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• A health-promoting lifestyle can fully mediate the association between mobile health technology acceptance and cognitive function. • The beneficial healthy lifestyle behaviors, especially exercise and stress management, can actively promote the influence of mobile health technology acceptance on cognitive function of older people with chronic diseases. • Interventions to maintain or improve cognitive function in older adults with chronic diseases should also encourage the acceptance of mobile health technology and adoption of health-promoting lifestyle. This study used a cross-sectional design to investigate the mediating role of a health-promoting lifestyle on mobile health technology acceptance and cognitive function. A total of 219 older adults, age 65 years or older, with diagnosed chronic diseases, were recruited from a northern Taiwan community. Participants completed questionnaires including demographic characteristics, the Senior Technology Acceptance Scale, the Health Promoting Lifestyle Profile Scale, and the Montreal Cognitive Assessment. Mediation analysis was conducted using the PROCESS macro. After controlling for demographic factors, health-promoting lifestyle proved to be a full mediator of mobile health technology acceptance and cognitive function. The results indicate that interventions to maintain or improve cognitive function in older adults with chronic diseases who are living in the community should also encourage the acceptance of mobile health technology and adoption of health-promoting lifestyle. [ABSTRACT FROM AUTHOR]

• The cognitive-motor dual-task training program based on mobile health technology developed by nurses exhibited high feasibility and acceptability in older people with cognitive frailty. • The intervention was found to significantly improve cognitive function, frailty, physical function, and fear of falling in older individuals with cognitive frailty, and showed a specific long-term maintenance effect. • Nurses can use their professional advantages to play an important role in the electronic health management of cognitive frailty of the elderly, which provides a valuable reference for non-pharmacological intervention of the older people in the post-epidemic era. To evaluate the feasibility and effect of nurse-led cognitive-motor dual-task training based on mobile health technology in people with cognitive frailty and investigate its potential for transforming practice in this population. From September 2021 to May 2022, a total of 74 older adults with cognitive frailty were screened at a Cognitive Memory Clinic of a tertiary hospital in Beijing. The control and intervention groups received health education related to cognitive frailty; additionally the intervention group received cognitive-motor dual-task training based on mobile health technology at home for 12 weeks, three times a week. At baseline (T 0), post-intervention (T 1) and one-year follow-up (T 2), Montreal Cognitive Assessment-Peking version (MoCA-P), Fried Frailty phenotype (FP), Short Physical Performance Battery (SPPB), and Modified Fall Efficacy Scale (MFES) were employed to evaluate cognitive function, frailty, physical function, and fear of falling, and to assess the impact of cognitive-motor dual-task training on these measures. The data collected for 65 out of 74 participants enrolled for the study was completed after 12 weeks intervention, including 34 participants in the control group and 31 participants in the intervention group. During the intervention, the majority (83.8 %) of the participants in the intervention group were able to complete the recommended intervention dose for 12 weeks under the guidance of nurses and the presence of caregivers. At the end of the 12-week intervention, the intervention group showed statistically significant improvements compared to the control in scores of MoCA-P (t =4.017, p <0.001), FP (t =3.739, p <0.001), MFES (t =4.283, p <0.001) and SPPB (t =3.548, p <0.001). At after one-year follow-up, the scores of MoCA-P (t =3.237, p <0.05), FP(t =3.725, p <0.001), and MFES (t =4.473, p <0.001) in the two groups remained statistically significant. Cognition, frailty and fear of falling were significantly affected by intergroup effects (P group <0.05), time effects (P time <0.05) and interaction effects (P group*time <0.001). The cognitive-motor dual-task training program based on mobile health technology developed by nurses exhibited high feasibility and acceptability in older people with cognitive frailty. The intervention was found to significantly improve cognitive function, frailty, physical function, and fear of falling in older individuals with cognitive frailty, and showed a specific long-term maintenance effect. This study provides evidence for the promotion and application of mobile health technology, and serves as a practical basis for home health intervention for older individuals in the post-pandemic era. [ABSTRACT FROM AUTHOR]

To assess implementation outcomes and potential efficacy of a user-informed, theory-based digital health intervention developed to improve adolescent and young adult (AYA) male sexual health. We conducted a pilot randomized controlled trial of sexually active male emergency department (ED) patients aged 14–21 years. Participants were randomized to the intervention (Dr. Eric, E mergency R oom I nterventions to improve C are) or usual care. Dr. Eric consists of an ED-based sexual health app followed by 3 months of interactive text messages. We assessed the feasibility, adoption, and fidelity among users. Condom use, defined as the number of sexual encounters with condoms divided by the total number of sexual encounters over the past 4 weeks, was the primary efficacy outcome. We enrolled 119 patients; mean age was 17.9 years, 87% were Hispanic, and half used condoms at last intercourse. Dr. Eric demonstrated feasibility, with high rates of consent (86.2%) and follow-up (81.5%). Intervention participants found Dr. Eric acceptable, liking (86.2%) and recommending (87.9%) the program. 98% of intervention participants interacted with all five app modules; one-quarter opted out of text messaging. At 6-week follow-up, the intervention group more often used a condom compared to the control group [OR 3.57, 95% confidence interval (1.93, 6.60)], p <.001]; however, this significant difference did not sustain at 13 weeks. Dr. Eric was feasible to deliver and acceptable to male AYA patients. To our knowledge, Dr. Eric is the first intervention to demonstrate evidence of short-term efficacy for improving condom use among male AYA in the ED. [ABSTRACT FROM AUTHOR]

Background: Improving mother and child nutrition during the first 1000 days of life (DoL) is one of the major areas where mHealth has demonstrated great promise. A lot of mHealth is already used in society. However, no study examines the content and features of mobile health. Aim: This study aims to examine the difference in content and features of mHealth intervention for maternal and child nutrition throughout the first 1000 days of life. Thus, new apps can be enhanced. Methods: The online journal databases that offer free papers from Scopus-indexed journals published in 2017–2022 served as the primary sources for the literature included in this study. Several keywords were used in the literature search, which used the databases Google Scholar, Science Direct, and PubMed. A total of 8 articles were included in the literature review. Results: Existing mHealth provides content and features to support and improve the health status of pregnant women, breastfeeding mothers, and children aged 0–24 months. mHealth interventions have the potential to improve maternal and child nutrition health in the first 1000 days of life by providing education, communication, support, data collection and analysis, cultural appropriateness, accessibility, and capacity building for health workers. However, it is crucial to address challenges such as evidence-based design, privacy and security, sustainability, and data management, and to ensure cultural appropriateness and accessibility for all populations. Conclusions: The more complete the content, features, and uses of mHealth, the greater the users' acceptance. [ABSTRACT FROM AUTHOR]

The innovative application of emerging mobile and wearable health information and sensing technologies (mHealth) holds great promise for lowering healthcare costs and enhancing well-being in various ways. Numerous fields are seeing the development of these applications. Still, to fully understand the opportunities and difficulties associated with using mobile technologies to enhance health outcomes, more thorough research is required. There is currently little data supporting the effectiveness of mHealth. Even though these technologies seem harmless and enticing, more research is required to determine the best times, locations, and users for mHealth methods, applications, and devices. As a result, a remote medical hub waitperson is essential to sustainably providing high-quality telemedicine services. This paper offers a thorough analysis of the delivery of medical services through telemedicine applications, with a focus on medical center servers. It also draws attention to the problems and obstacles that still need to be resolved to deliver health services via telehealth in the medical center. These ideas are used in this work to define a high standard for the developing field of mHealth research, explore potential future directions, and describe existing assessment standards. [ABSTRACT FROM AUTHOR]

With the advent of the digital era, the application of mobile technology in higher education has become increasingly prevalent, particularly in the management of students' mental health. University students face multiple pressures, including academic, social, and career-related challenges, which have led to a rise in mental health issues. As a result, the utilization of mobile technology to collect and analyze students' mental states and behavioral data in realtime has emerged as significant research focus. Current studies predominantly rely on traditional survey methods, which fail to capture students' dynamic mental states in real-time and often lack an in-depth understanding of complex behavioral patterns. Moreover, few existing studies have examined the integration of multi-source data, thereby limiting comprehensive analyses of mental health risks. This study proposes a dynamic mental behavior inference and mental health risk assessment framework for university students based on multi-source data integration. The framework aims to analyze students' mental health conditions comprehensively by integrating diverse data from mobile technology. Experimental results and analyses were presented to verify the framework's effectiveness and practicality, providing new insights for mental health management in higher education and laying the foundation for future research. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the impact of direct anterior approach (DAA) or posterior approach (PA) on step and stair counts after total hip arthroplasty using a remotely monitored mobile application with a smartwatch while controlling for baseline characteristics. This is a secondary data analysis from a prospective cohort study of patients utilizing a smartphone-based care management platform. The primary outcomes were step and stair counts and changes from baseline through one year. Step and stair counts were available for 1,501 and 847 patients, respectively. Longitudinal regression models were created to control for baseline characteristics. Patients in the DAA group had significantly lower body mass index (P =.049) and comorbidities (P =.028), but there were no significant differences in age (P =.225) or sex (P =.315). The DAA patients had a higher average and improvement from baseline in step count at 2 and 3 weeks postoperatively after controlling for patient characteristics (P =.028 and P =.044, respectively). The average stair counts were higher for DAA patients at one month postoperatively (P =.035), but this difference was not significant after controlling for patient demographics. Average stair ascending speeds and changes from baseline were not different between DAA and PA patients. Descending stair speed was higher at 2 weeks postoperatively for DAA patients, but was no longer higher after controlling for baseline demographics. After controlling for baseline characteristics, DAA patients demonstrate earlier improvement in step count than PA patients after total hip arthroplasty. However, patient selection and surgeon training may continue to influence outcomes through a surgical approach. [ABSTRACT FROM AUTHOR]

The most common health-related apps are lifestyle apps, i.e., fitness, nutrition, diet, and meditation apps, which account for half of all m-health apps on the market. Mobile app-based interventions have been shown to be effective in improving diet-related health outcomes. The aim of this study is to map the usage patterns of lifestyle apps (fitness, diet, and relaxation apps) and identify the role of each factor in the usability of MAUQ (m-Health App Usability Questionnaire) factor - ease of use, interface satisfaction, and usefulness - in overall satisfaction. Data were collected through an online survey in Hungary with 348 users of various lifestyle applications, i.e., fitness (30.2%), nutrition (31.3%), and mindfulness (38.5%) apps. Respondents showed a preference for free apps over paid ones and predominantly used iOS operating systems. The partial least squares structural equation modelling (PLS-SEM) method was used to identify the role of usability dimensions in overall satisfaction. The satisfaction of lifestyle app users is positively influenced by 'Ease of Use' and 'Interface and Satisfaction'. However, effectiveness (positive physical and mental health outcomes) negatively influences satisfaction. Research can be particularly useful for app developers, as usability and design (features) play a particularly important role in satisfaction, so these are primary considerations in development. [ABSTRACT FROM AUTHOR]

In the past few years, mobile health services (MHS)--a growing topic in the healthcare sector--have received increasing attention. Technologies related to mobile health, or m-health, have several advantages for people, businesses, and medical professionals. Indeed, using m-health can help older people become more proactive patients and lessen the strain on health systems and financial resources. However, the potentially revolutionary impact of m-health is constrained since a large number of senior citizens oppose its adoption, which creates an age-based digital divide. Few studies have examined how public consumers of MHS adopt MHS, even though prior study on this topic is very substantial. We discover that user attitudes towards MHS are positively impacted by perceived benefits and usefulness, perceived barriers, and external cues. Perceived service availability also has a major impact on perceived benefits and usefulness, which, when combined with attitude, directly increases intention. Additionally, we discover that the MHS's utilization intent has moderating effects. The final section discusses the implications for mobile health marketing. The results indicate that perceived ease of use, vulnerability, and severity are more significant factors for middle-aged and older users to use MHS. The moderator analysis confirmed that different age groups have specific moderating effects on the adoption of mobile health services. [ABSTRACT FROM AUTHOR]

The potential of mobile health applications to improve healthcare outcomes and accessibility, particularly among older individuals, is significant. However, challenges such as small fonts, complex navigation, and unintuitive design hinder their effective use by older adults. Addressing these issues is crucial for ensuring equitable access to healthcare. This study aims to identify the unique difficulties older people face with mobile health apps and explore innovative ways to enhance usability and accessibility. Through design analysis and user testing, the research seeks to address these obstacles and propose solutions. The study examines usability issues and suggests a practical framework that integrates assistive technology and dynamic procurement processes to ensure the availability of user-friendly apps. Emphasizing the importance of user testing with older adults, the research offers actionable insights for improving the overall experience of seniors using mobile health apps. The framework provides practical solutions for resolving these problems, ultimately improving seniors' overall experience utilizing the system. [ABSTRACT FROM AUTHOR]

Ecological momentary assessment (EMA) is a methodological approach to studying intraindividual variation over time. This study aimed to use EMA to determine the variability of cognition in individuals with chronic stroke, identify the latent classes of cognitive variability, and examine any differences in daily activities, social functioning, and neuropsychological performance between these latent classes. Participants (N = 202) with mild-to-moderate stroke and over 3-month post-stroke completed a study protocol, including smartphone-based EMA and two lab visits. Participants responded to five EMA surveys daily for 14 days to assess cognition. They completed patient-reported measures and neuropsychological assessments during lab visits. Using latent class analysis, we derived four indicators to quantify cognitive variability and identified latent classes among participants. We used ANOVA and Chi-square to test differences between these latent classes in daily activities, social functioning, and neuropsychological performance. The latent class analysis converged on a three-class model. The moderate and high variability classes demonstrated significantly greater problems in daily activities and social functioning than the low class. They had significantly higher proportions of participants with problems in daily activities and social functioning than the low class. Neuropsychological performance was not statistically different between the three classes, although a trend approaching statistically significant difference was observed in working memory and executive function domains. EMA could capture intraindividual cognitive variability in stroke survivors. It offers a new approach to understanding the impact and mechanism of post-stroke cognitive problems in daily life and identifying individuals benefiting from self-regulation interventions. [ABSTRACT FROM AUTHOR]

Objective: Meditation apps are the most widely used mental health apps. The precise mechanisms underlying their effects remain unclear. In particular, the degree to which affect experienced during meditation is associated with outcomes has not been established. Method: We used the meditation app arm of a recently completed randomized controlled trial comparing a self-guided meditation app (Healthy Minds Program) to a waitlist control. Predominantly distressed public school employees (n = 243, 80.9% with clinically elevated depression and/or anxiety) reported positive and negative affect during meditation practice. Data were analyzed using two-level multivariate latent growth curve models (observations nested within participants) that simultaneously attended to both positive and negative affect. We examined whether positive and negative affect during meditation changed over time and whether these changes were associated with changes in psychological distress (parent trial's preregistered primary outcome) at posttest or 3-month follow-up. Results: On average, participants reported decreased negative affect but no change in positive affect during meditation over time. Increased positive affect and decreased negative affect during meditation were associated with improvements in distress at posttest and follow-up. Change in positive affect was a stronger predictor of distress at follow-up than change in negative affect. Conclusions: Despite notions embedded within mainstream mindfulness meditation training that deemphasize the importance of the affective experience of practice (i.e., nonjudgmental awareness of present moment experience, regardless of valence), results indicate that these experiences contain signals associated with outcomes. Monitoring affect during meditation may be worthwhile to guide intervention delivery (i.e., measurement-based care, precision medicine). What is the public health significance of this article?: This study suggests that affect experienced during meditation is associated with both short- and long-term changes in psychological distress that occur in the context of smartphone app-delivered meditation training. While both increases in positive affect and decreases in negative affect were associated with improvements in distress, increases in positive affect were the stronger predictor of long-term improvements in distress. [ABSTRACT FROM AUTHOR]

• Worthlessness predicted perceived social isolation contemporaneously and temporally. • Out-of-home activities alleviated perceived social isolation and negative emotions. • EMA and network analysis are valuable for studying social isolation dynamics. To assess contemporaneous and temporal dynamics of perceived social isolation (PSI), secondary conditions, and daily activity patterns in individuals post-stroke. Longitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of an individual's lived experiences. We conducted dynamic network analyses to examine longitudinal associations among EMA variables. Home and Community. 202 individuals with mild-to-moderate chronic stroke (median age=60 years; 45% women; 44% black; 90% ischemic stroke; median NIHSS score=2; N=202). Not applicable. EMA questions measured PSI, secondary conditions (pain, tiredness, stress, anxiety, worthlessness, difficulty concentrating, and cheerfulness), and daily activity patterns (being at home, being alone, and participating in productive activities). The median EMA response rate was 84%. The contemporaneous model showed that PSI was associated with being home, alone, and all symptoms except pain. The temporal model revealed a pathway indicating that feelings of worthlessness predicted PSI (regression coefficient=0.06, P =.019), followed by stress (regression coefficient=0.06, P =.024), and then by being not at home (regression coefficient=-0.04, P =.013). Implementing dynamic network analyses on EMA data can uncover dynamic connections among PSI, secondary conditions, and daily activity patterns after stroke. This study found a significant temporal association between PSI and negative emotions. Feeling isolated was followed by feeling stressed, which was followed by a tendency to be out of home, indicating adaptive behaviors in individuals with stroke. These findings highlight the importance of engaging in out-of-home or outdoor activities to mitigate PSI and negative emotions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Taibah University Medical Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

OBJECTIVE: Mobile health (mHealth) technologies have emerged as promising tools in the field of neuro-physiotherapy, offering innovative solutions for enhancing clinical decision-making processes. This scoping review explores the existing literature on the use of mHealth applications in neuro-physiotherapy with a specific focus on their impact on clinical decision-making. MATERIALS AND METHODS: The PubMed, Google Scholar, Cochrane Library, ScienceDirect, and Scopus databases were comprehensively searched for both qualitative and quantitative peer-reviewed articles written in the English language and published till 2023 that focus on mHealth applications in neuro-physiotherapy and clinical decision-making. RESULTS: The key findings from the 14 included studies highlighted the diverse array of mHealth applications employed in neuro-physiotherapy, ranging from wearable sensors and mobile apps to virtual reality platforms. Synthesis of the evidence from these studies demonstrated the potential of these technologies in clinical decision-making and improving patient outcomes, patients' and therapists' perspectives of these applications, their clinical clues, and the challenges with their use. CONCLUSIONS: The findings from the review underscore the need for continued exploration of these technologies to optimize their effectiveness in rehabilitation settings and ultimately improve clinical decision-making and patient care in neuro-physiotherapy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Cukurova University Institute of Social Sciences is the property of Cukurova University Institute of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Access to early evidence-based intervention for mental health concerns is limited. • mHealth is a possible avenue for prevention of mental health concerns in children. • An iterative development process produced a viable anxiety self-help app for families. • Anchors Away was found to be an acceptable form of childhood mental health self-help. • Families enjoyed and were open to buying this app for child mental health prevention. Behavioral intervention technology (BIT) is one avenue for extending the reach of evidence-based pediatric mental health. We developed Anchors Away: Anxiety at Bay, a family-friendly stand-alone mobile health app for children ages 6 to 11 that delivers CBT strategies tailored to mental health struggles typical for youth. This study assessed the acceptability, appropriateness, feasibility, and fidelity following an iterative model of development that provided quantitative and qualitative data. We made changes based on feedback from pilot data and clinical expert stakeholders and conducted a randomized controlled trial (RCT). Findings largely supported the acceptability and feasibility of the app. 71.8% of those randomized to the app show some degree of engagement with the child-side of the app and 47.4% completed all game modules. Most participants (80%) reported that they would purchase the app and found it to be easy to use and helpful in meeting their child's specific anxiety and emotion regulation challenges. [ABSTRACT FROM AUTHOR]

Mobile apps have become a go-to solution for interventions in many health areas that aim to influence user behavior, such as physical activity or nutrition. However, many apps still fail to bind users long-term. Although research has been conducted on individual mechanisms aiming to improve long-term user adoption, such as gamification, we lack an understanding of different approaches at the time of this research. Thus, in this research, we investigate different approaches and persuasive technology that aim to keep the users' engagement with the app in use. Therefore, this study reviews existing literature and tries to identify how motivational mechanisms can be successfully implemented. In the course of this, identify 14 major motivational mechanisms to increase long-term user adoption of mobile apps. Based on these insights, we want to give advice on how to improve their impact through combination as well as give advice on future implementation. [ABSTRACT FROM AUTHOR]

The sustainability and viability of the public health services accessible to all has been widely discussed worldwide, due to the aging of the population, increasing burden of chronic diseases and consequent rising of healthcare expenditures. The mortality and burden of cardiovascular disease is one of the most important drivers of public and private expenses and hypertension and non-compliance with medical prescription one of the leading contributors. Therefore, strategies that promote therapeutic adherence are necessary to control cardiovascular disease and its major outcomes. A solution named PACTO was developed and emphasizes the role of the patient as an active partner of health professionals. PACTO is a mHealth solution with a variety of functionalities that accompanies the patient throughout the treatment journey, with multiple incentives that promote behavioural changes. Finally, PACTO may work in interoperability with other applications already in use in primary healthcare, reducing its initial investment and clinical teams' overload, while increasing adherence, patients' health and quality of life, and sustainability of health systems. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) application concerns the use of wireless and mobile technologies to support the accomplishment of health objectives. With a doctor's help, consumers may monitor their health metrics. This is exceptionally focal with the persistent torrent of the unstructured and structured data sets created on periodic intervals from the augmentation of mobile health applications in distinct healthcare systems and products universally. In this study, the leading principle was to examine the impact of perceived usefulness and perceived ease of use on the intentions to adopt Mobile Health application and elucidate with a extensive knowledge of machine learning techniques that might be applied to improve the expertise and knowledge of a mobile health application.12 measurements are based on perceived usefulness in the article, and 8 are based on perceived ease of use. Information was gathered from 200 respondents, of whom 102 used mobile health applications, considered them helpful, and preferred to use them again. The outcomes from the proposed machine learning prototypes have been shown to be quite accurate and effective. A comparison study was conducted based on accuracy values, and the findings indicate that Random Forest and Decision Tree models provide more accuracy than the other models. The research shows that Random Forest and Decision Tree models, which have 100% accuracy rate, produce the most accurate results. Therefore, the study is applicable to forecast accuracy and is valuable. for patients' readiness to use mHealth applications and can be used to Contact doctors and get health information available at any time and from any location. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) system is analysing human behaviour using mobile health technology. Mobile Health data (MHEALTH) uses electronic devices to collect data and identify the activity of the patient in real-time. Recordings of 10 patients' vital signs from various circumstances are included in the dataset. With a sensor attached to their bodies, they were required to carry out a number of physical tasks. Due to the lack of accuracy in the other state-of-the-art algorithms, we proposed Human Activity Recognition Neural Network (HARNet) architecture for automatic recognition of human activity using CNN and LSTM with the transfer learning of SVM. Here, the human health behaviour was analysed and classified using different ML and DL algorithms. The hybrid techniques of CNN and LSTM are selected across the different DL algorithms and it is used to extract independent and discriminating features, which aids the SVM classifier to attain good classification. When compared to other DL methods, HARNet performed better, achieving 99.8% accuracy. Overall, HAR systems have many potential applications in various fields, including healthcare, wellness, sports and surveillance. They have relationships to many different academic disciplines, including sociology, human–computer interaction, medical and may offer individualized help for different domains. [ABSTRACT FROM AUTHOR]

Background: Social participation is crucial for healthy aging, improving physical and mental health, cognitive function, and quality of life among older adults. However, social participation tends to decline with age due to factors like loss of social networks and health issues. Mobile health apps show promise in promoting healthy behaviors among older adults, but their effectiveness in increasing social participation remains understudied. Objective: This randomized controlled trial aimed to evaluate the efficacy of a mobile app called Encouragement of Social Participation (ESP, "Shakai Sanka no Susume;" Hitachi) in promoting social participation and physical activity among community-dwelling older adults. Methods: The study recruited 181 community-dwelling adults aged 60 years or older from 2 municipalities in Japan and through a web-based research panel. Participants were randomly assigned to either the intervention group (n=87), which used the ESP app for 12 weeks, or the control group (n=94), which used only Google Fit. The ESP app incorporated features such as self-monitoring of social participation, personalized feedback, gamification elements, and educational content. Primary outcomes were changes in social participation frequency over the previous 2 months and changes in step counts, measured at baseline and week 12. Secondary outcomes included changes in specific types of social activities and subjective well-being. Data were analyzed using analysis of covariance and linear mixed-effects models. Results: The intervention group showed a significantly greater increase in social participation frequency compared with the control group (adjusted difference 3.03; 95% CI 0.17-5.90; P =.04). Specifically, the intervention group demonstrated higher frequencies of participation in hobbies (adjusted difference: 0.82; 95% CI 0.01-1.63) and cultural clubs (adjusted difference 0.65; 95% CI 0.07-1.23) compared with the control group. However, there were no significant differences in weekly step counts between the groups. Subgroup analyses suggested potentially larger effects among participants who were older than 70 years, female, had lower educational attainment, and were recruited from community settings, although only females and the lower educational attainment subgroups demonstrated 95% CIs that did not encompass zero. Conclusions: The ESP mobile app effectively promoted social participation among community-dwelling older adults, particularly in hobbies and cultural club activities. However, it did not significantly impact physical activity levels as measured by step counts. These findings suggest that mobile apps can be valuable tools for encouraging social engagement in older populations, potentially contributing to healthy aging. Future research should focus on optimizing app features to maintain long-term engagement and exploring strategies to enhance physical activity alongside social participation. Trial Registration: University Medical Information Network Clinical Trial Registry UMIN000049045; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000055781 [ABSTRACT FROM AUTHOR]

Over the past quarter-century, mobile health (mHealth) technologies have experienced significant changes in adoption rates, adaptation strategies, and instances of abandonment. Understanding the underlying factors driving these trends is essential for optimizing the design, implementation, and sustainability of interventions using these technologies. The evolution of mHealth adoption has followed a progressive trajectory, starting with cautious exploration and later accelerating due to technological advancements, increased smartphone penetration, and growing acceptance of digital health solutions by both health care providers and patients. However, alongside widespread adoption, challenges related to usability, interoperability, privacy concerns, and socioeconomic disparities have emerged, necessitating ongoing adaptation efforts. While many mHealth initiatives have successfully adapted to address these challenges, technology abandonment remains common, often due to unsustainable business models, inadequate user engagement, and insufficient evidence of effectiveness. This paper utilizes the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework to examine the interplay between the academic and industry sectors in patterns of adoption, adaptation, and abandonment, using 3 major mHealth innovations as examples: health-related SMS text messaging, mobile apps and wearables, and social media for health communication. Health SMS text messaging has demonstrated significant potential as a tool for health promotion, disease management, and patient engagement. The proliferation of mobile apps and devices has facilitated a shift from in-person and in-clinic practices to mobile- and wearable-centric solutions, encompassing everything from simple activity trackers to advanced health monitoring devices. Social media, initially characterized by basic text-based interactions in chat rooms and online forums, underwent a paradigm shift with the emergence of platforms such as MySpace and Facebook. This transition ushered in an era of mass communication through social media. The rise of microblogging and visually focused platforms such as Twitter(now X), Instagram, Snapchat, and TikTok, along with the integration of live streaming and augmented reality features, exemplifies the ongoing innovation within the social media landscape. Over the past 25 years, there have been remarkable strides in the adoption and adaptation of mHealth technologies, driven by technological innovation and a growing recognition of their potential to revolutionize health care delivery. Each mobile technology uniquely enhances public health and health care by catering to different user needs. SMS text messaging offers wide accessibility and proven effectiveness, while mobile apps and wearables provide comprehensive functionalities for more in-depth health management. Social media platforms amplify these efforts with their vast reach and community-building potential, making it essential to select the right tool for specific health interventions to maximize impact and engagement. Nevertheless, continued efforts are needed to address persistent challenges and mitigate instances of abandonment, ensuring that mHealth interventions reach their full potential in improving health outcomes and advancing equitable access to care. [ABSTRACT FROM AUTHOR]

Digital health interventions have gained prominence in recent years, offering innovative solutions to improve health care delivery and patient outcomes. Researchers are increasingly using qualitative approaches to explore patient experiences of using digital health interventions. Yet, the qualitative methods used in these studies can vary widely, and some methods are frequently misapplied. We highlight the methods we find most fit for purpose to explore user experiences of digital tools and propose 5 questions for researchers to use to help them select a qualitative method that best suits their research aims. [ABSTRACT FROM AUTHOR]

Background: Digital and mobile health interventions using personalization via reinforcement learning algorithms have the potential to reach large number of people to support physical activity and help manage diabetes and depression in daily life. Objective: The Diabetes and Mental Health Adaptive Notification and Tracking Evaluation (DIAMANTE) study tested whether a digital physical activity intervention using personalized text messaging via reinforcement learning algorithms could increase step counts in a diverse, multilingual sample of people with diabetes and depression symptoms. Methods: From January 2020 to June 2022, participants were recruited from 4 San Francisco, California–based public primary care clinics and through web-based platforms to participate in the 24-week randomized controlled trial. Eligibility criteria included English or Spanish language preference and a documented diagnosis of diabetes and elevated depression symptoms. The trial had 3 arms: a Control group receiving a weekly mood monitoring message, a Random messaging group receiving randomly selected feedback and motivational text messages daily, and an Adaptive messaging group receiving text messages selected by a reinforcement learning algorithm daily. Randomization was performed with a 1:1:1 allocation. The primary outcome, changes in daily step counts, was passively collected via a mobile app. The primary analysis assessed changes in daily step count using a linear mixed-effects model. An a priori subanalysis compared the primary step count outcome within recruitment samples. Results: In total, 168 participants were analyzed, including those with 24% (40/168) Spanish language preference and 37.5% (63/168) from clinic-based recruitment. The results of the linear mixed-effects model indicated that participants in the Adaptive arm cumulatively gained an average of 3.6 steps each day (95% CI 2.45-4.78; P <.001) over the 24-week intervention (average of 608 total steps), whereas both the Control and Random arm participants had significantly decreased rates of change. Postintervention estimates suggest that participants in the Adaptive messaging arm showed a significant step count increase of 19% (606/3197; P <.001), in contrast to 1.6% (59/3698) and 3.9% (136/3480) step count increase in the Random and Control arms, respectively. Intervention effectiveness differences were observed between participants recruited from the San Francisco clinics and those recruited via web-based platforms, with the significant step count trend persisting across both samples for participants in the Adaptive group. Conclusions: Our study supports the use of reinforcement learning algorithms for personalizing text messaging interventions to increase physical activity in a diverse sample of people with diabetes and depression. It is the first to test this approach in a large, diverse, and multilingual sample. Trial Registration: ClinicalTrials.gov NCT03490253; https://clinicaltrials.gov/study/NCT03490253 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-034723 [ABSTRACT FROM AUTHOR]

Background: Hypertension management apps (HMAs) can be effective in controlling blood pressure, but their actual impact is often suboptimal. Establishing a user satisfaction evaluation indicator system for HMAs can assist app developers in enhancing app design and functionality, while also helping users identify apps that best meet their needs. This approach aims to improve the overall effectiveness of app usage. Objective: This study aims to systematically collect data on HMAs and their user reviews in the United States and China. It analyzes app usage patterns and functional characteristics, identifies factors influencing user satisfaction from existing research, and develops a satisfaction evaluation indicator system to provide more accurate recommendations for improving user satisfaction. Methods: We conducted a descriptive statistical analysis to assess the development status of HMAs in both countries and applied the task-technology fit model to evaluate whether the app functionalities align with business needs. We separately summarized the factors influencing user satisfaction in both countries from previous research, utilized the analytic hierarchy process to develop an evaluation indicator system for HMA user satisfaction, and calculated satisfaction levels. Based on these findings, we propose improvements to enhance app functionality and user satisfaction. Results: In terms of current development status, there were fewer HMAs and user reviews in China compared with the United States. Regarding app functional availability, fewer than 5% (4/91) of the apps achieved a demand fulfillment rate exceeding 80% (8/10). Overall, user satisfaction in both countries was low. Conclusions: In the United States, user satisfaction was lowest for advertising distribution, data synchronization, and reliability. By contrast, Chinese apps need improvements in cost efficiency and compatibility. [ABSTRACT FROM AUTHOR]

Background: Digital health interventions are a promising method for delivering timely support to underresourced family caregivers. The uptake of digital health interventions among caregivers may be improved by engaging caregivers in participatory design (PD). In recent years, there has been a shift toward conducting PD remotely, which may enable participation by previously hard-to-reach groups. However, little is known regarding how best to facilitate engagement in remote PD among family caregivers. Objective: This study aims to (1) understand the context, quality, and outcomes of family caregivers' engagement experiences in remote PD and (2) learn which aspects of the observed PD approach facilitated engagement or need to be improved. Methods: We analyzed qualitative and quantitative data from evaluation and reflection surveys and interviews completed by research and community partners (family caregivers) across 4 remote PD studies. Studies focused on building digital health interventions for family caregivers. For each study, community partners met with research partners for 4 to 5 design sessions across 6 months. After each session, partners completed an evaluation survey. In 1 of the 4 studies, research and community partners completed a reflection survey and interview. Descriptive statistics were used to summarize quantitative evaluation and reflection survey data, while reflexive thematic analysis was used to understand qualitative data. Results: In 62.9% (83/132) of evaluations across projects 1-3, participants described the session as "very effective." In 74% (28/38) of evaluations for project 4, participants described feeling "extremely satisfied" with the session. Qualitative data relating to the engagement context identified that the identities of partners, the technological context of remote PD, and partners' understanding of the project and their role all influenced engagement. Within the domain of engagement quality, relationship-building and co-learning; satisfaction with prework, design activities, time allotted, and the final prototype; and inclusivity and the distribution of influence contributed to partners' experience of engagement. Outcomes of engagement included partners feeling an ongoing interest in the project after its conclusion, gratitude for participation, and a sense of meaning and self-esteem. Conclusions: These results indicate high satisfaction with remote PD processes and few losses specific to remote PD. The results also demonstrate specific ways in which processes can be changed to improve partner engagement and outcomes. Community partners should be involved from study inception in defining the problem to be solved, the approach used, and their roles within the project. Throughout the design process, online tools may be used to check partners' satisfaction with design processes and perceptions of inclusivity and power-sharing. Emphasis should be placed on increasing the psychosocial benefits of engagement (eg, sense of community and purpose) and increasing opportunities to participate in disseminating findings and in future studies. [ABSTRACT FROM AUTHOR]

Since the early studies exploring the use of SMS text messaging for health intervention, text messaging has played a pivotal role in the advancement of mobile health. As an intervention modality, text messaging has provided vital learnings for the design and delivery of interventions, particularly in low-resource settings. Despite the advances in technology over the last 25 years, text messaging is still being used in largely the same way to deliver health information, behavior change interventions, and support. The strong, consistent evidence for the benefits of this type of intervention has made text messaging a routine part of health interventions around the world. Key to its success is its simplicity, alongside the benefit of being arguably the most accessible form of consumer digital health intervention. Text message interventions are well suited for public health interventions due to their low cost, vast reach, frequent use, high read rates, and ability to be tailored and personalized. Furthermore, the nature of text messaging interventions makes them ideal for the delivery of multilingual, culturally tailored interventions, which is important in the context of increasing cultural diversity in many countries internationally. Indeed, studies assessing text message–based health interventions have shown them to be effective across sociodemographic and ethnic groups and have led to their adoption into national-level health promotion programs. With a growing focus on artificial intelligence, robotics, sensors, and other advances in digital health, there is an opportunity to integrate these technologies into text messaging programs. Simultaneously, it is essential that equity remains at the forefront for digital health researchers, developers, and implementers. Ensuring digital health solutions address inequities in health experienced across the world while taking action to maximize digital inclusion will ensure the true potential of digital health is realized. Text messaging has the potential to continue to play a pivotal role in the delivery of equitable digital health tools to communities around the world for many years to come. Further new technologies can build on the humble text message, leveraging its success to advance the field of digital health. This Viewpoint presents a retrospective of text messaging in health, drawing on the example of text message–based interventions for smoking cessation, and presents evidence for the continued relevance of this mobile health modality in 2025 and beyond. [ABSTRACT FROM AUTHOR]

Background: Digital health care apps, including digital therapeutics, have the potential to increase accessibility and improve patient engagement by overcoming the limitations of traditional facility-based medical treatments. However, there are no established tools capable of quantitatively measuring long-term engagement at present. Objective: This study aimed to evaluate an existing engagement index (EI) in a commercial health management app for long-term use and compare it with a newly developed EI. Methods: Participants were recruited from cancer survivors enrolled in a randomized controlled trial that evaluated the impact of mobile health apps on recovery. Of these patients, 240 were included in the study and randomly assigned to the Noom app (Noom Inc). The newly developed EI was compared with the existing EI, and a long-term use analysis was conducted. Furthermore, the new EI was evaluated based on adapted measurements from the Web Matrix Visitor Index, focusing on click depth, recency, and loyalty indices. Results: The newly developed EI model outperformed the existing EI model in terms of predicting EI of a 6- to 9-month period based on the EI of a 3- to 6-month period. The existing model had a mean squared error of 0.096, a root mean squared error of 0.310, and an R 2 of 0.053. Meanwhile, the newly developed EI models showed improved performance, with the best one achieving a mean squared error of 0.025, root mean squared error of 0.157, and R 2 of 0.610. The existing EI exhibited significant associations: the click depth index (hazard ratio [HR] 0.49, 95% CI 0.29-0.84; P <.001) and loyalty index (HR 0.17, 95% CI 0.09-0.31; P <.001) were significantly associated with improved survival, whereas the recency index exhibited no significant association (HR 1.30, 95% CI 1.70-2.42; P =.41). Among the new EI models, the EI with a menu combination of menus available in the app's free version yielded the most promising result. Furthermore, it exhibited significant associations with the loyalty index (HR 0.32, 95% CI 0.16-0.62; P <.001) and the recency index (HR 0.47, 95% CI 0.30-0.75; P <.001). Conclusions: The newly developed EI model outperformed the existing model in terms of the prediction of long-term user engagement and compliance in a mobile health app context. We emphasized the importance of log data and suggested avenues for future research to address the subjectivity of the EI and incorporate a broader range of indices for comprehensive evaluation. [ABSTRACT FROM AUTHOR]

Digital mental health interventions are routinely integrated into mental health services internationally and can contribute to reducing the global mental health treatment gap identified by the World Health Organization. Research teams designing and delivering evaluations frequently invest substantial effort in deliberating on ethical and legal challenges around digital mental health interventions. In this article, we reflect on our own research experience with digital mental health intervention design and evaluation to identify 8 of the most critical challenges that we or others have faced, and that have ethical or legal consequences. These include: (1) harm caused by online recruitment work; (2) monitoring of intervention safety; (3) exclusion of specific demographic or clinical groups; (4) inadequate robustness of effectiveness and cost-effectiveness findings; (5) adequately conceptualizing and supporting engagement and adherence; (6) structural barriers to implementation; (7) data protection and intellectual property; and (8) regulatory ambiguity relating to digital mental health interventions that are medical devices. As we describe these challenges, we have highlighted serious consequences that can or have occurred, such as substantial delays to studies if regulations around Software as a Medical Device (SaMD) are not fully understood, or if regulations change substantially during the study lifecycle. Collectively, the challenges we have identified highlight a substantial body of required knowledge and expertise, either within the team or through access to external experts. Ensuring access to knowledge requires careful planning and adequate financial resources (for example, paying public contributors to engage in debate on critical ethical issues or paying for legal opinions on regulatory issues). Access to such resources can be planned for on a per-study basis and enabled through funding proposals. However, organizations regularly engaged in the development and evaluation of digital mental health interventions should consider creating or supporting structures such as advisory groups that can retain necessary competencies, such as in medical device regulation. [ABSTRACT FROM AUTHOR]

Background: Mobile technologies are increasingly being used in health care and public health practice for patient communication, monitoring, and education. Mobile health (mHealth) tools have also been used to facilitate adherence to chronic musculoskeletal pain (CMP) management, which is critical to achieving improved pain outcomes, quality of life, and cost-effective health care. Objective: The aim of this systematic review was to evaluate the 25-year trend of the literature on the adherence, usability, feasibility, and acceptability of mHealth interventions in CMP management among patients and health care providers. Methods: We searched the PubMed, Cochrane CENTRAL, MEDLINE, EMBASE, and Web of Science databases for studies assessing the role of mHealth in CMP management from January 1999 to December 2023. Outcomes of interest included the effect of mHealth interventions on patient adherence; pain-specific clinical outcomes after the intervention; and the usability, feasibility, and acceptability of mHealth tools and platforms in chronic pain management among target end users. Results: A total of 89 articles (26,429 participants) were included in the systematic review. Mobile apps were the most commonly used mHealth tools (78/89, 88%) among the included studies, followed by mobile app plus monitor (5/89, 6%), mobile app plus wearable sensor (4/89, 4%), and web-based mobile app plus monitor (1/89, 1%). Usability, feasibility, and acceptability or patient preferences for mHealth interventions were assessed in 26% (23/89) of the studies and observed to be generally high. Overall, 30% (27/89) of the studies used a randomized controlled trial (RCT), cohort, or pilot design to assess the impact of the mHealth intervention on patients' adherence, with significant improvements (all P <.05) observed in 93% (25/27) of these studies. Significant (judged at P <.05) between-group differences were reported in 27 of the 29 (93%) RCTs that measured the effect of mHealth on CMP-specific clinical outcomes. Conclusions: There is great potential for mHealth tools to better facilitate adherence to CMP management, and the current evidence supporting their effectiveness is generally high. Further research should focus on the cost-effectiveness of mHealth interventions for better incorporating these tools into health care practices. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42024524634; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=524634 [ABSTRACT FROM AUTHOR]

Background: Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression. Objective: This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time. Methods: In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. Results: Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns. Conclusions: The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms. [ABSTRACT FROM AUTHOR]

Background: Telemedicine has demonstrated efficacy as a supplement to traditional in-person care when treating certain diseases. Nevertheless, more investigation is needed to comprehensively assess its potential as an alternative to in-person care and its influence on access to care. The successful treatment of short stature relies on timely and regular intervention, particularly in rural and economically disadvantaged regions where the disease is more prevalent. Objective: This study evaluated the clinical outcomes, health-seeking behaviors, and cost of telemedicine integrated into care for children with short stature in China. Methods: Our study involved 1241 individuals diagnosed with short stature at the pediatric outpatient clinic of Peking University Third Hospital between 2012 and 2023. Patients were divided into in-person care (IPC; 1183 patients receiving only in-person care) and telemedicine integrated care (TIC; 58 patients receiving both in-person and virtual care) groups. For both groups, the initial 71.43% (average of 58 percentages, with each percentage representing the ratio of patients in the treatment group) of visits were categorized into the pretelemedicine phase. We used propensity score matching to select individuals with similar baseline conditions. We used 7 variables such as age, gender, and medical insurance for the 1:5 closest neighbor match. Eventually, 115 patients in the IPC group and 54 patients in the TIC group were selected. The primary clinical outcome was the change in the standard height percentage. Health-seeking behavior was described by visit intervals in the pre- and post-telemedicine phases. The cost analysis compared costs both between different groups and between different visit modalities of the TIC group in the post-telemedicine phase. Results: In terms of clinical effectiveness, we demonstrated that the increase in height among the TIC group (Δ z TIC=0.74) was more substantial than that for the IPC group (Δ z IPC=0.51, P =.01; paired t test), while no unfavorable changes in other endpoints such as BMI or insulin-like growth factor 1 (IGF-1) levels were observed. As for health-seeking behaviors, the results showed that, during the post-telemedicine phase, the IPC group had a visit interval of 71.08 (IQR 50.75-90.73) days, significantly longer than the prior period (51.25 [IQR 34.75-82.00] days, P <.001; U test), whereas the TIC group's visit interval remained unchanged. As for the cost per visit, there was no difference in the average cost per visit between the 2 groups nor between the pre- and post-telemedicine phases. During the post-telemedicine phase, within the TIC group, in-person visits had a higher average total cost, elevated medical and labor expenses, and greater medical cost compared with virtual visits. Conclusions: We contend that the rise in medical visits facilitated by integrating telemedicine into care effectively restored the previously constrained number of medical visits to their usual levels, without increasing costs. Our research underscores that administering prompt treatment may enable physicians to seize a crucial treatment opportunity for children with short stature, thus attaining superior results. [ABSTRACT FROM AUTHOR]

Background: There has been a rapid expansion of digital health care services, making the need for measuring and improving digital health readiness a priority. In response, our study team developed the Mobile-Centered Digital Health Readiness: Health Literacy and Equity Scale (mDiHERS) to measure digital health readiness. Objective: We aim to develop and validate a scale that assesses digital health readiness, encompassing literacy and equity, and to ensure the effective use of mobile-centered digital health services. Methods: This study was conducted from October 2021 to October 2022 to develop and validate the mDiHERS. Participants included patients with inflammatory bowel disease, which is a chronic condition requiring continuous management, and experts in medical and nursing informatics. The scale development involved a literature review, focus group interviews, and content validity evaluations. A total of 440 patients with inflammatory bowel disease were recruited for the validation phase, with 403 completing the survey. The scale's validity and reliability were assessed through exploratory factor analysis and Cronbach α. The scale was translated into English by translators and bilingual and native researchers, ensuring its applicability in diverse settings. Results: The mDiHERS consists of 36 items across 6 domains, with a 5-point Likert scale for responses. The validation process confirmed the scale's construct validity, with 4 factors explaining 65.05% of the total variance. The scale's reliability was established with Cronbach α values ranging from 0.84 to 0.91. The scale's development considered the technical proficiency necessary for engaging with health mobile apps and devices, reflecting the importance of subjective confidence and objective skills in digital health literacy. Conclusions: The mDiHERS is a validated tool for measuring patients' readiness and ability to use digital health services. The mDiHERS assesses user characteristics, digital accessibility, literacy, and equity to contribute to the effective use of digital health services and improve accessibility. The development and validation of the mDiHERS emphasize the importance of confidence and competence in managing health digitally. Continuous improvements are necessary to ensure that all patients can benefit from digital health care. [ABSTRACT FROM AUTHOR]

Background: Symptom-checkers have become important tools for self-triage, assisting patients to determine the urgency of medical care. To be safe and effective, these tools must be validated, particularly to avoid potentially hazardous undertriage without leading to inefficient overtriage. Only limited safety data from studies including small sample sizes have been available so far. Objective: The objective of our study was to prospectively investigate the safety of patients' self-triage in a large patient sample. We used SMASS (Swiss Medical Assessment System; in4medicine, Inc) pathfinder, a symptom-checker based on a computerized transparent neural network. Methods: We recruited 2543 patients into this single-center, prospective clinical trial conducted at the cantonal hospital of Baden, Switzerland. Patients with an Emergency Severity Index of 1-2 were treated by the team of the emergency department, while those with an index of 3-5 were seen at the walk-in clinic by general physicians. We compared the triage recommendation obtained by the patients' self-triage with the assessment of clinical urgency made by 3 successive interdisciplinary panels of physicians (panels A, B, and C). Using the Clopper-Pearson CI, we assumed that to confirm the symptom-checkers' safety, the upper confidence bound for the probability of a potentially hazardous undertriage should lie below 1%. A potentially hazardous undertriage was defined as a triage in which either all (consensus criterion) or the majority (majority criterion) of the experts of the last panel (panel C) rated the triage of the symptom-checker to be "rather likely" or "likely" life-threatening or harmful. Results: Of the 2543 patients, 1227 (48.25%) were female and 1316 (51.75%) male. None of the patients reached the prespecified consensus criterion for a potentially hazardous undertriage. This resulted in an upper 95% confidence bound of 0.1184%. Further, 4 cases met the majority criterion. This resulted in an upper 95% confidence bound for the probability of a potentially hazardous undertriage of 0.3616%. The 2-sided 95% Clopper-Pearson CI for the probability of overtriage (n=450 cases,17.69%) was 16.23% to 19.24%, which is considerably lower than the figures reported in the literature. Conclusions: The symptom-checker proved to be a safe triage tool, avoiding potentially hazardous undertriage in a real-life clinical setting of emergency consultations at a walk-in clinic or emergency department without causing undesirable overtriage. Our data suggest the symptom-checker may be safely used in clinical routine. Trial Registration: ClinicalTrials.gov NCT04055298; https://clinicaltrials.gov/study/NCT04055298 [ABSTRACT FROM AUTHOR]

Background: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. Objective: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs. Methods: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported. Results: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies. Conclusions: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user. [ABSTRACT FROM AUTHOR]

Background: With 1 in 3 adults globally living with chronic conditions and the rise in smartphone ownership, mobile health apps have become a prominent tool for managing lifestyle-related health behaviors and mental health. However, high rates of app abandonment pose challenges to their effectiveness. Objective: We explored the abandonment of apps used for managing physical activity, diet, alcohol, smoking, and mental health in free-living conditions, examining the duration of app use before abandonment and the underlying reasons. Methods: A scoping review was conducted based on the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines and eligibility criteria were designed according to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) framework. In total, 4 databases were searched (MEDLINE, Scopus, Embase, and PsycINFO) to identify quantitative and qualitative studies with outcome measures related to app abandonment in adults with free-living conditions, including reasons for abandonment and duration of use, for mobile apps related to WHO (World Health Organization) modifiable health behaviors and mental health. The included studies' risk of bias was appraised based on the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and COREQ (Consolidated Criteria for Reporting Qualitative Research) checklists. To enable data synthesis across different methodologies, app domains, demographic data, and outcome measures were categorized. Results are presented in 2 sections: quantitatively in a scatterplot to understand when users abandon apps and qualitatively through basic qualitative content analysis to identify the underlying reasons. Results: Eighteen eligible studies (525,824 participants) published between 2014 and 2022, predominantly from the United States, Canada, the United Kingdom, and Germany, were identified. Findings revealed a curvilinear pattern of app abandonment, with sharper abandonment soon after acquisition, followed by a slowing rate of abandonment over time. Taken together, a median of 70% of users discontinued use within the first 100 days. The abandonment rate appeared to vary by app domain, with apps focusing on alcohol and smoking exhibiting faster abandonment, and physical activity and mental health exhibiting longer usage durations. In total, 22 unique reasons for abandonment were organized into six categories: (1) technical and functional issues, (2) privacy concerns, (3) poor user experience, (4) content and features, (5) time and financial costs, and (6) evolving user needs and goals. Conclusions: This study highlights the complex nature of health app abandonment and the need for an improved understanding of user engagement over time, underscoring the importance of addressing various factors contributing to abandonment, from technical issues to evolving user needs. Our findings also emphasize the need for longitudinal studies and a consistent definition of app abandonment to better understand and mitigate this phenomenon, thereby enhancing the effectiveness of health apps in supporting public health initiatives. [ABSTRACT FROM AUTHOR]

Background: Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy among people with HIV and substance use disorders. Objective: This study aimed to use longitudinal and detailed system logs and weekly survey data to test a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management were treated as joint mediators, and antiretroviral therapy adherence was treated as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs reception) and by communication levels (intraindividual vs dyadic vs network). Methods: Tailored for people with HIV living with substance use disorders, the mHealth app was distributed among 208 participants aged >18 years from 2 US health clinics. Supervised by medical professionals, participants received weekly surveys through the app to report their health status and medication adherence data. System use was passively collected through the app, operationalized as transformed click-level data, aggregated weekly, and connected to survey responses with a 7-day lagged window. Using the weekly check-in record provided by participants as the unit of analysis (N=681), linear regression and structure equation models with cluster-robust SEs were used for analyses, controlling within-person autocorrelation and group-level error correlations. Racial groups were examined as moderators in the structure equation models. Results: We found that (1) intensity, not initiation, of system use; (2) dyadic message expression and reception; and (3) network expression positively predicted medication adherence through joint mediators (substance use and confidence in HIV management). However, intraindividual reception (ie, rereading saved entries for personal motivation) negatively predicts medication adherence through joint mediators. We also found Black participants have distinct usage patterns, suggesting the need to tailor mHealth interventions for this subgroup. Conclusions: These findings highlight the importance of considering the intensity of system engagement, rather than initiation alone, when designing mHealth interventions for people with HIV and tailoring these systems to Black communities. [ABSTRACT FROM AUTHOR]

Background: Nonpharmacological interventions for veterans are needed to help them manage chronic pain and posttraumatic stress disorder (PTSD) symptoms. Complementary and integrative health (CIH) interventions such as Mission Reconnect (MR) seek to provide veterans with the option of a partnered, self-directed intervention that teaches CIH skills remotely to support symptom management. Objective: The purpose of this study was to describe the physical, psychological, and social outcomes of a self-directed mobile- and web-based CIH intervention for veterans with comorbid chronic pain and PTSD and their partners and qualitatively examine their MR user experience. Methods: A sample of veteran-partner dyads (n=364) were recruited to participate in a mixed methods multisite waitlist control randomized controlled trial to measure physical, psychological, and social outcomes, with pain as the primary outcome and PTSD, depression, stress, sleep, quality of life, and relationships as secondary outcomes. Linear mixed models were constructed for primary and secondary patient-reported outcomes. The quantitative analysis was triangulated using qualitative interviews from a subsample of dyads (n=35) to examine participants' perceptions of their program experience. Results: Dyads were randomized to 2 groups: intervention (MR; 140/364, 38.5%) and waitlist control (136/364, 37.4%). No significant change was observed in overall pain, sleep, PTSD, quality of life, relationship satisfaction, overall self-compassion, or compassion for others. A significant reduction in pain interference in mood (P =.008) and sleep (P =.008) was observed among the veteran MR group that was not observed in the waitlist control group. We also observed a positive effect of the MR intervention on a reduction in negative affect associated with pain (P =.049), but this effect did not exceed the adjusted significance threshold (P =.01). Significant improvements were also observed for partners in the affection (P=.007) and conflict (P=.001) subdomains of the consensus and satisfaction domains. In contrast to quantitative results, qualitative data indicated that intervention impacts included improved sleep and reduced pain, anxiety, and stress and, in contrast to the survey data, overall improvement in PTSD symptoms and social relationships. Participants' overall impressions of MR highlight usability and navigation, perceptions on packaging and content, and barriers to and facilitators of MR use. Conclusions: Adjunctive CIH-based modalities can be delivered using web and mobile apps but should be developed and tailored using established best practices. MR may be beneficial for veterans with pain and PTSD and their partners. Further pragmatic trials and implementation efforts are warranted. Trial Registration: ClinicalTrials.gov NCT03593772; https://clinicaltrials.gov/study/NCT03593772 International Registered Report Identifier (IRRID): RR2-10.2196/13666 [ABSTRACT FROM AUTHOR]

Background: Chronic heart failure has become a serious threat to the health of the global population, and self-management is key to treating chronic heart failure. The emergence of mobile health (mHealth) provides new ideas for the self-management of chronic heart failure in which the informal caregiver plays an important role. Current research has mainly studied the experiences with using mHealth among patients with chronic heart failure from the perspective of individual patients, and there is a lack of research from the dichotomous perspective. Objective: The aim of this study was to explore the experiences with mHealth use among patients with chronic heart failure and their informal caregivers from a dichotomous perspective. Methods: This descriptive phenomenological study from a post-positivist perspective used a dyadic interview method, and face-to-face semistructured interviews were conducted with patients with chronic heart failure and their informal caregivers. Data were collected and managed using NVivo 12 software, and data analysis used thematic analysis to identify and interpret participants' experiences and perspectives. The thematic analysis included familiarizing ourselves with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. Results: A total of 14 dyads of patients with chronic heart failure and their informal caregivers (13 men and 15 women) participated in this study, including 3 couples and 11 parent-child pairs. We constructed 4 key themes and their subthemes related to the experiences with mHealth use: (1) opposing experiences with mHealth as human interaction or trauma (great experience with mHealth use; trauma), (2) supplement instead of replacement (it is useful but better as a reference; offline is unavoidable sometimes), (3) both agreement and disagreement over who should be the adopter of mHealth (achieving consensus regarding who should adopt mHealth; conflict occurs when considering patients as the adopter of mHealth), (4) for better mHealth (applying mHealth with caution; suggestions for improved mHealth). Conclusions: This study reported that the experiences with mHealth use among patients with chronic heart failure and their informal caregivers were mixed, and it highlighted the human touch of mHealth and the importance of network security. These results featured mHealth as a complement to offline hospitals rather than a replacement. In the context of modern or changing Chinese culture, we encourage patients to use mHealth by themselves and their informal caregivers to provide help when necessary. In addition, we need to use mHealth carefully, and future mHealth designs should focus more on ease of use and be oriented more toward older adults. [ABSTRACT FROM AUTHOR]

Background: Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field. Objective: This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations. Methods: A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis. Results: The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts. Conclusions: Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use. [ABSTRACT FROM AUTHOR]

Background: Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. Objective: This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. Methods: Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. Results: We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)–adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P =.009), % predicted maximum HR (FDR-adjusted P <.001), and systolic blood pressure-to-workload slope (FDR-adjusted P =.01) were associated with nonactive HR but not associated with daily steps. Conclusions: Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels. [ABSTRACT FROM AUTHOR]

Background: With advances in medical technologies, more children with chronic diseases are now living on into adulthood. The development of proficient self-management skills is essential for adolescents and young adults to transition from pediatric to adult health care services. An innovative way to improve the current care model and foster self-management skills could be through eHealth or mHealth (mobile health) interventions, in particular, when considering the rising ownership of digital technology by adolescents and young adults. Objective: This systematic review aimed to evaluate the features, acceptability, usability, engagement, and intervention efficacy of eHealth and mHealth interventions that support self-management and health care transition in adolescents and young adults with chronic disease. Methods: This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines (registration number CRD42023378355). We systematically searched the MEDLINE complete, Embase, Cochrane Library, CINAHL complete, and ProQuest Health & Medical complete. We considered only articles published in or since 2019, as we aimed to extend the data collected by 2 previous systematic reviews. Results: A total of 16,752 studies were screened. After removing duplicates, 14,507 studies were excluded based on the title and abstract. Ultimately, 22 studies were included. The interventions ranged from simple text messages to complex interventions involving web-based games and engagement of health care providers, which were summarized into 6 themes: medication monitoring and reminders, symptom tracking and monitoring, management goal setting, knowledge education and self-management skills training, incentives and reinforcement, and communication. Most adolescents and young adults felt the eHealth and mHealth interventions were feasible, as they were convenient, easy to use, and accessible in the context of helping manage their health. However, user engagement was variable and presented a gradual decline in youth engagement with these apps over time. Barriers that prevent user engagement are diverse, such as time-consuming video uploads, noncontinuous access to a phone, reading literacy levels, language, and false impressions. Moreover, adolescents and young adults had different preferred styles of message delivery and functions, especially the engaging elements, disease-specific information, and opportunities to communicate with peers, health care providers, and app teams. Conclusions: There remains limited data about the effectiveness of eHealth and mHealth interventions facilitating the self-management and health care transition of adolescents and young adults with chronic diseases. Based on the available evidence, they were receptive to and interested in receiving information and managing their health using mobile apps or websites. Considering adolescents and young adults had different preferred styles of message delivery and features, to improve user engagement and provide focused interventions, it would be better to involve them early in the design process to identify their specific needs, as well as collaborate with health care providers and app teams to obtain suggestions. [ABSTRACT FROM AUTHOR]

Background: Cardiac rehabilitation is known to reduce coronary artery disease (CAD) severity and symptoms, but adoption of a healthy postrehabilitation lifestyle remains challenging. Innovative eHealth solutions could help, but behavioral change–based eHealth maintenance programs for patients with CAD are scarce. RehaPlus+ aims to improve postrehabilitation outcomes with a personalized eHealth intervention built on behavioral change concepts emphasizing healthy lifestyle changes, especially regular physical activity (PA). Objective: This study aims to evaluate the effectiveness of the personalized eHealth program RehaPlus+ for promoting regular PA against usual care. Methods: A total of 169 patients with CAD who had undergone stent implantation or bypass surgery were recruited after completing center-based phase II rehabilitation. They were then divided, without blinding, into 2 groups using a quasi-experimental approach: a case manager–assisted 24-week eHealth program (RehaPlus+; n=84) and a conventional physician-assisted outpatient program (usual care; n=85). The study was designed as a noninferiority trial. RehaPlus+ participants received motivational messages twice weekly for 6 months, and the usual care group engaged in a 6-month outpatient program (twenty-four 90-minute strength and endurance training sessions). The primary outcomes, evaluated using the self-assessed Bewegungs- und Sportaktivität questionnaire, were regular PA (≥150 min/wk) and weekly activities of daily living (ADLs) 6 months after rehabilitation. Secondary outcomes involved PA during work and floors climbed weekly (measured by Bewegungs- und Sportaktivität questionnaire), psychological well-being (assessed by the 5-item World Health Organization Well-Being Index), cardiac self-efficacy, health-related quality of life (measured by the 36-Item Short Form Survey), and work ability (using the Work Ability Index). Results: Data of 105 patients (RehaPlus+: n=44, 41.9%; usual care: n=61, 58.1%; male patients: n=80, 76.2%; female patients: n=25, 23.8%; mean age 56.0, SD 7.3 years) were available at the 6-month follow-up. At 6 months after discharge from phase II cardiac rehabilitation, the RehaPlus+ group exhibited 182 (SD 208) minutes per week of PA and the usual care group exhibited 119 (SD 175) minutes per week of PA (P =.15), with no interaction effect (P =.12). The RehaPlus+ group showed an ADL level of 443 (SD 538) minutes per week compared to the usual care group with 308 (SD 412) minutes per week at the 6-month follow-up, with no interaction effect (P =.84). The differences observed in PA and ADL levels between the RehaPlus+ and usual care groups were within the predefined 1-sided noninferiority margin, indicating that the RehaPlus+ intervention is not inferior to usual care based on these outcomes. There were no differences between the groups for all secondary outcomes (P >.05). Conclusions: RehaPlus+ is not inferior to the usual care program, as both groups improved PA and ADLs to a similar extent. These findings emphasize the potential of eHealth interventions to assist in maintaining healthy lifestyles after rehabilitation. Trial Registration: ClinicalTrials.gov NCT06162793; https://clinicaltrials.gov/study/NCT06162793 [ABSTRACT FROM AUTHOR]

Background: Mobile health (mHealth) interventions have gained popularity in augmenting psychiatric care for adults with psychosis. Interest has grown in leveraging mHealth to empower individuals living with severe mental illness and extend continuity of care beyond the hospital to the community. However, reported outcomes have been mixed, likely attributed in part to the intervention and adopted outcomes, which affected between-study comparisons. Objective: This study aimed to critically review outcome measures used to evaluate mHealth interventions for adults with psychosis in relation to the characteristics of mHealth interventions. Methods: A systematic mapping review was conducted. We searched PubMed, CINAHL, Embase, PsycINFO, and Cochrane Libraries from 1973 to the present. Selection criteria included randomized controlled studies of mHealth interventions in adults diagnosed with schizophrenia spectrum disorders. Reviewers worked in pairs to screen and extract data from included studies independently using a standardized form; disagreements were resolved by consensus with an independent reviewer. We report our findings in line with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: A total of 1703 citations were screened; 29 publications reporting on 23 studies were included in this review. mHealth interventions for psychosis span a wide range, with psychological therapy being the most-deployed intervention (12/23, 52%), followed by psychoeducation (8/23, 35%) and active self-monitoring (8/23, 35%). Several mHealth interventions for psychosis targeted multiple pillars of biopsychosocial well-being (10/23, 43%); the bulk of interventions (16/23, 70%) incorporated features promoting users' self-management. The majority of mHealth interventions were delivered through applications (14/23, 61%) as the main medium and smartphones (17/23, 74%) as the main channel of delivery. Interventions were primarily administered in the outpatient and community settings (16/23, 70%); many were also blended with in-person sessions (11/23, 48%) or guided remotely (6/23, 26%) by persons, including health care providers or trained peer supporters. The severity of psychosis-related symptoms (21/23, 91%) was the most prevalent outcome, of which positive symptoms (13/23, 57%), mood and anxiety (10/23, 43%), and overall psychopathology severity (9/23, 39%) were most commonly measured. Patient-centric outcomes, including well-being (17/23, 74%)—particularly quality of life (10/23, 43%)—and user experience (15/23, 65%), including feasibility (7/23, 30%), acceptability (7/23, 30%), and engagement (7/23, 26%). Notably, outcome choices remained diverse despite stratification by type of mHealth intervention. Conclusions: mHealth interventions for psychosis encompass a wide range of modalities and use outcome measures that probe various social and behavioral determinants of health. These should be considered complex interventions, and a holistic evaluation approach combining clinical and patient-centric outcomes is recommended. [ABSTRACT FROM AUTHOR]

Background: Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist. Objective: We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions? Methods: We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection. Results: For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F 1,144=0.0227; P =.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (χ 21=4.4; P =.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks. Conclusions: We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning. [ABSTRACT FROM AUTHOR]

Background: eHealth can help replicate the benefits of conventional surgical prehabilitation programs and overcome organizational constraints related to human resources and health care–related costs. Objective: We aimed to assess the impact of an optimized perioperative program using a personalized mobile app designed for preparation and recovery after radical prostatectomy (RP). Methods: We report on a series of 122 consecutive robot-assisted RP before and after the implementation of the betty.care app (cohort A: standard of care, n=60; cohort B: optimized program, n=62). The primary end point was continence recovery, defined as "0 or 1 safety pad per day" at 6 weeks after surgery. Secondary end points were length of stay, same-day discharge, complications, readmissions, and number of days alive and out of hospital within 30 days from surgery. Results: Both cohorts were comparable in terms of age, prostate-specific antigen, prostate volume, and disease aggressiveness. Intraoperative parameters (lymph node dissection, operative time, and bilateral nerve-sparing surgery) were comparable in both groups, except for blood loss, which was significantly higher in cohort B (182 vs 125 cc; P =.008). The 6-week continence rate was improved in cohort B in both univariable and multivariable analyses (92% vs 75%; P =.01). There were trends favoring cohort B for all secondary end points with a minimal 30% benefit compared with cohort A. Grade 2 or more complications occurred less frequently in cohort B (13% vs 3.2%; P =.042). Same-day discharge and readmission rates were 35% and 53% (P =.043), and 3.3% and 1.6% (P =.54) in cohorts A and B, respectively. Mean length of stay was reduced by 0.2 days in cohort B (0.58 vs 0.78 days; P =.10). The main limitation was the absence of randomization. Conclusions: The implementation of a mobile app that provides a holistic approach to the perioperative period, integrating prehabilitation, rehabilitation, and remote monitoring, could lead to the improvement of important functional outcomes after RP and could replicate an on-site prehabilitation program. Multicenter validation is needed. [ABSTRACT FROM AUTHOR]

Background: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. Objective: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. Methods: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. Results: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=–93.61, P <.001), increased sleep variability (β=0.96, P <.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P =.001; sleep offset: β=1.12, P <.001; M10 onset: β=0.73, P =.003; HR acrophase: β=0.71, P =.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = –31.51, P =.002) and summer (β of PHQ-8 × summer = –42.61, P <.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. Conclusions: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression. [ABSTRACT FROM AUTHOR]

Background: The shift in medical care toward prediction and prevention has led to the emergence of digital health care as a valuable tool for managing health issues. Aiding long-term follow-up care for cancer survivors and contributing to improved survival rates. However, potential barriers to mobile health usage, including age-related disparities and challenges in user retention for commercial health apps, highlight the need to assess the impact of patients' abilities and health status on the adoption of these interventions. Objective: This study aims to investigate the app adherence and user experience of commercial health care apps among cancer survivors using an extended technology acceptance model (TAM). Methods: The study enrolled 264 cancer survivors. We collected survey results from May to August 2022 and app usage records from the app companies. The survey questions were created based on the TAM. Results: We categorized 264 participants into 3 clusters based on their app usage behavior: short use (n=77), medium use (n=101), and long use (n=86). The mean usage days were 9 (SD 11) days, 58 (SD 20) days, and 84 (SD 176) days, respectively. Analysis revealed significant differences in perceived usefulness (P =.01), interface satisfaction (P <.01), equity (P <.01), and utility (P =.01) among the clusters. Structural equation modeling indicated that perceived ease-of-use significantly influenced perceived usefulness (β=0.387, P <.01), and both perceived usefulness and attitude significantly affected behavioral intention and actual usage. Conclusions: This study showed the importance of positive user experience and clinician recommendations in facilitating the effective usage of digital health care tools among cancer survivors and contributing to the evolving landscape of medical care. [ABSTRACT FROM AUTHOR]

Background: Symptom checker apps (SCA) aim to enable individuals without medical training to classify perceived symptoms and receive guidance on appropriate actions, such as self-care or seeking professional medical attention. However, there is a lack of detailed understanding regarding the contexts in which individuals use SCA and their opinions on these tools. Objective: This mixed methods study aims to explore the circumstances under which medical laypeople use SCA and to identify which aspects users find noteworthy after using SCA. Methods: A total of 48 SCA users documented their medical symptoms, provided open-ended responses, and recorded their SCA use along with other variables over 6 weeks in a longitudinal study. Generalized linear mixed models with and those without regularization were applied to consider the hierarchical structure of the data, and the models' outcomes were evaluated for comparison. Qualitative data were analyzed through Kuckartz qualitative content analysis. Results: Significant predictors of SCA use included the initial occurrence of symptoms, day of measurement (odds ratio [OR] 0.97), self-rated health (OR 0.80, P <.001), and the following International Classification in Primary Care-2–classified symptoms, that are general and unspecified (OR 3.33, P <.001), eye (OR 5.56, P =.001), cardiovascular (OR 8.33, P <.001), musculoskeletal (OR 5.26, P <.001), and skin (OR 4.76, P <.001). The day of measurement and self-rated health showed minor importance due to their small effect sizes. Qualitative analysis highlighted four main themes: (1) reasons for using SCA, (2) diverse affective responses, (3) a broad spectrum of behavioral reactions, and (4) unmet needs including a lack of personalization. Conclusions: The emergence of new and unfamiliar symptoms was a strong determinant for SCA use. Specific International Classification in Primary Care–rated symptom clusters, particularly those related to cardiovascular, eye, skin, general, and unspecified symptoms, were also highly predictive of SCA use. The varied applications of SCA fit into the concept of health literacy as bricolage, where SCA is leveraged as flexible tools by patients based on individual and situational requirements, functioning alongside other health care resources. [ABSTRACT FROM AUTHOR]