Background: The use of mobile technology to meet health needs, widely referred to as mobile health (mHealth), has played a critical role in providing self-management support for chronic health conditions. However, despite its potential benefits, mHealth technologies such as self-management support apps for spinal cord injury (SCI) have received little research attention, and an understanding of their public availability is lacking. Therefore, an overview of these apps is needed to complement findings from the literature for a complete understanding of mHealth self-management support tools for SCI to support the selection and improvement of existing apps and the development of new ones. Objective: This study aimed to identify and describe quantity, quality, focus, strengths, and weaknesses of self-management support apps for SCI available on major mobile app digital distribution platforms. Methods: A systematic search of the Google Play Store and Apple App Store was conducted to identify and summarize apps for SCI that have been updated since 2017. A supplementary systematic literature review was conducted across 11 bibliographic databases to identify publications that provided more detailed descriptions of the identified apps than what is typically available in app stores. The data synthesis was guided by self-management tasks and skills taxonomies. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines informed the reporting. Results: The 13 apps included in the final synthesis were launched between 2013 and 2021, mostly originating in the United States, with availability in 72 countries and support for 14 languages. Most apps used the Android operating system (10/13, 77%), while 31% (4/13) used iOS. The identified apps mainly focused on activities of daily living, physical activity promotion, health literacy, and therapeutic exercise. All 3 self-management tasks (medical, role, and emotional management) and most self-management skills and support activities were supported by the apps. The mean Mobile App Rating Scale score was 3.86 (SD 0.54), indicating good overall quality. No publications were found describing these apps. Conclusions: Despite their good overall quality, as measured by the Mobile App Rating Scale assessment, the 13 identified apps, alone or combined, do not appear to offer a comprehensive self-management approach that incorporates theory-based strategies. Besides working to improve comprehensiveness, future research and practice should consider adopting new technologies, such as artificial intelligence, to enhance future self-management support apps for SCI. Furthermore, adopting new app development methods, such as low-code development platforms, could help reduce barriers to development, such as time, cost, and securing scarce expertise.

Background: Obsessive-compulsive disorder (OCD) is the third most prevalent mental health disorder in Singapore, with a high degree of burden and large treatment gaps. Self-guided programs on mobile apps are accessible and affordable interventions, with the potential to address subclinical OCD before symptoms escalate. Objective: This randomized controlled trial aimed to examine the efficacy of a self-guided OCD program on the mobile health (mHealth) app Intellect in improving subclinical OCD and maladaptive perfectionism (MP) as a potential moderator of this predicted relationship. Methods: University students (N=225) were randomly assigned to an 8-day, self-guided app program on OCD (intervention group) or cooperation (active control). Self-reported measures were obtained at baseline, after the program, and at a 4-week follow-up. The primary outcome measure was OCD symptom severity (Obsessive Compulsive Inventory–Revised [OCI-R]). Baseline MP was assessed as a potential moderator. Depression, anxiety, and stress (Depression Anxiety and Stress Scales-21) were controlled for during statistical analyses. Results: The final sample included 192 participants. The intervention group reported significantly lower OCI-R scores compared with the active control group after the intervention (partial eta-squared [η_p²]=0.031; P=.02) and at 4-week follow-up (η_p²=0.021; P=.044). A significant, weak positive correlation was found between MP and OCI-R levels at baseline (r=0.28; P<.001). MP was not found to moderate the relationship between condition and OCI-R scores at postintervention (P=.70) and at 4-week follow-up (P=.88). Conclusions: This study provides evidence that the self-guided OCD program on the Intellect app is effective in reducing subclinical OCD among university students in Singapore. Future studies should include longer follow-up durations and study MP as a moderator in a broader spectrum of OCD symptom severity. Trial Registration: ClinicalTrials.gov NCT06202677; https://clinicaltrials.gov/study/NCT06202677

Background: Mobile Health (mHealth) interventions have the potential to improve health outcomes in low- and middle-income countries (LMIC) by aiding health workers to strengthen service delivery, and patients and communities to manage and prevent diseases. It is crucial to understand how best to implement mHealth within already burdened health services to maximally improve health outcomes and sustain the intervention in LMIC. Objective: We aimed to identify key barriers to and facilitators of the implementation of mHealth interventions for infectious diseases in LMIC, drawing on a health systems analysis framework. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select qualitative or mixed methods studies reporting on determinants of already implemented infectious disease mHealth interventions in LMIC. We searched MEDLINE, Embase, PubMed, CINAHL, Social Science Citation Index and Global Health. We extracted characteristics of the mHealth interventions and implementation experiences, followed by an analysis of determinants using the Tailored Implementation of Chronic Diseases Framework. Results: We identified 10494 titles for screening, among which 20 studies met our eligibility criteria. Nine studies examined mHealth smartphone applications, and 11 examined Short Messaging Services interventions. The interventions addressed HIV (n=7), malaria (n=4), tuberculosis (n=4), pneumonia (n=2), dengue (n=1), HPV (n=1), COVID-19 (n=1) and respiratory illnesses or childhood infectious diseases (n=2), with two studies addressing multiple diseases. Ten interventions were intended for use by health workers and the remainder targeted patients, at-risk individuals or community members. Access to reliable technological resources, familiarity with technology and training and support were key determinants of implementation. Additional themes included mHealth intervention design affecting ease of use, or users forgetting to use the mHealth interventions. Conclusions: Acceptance of the intervention and the capacity of existing health care system infrastructure and resources are two key factors affecting implementation of mHealth interventions. Understanding the interaction between mHealth interventions, its implementation, and health systems will improve their uptake in LMIC.

Background: Pre-exposure prophylaxis (PrEP) is an effective strategy to reduce the risk of HIV infection. However, the efficacy of PrEP is highly dependent on adherence. Meanwhile, adherence changes over time, making it difficult to manage effectively. Objective: Our study aimed to explore and predict the patterns of change in PrEP adherence among Men who have sex with men (MSM), and evaluate the impact of the WeChat-based reminder intervention on adherence, thus providing more information for PrEP implementation strategies. Methods: From November 2019 to June 2023, in a randomized controlled longitudinal study of the PrEP demonstration project in Western China (Chongqing, Sichuan, and Xinjiang) based on mHealth reminder intervention, participants were randomly divided into reminder and no-reminder groups, with those in the reminder group receiving daily reminders based on the WeChat App. Participants were followed up and self-reported their medication adherence every 12 weeks for a total of 5 follow-up visits. We used the Growth mixture model (GMM) to explore potential categories and longitudinal trajectories of adherence among MSM, and patterns of change in PrEP adherence were predicted and evaluated based on Decision tree. Results: A total of 446 MSM were included in the analysis. The GMM identified 3 trajectories of adherence: Intermediate adherence group (N=34, 7.62%), Low adherence ascending group (N=126, 28.25%) and High adherence decline group (N=286, 64.13%). We included eight variables that were significant in the univariate analysis in the Decision tree prediction model. We found four factors and eight prediction rules, and the results showed that HIV knowledge score, education attainment, mHealth intervention, and HIV testing were key nodes in the patterns of change in adherence. After ten-fold cross-validation, the final prediction model had an accuracy of 75%, and the classification accuracy of low and intermediate adherence was 78.12%. Conclusions: WeChat-based reminder intervention was beneficial for adherence. A short set of questions and prediction rules that can be applied in future large-scale validation studies aimed at developing and validating a short adherence assessment tool and implementing it in PrEP practices among MSM. Clinical Trial: Chinese Clinical Trial Registry ChiCTR1900026414; https://www.chictr.org.cn/showprojEN.html?proj=35077

Background: Smartphone apps and wearable activity trackers are increasingly recognized for their potential to promote physical activity (PA). While studies suggest that the use of commercial mobile health tools is associated with higher PA levels, most existing evidence is cross-sectional, leaving a gap in longitudinal data. Objective: This study aims to identify app-use patterns that are prospectively associated with increases in and maintenance of PA. The primary objective was to test whether continued app use is linked to adherence to the recommended PA levels (ie, 23 metabolic equivalent task [MET] hours per week for adults or 10 MET hours/week for individuals aged >65 years) during a follow-up assessment. The secondary objective was to explore which functions and features of PA apps predict changes in PA levels. Methods: A 2-wave longitudinal survey was conducted, with baseline and follow-up assessments separated by 6 months. A total of 20,573 Japanese-speaking online respondents participated in the baseline survey, and 16,286 (8289 women; mean age 54.7 years, SD 16.8 years) completed the follow-up. At both time points, participants reported their current PA levels and whether they were using any PA apps or wearables. Each participant was classified into 1 of the following 4 categories: continued users (those using apps at both the baseline and follow-up; n=2150, 13.20%), new users (those who started using apps before the follow-up; n=1462, 8.98%), discontinued users (those who had used apps at baseline but not at follow-up; n=1899, 11.66%), and continued nonusers (those who had never used apps; n=10,775, 66.16%). Results: The majority of continued users (1538/2150, 71.53%) either improved or maintained their PA at the recommended levels over 6 months. By contrast, discontinued users experienced the largest reduction in PA (−7.95 MET hours/week on average), with more than half failing to meet the recommended levels at the follow-up (n=968, 50.97%). Analyses of individual app functions revealed that both energy analysis (eg, app calculation of daily energy expenditure) and journaling (eg, users manually entering notes and maintaining an exercise diary) were significantly associated with increases in PA. Specifically, energy analysis was associated with an odds ratio (OR) of 1.67 (95% CI 1.05-2.64, P=.03), and journaling had an OR of 1.76 (95% CI 1.12-2.76, P=.01). By contrast, individuals who maintained the recommended PA levels at the follow-up were more likely to use the goal setting (OR 1.73, 95% CI 1.21-2.48, P=.003), sleep information (OR 1.66, 95% CI 1.03-2.68, P=.04), and blood pressure recording (OR 2.05, 95% CI 1.10-3.83, P=.02) functions. Conclusions: The results highlight the importance of continued app use in both increasing and maintaining PA levels. Different app functions may contribute to these outcomes, with features such as goal setting and journaling playing a key role in increasing PA, while functions related to overall health, such as sleep tracking and blood pressure monitoring, are more associated with maintaining high PA levels.

Background: Adolescence is a period characterized by an increased susceptibility to developing risky alcohol consumption habits. This susceptibility can be influenced by social and situational factors encountered in daily life, which, in conjunction with emotions and thoughts, contribute to behavioral patterns related to alcohol use even in the early stages of alcohol experimentation, when initial experiences with alcohol are formed, and regular consumption is still evolving. Objective: This study aimed to examine the association between detailed behavioral and movement patterns, along with emotional and cognitive factors, and the early onset of alcohol use in the everyday lives of adolescents. Methods: A total of 65 healthy adolescents (33 male, twenty-nine 14-year-olds, and thirty-six 16-year-olds) underwent mobile-based ecological momentary assessments on alcohol (once a day at 9 AM, assessing alcohol use the day before), positive and negative affect, craving, rumination, and social context (6 prompts/day at 9 AM, 11 AM, 2 PM, 4 PM, 6 PM and 8 PM), type of day (weekdays or weekends, with weekend including Fridays, Saturdays, and Sundays), and using geospatial measures (specifically roaming entropy and number and type of trigger points for alcohol use met) over 14 days. After adjusting for a compliance rate of at least 50%, 52 participants (26 male and twenty-four 14-year-olds) were included in the analyses. Results: Generalized linear multilevel models revealed that higher positive affect (b=0.685, P=.007), higher rumination (b=0.586, P=.02), and a larger movement radius (roaming entropy) (b=8.126, P=.02) were positively associated with alcohol use on the same day. However, social context (b=–0.076, P=.90), negative affect (b=–0.077, P=.80), or potential trigger points (all P>.05) did not show significant associations. Alcohol use varied depending on the type of day, with more alcohol use on weekends (b=1.082, P<.001) and age (t₅₀=–2.910, P=.005), with 16-year-olds (mean 1.61, SD 1.66) reporting more days of alcohol consumption than 14-year-olds (mean 0.548, SD 0.72). Conclusions: Our findings support previously identified factors as significant contributors to very early and low levels of alcohol consumption through fine-grained analysis of daily behaviors. These factors include positive affect, rumination, weekend days, and age. In addition, we emphasize that exploratory environmental movement behavior (roaming entropy) is also significantly associated with adolescent alcohol use, highlighting its importance as an additional factor. Trial Registration:

Background: Mindfulness practices, such as breathing meditation (BM), reduce stress and enhance mood. One such practice is mindful eating, where a practitioner focuses on the five senses while eating or drinking. A novel set of prototypes has been developed, incorporating principles of mindful eating. These prototypes include matcha green tea and a mobile app that provides audio guidance for meditation during the preparation and consumption of the beverage (hereafter referred to as guided tea meditation [GTM]). Objective: This study assessed the feasibility and efficacy of GTM, evaluating meditation time, frequency, and prototype acceptability over 8 weeks, alongside changes in stress and mood. Additionally, other benefits of GTM were explored. Methods: A comparator group was established in which participants performed traditional BM without an app or audio guide (active control). This unblinded randomized controlled trial involved 100 healthy American volunteers (n=49 GTM, n=51 BM). During the 8-week study period, participants were encouraged to perform either GTM or BM for 10 minutes daily. The meditation activity was self-reported the following day. Only the GTM group assessed the prototype acceptability. The Perceived Stress Scale-10 was used to measure stress levels, while the Two-Dimensional Mood Scale was used to evaluate mood changes. Other meditation benefits were explored using a questionnaire. All questionnaires were presented and completed via an app. An intention-to-treat analysis was performed. Results: No significant between-group differences were found in total meditation time (P=.15) or frequency (P=.36). However, the weekly time and frequency of the GTM group remained above 50 minutes per week and 4 days per week, respectively. Over half of the GTM participants (≥28/49, ≥57%) accepted the prototype. The GTM group exhibited significant stress reductions at weeks 4 and 8 (both P<.001), similar to the BM group. Improvements in mood metrics were observed after a single GTM session on days 1 and 56, similar to the BM group. Moreover, increases in premeditation scores for relaxed and calm from day 1 to day 56 were significantly higher for the GTM group (P=.04 and .048, respectively). The majority of participants (≥25/49, ≥51%) assigned to GTM experienced positive changes in happiness, time management, quality of life, relationships, sleep, and work performance as they continued meditating. However, no significant between-group differences were found in these exploratory outcomes (P>.08). Conclusions: We believe that GTM exhibits good feasibility. Meanwhile, GTM reduced stress, improved mood, and let the practitioners feel other benefits, similar to BM. Long-term practitioners of GTM may even feel more relaxed and calmer in the state of premeditation than those who practice BM. Clinical Trial: ClinicalTrials.gov NCT05832645; https://clinicaltrials.gov/study/NCT05832645

Background: Personalized intelligent exercise prescriptions have demonstrated significant benefits in increasing physical activity and improving individual health. However, the health benefits of these prescriptions depend on long-term adherence. Therefore, it is essential to analyze the factors influencing adherence to personalized intelligent exercise prescriptions and explore the intrinsic relationship between individual behavioral motivation and adherence. This understanding can help improve adherence and maximize the effectiveness of such prescriptions. Objective: This study aims to identify the factors influencing adherence behavior among middle-aged and older community residents who have been prescribed personalized exercise regimens through an electronic health promotion system. It also explores how these factors affect the initiation and maintenance of adherence behavior. Methods: We used purposive sampling to conduct individual, face-to-face semistructured interviews based on the Transtheoretical Model (TTM) with 12 middle-aged and older community residents who had been following personalized exercise regimens for 8 months. These residents had received detailed exercise health education and guidance from staff. The interviews were recorded, transcribed verbatim, and analyzed using NVivo software through grounded theory. We then applied the TTM and multibehavioral motivation theory to analyze the factors influencing adherence. Additionally, the relationship between behavioral motivations and adherence was explored. Results: Using the behavior change stages of the TTM, open coding yielded 21 initial categories, which were then organized into 8 main categories through axial coding: intrinsic motivation, extrinsic motivation, benefit motivation, pleasure motivation, achievement motivation, perceived barriers, self-regulation, and optimization strategies. Selective coding further condensed these 8 main categories into 3 core categories: “multitheory motivation,” “obstacle factors,” and “solution strategies.” Using the coding results, a 3-level model of factors influencing adherence to intelligent personalized exercise prescriptions was developed. Based on this, an implementation path for promoting adherence to intelligent personalized exercise prescriptions was proposed by integrating the model with the TTM. Conclusions: Adherence to personalized exercise prescriptions is influenced by both facilitating factors (eg, multibehavioral motivation, optimization strategies) and obstructive factors (eg, perceived barriers). Achieving and maintaining adherence is a gradual process, shaped by a range of motivations and factors. Personalized solutions, long-term support, feedback mechanisms, and social support networks are essential for promoting adherence. Future efforts should focus on enhancing adherence by strengthening multibehavioral motivation, optimizing solutions, and addressing barriers to improve overall adherence.

Background: The use of digital interventions by patients for remote monitoring and management of health and disease is increasing. This observational study examined the feasibility, use, and safety of a digital smartphone app for routine monitoring of blood pressure (BP), medication, and symptoms of COVID-19 during the COVID-19 pandemic. Objective: The objective of this study was to deploy and test electronic data recording using a smartphone app developed for routine monitoring of BP in patients with primary hypertension. We tested the app for ease of data entry in BP management and tracking symptoms of new-onset COVID-19 to determine if participants found this app approach useful and sustainable. Methods: This remote, decentralized, 12-week, prospective, observational study was conducted in a community setting within the United States. Participants were approached and recruited from affiliated sites where they were enrolled in an ongoing remote decentralized study (CURE-19) of participants experiencing the COVID-19 pandemic. Potential participants were asked to complete a digital screener to determine eligibility and given informed consent forms to read and consent to using the Curebase digital platform. Following enrollment, participants downloaded the digital app to their smartphones for all data collection. Participants recorded daily BP, associated medication use, and emergent symptoms associated with SARS-CoV-2 infection. In addition, usability (adherence, acceptability, and user experience) was assessed using standard survey questions. Adverse events were collected based on participant self-report. Compliance and engagement were determined from user data entry rates. Feasibility and participant feedback were assessed upon study completion using the User Experience Questionnaire. Results: Of the 389 participants who enrolled in and completed the study, 380 (98%) participants downloaded and entered BP routines in week 1. App engagement remained high; 239 (62.9%) of the 380 participants remained in the study for the full 12-week observation period, and 201 (84.1%) of the 239 participants entered full BP routines into the digital app 80% or more of the time. The smartphone app scored an overall positive evaluation as assessed by the User Experience Questionnaire and was benchmarked as “excellent” for domains of perspicuity, efficiency, and dependability and “above average” for domains of attractiveness and stimulation. Highly adherent participants with hypertension demonstrated well-controlled BP, with no significant changes in average systolic or diastolic BP between week 1 and week 12 (all P>.05). Participants were able to record BP medications and symptoms of SARS-CoV-2 infection. No adverse events attributable to the use of the smartphone app were reported during the observational period. Conclusions: The high retention, engagement and acceptability and positive feedback in this study demonstrates that routine monitoring of BP and medications using a smartphone app is feasible for patients with hypertension in a community setting. Remote monitoring of BP and data collection could be coupled with hypertensive medication in a combination product (drug+digital) for precision management of hypertension.

Background: The use of Smart technology in the management of all forms of diabetes mellitus has grown significantly in the past ten years. Technologies such as the SmartWatch have been proposed as a method of assisting in the monitoring of blood glucose levels as well as other alert prompts such as medication adherence and daily physical activity targets. These important outcomes reach across all forms of diabetes and have the potential to increase compliance of self-monitoring with the aim of improving long term outcomes such as HbA1c. Objective: This systematic review aims to explore the literature for evidence of SmartWatch technology in type 1,2 and gestational diabetes. Methods: A systematic review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to August 2023), EMBASE (January 1980 to August 2023), Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, and ACM Digital Libraries, and Web of Science databases. Type 1, type 2 and gestational diabetes were eligible for inclusion. Quantitative studies such as prospective cohort or randomised clinical trials that explored the feasibility, usability or effect of SmartWatch technology in people with diabetes were eligible. Outcomes of interest were changes in blood glucose or HbA1c, physical activity levels, medication adherence and feasibility or usability scores. Results: Of the 8558 titles and abstracts screened, five studies were included for qualitative synthesis in this review. A total of 322 participants with either type 1 or type 2 diabetes mellitus were included in the review. Four studies focused on the feasibility and usability of SmartWatch technology in diabetes management. One study conducted a proof of concept randomised clinical trial including SmartWatch technology for exercise time prescriptions for participants with type 2 diabetes mellitus. Adherence of participants to SmartWatch technology varied between included studies with one reporting input submissions of 58% to another reporting that participants logged 50% more entries than they were required to. One study reported significantly improved glycaemic control with integrated SmartWatch technology with increased exercise prescriptions, however, this study was not powered and requires a longer observational period. Conclusions: This systematic review has highlighted the lack of robust randomised clinical trials that explore the efficacy of SmartWatch technology in the management of patients with type 1, 2 and gestational diabetes. Further research is required to establish the role of integrated SmartWatch technology in important outcomes such as glycaemic control, exercise participation, drug adherence and diet monitoring in people with all forms of diabetes mellitus.