TY - JOUR AU - Chettri, Shibani AU - Wang, Vivian AU - Balkin, Asher Eli AU - Rayo, F. Michael AU - Lee, N. Clara PY - 2022/4/1 TI - Development of a Mobile App for Clinical Research: Challenges and Implications for Investigators JO - JMIR Mhealth Uhealth SP - e32244 VL - 10 IS - 4 KW - mHealth KW - mobile app KW - patient-collected data KW - data security KW - mobile health KW - patient data KW - clinical research KW - research facilities UR - https://mhealth.jmir.org/2022/4/e32244 UR - http://dx.doi.org/10.2196/32244 UR - http://www.ncbi.nlm.nih.gov/pubmed/35363154 ID - info:doi/10.2196/32244 ER - TY - JOUR AU - Fonseka, N. Lakshan AU - Woo, P. Benjamin K. PY - 2022/4/7 TI - Wearables in Schizophrenia: Update on Current and Future Clinical Applications JO - JMIR Mhealth Uhealth SP - e35600 VL - 10 IS - 4 KW - wearables KW - smartwatch KW - schizophrenia KW - digital phenotype KW - wearable KW - mHealth KW - mobile health KW - review KW - clinical application KW - clinical utility KW - clinical use KW - literature search KW - diagnosis KW - prevention UR - https://mhealth.jmir.org/2022/4/e35600 UR - http://dx.doi.org/10.2196/35600 UR - http://www.ncbi.nlm.nih.gov/pubmed/35389361 ID - info:doi/10.2196/35600 ER - TY - JOUR AU - Cho, Jaeho Peter AU - Yi, Jaehan AU - Ho, Ethan AU - Shandhi, Hasan Md Mobashir AU - Dinh, Yen AU - Patil, Aneesh AU - Martin, Leatrice AU - Singh, Geetika AU - Bent, Brinnae AU - Ginsburg, Geoffrey AU - Smuck, Matthew AU - Woods, Christopher AU - Shaw, Ryan AU - Dunn, Jessilyn PY - 2022/4/8 TI - Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design JO - JMIR Mhealth Uhealth SP - e29510 VL - 10 IS - 4 KW - bring your own device KW - wearable device KW - mHealth UR - https://mhealth.jmir.org/2022/4/e29510 UR - http://dx.doi.org/10.2196/29510 UR - http://www.ncbi.nlm.nih.gov/pubmed/34913871 ID - info:doi/10.2196/29510 ER - TY - JOUR AU - Hamberger, Marietta AU - Ikonomi, Nensi AU - Schwab, D. Julian AU - Werle, D. Silke AU - Fürstberger, Axel AU - Kestler, MR Angelika AU - Holderried, Martin AU - Kaisers, X. Udo AU - Steger, Florian AU - Kestler, A. Hans PY - 2022/4/13 TI - Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives JO - JMIR Mhealth Uhealth SP - e32696 VL - 10 IS - 4 KW - mHealth KW - mobile apps KW - patient empowerment KW - digital health KW - interaction empowerment KW - patient-doctor relationship KW - health care network KW - intersectoral communication UR - https://mhealth.jmir.org/2022/4/e32696 UR - http://dx.doi.org/10.2196/32696 UR - http://www.ncbi.nlm.nih.gov/pubmed/35416786 ID - info:doi/10.2196/32696 ER - TY - JOUR AU - Mann, M. Devin AU - Lawrence, Katharine PY - 2022/4/15 TI - Reimagining Connected Care in the Era of Digital Medicine JO - JMIR Mhealth Uhealth SP - e34483 VL - 10 IS - 4 KW - health information technology KW - telehealth KW - remote patient monitoring KW - mobile health KW - mHealth KW - eHealth KW - digital health KW - innovation KW - process model KW - information technology KW - digital medicine KW - automation UR - https://mhealth.jmir.org/2022/4/e34483 UR - http://dx.doi.org/10.2196/34483 UR - http://www.ncbi.nlm.nih.gov/pubmed/35436238 ID - info:doi/10.2196/34483 ER - TY - JOUR AU - Adamowicz, Lukas AU - Christakis, Yiorgos AU - Czech, D. Matthew AU - Adamusiak, Tomasz PY - 2022/4/21 TI - SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing JO - JMIR Mhealth Uhealth SP - e36762 VL - 10 IS - 4 KW - wearable sensors KW - digital medicine KW - gait analysis KW - human movement analysis KW - digital biomarkers KW - uHealth KW - wearable KW - sensor KW - gait KW - movement KW - mobility KW - physical activity KW - sleep KW - Python KW - coding KW - open source KW - software package KW - algorithm KW - machine learning KW - data science KW - computer programming UR - https://mhealth.jmir.org/2022/4/e36762 UR - http://dx.doi.org/10.2196/36762 UR - http://www.ncbi.nlm.nih.gov/pubmed/35353039 ID - info:doi/10.2196/36762 ER - TY - JOUR AU - Forchuk, Cheryl AU - Serrato, Jonathan AU - Lizotte, Daniel AU - Mann, Rupinder AU - Taylor, Gavin AU - Husni, Sara PY - 2022/4/29 TI - Developing a Smart Home Technology Innovation for People With Physical and Mental Health Problems: Considerations and Recommendations JO - JMIR Mhealth Uhealth SP - e25116 VL - 10 IS - 4 KW - smart home KW - smart technology KW - mental health KW - physical health, eHealth KW - comorbidity KW - innovation KW - communication KW - connection KW - uHealth KW - ubiquitous health KW - digital health UR - https://mhealth.jmir.org/2022/4/e25116 UR - http://dx.doi.org/10.2196/25116 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486422 ID - info:doi/10.2196/25116 ER - TY - JOUR AU - Triantafyllidis, Andreas AU - Kondylakis, Haridimos AU - Katehakis, Dimitrios AU - Kouroubali, Angelina AU - Koumakis, Lefteris AU - Marias, Kostas AU - Alexiadis, Anastasios AU - Votis, Konstantinos AU - Tzovaras, Dimitrios PY - 2022/4/4 TI - Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review JO - JMIR Mhealth Uhealth SP - e32344 VL - 10 IS - 4 KW - mHealth KW - deep learning KW - chronic disease KW - review KW - mobile phone N2 - Background: Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. Objective: The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Methods: A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance. Results: In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient?s condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes. Conclusions: The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions. UR - https://mhealth.jmir.org/2022/4/e32344 UR - http://dx.doi.org/10.2196/32344 UR - http://www.ncbi.nlm.nih.gov/pubmed/35377325 ID - info:doi/10.2196/32344 ER - TY - JOUR AU - Arroyo, Carmen Amber AU - Zawadzki, J. Matthew PY - 2022/4/4 TI - The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review JO - JMIR Mhealth Uhealth SP - e33527 VL - 10 IS - 4 KW - behavior change techniques KW - sleep KW - mHealth KW - apps KW - digital health KW - mobile phone N2 - Background: Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep. Objective: This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep? Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020. Results: A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes. Conclusions: Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes). UR - https://mhealth.jmir.org/2022/4/e33527 UR - http://dx.doi.org/10.2196/33527 UR - http://www.ncbi.nlm.nih.gov/pubmed/35377327 ID - info:doi/10.2196/33527 ER - TY - JOUR AU - Antoun, Jumana AU - Itani, Hala AU - Alarab, Natally AU - Elsehmawy, Amir PY - 2022/4/8 TI - The Effectiveness of Combining Nonmobile Interventions With the Use of Smartphone Apps With Various Features for Weight Loss: Systematic Review and Meta-analysis JO - JMIR Mhealth Uhealth SP - e35479 VL - 10 IS - 4 KW - obesity KW - weight loss KW - mobile app KW - self-monitoring KW - behavioral KW - tracker KW - behavioral coaching KW - coach KW - dietitian KW - mobile phone N2 - Background: The effectiveness of smartphone apps for weight loss is limited by the diversity of interventions that accompany such apps. This research extends the scope of previous systematic reviews by including 2 subgroup analyses based on nonmobile interventions that accompanied smartphone use and human-based versus passive behavioral interventions. Objective: The primary objective of this study is to systematically review and perform a meta-analysis of studies that evaluated the effectiveness of smartphone apps on weight loss in the context of other interventions combined with app use. The secondary objective is to measure the impact of different mobile app features on weight loss and mobile app adherence. Methods: We conducted a systematic review and meta-analysis of relevant studies after an extensive search of the PubMed, MEDLINE, and EBSCO databases from inception to January 31, 2022. Gray literature, such as abstracts and conference proceedings, was included. Working independently, 2 investigators extracted the data from the articles, resolving disagreements by consensus. All randomized controlled trials that used smartphone apps in at least 1 arm for weight loss were included. The weight loss outcome was the change in weight from baseline to the 3- and 6-month periods for each arm. Net change estimates were pooled across the studies using random-effects models to compare the intervention group with the control group. The risk of bias was assessed independently by 2 authors using the Cochrane Collaboration tool for assessing the risk of bias in randomized trials. Results: Overall, 34 studies were included that evaluated the use of a smartphone app in at least 1 arm. Compared with controls, the use of a smartphone app?based intervention showed a significant weight loss of ?1.99 kg (95% CI ?2.19 to ?1.79 kg; I2=81%) at 3 months and ?2.80 kg (95% CI ?3.03 to ?2.56 kg; I2=91%) at 6 months. In the subgroup analysis, based on the various intervention components that were added to the mobile app, the combination of the mobile app, tracker, and behavioral interventions showed a statistically significant weight loss of ?2.09 kg (95% CI ?2.32 to ?1.86 kg; I2=91%) and ?3.77 kg (95% CI ?4.05 to ?3.49 kg; I2=90%) at 3 and 6 months, respectively. When a behavioral intervention was present, only the combination of the mobile app with intensive behavior coaching or feedback by a human coach showed a statistically significant weight loss of ?2.03 kg (95% CI ?2.80 to ?1.26 kg; I2=83%) and ?2.63 kg (95% CI ?2.97 to ?2.29 kg; I2=91%) at 3 and 6 months, respectively. Neither the type nor the number of mobile app features was associated with weight loss. Conclusions: Smartphone apps have a role in weight loss management. Nevertheless, the human-based behavioral component remained key to higher weight loss results. UR - https://mhealth.jmir.org/2022/4/e35479 UR - http://dx.doi.org/10.2196/35479 UR - http://www.ncbi.nlm.nih.gov/pubmed/35394443 ID - info:doi/10.2196/35479 ER - TY - JOUR AU - Wang, Wentao AU - Cheng, Jing AU - Song, Weijun AU - Shen, Yi PY - 2022/4/8 TI - The Effectiveness of Wearable Devices as Physical Activity Interventions for Preventing and Treating Obesity in Children and Adolescents: Systematic Review and Meta-analysis JO - JMIR Mhealth Uhealth SP - e32435 VL - 10 IS - 4 KW - wearable devices KW - obesity KW - children KW - adolescents KW - meta-analysis N2 - Background: The prevalence of obesity in children and adolescents remains a global public health issue. Wearable devices may offer new opportunities for prevention and intervention in obesity. Previous systematic reviews have only examined the effect of the wearable device interventions on preventing and treating obesity in adults. However, no systematic review has provided an evaluation of wearable devices as physical activity interventions for preventing and treating obesity in children and adolescents. Objective: The purpose of this review and meta-analysis was to evaluate the effectiveness of wearable devices as physical activity interventions on obesity-related anthropometric outcomes in children and adolescents. Methods: Research articles retrieved from PubMed, EMBASE, Cochrane Library, Scopus, and EBSCO from inception to February 1, 2021, were reviewed. The search was designed to identify studies utilizing wearable devices for preventing and treating obesity in children and adolescents. The included studies were evaluated for risk of bias following the Cochrane recommendation. Meta-analyses were conducted to evaluate the effectiveness of wearable devices as physical activity interventions on body weight, body fat, BMI z-score (BMI-Z), BMI, and waist circumference. Subgroup analyses were performed to determine whether the characteristics of the interventions had an impact on the effect size. Results: A total of 12 randomized controlled trials (3227 participants) were selected for meta-analysis. Compared with the control group, wearable device interventions had statistically significant beneficial effects on BMI (mean difference [MD] ?0.23; 95% CI ?0.43 to ?0.03; P=.03; I2=2%), BMI-Z (MD ?0.07; 95% CI ?0.13 to ?0.01; P=.01; I2=81%), body weight (MD ?1.08; 95% CI ?2.16 to ?0.00; P=.05; I2=58%), and body fat (MD ?0.72; 95% CI ?1.19 to ?0.25; P=.003; I2=5%). However, no statistically significant effect was found on waist circumference (MD 0.55; 95% CI ?0.21 to 1.32; P=.16; I2=0%). The subgroup analysis showed that for participants with overweight or obesity (MD ?0.75; 95% CI ?1.18 to ?0.31; P<.01; I2=0%), in the short-term (MD ?0.62; 95% CI ?1.03 to ?0.21; P<.01; I2=0%), wearable-based interventions (MD ?0.56; 95% CI ?0.95 to ?0.18; P<.01; I2=0%) generally resulted in greater intervention effect size on BMI. Conclusions: Evidence from this meta-analysis shows that wearable devices as physical activity interventions may be useful for preventing and treating obesity in children and adolescents. Future research is needed to identify the most effective physical activity indicators of wearable devices to prevent and treat obesity in children and adolescents. UR - https://mhealth.jmir.org/2022/4/e32435 UR - http://dx.doi.org/10.2196/32435 UR - http://www.ncbi.nlm.nih.gov/pubmed/35394447 ID - info:doi/10.2196/32435 ER - TY - JOUR AU - Qirtas, Muhammad Malik AU - Zafeiridi, Evi AU - Pesch, Dirk AU - White, Bantry Eleanor PY - 2022/4/12 TI - Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review JO - JMIR Mhealth Uhealth SP - e34638 VL - 10 IS - 4 KW - passive sensing KW - loneliness KW - social isolation KW - smartphone KW - sensors KW - wearables KW - monitoring KW - scoping review KW - eHealth KW - mHealth KW - mobile phone N2 - Background: Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation. Objective: This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users? daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues. Methods: A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems. Results: After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation. Conclusions: Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants? behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns. UR - https://mhealth.jmir.org/2022/4/e34638 UR - http://dx.doi.org/10.2196/34638 UR - http://www.ncbi.nlm.nih.gov/pubmed/35412465 ID - info:doi/10.2196/34638 ER - TY - JOUR AU - Chevance, Guillaume AU - Golaszewski, M. Natalie AU - Tipton, Elizabeth AU - Hekler, B. Eric AU - Buman, Matthew AU - Welk, J. Gregory AU - Patrick, Kevin AU - Godino, G. Job PY - 2022/4/13 TI - Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis JO - JMIR Mhealth Uhealth SP - e35626 VL - 10 IS - 4 KW - wearables KW - activity monitors KW - physical activity KW - validity KW - accelerometry N2 - Background: Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. Objective: The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. Methods: We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. Results: A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of ?2.99 beats per minute (k comparison=74), ?2.77 kcal per minute (k comparison=29), and ?3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: ?23.99 to 18.01, ?12.75 to 7.41, and ?13.07 to 6.86, respectively). Conclusions: Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes. UR - https://mhealth.jmir.org/2022/4/e35626 UR - http://dx.doi.org/10.2196/35626 UR - http://www.ncbi.nlm.nih.gov/pubmed/35416777 ID - info:doi/10.2196/35626 ER - TY - JOUR AU - Alhussein, Ghada AU - Hadjileontiadis, Leontios PY - 2022/4/21 TI - Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis JO - JMIR Mhealth Uhealth SP - e32557 VL - 10 IS - 4 KW - mHealth KW - digital health KW - osteoporosis KW - self-management KW - systematic review KW - meta-analysis KW - chronic disease KW - bone health KW - nutrition KW - physical activity KW - risk assessment KW - mobile phone N2 - Background: Osteoporosis is the fourth most common chronic disease worldwide. The adoption of preventative measures and effective self-management interventions can help improve bone health. Mobile health (mHealth) technologies can play a key role in the care and self-management of patients with osteoporosis. Objective: This study presents a systematic review and meta-analysis of the currently available mHealth apps targeting osteoporosis self-management, aiming to determine the current status, gaps, and challenges that future research could address, as well as propose appropriate recommendations. Methods: A systematic review of all English articles was conducted, in addition to a survey of all apps available in iOS and Android app stores as of May 2021. A comprehensive literature search (2010 to May 2021) of PubMed, Scopus, EBSCO, Web of Science, and IEEE Xplore was conducted. Articles were included if they described apps dedicated to or useful for osteoporosis (targeting self-management, nutrition, physical activity, and risk assessment) delivered on smartphone devices for adults aged ?18 years. Of the 32 articles, a random effects meta-analysis was performed on 13 (41%) studies of randomized controlled trials, whereas the 19 (59%) remaining studies were only included in the narrative synthesis as they did not provide enough data. Results: In total, 3906 unique articles were identified. Of these 3906 articles, 32 (0.81%) articles met the inclusion criteria and were reviewed in depth. The 32 studies comprised 14,235 participants, of whom, on average, 69.5% (n=9893) were female, with a mean age of 49.8 (SD 17.8) years. The app search identified 23 relevant apps for osteoporosis self-management. The meta-analysis revealed that mHealth-supported interventions resulted in a significant reduction in pain (Hedges g ?1.09, 95% CI ?1.68 to ?0.45) and disability (Hedges g ?0.77, 95% CI ?1.59 to 0.05). The posttreatment effect of the digital intervention was significant for physical function (Hedges g 2.54, 95% CI ?4.08 to 4.08) but nonsignificant for well-being (Hedges g 0.17, 95% CI ?1.84 to 2.17), physical activity (Hedges g 0.09, 95% CI ?0.59 to 0.50), anxiety (Hedges g ?0.29, 95% CI ?6.11 to 5.53), fatigue (Hedges g ?0.34, 95% CI ?5.84 to 5.16), calcium (Hedges g ?0.05, 95% CI ?0.59 to 0.50), vitamin D intake (Hedges g 0.10, 95% CI ?4.05 to 4.26), and trabecular score (Hedges g 0.06, 95% CI ?1.00 to 1.12). Conclusions: Osteoporosis apps have the potential to support and improve the management of the disease and its symptoms; they also appear to be valuable tools for patients and health professionals. However, most of the apps that are currently available lack clinically validated evidence of their efficacy and focus on a limited number of symptoms. A more holistic and personalized approach within a cocreation design ecosystem is needed. Trial Registration: PROSPERO 2021 CRD42021269399; https://tinyurl.com/2sw454a9 UR - https://mhealth.jmir.org/2022/4/e32557 UR - http://dx.doi.org/10.2196/32557 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451968 ID - info:doi/10.2196/32557 ER - TY - JOUR AU - Jalali, Niloofar AU - Sahu, Sundar Kirti AU - Oetomo, Arlene AU - Morita, Pelegrini Plinio PY - 2022/4/1 TI - Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study JO - JMIR Mhealth Uhealth SP - e28811 VL - 10 IS - 4 KW - public health KW - Internet of Things (IoT) KW - big data KW - sleep monitoring KW - health monitoring KW - mobile phone N2 - Background: Sleep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. Objective: The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations. Methods: From the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ?6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household?s record was divided into different subsets based on seasonal, weekday, and seasonal weekday scales. Results: Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450; P<.001; odds ratio [OR] 1.8, 95% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450; P<.001; OR 1.8, 95% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450; P<.001; OR 5.6, 95% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445; P<.001; OR 2.06, 95% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455; P<.001; OR 1.6, 95% CI 1.3-2.3). These results are in accordance with existing literature. Conclusions: This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change). UR - https://mhealth.jmir.org/2022/4/e28811 UR - http://dx.doi.org/10.2196/28811 UR - http://www.ncbi.nlm.nih.gov/pubmed/35363147 ID - info:doi/10.2196/28811 ER - TY - JOUR AU - Macharia, Paul AU - Pérez-Navarro, Antoni AU - Sambai, Betsy AU - Inwani, Irene AU - Kinuthia, John AU - Nduati, Ruth AU - Carrion, Carme PY - 2022/4/15 TI - An Unstructured Supplementary Service Data?Based mHealth App Providing On-Demand Sexual Reproductive Health Information for Adolescents in Kibra, Kenya: Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e31233 VL - 10 IS - 4 KW - adolescents KW - sexual reproductive health KW - mobile phones KW - randomized controlled trial N2 - Background: Adolescents transitioning from childhood to adulthood need to be equipped with sexual reproductive health (SRH) knowledge, skills, attitudes, and values that empower them. Accessible, reliable, appropriate, and friendly information can be provided through mobile phone?based health interventions. Objective: This study aims to investigate the effectiveness and impact of an Unstructured Supplementary Service Data (USSD)?based app in increasing adolescents? knowledge about contraceptives, gender-based stereotypes, sexually transmitted infections (STIs), abstinence, and perceived vulnerability, and helping adolescents make informed decisions about their SRH. Methods: A randomized controlled trial (RCT) methodology was applied to investigate the potential of a USSD-based app for providing on-demand SRH information. To be eligible, adolescents aged 15 to 19 years residing in Kibra, Kenya, had to have access to a phone and be available for the 3-month follow-up visit. Participants were randomly assigned to the intervention (n=146) and control (n=154) groups using sequentially numbered, opaque, sealed envelopes. The primary outcome was improved SRH knowledge. The secondary outcome was improved decision-making on SRH. The outcomes were measured using validated tools on adolescent SRH and user perceptions during the follow-up visit. A paired sample t test was used to compare the changes in knowledge scores in both groups. The control group did not receive any SRH information. Results: During the RCT, 54.9% (62/109) of adolescents used the USSD-based app at least once. The mean age by randomization group was 17.3 (SD 1.23) years for the control group and 17.3 (SD 1.12) years for the intervention group. There was a statistically significant difference in the total knowledge scores in the intervention group (mean 10.770, SD 2.012) compared with the control group (mean 10.170, SD 2.412) conditions (t179=2.197; P=.03). There was a significant difference in abstinence (P=.01) and contraceptive use (P=.06). Of the individuals who used the app, all participants felt the information received could improve decision-making regarding SRH. Information on STIs was of particular interest, with 27% (20/62) of the adolescents seeking information in this area, of whom 55% (11/20) were female. In relation to improved decision-making, 21.6% (29/134) of responses showed the adolescents were able to identify STIs and were likely to seek treatment; 51.7% (15/29) of these were female. Ease of use was the most important feature of the app for 28.3% (54/191) of the responses. Conclusions: Adolescents require accurate and up-to-date SRH information to guide their decision-making and improve health outcomes. As adolescents already use mobile phones in their day-to-day lives, apps provide an ideal platform for this information. A USSD-based app could be an appropriate tool for increasing SRH knowledge among adolescents in low-resource settings. Adolescents in the study valued the information provided because it helped them identify SRH topics on which they needed more information. Trial Registration: Pan African Clinical Trial Registry PACTR202204774993198; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=22623 UR - https://mhealth.jmir.org/2022/4/e31233 UR - http://dx.doi.org/10.2196/31233 UR - http://www.ncbi.nlm.nih.gov/pubmed/35436230 ID - info:doi/10.2196/31233 ER - TY - JOUR AU - Zhou, Joanne AU - Lamichhane, Bishal AU - Ben-Zeev, Dror AU - Campbell, Andrew AU - Sano, Akane PY - 2022/4/11 TI - Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis JO - JMIR Mhealth Uhealth SP - e31006 VL - 10 IS - 4 KW - schizophrenia KW - psychotic relapse KW - machine learning KW - clustering KW - mobile phone KW - routine KW - Gaussian mixture models KW - partition around medoids KW - dynamic time warping KW - balanced random forest N2 - Background: Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. Objective: In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse. Methods: We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age. Results: The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042. Conclusions: Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions. UR - https://mhealth.jmir.org/2022/4/e31006 UR - http://dx.doi.org/10.2196/31006 UR - http://www.ncbi.nlm.nih.gov/pubmed/35404256 ID - info:doi/10.2196/31006 ER - TY - JOUR AU - Cook, Paul AU - Jankowski, Catherine AU - Erlandson, M. Kristine AU - Reeder, Blaine AU - Starr, Whitney AU - Flynn Makic, Beth Mary PY - 2022/4/14 TI - Low- and High-Intensity Physical Activity Among People with HIV: Multilevel Modeling Analysis Using Sensor- and Survey-Based Predictors JO - JMIR Mhealth Uhealth SP - e33938 VL - 10 IS - 4 KW - ecological momentary assessment KW - fatigue KW - HIV KW - physical activity KW - stress KW - mobile phone N2 - Background: High-intensity physical activity improves the health of people with HIV. Even when people have good intentions to engage in physical activity, they often find it difficult to maintain physical activity behavior in the long term. Two Minds Theory is a neurocognitive model that explains gaps between people?s intentions and behaviors based on the operations of 2 independent mental systems. This model predicts that everyday experiences will affect physical activity and that factors outside people?s awareness, such as sleep and stress, can have particularly strong effects on physical activity behaviors. Objective: We designed this study to test the effects of daily experiences on physical activity among people with HIV, including measures of people?s conscious experiences using daily electronic surveys and measures of nonconscious influences using sensor devices. Methods: In this study, 55 people with HIV wore a Fitbit Alta for 30 days to monitor their physical activity, sleep, and heart rate variability (HRV) as a physiological indicator of stress. Participants also used their smartphones to complete daily electronic surveys for the same 30 days about fatigue, self-efficacy, mood, stress, coping, motivation, and barriers to self-care. Time-lagged, within-person, multilevel models were used to identify the best prospective predictors of physical activity, considering the daily survey responses of people with HIV and sensor data as predictors of their physical activity the following day. We also tested baseline surveys as predictors of physical activity for comparison with daily variables. Results: Different people had different average levels of physical activity; however, physical activity also varied substantially from day to day, and daily measures were more predictive than baseline surveys. This suggests a chance to intervene based on day-to-day variations in physical activity. High-intensity physical activity was more likely when people with HIV reported less subjective fatigue on the prior day (r=?0.48) but was unrelated to actual sleep based on objective sensor data. High-intensity physical activity was also predicted by higher HRV (r=0.56), indicating less stress, lower HIV-related stigma (r=?0.21), fewer barriers to self-care (r=?0.34), and less approach coping (r=?0.34). Similar variables predicted lower-level physical activity measured based on the number of steps per day of people with HIV. Conclusions: Some predictors of physical activity, such as HRV, were only apparent based on sensor data, whereas others, such as fatigue, could be measured via self-report. Findings about coping were unexpected; however, other findings were in line with the literature. This study extends our prior knowledge on physical activity by demonstrating a prospective effect of everyday experiences on physical activity behavior, which is in line with the predictions of Two Minds Theory. Clinicians can support the physical activity of people with HIV by helping their patients reduce their daily stress, fatigue, and barriers to self-care. UR - https://mhealth.jmir.org/2022/4/e33938 UR - http://dx.doi.org/10.2196/33938 UR - http://www.ncbi.nlm.nih.gov/pubmed/35436236 ID - info:doi/10.2196/33938 ER - TY - JOUR AU - Kutyba, Jolanta Justyna AU - J?drzejczak, Wiktor W. AU - Gos, El?bieta AU - Raj-Koziak, Danuta AU - Skarzynski, Henryk Piotr PY - 2022/4/21 TI - Chronic Tinnitus and the Positive Effects of Sound Treatment via a Smartphone App: Mixed-Design Study JO - JMIR Mhealth Uhealth SP - e33543 VL - 10 IS - 4 KW - tinnitus KW - mobile app KW - smartphone KW - sound therapy KW - telemedicine KW - mobile phone N2 - Background: Tinnitus is a phantom auditory sensation in the absence of an external stimulus. It is accompanied by a broad range of negative emotional symptoms and a significantly lower quality of life. So far, there is no cure for tinnitus, although various treatment options have been tried. One of them is mobile technology employing dedicated apps based on sound therapy. The apps can be managed by the patient and tailored according to their needs. Objective: The study aims to assess the effect of a mobile app that generates background sounds on the severity of tinnitus. Methods: The study involved 68 adults who had chronic tinnitus. Participants were divided into a study group (44 patients) and a control group (24 patients). For 6 months those in the study group used a free mobile app that enriched the sound environment with a background sound. Participants were instructed to use the app for at least 30 minutes a day using their preferred sound. The participants in the control group did not use the app. Subjective changes in the day-to-day functioning of both groups were evaluated using the Tinnitus Handicap Inventory (THI) questionnaire, a visual analog scale, and a user survey. Results: After 3 months of using the app, the THI global score significantly decreased (P<.001) in the study group, decreasing again at 6 months (P<.001). The largest improvements were observed in the emotional and catastrophic reactions subscales. A clinically important change in the THI was reported by 39% of the study group (17/44). Almost 90% of the study participants (39/44) chose environmental sounds to listen to, the most popular being rain and ocean waves. In the control group, tinnitus severity did not change over 3 or 6 months. Conclusions: Although the participants still experienced limitations caused by tinnitus, the advantage of the app was that it led to lower negative emotions and thus reduced overall tinnitus severity. It is worth considering whether a mobile app might be incorporated into the management of tinnitus in a professional setting. UR - https://mhealth.jmir.org/2022/4/e33543 UR - http://dx.doi.org/10.2196/33543 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451975 ID - info:doi/10.2196/33543 ER - TY - JOUR AU - Ramachandram, Dhanesh AU - Ramirez-GarciaLuna, Luis Jose AU - Fraser, J. Robert D. AU - Martínez-Jiménez, Aurelio Mario AU - Arriaga-Caballero, E. Jesus AU - Allport, Justin PY - 2022/4/22 TI - Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study JO - JMIR Mhealth Uhealth SP - e36977 VL - 10 IS - 4 KW - wound KW - tissue segmentation KW - automated tissue identification KW - deep learning KW - mobile imaging KW - mobile phone N2 - Background: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. Objective: This study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks. Methods: A data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical?s Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices. Results: A poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff ? value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. Conclusions: The interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale. UR - https://mhealth.jmir.org/2022/4/e36977 UR - http://dx.doi.org/10.2196/36977 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451982 ID - info:doi/10.2196/36977 ER - TY - JOUR AU - Wendrich, Karine AU - Krabbenborg, Lotte PY - 2022/4/27 TI - Digital Self-monitoring of Multiple Sclerosis: Interview Study With Dutch Health Care Providers on the Expected New Configuration of Roles and Responsibilities JO - JMIR Mhealth Uhealth SP - e30224 VL - 10 IS - 4 KW - digital self-monitoring KW - smartphone apps KW - multiple sclerosis KW - technology assessment KW - health care providers KW - user participation KW - mobile phone N2 - Background: Digital self-monitoring allows patients to produce and share personal health data collected at home. This creates a novel situation in which health care providers and patients must engage in a reconfiguration of roles and responsibilities. Although existing research pays considerable attention to the perceptions of patients regarding digital self-monitoring, less attention has been paid to the needs, wishes, and concerns of health care providers. As several companies and public institutions are developing and testing digital self-monitoring at the time of writing, it is timely and relevant to explore how health care providers envision using these technologies in their daily work practices. Our findings can be considered in decision-making processes concerning the further development and implementation of digital self-monitoring. Objective: This study aims to explore how health care providers envisage using smartphone apps for digital self-monitoring of multiple sclerosis (MS) in their daily work practices, with a particular focus on physician-patient communication and on how health care providers respond to self-monitoring data and delegate tasks and responsibilities to patients. Methods: We conducted semistructured in-depth interviews with 14 MS health care providers: 4 neurologists, 7 MS specialist nurses, and 3 rehabilitation professionals. They are affiliated with 3 different hospitals in the Netherlands that will participate in a pilot study to assess the efficiency and effectiveness of a specific smartphone app for self-monitoring. Results: The interviewed health care providers seemed willing to use these smartphone apps and valued the quantitative data they produce that can complement the narratives that patients provide during medical appointments. The health care providers primarily want to use digital self-monitoring via prescription, meaning that they want a standardized smartphone app and want to act as its gatekeepers. Furthermore, they envisioned delegating particular tasks and responsibilities to patients via digital self-monitoring, such as sharing data with the health care providers or acting on the data, if necessary. The health care providers expected patients to become more proactive in the management of their disease. However, they also acknowledged that not all patients are willing or able to use digital self-monitoring apps and were concerned about the potential psychological and emotional burden on patients caused by this technology. Conclusions: Our findings show that health care providers envisage a particular type of patient empowerment and personalized health care in which tensions arise between health care providers acting as gatekeepers and patient autonomy, between patient empowerment and patient disempowerment, and between the weight given to quantitative objective data and that given to patients? subjective experiences. In future research, it would be very interesting to investigate the actual experiences of health care providers with regard to digital self-monitoring to ascertain how the tensions mentioned in this paper play out in practice. UR - https://mhealth.jmir.org/2022/4/e30224 UR - http://dx.doi.org/10.2196/30224 UR - http://www.ncbi.nlm.nih.gov/pubmed/35475770 ID - info:doi/10.2196/30224 ER - TY - JOUR AU - Cai, Yiyuan AU - Gong, Wenjie AU - He, Wenjun AU - He, Hua AU - Hughes, P. James AU - Simoni, Jane AU - Xiao, Shuiyuan AU - Gloyd, Stephen AU - Lin, Meijuan AU - Deng, Xinlei AU - Liang, Zichao AU - Dai, Bofeng AU - Liao, Jing AU - Hao, Yuantao AU - Xu, Roman Dong PY - 2022/4/19 TI - Residual Effect of Texting to Promote Medication Adherence for Villagers with Schizophrenia in China: 18-Month Follow-up Survey After the Randomized Controlled Trial Discontinuation JO - JMIR Mhealth Uhealth SP - e33628 VL - 10 IS - 4 KW - medication adherence KW - mobile texting KW - lay health worker KW - resource-poor community KW - primary health care KW - quality of care KW - mHealth KW - schizophrenia KW - maintenance KW - residual effect KW - mental health KW - patient outcomes N2 - Background: Reducing the treatment gap for mental health in low- and middle-income countries is a high priority. Even with treatment, adherence to antipsychotics is rather low. Our integrated intervention package significantly improved medication adherence within 6 months for villagers with schizophrenia in resource-poor communities in rural China. However, considering the resource constraint, we need to test whether the effect of those behavior-shaping interventions may be maintained even after the suspension of the intervention. Objective: The aim of this study is to explore the primary outcome of adherence and other outcomes at an 18-month follow-up after the intervention had been suspended. Methods: In a 6-month randomized trial, 277 villagers with schizophrenia were randomized to receive either a government community mental health program (686 Program) or the 686 Program plus Lay health supporters, e-platform, award, and integration (LEAN), which included health supporters for medication or care supervision, e-platform access for sending mobile SMS text messaging reminders and education message, a token gift for positive behavior changes (eg, continuing taking medicine), and integrating the e-platform with the existing 686 Program. After the 6-month intervention, both groups received only the 686 Program for 18 months (phase 2). Outcomes at both phases included antipsychotic medication adherence, functioning, symptoms, number of rehospitalization, suicide, and violent behaviors. The adherence and functioning were assessed at the home visit by trained assessors. We calculated the adherence in the past 30 days by counting the percentage of dosages taken from November to December 2018 by unannounced home-based pill counts. The functioning was assessed using the World Health Organization Disability Assessment Schedule 2.0. The symptoms were evaluated using the Clinical Global Impression?Schizophrenia during their visits to the 686 Program psychiatrists. Other outcomes were routinely collected in the 686 Program system. We used intention-to-treat analysis, and missing data were dealt with using multiple imputation. The generalized estimating equation model was used to assess program effects on adherence, functioning, and symptoms. Results: In phase 1, antipsychotic adherence and rehospitalization incidence improved significantly. However, in phase 2, the difference of the mean of antipsychotic adherence (adjusted mean difference 0.05, 95% CI ?0.06 to 0.16; P=.41; Cohen d effect size=0.11) and rehospitalization incidence (relative risk 0.65, 95% CI 0.32-1.33; P=.24; number needed to treat 21.83, 95% CI 8.30-34.69) was no longer statistically significant, and there was no improvement in other outcomes in either phase (P?.05). Conclusions: The simple community-based LEAN intervention could not continually improve adherence and reduce the rehospitalization of people with schizophrenia. Our study inclined to suggest that prompts for medication may be necessary to maintain medication adherence for people with schizophrenia, although we cannot definitively exclude other alternative interpretations. UR - https://mhealth.jmir.org/2022/4/e33628 UR - http://dx.doi.org/10.2196/33628 UR - http://www.ncbi.nlm.nih.gov/pubmed/35438649 ID - info:doi/10.2196/33628 ER - TY - JOUR AU - Yamamoto, Kazumichi AU - Ito, Masami AU - Sakata, Masatsugu AU - Koizumi, Shiho AU - Hashisako, Mizuho AU - Sato, Masaaki AU - Stoyanov, R. Stoyan AU - Furukawa, A. Toshi PY - 2022/4/14 TI - Japanese Version of the Mobile App Rating Scale (MARS): Development and Validation JO - JMIR Mhealth Uhealth SP - e33725 VL - 10 IS - 4 KW - mobile health apps KW - MHAs KW - mHealth KW - mobile application KW - mobile application rating scale KW - MARS KW - scale development KW - mental health KW - mobile health applications N2 - Background: The number of mobile health (mHealth) apps continues to rise each year. Widespread use of the Mobile App Rating Scale (MARS) has allowed objective and multidimensional evaluation of the quality of these apps. However, no Japanese version of MARS has been made available to date. Objective: The purposes of this study were (1) to develop a Japanese version of MARS and (2) to assess the translated version?s reliability and validity in evaluating mHealth apps. Methods: To develop the Japanese version of MARS, cross-cultural adaptation was used using a universalist approach. A total of 50 mental health apps were evaluated by 2 independent raters. Internal consistency and interrater reliability were then calculated. Convergent and divergent validity were assessed using multitrait scaling analysis and concurrent validity. Results: After cross-cultural adaptation, all 23 items from the original MARS were included in the Japanese version. Following translation, back-translation, and review by the author of the original MARS, a Japanese version of MARS was finalized. Internal consistency was acceptable by all subscales of objective and subjective quality (Cronbach ?=.78-.89). Interrater reliability was deemed acceptable, with the intraclass correlation coefficient (ICC) ranging from 0.61 to 0.79 for all subscales, except for ?functionality,? which had an ICC of 0.40. Convergent/divergent validity and concurrent validity were also considered acceptable. The rate of missing responses was high in several items in the ?information? subscale. Conclusions: A Japanese version of MARS was developed and shown to be reliable and valid to a degree that was comparable to the original MARS. This Japanese version of MARS can be used as a standard to evaluate the quality and credibility of mHealth apps. UR - https://mhealth.jmir.org/2022/4/e33725 UR - http://dx.doi.org/10.2196/33725 UR - http://www.ncbi.nlm.nih.gov/pubmed/35197241 ID - info:doi/10.2196/33725 ER - TY - JOUR AU - García-Sánchez, Sebastián AU - Somoza-Fernández, Beatriz AU - de Lorenzo-Pinto, Ana AU - Ortega-Navarro, Cristina AU - Herranz-Alonso, Ana AU - Sanjurjo, María PY - 2022/4/20 TI - Mobile Health Apps Providing Information on Drugs for Adult Emergency Care: Systematic Search on App Stores and Content Analysis JO - JMIR Mhealth Uhealth SP - e29985 VL - 10 IS - 4 KW - emergency drugs KW - emergency medicine KW - emergency departments KW - emergency professionals KW - medication errors KW - drug characteristics KW - drug management KW - apps KW - mHealth KW - mobile health KW - digital health KW - smartphone KW - mobile phone N2 - Background: Drug-referencing apps are among the most frequently used by emergency health professionals. To date, no study has analyzed the quantity and quality of apps that provide information on emergency drugs. Objective: This study aimed to identify apps designed to assist emergency professionals in managing drugs and to describe and analyze their characteristics. Methods: We performed an observational, cross-sectional, descriptive study of apps that provide information on drugs for adult emergency care. The iOS and Android platforms were searched in February 2021. The apps were independently evaluated by 2 hospital clinical pharmacists. We analyzed developer affiliation, cost, updates, user ratings, and number of downloads. We also evaluated the main topic (emergency drugs or emergency medicine), the number of drugs described, the inclusion of bibliographic references, and the presence of the following drug information: commercial presentations, usual dosage, dose adjustment for renal failure, mechanism of action, therapeutic indications, contraindications, interactions with other medicinal products, use in pregnancy and breastfeeding, adverse reactions, method of preparation and administration, stability data, incompatibilities, identification of high-alert medications, positioning in treatment algorithms, information about medication reconciliation, and cost. Results: Overall, 49 apps were identified. Of these 49 apps, 32 (65%) were found on both digital platforms; 11 (22%) were available only for Android, and 6 (12%) were available only for iOS. In total, 41% (20/49) of the apps required payment (ranging from ?0.59 [US $0.64] to ?179.99 [US $196.10]) and 22% (11/49) of the apps were developed by non?health care professionals. The mean weighted user rating was 4.023 of 5 (SD 0.71). Overall, 45% (22/49) of the apps focused on emergency drugs, and 55% (27/49) focused on emergency medicine. More than half (29/47, 62%) did not include bibliographic references or had not been updated for more than a year (29/49, 59%). The median number of drugs was 66 (range 4 to >5000). Contraindications (26/47, 55%) and adverse reactions (24/47, 51%) were found in only half of the apps. Less than half of the apps addressed dose adjustment for renal failure (15/47, 32%), interactions (10/47, 21%), and use during pregnancy and breastfeeding (15/47, 32%). Only 6% (3/47) identified high-alert medications, and 2% (1/47) included information about medication reconciliation. Health-related developer, main topic, and greater amount of drug information were not statistically associated with higher user ratings (P=.99, P=.09, and P=.31, respectively). Conclusions: We provide a comprehensive review of apps with information on emergency drugs for adults. Information on authorship, drug characteristics, and bibliographic references is frequently scarce; therefore, we propose recommendations to consider when developing an app of these characteristics. Future efforts should be made to increase the regulation of drug-referencing apps and to conduct a more frequent and documented review of their clinical content. UR - https://mhealth.jmir.org/2022/4/e29985 UR - http://dx.doi.org/10.2196/29985 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442212 ID - info:doi/10.2196/29985 ER - TY - JOUR AU - Kawichai, Surinda AU - Songtaweesin, Natalie Wipaporn AU - Wongharn, Prissana AU - Phanuphak, Nittaya AU - Cressey, R. Tim AU - Moonwong, Juthamanee AU - Vasinonta, Anuchit AU - Saisaengjan, Chutima AU - Chinbunchorn, Tanat AU - Puthanakit, Thanyawee PY - 2022/4/21 TI - A Mobile Phone App to Support Adherence to Daily HIV Pre-exposure Prophylaxis Engagement Among Young Men Who Have Sex With Men and Transgender Women Aged 15 to 19 Years in Thailand: Pilot Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e25561 VL - 10 IS - 4 KW - mHealth KW - PrEP adherence KW - adolescents KW - men who have sex with men KW - transgender women KW - mobile phone N2 - Background: Widespread smartphone use provides opportunities for mobile health HIV prevention strategies among at-risk populations. Objective: This study aims to investigate engagement in a theory-based (information?motivation?behavioral skills model) mobile phone app developed to support HIV pre-exposure prophylaxis (PrEP) adherence among Thai young men who have sex with men (YMSM) and young transgender women (YTGW) in Bangkok, Thailand. Methods: A randomized controlled trial was conducted among HIV-negative YMSM and YTGW aged 15-19 years initiating daily oral PrEP. Participants were randomized to receive either youth-friendly PrEP services (YFS) for 6 months, including monthly contact with site staff (clinic visits or telephone follow-up) and staff consultation access, or YFS plus use of a PrEP adherence support app (YFS+APP). The target population focus group discussion findings and the information?motivation?behavioral skills model informed app development. App features were based on the 3Rs?risk assessment of self-HIV acquisition risk, reminders to take PrEP, and rewards as redeemable points. Dried blood spots quantifying of tenofovir diphosphate were collected at months 3 and 6 to assess PrEP adherence. Tenofovir diphosphate ?350-699 fmol/punch was classified as fair adherence and ?700 fmol/punch as good adherence. Data analysis on app use paradata and exit interviews were conducted on the YFS+APP arm after 6 months of follow-up. Results: Between March 2018 and June 2019, 200 participants with a median age of 18 (IQR 17-19) years were enrolled. Overall, 74% (148/200) were YMSM; 87% (87/100) of participants who received YFS+APP logged in to the app and performed weekly HIV acquisition risk assessments (log-in and risk assessment [LRA]). The median duration between the first and last log-in was 3.5 (IQR 1.6-5.6) months, with a median frequency of 6 LRAs (IQR 2-10). Moreover, 22% (22/100) of the participants in the YFS+APP arm were frequent users (LRA?10) during the 6-month follow-up period. YMSM were 9.3 (95% CI 1.2-74.3) times more likely to be frequent app users than YTGW (P=.04). Frequent app users had higher proportions (12%-16%) of PrEP adherence at both months 3 and 6 compared with infrequent users (LRA<10) and the YFS arm, although this did not reach statistical significance. Of the 100 participants in the YFS+APP arm, 23 (23%) were interviewed. The risk assessment function is perceived as the most useful app feature. Further aesthetic adaptations and a more comprehensive rewards system were suggested by the interviewees. Conclusions: Higher rates of PrEP adherence among frequent app users were observed; however, this was not statistically significant. A short app use duration of 3 months suggests that they may be useful in establishing habits in taking daily PrEP, but not long-term adherence. Further studies on the specific mechanisms of mobile phone apps that influence health behaviors are needed. Trial Registration: ClinicalTrials.gov NCT03778892; https://clinicaltrials.gov/ct2/show/NCT03778892 UR - https://mhealth.jmir.org/2022/4/e25561 UR - http://dx.doi.org/10.2196/25561 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451976 ID - info:doi/10.2196/25561 ER - TY - JOUR AU - Rodríguez Sánchez-Laulhé, Pablo AU - Luque-Romero, Gabriel Luis AU - Barrero-García, José Francisco AU - Biscarri-Carbonero, Ángela AU - Blanquero, Jesús AU - Suero-Pineda, Alejandro AU - Heredia-Rizo, Marcos Alberto PY - 2022/4/7 TI - An Exercise and Educational and Self-management Program Delivered With a Smartphone App (CareHand) in Adults With Rheumatoid Arthritis of the Hands: Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e35462 VL - 10 IS - 4 KW - rheumatoid arthritis KW - telerehabilitation KW - self-management KW - mHealth KW - primary health care KW - physical therapy KW - exercise therapy KW - mobile applications KW - telehealth KW - health education KW - mobile phone N2 - Background: Rheumatoid arthritis (RA) is a prevalent autoimmune disease that usually involves problems of the hand or wrist. Current evidence recommends a multimodal therapy including exercise, self-management, and educational strategies. To date, the efficacy of this approach, as delivered using a smartphone app, has been scarcely investigated. Objective: This study aims to assess the short- and medium-term efficacy of a digital app (CareHand) that includes a tailored home exercise program, together with educational and self-management recommendations, compared with usual care, for people with RA of the hands. Methods: A single-blinded randomized controlled trial was conducted between March 2020 and February 2021, including 36 participants with RA of the hands (women: 22/36, 61%) from 2 community health care centers. Participants were allocated to use the CareHand app, consisting of tailored exercise programs, and self-management and monitoring tools or to a control group that received a written home exercise routine and recommendations, as per the usual protocol provided at primary care settings. Both interventions lasted for 3 months (4 times a week). The primary outcome was hand function, assessed using the Michigan Hand Outcome Questionnaire (MHQ). Secondary measures included pain and stiffness intensity (visual analog scale), grip strength (dynamometer), pinch strength (pinch gauge), and upper limb function (shortened version of the Disabilities of the Arm, Shoulder, and Hand questionnaire). All measures were collected at baseline and at a 3-month follow-up. Furthermore, the MHQ and self-reported stiffness were assessed 6 months after baseline, whereas pain intensity and scores on the shortened version of the Disabilities of the Arm, Shoulder, and Hand questionnaire were collected at the 1-, 3-, and 6-month follow-ups. Results: In total, 30 individuals, corresponding to 58 hands (CareHand group: 26/58, 45%; control group: 32/58, 55%), were included in the analysis; 53% (19/36) of the participants received disease-modifying antirheumatic drug treatment. The ANOVA demonstrated a significant time×group effect for the total score of the MHQ (F1.62,85.67=9.163; P<.001; ?2=0.15) and for several of its subscales: overall hand function, work performance, pain, and satisfaction (all P<.05), with mean differences between groups for the total score of 16.86 points (95% CI 8.70-25.03) at 3 months and 17.21 points (95% CI 4.78-29.63) at 6 months. No time×group interaction was observed for the secondary measures (all P>.05). Conclusions: Adults with RA of the hands who used the CareHand app reported better results in the short and medium term for overall hand function, work performance, pain, and satisfaction, compared with usual care. The findings of this study suggest that the CareHand app is a promising tool for delivering exercise therapy and self-management recommendations to this population. Results must be interpreted with caution because of the lack of efficacy of the secondary outcomes. Trial Registration: ClinicalTrials.gov NCT04263974; https://clinicaltrials.gov/ct2/show/NCT04263974 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-020-04713-4 UR - https://mhealth.jmir.org/2022/4/e35462 UR - http://dx.doi.org/10.2196/35462 UR - http://www.ncbi.nlm.nih.gov/pubmed/35389367 ID - info:doi/10.2196/35462 ER - TY - JOUR AU - Zhang, Lingmin AU - Li, Pengxiang PY - 2022/4/8 TI - Problem-Based mHealth Literacy Scale (PB-mHLS): Development and Validation JO - JMIR Mhealth Uhealth SP - e31459 VL - 10 IS - 4 KW - mobile health KW - mHealth literacy KW - instrument development KW - problem-based framework N2 - Background: Mobile devices have greatly facilitated the use of digital health resources, particularly during the COVID-19 pandemic. Mobile health (mHealth) has become a common and important way to monitor and improve health conditions for people from different social classes. The ability to utilize mHealth affects its effectiveness; therefore, the widespread application of mHealth technologies calls for an instrument that can accurately measure health literacy in the era of mobile media. Objective: We aimed to (1) identify the components of mHealth literacy for ordinary users and (2) develop a systematic scale for appropriately measuring individuals? self-perceived mHealth literacy through a problem-based framework. Methods: We conducted an exploratory study involving in-depth interviews and observations (15 participants) in January 2020 and used exploratory factor analysis and confirmatory factor analysis to identify the components of mHealth literacy and develop an item pool. In February 2020, we conducted a pilot survey with 148 participants to explore the factor structures of items identified during the exploratory study. Subsequently, 2 surveys were administrated using quota sampling. The first survey (conducted in Guangdong, China) collected 552 responses during March 2020; we assessed composite reliability, convergent validity, and discriminant validity. The second survey (conducted in China nationwide) collected 433 responses during October 2021; we assessed criterion-related validity using structural equation modeling. Results: We identified 78 items during the exploratory study. The final scale?the Problem-Based mHealth Literacy Scale?consists of 33 items that reflect 8 domains of mHealth literacy. The first web-based survey suggested that mHealth literacy consists of 8 factors (ie, subscales), namely, mHealth desire, mobile phone operational skills, acquiring mHealth information, acquiring mHealth services, understanding of medical terms, mobile-based patient?doctor communication, evaluating mHealth information, and mHealth decision-making. These factors were found to be reliable (composite reliability >0.7), with good convergent validity (average variance extracted >0.5) and discriminant validity (square root of average variance extracted are greater than the correlation coefficients between factors). The findings also revealed that these 8 factors should be grouped under a second-order factor model (?2/df=2.701; comparative fit index 0.921; root mean square error of approximation 0.056; target coefficient 0.831). The second survey revealed that mHealth use had a significant impact (?=0.43, P<.001) on mHealth literacy and that mHealth literacy had a significant impact (?=0.23, P<.001) on health prevention behavior. Conclusions: This study revealed the distinctiveness of mHealth literacy by placing mHealth needs, the ability to understand medical terms, and the skills in patient?doctor interactions in the foreground. The Problem-Based mHealth Literacy Scale is a useful instrument for comprehensively measuring individuals? mHealth literacy and extends the concept of health literacy to the context of mobile communication. UR - https://mhealth.jmir.org/2022/4/e31459 UR - http://dx.doi.org/10.2196/31459 UR - http://www.ncbi.nlm.nih.gov/pubmed/35394446 ID - info:doi/10.2196/31459 ER - TY - JOUR AU - Mackey, Rachel AU - Gleason, Ann AU - Ciulla, Robert PY - 2022/4/15 TI - A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study JO - JMIR Mhealth Uhealth SP - e32643 VL - 10 IS - 4 KW - mobile health apps KW - app rating KW - app analysis methodology KW - app market research KW - mobile phone N2 - Background: Selecting and integrating health-related apps into patient care is impeded by the absence of objective guidelines for identifying high-quality apps from the many thousands now available. Objective: This study aimed to evaluate the App Rating Inventory, which was developed by the Defense Health Agency?s Connected Health branch, to support clinical decisions regarding app selection and evaluate medical and behavioral apps. Methods: To enhance the tool?s performance, eliminate item redundancy, reduce scoring system subjectivity, and ensure a broad application of App Rating Inventory?derived results, inventory development included 3 rounds of validation testing and 2 trial periods conducted over a 6-month interval. The development focused on content validity testing, dimensionality (ie, whether the tool?s criteria performed as operationalized), factor and commonality analysis, and interrater reliability (reliability scores improved from 0.62 to 0.95 over the course of development). Results: The development phase culminated in a review of 248 apps for a total of 6944 data points and a final 28-item, 3-category app rating system. The App Rating Inventory produces scores for the following three categories: evidence (6 items), content (11 items), and customizability (11 items). The final (fourth) metric is the total score, which constitutes the sum of the 3 categories. All 28 items are weighted equally; no item is considered more (or less) important than any other item. As the scoring system is binary (either the app contains the feature or it does not), the ratings? results are not dependent on a rater?s nuanced assessments. Conclusions: Using predetermined search criteria, app ratings begin with an environmental scan of the App Store and Google Play. This first step in market research funnels hundreds of apps in a given disease category down to a manageable top 10 apps that are, thereafter, rated using the App Rating Inventory. The category and final scores derived from the rating system inform the clinician about whether an app is evidence informed and easy to use. Although a rating allows a clinician to make focused decisions about app selection in a context where thousands of apps are available, clinicians must weigh the following factors before integrating apps into a treatment plan: clinical presentation, patient engagement and preferences, available resources, and technology expertise. UR - https://mhealth.jmir.org/2022/4/e32643 UR - http://dx.doi.org/10.2196/32643 UR - http://www.ncbi.nlm.nih.gov/pubmed/35436227 ID - info:doi/10.2196/32643 ER - TY - JOUR AU - Greysen, Ryan S. AU - Waddell, J. Kimberly AU - Patel, S. Mitesh PY - 2022/4/27 TI - Exploring Wearables to Focus on the ?Sweet Spot? of Physical Activity and Sleep After Hospitalization: Secondary Analysis JO - JMIR Mhealth Uhealth SP - e30089 VL - 10 IS - 4 KW - sleep KW - physical activity KW - hospitalization KW - wearables KW - health care KW - digital health KW - patient reported outcomes KW - hospital N2 - Background: Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge. Objective: This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge. Methods: This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup. Results: For the full sample of 120 participants, we observed a ?T-shaped? distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95% CI 0.3 to 1.5; P=.02), community mobility (z-score ?0.77, 95% CI ?1.3 to ?0.15; P=.02), and perceived physical health (z-score ?0.73, 95% CI ?1.3 to ?0.13; P=.003), compared with those who were more physically active (?5000 steps/day). Conclusions: Participants within the ?sweet spot? that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the ?sweet spot.? Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a ?sweet spot? of recommended levels for both sleep and activity needed for optimal recovery. Trial Registration: ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279 UR - https://mhealth.jmir.org/2022/4/e30089 UR - http://dx.doi.org/10.2196/30089 UR - http://www.ncbi.nlm.nih.gov/pubmed/35476034 ID - info:doi/10.2196/30089 ER -