TY - JOUR AU - Redman, M. Leanne AU - Gilmore, Anne L. AU - Breaux, Jeffrey AU - Thomas, M. Diana AU - Elkind-Hirsch, Karen AU - Stewart, Tiffany AU - Hsia, S. Daniel AU - Burton, Jeffrey AU - Apolzan, W. John AU - Cain, E. Loren AU - Altazan, D. Abby AU - Ragusa, Shelly AU - Brady, Heather AU - Davis, Allison AU - Tilford, Mick J. AU - Sutton, F. Elizabeth AU - Martin, K. Corby PY - DA - 2017/09/13 TI - Effectiveness of SmartMoms, a Novel eHealth Intervention for Management of Gestational Weight Gain: Randomized Controlled Pilot Trial JO - JMIR Mhealth Uhealth SP - e133 VL - 5 IS - 9 KW - pregnancy KW - gestational weight gain KW - lifestyle modification KW - intervention AB - Background: Two-thirds of pregnant women exceed gestational weight gain (GWG) recommendations. Because excess GWG is associated with adverse outcomes for mother and child, development of scalable and cost-effective approaches to deliver intensive lifestyle programs during pregnancy is urgent. Objective: The aim of this study was to decrease the proportion of women who exceed the Institute of Medicine (IOM) 2009 GWG guidelines. Methods: In a parallel-arm randomized controlled trial, 54 pregnant women (age 18-40 years) who were overweight (n=25) or obese (n=29) were enrolled to test whether an intensive lifestyle intervention (called SmartMoms) decreased the proportion of women with excess GWG, defined as exceeding the 2009 IOM guidelines, compared to no intervention (usual care group). The SmartMoms intervention was delivered through mobile phone (remote group) or in a traditional in-person, clinic-based setting (in-person group), and included a personalized dietary intake prescription, self-monitoring weight against a personalized weight graph, activity tracking with a pedometer, receipt of health information, and continuous personalized feedback from counselors. Results: A significantly smaller proportion of women exceeded the IOM 2009 GWG guidelines in the SmartMoms intervention groups (in-person: 56%, 10/18; remote: 58%, 11/19) compared to usual care (85%, 11/13; P=.02). The remote intervention was a lower cost to participants (mean US $97, SD $6 vs mean US $347, SD $40 per participant; P<.001) and clinics (US $215 vs US $419 per participant) and with increased intervention adherence (76.5% vs 60.8%; P=.049). Conclusions: An intensive lifestyle intervention for GWG can be effectively delivered via a mobile phone, which is both cost-effective and scalable. Trial Registration: Clinicaltrials.gov NCT01610752; https://clinicaltrials.gov/ct2/show/NCT01610752 (Archived by WebCite at http://www.webcitation.org/6sarNB4iW) UR - http://mhealth.jmir.org/2017/9/e133/ DO - 10.2196/mhealth.8228 UR - http://www.ncbi.nlm.nih.gov/pubmed/28903892 ID - info:doi/10.2196/mhealth.8228 ER - TY - JOUR AU - Rathbone, Leigh Amy AU - Prescott, Julie PY - DA - 2017/08/24 TI - The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review JO - J Med Internet Res SP - e295 VL - 19 IS - 8 KW - mHealth KW - smartphone KW - health KW - review KW - systematic KW - short message service KW - treatment efficacy KW - portable electronic applications KW - intervention study AB - Background: The initial introduction of the World Wide Web in 1990 brought around the biggest change in information acquisition. Due to the abundance of devices and ease of access they subsequently allow, the utility of mobile health (mHealth) has never been more endemic. A substantial amount of interactive and psychoeducational apps are readily available to download concerning a wide range of health issues. mHealth has the potential to reduce waiting times for appointments; eradicate the need to meet in person with a clinician, successively diminishing the workload of mental health professionals; be more cost effective to practices; and encourage self-care tactics. Previous research has given valid evidence with empirical studies proving the effectiveness of physical and mental health interventions using mobile apps. Alongside apps, there is evidence to show that receiving short message service (SMS) messages, which entail psychoeducation, medication reminders, and links to useful informative Web pages can also be advantageous to a patient?s mental and physical well-being. Available mHealth apps and SMS services and their ever improving quality necessitates a systematic review in the area in reference to reduction of symptomology, adherence to intervention, and usability. Objective: The aim of this review was to study the efficacy, usability, and feasibility of mobile apps and SMS messages as mHealth interventions for self-guided care. Methods: A systematic literature search was carried out in JMIR, PubMed, PsychINFO, PsychARTICLES, Google Scholar, MEDLINE, and SAGE. The search spanned from January 2008 to January 2017. The primary outcome measures consisted of weight management, (pregnancy) smoking cessation, medication adherence, depression, anxiety and stress. Where possible, adherence, feasibility, and usability outcomes of the apps or SMS services were evaluated. Between-group and within-group effect sizes (Cohen d) for the mHealth intervention method group were determined. Results: A total of 27 studies, inclusive of 4658 participants were reviewed. The papers included randomized controlled trials (RCTs) (n=19), within-group studies (n=7), and 1 within-group study with qualitative aspect. Studies show improvement in physical health and significant reductions of anxiety, stress, and depression. Within-group and between-group effect sizes ranged from 0.05-3.37 (immediately posttest), 0.05-3.25 (1-month follow-up), 0.08-3.08 (2-month follow-up), 0.00-3.10 (3-month follow-up), and 0.02-0.27 (6-month follow-up). Usability and feasibility of mHealth interventions, where reported, also gave promising, significant results. Conclusions: The review shows the promising and emerging efficacy of using mobile apps and SMS text messaging as mHealth interventions. UR - http://www.jmir.org/2017/8/e295/ DO - 10.2196/jmir.7740 UR - http://www.ncbi.nlm.nih.gov/pubmed/28838887 ID - info:doi/10.2196/jmir.7740 ER - TY - JOUR AU - O'Reilly, Martin AU - Duffin, Joe AU - Ward, Tomas AU - Caulfield, Brian PY - DA - 2017/08/21 TI - Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation JO - JMIR Rehabil Assist Technol SP - e9 VL - 4 IS - 2 KW - exercise therapy KW - biomedical technology KW - lower extremity KW - physical therapy specialty AB - Background: Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers. Objective: We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation. Methods: A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated. Results: The tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh. Conclusions: A tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayed excellent system accuracy even when assessing acute deviations in compound exercises with a single IMU. UR - http://rehab.jmir.org/2017/2/e9/ DO - 10.2196/rehab.7259 UR - http://www.ncbi.nlm.nih.gov/pubmed/28827210 ID - info:doi/10.2196/rehab.7259 ER - TY - JOUR AU - Maddison, Ralph AU - Gemming, Luke AU - Monedero, Javier AU - Bolger, Linda AU - Belton, Sarahjane AU - Issartel, Johann AU - Marsh, Samantha AU - Direito, Artur AU - Solenhill, Madeleine AU - Zhao, Jinfeng AU - Exeter, John Daniel AU - Vathsangam, Harshvardhan AU - Rawstorn, Charles Jonathan PY - DA - 2017/08/17 TI - Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study JO - JMIR Mhealth Uhealth SP - e122 VL - 5 IS - 8 KW - telemedicine KW - smartphone KW - validation studies KW - geographic information systems KW - locomotion KW - physical activity KW - humans AB - Background: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data?which includes billions of digital traces?offers scientists a new lens to examine PA in fine-grained detail and allows us to track people?s geocoded movement patterns to determine their interaction with the environment. Objective: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. Methods: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. Results: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [?0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. Conclusions: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics. UR - http://mhealth.jmir.org/2017/8/e122/ DO - 10.2196/mhealth.7167 UR - http://www.ncbi.nlm.nih.gov/pubmed/28818819 ID - info:doi/10.2196/mhealth.7167 ER - TY - JOUR AU - Duncan, Mitch AU - Murawski, Beatrice AU - Short, E. Camille AU - Rebar, L. Amanda AU - Schoeppe, Stephanie AU - Alley, Stephanie AU - Vandelanotte, Corneel AU - Kirwan, Morwenna PY - DA - 2017/08/14 TI - Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors JO - Interact J Med Res SP - e13 VL - 6 IS - 2 KW - health behavior KW - public health KW - exercise KW - sleep KW - behavior change KW - fitness trackers KW - adult, mobile applications AB - Background: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet, it is unknown how the implementation of BCTs differs between these behaviors. Objective: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers (self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type of BCTs differ between behaviors. Methods: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a 1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the number of BCTs implemented was assessed using kappa statistics. Results: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and sedentary behavior (n=6). Conclusions: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown. UR - http://www.i-jmr.org/2017/2/e13/ DO - 10.2196/ijmr.6685 UR - http://www.ncbi.nlm.nih.gov/pubmed/28807889 ID - info:doi/10.2196/ijmr.6685 ER - TY - JOUR AU - Alinia, Parastoo AU - Cain, Chris AU - Fallahzadeh, Ramin AU - Shahrokni, Armin AU - Cook, Diane AU - Ghasemzadeh, Hassan PY - DA - 2017/08/11 TI - How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities JO - JMIR Mhealth Uhealth SP - e106 VL - 5 IS - 8 KW - activities of daily living KW - activity tracker KW - mobility limitations KW - mobile health AB - Background: As commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools. Objective: The aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker. Methods: We recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard. Results: All the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC<0.60), and the error rate was significantly higher in walker experiment compared to other activities. There was no significant difference between the trackers and the gold standard in the shopping cart experiment. The wrist worn tracker, Flex, counted several steps when eating (P<.01). The chest tracker was identified as the most promising site to capture steps in more intense activities, while the wrist was the optimal wearing site in less intense activities. Conclusions: This feasibility study focused on 6 PAs and demonstrated that Fitbit trackers were most accurate when walking on a treadmill and least accurate during walking with a walking aid and for low-intensity activities. This may suggest excluding participants with assistive devices from studies that focus on PA interventions using commercially available trackers. This study also indicates that the wearing site of the tracker is an important factor impacting the accuracy performance. A larger scale study with a more diverse population, various activity tracker vendors, and a larger activity set are warranted to generalize our results. UR - http://mhealth.jmir.org/2017/8/e106/ DO - 10.2196/mhealth.6321 UR - http://www.ncbi.nlm.nih.gov/pubmed/28801304 ID - info:doi/10.2196/mhealth.6321 ER - TY - JOUR AU - Lonini, Luca AU - Gupta, Aakash AU - Deems-Dluhy, Susan AU - Hoppe-Ludwig, Shenan AU - Kording, Konrad AU - Jayaraman, Arun PY - DA - 2017/08/10 TI - Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models JO - JMIR Rehabil Assist Technol SP - e8 VL - 4 IS - 2 KW - activities of daily living KW - machine learning KW - wearables KW - rehabilitation KW - orthotic devices AB - Background: Wearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition. Objective: The objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals. Methods: Data were collected from 11 healthy controls as well as from 11 age-matched individuals with disabilities who used a standard stance control knee-ankle-foot orthosis (KAFO), and then a computer-controlled adaptive KAFO (Ottobock C-Brace). All subjects performed a structured set of functional activities while wearing an accelerometer on their waist, and random forest classifiers were used as activity classification models. We examined both global models, which are trained on other subjects (healthy or disabled individuals), and personal models, which are trained and tested on the same subject. Results: Median accuracies of global and personal models trained with data from the new KAFO were significantly higher (61% and 76%, respectively) than those of models that use data from the original KAFO (55% and 66%, respectively) (Wilcoxon signed-rank test, P=.006 and P=.01). These models also massively outperformed a global model trained on healthy subjects, which only achieved a median accuracy of 53%. Device-specific models conferred a major advantage for activity recognition. Conclusions: Our results suggest that when patients use a new assistive device, labeled data from activities performed with the specific device are needed for maximal precision activity recognition. Personal device-specific models yield the highest accuracy in such scenarios, whereas models trained on healthy individuals perform poorly and should not be used in patient populations. UR - http://rehab.jmir.org/2017/2/e8/ DO - 10.2196/rehab.7317 UR - http://www.ncbi.nlm.nih.gov/pubmed/28798008 ID - info:doi/10.2196/rehab.7317 ER - TY - JOUR AU - Lentferink, J. Aniek AU - Oldenhuis, KE Hilbrand AU - de Groot, Martijn AU - Polstra, Louis AU - Velthuijsen, Hugo AU - van Gemert-Pijnen, EWC Julia PY - DA - 2017/08/01 TI - Key Components in eHealth Interventions Combining Self-Tracking and Persuasive eCoaching to Promote a Healthier Lifestyle: A Scoping Review JO - J Med Internet Res SP - e277 VL - 19 IS - 8 KW - telemedicine KW - review KW - health promotion KW - remote sensing technology AB - Background: The combination of self-tracking and persuasive eCoaching in automated interventions is a new and promising approach for healthy lifestyle management. Objective: The aim of this study was to identify key components of self-tracking and persuasive eCoaching in automated healthy lifestyle interventions that contribute to their effectiveness on health outcomes, usability, and adherence. A secondary aim was to identify the way in which these key components should be designed to contribute to improved health outcomes, usability, and adherence. Methods: The scoping review methodology proposed by Arskey and O?Malley was applied. Scopus, EMBASE, PsycINFO, and PubMed were searched for publications dated from January 1, 2013 to January 31, 2016 that included (1) self-tracking, (2) persuasive eCoaching, and (3) healthy lifestyle intervention. Results: The search resulted in 32 publications, 17 of which provided results regarding the effect on health outcomes, 27 of which provided results regarding usability, and 13 of which provided results regarding adherence. Among the 32 publications, 27 described an intervention. The most commonly applied persuasive eCoaching components in the described interventions were personalization (n=24), suggestion (n=19), goal-setting (n=17), simulation (n=17), and reminders (n=15). As for self-tracking components, most interventions utilized an accelerometer to measure steps (n=11). Furthermore, the medium through which the user could access the intervention was usually a mobile phone (n=10). The following key components and their specific design seem to influence both health outcomes and usability in a positive way: reduction by setting short-term goals to eventually reach long-term goals, personalization of goals, praise messages, reminders to input self-tracking data into the technology, use of validity-tested devices, integration of self-tracking and persuasive eCoaching, and provision of face-to-face instructions during implementation. In addition, health outcomes or usability were not negatively affected when more effort was requested from participants to input data into the technology. The data extracted from the included publications provided limited ability to identify key components for adherence. However, one key component was identified for both usability and adherence, namely the provision of personalized content. Conclusions: This scoping review provides a first overview of the key components in automated healthy lifestyle interventions combining self-tracking and persuasive eCoaching that can be utilized during the development of such interventions. Future studies should focus on the identification of key components for effects on adherence, as adherence is a prerequisite for an intervention to be effective. UR - http://www.jmir.org/2017/8/e277/ DO - 10.2196/jmir.7288 UR - http://www.ncbi.nlm.nih.gov/pubmed/28765103 ID - info:doi/10.2196/jmir.7288 ER - TY - JOUR AU - Bian, Jiang AU - Guo, Yi AU - Xie, Mengjun AU - Parish, E. Alice AU - Wardlaw, Isaac AU - Brown, Rita AU - Modave, François AU - Zheng, Dong AU - Perry, T. Tamara PY - DA - 2017/07/25 TI - Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents JO - JMIR Mhealth Uhealth SP - e105 VL - 5 IS - 7 KW - mobile health KW - mHealth KW - asthma KW - Fitbit KW - physical activity KW - sleep KW - sleep quality AB - Background: Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients? asthma outcomes have not been established. Objective: The objective of this study was to explore the association between self?reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI). Methods: We conducted an 8?week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient?reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep?related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F?SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest. Results: We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=?.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=?.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62). Conclusions: Our findings support the potential of using wrist-worn devices to continuously monitor two important factors?physical activity and sleep?associated with patients? asthma outcomes and to develop a personalized asthma management platform. UR - http://mhealth.jmir.org/2017/7/e105/ DO - 10.2196/mhealth.7346 UR - http://www.ncbi.nlm.nih.gov/pubmed/28743679 ID - info:doi/10.2196/mhealth.7346 ER - TY - JOUR AU - Sirard, R. John AU - Masteller, Brittany AU - Freedson, S. Patty AU - Mendoza, Albert AU - Hickey, Amanda PY - DA - 2017/07/19 TI - Youth Oriented Activity Trackers: Comprehensive Laboratory- and Field-Based Validation JO - J Med Internet Res SP - e250 VL - 19 IS - 7 KW - child KW - movement KW - fitness trackers AB - Background: Commercial activity trackers are growing in popularity among adults and some are beginning to be marketed to children. There is, however, a paucity of independent research examining the validity of these devices to detect physical activity of different intensity levels. Objectives: The purpose of this study was to determine the validity of the output from 3 commercial youth-oriented activity trackers in 3 phases: (1) orbital shaker, (2) structured indoor activities, and (3) 4 days of free-living activity. Methods: Four units of each activity tracker (Movband [MB], Sqord [SQ], and Zamzee [ZZ]) were tested in an orbital shaker for 5-minutes at three frequencies (1.3, 1.9, and 2.5 Hz). Participants for Phase 2 (N=14) and Phase 3 (N=16) were 6-12 year old children (50% male). For Phase 2, participants completed 9 structured activities while wearing each tracker, the ActiGraph GT3X+ (AG) research accelerometer, and a portable indirect calorimetry system to assess energy expenditure (EE). For Phase 3, participants wore all 4 devices for 4 consecutive days. Correlation coefficients, linear models, and non-parametric statistics evaluated the criterion and construct validity of the activity tracker output. Results: Output from all devices was significantly associated with oscillation frequency (r=.92-.99). During Phase 2, MB and ZZ only differentiated sedentary from light intensity (P<.01), whereas the SQ significantly differentiated among all intensity categories (all comparisons P<.01), similar to AG and EE. During Phase 3, AG counts were significantly associated with activity tracker output (r=.76, .86, and .59 for the MB, SQ, and ZZ, respectively). Conclusions: Across study phases, the SQ demonstrated stronger validity than the MB and ZZ. The validity of youth-oriented activity trackers may directly impact their effectiveness as behavior modification tools, demonstrating a need for more research on such devices. UR - http://www.jmir.org/2017/7/e250/ DO - 10.2196/jmir.6360 UR - http://www.ncbi.nlm.nih.gov/pubmed/28724509 ID - info:doi/10.2196/jmir.6360 ER - TY - JOUR AU - Knell, Gregory AU - Gabriel, Pettee Kelley AU - Businelle, S. Michael AU - Shuval, Kerem AU - Wetter, W. David AU - Kendzor, E. Darla PY - DA - 2017/07/18 TI - Ecological Momentary Assessment of Physical Activity: Validation Study JO - J Med Internet Res SP - e253 VL - 19 IS - 7 KW - accelerometry KW - behavioral risk factor surveillance system KW - ecological momentary assessment KW - self-report KW - data accuracy AB - Background: Ecological momentary assessment (EMA) may elicit physical activity (PA) estimates that are less prone to bias than traditional self-report measures while providing context. Objectives: The objective of this study was to examine the convergent validity of EMA-assessed PA compared with accelerometry. Methods: The participants self-reported their PA using International Physical Activity Questionnaire (IPAQ) and Behavioral Risk Factor Surveillance System (BRFSS) and wore an accelerometer while completing daily EMAs (delivered through the mobile phone) for 7 days. Weekly summary estimates included sedentary time and moderate-, vigorous-, and moderate-to vigorous-intensity physical activity (MVPA). Spearman coefficients and Lin?s concordance correlation coefficients (LCC) examined the linear association and agreement for EMA and the questionnaires as compared with accelerometry. Results: Participants were aged 43.3 (SD 13.1) years, 51.7% (123/238) were African American, 74.8% (178/238) were overweight or obese, and 63.0% (150/238) were low income. The linear associations of EMA and traditional self-reports with accelerometer estimates were statistically significant (P<.05) for sedentary time (EMA: ?=.16), moderate-intensity PA (EMA: ?=.29; BRFSS: ?=.17; IPAQ: ?=.24), and MVPA (EMA: ?=.31; BRFSS: ?=.17; IPAQ: ?=.20). Only EMA estimates of PA were statistically significant compared with accelerometer for agreement. Conclusions: The mobile EMA showed better correlation and agreement to accelerometer estimates than traditional self-report methods. These findings suggest that mobile EMA may be a practical alternative to accelerometers to assess PA in free-living settings. UR - http://www.jmir.org/2017/7/e253/ DO - 10.2196/jmir.7602 UR - http://www.ncbi.nlm.nih.gov/pubmed/28720556 ID - info:doi/10.2196/jmir.7602 ER - TY - JOUR AU - Frias, Juan AU - Virdi, Naunihal AU - Raja, Praveen AU - Kim, Yoona AU - Savage, George AU - Osterberg, Lars PY - DA - 2017/07/11 TI - Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial JO - J Med Internet Res SP - e246 VL - 19 IS - 7 KW - digital medicine KW - hypertension KW - type 2 diabetes KW - patient engagement, medication adherence KW - therapeutic inertia AB - Background: Hypertension and type 2 diabetes mellitus are major modifiable risk factors for cardiac, cerebrovascular, and kidney diseases. Reasons for poor disease control include nonadherence, lack of patient engagement, and therapeutic inertia. Objective: The aim of this study was to assess the impact on clinic-measured blood pressure (BP) and glycated hemoglobin (HbA1c) using a digital medicine offering (DMO) that measures medication ingestion adherence, physical activity, and rest using digital medicines (medication taken with ingestible sensor), wearable sensor patches, and a mobile device app. Methods: Participants with elevated systolic BP (SBP ?140 mm Hg) and HbA1c (?7%) failing antihypertensive (?2 medications) and oral diabetes therapy were enrolled in this three-arm, 12-week, cluster-randomized study. Participants used DMO (includes digital medicines, the wearable sensor patch, and the mobile device app) for 4 or 12 weeks or received usual care based on site randomization. Providers in the DMO arms could review the DMO data via a Web portal. In all three arms, providers were instructed to make medical decisions (medication titration, adherence counseling, education, and lifestyle coaching) on all available clinical information at each visit. Primary outcome was change in SBP at week 4. Other outcomes included change in SBP and HbA1c at week 12, and low-density lipoprotein cholesterol (LDL-C) and diastolic blood pressure (DBP) at weeks 4 and 12, as well as proportion of patients at BP goal (<140/90 mm Hg) at weeks 4 and 12, medical decisions, and medication adherence patterns. Results: Final analysis included 109 participants (12 sites; age: mean 58.7, SD years; female: 49.5%, 54/109; Hispanic: 45.9%, 50/109; income ? US $20,000: 56.9%, 62/109; and ? high school education: 52.3%, 57/109). The DMO groups had 80 participants (7 sites) and usual care had 29 participants (5 sites). At week 4, DMO resulted in a statistically greater SBP reduction than usual care (mean ?21.8, SE 1.5 mm Hg vs mean ?12.7, SE 2.8 mmHg; mean difference ?9.1, 95% CI ?14.0 to ?3.3 mm Hg) and maintained a greater reduction at week 12. The DMO groups had greater reductions in HbA1c, DBP, and LDL-C, and a greater proportion of participants at BP goal at weeks 4 and 12 compared with usual care. The DMO groups also received more therapeutic interventions than usual care. Medication adherence was ?80% while using the DMO. The most common adverse event was a self-limited rash at the wearable sensor site (12%, 10/82). Conclusions: For patients failing hypertension and diabetes oral therapy, this DMO, which provides dose-by-dose feedback on medication ingestion adherence, can help lower BP, HbA1c, and LDL-C, and promote patient engagement and provider decision making. Trial Registration: Clinicaltrials.gov NCT02827630; https://clinicaltrials.gov/show/NCT02827630 (Archived by WebCite at http://www.webcitation.org/6rL8dW2VF) UR - http://www.jmir.org/2017/7/e246/ DO - 10.2196/jmir.7833 UR - http://www.ncbi.nlm.nih.gov/pubmed/28698169 ID - info:doi/10.2196/jmir.7833 ER - TY - JOUR AU - Gaudet, Jeffrey AU - Gallant, François AU - Bélanger, Mathieu PY - DA - 2017/07/06 TI - A Bit of Fit: Minimalist Intervention in Adolescents Based on a Physical Activity Tracker JO - JMIR Mhealth Uhealth SP - e92 VL - 5 IS - 7 KW - health behavior KW - health promotion KW - mHealth KW - physical activity tracker AB - Background: Only 5% of Canadian youth meet the recommended 60 minutes of moderate to vigorous physical activity (MVPA) per day, with leisure time being increasingly allocated to technology usage. Direct-to-consumer mHealth devices that promote physical activity, such as wrist-worn physical activity trackers, have features with potential appeal to youth. Objective: The primary purpose of this study was to determine whether a minimalist physical activity tracker-based intervention would lead to an increase in physical activity in young adolescents. A secondary aim of this study was to assess change in physical activity across a 7-week intervention, as measured by the tracker. Methods: Using a quasi-experimental crossover design, two groups of 23 young adolescents (aged 13-14 years) were randomly assigned to immediate intervention or delayed intervention. The intervention consisted of wearing a Fitbit-Charge-HR physical activity tracker over a 7-week period. Actical accelerometers were used to measure participants? levels of MVPA before and at the end of intervention periods for each group. Covariates such as age, sex, stage of change for physical activity behavior, and goal commitment were also measured. Results: There was an increase in physical activity over the course of the study period, though it was not related to overall physical activity tracker use. An intervention response did, however, occur in a subset of participants. Specifically, exposure to the physical activity tracker was associated with an average daily increase in MVPA by more than 15 minutes (P=.01) among participants who reported being in the action and maintenance stages of behavior change in relation to participation in physical activity. Participants in the precontemplation, contemplation, and preparation stages of behavior change had no change in their level of MVPA (P=.81). Conclusions: These results suggest that physical activity trackers may elicit improved physical activity related behavior in young adolescents demonstrating a readiness to be active. Future studies should seek to investigate if integrating physical activity trackers as part of more intensive interventions leads to greater increases in physical activity across different levels of stages of behavior change and if these changes can be sustained over longer periods of time. UR - http://mhealth.jmir.org/2017/7/e92/ DO - 10.2196/mhealth.7647 UR - http://www.ncbi.nlm.nih.gov/pubmed/28684384 ID - info:doi/10.2196/mhealth.7647 ER - TY - JOUR AU - Modave, François AU - Guo, Yi AU - Bian, Jiang AU - Gurka, J. Matthew AU - Parish, Alice AU - Smith, D. Megan AU - Lee, M. Alexandra AU - Buford, W. Thomas PY - DA - 2017/06/28 TI - Mobile Device Accuracy for Step Counting Across Age Groups JO - JMIR Mhealth Uhealth SP - e88 VL - 5 IS - 6 KW - mobile KW - devices KW - physical activity KW - weight reduction KW - adults AB - Background: Only one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm. Objective: We aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups. Methods: We recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant. Results: Among the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years. Conclusions: Our analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program. UR - https://mhealth.jmir.org/2017/6/e88/ DO - 10.2196/mhealth.7870 UR - http://www.ncbi.nlm.nih.gov/pubmed/28659255 ID - info:doi/10.2196/mhealth.7870 ER - TY - JOUR AU - Li, C. Linda AU - Sayre, C. Eric AU - Xie, Hui AU - Clayton, Cam AU - Feehan, M. Lynne PY - DA - 2017/06/26 TI - A Community-Based Physical Activity Counselling Program for People With Knee Osteoarthritis: Feasibility and Preliminary Efficacy of the Track-OA Study JO - JMIR Mhealth Uhealth SP - e86 VL - 5 IS - 6 KW - osteoarthritis KW - physical activity KW - sedentary behavior KW - sedentary lifestyle KW - wearables KW - digital technology KW - fitness trackers KW - exercise AB - Background: Physical activity can improve health outcomes in people with knee osteoarthritis (OA); however, participation in physical activity is very low in this population. Objective: The objective of our study was to assess the feasibility and preliminary efficacy of the use of wearables (Fitbit Flex) and telephone counselling by a physical therapist (PT) for improving physical activity in people with a physician-confirmed diagnosis of knee OA, or who have passed 2 validated criteria for early OA. Methods: We conducted a community-based feasibility randomized controlled trial. The immediate group (n=17) received a brief education session by a physical therapist, a Fitbit Flex activity tracker, and a weekly telephone call for activity counselling with the physical therapist. The delayed group (n=17) received the same intervention 1 month later. All participants were assessed at baseline (T0), and the end of 1 month (T1) and 2 months (T2). Outcomes were (1) mean moderate to vigorous physical activity time, (2) mean time spent on sedentary behavior, (3) Knee Injury and Osteoarthritis Outcome Score (KOOS), and (4) Partners in Health Scale. Feasibility data were summarized with descriptive statistics. We used analysis of covariance to evaluate the effect of the group type on the outcome measures at T1 and T2, after adjusting for blocking and T0. We assessed planned contrasts of changes in outcome measures over measurement periods. Results: We identified 46 eligible individuals; of those, 34 (74%) enrolled and no one dropped out. All but 1 participant adhered to the intervention protocol. We found a significant effect, with the immediate intervention group having improved in the moderate to vigorous physical activity time and in the Partners in Health Scale at T0 to T1 compared with the delayed intervention group. The planned contrast of the immediate intervention group at T0 to T1 versus the delayed group at T1 to T2 showed a significant effect in the sedentary time and the KOOS symptoms subscale, favoring the delayed group. Conclusions: This study demonstrated the feasibility of a behavioral intervention, supported by the use of a wearable device, to promote physical activity among people with knee OA. Trial Registration: ClinicalTrials.gov NCT02313506; https://clinicaltrials.gov/ct2/show/NCT02313506 (Archived by WebCite at http://www.webcitation.org/6r4P3Bub0) UR - http://mhealth.jmir.org/2017/6/e86/ DO - 10.2196/mhealth.7863 UR - http://www.ncbi.nlm.nih.gov/pubmed/28652228 ID - info:doi/10.2196/mhealth.7863 ER - TY - JOUR AU - Castellano-Tejedor, Carmina AU - Moreno, Jordi AU - Ciudin, Andrea AU - Parramón, Gemma AU - Lusilla-Palacios, Pilar PY - DA - 2017/05/31 TI - PREventive Care Infrastructure based On Ubiquitous Sensing (PRECIOUS): A Study Protocol JO - JMIR Res Protoc SP - e105 VL - 6 IS - 5 KW - mHealth KW - motivational interviewing KW - physical activity KW - diet KW - sustained motivation KW - adherence AB - Background: mHealth has experienced a huge growth during the last decade. It has been presented as a new and promising pathway to increase self-management of health and chronic conditions in several populations. One of the most prolific areas of mHealth has been healthy lifestyles promotion. However, few mobile apps have succeeded in engaging people and ensuring sustained use. Objective: This paper describes the pilot test protocol of the PReventive Care Infrastructure based on Ubiquitous Sensing (PRECIOUS) project, aimed at validating the PRECIOUS system with end users. This system includes, within a motivational framework, the Bodyguard2 sensor (accelerometer with heart rate monitoring) and the PRECIOUS app. Methods: This is a pilot experimental study targeting morbidly obese prediabetic patients who will be randomized to three conditions: (1) Group 1 - Control group (Treatment as usual with the endocrinologist and the nurse + Bodyguard2), (2) Group 2 - PRECIOUS system (Bodyguard2 + PRECIOUS app), and (3) Group 3 - PRECIOUS system (Bodyguard2 + PRECIOUS app + Motivational Interviewing). The duration of the study will be 3 months with scheduled follow-up appointments within the scope of the project at Weeks 3, 5, 8, and 12. During the study, several measures related to healthy lifestyles, weight management, and health-related quality of life will be collected to explore the effectiveness of PRECIOUS to foster behavior change, as well as user acceptance, usability, and satisfaction with the solution. Results: Because of the encouraging results shown in similar scientific work analyzing health apps acceptance in clinical settings, we expect patients to widely accept and express satisfaction with PRECIOUS. We also expect to find acceptable usability of the preventive health solution. The recruitment of the pilot study has concluded with the inclusion of 31 morbidly obese prediabetic patients. Results are expected to be available in mid-2017. Conclusions: Adopting and maintaining healthy habits may be challenging in people with chronic conditions who usually need regular support to ensure mid/long-term adherence to recommendations and behavior change. Thus, mHealth could become a powerful and efficient tool since it allows continuous communication with users and immediate feedback. The PRECIOUS system is an innovative preventive health care solution aimed at enhancing inner motivation from users to change their lifestyles and adopt healthier habits. PRECIOUS includes ubiquitous sensors and a scientifically grounded app to address three main components of health: physical activity, diet, and stress levels. Trial Registration: Clinicaltrials.gov NCT02818790; https://clinicaltrials.gov/ct2/show/NCT02818790 (Archived by WebCite at http://www.webcitation.org/6qfzdfMoU) UR - http://www.researchprotocols.org/2017/5/e105/ DO - 10.2196/resprot.6973 UR - http://www.ncbi.nlm.nih.gov/pubmed/28566263 ID - info:doi/10.2196/resprot.6973 ER - TY - JOUR AU - Bedard, Chloe AU - King-Dowling, Sara AU - McDonald, Madeline AU - Dunton, Genevieve AU - Cairney, John AU - Kwan, Matthew PY - DA - 2017/05/31 TI - Understanding Environmental and Contextual Influences of Physical Activity During First-Year University: The Feasibility of Using Ecological Momentary Assessment in the MovingU Study JO - JMIR Public Health Surveill SP - e32 VL - 3 IS - 2 KW - exercise KW - compliance KW - feasibility studies KW - young adult KW - students AB - Background: It is well established that drastic declines in physical activity (PA) occur during young adults? transition into university; however, our understanding of contextual and environmental factors as it relates to young adults? PA is limited. Objective: The purpose of our study was to examine the feasibility of using wrist-worn accelerometers and the use of ecological momentary assessment (EMA) to assess the context and momentary correlates of PA on multiple occasions each day during first-year university. Methods: First-year university students were asked to participate in the study. The participants completed a brief questionnaire and were subsequently asked to wear an ActiGraph GT9X-Link accelerometer and respond to a series of EMA prompts (7/day) via their phones for 5 consecutive days. Results: A total of 96 first-year university students with smartphones agreed to participate in the study (mean age 18.3 [SD 0.51]; n=45 females). Overall, there was good compliance for wearing the accelerometers, with 91% (78/86) of the participants having ?2 days of ?10 hours of wear time (mean=3.53 valid days). Students were generally active, averaging 10,895 steps/day (SD 3413) or 1123.23 activity counts/min (SD 356.10). Compliance to EMA prompts was less desirable, with 64% (55/86) of the participants having usable EMA data (responding to a minimum of ?3 days of 3 prompts/day or ?4 days of 2 prompts/day), and only 47% (26/55) of these participants were considered to have excellent EMA compliance (responding to ?5 days of 4 prompts/day or ? 4 days of 5 prompts/day). Conclusions: This study represents one of the first studies to use an intensive real-time data capture strategy to examine time-varying correlates of PA among first-year university students. These data will aim to describe the physical and social contexts in which PA occurs and examine the relationships between momentary correlates of PA among the first-year university students. Overall, current results suggest that wrist-worn accelerometers and EMA are feasible methods for data collection among the young adult population; however, more work is needed to understand how to improve upon compliance to a real-time data capture method such as EMA. UR - http://publichealth.jmir.org/2017/2/e32/ DO - 10.2196/publichealth.7010 UR - http://www.ncbi.nlm.nih.gov/pubmed/28566264 ID - info:doi/10.2196/publichealth.7010 ER - TY - JOUR AU - Kohler, Simone AU - Behrens, Gundula AU - Olden, Matthias AU - Baumeister, E. Sebastian AU - Horsch, Alexander AU - Fischer, Beate AU - Leitzmann, F. Michael PY - DA - 2017/05/30 TI - Design and Evaluation of a Computer-Based 24-Hour Physical Activity Recall (cpar24) Instrument JO - J Med Internet Res SP - e186 VL - 19 IS - 5 KW - web-based method KW - validity KW - reliability KW - usability KW - lifestyle behavior KW - physical activity KW - sedentary behavior AB - Background: Widespread access to the Internet and an increasing number of Internet users offers the opportunity of using Web-based recalls to collect detailed physical activity data in epidemiologic studies. Objective: The aim of this investigation was to evaluate the validity and reliability of a computer-based 24-hour physical activity recall (cpar24) instrument with respect to the recalled 24-h period. Methods: A random sample of 67 German residents aged 22 to 70 years was instructed to wear an ActiGraph GT3X+ accelerometer for 3 days. Accelerometer counts per min were used to classify activities as sedentary (<100 counts per min), light (100-1951 counts per min), and moderate to vigorous (?1952 counts per min). On day 3, participants were also requested to specify the type, intensity, timing, and context of all activities performed during day 2 using the cpar24. Using metabolic equivalent of task (MET), the cpar24 activities were classified as sedentary (<1.5 MET), light (1.5-2.9 MET), and moderate to vigorous (?3.0 MET). The cpar24 was administered twice at a 3-h interval. The Spearman correlation coefficient (r) was used as primary measure of concurrent validity and test-retest reliability. Results: As compared with accelerometry, the cpar24 underestimated light activity by ?123 min (median difference, P difference <.001) and overestimated moderate to vigorous activity by 89 min (P difference <.001). By comparison, time spent sedentary assessed by the 2 methods was similar (median difference=+7 min, P difference=.39). There was modest agreement between the cpar24 and accelerometry regarding sedentary (r=.54), light (r=.46), and moderate to vigorous (r=.50) activities. Reliability analyses revealed modest to high intraclass correlation coefficients for sedentary (r=.75), light (r=.65), and moderate to vigorous (r=.92) activities and no statistically significant differences between replicate cpar24 measurements (median difference for sedentary activities=+10 min, for light activities=?5 min, for moderate to vigorous activities=0 min, all P difference ?.60). Conclusion: These data show that the cpar24 is a valid and reproducible Web-based measure of physical activity in adults. UR - http://www.jmir.org/2017/5/e186/ DO - 10.2196/jmir.7620 UR - http://www.ncbi.nlm.nih.gov/pubmed/28559229 ID - info:doi/10.2196/jmir.7620 ER - TY - JOUR AU - O'Brien, K. Megan AU - Shawen, Nicholas AU - Mummidisetty, K. Chaithanya AU - Kaur, Saninder AU - Bo, Xiao AU - Poellabauer, Christian AU - Kording, Konrad AU - Jayaraman, Arun PY - DA - 2017/05/25 TI - Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting JO - J Med Internet Res SP - e184 VL - 19 IS - 5 KW - smartphone KW - activities of daily living KW - ambulatory monitoring KW - machine learning KW - stroke rehabilitation AB - Background: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions. Objective: In this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting). Methods: Thirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone?s accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities. Results: A classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%). Conclusions: Stroke-based training data is needed for high quality AR among gait-impaired individuals with stroke. Additionally, AR systems for home and community monitoring would likely benefit from including at-home activities in the training data. UR - http://www.jmir.org/2017/5/e184/ DO - 10.2196/jmir.7385 UR - http://www.ncbi.nlm.nih.gov/pubmed/28546137 ID - info:doi/10.2196/jmir.7385 ER - TY - JOUR AU - Liao, Gen-Yih AU - Chien, Yu-Tai AU - Chen, Yu-Jen AU - Hsiung, Hsiao-Fang AU - Chen, Hsiao-Jung AU - Hsieh, Meng-Hua AU - Wu, Wen-Jie PY - DA - 2017/05/25 TI - What to Build for Middle-Agers to Come? Attractive and Necessary Functions of Exercise-Promotion Mobile Phone Apps: A Cross-Sectional Study JO - JMIR Mhealth Uhealth SP - e65 VL - 5 IS - 5 KW - physical exercise KW - middle aged KW - mobile application KW - self efficacy KW - consumer preference AB - Background: Physical activity is important for middle-agers to maintain health both in middle age and in old age. Although thousands of exercise-promotion mobile phone apps are available for download, current literature offers little understanding regarding which design features can enhance middle-aged adults? quality perception toward exercise-promotion apps and which factor may influence such perception. Objectives: The aims of this study were to understand (1) which design features of exercise-promotion apps can enhance quality perception of middle-agers, (2) whether their needs are matched by current functions offered in app stores, and (3) whether physical activity (PA) and mobile phone self-efficacy (MPSE) influence quality perception. Methods: A total of 105 middle-agers participated and filled out three scales: the International Physical Activity Questionnaire (IPAQ), the MPSE scale, and the need for design features questionnaire. The design features were developed based on the Coventry, Aberdeen, and London?Refined (CALO-RE) taxonomy. Following the Kano quality model, the need for design features questionnaire asked participants to classify design features into five categories: attractive, one-dimensional, must-be, indifferent, and reverse. The quality categorization was conducted based on a voting approach and the categorization results were compared with the findings of a prevalence study to realize whether needs match current availability. In total, 52 multinomial logistic regression models were analyzed to evaluate the effects of PA level and MPSE on quality perception of design features. Results: The Kano analysis on the total sample revealed that visual demonstration of exercise instructions is the only attractive design feature, whereas the other 51 design features were perceived with indifference. Although examining quality perception by PA level, 21 features are recommended to low level, 6 features to medium level, but none to high-level PA. In contrast, high-level MPSE is recommended with 14 design features, medium level with 6 features, whereas low-level participants are recommended with 1 feature. The analysis suggests that the implementation of demanded features could be low, as the average prevalence of demanded design features is 20% (4.3/21). Surprisingly, social comparison and social support, most implemented features in current apps, were categorized into the indifferent category. The magnitude of effect is larger for MPSE because it effects quality perception of more design features than PA. Delving into the 52 regression models revealed that high MPSE more likely induces attractive or one- dimensional categorization, suggesting the importance of technological self-efficacy on eHealth care promotion. Conclusions: This study is the first to propose middle-agers? needs in relation to mobile phone exercise-promotion. In addition to the tailor-made recommendations, suggestions are offered to app designers to enhance the performance of persuasive features. An interesting finding on change of quality perception attributed to MPSE is proposed as future research. UR - http://mhealth.jmir.org/2017/5/e65/ DO - 10.2196/mhealth.6233 UR - http://www.ncbi.nlm.nih.gov/pubmed/28546140 ID - info:doi/10.2196/mhealth.6233 ER - TY - JOUR AU - Skrepnik, Nebojsa AU - Spitzer, Andrew AU - Altman, Roy AU - Hoekstra, John AU - Stewart, John AU - Toselli, Richard PY - DA - 2017/05/09 TI - Assessing the Impact of a Novel Smartphone Application Compared With Standard Follow-Up on Mobility of Patients With Knee Osteoarthritis Following Treatment With Hylan G-F 20: A Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e64 VL - 5 IS - 5 KW - mobile health KW - mHealth KW - mobile apps KW - osteoarthritis KW - osteoarthritis, knee KW - hylan G-F 20 KW - Synvisc AB - Background: Osteoarthritis (OA) is a leading cause of disability in the United States. Although no disease-modifying therapies exist, patients with knee OA who increase walking may reduce risk of functional limitations. Objective: The objective of the study is to evaluate the impact of a mobile app (OA GO) plus wearable activity monitor/pedometer (Jawbone UP 24) used for 90 days on the mobility of patients with knee OA treated with hylan G-F 20. Methods: Patients with knee OA aged 30 to 80 years who were eligible to receive hylan G-F 20 and were familiar with smartphone technology were enrolled in this randomized, multicenter, open-label study. Patients who had a body mass index above 35 kg/m2 were excluded. All patients received a single 6-mL injection of hylan G-F 20 and wore the Jawbone monitor. The patients were then randomized 1:1 to Jawbone and OA GO (Group A; n=107) with visible feedback (unblinded) or Jawbone only (Group B; n=104) with no visible feedback (blinded). The primary endpoint was mean change from baseline in steps per day at day 90 between Groups A and B. Results: Baseline characteristics were similar between groups. There were significant differences between the increases in least squares (LS) mean number of steps per day (1199 vs 467, P=.03) and the mean percentage change (35.8% vs 11.5%, P=.02) from baseline in favor of Group A over Group B. There was a greater reduction in pain from baseline during the 6-minute walk test in Group A versus Group B. (LS mean change: ?55.3 vs ?33.8, P=.007). Most patients (65.4%) and surveys of physicians (67.3%) reported they would be likely or very likely to use/recommend the devices. Patient Activity Measure-13 scores improved from baseline (LS mean change for Groups A and B: 5.0 vs 6.9), with no significant differences between groups. The occurrence of adverse events was similar in the 2 groups. Conclusions: Use of a novel smartphone app in conjunction with a wearable activity monitor provided additional improvement on mobility parameters such as steps per day and pain with walking in the 6-minute walk test in patients with knee OA who were treated with hylan G-F 20. Results also highlight the amenability of patients and physicians to using mobile health technology in the treatment of OA and suggest further study is warranted. UR - http://mhealth.jmir.org/2017/5/e64/ DO - 10.2196/mhealth.7179 UR - http://www.ncbi.nlm.nih.gov/pubmed/28487266 ID - info:doi/10.2196/mhealth.7179 ER - TY - JOUR AU - Ng, Kwok AU - Tynjälä, Jorma AU - Kokko, Sami PY - DA - 2017/05/04 TI - Ownership and Use of Commercial Physical Activity Trackers Among Finnish Adolescents: Cross-Sectional Study JO - JMIR Mhealth Uhealth SP - e61 VL - 5 IS - 5 KW - social determinants of health KW - mobile phone KW - health promotion KW - disabled children KW - physical activity KW - adolescent AB - Background: Mobile phone apps for monitoring and promoting physical activity (PA) are extremely popular among adults. Devices, such as heart rate monitors or sports watches (HRMs/SWs) that work with these apps are at sufficiently low costs to be available through the commercial markets. Studies have reported an increase in PA levels among adults with devices; however, it is unknown whether the phenomena are similar during early adolescence. At a time when adolescents start to develop their own sense of independence and build friendship, the ease of smartphone availability in developed countries needs to be investigated in important health promoting behaviors such as PA. Objective: The objective of this study was to investigate the ownership and usage of PA trackers (apps and HRM/SW) among adolescents in a national representative sample and to examine the association between use of devices and PA levels. Methods: The Finnish school-aged physical activity (SPA) study consisted of 4575 adolescents, aged 11-, 13-, and 15-years, who took part in a web-based questionnaire during school time about PA behaviors between April and May 2016. Binary logistic regression analyses were used to test the associations between moderate to vigorous physical activity (MVPA) and devices, after controlling for gender, age, disability, and family affluence. Results: PA tracking devices have been categorized into two types, which are accessible to adolescents: (1) apps and (2) HRM/SW. Half the adolescents (2351/4467; 52.63%) own apps for monitoring PA, yet 16.12% (720/4467) report using apps. Fewer adolescents (782/4413; 17.72%) own HRM/SW and 9.25% (408/4413) use HRM/SW. In this study, users of HRM/SW were 2.09 times (95% CI 1.64-2.67), whereas users of apps were 1.4 times (95% CI 1.15-1.74) more likely to meet PA recommendations of daily MVPA for at least 60 min compared with adolescents without HRM/SW or without apps. Conclusions: To our knowledge, this is the first study that describes the situation in Finland with adolescents using PA trackers and its association with PA levels. Implications of the use of apps and HRM/SW by adolescents are discussed. UR - http://mhealth.jmir.org/2017/5/e61/ DO - 10.2196/mhealth.6940 UR - http://www.ncbi.nlm.nih.gov/pubmed/28473304 ID - info:doi/10.2196/mhealth.6940 ER - TY - JOUR AU - Masteller, Brittany AU - Sirard, John AU - Freedson, Patty PY - DA - 2017/4/28 TI - The Physical Activity Tracker Testing in Youth (P.A.T.T.Y.) Study: Content Analysis and Children?s Perceptions JO - JMIR Mhealth Uhealth SP - e55 VL - 5 IS - 4 KW - child KW - physical activity KW - qualitative research AB - Background: Activity trackers are widely used by adults and several models are now marketed for children. Objective: The aims of this study were to (1) perform a content analysis of behavioral change techniques (BCTs) used by three commercially available youth-oriented activity trackers and (2) obtain feedback describing children?s perception of these devices and the associated websites. Methods: A content analysis recorded the presence of 36 possible BCTs for the MovBand (MB), Sqord (SQ), and Zamzee (ZZ) activity trackers. In addition, 16 participants (mean age 8.6 years [SD 1.6]; 50% female [8/16]) received all three trackers and were oriented to the devices and websites. Participants were instructed to wear the trackers on 4 consecutive days and spend ?10 min/day on each website. A cognitive interview and survey were administered when the participant returned the devices. Qualitative data analysis was used to analyze the content of the cognitive interviews. Chi-square analyses were used to determine differences in behavioral monitoring and social interaction features between websites. Results: The MB, SQ, and ZZ devices or websites included 8, 15, and 14 of the possible 36 BCTs, respectively. All of the websites had a behavioral monitoring feature (charts for tracking activity), but the percentage of participants indicating that they ?liked? those features varied by website (MB: 8/16, 50%; SQ: 6/16, 38%; ZZ: 11/16, 69%). Two websites (SQ and ZZ) included an ?avatar? that the user could create to represent themselves on the website. Participants reported that they ?liked? creating and changing their avatar (SQ: 12/16, 75%, ZZ: 15/16, 94%), which was supported by the qualitative analyses of the cognitive interviews. Most participants (75%) indicated that they would want to wear the devices more if their friends were wearing a tracker. No significant differences were observed between SQ and ZZ devices in regards to liking or use of social support interaction features (P=.21 to .37). Conclusions: The websites contained several BCTs consistent with previously identified strategies. Children ?liked? the social aspects of the websites more than the activity tracking features. Developers of commercial activity trackers for youth may benefit from considering a theoretical perspective during the website design process. UR - http://mhealth.jmir.org/2017/4/e55/ DO - 10.2196/mhealth.6347 UR - http://www.ncbi.nlm.nih.gov/pubmed/28455278 ID - info:doi/10.2196/mhealth.6347 ER - TY - JOUR AU - Brinton, E. Julia AU - Keating, D. Mike AU - Ortiz, M. Alexa AU - Evenson, R. Kelly AU - Furberg, D. Robert PY - DA - 2017/04/27 TI - Establishing Linkages Between Distributed Survey Responses and Consumer Wearable Device Datasets: A Pilot Protocol JO - JMIR Res Protoc SP - e66 VL - 6 IS - 4 KW - Fitbit KW - Mturk KW - mHealth KW - clinical research protocol KW - consumer wearable KW - physical activity tracker AB - Background: As technology increasingly becomes an integral part of everyday life, many individuals are choosing to use wearable technology such as activity trackers to monitor their daily physical activity and other health-related goals. Researchers would benefit from learning more about the health of these individuals remotely, without meeting face-to-face with participants and avoiding the high cost of providing consumer wearables to participants for the study duration. Objective: The present study seeks to develop the methods to collect data remotely and establish a linkage between self-reported survey responses and consumer wearable device biometric data, ultimately producing a de-identified and linked dataset. Establishing an effective protocol will allow for future studies of large-scale deployment and participant management. Methods: A total of 30 participants who use a Fitbit will be recruited on Mechanical Turk Prime and asked to complete a short online self-administered questionnaire. They will also be asked to connect their personal Fitbit activity tracker to an online third-party software system, called Fitabase, which will allow access to 1 month?s retrospective data and 1 month?s prospective data, both from the date of consent. Results: The protocol will be used to create and refine methods to establish linkages between remotely sourced and de-identified survey responses on health status and consumer wearable device data. Conclusions: The refinement of the protocol will inform collection and linkage of similar datasets at scale, enabling the integration of consumer wearable device data collection in cross-sectional and prospective cohort studies. UR - http://www.researchprotocols.org/2017/4/e66/ DO - 10.2196/resprot.6513 UR - http://www.ncbi.nlm.nih.gov/pubmed/28450274 ID - info:doi/10.2196/resprot.6513 ER - TY - JOUR AU - Van Blarigan, L. Erin AU - Kenfield, A. Stacey AU - Tantum, Lucy AU - Cadmus-Bertram, A. Lisa AU - Carroll, R. Peter AU - Chan, M. June PY - DA - 2017/04/18 TI - The Fitbit One Physical Activity Tracker in Men With Prostate Cancer: Validation Study JO - JMIR Cancer SP - e5 VL - 3 IS - 1 KW - prostatic neoplasms KW - exercise AB - Background: Physical activity after cancer diagnosis improves quality of life and may lengthen survival. However, objective data in cancer survivors are limited and no physical activity tracker has been validated for use in this population. Objective: The aim of this study was to validate the Fitbit One?s measures of physical activity over 7 days in free-living men with localized prostate cancer. Methods: We validated the Fitbit One against the gold-standard ActiGraph GT3X+ accelerometer in 22 prostate cancer survivors under free-living conditions for 7 days. We also compared these devices with the HJ-322U Tri-axis USB Omron pedometer and a physical activity diary. We used descriptive statistics (eg, mean, standard deviation, median, interquartile range) and boxplots to examine the distribution of average daily light, moderate, and vigorous physical activity and steps measured by each device and the diary. We used Pearson and Spearman rank correlation coefficients to compare measures of physical activity and steps between the devices and the diary. Results: On average, the men wore the devices for 5.8 days. The mean (SD) moderate-to-vigorous physical activity (MVPA; minutes/day) measured was 100 (48) via Fitbit, 51 (29) via ActiGraph, and 110 (78) via diary. The mean (SD) steps/day was 8724 (3535) via Fitbit, 8024 (3231) via ActiGraph, and 6399 (3476) via pedometer. Activity measures were well correlated between the Fitbit and ActiGraph: 0.85 for MPVA and 0.94 for steps (all P<.001). The Fitbit?s step measurements were well correlated with the pedometer (0.67, P=.001), and the Fitbit?s measure of MVPA was well correlated with self-reported activity in the diary (0.84; P<.001). Conclusions: Among prostate cancer survivors, the Fitbit One?s activity and step measurements were well correlated with the ActiGraph GT3X+ and Omron pedometer. However, the Fitbit One measured two times more MVPA on average compared with the ActiGraph. UR - http://cancer.jmir.org/2017/1/e5/ DO - 10.2196/cancer.6935 UR - http://www.ncbi.nlm.nih.gov/pubmed/28420602 ID - info:doi/10.2196/cancer.6935 ER - TY - JOUR AU - Furberg, D. Robert AU - Taniguchi, Travis AU - Aagaard, Brian AU - Ortiz, M. Alexa AU - Hegarty-Craver, Meghan AU - Gilchrist, H. Kristin AU - Ridenour, A. Ty PY - DA - 2017/03/17 TI - Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations JO - JMIR Res Protoc SP - e44 VL - 6 IS - 3 KW - psychophysiology KW - law enforcement KW - sensor, wearable KW - clinical trial KW - digital health AB - Background: Stress experienced by law enforcement officers is often extreme and is in many ways unique among professions. Although past research on officer stress is informative, it is limited, and most studies measure stress using self-report questionnaires or observational studies that have limited generalizability. We know of no research studies that have attempted to track direct physiological stress responses in high fidelity, especially within an operational police setting. The outcome of this project will have an impact on both practitioners and policing researchers. To do so, we will establish a capacity to obtain complex, multisensor data; process complex datasets; and establish the methods needed to conduct idiopathic clinical trials on behavioral interventions in similar contexts. Objective: The objective of this pilot study is to demonstrate the practicality and utility of wrist-worn biometric sensor-based research in a law enforcement agency. Methods: We will use nonprobability convenience-based sampling to recruit 2-3 participants from the police department in Durham, North Carolina, USA. Results: Data collection was conducted in 2016. We will analyze data in early 2017 and disseminate our results via peer reviewed publications in late 2017. Conclusions: We developed the Biometrics & Policing Demonstration project to provide a proof of concept on collecting biometric data in a law enforcement setting. This effort will enable us to (1) address the regulatory approvals needed to collect data, including human participant considerations, (2) demonstrate the ability to use biometric tracking technology in a policing setting, (3) link biometric data to law enforcement data, and (4) explore project results for law enforcement policy and training. UR - http://www.researchprotocols.org/2017/3/e44/ DO - 10.2196/resprot.7499 UR - http://www.ncbi.nlm.nih.gov/pubmed/28314707 ID - info:doi/10.2196/resprot.7499 ER - TY - JOUR AU - Dooley, E. Erin AU - Golaszewski, M. Natalie AU - Bartholomew, B. John PY - DA - 2017/03/16 TI - Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices JO - JMIR Mhealth Uhealth SP - e34 VL - 5 IS - 3 KW - motor activity KW - physical exertion KW - exercise KW - monitoring, physiologic KW - energy metabolism KW - heart rate KW - photoplethysmography AB - Background: Physical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown. Objective: The objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities. Methods: A total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE). Results: For the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (P<.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (P<.001) and higher HR during light intensity (P<.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (P<.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (P<.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (P<.001). Conclusions: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist. UR - http://mhealth.jmir.org/2017/3/e34/ DO - 10.2196/mhealth.7043 UR - http://www.ncbi.nlm.nih.gov/pubmed/28302596 ID - info:doi/10.2196/mhealth.7043 ER - TY - JOUR AU - Wen, Dong AU - Zhang, Xingting AU - Liu, Xingyu AU - Lei, Jianbo PY - DA - 2017/03/07 TI - Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions JO - J Med Internet Res SP - e68 VL - 19 IS - 3 KW - fitness trackers KW - monitoring, physiologic KW - motor activity KW - activities of daily living KW - health status AB - Background: Wearable devices are gaining increasing market attention; however, the monitoring accuracy and consistency of the devices remains unknown. Objective: The purpose of this study was to assess the consistency of the monitoring measurements of the latest wearable devices in the state of normal activities to provide advice to the industry and support to consumers in making purchasing choices. Methods: Ten pieces of representative wearable devices (2 smart watches, 4 smart bracelets of Chinese brands or foreign brands, and 4 mobile phone apps) were selected, and 5 subjects were employed to simultaneously use all the devices and the apps. From these devices, intact health monitoring data were acquired for 5 consecutive days and analyzed on the degree of differences and the relationships of the monitoring measurements ??by the different devices. Results: The daily measurements by the different devices fluctuated greatly, and the coefficient of variation (CV) fluctuated in the range of 2-38% for the number of steps, 5-30% for distance, 19-112% for activity duration, .1-17% for total energy expenditure (EE), 22-100% for activity EE, 2-44% for sleep duration, and 35-117% for deep sleep duration. After integrating the measurement data of 25 days among the devices, the measurements of the number of steps (intraclass correlation coefficient, ICC=.89) and distance (ICC=.84) displayed excellent consistencies, followed by those of activity duration (ICC=.59) and the total EE (ICC=.59) and activity EE (ICC=.57). However, the measurements for sleep duration (ICC=.30) and deep sleep duration (ICC=.27) were poor. For most devices, there was a strong correlation between the number of steps and distance measurements (R2>.95), and for some devices, there was a strong correlation between activity duration measurements and EE measurements (R2>.7). A strong correlation was observed in the measurements of steps, distance and EE from smart watches and mobile phones of the same brand, Apple or Samsung (r>.88). Conclusions: Although wearable devices are developing rapidly, the current mainstream devices are only reliable in measuring the number of steps and distance, which can be used as health assessment indicators. However, the measurement consistencies of activity duration, EE, sleep quality, and so on, are still inadequate, which require further investigation and improved algorithms. UR - http://www.jmir.org/2017/3/e68/ DO - 10.2196/jmir.6874 UR - http://www.ncbi.nlm.nih.gov/pubmed/28270382 ID - info:doi/10.2196/jmir.6874 ER - TY - JOUR AU - Lyons, J. Elizabeth AU - Swartz, C. Maria AU - Lewis, H. Zakkoyya AU - Martinez, Eloisa AU - Jennings, Kristofer PY - DA - 2017/03/06 TI - Feasibility and Acceptability of a Wearable Technology Physical Activity Intervention With Telephone Counseling for Mid-Aged and Older Adults: A Randomized Controlled Pilot Trial JO - JMIR Mhealth Uhealth SP - e28 VL - 5 IS - 3 KW - physical activity KW - technology KW - mobile health KW - health behavior KW - self-control AB - Background: As adults age, their physical activity decreases and sedentary behavior increases, leading to increased risk of negative health outcomes. Wearable electronic activity monitors have shown promise for delivering effective behavior change techniques. However, little is known about the feasibility and acceptability of non-Fitbit wearables (Fitbit, Inc, San Francisco, California) combined with telephone counseling among adults aged more than 55 years. Objective: The purpose of our study was to determine the feasibility, acceptability, and effect on physical activity of an intervention combining a wearable physical activity monitor, tablet device, and telephone counseling among adults aged 55-79 years. Methods: Adults (N=40, aged 55-79 years, body mass index=25-35, <60 min of activity per week) were randomized to receive a 12-week intervention or to a wait list control. Intervention participants received a Jawbone Up24 monitor, a tablet with the Jawbone Up app installed, and brief weekly telephone counseling. Participants set daily and weekly step goals and used the monitor?s idle alert to notify them when they were sedentary for more than 1 h. Interventionists provided brief counseling once per week by telephone. Feasibility was measured using observation and study records, and acceptability was measured by self-report using validated items. Physical activity and sedentary time were measured using ActivPAL monitors following standard protocols. Body composition was measured using dual-energy x-ray absorptiometry scans, and fitness was measured using a 6-min walk test. Results: Participants were 61.48 years old (SD 5.60), 85% (34/40) female, 65% (26/40) white. Average activity monitor wear time was 81.85 (SD 3.73) of 90 days. Of the 20 Up24 monitors, 5 were reported broken and 1 lost. No related adverse events were reported. Acceptability items were rated at least 4 on a scale of 1-5. Effect sizes for most outcomes were small, including stepping time per day (d=0.35), steps per day (d=0.26), sitting time per day (d=0.21), body fat (d=0.17), and weight (d=0.33). Conclusions: The intervention was feasible and acceptable in this population. Effect sizes were similar to the sizes found using other wearable electronic activity monitors, indicating that when combined with telephone counseling, wearable activity monitors are a potentially effective tool for increasing physical activity and decreasing sedentary behavior. Trial registration: Clinicaltrials.gov NCT01869348; https://clinicaltrials.gov/ct2/show/NCT01869348 (Archived by WebCite at http://www.webcitation.org/6odlIolqy) UR - http://mhealth.jmir.org/2017/3/e28/ DO - 10.2196/mhealth.6967 UR - http://www.ncbi.nlm.nih.gov/pubmed/28264796 ID - info:doi/10.2196/mhealth.6967 ER - TY - JOUR AU - Pobiruchin, Monika AU - Suleder, Julian AU - Zowalla, Richard AU - Wiesner, Martin PY - DA - 2017/02/28 TI - Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study JO - JMIR Mhealth Uhealth SP - e24 VL - 5 IS - 2 KW - athlete KW - wearables KW - mobile phones KW - physical activity KW - activity monitoring AB - Background: Today, runners use wearable technology such as global positioning system (GPS)?enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors. Objective: The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially ?smart? devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices. Methods: A pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other. Results: A total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event. Conclusions: A great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances. UR - http://mhealth.jmir.org/2017/2/e24/ DO - 10.2196/mhealth.6395 UR - http://www.ncbi.nlm.nih.gov/pubmed/28246070 ID - info:doi/10.2196/mhealth.6395 ER - TY - JOUR AU - Hendrikx, Jos AU - Ruijs, S. Loes AU - Cox, GE Lieke AU - Lemmens, MC Paul AU - Schuijers, GP Erik AU - Goris, HC Annelies PY - DA - 2017/02/02 TI - Clinical Evaluation of the Measurement Performance of the Philips Health Watch: A Within-Person Comparative Study JO - JMIR Mhealth Uhealth SP - e10 VL - 5 IS - 2 KW - sedentary lifestyle KW - monitoring, ambulatory KW - monitoring, physiologic KW - accelerometry KW - actigraphy KW - photoplethysmography KW - heart rate KW - energy metabolism KW - adult KW - humans AB - Background: Physical inactivity is an important modifiable risk factor for chronic diseases. A new wrist-worn heart rate and activity monitor has been developed for unobtrusive data collection to aid prevention and management of lifestyle-related chronic diseases by means of behavioral change programs. Objective: The objective of the study was to evaluate the performance of total energy expenditure and resting heart rate measures of the Philips health watch. Secondary objectives included the assessment of accuracy of other output parameters of the monitor: heart rate, respiration rate at rest, step count, and activity type recognition. Methods: A within-person comparative study was performed to assess the performance of the health watch against (medical) reference measures. Participants executed a protocol including 15 minutes of rest and various activities of daily life. A two one-sided tests approach was adopted for testing equivalence. In addition, error metrics such as mean error and mean absolute percentage error (MAPE) were calculated. Results: A total of 29 participants (14 males; mean age 41.2, SD 14.4, years; mean weight 77.2, SD 10.2, kg; mean height 1.8, SD 0.1, m; mean body mass index 25.1, SD 3.1, kg/m2) completed the 81-minute protocol. Their mean resting heart rate in beats per minute (bpm) was 64 (SD 7.3). With a mean error of ?10 (SD 38.9) kcal and a MAPE of 10% (SD 8.7%), total energy expenditure estimation of the health watch was found to be within the 15% predefined equivalence margin in reference to a portable indirect calorimeter. Resting heart rate determined during a 15-minute rest protocol was found to be within a 10% equivalence margin in reference to a wearable electrocardiogram (ECG) monitor, with a mean deviation of 0 bpm and a maximum deviation of 3 bpm. Heart rate was within 10 bpm and 10% of the ECG monitor reference for 93% of the duration of the protocol. Step count estimates were on average 21 counts lower than a waist-mounted step counter over all walking activities combined, with a MAPE of 3.5% (SD 2.4%). Resting respiration rate was on average 0.7 (SD 1.1) breaths per minute lower than the reference measurement by the spirometer embedded in the indirect calorimeter during the 15-minute rest, resulting in a MAPE of 8.3% (SD 7.0%). Activity type recognition of walking, running, cycling, or other was overall 90% accurate in reference to the activities performed. Conclusions: The health watch can serve its medical purpose of measuring resting heart rate and total energy expenditure over time in an unobtrusive manner, thereby providing valuable data for the prevention and management of lifestyle-related chronic diseases. Trial Registration: Netherlands trial register NTR5552; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5552 (Archived by WebCite at http://www.webcitation.org/6neYJgysl) UR - http://mhealth.jmir.org/2017/2/e10/ DO - 10.2196/mhealth.6893 UR - http://www.ncbi.nlm.nih.gov/pubmed/28153815 ID - info:doi/10.2196/mhealth.6893 ER - TY - JOUR AU - Gualtieri, Lisa AU - Rosenbluth, Sandra AU - Phillips, Jeffrey PY - DA - 2016/12/15 TI - Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group JO - iproc SP - e1 VL - 2 IS - 1 KW - wearable activity trackers KW - fitness trackers KW - trackers KW - physical activity KW - chronic disease KW - behavior change KW - wellness group KW - wellness KW - older adults KW - digital health AB - Background: Wearable activity trackers (trackers) are increasingly popular devices used to track step count and other health indicators. Trackers have the potential to benefit those in need of increased physical activity, such as adults who are older and who face significant health challenges. These populations are least likely to purchase trackers and most likely to face challenges in using them, yet may derive educational, motivational, and health benefits from their use once these barriers are removed. Objective: The aim of this research was to investigate the use of trackers by older adults with chronic medical conditions who had never used trackers previously. Our primary research questions were (1) if participants would accept and use trackers to increase their physical activity; (2) if there were barriers to use besides cost and training; (3) if trackers would educate participants on their baseline and ongoing activity levels and support behavior change; and (4) if clinical outcomes would show improvements in participants? health. Methods: This study was conducted with 10 patients in a 12 week physician-led wellness group offered by Family Doctors, LLC. Patients were given trackers in the second week of the wellness group and were interviewed 2-4 weeks after it ended. The study investigators analyzed the interview notes to extract themes about the participants? attitudes and behavior changes and collected and analyzed participants? clinical data, including weight and LDL-Cholesterol (LDL), over the course of the study. Results: Over the 12-14 weeks of tracker use, improvements were seen in clinical outcomes, attitudes towards the trackers, and physical activity behaviors. Participants lost an average of a half-pound per week (SD=0.408), with a mean total weight loss of 5.97 pounds (P=.0038). Other short-term clinical outcomes included a 9.2% decrease in LDL levels (P=.0377). All participants reported an increase in well-being and confidence in their ability to lead more active lives. We identified 6 major attitudinal themes from our qualitative analysis of the interview notes: (1) barriers to tracker purchase included cost, perceived value, and choice confusion; (2) attitudes towards the trackers shifted for many, from half of the participants expressing excitement and hope and half expressing hesitation or trepidation, to all participants feeling positive towards their tracker at the time of the interviews; (3) trackers served as educational tools for baseline activity levels; (4) trackers provided concrete feedback on physical activity, which motivated behavior change; (5) tracker use reinforced wellness group activities and goals; and (6) although commitment to tracker use did not waver, external circumstances influenced some participants? ongoing use. Conclusions: Our findings suggest that adding trackers to wellness groups comprising older adults with chronic medical conditions can support education and behavior change to be more physically active. The trackers increased participant self-efficacy by providing a tangible, visible reminder of a commitment to increasing activity and immediate feedback on step count and progress towards a daily step goal. While acceptance was high and attitudes ultimately positive, training and support are needed and short-term drop-off in participant use is to be expected. Future research will further consider the potential of trackers in older adults with chronic medical conditions who are unlikely to purchase them, and studies will use larger samples, continue over a longer period of time, and evaluate outcomes independent of a wellness group. UR - http://www.iproc.org/2016/1/e1/ DO - 10.2196/iproc.6245 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/iproc.6245 ER - TY - JOUR AU - Althoff, Tim AU - White, W. Ryen AU - Horvitz, Eric PY - DA - 2016/12/06 TI - Influence of Pokémon Go on Physical Activity: Study and Implications JO - J Med Internet Res SP - e315 VL - 18 IS - 12 KW - physical activity KW - Pokémon Go KW - mobile health KW - mHealth KW - wearable devices KW - mobile applications KW - games KW - exergames KW - public health AB - Background: Physical activity helps people maintain a healthy weight and reduces the risk for several chronic diseases. Although this knowledge is widely recognized, adults and children in many countries around the world do not get recommended amounts of physical activity. Although many interventions are found to be ineffective at increasing physical activity or reaching inactive populations, there have been anecdotal reports of increased physical activity due to novel mobile games that embed game play in the physical world. The most recent and salient example of such a game is Pokémon Go, which has reportedly reached tens of millions of users in the United States and worldwide. Objective: The objective of this study was to quantify the impact of Pokémon Go on physical activity. Methods: We study the effect of Pokémon Go on physical activity through a combination of signals from large-scale corpora of wearable sensor data and search engine logs for 32,000 Microsoft Band users over a period of 3 months. Pokémon Go players are identified through search engine queries and physical activity is measured through accelerometers. Results: We find that Pokémon Go leads to significant increases in physical activity over a period of 30 days, with particularly engaged users (ie, those making multiple search queries for details about game usage) increasing their activity by 1473 steps a day on average, a more than 25% increase compared with their prior activity level (P<.001). In the short time span of the study, we estimate that Pokémon Go has added a total of 144 billion steps to US physical activity. Furthermore, Pokémon Go has been able to increase physical activity across men and women of all ages, weight status, and prior activity levels showing this form of game leads to increases in physical activity with significant implications for public health. In particular, we find that Pokémon Go is able to reach low activity populations, whereas all 4 leading mobile health apps studied in this work largely draw from an already very active population. Conclusions: Mobile apps combining game play with physical activity lead to substantial short-term activity increases and, in contrast to many existing interventions and mobile health apps, have the potential to reach activity-poor populations. Future studies are needed to investigate potential long-term effects of these applications. UR - http://www.jmir.org/2016/12/e315/ DO - 10.2196/jmir.6759 UR - http://www.ncbi.nlm.nih.gov/pubmed/27923778 ID - info:doi/10.2196/jmir.6759 ER - TY - JOUR AU - Gualtieri, Lisa AU - Rosenbluth, Sandra AU - Phillips, Jeffrey PY - DA - 2016/11/30 TI - Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group JO - JMIR Res Protoc SP - e237 VL - 5 IS - 4 KW - wearable activity trackers KW - fitness trackers KW - trackers KW - physical activity KW - chronic disease KW - behavior change KW - wellness group KW - wellness KW - older adults KW - digital health AB - Background: Wearable activity trackers (trackers) are increasingly popular devices used to track step count and other health indicators. Trackers have the potential to benefit those in need of increased physical activity, such as adults who are older and face significant health challenges. These populations are least likely to purchase trackers and most likely to face challenges in using them, yet may derive educational, motivational, and health benefits from their use once these barriers are removed. Objective: The aim of this pilot research is to investigate the use of trackers by adults with chronic medical conditions who have never used trackers previously. Specifically, we aim to determine (1) if participants would accept and use trackers to increase their physical activity; (2) if there were barriers to use besides cost and training; (3) if trackers would educate participants on their baseline and ongoing activity levels and support behavior change; and (4) if clinical outcomes would show improvements in participants? health. Methods: This study was conducted with patients (N=10) in a 12-week physician-led wellness group offered by Family Doctors, LLC. Patients were given trackers in the second week of The Wellness Group and were interviewed 2 to 4 weeks after it ended. The study investigators analyzed the interview notes to extract themes about the participants? attitudes and behavior changes and collected and analyzed participants? clinical data, including weight and low-density lipoprotein (LDL) cholesterol over the course of the study. Results: Over the 12 to 14 weeks of tracker use, improvements were seen in clinical outcomes, attitudes towards the trackers, and physical activity behaviors. Participants lost an average of 0.5 lbs per week (SD 0.4), with a mean total weight loss of 5.97 lbs (P=.004). Other short-term clinical outcomes included a 9.2% decrease in LDL levels (P=.038). All participants reported an increase in well-being and confidence in their ability to lead more active lives. We identified the following 6 major attitudinal themes from our qualitative analysis of the interview notes: (1) barriers to tracker purchase included cost, perceived value, and choice confusion; (2) attitudes towards the trackers shifted for many, from half of the participants expressing excitement and hope and half expressing hesitation or trepidation, to all participants feeling positive towards their tracker at the time of the interviews; (3) trackers served as educational tools for baseline activity levels; (4) trackers provided concrete feedback on physical activity, which motivated behavior change; (5) tracker use reinforced wellness group activities and goals; and (6) although commitment to tracker use did not waver, external circumstances influenced some participants? ongoing use. Conclusions: Our findings suggest that adding trackers to wellness groups comprising primarily older adults with chronic medical conditions can support education and behavior change to be more physically active. The trackers increased participant self-efficacy by providing a tangible, visible reminder of a commitment to increasing activity and immediate feedback on step count and progress towards a daily step goal. While acceptance was high and attitudes ultimately positive, training and support are needed and short-term drop-off in participant use is to be expected. Future research will further consider the potential of trackers in older adults with chronic medical conditions who are unlikely to purchase them, and studies will use larger samples, continue over a longer period of time, and evaluate outcomes independent of a wellness group. UR - http://www.researchprotocols.org/2016/4/e237/ DO - 10.2196/resprot.6534 UR - http://www.ncbi.nlm.nih.gov/pubmed/27903490 ID - info:doi/10.2196/resprot.6534 ER - TY - JOUR AU - Innominato, F. Pasquale AU - Komarzynski, Sandra AU - Mohammad-Djafari, Ali AU - Arbaud, Alexandre AU - Ulusakarya, Ayhan AU - Bouchahda, Mohamed AU - Haydar, Mazen AU - Bossevot-Desmaris, Rachel AU - Plessis, Virginie AU - Mocquery, Magali AU - Bouchoucha, Davina AU - Afshar, Mehran AU - Beau, Jacques AU - Karaboué, Abdoulaye AU - Morère, Jean-François AU - Fursse, Joanna AU - Rovira Simon, Jordi AU - Levi, Francis PY - DA - 2016/11/25 TI - Clinical Relevance of the First Domomedicine Platform Securing Multidrug Chronotherapy Delivery in Metastatic Cancer Patients at Home: The inCASA European Project JO - J Med Internet Res SP - e305 VL - 18 IS - 11 KW - domomedicine KW - chronotherapy KW - actigraphy KW - MDASI KW - telemonitoring AB - Background: Telehealth solutions can improve the safety of ambulatory chemotherapy, contributing to the maintenance of patients at their home, hence improving their well-being, all the while reducing health care costs. There is, however, need for a practicable multilevel monitoring solution, encompassing relevant outputs involved in the pathophysiology of chemotherapy-induced toxicity. Domomedicine embraces the delivery of complex care and medical procedures at the patient?s home based on modern technologies, and thus it offers an integrated approach for increasing the safety of cancer patients on chemotherapy. Objective: The objective was to evaluate patient compliance and clinical relevance of a novel integrated multiparametric telemonitoring domomedicine platform in cancer patients receiving multidrug chemotherapy at home. Methods: Self-measured body weight, self-rated symptoms using the 19-item MD Anderson Symptom Inventory (MDASI), and circadian rest-activity rhythm recording with a wrist accelerometer (actigraph) were transmitted daily by patients to a server via the Internet, using a dedicated platform installed at home. Daily body weight changes, individual MDASI scores, and relative percentage of activity in-bed versus out-of-bed (I7 were enrolled in-person to participate in the study for 6 months and were randomized into either the intervention arm that received the full complement of the intervention or a control arm that received only pedometers. The primary outcome was change in physical activity. We also assessed the effect of the intervention on HbA1c, weight, and participant engagement. Results: All participants (intervention: n=64; control: n=62) were included in the analyses. The intervention group had significantly higher monthly step counts in the third (risk ratio [RR] 4.89, 95% CI 1.20 to 19.92, P=.03) and fourth (RR 6.88, 95% CI 1.21 to 39.00, P=.03) months of the study compared to the control group. However, over the 6-month follow-up period, monthly step counts did not differ statistically by group (intervention group: 9092 steps; control group: 3722 steps; RR 2.44, 95% CI 0.68 to 8.74, P=.17). HbA1c decreased by 0.07% (95% CI ?0.47 to 0.34, P=.75) in the TTM group compared to the control group. Within groups, HbA1c decreased significantly from baseline in the TTM group by ?0.43% (95% CI ?0.75 to ?0.12, P=.01), but nonsignificantly in the control group by ?0.21% (95% CI ?0.49 to 0.06, P=.13). Similar changes were observed for other secondary outcomes. Conclusion: Personalized text messaging can be used to improve outcomes in patients with T2DM by employing optimal patient engagement measures. UR - http://www.jmir.org/2016/11/e307/ DO - 10.2196/jmir.6439 UR - http://www.ncbi.nlm.nih.gov/pubmed/27864165 ID - info:doi/10.2196/jmir.6439 ER - TY - JOUR AU - Kim, Young Ju AU - Wineinger, E. Nathan AU - Taitel, Michael AU - Radin, M. Jennifer AU - Akinbosoye, Osayi AU - Jiang, Jenny AU - Nikzad, Nima AU - Orr, Gregory AU - Topol, Eric AU - Steinhubl, Steve PY - DA - 2016/11/17 TI - Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors JO - J Med Internet Res SP - e292 VL - 18 IS - 11 KW - health behavior KW - mobile health KW - mobile apps KW - reward KW - self blood pressure monitoring KW - blood glucose self-monitoring AB - Background: The advent of digital technology has enabled individuals to track meaningful biometric data about themselves. This novel capability has spurred nontraditional health care organizations to develop systems that aid users in managing their health. One of the most prolific systems is Walgreens Balance Rewards for healthy choices (BRhc) program, an incentivized, Web-based self-monitoring program. Objective: This study was performed to evaluate health data self-tracking characteristics of individuals enrolled in the Walgreens? BRhc program, including the impact of manual versus automatic data entries through a supported device or apps. Methods: We obtained activity tracking data from a total of 455,341 BRhc users during 2014. Upon identifying users with sufficient follow-up data, we explored temporal trends in user participation. Results: Thirty-four percent of users quit participating after a single entry of an activity. Among users who tracked at least two activities on different dates, the median length of participating was 8 weeks, with an average of 5.8 activities entered per week. Furthermore, users who participated for at least twenty weeks (28.3% of users; 33,078/116,621) consistently entered 8 to 9 activities per week. The majority of users (77%; 243,774/315,744) recorded activities through manual data entry alone. However, individuals who entered activities automatically through supported devices or apps participated roughly four times longer than their manual activity-entering counterparts (average 20 and 5 weeks, respectively; P<.001). Conclusions: This study provides insights into the utilization patterns of individuals participating in an incentivized, Web-based self-monitoring program. Our results suggest automated health tracking could significantly improve long-term health engagement. UR - http://www.jmir.org/2016/11/e292/ DO - 10.2196/jmir.6371 UR - http://www.ncbi.nlm.nih.gov/pubmed/27856407 ID - info:doi/10.2196/jmir.6371 ER - TY - JOUR AU - Sathyanarayana, Aarti AU - Joty, Shafiq AU - Fernandez-Luque, Luis AU - Ofli, Ferda AU - Srivastava, Jaideep AU - Elmagarmid, Ahmed AU - Arora, Teresa AU - Taheri, Shahrad PY - DA - 2016/11/04 TI - Sleep Quality Prediction From Wearable Data Using Deep Learning JO - JMIR Mhealth Uhealth SP - e125 VL - 4 IS - 4 KW - wearables KW - sleep quality KW - sleep efficiency KW - actigraphy KW - body sensor networks KW - mobile health KW - connected health KW - accelerometer KW - physical activity KW - pervasive health KW - consumer health informatics KW - deep learning AB - Background: The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective: The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods: Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results: Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. ?CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Conclusions: Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. UR - http://mhealth.jmir.org/2016/4/e125/ DO - 10.2196/mhealth.6562 UR - http://www.ncbi.nlm.nih.gov/pubmed/27815231 ID - info:doi/10.2196/mhealth.6562 ER - TY - JOUR AU - Powell, Lauren AU - Parker, Jack AU - Martyn St-James, Marrissa AU - Mawson, Susan PY - DA - 2016/10/07 TI - The Effectiveness of Lower-Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: A Systematic Review JO - J Med Internet Res SP - e259 VL - 18 IS - 10 KW - wearable technology KW - stroke KW - gait KW - rehabilitation AB - Background: With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Regaining ambulation is a top priority for an increasing number of stroke survivors. However, despite an increase in research exploring these devices for lower limb rehabilitation, little is known of the effectiveness. Objective: This review aims to assess the effectiveness of lower limb wearable technology for improving activity and participation in adult stroke survivors. Methods: Randomized controlled trials (RCTs) of lower limb wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs. Results: In the review, we included 11 RCTs with collectively 550 participants at baseline and 474 participants at final follow-up including control groups and participants post stroke. Participants' stroke type and severity varied. Only one study found significant between-group differences for systems functioning and activity. Across the included RCTs, the lowest number of participants was 12 and the highest was 151 with a mean of 49 participants. The lowest number of participants to drop out of an RCT was zero in two of the studies and 19 in one study. Significant between-group differences were found across three of the 11 included trials. Out of the activity and participation measures alone, P values ranged from P=.87 to P ?.001. Conclusions: This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes, appropriateness of the RCT methodology for complex interventions, a lack of appropriate analysis of outcome data, and participant stroke severity. UR - http://www.jmir.org/2016/10/e259/ DO - 10.2196/jmir.5891 UR - http://www.ncbi.nlm.nih.gov/pubmed/27717920 ID - info:doi/10.2196/jmir.5891 ER - TY - JOUR AU - Ehrler, Frederic AU - Weber, Chloé AU - Lovis, Christian PY - DA - 2016/10/06 TI - Influence of Pedometer Position on Pedometer Accuracy at Various Walking Speeds: A Comparative Study JO - J Med Internet Res SP - e268 VL - 18 IS - 10 KW - frail elderly KW - mHealth KW - walking KW - motor activity AB - Background: Demographic growth in conjunction with the rise of chronic diseases is increasing the pressure on health care systems in most OECD countries. Physical activity is known to be an essential factor in improving or maintaining good health. Walking is especially recommended, as it is an activity that can easily be performed by most people without constraints. Pedometers have been extensively used as an incentive to motivate people to become more active. However, a recognized problem with these devices is their diminishing accuracy associated with decreased walking speed. The arrival on the consumer market of new devices, worn indifferently either at the waist, wrist, or as a necklace, gives rise to new questions regarding their accuracy at these different positions. Objective: Our objective was to assess the performance of 4 pedometers (iHealth activity monitor, Withings Pulse O2, Misfit Shine, and Garmin vívofit) and compare their accuracy according to their position worn, and at various walking speeds. Methods: We conducted this study in a controlled environment with 21 healthy adults required to walk 100 m at 3 different paces (0.4 m/s, 0.6 m/s, and 0.8 m/s) regulated by means of a string attached between their legs at the level of their ankles and a metronome ticking the cadence. To obtain baseline values, we asked the participants to walk 200 m at their own pace. Results: A decrease of accuracy was positively correlated with reduced speed for all pedometers (12% mean error at self-selected pace, 27% mean error at 0.8 m/s, 52% mean error at 0.6 m/s, and 76% mean error at 0.4 m/s). Although the position of the pedometer on the person did not significantly influence its accuracy, some interesting tendencies can be highlighted in 2 settings: (1) positioning the pedometer at the waist at a speed greater than 0.8 m/s or as a necklace at preferred speed tended to produce lower mean errors than at the wrist position; and (2) at a slow speed (0.4 m/s), pedometers worn at the wrist tended to produce a lower mean error than in the other positions. Conclusions: At all positions, all tested pedometers generated significant errors at slow speeds and therefore cannot be used reliably to evaluate the amount of physical activity for people walking slower than 0.6 m/s (2.16 km/h, or 1.24 mph). At slow speeds, the better accuracy observed with pedometers worn at the wrist could constitute a valuable line of inquiry for the future development of devices adapted to elderly people. UR - http://www.jmir.org/2016/10/e268/ DO - 10.2196/jmir.5916 UR - http://www.ncbi.nlm.nih.gov/pubmed/27713114 ID - info:doi/10.2196/jmir.5916 ER - TY - JOUR AU - Dominick, M. Gregory AU - Winfree, N. Kyle AU - Pohlig, T. Ryan AU - Papas, A. Mia PY - DA - 2016/09/19 TI - Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions JO - JMIR Mhealth Uhealth SP - e110 VL - 4 IS - 3 KW - Fitbit KW - activity tracker KW - actigraphy KW - physical activity KW - aerobic exercise KW - validity AB - Background: Wearable activity monitors such as Fitbit enable users to track various attributes of their physical activity (PA) over time and have the potential to be used in research to promote and measure PA behavior. However, the measurement accuracy of Fitbit in absolute free-living conditions is largely unknown. Objective: To examine the measurement congruence between Fitbit Flex and ActiGraph GT3X for quantifying steps, metabolic equivalent tasks (METs), and proportion of time in sedentary activity and light-, moderate-, and vigorous-intensity PA in healthy adults in free-living conditions. Methods: A convenience sample of 19 participants (4 men and 15 women), aged 18-37 years, concurrently wore the Fitbit Flex (wrist) and ActiGraph GT3X (waist) for 1- or 2-week observation periods (n=3 and n=16, respectively) that included self-reported bouts of daily exercise. Data were examined for daily activity, averaged over 14 days and for minutes of reported exercise. Average day-level data included steps, METs, and proportion of time in different intensity levels. Minute-level data included steps, METs, and mean intensity score (0 = sedentary, 3 = vigorous) for overall reported exercise bouts (N=120) and by exercise type (walking, n=16; run or sports, n=44; cardio machine, n=20). Results: Measures of steps were similar between devices for average day- and minute-level observations (all P values > .05). Fitbit significantly overestimated METs for average daily activity, for overall minutes of reported exercise bouts, and for walking and run or sports exercises (mean difference 0.70, 1.80, 3.16, and 2.00 METs, respectively; all P values < .001). For average daily activity, Fitbit significantly underestimated the proportion of time in sedentary and light intensity by 20% and 34%, respectively, and overestimated time by 3% in both moderate and vigorous intensity (all P values < .001). Mean intensity scores were not different for overall minutes of exercise or for run or sports and cardio-machine exercises (all P values > .05). Conclusions: Fitbit Flex provides accurate measures of steps for daily activity and minutes of reported exercise, regardless of exercise type. Although the proportion of time in different intensity levels varied between devices, examining the mean intensity score for minute-level bouts across different exercise types enabled interdevice comparisons that revealed similar measures of exercise intensity. Fitbit Flex is shown to have measurement limitations that may affect its potential utility and validity for measuring PA attributes in free-living conditions. UR - http://mhealth.jmir.org/2016/3/e110/ DO - 10.2196/mhealth.6281 UR - http://www.ncbi.nlm.nih.gov/pubmed/27644334 ID - info:doi/10.2196/mhealth.6281 ER - TY - JOUR AU - Gomersall, R. Sjaan AU - Ng, Norman AU - Burton, W. Nicola AU - Pavey, G. Toby AU - Gilson, D. Nicholas AU - Brown, J. Wendy PY - DA - 2016/09/07 TI - Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry JO - J Med Internet Res SP - e239 VL - 18 IS - 9 KW - activity tracker KW - physical activity KW - sedentary behavior KW - accelerometry KW - Fitbit KW - Jawbone AB - Background: Activity trackers are increasingly popular with both consumers and researchers for monitoring activity and for promoting positive behavior change. However, there is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings. Objective: The aim was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior with data from the ActiGraph GT3X+ accelerometer in a free-living context. Methods: Thirty-two participants were recruited using convenience sampling; 29 provided valid data for this study (female: 90%, 26/29; age: mean 39.6, SD 11.0 years). On two occasions for 7 days each, participants wore an ActiGraph GT3X+ accelerometer on their right hip and either a hip-worn Fitbit One (n=14) or wrist-worn Jawbone UP (n=15) activity tracker. Daily estimates of steps and very active minutes were derived from the Fitbit One (n=135 days) and steps, active time, and longest idle time from the Jawbone UP (n=154 days). Daily estimates of steps, MVPA, and longest sedentary bout were derived from the corresponding days of ActiGraph data. Correlation coefficients and Bland-Altman plots with examination of systematic bias were used to assess convergent validity and agreement between the devices and the ActiGraph. Cohen?s kappa was used to assess the agreement between each device and the ActiGraph for classification of active versus inactive (?10,000 steps per day and ?30 min/day of MVPA) comparable with public health guidelines. Results: Correlations with ActiGraph estimates of steps and MVPA ranged between .72 and .90 for Fitbit One and .56 and .75 for Jawbone UP. Compared with ActiGraph estimates, both devices overestimated daily steps by 8% (Fitbit One) and 14% (Jawbone UP). However, mean differences were larger for daily MVPA (Fitbit One: underestimated by 46%; Jawbone UP: overestimated by 50%). There was systematic bias across all outcomes for both devices. Correlations with ActiGraph data for longest idle time (Jawbone UP) ranged from .08 to .19. Agreement for classifying days as active or inactive using the ?10,000 steps/day criterion was substantial (Fitbit One: ?=.68; Jawbone UP: ?=.52) and slight-fair using the criterion of ?30 min/day of MVPA (Fitbit One: ?=.40; Jawbone UP: ?=.14). Conclusions: There was moderate-strong agreement between the ActiGraph and both Fitbit One and Jawbone UP for the estimation of daily steps. However, due to modest accuracy and systematic bias, they are better suited for consumer-based self-monitoring (eg, for the public consumer or in behavior change interventions) rather than to evaluate research outcomes. The outcomes that relate to health-enhancing MVPA (eg, ?very active minutes? for Fitbit One or ?active time? for Jawbone UP) and sedentary behavior (?idle time? for Jawbone UP) should be used with caution by consumers and researchers alike. UR - http://www.jmir.org/2016/9/e239/ DO - 10.2196/jmir.5531 UR - http://www.ncbi.nlm.nih.gov/pubmed/27604226 ID - info:doi/10.2196/jmir.5531 ER - TY - JOUR AU - Painter, Stefanie AU - Ditsch, Gary AU - Ahmed, Rezwan AU - Hanson, Buck Nicholas AU - Kachin, Kevin AU - Berger, Jan PY - DA - 2016/08/22 TI - Retrofit Weight-Loss Outcomes at 6, 12, and 24 Months and Characteristics of 12-Month High Performers: A Retrospective Analysis JO - JMIR Mhealth Uhealth SP - e101 VL - 4 IS - 3 KW - behavior KW - body mass index KW - BMI KW - engagement KW - fitness KW - self-monitoring KW - obesity KW - overweight KW - weight loss AB - Background: Obesity is the leading cause of preventable death costing the health care system billions of dollars. Combining self-monitoring technology with personalized behavior change strategies results in clinically significant weight loss. However, there is a lack of real-world outcomes in commercial weight-loss program research. Objective: Retrofit is a personalized weight management and disease-prevention solution. This study aimed to report Retrofit?s weight-loss outcomes at 6, 12, and 24 months and characterize behaviors, age, and sex of high-performing participants who achieved weight loss of 10% or greater at 12 months. Methods: A retrospective analysis was performed from 2011 to 2014 using 2720 participants enrolled in a Retrofit weight-loss program. Participants had a starting body mass index (BMI) of >25 kg/m² and were at least 18 years of age. Weight measurements were assessed at 6, 12, and 24 months in the program to evaluate change in body weight, BMI, and percentage of participants who achieved 5% or greater weight loss. A secondary analysis characterized high-performing participants who lost ?10% of their starting weight (n=238). Characterized behaviors were evaluated, including self-monitoring through weigh-ins, number of days wearing an activity tracker, daily step count average, and engagement through coaching conversations via Web-based messages, and number of coaching sessions attended. Results: Average weight loss at 6 months was ?5.55% for male and ?4.86% for female participants. Male and female participants had an average weight loss of ?6.28% and ?5.37% at 12 months, respectively. Average weight loss at 24 months was ?5.03% and ?3.15% for males and females, respectively. Behaviors of high-performing participants were assessed at 12 months. Number of weigh-ins were greater in high-performing male (197.3 times vs 165.4 times, P=.001) and female participants (222 times vs 167 times, P<.001) compared with remaining participants. Total activity tracker days and average steps per day were greater in high-performing females (304.7 vs 266.6 days, P<.001; 8380.9 vs 7059.7 steps, P<.001, respectively) and males (297.1 vs 255.3 days, P<.001; 9099.3 vs 8251.4 steps, P=.008, respectively). High-performing female participants had significantly more coaching conversations via Web-based messages than remaining female participants (341.4 vs 301.1, P=.03), as well as more days with at least one such electronic message (118 vs 108 days, P=.03). High-performing male participants displayed similar behavior. Conclusions: Participants on the Retrofit program lost an average of ?5.21% at 6 months, ?5.83% at 12 months, and ?4.09% at 24 months. High-performing participants show greater adherence to self-monitoring behaviors of weighing in, number of days wearing an activity tracker, and average number of steps per day. Female high performers have higher coaching engagement through conversation days and total number of coaching conversations. UR - http://mhealth.jmir.org/2016/3/e101/ DO - 10.2196/mhealth.5873 UR - http://www.ncbi.nlm.nih.gov/pubmed/27549134 ID - info:doi/10.2196/mhealth.5873 ER - TY - JOUR AU - Jones, Donald AU - Skrepnik, Nebojsa AU - Toselli, M. Richard AU - Leroy, Bruno PY - DA - 2016/08/09 TI - Incorporating Novel Mobile Health Technologies Into Management of Knee Osteoarthritis in Patients Treated With Intra-Articular Hyaluronic Acid: Rationale and Protocol of a Randomized Controlled Trial JO - JMIR Res Protoc SP - e164 VL - 5 IS - 3 KW - mHealth KW - osteoarthritis KW - pain KW - physical therapy AB - Background: Osteoarthritis (OA) of the knee is one of the leading causes of disability in the United States. One relatively new strategy that could be helpful in the management of OA is the use of mHealth technologies, as they can be used to increase physical activity and promote exercise, which are key components of knee OA management. Objective: Currently, no published data on the use of a mHealth approach to comprehensively monitor physical activity in patients with OA are available, and similarly, no data on whether mHealth technologies can impact outcomes are available. Our objective is to evaluate the effectiveness of mHealth technology as part of a tailored, comprehensive management strategy for patients with knee OA. Methods: The study will assess the impact of a smartphone app that integrates data from a wearable activity monitor (thereby both encouraging changes in mobility as well as tracking them) combined with education about the benefits of walking on patient mobility. The results from the intervention group will be compared with data from a control group of individuals who are given the same Arthritis Foundation literature regarding the benefits of walking and wearable activity monitors but who do not have access to the data from those monitors. Activity monitors will capture step count estimates and will compare those with patients? step goals, calories burned, and distance walked. Patients using the novel smartphone app will be able to enter information on their daily pain, mood, and sleep quality. The relationships among activity and pain, activity and mood, and sleep will be assessed, as will patient satisfaction with and adherence to the mobile app. Results: We present information on an upcoming trial that will prospectively assess the ability of a mobile app to improve mobility for knee OA patients who are treated with intra-articular hyaluronic acid. Conclusions: We anticipate the results of this study will support the concept that mHealth technologies provide continuous, real-time feedback to patients with OA on their overall level of activity for a more proactive, personalized approach to treatment that may help modify behavior and assist with self-management through treatment support in the form of motivational messages and reminders. UR - http://www.researchprotocols.org/2016/3/e164/ DO - 10.2196/resprot.5940 UR - http://www.ncbi.nlm.nih.gov/pubmed/27506148 ID - info:doi/10.2196/resprot.5940 ER - TY - JOUR AU - Moy, L. Marilyn AU - Martinez, H. Carlos AU - Kadri, Reema AU - Roman, Pia AU - Holleman, G. Robert AU - Kim, Myra Hyungjin AU - Nguyen, Q. Huong AU - Cohen, D. Miriam AU - Goodrich, E. David AU - Giardino, D. Nicholas AU - Richardson, R. Caroline PY - DA - 2016/08/08 TI - Long-Term Effects of an Internet-Mediated Pedometer-Based Walking Program for Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial JO - J Med Internet Res SP - e215 VL - 18 IS - 8 KW - bronchitis, chronic KW - emphysema KW - pulmonary disease, chronic obstructive KW - quality of life KW - exercise KW - motor activity KW - Internet AB - Background: Regular physical activity (PA) is recommended for persons with chronic obstructive pulmonary disease (COPD). Interventions that promote PA and sustain long-term adherence to PA are needed. Objective: We examined the effects of an Internet-mediated, pedometer-based walking intervention, called Taking Healthy Steps, at 12 months. Methods: Veterans with COPD (N=239) were randomized in a 2:1 ratio to the intervention or wait-list control. During the first 4 months, participants in the intervention group were instructed to wear the pedometer every day, upload daily step counts at least once a week, and were provided access to a website with four key components: individualized goal setting, iterative feedback, educational and motivational content, and an online community forum. The subsequent 8-month maintenance phase was the same except that participants no longer received new educational content. Participants randomized to the wait-list control group were instructed to wear the pedometer, but they did not receive step-count goals or instructions to increase PA. The primary outcome was health-related quality of life (HRQL) assessed by the St George?s Respiratory Questionnaire Total Score (SGRQ-TS); the secondary outcome was daily step count. Linear mixed-effect models assessed the effect of intervention over time. One participant was excluded from the analysis because he was an outlier. Within the intervention group, we assessed pedometer adherence and website engagement by examining percent of days with valid step-count data, number of log-ins to the website each month, use of the online community forum, and responses to a structured survey. Results: Participants were 93.7% male (223/238) with a mean age of 67 (SD 9) years. At 12 months, there were no significant between-group differences in SGRQ-TS or daily step count. Between-group difference in daily step count was maximal and statistically significant at month 4 (P<.001), but approached zero in months 8-12. Within the intervention group, mean 76.7% (SD 29.5) of 366 days had valid step-count data, which decreased over the months of study (P<.001). Mean number of log-ins to the website each month also significantly decreased over the months of study (P<.001). The online community forum was used at least once during the study by 83.8% (129/154) of participants. Responses to questions assessing participants? goal commitment and intervention engagement were not significantly different at 12 months compared to 4 months. Conclusions: An Internet-mediated, pedometer-based PA intervention, although efficacious at 4 months, does not maintain improvements in HRQL and daily step counts at 12 months. Waning pedometer adherence and website engagement by the intervention group were observed. Future efforts should focus on improving features of PA interventions to promote long-term behavior change and sustain engagement in PA. ClinicalTrial: Clinicaltrials.gov NCT01102777; https://clinicaltrials.gov/ct2/show/NCT01102777 (Archived by WebCite at http://www.webcitation.org/6iyNP9KUC) UR - http://www.jmir.org/2016/8/e215/ DO - 10.2196/jmir.5622 UR - http://www.ncbi.nlm.nih.gov/pubmed/27502583 ID - info:doi/10.2196/jmir.5622 ER - TY - JOUR AU - Bruening, Meg AU - van Woerden, Irene AU - Todd, Michael AU - Brennhofer, Stephanie AU - Laska, N. Melissa AU - Dunton, Genevieve PY - DA - 2016/07/27 TI - A Mobile Ecological Momentary Assessment Tool (devilSPARC) for Nutrition and Physical Activity Behaviors in College Students: A Validation Study JO - J Med Internet Res SP - e209 VL - 18 IS - 7 KW - validation study KW - ecological momentary assessment KW - nutritional status KW - physical activity KW - sedentary activity KW - emerging adults AB - Background: The majority of nutrition and physical activity assessments methods commonly used in scientific research are subject to recall and social desirability biases, which result in over- or under-reporting of behaviors. Real-time mobile-based ecological momentary assessments (mEMAs) may result in decreased measurement biases and minimize participant burden. Objective: The aim was to examine the validity of a mEMA methodology to assess dietary and physical activity levels compared to 24-hour dietary recalls and accelerometers. Methods: This study was a pilot test of the SPARC (Social impact of Physical Activity and nutRition in College) study, which aimed to determine the mechanism by which friendship networks impact weight-related behaviors among young people. An mEMA app, devilSPARC, was developed to assess weight-related behaviors in real time. A diverse sample of 109 freshmen and community mentors attending a large southwestern university downloaded the devilSPARC mEMA app onto their personal mobile phones. Participants were prompted randomly eight times per day over the course of 4 days to complete mEMAs. During the same 4-day period, participants completed up to three 24-hour dietary recalls and/or 4 days of accelerometry. Self-reported mEMA responses were compared to 24-hour dietary recalls and accelerometry measures using comparison statistics, such as match rate, sensitivity and specificity, and mixed model odds ratios, adjusted for within-person correlation among repeated measurements. Results: At the day level, total dietary intake data reported through the mEMA app reflected eating choices also captured by the 24-hour recall. Entrées had the lowest match rate, and fruits and vegetables had the highest match rate. Widening the window of aggregation of 24-hour dietary recall data on either side of the mEMA response resulted in increased specificity and decreased sensitivity. For physical activity behaviors, levels of activity reported through mEMA differed for sedentary versus non-sedentary activity at the day level as measured by accelerometers. Conclusions: The devilSPARC mEMA app is valid for assessing eating behaviors and the presence of sedentary activity at the day level. This mEMA may be useful in studies examining real-time weight-related behaviors. UR - http://www.jmir.org/2016/7/e209/ DO - 10.2196/jmir.5969 UR - http://www.ncbi.nlm.nih.gov/pubmed/27465701 ID - info:doi/10.2196/jmir.5969 ER - TY - JOUR AU - Ortiz, M. Alexa AU - Tueller, J. Stephen AU - Cook, L. Sarah AU - Furberg, D. Robert PY - DA - 2016/07/25 TI - ActiviTeen: A Protocol for Deployment of a Consumer Wearable Device in an Academic Setting JO - JMIR Res Protoc SP - e153 VL - 5 IS - 3 KW - mHealth KW - clinical research protocol KW - Fitbit KW - physical activity tracker KW - survival analaysis KW - technology deployment KW - education AB - Background: Regular physical activity (PA) can be an important indicator of health across an individual?s life span. Consumer wearables, such as Fitbit or Jawbone, are becoming increasingly popular to track PA. With the increased adoption of activity trackers comes the increased generation of valuable individual-based data. Generated data has the potential to provide detailed insights into the user?s behavior and lifestyle. Objective: The primary objective of the described study is to evaluate the feasibility of individual data collection from the selected consumer wearable device (the Fitbit Zip). The rate of user attrition and barriers preventing the use of consumer wearable devices will also be evaluated as secondary objectives. Methods: The pilot study will occur in two stages and employs a long-term review and analysis with a convenience sample of 30 students attending Research Triangle High School. For the first stage, students will initially be asked to wear the Fitbit Zip over the course of 4 weeks. During which time, their activity data and step count will be collected. Students will also be asked to complete a self-administered survey at the beginning and conclusion of the first stage. The second stage will continue to collect students? activity data and step count over an additional 3-month period. Results: We are anticipating results for this study by the end of 2016. Conclusion: This study will provide insight into the data collection procedures surrounding consumer wearable devices and could serve as the future foundation for other studies deploying consumer wearable devices in educational settings. UR - http://www.researchprotocols.org/2016/3/e153/ DO - 10.2196/resprot.5934 UR - http://www.ncbi.nlm.nih.gov/pubmed/27457824 ID - info:doi/10.2196/resprot.5934 ER - TY - JOUR AU - Pande, Amit AU - Mohapatra, Prasant AU - Nicorici, Alina AU - Han, J. Jay PY - DA - 2016/07/19 TI - Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy JO - JMIR Rehabil Assist Technol SP - e7 VL - 3 IS - 2 KW - accelerometry KW - physical activity KW - heart rate KW - neuromuscular disease KW - children AB - Background: Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention. Objective: This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data. Methods: There were 7 boys with DMD, 6 healthy control boys, and 22 control adults recruited. Data were collected using smartphone accelerometer and chest-worn heart rate sensors. The gold standard EE values were obtained from the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 years) with DMD and controls. Data from this sensor setup were collected simultaneously during a series of concurrent activities. Linear regression and nonlinear machine-learning?based approaches were used to analyze the relationship between accelerometer and heart rate readings and COSMED values. Results: Existing calorimetry equations using linear regression and nonlinear machine-learning?based models, developed for healthy adults and young children, give low correlation to actual EE values in children with disabilities (14%-40%). The proposed model for boys with DMD uses ensemble machine learning techniques and gives a 91% correlation with actual measured EE values (root mean square error of 0.017). Conclusions: Our results confirm that the methods developed to determine EE using accelerometer and heart rate sensor values in normal adults are not appropriate for children with disabilities and should not be used. A much more accurate model is obtained using machine-learning?based nonlinear regression specifically developed for this target population. UR - http://rehab.jmir.org/2016/2/e7/ DO - 10.2196/rehab.4340 UR - http://www.ncbi.nlm.nih.gov/pubmed/28582264 ID - info:doi/10.2196/rehab.4340 ER - TY - JOUR AU - Chang, Cherng-Shiow Rebecca AU - Lu, Hsi-Peng AU - Yang, Peishan AU - Luarn, Pin PY - DA - 2016/07/05 TI - Reciprocal Reinforcement Between Wearable Activity Trackers and Social Network Services in Influencing Physical Activity Behaviors JO - JMIR Mhealth Uhealth SP - e84 VL - 4 IS - 3 KW - Wearable activity trackers KW - wearables KW - physical activity KW - social support KW - social network services KW - behavior change techniques AB - Background: Wearable activity trackers (WATs) are emerging consumer electronic devices designed to support physical activities (PAs), which are based on successful behavior change techniques focusing on goal-setting and frequent behavioral feedbacks. Despite their utility, data from both recent academic and market research have indicated high attrition rates of WAT users. Concurrently, evidence shows that social support (SS), delivered/obtained via social network services or sites (SNS), could increase adherence and engagement of PA intervention programs. To date, relatively few studies have looked at how WATs and SS may interact and affect PAs. Objective: The purpose of this study was to explore how these two Internet and mobile technologies, WATs and SNS, could work together to foster sustainable PA behavior changes and habits among middle-aged adults (40-60 years old) in Taiwan. Methods: We used purposive sampling of Executive MBA Students from National Taiwan University of Science and Technology to participate in our qualitative research. In-depth interviews and focus groups were conducted with a total of 15 participants, including 9 WAT users and 6 nonusers. Analysis of the collected materials was done inductively using the thematic approach with no preset categories. Two authors from different professional backgrounds independently annotated and coded the transcripts, and then discussed and debated until consensus was reached on the final themes. Results: The thematic analysis revealed six themes: (1) WATs provided more awareness than motivation in PA with goal-setting and progress monitoring, (2) SS, delivered/obtained via SNS, increased users? adherence and engagement with WATs and vice versa, (3) a broad spectrum of configurations would be needed to deliver WATs with appropriately integrated SS functions, (4) WAT design, style, and appearance mattered even more than those of smartphones, as they are body-worn devices, (5) the user interfaces of WATs left a great deal to be desired, and (6) privacy concerns must be addressed before more mainstream consumers would consider adopting WATs. Conclusions: Participants perceived WATs as an awareness tool to understand one?s PA level. It is evident from our study that SS, derived from SNS and other pertinent vehicles such as the LINE social messaging application (similar to WhatsApp and WeChat), will increase the engagement and adherence of WAT usage. Combining WATs and SNS enables cost-effective, scalable PA intervention programs with end-to-end services and data analytics capabilities, to elevate WATs from one-size-fits-all consumer electronics to personalized PA assistants. UR - http://mhealth.jmir.org/2016/3/e84/ DO - 10.2196/mhealth.5637 UR - http://www.ncbi.nlm.nih.gov/pubmed/27380798 ID - info:doi/10.2196/mhealth.5637 ER - TY - JOUR AU - Valenti, Giulio AU - Bonomi, G. Alberto AU - Westerterp, R. Klaas PY - DA - 2016/06/07 TI - Walking as a Contributor to Physical Activity in Healthy Older Adults: 2 Week Longitudinal Study Using Accelerometry and the Doubly Labeled Water Method JO - JMIR mHealth uHealth SP - e56 VL - 4 IS - 2 KW - aging KW - walking KW - physical activity KW - accelerometry KW - monitoring, ambulatory/instrumentation AB - Background: Physical activity is recommended to promote healthy aging. Defining the importance of activities such as walking in achieving higher levels of physical activity might provide indications for interventions. Objective: To describe the importance of walking in achieving higher levels of physical activity in older adults. Methods: The study included 42 healthy subjects aged between 51 and 84 years (mean body mass index 25.6 kg/m2 [SD 2.6]). Physical activity, walking, and nonwalking activity were monitored with an accelerometer for 2 weeks. Physical activity was quantified by accelerometer-derived activity counts. An algorithm based on template matching and signal power was developed to classify activity counts into nonwalking counts, short walk counts, and long walk counts. Additionally, in a subgroup of 31 subjects energy expenditure was measured using doubly labeled water to derive physical activity level (PAL). Results: Subjects had a mean PAL of 1.84 (SD 0.19, range 1.43-2.36). About 20% of the activity time (21% [SD 8]) was spent walking, which accounted for about 40% of the total counts (43% [SD 11]). Short bouts composed 83% (SD 9) of walking time, providing 81% (SD 11) of walking counts. A stepwise regression model to predict PAL included nonwalking counts and short walk counts, explaining 58% of the variance of PAL (standard error of the estimate=0.12). Walking activities produced more counts per minute than nonwalking activities (P<.001). Long walks produced more counts per minute than short walks (P=.001). Nonwalking counts were independent of walking counts (r=?.05, P=.38). Conclusions: Walking activities are a major contributor to physical activity in older adults. Walking activities occur at higher intensities than nonwalking activities, which might prevent individuals from engaging in more walking activity. Finally, subjects who engage in more walking activities do not tend to compensate by limiting nonwalking activities. Trial Registration: ClinicalTrials.gov NCT01609764; https://clinicaltrials.gov/ct2/show/NCT01609764 (Archived by WebCite at http://www.webcitation.org/6grls0wAp) UR - http://mhealth.jmir.org/2016/2/e56/ DO - 10.2196/mhealth.5445 UR - http://www.ncbi.nlm.nih.gov/pubmed/27268471 ID - info:doi/10.2196/mhealth.5445 ER - TY - JOUR AU - Dunton, Fridlund Genevieve AU - Dzubur, Eldin AU - Intille, Stephen PY - DA - 2016/06/01 TI - Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity JO - J Med Internet Res SP - e106 VL - 18 IS - 6 KW - mobile phones KW - ecological momentary assessment KW - accelerometer KW - physical activity AB - Background: Objective physical activity monitors (eg, accelerometers) have high rates of nonwear and do not provide contextual information about behavior. Objective: This study tested performance and value of a mobile phone app that combined objective and real-time self-report methods to measure physical activity using sensor-informed context-sensitive ecological momentary assessment (CS-EMA). Methods: The app was programmed to prompt CS-EMA surveys immediately after 3 types of events detected by the mobile phone?s built-in motion sensor: (1) Activity (ie, mobile phone movement), (2) No-Activity (ie, mobile phone nonmovement), and (3) No-Data (ie, mobile phone or app powered off). In addition, the app triggered random (ie, signal-contingent) ecological momentary assessment (R-EMA) prompts (up to 7 per day). A sample of 39 ethnically diverse high school students in the United States (aged 14-18, 54% female) tested the app over 14 continuous days during nonschool time. Both CS-EMA and R-EMA prompts assessed activity type (eg, reading or doing homework, eating or drinking, sports or exercising) and contextual characteristics of the activity (eg, location, social company, purpose). Activity was also measured with a waist-worn Actigraph accelerometer. Results: The average CS-EMA + R-EMA prompt compliance and survey completion rates were 80.5% and 98.5%, respectively. More moderate-to-vigorous intensity physical activity was recorded by the waist-worn accelerometer in the 30 minutes before CS-EMA activity prompts (M=5.84 minutes) than CS-EMA No-Activity (M=1.11 minutes) and CS-EMA No-Data (M=0.76 minute) prompts (P?s<.001). Participants were almost 5 times as likely to report going somewhere (ie, active or motorized transit) in the 30 minutes before CS-EMA Activity than R-EMA prompts (odds ratio=4.91, 95% confidence interval=2.16-11.12). Conclusions: Mobile phone apps using motion sensor?informed CS-EMA are acceptable among high school students and may be used to augment objective physical activity data collected from traditional waist-worn accelerometers. UR - http://www.jmir.org/2016/6/e106/ DO - 10.2196/jmir.5398 UR - http://www.ncbi.nlm.nih.gov/pubmed/27251313 ID - info:doi/10.2196/jmir.5398 ER - TY - JOUR AU - Brakenridge, L. Charlotte AU - Fjeldsoe, S. Brianna AU - Young, C. Duncan AU - Winkler, H. Elisabeth A. AU - Dunstan, W. David AU - Straker, M. Leon AU - Brakenridge, J. Christian AU - Healy, N. Genevieve PY - DA - 2016/05/25 TI - Organizational-Level Strategies With or Without an Activity Tracker to Reduce Office Workers? Sitting Time: Rationale and Study Design of a Pilot Cluster-Randomized Trial JO - JMIR Res Protoc SP - e73 VL - 5 IS - 2 KW - wearable device KW - self-monitoring KW - sedentary lifestyle KW - office workers KW - light intensity activity KW - ecological model KW - workplace KW - trial KW - objective KW - activity monitor AB - Background: The office workplace is a key setting in which to address excessive sitting time and inadequate physical activity. One major influence on workplace sitting is the organizational environment. However, the impact of organizational-level strategies on individual level activity change is unknown. Further, the emergence of sophisticated, consumer-targeted wearable activity trackers that facilitate real-time self-monitoring of activity, may be a useful adjunct to support organizational-level strategies, but to date have received little evaluation in this workplace setting. Objective: The aim of this study is to evaluate the feasibility, acceptability, and effectiveness of organizational-level strategies with or without an activity tracker on sitting, standing, and stepping in office workers in the short (3 months, primary aim) and long-term (12 months, secondary aim). Methods: This study is a pilot, cluster-randomized trial (with work teams as the unit of clustering) of two interventions in office workers: organizational-level support strategies (eg, visible management support, emails) or organizational-level strategies plus the use of a waist-worn activity tracker (the LUMOback) that enables self-monitoring of sitting, standing, and stepping time and enables users to set sitting and posture alerts. The key intervention message is to ?Stand Up, Sit Less, and Move More.? Intervention elements will be implemented from within the organization by the Head of Workplace Wellbeing. Participants will be recruited via email and enrolled face-to-face. Assessments will occur at baseline, 3, and 12 months. Time spent sitting, sitting in prolonged (?30 minute) bouts, standing, and stepping during work hours and across the day will be measured with activPAL3 activity monitors (7 days, 24 hours/day protocol), with total sitting time and sitting time during work hours the primary outcomes. Web-based questionnaires, LUMOback recorded data, telephone interviews, and focus groups will measure the feasibility and acceptability of both interventions and potential predictors of behavior change. Results: Baseline and follow-up data collection has finished. Results are expected in 2016. Conclusions: This pilot, cluster-randomized trial will evaluate the feasibility, acceptability, and effectiveness of two interventions targeting reductions in sitting and increases in standing and stepping in office workers. Few studies have evaluated these intervention strategies and this study has the potential to contribute both short and long-term findings. UR - http://www.researchprotocols.org/2016/2/e73/ DO - 10.2196/resprot.5438 UR - http://www.ncbi.nlm.nih.gov/pubmed/27226457 ID - info:doi/10.2196/resprot.5438 ER - TY - JOUR AU - Allen, Nelson Luke AU - Christie, Pepall Gillian PY - DA - 2016/05/10 TI - The Emergence of Personalized Health Technology JO - J Med Internet Res SP - e99 VL - 18 IS - 5 KW - personalized health technology KW - population health KW - frugal innovation KW - ethics KW - socioeconomic factors, inequalities KW - technology, health UR - http://www.jmir.org/2016/5/e99/ DO - 10.2196/jmir.5357 UR - http://www.ncbi.nlm.nih.gov/pubmed/27165944 ID - info:doi/10.2196/jmir.5357 ER - TY - JOUR AU - Sanders, P. James AU - Loveday, Adam AU - Pearson, Natalie AU - Edwardson, Charlotte AU - Yates, Thomas AU - Biddle, JH Stuart AU - Esliger, W. Dale PY - DA - 2016/05/04 TI - Devices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review JO - J Med Internet Res SP - e90 VL - 18 IS - 5 KW - sitting time KW - physical activity KW - measurement KW - feedback KW - activity monitor KW - scoping review AB - Background: It is well documented that meeting the guideline levels (150 minutes per week) of moderate-to-vigorous physical activity (PA) is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting. Therefore, there is a need to change both behaviors. Self-monitoring of behavior is one of the most robust behavior-change techniques available. The growing number of technologies in the consumer electronics sector provides a unique opportunity for individuals to self-monitor their behavior. Objective: The aim of this study is to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary time and/or PA. Methods: To identify technologies, four scientific databases were systematically searched using key terms related to behavior, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1, 2015. Results: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored PA, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring PA and/or sedentary behavior; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on PA (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary time (activPAL VT, the Lumo Back, and Darma). Conclusions: There are a large number of devices that self-monitor PA; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behavior change interventions, although the growing field of wearable technology may facilitate this to change. UR - http://www.jmir.org/2016/5/e90/ DO - 10.2196/jmir.5373 UR - http://www.ncbi.nlm.nih.gov/pubmed/27145905 ID - info:doi/10.2196/jmir.5373 ER - TY - JOUR AU - Mercer, Kathryn AU - Li, Melissa AU - Giangregorio, Lora AU - Burns, Catherine AU - Grindrod, Kelly PY - DA - 2016/04/27 TI - Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis JO - JMIR mHealth uHealth SP - e40 VL - 4 IS - 2 KW - older adults KW - physical activity KW - wearables KW - mobile health KW - chronic disease management AB - Background: Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults. Objective: The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population. Methods: Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa. Results: The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce. Conclusions: Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults. UR - http://mhealth.jmir.org/2016/2/e40/ DO - 10.2196/mhealth.4461 UR - http://www.ncbi.nlm.nih.gov/pubmed/27122452 ID - info:doi/10.2196/mhealth.4461 ER - TY - JOUR AU - McMahon, K. Siobhan AU - Lewis, Beth AU - Oakes, Michael AU - Guan, Weihua AU - Wyman, F. Jean AU - Rothman, J. Alexander PY - DA - 2016/04/13 TI - Older Adults? Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity JO - JMIR mHealth uHealth SP - e35 VL - 4 IS - 2 KW - Aged KW - Mobile Health KW - Self-Appraisal KW - Physical Activity KW - Motivation KW - Monitoring KW - Ambulatory KW - Wearables AB - Background: Physical activity contributes to older adults? autonomy, mobility, and quality of life as they age, yet fewer than 1 in 5 engage in activities as recommended. Many older adults track their exercise using pencil and paper, or their memory. Commercially available physical activity monitors (PAM) have the potential to facilitate these tracking practices and, in turn, physical activity. An assessment of older adults? long-term experiences with PAM is needed to understand this potential. Objective: To assess short and long-term experiences of adults >70 years old using a PAM (Fitbit One) in terms of acceptance, ease-of-use, and usefulness: domains in the technology acceptance model. Methods: This prospective study included 95 community-dwelling older adults, all of whom received a PAM as part of randomized controlled trial piloting a fall-reducing physical activity promotion intervention. Ten-item surveys were administered 10 weeks and 8 months after the study started. Survey ratings are described and analyzed over time, and compared by sex, education, and age. Results: Participants were mostly women (71/95, 75%), 70 to 96 years old, and had some college education (68/95, 72%). Most participants (86/95, 91%) agreed or strongly agreed that the PAM was easy to use, useful, and acceptable both 10 weeks and 8 months after enrolling in the study. Ratings dropped between these time points in all survey domains: ease-of-use (median difference 0.66 points, P=.001); usefulness (median difference 0.16 points, P=.193); and acceptance (median difference 0.17 points, P=.032). Differences in ratings by sex or educational attainment were not statistically significant at either time point. Most participants 80+ years of age (28/37, 76%) agreed or strongly agreed with survey items at long-term follow-up, however their ratings were significantly lower than participants in younger age groups at both time points. Conclusions: Study results indicate it is feasible for older adults (70-90+ years of age) to use PAMs when self-tracking their physical activity, and provide a basis for developing recommendations to integrate PAMs into promotional efforts. Trial Registration: Clinicaltrials.gov NCT02433249; https://clinicaltrials.gov/ct2/show/NCT02433249 (Archived by WebCite at http://www.webcitation.org/6gED6eh0I) UR - http://mhealth.jmir.org/2016/2/e35/ DO - 10.2196/mhealth.5120 UR - http://www.ncbi.nlm.nih.gov/pubmed/27076486 ID - info:doi/10.2196/mhealth.5120 ER - TY - JOUR AU - Thorup, Charlotte AU - Hansen, John AU - Grønkjær, Mette AU - Andreasen, Jesper Jan AU - Nielsen, Gitte AU - Sørensen, Elgaard Erik AU - Dinesen, Irene Birthe PY - DA - 2016/04/04 TI - Cardiac Patients? Walking Activity Determined by a Step Counter in Cardiac Telerehabilitation: Data From the Intervention Arm of a Randomized Controlled Trial JO - J Med Internet Res SP - e69 VL - 18 IS - 4 KW - heart disease KW - rehabilitation KW - step counters KW - physical activity KW - telerehabilitation AB - Background: Walking represents a large part of daily physical activity. It reduces both overall and cardiovascular diseases and mortality and is suitable for cardiac patients. A step counter measures walking activity and might be a motivational tool to increase and maintain physical activity. There is a lack of knowledge about both cardiac patients? adherence to step counter use in a cardiac telerehabilitation program and how many steps cardiac patients walk up to 1 year after a cardiac event. Objective: The purpose of this substudy was to explore cardiac patients? walking activity. The walking activity was analyzed in relation to duration of pedometer use to determine correlations between walking activity, demographics, and medical and rehabilitation data. Methods: A total of 64 patients from a randomized controlled telerehabilitation trial (Teledi@log) from Aalborg University Hospital and Hjoerring Hospital, Denmark, from December 2012 to March 2014 were included in this study. Inclusion criteria were patients hospitalized with acute coronary syndrome, heart failure, and coronary artery bypass grafting or valve surgery. In Teledi@log, the patients received telerehabilitation technology and selected one of three telerehabilitation settings: a call center, a community health care center, or a hospital. Monitoring of steps continued for 12 months and a step counter (Fitbit Zip) was used to monitor daily steps. Results: Cardiac patients walked a mean 5899 (SD 3274) steps per day, increasing from mean 5191 (SD 3198) steps per day in the first week to mean 7890 (SD 2629) steps per day after 1 year. Adherence to step counter use lasted for a mean 160 (SD 100) days. The patients who walked significantly more were younger (P=.01) and continued to use the pedometer for a longer period (P=.04). Furthermore, less physically active patients weighed more. There were no significant differences in mean steps per day for patients in the three rehabilitation settings or in the disease groups. Conclusions: This study indicates that cardiac telerehabilitation at a call center can support walking activity just as effectively as telerehabilitation at either a hospital or a health care center. In this study, the patients tended to walk fewer steps per day than cardiac patients in comparable studies, but our study may represent a more realistic picture of walking activity due to the continuation of step counter use. Qualitative studies on patients? behavior and motivation regarding step counter use are needed to shed light on adherence to and motivation to use step counters. Trial Registration: ClinicalTrails.gov NCT01752192; https://clinicaltrials.gov/ct2/show/NCT01752192 (Archived by WebCite at http://www.webcitation.org/6fgigfUyV) UR - http://www.jmir.org/2016/4/e69/ DO - 10.2196/jmir.5191 UR - http://www.ncbi.nlm.nih.gov/pubmed/27044310 ID - info:doi/10.2196/jmir.5191 ER - TY - JOUR AU - Mercer, Kathryn AU - Giangregorio, Lora AU - Schneider, Eric AU - Chilana, Parmit AU - Li, Melissa AU - Grindrod, Kelly PY - DA - 2016/01/27 TI - Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation JO - JMIR mHealth uHealth SP - e7 VL - 4 IS - 1 KW - chronic disease KW - physical activity KW - sedentary lifestyle KW - wearables AB - Background: Physical inactivity and sedentary behavior increase the risk of chronic illness and death. The newest generation of ?wearable? activity trackers offers potential as a multifaceted intervention to help people become more active. Objective: To examine the usability and usefulness of wearable activity trackers for older adults living with chronic illness. Methods: We recruited a purposive sample of 32 participants over the age of 50, who had been previously diagnosed with a chronic illness, including vascular disease, diabetes, arthritis, and osteoporosis. Participants were between 52 and 84 years of age (mean 64); among the study participants, 23 (72%) were women and the mean body mass index was 31 kg/m2. Participants tested 5 trackers, including a simple pedometer (Sportline or Mio) followed by 4 wearable activity trackers (Fitbit Zip, Misfit Shine, Jawbone Up 24, and Withings Pulse) in random order. Selected devices represented the range of wearable products and features available on the Canadian market in 2014. Participants wore each device for at least 3 days and evaluated it using a questionnaire developed from the Technology Acceptance Model. We used focus groups to explore participant experiences and a thematic analysis approach to data collection and analysis. Results: Our study resulted in 4 themes: (1) adoption within a comfort zone; (2) self-awareness and goal setting; (3) purposes of data tracking; and (4) future of wearable activity trackers as health care devices. Prior to enrolling, few participants were aware of wearable activity trackers. Most also had been asked by a physician to exercise more and cited this as a motivation for testing the devices. None of the participants planned to purchase the simple pedometer after the study, citing poor accuracy and data loss, whereas 73% (N=32) planned to purchase a wearable activity tracker. Preferences varied but 50% felt they would buy a Fitbit and 42% felt they would buy a Misfit, Jawbone, or Withings. The simple pedometer had a mean acceptance score of 56/95 compared with 63 for the Withings, 65 for the Misfit and Jawbone, and 68 for the Fitbit. To improve usability, older users may benefit from devices that have better compatibility with personal computers or less-expensive Android mobile phones and tablets, and have comprehensive paper-based user manuals and apps that interpret user data. Conclusions: For older adults living with chronic illness, wearable activity trackers are perceived as useful and acceptable. New users may need support to both set up the device and learn how to interpret their data. UR - http://mhealth.jmir.org/2016/1/e7/ DO - 10.2196/mhealth.4225 UR - http://www.ncbi.nlm.nih.gov/pubmed/26818775 ID - info:doi/10.2196/mhealth.4225 ER - TY - JOUR AU - Cadmus-Bertram, Lisa AU - Marcus, H. Bess AU - Patterson, E. Ruth AU - Parker, A. Barbara AU - Morey, L. Brittany PY - DA - 2015/11/19 TI - Use of the Fitbit to Measure Adherence to a Physical Activity Intervention Among Overweight or Obese, Postmenopausal Women: Self-Monitoring Trajectory During 16 Weeks JO - JMIR mHealth uHealth SP - e96 VL - 3 IS - 4 KW - exercise KW - health behavior KW - health promotion KW - Internet KW - mHealth KW - motor activity KW - physical activity KW - technology KW - women AB - Background: Direct-to-consumer trackers and devices have potential to enhance theory-based physical activity interventions by offering a simple and pleasant way to help participants self-monitor their behavior. A secondary benefit of these devices is the opportunity for investigators to objectively track adherence to physical activity goals across weeks or even months, rather than relying on self-report or a small number of accelerometry wear periods. The use of consumer trackers for continuous monitoring of adherence has considerable potential to enhance physical activity research, but few studies have been published in this rapidly developing area. Objective: The objective of the study was to assess the trajectory of physical activity adherence across a 16-week self-monitoring intervention, as measured by the Fitbit tracker. Methods: Participants were 25 overweight or obese, postmenopausal women enrolled in the intervention arm of a randomized controlled physical activity intervention trial. Each participant received a 16-week technology-based intervention that used the Fitbit physical activity tracker and website. The overall study goal was 150 minutes/week of moderate to vigorous intensity physical activity (MVPA) and 10,000 steps/day; however, goals were set individually for each participant and updated at Week 4 based on progress. Adherence data were collected by the Fitbit and aggregated by Fitabase. Participants also wore an ActiGraph GT3X+ accelerometer for 7 days prior to the intervention and again during Week 16. Results: The median participant logged 10 hours or more/day of Fitbit wear on 95% of the 112 intervention days, with no significant decline in wear over the study period. Participants averaged 7540 (SD 2373) steps/day and 82 minutes/week (SD 43) of accumulated ?fairly active? and ?very active? minutes during the intervention. At Week 4, 80% (20/25) of women chose to maintain/increase their individual MVPA goal and 72% (18/25) of participants chose to maintain/increase their step goal. Physical activity levels were relatively stable after peaking at 3 weeks, with only small declines of 8% for steps (P=.06) and 14% for MVPA (P=.05) by 16 weeks. Conclusions: These data indicate that a sophisticated, direct-to-consumer activity tracker encouraged high levels of self-monitoring that were sustained over 16 weeks. Further study is needed to determine how to motivate additional gains in physical activity and evaluate the long-term utility of the Fitbit tracker as part of a strategy for chronic disease prevention. Trial Registration: Clinicaltrials.gov NCT01837147; http://clinicaltrials.gov/ct2/show/NCT01837147 (Archived by WebCite at http://www.webcitation.org/6d0VeQpvB) UR - http://mhealth.jmir.org/2015/4/e96/ DO - 10.2196/mhealth.4229 UR - http://www.ncbi.nlm.nih.gov/pubmed/26586418 ID - info:doi/10.2196/mhealth.4229 ER - TY - JOUR AU - Gay, Valerie AU - Leijdekkers, Peter PY - DA - 2015/11/18 TI - Bringing Health and Fitness Data Together for Connected Health Care: Mobile Apps as Enablers of Interoperability JO - J Med Internet Res SP - e260 VL - 17 IS - 11 KW - health informatics KW - connected health KW - pervasive and mobile computing KW - ubiquitous and mobile devices AB - Background: A transformation is underway regarding how we deal with our health. Mobile devices make it possible to have continuous access to personal health information. Wearable devices, such as Fitbit and Apple?s smartwatch, can collect data continuously and provide insights into our health and fitness. However, lack of interoperability and the presence of data silos prevent users and health professionals from getting an integrated view of health and fitness data. To provide better health outcomes, a complete picture is needed which combines informal health and fitness data collected by the user together with official health records collected by health professionals. Mobile apps are well positioned to play an important role in the aggregation since they can tap into these official and informal health and data silos. Objective: The objective of this paper is to demonstrate that a mobile app can be used to aggregate health and fitness data and can enable interoperability. It discusses various technical interoperability challenges encountered while integrating data into one place. Methods: For 8 years, we have worked with third-party partners, including wearable device manufacturers, electronic health record providers, and app developers, to connect an Android app to their (wearable) devices, back-end servers, and systems. Results: The result of this research is a health and fitness app called myFitnessCompanion, which enables users to aggregate their data in one place. Over 6000 users use the app worldwide to aggregate their health and fitness data. It demonstrates that mobile apps can be used to enable interoperability. Challenges encountered in the research process included the different wireless protocols and standards used to communicate with wireless devices, the diversity of security and authorization protocols used to be able to exchange data with servers, and lack of standards usage, such as Health Level Seven, for medical information exchange. Conclusions: By limiting the negative effects of health data silos, mobile apps can offer a better holistic view of health and fitness data. Data can then be analyzed to offer better and more personalized advice and care. UR - http://www.jmir.org/2015/11/e260/ DO - 10.2196/jmir.5094 UR - http://www.ncbi.nlm.nih.gov/pubmed/26581920 ID - info:doi/10.2196/jmir.5094 ER - TY - JOUR AU - Hurley, C. Jane AU - Hollingshead, E. Kevin AU - Todd, Michael AU - Jarrett, L. Catherine AU - Tucker, J. Wesley AU - Angadi, S. Siddhartha AU - Adams, A. Marc PY - DA - 2015/09/11 TI - The Walking Interventions Through Texting (WalkIT) Trial: Rationale, Design, and Protocol for a Factorial Randomized Controlled Trial of Adaptive Interventions for Overweight and Obese, Inactive Adults JO - JMIR Res Protoc SP - e108 VL - 4 IS - 3 KW - just in time adaptive interventions KW - Fitbit KW - exercise KW - overweight KW - inactive KW - text messaging KW - SMS KW - percentile schedule of reinforcement KW - mHealth AB - Background: Walking is a widely accepted and frequently targeted health promotion approach to increase physical activity (PA). Interventions to increase PA have produced only small improvements. Stronger and more potent behavioral intervention components are needed to increase time spent in PA, improve cardiometabolic risk markers, and optimize health. Objective: Our aim is to present the rationale and methods from the WalkIT Trial, a 4-month factorial randomized controlled trial (RCT) in inactive, overweight/obese adults. The main purpose of the study was to evaluate whether intensive adaptive components result in greater improvements to adults? PA compared to the static intervention components. Methods: Participants enrolled in a 2x2 factorial RCT and were assigned to one of four semi-automated, text message?based walking interventions. Experimental components included adaptive versus static steps/day goals, and immediate versus delayed reinforcement. Principles of percentile shaping and behavioral economics were used to operationalize experimental components. A Fitbit Zip measured the main outcome: participants? daily physical activity (steps and cadence) over the 4-month duration of the study. Secondary outcomes included self-reported PA, psychosocial outcomes, aerobic fitness, and cardiorespiratory risk factors assessed pre/post in a laboratory setting. Participants were recruited through email listservs and websites affiliated with the university campus, community businesses and local government, social groups, and social media advertising. Results: This study has completed data collection as of December 2014, but data cleaning and preliminary analyses are still in progress. We expect to complete analysis of the main outcomes in late 2015 to early 2016. Conclusions: The Walking Interventions through Texting (WalkIT) Trial will further the understanding of theory-based intervention components to increase the PA of men and women who are healthy, insufficiently active and are overweight or obese. WalkIT is one of the first studies focusing on the individual components of combined goal setting and reward structures in a factorial design to increase walking. The trial is expected to produce results useful to future research interventions and perhaps industry initiatives, primarily focused on mHealth, goal setting, and those looking to promote behavior change through performance-based incentives. Trial Registration: ClinicalTrials.gov NCT02053259; https://clinicaltrials.gov/ct2/show/NCT02053259 (Archived by WebCite at http://www.webcitation.org/6b65xLvmg). UR - http://www.researchprotocols.org/2015/3/e108/ DO - 10.2196/resprot.4856 UR - http://www.ncbi.nlm.nih.gov/pubmed/26362511 ID - info:doi/10.2196/resprot.4856 ER - TY - JOUR AU - Bonn, Erika Stephanie AU - Bergman, Patrick AU - Trolle Lagerros, Ylva AU - Sjölander, Arvid AU - Bälter, Katarina PY - DA - 2015/07/16 TI - A Validation Study of the Web-Based Physical Activity Questionnaire Active-Q Against the GENEA Accelerometer JO - JMIR Res Protoc SP - e86 VL - 4 IS - 3 KW - accelerometer KW - activity assessment KW - epidemiology KW - Internet KW - self report KW - validity AB - Background: Valid physical activity assessment in epidemiological studies is essential to study associations with various health outcomes. Objective: To validate the Web-based physical activity questionnaire Active-Q by comparing results of time spent at different physical activity levels with results from the GENEA accelerometer and to assess the reproducibility of Active-Q by comparing two admissions of the questionnaire. Methods: A total of 148 men (aged 33 to 86 years) responded to Active-Q twice and wore the accelerometer during seven consecutive days on two occasions. Time spent on six different physical activity levels including sedentary, light (LPA), moderate (MPA), and vigorous (VPA) as well as additional combined categories of sedentary-to-light and moderate-to-vigorous (MVPA) physical activity was assessed. Validity of Active-Q was determined using Spearman correlation coefficients with 95% confidence intervals (CI) and the Bland-Altman method. Reproducibility was assessed using intraclass correlation coefficients (ICCs) comparing two admissions of the questionnaire. Results: The validity correlation coefficients were statistically significant for time spent at all activity levels; sedentary (r=0.19, 95% CI: 0.04-0.34), LPA (r=0.15, 95% CI: 0.00-0.31), sedentary-to-light (r=0.35, 95% CI: 0.19-0.51), MPA (r=0.27, 95% CI: 0.12-0.42), VPA (r=0.54, 95% CI: 0.42-0.67), and MVPA (r=0.35, 95% CI: 0.21-0.48). The Bland-Altman plots showed a negative mean difference for time in LPA and positive mean differences for time spent in MPA, VPA and MVPA. The ICCs of test-retest reliability ranged between r=0.51-0.80 for the different activity levels in Active-Q. Conclusions: More moderate and vigorous activities and less light activities were reported in Active-Q compared to accelerometer measurements. Active-Q shows comparable validity and reproducibility to other physical activity questionnaires used today. UR - http://www.researchprotocols.org/2015/3/e86/ DO - 10.2196/resprot.3896 UR - http://www.ncbi.nlm.nih.gov/pubmed/26183896 ID - info:doi/10.2196/resprot.3896 ER - TY - JOUR AU - Khoo Chee Han, Christopher AU - Shanmugam, AL Rukmanikanthan AU - Choon Siew Kit, David PY - DA - 2014/11/24 TI - Accuracy, Consistency, and Reproducibility of the Triaxial Accelerometer in the iPod Touch: A Pilot Study JO - JMIR mHealth uHealth SP - e39 VL - 2 IS - 4 KW - accelerometry KW - tri-axial accelerometer KW - iPod Touch AB - Background: The use of a mobile consumer communicative device as a motion analysis tool for patients has been researched and documented previously, examining the triaxial accelerometer embedded in such devices. However, there have been few reports in the literature testing the sensitivity of an embedded triaxial accelerometer. Objective: Our goal in this study was to test the accuracy, consistency, and reproducibility of the triaxial accelerometer in the iPod Touch. Methods: In this pilot study, we subjected the triaxial accelerometer in the iPod Touch to a free fall from a height of 100 cm in order to test its accuracy, consistency, and reproducibility under dynamic conditions. Results: The resultant vectorial sum acceleration was mean 0.999 g (standard gravity; SD 1.51%; 95% CI 0.99-1.01), indicating very high accuracy and sensitivity under dynamic conditions. Conclusions: Our results highlighted the reproducibility of the capability of the triaxial accelerometer in the iPod Touch to capture data accurately and consistently. Thus, the device has huge potential as a motion analysis tool for measuring gait and studying balance and mobility in patients before and after surgery. UR - http://mhealth.jmir.org/2014/4/e39/ DO - 10.2196/mhealth.3008 UR - http://www.ncbi.nlm.nih.gov/pubmed/25486896 ID - info:doi/10.2196/mhealth.3008 ER - TY - JOUR AU - Vooijs, Martijn AU - Alpay, L. Laurence AU - Snoeck-Stroband, B. Jiska AU - Beerthuizen, Thijs AU - Siemonsma, C. Petra AU - Abbink, J. Jannie AU - Sont, K. Jacob AU - Rövekamp, A. Ton PY - DA - 2014/10/27 TI - Validity and Usability of Low-Cost Accelerometers for Internet-Based Self-Monitoring of Physical Activity in Patients With Chronic Obstructive Pulmonary Disease JO - Interact J Med Res SP - e14 VL - 3 IS - 4 KW - accelerometers KW - activity monitoring KW - chronic obstructive pulmonary disease KW - validity KW - usability AB - Background: The importance of regular physical activity for patients with chronic obstructive pulmonary disease (COPD) is well-established. However, many patients do not meet the recommended daily amount. Accelerometers might provide patients with the information needed to increase physical activity in daily life. Objective: Our objective was to assess the validity and usability of low-cost Internet-connected accelerometers. Furthermore we explored patients? preferences with regards to the presentation of and feedback on monitored physical activity. Methods: To assess concurrent validity we conducted a field validation study with patients who wore two low-cost accelerometers, Fitbit and Physical Activity Monitor (PAM), at the same time along with a sophisticated multisensor accelerometer (SenseWear Armband) for 48 hours. Data on energy expenditure assessed from registrations from the two low-cost accelerometers were compared to the well validated SenseWear Armband which served as a reference criterion. Usability was examined in a cross-over study with patients who, in succession, wore the Fitbit and the PAM for 7 consecutive days and filled out a 16 item questionnaire with regards to the use of the corresponding device Results: The agreement between energy expenditure (METs) from the SenseWear Armband with METs estimated by the Fitbit and PAM was good (r=.77) and moderate (r=.41), respectively. The regression model that was developed for the Fitbit explained 92% whereas the PAM-model could explain 89% of total variance in METs measured by the SenseWear. With regards to the usability, both the Fitbit and PAM were well rated on all items. There were no significant differences between the two devices. Conclusions: The low-cost Fitbit and PAM are valid and usable devices to measure physical activity in patients with COPD. These devices may be useful in long-term interventions aiming at increasing physical activity levels in these patients. UR - http://www.i-jmr.org/2014/4/e14/ DO - 10.2196/ijmr.3056 UR - http://www.ncbi.nlm.nih.gov/pubmed/25347989 ID - info:doi/10.2196/ijmr.3056 ER - TY - JOUR AU - Lyons, J. Elizabeth AU - Lewis, H. Zakkoyya AU - Mayrsohn, G. Brian AU - Rowland, L. Jennifer PY - DA - 2014/08/15 TI - Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis JO - J Med Internet Res SP - e192 VL - 16 IS - 8 KW - electronic activity monitor KW - mobile KW - mhealth KW - physical activity KW - behavior change technique AB - Background: Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. Objective: The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Methods: Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. Results: All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Conclusions: Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings. UR - http://www.jmir.org/2014/8/e192/ DO - 10.2196/jmir.3469 UR - http://www.ncbi.nlm.nih.gov/pubmed/25131661 ID - info:doi/10.2196/jmir.3469 ER - TY - JOUR AU - Wu, Wanmin AU - Dasgupta, Sanjoy AU - Ramirez, E. Ernesto AU - Peterson, Carlyn AU - Norman, J. Gregory PY - DA - 2012/10/05 TI - Classification Accuracies of Physical Activities Using Smartphone Motion Sensors JO - J Med Internet Res SP - e130 VL - 14 IS - 5 KW - Activity classification KW - machine learning KW - accelerometer KW - gyroscope KW - smartphone AB - Background: Over the past few years, the world has witnessed an unprecedented growth in smartphone use. With sensors such as accelerometers and gyroscopes on board, smartphones have the potential to enhance our understanding of health behavior, in particular physical activity or the lack thereof. However, reliable and valid activity measurement using only a smartphone in situ has not been realized. Objective: To examine the validity of the iPod Touch (Apple, Inc.) and particularly to understand the value of using gyroscopes for classifying types of physical activity, with the goal of creating a measurement and feedback system that easily integrates into individuals? daily living. Methods: We collected accelerometer and gyroscope data for 16 participants on 13 activities with an iPod Touch, a device that has essentially the same sensors and computing platform as an iPhone. The 13 activities were sitting, walking, jogging, and going upstairs and downstairs at different paces. We extracted time and frequency features, including mean and variance of acceleration and gyroscope on each axis, vector magnitude of acceleration, and fast Fourier transform magnitude for each axis of acceleration. Different classifiers were compared using the Waikato Environment for Knowledge Analysis (WEKA) toolkit, including C4.5 (J48) decision tree, multilayer perception, naive Bayes, logistic, k-nearest neighbor (kNN), and meta-algorithms such as boosting and bagging. The 10-fold cross-validation protocol was used. Results: Overall, the kNN classifier achieved the best accuracies: 52.3%?79.4% for up and down stair walking, 91.7% for jogging, 90.1%?94.1% for walking on a level ground, and 100% for sitting. A 2-second sliding window size with a 1-second overlap worked the best. Adding gyroscope measurements proved to be more beneficial than relying solely on accelerometer readings for all activities (with improvement ranging from 3.1% to 13.4%). Conclusions: Common categories of physical activity and sedentary behavior (walking, jogging, and sitting) can be recognized with high accuracies using both the accelerometer and gyroscope onboard the iPod touch or iPhone. This suggests the potential of developing just-in-time classification and feedback tools on smartphones. UR - http://www.jmir.org/2012/5/e130/ DO - 10.2196/jmir.2208 UR - http://www.ncbi.nlm.nih.gov/pubmed/23041431 ID - info:doi/10.2196/jmir.2208 ER -