JMIR Publications

JMIR mHealth and uHealth

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

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Journal Description

JMIR mhealth and uhealth (mobile and ubiquitous health) (JMU, ISSN 2291-5222) is a new spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2015: 4.532). JMIR mHealth and uHealth has a projected impact factor (2015) of about 2.03. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.

JMIR mHealth and uHealth publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
In addition to peer-reviewing paper submissions by researchers, JMIR mHealth and uHealth offers peer-review of medical apps itself (developers can submit an app for peer-review here).

JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.

JMIR mHealth and uHealth is indexed in PubMed Central/PubMed, and Thomson Reuters' Science Citation Index Expanded (SCIE).

JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.

 

Recent Articles:

  • Smartphone user. Image source: https://pixabay.com/en/smartphone-mobile-headphones-audio-923081/. License: CC0 Public Domain.

    An mHealth Intervention Using a Smartphone App to Increase Walking Behavior in Young Adults: A Pilot Study

    Abstract:

    Background: Physical inactivity is a growing concern for society and is a risk factor for cardiovascular disease, obesity, and other chronic diseases. Objective: This study aimed to determine the efficacy of the Accupedo-Pro Pedometer mobile phone app intervention, with the goal of increasing daily step counts in young adults. Methods: Mobile phone users (n=58) between 17-26 years of age were randomized to one of two conditions (experimental and control). Both groups downloaded an app that recorded their daily step counts. Baseline data were recorded and followed-up at 5 weeks. Both groups were given a daily walking goal of 30 minutes, but the experimental group participants were told the equivalent goal in steps taken, via feedback from the app. The primary outcome was daily step count between baseline and follow-up. Results: A significant time x group interaction effect was observed for daily step counts (P=.04). Both the experimental (P<.001) and control group (P=.03) demonstrated a significant increase in daily step counts, with the experimental group walking an additional 2000 steps per day. Conclusions: The results of this study demonstrate that a mobile phone app can significantly increase physical activity in a young adult sample by setting specific goals, using self-monitoring, and feedback.

  • Mobile Sensing and Support for People with Depression: A Pilot Trial in the Wild. Image sourced and copyright owned by makora AG.

    Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

    Abstract:

    Background: Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. Objective: The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. Methods: A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. Results: A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. Conclusions: Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.

  • Source: https://unsplash.com/search/exercise?photo=2-_WkjmC8x4; CC0 Public Domain.

    Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions

    Abstract:

    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.

  • A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study

    Abstract:

    Background: A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. Objective: We sought to (1) assess smokers’ compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. Methods: An explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. Results: Out of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app’s identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. Conclusions: User-initiated self-report is feasible for training a cessation app about an individual’s smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.

  • ACT-DL in Daily Life - © 2016 Maastricht University. Image sourced and copyright owned by authors Tim Batink et al.

    Acceptance and Commitment Therapy in Daily Life Training: A Feasibility Study of an mHealth Intervention

    Abstract:

    Background: With the development of mHealth, it is possible to treat patients in their natural environment. Mobile technology helps to bridge the gap between the therapist’s office and the “real world.” The ACT in Daily Life training (ACT-DL) was designed as an add-on intervention to help patients practice with acceptance and commitment therapy in their daily lives. The ACT-DL consists of two main components: daily monitoring using experience sampling and ACT training in daily life. Objectives: To assess the acceptability and feasibility of the ACT-DL in a general outpatient population. A secondary objective was to conduct a preliminary examination of the effectiveness of the ACT-DL. Methods: An observational comparative study was conducted. The experimental group consisted of 49 patients who volunteered for ACT-DL, and the control group consisted of 112 patients who did not volunteer. As part of an inpatient treatment program, both groups received a 6-week ACT training. Participants went home to continue their treatment on an outpatient basis, during which time the experimental group received the 4-week add-on ACT-DL. Acceptability and feasibility of the ACT-DL was assessed weekly by telephone survey. Effectiveness of the ACT-DL was evaluated with several self-report questionnaires (Flexibility Index Test (FIT-60): psychological flexibility, Brief Symptom Inventory: symptoms, Utrechtse Coping List: coping, and Quality of life visual analog scale (QoL-VAS): quality of life). Results: More than three-quarters of the participants (76%) completed the full 4-week training. User evaluations showed that ACT-DL stimulated the use of ACT in daily life: participants practiced over an hour a week (mean 78.8 minutes, standard deviation 54.4), doing 10.4 exercises (standard deviation 6.0) on average. Both ACT exercises and metaphors were experienced as useful components of the training (rated 5 out of 7). Repeated measures ANCOVA did not show significant effects of the ACT-DL on psychological flexibility (P=.88), symptoms (P=.39), avoidant coping (P=.28), or quality of life (P=.15). Conclusions: This is the first study that uses experience sampling to foster awareness in daily life in combination with acceptance and commitment therapy to foster skill building. Adherence to the ACT-DL was high for an intensive mHealth intervention. ACT-DL appears to be an acceptable and feasible mHealth intervention, suitable for a broad range of mental health problems. However, short-term effectiveness could not be demonstrated. Additional clinical trials are needed to examine both short-term and long-term effects.

  • Tablet for medical use. Image obtained from: https://pixabay.com/en/ehr-emr-electronic-medical-record-1476525/
Public Domain under Creative Commons CC0.

    User-Centered Design of a Tablet Waiting Room Tool for Complex Patients to Prioritize Discussion Topics for Primary Care Visits

    Abstract:

    Background: Complex patients with multiple chronic conditions often face significant challenges communicating and coordinating with their primary care physicians. These challenges are exacerbated by the limited time allotted to primary care visits. Objective: Our aim was to employ a user-centered design process to create a tablet tool for use by patients for visit discussion prioritization. Methods: We employed user-centered design methods to create a tablet-based waiting room tool that enables complex patients to identify and set discussion topic priorities for their primary care visit. In an iterative design process, we completed one-on-one interviews with 40 patients and their 17 primary care providers, followed by three design sessions with a 12-patient group. We audiorecorded and transcribed all discussions and categorized major themes. In addition, we met with 15 key health communication, education, and technology leaders within our health system to further review the design and plan for broader implementation of the tool. In this paper, we present the significant changes made to the tablet tool at each phase of this design work. Results: Patient feedback emphasized the need to make the tablet tool accessible for patients who lacked technical proficiency and to reduce the quantity and complexity of text presentation. Both patients and their providers identified specific content choices based on their personal experiences (eg, the ability to raise private or sensitive concerns) and recommended targeting new patients. Stakeholder groups provided essential input on the need to augment text with video and to create different versions of the videos to match sex and race/ethnicity of the actors with patients. Conclusions: User-centered design in collaboration with patients, providers, and key health stakeholders led to marked evolution in the initial content, layout, and target audience for a tablet waiting room tool intended to assist complex patients with setting visit discussion priorities.

  • Image sourced and copyright held by authors Quynh Pham et al.

    Beyond the Randomized Controlled Trial: A Review of Alternatives in mHealth Clinical Trial Methods

    Abstract:

    Background: Randomized controlled trials (RCTs) have long been considered the primary research study design capable of eliciting causal relationships between health interventions and consequent outcomes. However, with a prolonged duration from recruitment to publication, high-cost trial implementation, and a rigid trial protocol, RCTs are perceived as an impractical evaluation methodology for most mHealth apps. Objective: Given the recent development of alternative evaluation methodologies and tools to automate mHealth research, we sought to determine the breadth of these methods and the extent that they were being used in clinical trials. Methods: We conducted a review of the ClinicalTrials.gov registry to identify and examine current clinical trials involving mHealth apps and retrieved relevant trials registered between November 2014 and November 2015. Results: Of the 137 trials identified, 71 were found to meet inclusion criteria. The majority used a randomized controlled trial design (80%, 57/71). Study designs included 36 two-group pretest-posttest control group comparisons (51%, 36/71), 16 posttest-only control group comparisons (23%, 16/71), 7 one-group pretest-posttest designs (10%, 7/71), 2 one-shot case study designs (3%, 2/71), and 2 static-group comparisons (3%, 2/71). A total of 17 trials included a qualitative component to their methodology (24%, 17/71). Complete trial data collection required 20 months on average to complete (mean 21, SD 12). For trials with a total duration of 2 years or more (31%, 22/71), the average time from recruitment to complete data collection (mean 35 months, SD 10) was 2 years longer than the average time required to collect primary data (mean 11, SD 8). Trials had a moderate sample size of 112 participants. Two trials were conducted online (3%, 2/71) and 7 trials collected data continuously (10%, 7/68). Onsite study implementation was heavily favored (97%, 69/71). Trials with four data collection points had a longer study duration than trials with two data collection points: F4,56=3.2, P=.021, η2=0.18. Single-blinded trials had a longer data collection period compared to open trials: F2,58=3.8, P=.028, η2=0.12. Academic sponsorship was the most common form of trial funding (73%, 52/71). Trials with academic sponsorship had a longer study duration compared to industry sponsorship: F2,61=3.7, P=.030, η2=0.11. Combined, data collection frequency, study masking, sample size, and study sponsorship accounted for 32.6% of the variance in study duration: F4,55=6.6, P<.01, adjusted r2=.33. Only 7 trials had been completed at the time this retrospective review was conducted (10%, 7/71). Conclusions: mHealth evaluation methodology has not deviated from common methods, despite the need for more relevant and timely evaluations. There is a need for clinical evaluation to keep pace with the level of innovation of mHealth if it is to have meaningful impact in informing payers, providers, policy makers, and patients.

  • Image Source: Dueling Inhalers, copyright, Alan Levine.
http://tinyurl.com/zhtzf28, Licensed under Creative Commons Attribution cc-by 2.0 https://creativecommons.org/licenses/by/2.0/.

    The Effect of Smartphone Interventions on Patients With Chronic Obstructive Pulmonary Disease Exacerbations: A Systematic Review and Meta-Analysis

    Abstract:

    Background: The prevalence and mortality rates of chronic obstructive pulmonary disease (COPD) are increasing worldwide. Therefore, COPD remains a major public health problem. There is a growing interest in the use of smartphone technology for health promotion and disease management interventions. However, the effectiveness of smartphones in reducing the number of patients having a COPD exacerbation is poorly understood. Objective: To summarize and quantify the association between smartphone interventions and COPD exacerbations through a comprehensive systematic review and meta-analysis. Methods: A comprehensive search strategy was conducted across relevant databases (PubMed, Embase, Cochrane, CINHA, PsycINFO, and the Cochrane Library Medline) from inception to October 2015. We included studies that assessed the use of smartphone interventions in the reduction of COPD exacerbations compared with usual care. Full-text studies were excluded if the investigators did not use a smartphone device or did not report on COPD exacerbations. Observational studies, abstracts, and reviews were also excluded. Two reviewers extracted the data and conducted a risk of bias assessment using the US Preventive Services Task Force quality rating criteria. A random effects model was used to meta-analyze the results from included studies. Pooled odds ratios were used to measure the effectiveness of smartphone interventions on COPD exacerbations. Heterogeneity was measured using the I2statistic. Results: Of the 245 unique citations screened, 6 studies were included in the qualitative synthesis. Studies were relatively small with less than 100 participants in each study (range 30 to 99) and follow-up ranged from 4-9 months. The mean age was 70.5 years (SD 5.6) and 74% (281/380) were male. The studies varied in terms of country, type of smartphone intervention, frequency of data collection from the participants, and the feedback strategy. Three studies were included in the meta-analysis. The overall assessment of potential bias of the studies that were included in the meta-analysis was “Good” for one study and “Fair” for 2 studies. The pooled random effects odds ratio of patients having an exacerbation was 0.20 in patients using a smartphone intervention (95% CI 0.07-0.62), a reduction of 80% for smartphone interventions compared with usual care. However, there was moderate heterogeneity across the included studies (I2=59%). Conclusion: Although current literature on the role of smartphones in reducing COPD exacerbations is limited, findings from our review suggest that smartphones are useful in reducing the number of patients having a COPD exacerbation. Nevertheless, using smartphones require synergistic strategies to achieve the desired outcome. These results should be interpreted with caution due to the heterogeneity among the studies. Researchers should focus on conducting rigorous studies with adequately powered sample sizes to determine the validity and clinical utility of smartphone interventions in the management of COPD.

  • Ecological Momentary Assessment. Image sourced and copyright owned by authors Jason Fanning et al.

    Physical Activity, Mind Wandering, Affect, and Sleep: An Ecological Momentary Assessment

    Abstract:

    Background: A considerable portion of daily thought is spent in mind wandering. This behavior has been related to positive (eg, future planning, problem solving) and negative (eg, unhappiness, impaired cognitive performance) outcomes. Objective: Based on previous research suggesting future-oriented (ie, prospective) mind wandering may support autobiographical planning and self-regulation, this study examined associations between hourly mind wandering and moderate-to-vigorous physical activity (MVPA), and the impact of affect and daily sleep on these relations. Methods: College-aged adults (N=33) participated in a mobile phone-delivered ecological momentary assessment study for 1 week. Sixteen hourly prompts assessing mind wandering and affect were delivered daily via participants’ mobile phones. Perceived sleep quality and duration was assessed during the first prompt each day, and participants wore an ActiGraph accelerometer during waking hours throughout the study week. Results: Study findings suggest present-moment mind wandering was positively associated with future MVPA (P=.03), and this relationship was moderated by affective state (P=.04). Moreover, excessive sleep the previous evening was related to less MVPA across the following day (P=.007). Further, mind wandering was positively related to activity only among those who did not oversleep (P=.007). Conclusions: Together, these results have implications for multiple health behavior interventions targeting physical activity, affect, and sleep. Researchers may also build on this work by studying these relationships in the context of other important behaviors and psychosocial factors (eg, tobacco use, depression, loneliness).

  • Source: https://www.flickr.com/photos/kkendall/427215485; CC BY 2.0, Attribution K. Kendall.

    An Evaluation of a Smartphone–Assisted Behavioral Weight Control Intervention for Adolescents: Pilot Study

    Abstract:

    Background: The efficacy of adolescent weight control treatments is modest, and effective treatments are costly and are not widely available. Smartphones may be an effective method for delivering critical components of behavioral weight control treatment including behavioral self-monitoring. Objective: To examine the efficacy and acceptability of a smartphone assisted adolescent behavioral weight control intervention. Methods: A total of 16 overweight or obese adolescents (mean age=14.29 years, standard deviation=1.12) received 12 weeks of combined treatment that consisted of weekly in-person group behavioral weight control treatment sessions plus smartphone self-monitoring and daily text messaging. Subsequently they received 12 weeks of electronic-only intervention, totaling 24 weeks of intervention. Results: On average, participants attained modest but significant reductions in body mass index standard score (zBMI: 0.08 standard deviation units, t (13)=2.22, P=.04, d=0.63) over the in-person plus electronic-only intervention period but did not maintain treatment gains over the electronic-only intervention period. Participants self-monitored on approximately half of combined intervention days but less than 20% of electronic-only intervention days. Conclusions: Smartphones likely hold promise as a component of adolescent weight control interventions but they may be less effective in helping adolescents maintain treatment gains after intensive interventions.

  • The technology provided included a Wi-Fi–enabled scale, activity tracker, and access to a private dashboard. The dashboard was accessible via Web and mobile apps.

    Retrofit Weight-Loss Outcomes at 6, 12, and 24 Months and Characteristics of 12-Month High Performers: A Retrospective Analysis

    Abstract:

    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.

  • Text Messages. Image sourced and copyright held by authors Emma Cotten et al.

    Increasing Nonsedentary Behaviors in University Students Using Text Messages: Randomized Controlled Trial

    Abstract:

    Background: Sedentary behavior (SB) has been linked to many health problems such as type 2 diabetes and heart disease. Increasing the length and frequency of breaks from sitting and increasing the time spent standing and engaged in light and moderate physical activity are ways to decrease SB. Text message-based interventions have succeeded in aiding smoking cessation and increase both physical activity and healthy eating, but they have not been shown to reduce SB. Objective: The primary purpose of this pilot study was to determine the effectiveness of a text message-based intervention in increasing nonsedentary behaviors in university students. A secondary purpose was to (1) determine whether the intervention could enhance self-efficacy beliefs for decreasing SB and (2) whether these efficacious beliefs could predict actual SB. Methods: Eighty-two university students were recruited via mass emails and randomized into intervention (SB-related text messages) or control (text messages unrelated to SB) groups. Participants received daily text messages scheduled by the researcher encouraging breaks from sitting, standing, light- and moderate-intensity physical activity (PA). They then reported various SBs via Web-based questionnaires at four time points (baseline, 2, 4, and 6 weeks). Self-efficacious beliefs toward taking breaks from sitting and decreasing the amount of time spent sitting were assessed at the same time points. Results: Last observation carried forward (LOCF) method was used for incomplete data as an intent-to-treat (ITT) analysis (intervention group n=15, control group n=11). Small-to-moderate effects favoring the text intervention group were found at 6 weeks for break frequency -14.64 minutes, break length +.59 minutes, standing +24.30 min/day, light-intensity +74.34 min/day, and moderate-intensity + 9.97 min/day PA. Only light-intensity PA approached significance (P=.07). Self-efficacy beliefs also favored the text intervention group and reached significance (P=.032) for sitting less. Significant (P<.05) relations were found between the self-efficacy constructs and breaks, standing, and light or moderate PA. Conclusions: Text messages have the potential to increase nonsedentary behaviors in university students. These messages can increase self-efficacy beliefs to take more breaks and reduce sitting time. Efficacious beliefs can predict actual SB and to a lesser extent light- and moderate-intensity PA. Trial Registration: ClinicalTrials.gov NCT02562937; https://clinicaltrials.gov/ct2/show/NCT02562937 (Archived by WebCite at http://www.webcitation.org/6jVLwXE5M)

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    Date Submitted: Sep 23, 2016

    Open Peer Review Period: Sep 24, 2016 - Nov 19, 2016

    Background: The growth in mobile health (mHealth) technology is intersecting the demographic shift to an aging society. This presents unprecedented opportunity to maximize healthy aging. Regular physi...

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    Date Submitted: Sep 10, 2016

    Open Peer Review Period: Sep 12, 2016 - Nov 7, 2016

    In the recent years, there has been rapid increase in the number of mobile phone supported health interventions and accompanying literature assessing the efficacy of these interventions. The quality o...

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    Open Peer Review Period: Aug 30, 2016 - Oct 25, 2016

    Background: Although remote monitoring has proven its added value in various healthcare domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive...

    Background: Although remote monitoring has proven its added value in various healthcare domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive disorder (GHD). Objective: To evaluate the added value of a remote follow-up program for pregnant women diagnosed with GHD. Methods: A one year retrospective study was performed in the outpatient clinic of a 2nd level prenatal center where pregnant women with GHD received remote monitoring (RM) or conventional care (CC). Study endpoints include number of prenatal visits and admissions to the prenatal observation ward, gestational outcome, mode of delivery, neonatal outcome and admission to neonatal intensive care (NIC). Differences in continuous and categorical variables were tested using Student’s two sampled t-test and the χ² test, respectively, at nominal level α = 0.05. Results: Of 166 patients diagnosed with GHD, 53 received RM and 113 CC. After excluding 9 patients in the RM group and 15 in de CC group because of missing data, 44 patients in RM group and 98 in CC group were taken into final analysis. Both groups had similar demographics. The RM group had more women diagnosed with gestational hypertension but less with pre-eclampsia when compared with CC (79.55% versus 42.86% and 15.91% versus 43.88%). The RM group had less hospital and NIC admissions together with less hospital stay until delivery when compared with CC (31.81% versus 74.47%; 11.36% versus 31.63% and 18.18% versus 64.24%). A spontaneous start of the birth process was more likely and less inductions occurred in RM than in CC (56.81% versus 31.63% and 27.27% versus 48.98%). Conclusions: A RM follow – up of women with GHD is a promising tool in the obstetrician care. It opens the perspectives to reverse the current evolution of antenatal interventions leading to more interventions and as such to ever increasing medicalized antenatal care.

  • Analyzing mHealth: Joint Models for Intensively Collected User Engagement Data

    Date Submitted: Aug 19, 2016

    Open Peer Review Period: Aug 26, 2016 - Oct 21, 2016

    Background: Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealt...

    Background: Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in the mHealth context is necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention. Objective: The objective of this paper is to demonstrate the utility of a joint modeling approach to longitudinal engagement data in mHealth research. Methods: Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to separate naïve models that do not account for the relationship between drop-out and engagement. Results: The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app one day more per week was associated with a 33% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model. Conclusions: Naïve longitudinal models that do not account for informative missingness in mHealth data produce biased results. Joint models are appropriate for modeling intensively collected engagement outcomes in mHealth intervention research. Clinical Trial: Trial Registration: ClinicalTrials.gov NCT02364544.

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