Background: Electronic inhaler monitoring devices (EIMDs) can enhance medication adherence in patients with chronic obstructive pulmonary disease (COPD) and asthma, yet patient perceptions and experiences with these devices vary widely. A systematic qualitative synthesis is required to comprehensively understand patient perspectives on electronic inhaler monitoring devices, to lay the foundation for developing strategies to improve patient compliance. Objective: To systematically evaluate qualitative studies on the experiences of patients with COPD and asthma using EIMDs, providing insights to support their clinical application and improve patient engagement. Methods: This review synthesized qualitative data from reports found through a systematic search of PubMed, Web of Science, CINAHL, Embase, Cochrane Library, and PsycINFO from January 1983 to July 2024. The reports assessed patient experiences with EIMDs for COPD and asthma. The quality of included reports was appraised using the Critical Appraisal Skills Program criteria developed by the Centre for Evidence-Based Medicine, University of Oxford, UK. Results: Seven reports were included, encompassing data from 44 patients with COPD and 146 with asthma. Findings were organized into 9 sub-themes and 3 themes: positive experiences with EIMDs (usability and easy acceptance, enhanced self-management); stresses and challenges of using these devices (negative emotional stress, device trust issues, Social difficulties, economic burdens, technical challenges); and patient expectations from these devices (expectations related to device construction and function and external support). Conclusions: Patients have positive experiences using electronic monitoring devices for inhalation devices but also face various social, psychological, and technical challenges. Healthcare workers should consider patient experiences with EIMDs to tailor these devices to patient needs, ultimately enhancing device acceptance and adherence. Further research should focus on increasing EIMDs convenience and usability for patients with COPD and asthma. Clinical Trial: PROSPERO CRD42023480463.

Background: Cardiac rehabilitation (CR) is effective in preventing cardiovascular diseases; however, participation in CR programs remains limited due to the associated challenges. Integration of wearable devices and real-time monitoring offers a potential solution to enhance adherence to remote CR programs and their outcomes. Objective: In this study we aimed to evaluate the efficacy of a remote CR program using wearable devices and real-time monitoring with or without online coaching in improving exercise capacity in patients with coronary artery disease (CAD). Methods: We enrolled 50 patients with CAD in a remote CR program in this randomised, open-label, single-centre pilot trial (phase III). After baseline cardiopulmonary exercise tests (CPET), all patients were assigned a CPET-based home exercise program and were provided with a wearable device (Fitbit Sense) with real-time monitoring system (Recoval ©). The patients were randomly assigned to an intervention group with online coaching (OLC; n=25) or a control group (CON; n=25). The primary outcomes were changes in peak VO2 and anaerobic threshold (AT) VO2 at 12 weeks. The secondary outcomes were changes in CPET parameters, daily activity, anxiety levels, and health-related quality of life (HR-QOL). Results: Peak VO2 and AT VO2 increased significantly from baseline to 12 weeks in the OLC (+1.6 mL/kg/min, p<0.001; +1.0 mL/kg/min, p=0.001) and CON (+0.6 mL/kg/min, p=0.008; +1.3 mL/kg/min, p=0.002) groups with no significant between-group differences (p=0.65; p=0.9). In the latter half of the intervention, the daily distance and highly active time in the OLC group were significantly increased compared with those in the CON group (all p<0.05). Mental status and HR-QOL scores showed no significant differences between the groups. No major adverse cardiac events were reported. One patient in the OLC group dropped out due to lower limb muscle strain. Conclusions: Remote CR using wearable devices and a real-time monitoring system significantly improved exercise capacity in patients with CAD over a 12-week intervention program. Addition of regular online coaching to the intervention program further enhanced the physical activity levels, particularly in high-intensity activities. Future studies are warranted to validate these findings in more diverse populations and over longer intervention periods to optimize the benefits and safety of remote CR programs. Clinical Trial: UMIN 000047789; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000054483

Background: Exercise is a primary recommended treatment for knee osteoarthritis (KOA), as it helps alleviate symptoms and improves joint functionality. Personalized exercise programs, tailored to individual patient needs, have demonstrated promising results in maintaining physical fitness and enhancing overall well-being. In recent years, digital health applications have emerged as innovative tools for supervising and facilitating rehabilitation programs. Leveraging computer vision (CV) technology, these applications offer the potential to provide precise feedback and support personalized exercise interventions for KOA patients in a scalable and accessible manner. Objective: This study aims to evaluate the impact of a CV graded exercise intervention application over a 6-week period on clinical outcomes in KOA patients. The outcomes were compared to those achieved through conventional exercise education by videos. Methods: A randomized controlled trial was conducted with 60 participants aged 60–80 years, recruited through community administrators between July 2023 and September 2023. Participants were randomly assigned to one of two groups: the graded exercise application group (n=32) and the exercise education brochure group (n=28). The primary outcomes assessed were short-term changes in pain, physical function, and stiffness as measured by the Western Ontario and McMaster Universities Arthritis Index (WOMAC). Secondary outcomes included assessments of participants’ affective state, self-efficacy, quality of life, and user experience. Results: The study recruited 60 participants, including 26 males and 34 females. Analysis revealed statistically significant improvements in physical function (p=.02) and self-efficacy (p=.04) in the graded exercise application group compared to the exercise education brochure group after the intervention. While improvements in pain and stiffness were observed in both groups, these changes were not statistically significant. Additionally, participants in the graded exercise application group reported a positive user experience, highlighting the app's usability and engagement features as beneficial to their rehabilitation process. Conclusions: The findings suggest that the computer vision-based graded exercise intervention application effectively improves physical function and self-efficacy among KOA patients. This digital tool demonstrates the potential to enhance the quality and personalization of exercise rehabilitation compared to traditional methods. Future studies should explore the application’s long-term efficacy and replicability in larger community-based populations, with a focus on sustained engagement and adherence to rehabilitation programs. Clinical Trial: NCT06220565; https://clinicaltrials.gov/study/NCT06220565; approved by the Institutional Ethics Committee (H2022013I).

Background: Older adults with a history of fall often encounter challenges in participating in group exercise programs. Recent technological advances, such as activity trackers, can potentially enhance home-based exercise programs by providing continuous physical activity monitoring and feedback. Objective: To explore whether integrating wearable activity trackers with a home-based exercise intervention is effective in reducing fear of falling and improving physical function in older adults. Methods: This was a 12-week, parallel-group, randomized controlled trial involving 30 older adults (≥60 years) with a history of fall. Participants were randomly assigned in a 1:1 ratio to either a group combining an activity tracker with a home-based multi-component exercise intervention, which included in-person exercise sessions, exercise videos, and objective feedback via phone calls (AT+EX group) or to a group using the activity tracker only for self-monitoring (AT-only group). The primary and secondary outcomes included fall-related parameters (fear of falling assessed by the Activities-specific Balance Confidence [ABC] and the Falls Efficacy Scale-International [FES-I] scales), depression (Short Geriatric Depression Scale), cognition (Montreal Cognitive Assessment), physical function (grip strength, Short Physical Performance Battery [SPPB], Timed Up and Go [TUG] test, 2-min Step Test [2MST]), and body composition. Changes in the average daily step count were monitored and analyzed. Results: Overall, 28 participants (mean age, 74 years; 76.7% women) completed the 12-week follow-up period (28/30, 93%). In the AT+EX group, significant improvements were observed in fear of falling (ABC: P=.002; FES-I: P=.01). The AT-only group also showed a significant improvement in FES-I score (P=.01). Physical function significantly improved in the AT+EX group (SPPB, P=.004; TUG, P=.008; 2MST, P=.001), whereas the AT-only group showed significant improvement only in the TUG test (P=.002). However, no significant between-group differences were observed in the ABC score, FES-I score, or physical function. Despite no significant increase in daily step counts, both groups maintained close to 10,000 steps/d throughout the 12 weeks. Conclusions: Both groups showed improvements in the FES-I and TUG test scores without significant between-group differences. Wearable technology, with or without exercise intervention, seems to be an effective tool in reducing the fear of falling and improving physical function in older adults susceptible to falls. Clinical Trial: Clinical Research Information Service KCT0008230

Background: Cardiovascular diseases (CVD) are the leading cause of death and disability worldwide, and their prevention is a major public health priority. Detecting health issues early and assessing risk levels can significantly improve the chances of reducing mortality. Mobile applications (apps) can help estimate and manage CVD risks by providing users with personalized feedback, education, and motivation. Incorporating visual analysis into apps is an effective method for educating society. However, the usability evaluation and inclusion of visualization of these apps are often unclear and variable. Objective: The primary objective of this study is to review and compare the usability of existing apps designed to estimate CVD risk using the mHealth App Usability Questionnaire (MAUQ). This is not a traditional usability study involving user interaction design, but rather an assessment of how effectively these applications meet usability standards as defined by the MAUQ. Methods: First, we used predefined criteria to review 16 out of 2238 apps to estimate CVDs risk in the Google Play Store and the Apple App Store. Based on the apps characteristics (i.e., developed for healthcare professionals or patient use) and its functions (single or multiple CVD risk calculators), we conducted a descriptive analysis. Then we also compare the usability of existing apps using the MAUQ and calculated the agreement among three expert raters. Results: Most apps used the Framingham Risk Score (8/16; 50%) and Atherosclerotic Cardiovascular Disease Risk (7/16; 44%) prognostic models to estimate CVDs risk. The app with the highest overall MAUQ score was MDCalc Medical Calculator (6.76±0.25), and the lowest overall MAUQ score was obtained for CardioRisk Calculator (3.96±0.21). The app with the highest overall MAUQ score in the 'ease-of-use' domain was MDCalc Medical Calculator (7.00±0.00) in domain 'interface and satisfaction' was MDCalc Medical Calculator (6.67±0.33) and in domain, 'usefulness' ASCVD Risk Estimator Plus (6.80±0.32). Conclusions: We found that the Framingham Risk Score is the most widely used prognostic model in apps for estimating CVD risk. The 'ease-of-use' domain received the highest ratings. While more than half of the apps were suitable for both healthcare professionals and patients, only a few offered sophisticated visualizations for assessing CVD risk. Less than a quarter of the apps included visualizations, and those that did were single calculators. Our analysis of apps showed that they are an appropriate tool for estimating CVD risk.

Background: Encouraging physical activity improves mental health and is recommended in workplace mental health guidelines. Although mHealth interventions are promising for physical activity promotion, their impact on mental health outcomes is inconsistent. Furthermore, poor user retention rates of mHealth apps pose a major challenge. Objective: This study aimed to examine the effectiveness and implementation outcomes of the smartphone app ASHARE in Japanese workplace settings, leveraging a deep learning model to monitor depression and anxiety through physical activity. Methods: This hybrid effectiveness-implementation trial was a 3-month non-randomized controlled study conducted from October 2023 to September 2024. Work units and employees were recruited and allocated to the intervention or active control group based on preference. The intervention group installed the ASHARE app, whereas the control group participated in an existing multi-component workplace program promoting physical activity. Changes in physical activity and psychological distress levels were compared between the groups. User retention rates, participation rates, acceptability, appropriateness, feasibility, satisfaction, and potential harm were also assessed. Results: Eighty-four employees from seven work units participated (67 from five units in the intervention group and 17 from two units in the control group). Seventy-eight employees completed the 3-month follow-up survey (follow-up rate: 92.9%). Both groups showed increased physical activity, and the intervention group showed reduced psychological distress; however, the differences between groups were not statistically significant (P = .20; P = .36). In a sensitivity analysis of protocol-compliant employees (n = 21), psychological distress levels were significantly reduced in the intervention group compared to the control group (Coeff = -3.68, SE = 1.65, P = .03). The app’s 3-month user retention rate was 19.7% (12/61), which was lower than the participation rate in each component of the control programs. Implementation outcomes evaluated by employees were less favorable in the intervention group than in the control group, whereas health promotion managers found them to be similar. Conclusions: The ASHARE app did not show superior effectiveness compared with an existing multi-component workplace program for promoting physical activity. An implementation gap may exist between health promotion managers and employees, possibly contributing to the app’s low user retention rate. Future research should focus on examining the effectiveness of strategies to get engagement from managers and from segments of employees with favorable responses in the workplace at an early stage. Clinical Trial: UMIN-CTR UMIN000052374; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000059791

Background: Obesity represents a major public health crisis in the United States, imposing substantial health risks and economic costs. Medical nutrition therapy (MNT) is an evidence-based treatment where a registered dietitian provides personalized nutrition and lifestyle guidance to patients. MNT has been demonstrated to be effective for weight loss and managing chronic diseases in patients with obesity. With the rise of telehealth, MNT has gained popularity as an accessible alternative to traditional in-person care. While a nationwide program integrating MNT with a companion mobile app offers a comprehensive weight management solution, data supporting its clinical effectiveness is limited. Objective: This study aimed to evaluate the effectiveness of an MNT program with a companion mobile app on weight loss among adults with overweight and obesity. Methods: This retrospective cohort study included users of Nourish, an MNT program with a companion mobile app, who attended at least 1 appointment between August 2023 and October 2024 and had a baseline BMI≥30 kg/m² or a BMI between 27-30 kg/m² with diabetes or prediabetes. The primary outcome was the proportion of participants who achieved at least 5% weight loss; secondary outcomes included mean weight change, mean percent weight change, and the proportion of participants who achieved at least 3% weight loss. Statistical significance of weight change was determined using 2-tailed t tests. Subgroup analyses were performed by sex, BMI, follow-up time between weights, number of appointments completed, and levels of engagement according to appointment frequency and app usage. Results: In total, 3951 participants were included in the analysis. The mean age was 38 (SD 10) years, and 78% (3082/3951) of participants were female. Weight loss was reported as a program goal by 70% (2748/3951) of participants, while 31% (1204/3951) and 24% (939/3951) reported diabetes or prediabetes and a cardiovascular condition, respectively. Over a median follow-up of 2.2 months, 17% (689/3951) of participants achieved at least 5% weight loss. The mean weight change was –4.5 (SD 8.9) pounds, corresponding to a mean percent weight change of –2% (SD 3.9; P<.001). Males and participants aged 60 years or older were more likely to experience at least 5% weight loss. Longer follow-up time between weights and a higher number of completed appointments (≥5 appointments) were significantly associated with a significantly higher likelihood of achieving at least 5% weight loss (P<.001 for both). In addition, participants who were most engaged, based on appointment frequency and app usage, were more likely to achieve at least 5% weight loss compared with those who were less engaged (P<.001). Conclusions: Engagement with an MNT program and companion mobile app is associated with significant weight loss for adults with overweight and obesity and may serve as an effective, scalable weight management solution.

Background: Cervical cancer is currently the leading female cancer in Uganda. Most women are diagnosed with late-stage disease. Human papillomavirus (HPV) vaccination is the single most important primary preventive measure. While research regarding text message vaccine reminder use is strong in the U.S., their use has not yet been demonstrated in a pre-teen and adolescent population in Sub-Saharan Africa or other low- and middle-income countries. Objective: The objective of this pilot randomized controlled trial was to assess the impact of vaccine reminders with embedded interactive educational information on timeliness of HPV vaccination in Kampala, Uganda. Methods: In this randomized-controlled trial conducted in 2022, caregivers of adolescents needing a first or second HPV vaccine dose were recruited from an adolescent clinic and three community health centres in Kampala, Uganda. Families (n=154) were randomized 1:1 into intervention vs. usual care, stratified by dose (initiation, completion), language (English, Luganda) within each site. Intervention caregivers received a series of automated, personalized text messages or automated phone calls, based on family preference. Five messages were sent before the due date including both static and interactive educational information with five follow-up messages for those unvaccinated. Receipt of needed dose by 24 weeks post-enrolment was assessed by chi square, regression and Kaplan-Meier with log rank test. All analyses were intention-to-treat. Results: Overall, 154 caregivers enrolled (51.3% dose 1; 48.7% dose 2), and 64.3% spoke Luganda. Among the intervention arm, 62% requested text message and 38% automated phone reminders. There was no significant difference in requested mode by HPV vaccine dose or language. Intervention adolescents were more likely to receive a needed dose by 24 weeks (65.4% vs. 37.7%; p<0.001; RR 1.7 95% CI 1.2-2.4). There was no interaction by dose or language. There was no difference in vaccination by those requesting text message vs. phone reminders (65.3% vs 63.3%, p=0.86). The number needed to message for one additional vaccination was 3.6 (95% CI 2.3-8.2). Kaplan-Meier curves demonstrated more timely vaccination in the intervention arm (p<0.001). Conclusions: In this novel trial, text message and automated phone reminders were effective in promoting more timely HPV vaccination in this population. Clinical Trial: ClinicalTrials.gov Identifier: NCT05151367

Background: Sudden cardiac arrest is a major cause of mortality, necessitating immediate and high-quality cardiopulmonary resuscitation (CPR) for improved survival rates. High-quality CPR is defined by chest compressions at a rate of 100-120 per minute and a depth of 50-60 mm. Monitoring and maintaining these parameters in real time during emergencies remain a challenge. Objective: This study introduces a neural network model designed to predict and assess CPR quality using accelerometer data from a smartwatch. Methods: The study involved 83 participants performing CPR on mannequins, with accelerometer data collected via smartwatches worn by the participants. These data were aligned with gold-standard data from the mannequins. The accelerometer-derived compression data were segmented into 5-second intervals for training the neural network models. A total of 1226 neural network models were developed, incorporating variations in hyperparameters and dataset configurations to optimize performance. Results: The optimal model demonstrated the capability to accurately predict the number of compressions and the average compression depth within a 5-second interval. The model achieved an accuracy of ±3.8 mm for compression depth and an average deviation of 0.8 compressions. The results indicated that the neural network model could accurately assess CPR quality metrics, surpassing other models discussed in the literature. The large and diverse dataset used in this study contributed to the robustness and reliability of the model. Conclusions: This study validates the efficacy of a neural network model in accurately predicting CPR metrics using smartwatch accelerometer data. The model outperforms previous methods and shows promise for real-time feedback during CPR. Future work involves deploying the model directly on smartwatches for real-time application, potentially improving sudden cardiac arrest survival rates through immediate and accurate feedback on CPR quality.