Systematic review of context-aware digital behavior change interventions to improve health

What you need to know about the world. Toward a taxonomy of planetary health knowledge

Knowledge about the world is deemed a core competence to engage in shifting the world to a better place to live. Although the importance of this knowledge is emphasized in several political and educational frameworks, there is still a lack of a definition of the scope of the required knowledge. Looking for a suitable taxonomy about world knowledge for sustainable development we analyzed different approaches in this area-Sustainable Development Goals, Global Citizenship, and Planetary Health-concluded that none of these approaches satisfied the requirements of a knowledge taxonomy and identified a huge amount of overlapping content among these approaches. By merging these frameworks, we developed the Planetary Health Knowledge (PHK) taxonomy targeting knowledge about the health of the planet, human individuals, human systems, and their interaction. This hierarchical taxonomy exhaustively and disjunctively covers all relevant aspects of PHK in seven domains: Health, Nutrition, Environment, Safety, Education, Standard of Living, and Political and Economic Systems. We further discuss the existence of jingle-jangle fallacies in this field, quality criteria for evaluating taxonomies, and possibilities to use the PHK taxonomy.

Exploring the Acceptance of Just-in-Time Adaptive Lifestyle Support for People With Type 2 Diabetes: Qualitative Acceptability Study

BACKGROUND

The management of type 2 diabetes (T2D) requires individuals to adopt and maintain a healthy lifestyle. Personalized eHealth interventions can help individuals change their lifestyle behavior. Specifically, just-in-time adaptive interventions (JITAIs) offer a promising approach to provide tailored support to encourage healthy behaviors. Low-effort self-reporting via ecological momentary assessment (EMA) can provide insights into individuals' experiences and environmental factors and thus improve JITAI support, particularly for conditions that cannot be measured by sensors. We developed an EMA-driven JITAI to offer tailored support for various personal and environmental factors influencing healthy behavior in individuals with T2D.

OBJECTIVE

This study aimed to assess the acceptability of EMA-driven, just-in-time adaptive lifestyle support in individuals with T2D.

METHODS

In total, 8 individuals with T2D used the JITAI for 2 weeks. Participants completed daily EMAs about their activity, location, mood, overall condition, weather, and cravings and received tailored support via SMS text messaging. The acceptability of the JITAI was assessed through telephone-conducted, semistructured interviews. Interview topics included the acceptability of the EMA content and prompts, the intervention options, and the overall use of the JITAI. Data were analyzed using a hybrid approach of thematic analysis.

RESULTS

Participants with a mean age of 70.5 (SD 9) years, BMI of 32.1 (SD 5.3) kg/m², and T2D duration of 15.6 (SD 7.7) years had high self-efficacy scores in physical activity (ie, 32) and nutrition (ie, 29) and were mainly initiating or maintaining behavior changes. The identified themes were related to the intervention design, decision points, tailoring variables, intervention options, and mechanisms underlying adherence and retention. Participants provided positive feedback on several aspects of the JITAI, such as the motivating and enjoyable messages that appeared well tailored to some individuals. However, there were notable differences in individual experiences with the JITAI, particularly regarding intervention intensity and the perceived personalization of the EMA and messages. The EMA was perceived as easy to use and low in burden, but participants felt it provided too much of a snapshot and too little context, reducing the perceived tailoring of the intervention options. Challenges with the timing and frequency of prompts and the relevance of some tailoring variables were also observed. While some participants found the support relevant and motivating, others were less inclined to follow the advice. Participants expressed the need for even more personalized support tailored to their specific characteristics and circumstances.

CONCLUSIONS

This study showed that an EMA-driven JITAI can provide motivating and tailored support, but more personalization is needed to ensure that the lifestyle support more closely fits each individual's unique needs. Key areas for improvement include developing more individually tailored interventions, improving assessment methods to balance active and passive data collection, and integrating JITAIs within comprehensive lifestyle interventions.

Effectiveness of a Just-In-Time Adaptive App to Increase Daily Steps: An RCT

INTRODUCTION

Addressing the public health problem of physical inactivity, this study evaluates SNapp, a just-in-time adaptive app intervention to promote walking through dynamically tailored coaching content. It assesses SNapp's impact on daily steps and how users' perceptions regarding ease of use and usefulness moderated its effectiveness.

METHODS

SNapp was evaluated in an RCT from February 2021 to May 2022.This trial was preregistered in the Dutch Trial Register (NL7064). Analyses were conducted in November 2022. A total of 176 adults (76% female, mean age of 56 years) were randomized to a control group receiving a step counter app (n=89) or an intervention group receiving the app plus coaching content (n=87). SNapp's coaching content encompasses individually tailored feedback on step counts and advice to engage in more walking, taking preferences regarding behavior change techniques into account. Additionally, SNapp provides contextualized content calling attention to suitable walking locations in the user's environment. The primary outcome was daily step count as recorded by the step counter app. User perceptions regarding ease of use and usefulness were assessed via survey at 3-month follow-up.

RESULTS

Mixed models indicated that the intervention did not significantly impact step counts on average over time (B= -202.30, 95% CI= -889.7, 485.1), with the coefficient indicating that the intervention group walked fewer steps per day on average, though this difference was not statistically significant. Perceived ease of use did not moderate the intervention effect (Bgroup × perceived ease of use=38.60, 90% CI= -276.5, 353.7). Perceived usefulness significantly moderated the intervention effect (Bgroup × perceived usefulness=344.38, 90% CI=40.4, 648.3).

CONCLUSIONS

SNapp increased steps only in users who deemed the app useful, underscoring the importance of user perceptions in app-based interventions.

The application of artificial intelligence in health communication development: A scoping review

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health behavior interventions. A systematic search of articles published through February 2024, following PRISMA guidelines, identified 28 relevant studies from 13,723 screened. These studies, conducted across various countries, addressed health issues such as smoking cessation, musculoskeletal injuries, diabetes, chronic diseases and mental health using AI-driven tools like chatbots and apps. Despite AI's potential, a gap exists in aligning technical advancements with theoretical frameworks. The proposed AI Impact Communications Model (AI-ICM) aims to bridge this gap, offering a road map for future research and practice.

Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview

BACKGROUND

Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing.

OBJECTIVE

First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field.

METHODS

A scoping review was conducted based on the framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given.

RESULTS

In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages.

CONCLUSIONS

The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.

Provision of Digital Primary Health Care Services: Overview of Reviews

BACKGROUND

Digital health is a growing field, and many digital interventions have been implemented on a large scale since the COVID-19 pandemic, mainly in primary health care (PHC). The development of digital health interventions and their application in PHC are encouraged by the World Health Organization. The increased number of published scientific papers on this topic has resulted in an overwhelming amount of information, but there is no overview of reviews to summarize this evidence.

OBJECTIVE

This study aims to provide policy makers, health managers, and researchers with a summary of evidence on digital interventions used in PHC.

METHODS

This overview of reviews searched the Web of Science and MEDLINE databases for systematic and scoping reviews on assessments of digital technologies implemented in PHC published from January 2007 to March 2023. Only reviews that addressed digital interventions whose targets were real patients or health care providers (HCPs) were included.

RESULTS

A total of 236 records were identified from the search strategy, of which 42 (17.8%) full-text papers were selected for analysis, and 18 (7.6%) reviews met the eligibility criteria. In total, 61% (11/18) of the reviews focused their analysis on specific digital health interventions (client-to-provider telemedicine, provider-to-provider telemedicine, health worker decision support systems, systems for tracking patients' health status, client participation and self-care platforms, and provision of education and training to health workers), and 39% (7/18) of the reviews focused on specific topics related to PHC (preventive care, chronic disease management, behavioral health disorders, the COVID-19 pandemic, multicomponent PHC interventions, and care coordination). Most studies in the included reviews agreed on barriers to implementation, such as software and apps developed without involving end users, the lack of training of HCPs and patients in digital technology use, and the lack of reimbursement and billing strategies for remote consultations. However, they showed several mixed results related to health service quality and patients' clinical conditions and behavior changes.

CONCLUSIONS

Research in digital health applied to PHC is still concentrated in high-income countries, mainly in North America and Europe. The mixed results related to health service quality and patients' clinical conditions or behavior changes may have been caused by deficiencies in the process of implementing digital interventions. It is necessary to examine the entire impact pathway and the causal relationship among implementation, health service quality, and clinical condition outcomes to support the spread of digital health in PHC settings.

A randomized clinical trial testing digital mindset intervention for knee osteoarthritis pain and activity improvement

This randomized clinical trial evaluated the effectiveness of short, digital interventions in improving physical activity and pain for individuals with knee osteoarthritis. We compared a digital mindset intervention, focusing on adaptive mindsets (e.g., osteoarthritis is manageable), to a digital education intervention and a no-intervention group. 408 participants with knee osteoarthritis completed the study online in the US. The mindset intervention significantly improved mindsets compared to both other groups (P < 0.001) and increased physical activity levels more than the no-intervention group (mean = 28.6 points, P = 0.001), but pain reduction was not significant. The mindset group also showed significantly greater improvements in the perceived need for surgery, self-imposed physical limitations, fear of movement, and self-efficacy than the no-intervention and education groups. This trial demonstrates the effectiveness of brief digital interventions in educating about osteoarthritis and further highlights the additional benefits of improving mindsets to transform patients' approach to disease management. The study was prospectively registered (ClinicalTrials.gov: NCT05698368, 2023-01-26).

Content, Behaviour Change Techniques, and Quality of Postpartum Depression Apps to Be Recommended by Midwives: Systematic Search and Evaluation

BACKGROUND

Postpartum depression is a public health problem that affects a considerable percentage of women. Despite the proliferation of related apps, there are limited data available on the best apps to prevent postpartum depression. We identified which apps available in Spanish could be recommended by midwives based on their content, quality, and behaviour change techniques, as a complementary tool for preventing postpartum depression in women.

METHODS

A systematic search was performed to identify apps available on iOS App Store and Google Play, which were used to replicate how patients' access "postpartum depression prevention" apps. Apps' quality, behaviour change potential, and contents were assessed.

RESULTS

A total of 1408 apps were identified, of which 7 were retrieved for assessment (0.5%). The mean objective and subject quality were 3.1 (SD = 1.01) and 2.7 (SD = 1.27), respectively. A total of 24 topics were identified. The mean ABACUS score was 6.6 (SD = 3.64), and the mean number of topics addressed by the apps was 9.9 (SD = 5.90).

CONCLUSION

The results of the present study suggest that a specific free app is not available in Spanish for the prevention of postpartum depression, and only a small percentage of free apps should be recommended based on their quality, BCTs, and contents. The systematic review protocol was not registered.

How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review

To assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors. A scoping review was done across the EMBASE, PsycInfo, PsycNet, PubMed, and Web of Science databases using search terms related to ML/AI, behavioral science, and digital health to find live DBCIs using ML or AI to influence real-world health behaviors in patients or consumers. A total of 32 articles met inclusion criteria. Evidence regarding behavioral domains, target real-world behaviors, and type and purpose of ML and AI used were extracted. The types and quality of research evaluations done on the DBCIs and limitations of the research were also reviewed. Research occurred between October 9, 2023, and January 20, 2024. Twenty-three DBCIs used AI to influence real-world health behaviors. Most common domains were cardiometabolic health (n=5, 21.7%) and lifestyle interventions (n=4, 17.4%). The most common types of ML and AI used were classical ML algorithms (n=10, 43.5%), reinforcement learning (n=8, 34.8%), natural language understanding (n=8, 34.8%), and conversational AI (n=5, 21.7%). Evidence was generally positive, but had limitations such as inability to detect causation, low generalizability, or insufficient study duration to understand long-term outcomes. Despite evidence gaps related to the novelty of the technology, research supports the promise of using AI in DBCIs to manage complex input data and offer personalized, contextualized support for people changing real-world behaviors. Key opportunities are standardizing terminology and improving understanding of what ML and AI are.

Health lifestyles of six Zhiguo ethnic groups in China: a latent class analysis

BACKGROUND

Zhiguo ethnic groups, commonly known as "the directly-entering-socialism ethnic groups", represent Chinese ethnic minorities who have undergone a unique social development trajectory by transforming directly from primitive societies to the socialist stage. In recent decades, significant lifestyle transformations have occurred among Zhiguo ethnic groups. Understanding their health lifestyles can play a strategic role in China's pursuit of universal health coverage. This study aims to examine patterns of health-related lifestyle among Zhiguo ethnic groups and explore whether sociodemographic features and specific indicators related to health status are associated with particular classes.

METHODS

A cross-sectional study was conducted in Yunnan Province, China, from July to December 2022. Stratified random sampling method was employed to recruit residents belonging to six Zhiguo ethnic groups aged between 15 and 64. Latent class analysis was performed to identify clusters of health-related behaviors within each ethnic group. Logistic regression was utilized to determine the predictors of health lifestyles.

RESULTS

A total of 1,588 individuals from the Zhiguo ethnic groups participated in this study. Three latent classes representing prevalent health lifestyles among the Zhiguo ethnic groups were identified: "unhealthy lifestyle" (31.80%), "mixed lifestyle" (57.37%), and "healthy lifestyle" (10.83%). In the overall population, individuals belonging to the "healthy lifestyle" group exhibited a higher likelihood of being non-farmers (OR: 2.300, 95% CI: 1.347-3.927), women (OR: 21.459, 95% CI: 13.678-33.667), married individuals (OR: 1.897, 95% CI: 1.146-3.138), and those residing within a walking distance of less than 15 min from the nearest health facility (OR: 2.133, 95% CI: 1.415-3.215). Conversely, individuals in the age cohorts of 30-39 years (OR: 0.277, 95% CI: 0.137-0.558) and 40-49 years (OR: 0.471, 95% CI: 0.232-0.958) showed a decreased likelihood of adopting a healthy lifestyle.

CONCLUSIONS

A considerable proportion of the Zhiguo ethnic groups have not adopted healthy lifestyles. Targeted interventions aimed at improving health outcomes within these communities should prioritize addressing the clustering of unfavorable health behaviors, with particular emphasis on single male farmers aged 30-49, and expanding healthcare coverage for individuals residing more than 15 min away from accessible facilities.

Bidirectional relationships between nicotine vaping and maladaptive eating behaviors among young adults

Background

Past research indicates that young adult cigarette smokers are at risk of engaging in maladaptive eating behaviors (MEBs); however, whether this relationship extends to nicotine vaping is unclear. The current study assessed bidirectional associations between four types of MEBs and nicotine vaping among young adults.

Methods

1,303 young adults (20.5 ± 2.3 years; 63 % female) from a public, urban university were recruited and completed online surveys at six-month intervals from spring 2021 (W1) to spring 2023 (W5). Past 30-day nicotine vaping and four types of MEBs (susceptibility to external cues, emotional eating, routine restraint, and compensatory restraint) were evaluated.

Results

Longitudinal cross-lagged models examined the bidirectional relationships between past 30-day nicotine vaping and each type of MEB across five waves. Nicotine vaping predicted both susceptibility to external cues (β = 0.10, p <.05; Wave 2 to 3) and emotional eating (β = 0.08, p <.05; Wave 1 to 2). A significant cross-lag regression (Wave 4 to 5) showed nicotine vaping predicted to routine restraint (β = 0.08, p <.05), and routine restraint predicted to nicotine vaping (β = 0.12, p <.05).

Conclusions

Results indicated that nicotine vaping predicted MEBs; however, the type of MEB differed across waves, which may have been due to the COVID-19 pandemic context. Nicotine vaping predicted to MEBs reflecting vulnerability to the external environment and emotion regulation during a period of heightened restrictions, whereas later when pandemic restrictions had ceased nicotine vaping predicted only to routine restraint. Integrating research and practice on nicotine vaping and MEBs may inform public health efforts to decrease co-occurring health-risks in young adulthood.

Experiences of using the MINDSET Self-Management mobile health app among Hispanic Patients:Results of a qualitative study

BACKGROUND

Interventions focusing on epilepsy self-management (ESM) are vital for promoting the health of people living with epilepsy. E-technology and mobile health (mHealth) tools are becoming increasingly integrated into practice to promote self-management strategies for chronic diseases, enhance care delivery, and reduce health disparities. Management Information and Decision Support Epilepsy Tool (MINDSET), a bilingual decision support tool (available in English and Spanish), was found to be both feasible and effective in facilitating goal-based ESM in the clinic.

PURPOSE

To assess the experience of using MINDSET as an ESM intervention among Hispanic patients with epilepsy to inform future interventional studies.

METHODS

This study used a Qualitative Descriptive (QD) framework to provide a rich and straightforward description of patients' subjective experiences using MINDSET. Participants were enrolled in the intervention group of a larger parent study (RCT) to assess the efficacy of MINDSET among Hispanic People with Epilepsy (PWE). The purposive, convenient, criterion-based sample for this qualitative analysis comprised of 42 patients who agreed to participate in a semi-structured interview at the end of the larger RCT. This RCT was conducted between August 2017 and January 2019. Spanish and English-speaking Hispanic adult patients (n = 94) with epilepsy in Arizona (n = 53) and Texas (n = 41) were randomly assigned within 6 neurology clinics to treatment (MINDSET plus Usual Care, hereafter referred to as MINDSET; n = 46) and comparison (Usual Care Only; n = 48) conditions.

RESULTS

Patient demographics, epilepsy conditions, and ESM behavioral characteristics were representative of the intervention group. Study participants were Hispanic, mainly of Mexican descent (94 %), with a mean age of 39 years, mostly female (53 %), and most of the participants reported having had one or more seizures per month (54 %). The MINDSET intervention revealed five ESM themes: (1) Awareness and Realization of Epilepsy Self-Management, (2) Communication and Partnership with Health Care Providers HCP, (3) Epilepsy Self-Management and Quality of Life, (4) Seizure Control, and (5) Optimism and Agency.

CONCLUSION

The participants who used MINDSET as a self-management intervention reported an overall positive experience. Qualitative data in this study show that MINDSET is a valuable ESM tool for Hispanic patients with epilepsy. Findings from this qualitative study were consistent with results from a larger parent study that recognized MINDSET as an effective platform for improving epilepsy self-management adherence.

Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study

BACKGROUND

Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored.

OBJECTIVE

The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot.

METHODS

A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition-specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output.

RESULTS

AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities.

CONCLUSIONS

There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.

Social Isolation and Serious Mental Illness: The Role of Context-Aware Mobile Interventions

Social isolation is a common problem faced by individuals with serious mental illness (SMI), and current intervention approaches have limited effectiveness. This paper presents a blended intervention approach, called mobile Social Interaction Therapy by Exposure (mSITE), to address social isolation in individuals with serious mental illness. The approach combines brief in-person cognitive-behavioral therapy (CBT) with context-triggered mobile CBT interventions that are personalized using mobile sensing data. Our approach targets social behavior and is the first context-aware intervention for improving social outcomes in serious mental illness.

The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions

The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.

Exploring views and experiences of the general public's adoption of digital technologies for healthy lifestyle in Singapore: a qualitative study

Objective

Little is known about the general adult population's adoption of digital technology to support healthy lifestyle, especially when they are expected to take greater personal responsibility for managing their health and well-being today. The current qualitative study intended to gain an in-depth understanding of determinants of digital technology adoption for healthy lifestyle among community-dwelling adults in Singapore.

Design

A qualitative study design, with thematic framework analysis was applied to develop themes from the data.

Setting

Semi-structured individual interviews were conducted with participants either face-to-face or online through a videoconferencing platform.

Participants

14 women and 16 men from the general population who were between the ages of 22 and 71 years.

Results

Three major themes were developed: (1) digitally disempowered (2) safety and perceived risks and harm; (3) cultural values and drives. Adoption of technology among the general population is needs-driven, and contingent on individual, technological and other cross-cultural contextual factors.

Conclusion

Our findings highlight there is no one solution which fits all individuals, emphasizing the challenges of catering to diverse groups to reduce barriers to adoption of digital technologies for healthy lifestyle. Digital guidance and training, as well as social influences, can motivate technological adoption in the population. However, technical problems as well as data security and privacy concerns should first be adequately addressed. This study provides rich cross-cultural insights and informs policy-making due to its alignment with government public health initiatives to promote healthy lifestyle.

Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review

BACKGROUND

Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of noncommunicable diseases (NCDs), it is unclear what specific components make a DHI effective.

PURPOSE

This narrative umbrella review aimed to identify the most effective behavior change techniques (BCTs) in DHIs that address the prevention or management of NCDs.

METHODS

Five electronic databases were searched for articles published in English between January 2007 and December 2022. Studies were included if they were systematic reviews or meta-analyses of DHIs targeting the modification of one or more NCD-related risk factors in adults. BCTs were coded using the Behavior Change Technique Taxonomy v1. Study quality was assessed using AMSTAR 2.

RESULTS

Eighty-five articles, spanning 12 health domains and comprising over 865,000 individual participants, were included in the review. We found evidence that DHIs are effective in improving health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest that credible source, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, human coaching and personalization components increase the effectiveness of DHIs targeting the prevention and management of NCDs.

CONCLUSIONS

This review identifies the most common and effective BCTs used in DHIs, which warrant prioritization for integration into future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework

BACKGROUND

Online health communities (OHCs) have emerged as prominent platforms for behavior modification, and the digitization of online peer interactions has afforded researchers with unique opportunities to model multilevel mechanisms that drive behavior change. Existing studies, however, have been limited by a lack of methods that allow the capture of conversational context and socio-behavioral dynamics at scale, as manifested in these digital platforms.

OBJECTIVE

We develop, evaluate, and apply a novel methodological framework, Pragmatics to Reveal Intent in Social Media (PRISM), to facilitate granular characterization of peer interactions by combining multidimensional facets of human communication.

METHODS

We developed and applied PRISM to analyze peer interactions (N = 2.23 million) in QuitNet, an OHC for tobacco cessation. First, we generated a labeled set of peer interactions (n = 2,005) through manual annotation along three dimensions: communication themes (CTs), behavior change techniques (BCTs), and speech acts (SAs). Second, we used deep learning models to apply our qualitative codes at scale. Third, we applied our validated model to perform a retrospective analysis. Finally, using social network analysis (SNA), we portrayed large-scale patterns and relationships among the aforementioned communication dimensions embedded in peer interactions in QuitNet.

RESULTS

Qualitative analysis showed that the themes of social support and behavioral progress were common. The most used BCTs were feedback and monitoring and comparison of behavior, and users most commonly expressed their intentions using SAs-expressive and emotion. With additional in-domain pre-training, bidirectional encoder representations from Transformers (BERT) outperformed other deep learning models on the classification tasks. Content-specific SNA revealed that users' engagement or abstinence status is associated with the prevalence of various categories of BCTs and SAs, which also was evident from the visualization of network structures.

CONCLUSIONS

Our study describes the interplay of multilevel characteristics of online communication and their association with individual health behaviors.

The Influence of a Wearable-Based Reward Program on Health Care Costs: Retrospective, Propensity Score-Matched Cohort Study

BACKGROUND

Mobile health (mHealth) technology holds great promise as an easily accessible and effective solution to improve population health at scale. Despite the abundance of mHealth offerings, only a minority are grounded in evidence-based practice, whereas even fewer have line of sight into population-level health care spending, limiting the clinical utility of such tools.

OBJECTIVE

This study aimed to explore the influence of a health plan-sponsored, wearable-based, and reward-driven digital health intervention (DHI) on health care spending over 1 year. The DHI was delivered through a smartphone-based mHealth app available only to members of a large commercial health plan and leveraged a combination of behavioral economics, user-generated sensor data from the connected wearable device, and claims history to create personalized, evidence-based recommendations for each user.

METHODS

This study deployed a propensity score-matched, 2-group, and pre-post observational design. Adults (≥18 years of age) enrolled in a large, national commercial health plan and self-enlisted in the DHI for ≥7 months were allocated to the intervention group (n=56,816). Members who were eligible for the DHI but did not enlist were propensity score-matched to the comparison group (n=56,816). Average (and relative change from baseline) medical and pharmacy spending per user per month was computed for each member of the intervention and comparison groups during the pre- (ie, 12 months) and postenlistment (ie, 7-12 months) periods using claims data.

RESULTS

Baseline characteristics and medical spending were similar between groups (P=.89). On average, the total included sample population (N=113,632) consisted of young to middle-age (mean age 38.81 years), mostly White (n=55,562, 48.90%), male (n=46,731, 41.12%) and female (n=66,482, 58.51%) participants. Compared to a propensity score-matched cohort, DHI users demonstrated approximately US $10 per user per month lower average medical spending (P=.02) with a concomitant increase in preventive care activities and decrease in nonemergent emergency department admissions. These savings translated to approximately US $6.8 million in avoidable health care costs over the course of 1 year.

CONCLUSIONS

This employer-sponsored, digital health engagement program has a high likelihood for return on investment within 1 year owing to clinically meaningful changes in health-seeking behaviors and downstream medical cost savings. Future research should aim to elucidate health behavior-related mechanisms in support of these findings and continue to explore novel strategies to ensure equitable access of DHIs to underserved populations that stand to benefit the most.

Cost-effectiveness of Digital Tools for Behavior Change Interventions Among People With Chronic Diseases: Systematic Review

BACKGROUND

Chronic diseases, including cardiovascular diseases, diabetes, chronic obstructive pulmonary disease, and cerebrovascular diseases, contribute to the most significant disease burden worldwide, negatively impacting patients and their family members. People with chronic diseases have common modifiable behavioral risk factors, including smoking, alcohol overconsumption, and unhealthy diets. Digital-based interventions for promoting and sustaining behavioral changes have flourished in recent years, although evidence of the cost-effectiveness of such interventions remains inconclusive.

OBJECTIVE

In this study, we aimed to investigate the cost-effectiveness of digital health interventions for behavioral changes among people with chronic diseases.

METHODS

This systematic review evaluated published studies focused on the economic evaluation of digital tools for behavioral change among adults with chronic diseases. We followed the Population, Intervention, Comparator, and Outcomes framework to retrieve relevant publications from 4 databases: PubMed, CINAHL, Scopus, and Web of Science. We used the Joanna Briggs Institute's criteria for economic evaluation and randomized controlled trials to assess the risk of bias in the studies. Two researchers independently screened, assessed the quality, and extracted data from the studies selected for the review.

RESULTS

In total, 20 studies published between 2003 and 2021 fulfilled our inclusion criteria. All the studies were conducted in high-income countries. These studies used telephones, SMS text messaging, mobile health apps, and websites as digital tools for behavior change communication. Most digital tools for interventions focused on diet and nutrition (17/20, 85%) and physical activity (16/20, 80%), and a few focused on smoking and tobacco control (8/20, 40%), alcohol reduction (6/20, 30%), and reduction of salt intake (3/20, 15%). Most studies (17/20, 85%) used the health care payer perspective for economic analysis, and only 15% (3/20) used the societal perspective. Only 45% (9/20) of studies conducted a full economic evaluation. Most studies (7/20, 35%) based on full economic evaluation and 30% (6/20) of studies based on partial economic evaluation found digital health interventions to be cost-effective and cost-saving. Most studies had short follow-ups and failed to include proper indicators for economic evaluation, such as quality-adjusted life-years, disability-adjusted life-years, lack of discounting, and sensitivity analysis.

CONCLUSIONS

Digital health interventions for behavioral change among people with chronic diseases are cost-effective in high-income settings and can therefore be scaled up. Similar evidence from low- and middle-income countries based on properly designed studies for cost-effectiveness evaluation is urgently required. A full economic evaluation is needed to provide robust evidence for the cost-effectiveness of digital health interventions and their potential for scaling up in a wider population. Future studies should follow the National Institute for Health and Clinical Excellence recommendations to take a societal perspective, apply discounting, address parameter uncertainty, and apply a lifelong time horizon.

Athlete experiences of communication strategies in applied sports nutrition and future considerations for mobile app supportive solutions

Aim

This study aimed to explore athletes' experiences and opinions of communication strategies in applied sports nutrition, as well as capture suggestions for future mobile app supportive solutions.

Methods

A qualitative approach was used for this research. Data was generated from semi-structured focus groups (n = 9) with a purposive sample of 41 (male = 24, female = 17) full time professional athletes (mean age 24 ± 4.59) from five sports (football, rugby union, athletics, cycling, and boxing). Data was analyzed using reflexive thematic analysis.

Results

The analysis identified four higher order themes and five sub themes. Athletes appear dissatisfied with the levels of personalization in the nutrition support they receive. Limited practitioner contact time was suggested as a contributing factor to this problem. Athletes acknowledged the usefulness of online remote nutrition support and reported a desire for more personalized technology that can tailor support to their individual needs.

Conclusion

Athletes experienced a hybrid human-computer approach that combines in-person and remote digital methods to communicate with and receive information from practitioners. Mobile technology may now afford sports nutritionists with new opportunities to develop scalable solutions to support practice.

mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review

Background

The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management.

Objective

This review aimed to identify behavior change techniques (BCTs) commonly used in mHealth, assess their effectiveness based on the evidence reported in interventions and reviews to highlight the most appropriate techniques to design an optimal strategy to improve adherence to data reporting, and provide recommendations for future interventions and research.

Methods

We performed a systematic review of studies published between 2010 and 2021 in relevant scientific databases to identify and analyze mHealth interventions using BCTs that evaluated their effectiveness in terms of user adherence. Search terms included a mix of general (eg, data, information, and adherence), computer science (eg, mHealth and BCTs), and medicine (eg, personalized medicine) terms.

Results

This systematic review included 24 studies and revealed that the most frequently used BCTs in the studies were feedback and monitoring (n=20), goals and planning (n=14), associations (n=14), shaping knowledge (n=12), and personalization (n=7). However, we found mixed effectiveness of the techniques in mHealth outcomes, having more effective than ineffective outcomes in the evaluation of apps implementing techniques from the feedback and monitoring, goals and planning, associations, and personalization categories, but we could not infer causality with the results and suggest that there is still a need to improve the use of these and many common BCTs for better outcomes.

Conclusions

Personalization, associations, and goals and planning techniques were the most used BCTs in effective trials regarding adherence to mHealth apps. However, they are not necessarily the most effective since there are studies that use these techniques and do not report significant results in the proposed objectives; there is a notable overlap of BCTs within implemented app components, suggesting a need to better understand best practices for applying (a combination of) such techniques and to obtain details on the specific BCTs used in mHealth interventions. Future research should focus on studies with longer follow-up periods to determine the effectiveness of mHealth interventions on behavior change to overcome the limited evidence in the current literature, which has mostly small-sized and single-arm experiments with a short follow-up period.

Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study

BACKGROUND

App-based mobile health exercise interventions can motivate individuals to engage in more physical activity (PA). According to the Fogg Behavior Model, it is important that the individual receive prompts at the right time to be successfully persuaded into PA. These are referred to as just-in-time (JIT) interventions. The Playful Active Urban Living (PAUL) app is among the first to include 2 types of JIT prompts: JIT adaptive reminder messages to initiate a run or walk and JIT strength exercise prompts during a walk or run (containing location-based instruction videos). This paper reports on the feasibility of the PAUL app and its JIT prompts.

OBJECTIVE

The main objective of this study was to examine user experience, app engagement, and users' perceptions and opinions regarding the PAUL app and its JIT prompts and to explore changes in the PA behavior, intrinsic motivation, and the perceived capability of the PA behavior of the participants.

METHODS

In total, 2 versions of the closed-beta version of the PAUL app were evaluated: a basic version (Basic PAUL) and a JIT adaptive version (Smart PAUL). Both apps send JIT exercise prompts, but the versions differ in that the Smart PAUL app sends JIT adaptive reminder messages to initiate running or walking behavior, whereas the Basic PAUL app sends reminder messages at randomized times. A total of 23 participants were randomized into 1 of the 2 intervention arms. PA behavior (accelerometer-measured), intrinsic motivation, and the perceived capability of PA behavior were measured before and after the intervention. After the intervention, participants were also asked to complete a questionnaire on user experience, and they were invited for an exit interview to assess user perceptions and opinions of the app in depth.

RESULTS

No differences in PA behavior were observed (Z=-1.433; P=.08), but intrinsic motivation for running and walking and for performing strength exercises significantly increased (Z=-3.342; P<.001 and Z=-1.821; P=.04, respectively). Furthermore, participants increased their perceived capability to perform strength exercises (Z=2.231; P=.01) but not to walk or run (Z=-1.221; P=.12). The interviews indicated that the participants were enthusiastic about the strength exercise prompts. These were perceived as personal, fun, and relevant to their health. The reminders were perceived as important initiators for PA, but participants from both app groups explained that the reminder messages were often not sent at times they could exercise. Although the participants were enthusiastic about the functionalities of the app, technical issues resulted in a low user experience.

CONCLUSIONS

The preliminary findings suggest that the PAUL apps are promising and innovative interventions for promoting PA. Users perceived the strength exercise prompts as a valuable addition to exercise apps. However, to be a feasible intervention, the app must be more stable.

User Perceptions of eHealth and mHealth Services Promoting Physical Activity and Healthy Diets: Systematic Review

Background

Physical activity and a diet that follows general recommendations can help to prevent noncommunicable diseases. However, most adults do not meet current recommended guidelines, and support for behavior change needs to be strengthened. There is growing evidence that shows the benefits of eHealth and mobile health (mHealth) services in promoting healthy habits; however, their long-term effectiveness is uncertain because of nonadherence.

Objective

We aimed to explore users’ perceptions of acceptability, engagement, and usability of eHealth and mHealth services that promote physical activity, healthy diets, or both in the primary or secondary prevention of noncommunicable diseases.

Methods

We conducted a systematic review with a narrative synthesis. We performed the literature search in PubMed, PsycINFO, and CINAHL electronic databases in February 2021 and July 2021. The search was limited to papers published in English between 2016 and 2021. Papers on qualitative and mixed method studies that encompassed eHealth and mHealth services for adults with a focus on physical activity, healthy diet, or both in the primary or secondary prevention of noncommunicable diseases were included. Three authors screened the studies independently, and 2 of the authors separately performed thematic analysis of qualitative data.

Results

With an initial finding of 6308 articles and the removal of 427 duplicates, 23 articles were deemed eligible for inclusion in the review. Based on users’ preferences, an overarching theme—eHealth and mHealth services provide value but need to be tailored to individual needs—and 5 subthemes—interactive and integrated; varying and multifunctional; easy, pedagogic, and attractive; individualized and customizable; and reliable—emerged.

Conclusions

New evidence on the optimization of digital services that promote physical activity and healthy diets has been synthesized. The findings represent users’ perceptions of acceptability, engagement, and usability of eHealth and mHealth services and show that services should be personalized, dynamic, easily manageable, and reliable. These findings can help improve adherence to digital health-promoting services.

Combining Persuasive System Design Principles and Behavior Change Techniques in Digital Interventions Supporting Long-term Weight Loss Maintenance: Design and Development of eCHANGE

BACKGROUND

Long-term weight maintenance after weight loss is challenging, and innovative solutions are required. Digital technologies can support behavior change and, therefore, have the potential to be an effective tool for weight loss maintenance. However, to create meaningful and effective digital behavior change interventions that support end user values and needs, a combination of persuasive system design (PSD) principles and behavior change techniques (BCTs) might be needed.

OBJECTIVE

This study aimed to investigate how an evidence-informed digital behavior change intervention can be designed and developed by combining PSD principles and BCTs into design features to support end user values and needs for long-term weight loss maintenance.

METHODS

This study presents a concept for how PSD principles and BCTs can be translated into design features by combining design thinking and Agile methods to develop and deliver an evidence-informed digital behavior change intervention aimed at supporting weight maintenance. Overall, 45 stakeholders participated in the systematic and iterative development process comprising co-design workshops, prototyping, Agile development, and usability testing. This included prospective end users (n=17, 38%; ie, people with obesity who had lost ≥8% of their weight), health care providers (n=9, 20%), healthy volunteers (n=4, 9%), a service designer (n=1, 2%), and stakeholders from the multidisciplinary research and development team (n=14, 31%; ie, software developers; digital designers; and eHealth, behavior change, and obesity experts). Stakeholder input on how to operationalize the design features and optimize the technology was examined through formative evaluation and qualitative analyses using rapid and in-depth analysis approaches.

RESULTS

A total of 17 design features combining PSD principles and BCTs were identified as important to support end user values and needs based on stakeholder input during the design and development of eCHANGE, a digital intervention to support long-term weight loss maintenance. The design features were combined into 4 main intervention components: Week Plan, My Overview, Knowledge and Skills, and Virtual Coach and Smart Feedback System. To support a healthy lifestyle and continued behavior change to maintain weight, PSD principles such as tailoring, personalization, self-monitoring, reminders, rewards, rehearsal, praise, and suggestions were combined and implemented into the design features together with BCTs from the clusters of goals and planning, feedback and monitoring, social support, repetition and substitution, shaping knowledge, natural consequences, associations, antecedents, identity, and self-belief.

CONCLUSIONS

Combining and implementing PSD principles and BCTs in digital interventions aimed at supporting sustainable behavior change may contribute to the design of engaging and motivating interventions in line with end user values and needs. As such, the design and development of the eCHANGE intervention can provide valuable input for future design and tailoring of evidence-informed digital interventions, even beyond digital interventions in support of health behavior change and long-term weight loss maintenance.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04537988; https://clinicaltrials.gov/ct2/show/NCT04537988.

Personalized Digital Health Communications to Increase COVID-19 Vaccination in Underserved Populations: A Double Diamond Approach to Behavioral Design

The COVID-19 pandemic exacerbated pre-existing health disparities. People of historically underserved communities, including racial and ethnic minority groups and people with lower incomes and educational attainments, experienced disproportionate premature mortality, access to healthcare, and vaccination acceptance and adoption. At the same time, the pandemic increased reliance on digital devices, offering a unique opportunity to leverage digital communication channels to address health inequities, particularly related to COVID-19 vaccination. We offer a real-world, systematic approach to designing personalized behavior change email and text messaging interventions that address individual barriers with evidence-based behavioral science inclusive of underserved populations. Integrating design processes such as the Double Diamond model with evidence-based behavioral science intervention development offers a unique opportunity to create equitable interventions. Further, leveraging behavior change artificial intelligence (AI) capabilities allows for both personalizing and automating that personalization to address barriers to COVID-19 vaccination at scale. The result is an intervention whose broad component library meets the needs of a diverse population and whose technology can deliver the right components for each individual.

A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior

Background

It has recently been identified that manipulating carbohydrate availability around exercise activity can enhance training-induced metabolic adaptations. Despite this approach being accepted in the athletic populations, athletes do not systematically follow the guidelines. Digital environments appear to allow nutritionists to deliver this intervention at scale, reducing expensive human coaching time. Yet, digitally delivered dietary behavior change interventions for athletes and the coaching strategy to support them are still novel concepts within sports nutrition.

Methods/design

We aim to recruit 900 athletes across the UK. 500 athletes will be recruited to test the feasibility of a novel menu planner mobile application with coaching for 6 weeks. 250 athletes with pre-existing nutritionist support will also be recruited as control. We will then conduct a 4-week pilot sequential multiple assignment randomized trial (SMART) with an additional 150 athletes. In the SMART, athletes will be given the application and additional coaching according to their engagement responses. The primary outcomes are the mobile application and coach uptake, retention, engagement, and success in attaining carbohydrate periodization behavior. Secondary outcomes are changes in goal, weight, carbohydrate periodization self-efficacy, and beliefs about consequences. Due to the high attrition nature of digital interventions, all quantitative analyses will be carried out based on both the intention-to-treat and per-protocol principles.

Discussion

This study will be the first to investigate improving carbohydrate periodization using a digital approach and tailored coaching strategies under this context. Foundational evidence from this study will provide insights into the feasibility of the digital approach.

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review

BACKGROUND

Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated.

OBJECTIVE

This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings.

METHODS

A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted.

RESULTS

The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low.

CONCLUSIONS

Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Elevated Blood Pressure in Women of Childbearing Age in the United States

PURPOSE

Elevated blood pressure is frequently associated with adverse health issues among women during and after childbirth in the United States. The purpose of this study was to describe incidence of and determine predictors of prehypertension and hypertension among women of childbearing age in the United States.

STUDY DESIGN AND METHODS

Using secondary data analysis, existing data from the National Health and Nutrition Examination Survey (NHANES; 2013-2018) were used to address study aims. Inclusion criteria were women in the age range commonly considered to be of childbearing age, 15 to 44 years of age. Simple random sampling was to select subjects from the 2,932 women in the NHANES dataset who met inclusion criteria. We calculated a sample size as adequate for the statistical group comparison to be significant with a power of 95% to detect a difference among groups. An ordinal logistic regression model was created to discriminate predictors of normotensive blood pressure, prehypertension, and hypertension.

RESULTS

The sample (n = 393) included 300 women with normal blood pressure, 46 women with prehypertension, and 47 with hypertension. Older women (within childbearing age range), women with high body mass index, and African American women are highly likely to have prehypertension and hypertension.

CLINICAL IMPLICATIONS

Contrary to previous research, poverty income ratio was not associated with prehypertension and hypertension. Future research should test interventions that include promoting heathy lifestyles and address elevated body mass index. Interventions should be tailored to be culturally appropriate for African American women and older women within this age range.

Content, Behavior Change Techniques, and Quality of Pregnancy Apps in Spain: Systematic Search on App Stores

Background

Women consult information in mobile apps (apps) during pregnancy, and even obstetrics specialists highlight that pregnancy is the ideal moment for the use of apps as consultation sources. However, the high number of apps designed for pregnancy requires a careful assessment to determine their suitability before recommendation.

Objective

The aim of this study is to identify the apps available in Spanish that can be recommended based on their content, behavior change techniques (BCTs), and quality as a complementary tool during pregnancy.

Methods

A systematic search on app stores to identify apps was performed in the Apple App Store and Google Play with the subject term “pregnancy.” The apps meeting the following criteria were chosen: pregnancy-related content, free, and available in Spanish. An app was excluded if it was classified as a game or entertainment and thus lacking an educational or health aim and if it did not target the population under study. The selected apps were downloaded, and their quality was assessed using the Mobile Application Rating Scale (MARS), with the BCTs included evaluated using the BCT taxonomy version 1 and its content.

Results

A total of 457 apps were identified, 25 of which were downloaded for assessment (5.6%). The median for objective and subjective quality was 2.94 (IQR 2.71-3.46) and 1.75 (IQR 1.25-2.25), respectively. Regarding content, the median of topics included in the apps was 23 (IQR 16-23), with weight gain, nutrition, fetal development, and physical activity being the most common. The median number of BCTs was 12 (IQR 0.5-3.5). The most frequently identified BCTs in the apps were “Self-Monitoring of Outcomes,” followed by “Goal Behavior” and “Instructions.” Statistically significant correlations were observed between objective quality and content (ρ=0.624; P=.001), subjective quality and content (ρ=0.638; P=.001), objective quality and BCTs (ρ=0.672; P<.001), subjective quality and BCTs (ρ=0.623; P<.001), and BCTs and content (ρ=0.580; P=.002).

Conclusions

The results of this study suggest that only a small percentage of free pregnancy apps available in Spanish should be recommended. The apps with the best MARS scores were those that addressed a higher number of topics and included a higher number of BCTs. Those with the best content and quality, and a higher number of BCTs included could be recommended by health professionals.

Quality Analysis of YouTube Videos Presenting Pelvic Floor Exercises after Prostatectomy Surgery

Background: Prostate cancer (PC) is a major cause of disease and mortality among men. Surgical treatment involving the removal of the prostate may result in temporary or permanent erectile dysfunction (ED) and urinary incontinence (UI), with considerable impact on quality of life. Pelvic floor muscle training (PFMT) is one of the recommended techniques for the prevention, treatment, and rehabilitation of postoperative complications. The aim of this observational study was to assess the quality of YouTube videos—accessible to any patient—related to exercises after prostatectomy surgery. Methods: A systematic search was performed on YouTube on 24 September 2020. One hundred and fifty videos were selected and analyzed. Two statistical analyses were conducted based on machine-learning techniques, and videos were classified as ‘Relevant’ or ‘Non-Relevant’ using Principal Component Analysis (PCA) models. Two reviewers conducted independent analyses. Inter-observer agreement and individual correlations of video data were evaluated with the Intraclass Correlation Coefficient (ICC). Information quality, reliability, and accuracy were measured using the DISCERN Scale and Global Quality Score (GQS), while video popularity was evaluated using the Video Power Index (VPI). Results: DISCERN scored a mean of 3.35 and GQS scored 3.38. Average number of views was 124,354, mean duration was 14:42 min, mean days online was 1777, mean view ratio was 138.30, mean Likes was 1082, mean Dislikes was 68.58, and mean VPI was 92.28. Conclusions: The quality of the videos available on YouTube regarding the recommended pelvic floor exercises in PC surgery, according to the scores obtained, is High. Educational and health institutions, health professionals, government health authorities, and policy makers need to be involved in the proper development of policies to improve the information available on the web in order to have a positive impact on the healthy behavior of the population.

Experiences of Using a Self-management Mobile App Among Individuals With Heart Failure: Qualitative Study

BACKGROUND

Interventions that focus on the self-management of heart failure are vital to promoting health in patients with heart failure. Mobile health (mHealth) apps are becoming more integrated into practice to promote self-management strategies for chronic diseases, optimize care delivery, and reduce health disparities.

OBJECTIVE

The purpose of this study was to explore the experience of using a self-management mHealth intervention in individuals with heart failure to inform a future mHealth intervention study.

METHODS

This study used a qualitative descriptive design. Participants were enrolled in the intervention groups of a larger parent study using a mobile app related to self-management of heart failure. The purposive, convenient, criterion-based sample for this qualitative analysis comprised 10 patients who responded to phone calls and were willing to be interviewed. Inclusion criteria for the parent study were adults who were hospitalized at Nebraska Medical Center with a primary diagnosis and an episode of acute decompensated heart failure; discharged to home without services such as home health care; had access to a mobile phone; and were able to speak, hear, and understand English.

RESULTS

Study participants were middle-aged (mean age 55.8, SD 12 years; range 36-73 years). They had completed a mean of 13.5 (SD 2.2) years (range 11-17 years) of education. Of the 10 participants, 6 (60%) were male. Half of them (5/10, 50%) were New York Heart Association Classification Class III patients and the other half were Class IV patients. The intervention revealed four self-management themes, including (1) I didn't realize, and now I know; (2) It feels good to focus on my health; (3) I am the leader of my health care team; and (4) My health is improving.

CONCLUSIONS

Participants who used a self-management mHealth app intervention for heart failure reported an overall positive experience. Their statements were organized into four major themes. The education provided during the study increased self-awareness and promoted self-management of their heart failure. The mHealth app supported patient empowerment, resulting in better heart failure management and improved quality of life. Participants advocated for themselves by becoming the leader of their health, especially when communicating with their health care team. Finally, the mHealth app was used by the participants as a self-management tool to assist in symptom management and improve their overall health. Future research should study symptom evaluation, medication tracking, and possibly serve as a health provider communication platform to empower individuals to be leaders in their chronic disease management.

Digital Therapeutics in Parkinson's Disease: Practical Applications and Future Potential

Digital therapeutics, treatments delivered remotely and enabled by modern technology, facilitate the provision of personalized, evidence-based, interdisciplinary interventions to manage the complexities associated with Parkinson’s disease. In the context of the COVID-19 pandemic, the need for digital therapeutics has arguably never been greater. However, despite new advances in technology and a heightened interest due to the pandemic, digital therapeutics remain underdeveloped and underutilized. In this paper, we briefly review practical applications and emerging advances in digital therapeutic platforms that target motor and non-motor signs and healthy lifestyle behaviors such as regular exercise, a healthful diet and optimal sleep hygiene habits. Future applications which could transform personalized self-management and patient care are presented. Opportunities, drawbacks and barriers to access are discussed.

Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the 'right' time to deliver a restricted number of notifications adaptively, with respect to users' temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app's other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

To What Extent is Internet Activity Predictive of Psychological Well-Being?

BACKGROUND

Healthy internet activity (eg, making use of eHealth and online therapy) is positively associated with well-being. However, unhealthy internet activity (too much online time, problematic internet use/PIU, internet dependency/ID, etc.) is associated with reduced well-being, loneliness, and other related negative aspects. While most of the evidence is correlational, some research also shows that internet activity can be predictive for well-being.

OBJECTIVE

The aim of this article is to elaborate on the question as to what extent internet activity is predictive of psychological well-being by means of (a) a scoping review and (b) theoretical understanding which model the interrelation of internet activity and psychological well-being.

METHODOLOGY

We searched different electronic databases such as Web of Science by using the search terms "Internet" OR "App" OR "digital" OR "online" OR "mobile application" AND "Use" OR "Activity" OR "Behavior" OR "Engagement" AND "Well-being" OR "Loneliness" for (a, the scoping review) or CCAM for (b, the theoretical understanding).

RESULTS

The scoping review (a) summarizes recent findings: the extent to which internet activity is predictive for well-being depends on the internet activity itself: internet activity facilitating self-management is beneficial for well-being but too much internet activity, PIU and ID are detrimental to well-being. To understand (b) why, when and how internet activity is predictive for well-being, theoretical understanding and a model are required. While theories on either well-being or internet activity exist, not many theories take both aspects into account while also considering other behaviors. One such theory is the Compensatory Carry-Over Action Model (CCAM) which describes mechanisms on how internet use is related to other lifestyle behaviors and well-being, and that individuals are driven by the goal to adopt and maintain well-being - also called higher-level goals - in the CCAM. There are few studies testing the CCAM or selected aspects of it which include internet activity and well-being. Results demonstrate the potentials of such a multifactorial, sophisticated approach: it can help to improve health promotion in times of demographic change and in situations of lacking personnel resources in health care systems.

CONCLUSION AND RECOMMENDATION

Suggestions for future research are to employ theoretical approaches like the CCAM and testing intervention effects, as well as supporting individuals in different settings. The main aim should be to perform healthy internet activities to support well-being, and to prevent unhealthy internet activity. Behavior management and learning should accordingly aim at preventing problematic internet use and internet dependency.