Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review

Fine-tuning Large Language Models in Behavioral Psychology for Scalable Physical Activity Coaching

Personalized, smartphone-based coaching improves physical activity but relies on static, human-crafted messages. We introduce My Heart Counts (MHC)-Coach, a large language model fine-tuned on the Transtheoretical Model of Change. MHC-Coach generates messages tailored to an individual's psychology (their "stage of change"), providing personalized support to foster long-term physical activity behavior change. To evaluate MHC-Coach's efficacy, 632 participants compared human-expert and MHC-Coach text-based interventions encouraging physical activity. Among messages matched to an individual's stage of change, 68.0% (N=430) preferred MHC-Coach-generated messages (P < 0.001). Blinded behavioral science experts (N=2) rated MHC-Coach messages higher than human-expert messages for perceived effectiveness (4.4 vs. 2.8) and Transtheoretical Model alignment (4.1 vs. 3.5) on a 5-point Likert scale. This work demonstrates how language models can operationalize behavioral science frameworks for personalized health coaching, promoting long-term physical activity and potentially reducing cardiovascular disease risk at scale.

Patient-Centered Physical Activity Intervention in Lung Cancer Patients: A Clinical Severity and Functional Capacity Systematic Review and Meta-analysis

BACKGROUND

Increasing physical activity levels is a significant unmet need in cancer survivors, and it can likely be enhanced through a better understanding of the interventions developed. Some studies on patient-centered physical activity interventions have shown promising results in increasing daily activity levels among lung cancer survivors. However, the programs present a high heterogeneity, and there is no consensus on the parameters and their effectiveness.

OBJECTIVE

To examine the effectiveness of patient-centered physical activity interventions on clinical severity and functional capacity in lung cancer patients.

METHODS

A systematic review was performed on randomized controlled trials. A literature search was conducted using MEDLINE, Web of Science, Science Direct, and Cochrane Library (last search November 2023). The Cochrane tool and the Grading of Recommendations Assessment, Development, and Evaluation system were used for quality assessment. Pooled data were meta-analyzed for physical activity levels, functional capacity, and cancer-related symptoms.

RESULTS

Fourteen studies, encompassing 1123 lung cancer patients, were included. The treatment status of patients varied. The components of the physical activity programs showed heterogeneity. Results revealed significant differences favoring patient-centered physical activity interventions over the control group for physical activity levels (P < .05), functional capacity (P < .001), and cancer-related symptoms (P < .05).

CONCLUSION

The results indicate that patient-centered physical activity programs positively enhance physical activity levels, improve functional capacity, and reduce cancer-related symptoms in patients with lung cancer.

IMPLICATIONS FOR PRACTICE

Patient-centered physical activity interventions show promise in improving the care and management of lung cancer patients. These interventions provide a basis for encouraging lung cancer patients to actively participate in their treatment.

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.

Identifying Behavior Change Techniques in an Artificial Intelligence-Based Fitness App: A Content Analysis

In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and their impact on user engagement and behavior change are not explored sufficiently. This study investigates the Freeletics fitness app through a mixed-methods approach to evaluate the use of BCTs. In the quantitative analysis, fifteen unique BCTs were identified based on the Behavior Change Technique Taxonomy (V1). In the qualitative analysis, user reviews (n=400) were examined to understand perspectives on the app's effectiveness in promoting behavior change. Goal setting, action planning, self-monitoring of behavior, and social support were among the most prevalent BCTs identified in the Freeletics app, and their effectiveness in enhancing user engagement and promoting behavior change was also highlighted by user reviews. Among the areas of improvement identified in the study were the need for simplifying personalization options and addressing user concerns regarding the specificity of feedback. The study underscores the importance of integrating BCTs effectively within AI-based fitness apps to drive user engagement and facilitate behavior change. It contributes valuable insights into the design and implementation of BCTs in fitness apps and offers recommendations for developers, emphasizing the significance of goal setting, feedback mechanisms, self-monitoring, and social support. By understanding the impact of specific BCTs on user behavior and addressing user concerns, developers can create more effective fitness apps, ultimately promoting healthier lifestyles and positive behavior change.

Informing the development of a mobile application for the physical activity guidelines in multiple sclerosis: a qualitative, pluralistic approach

PURPOSE

The uptake of Physical Activity Guidelines (PAGs) for adults with multiple sclerosis (MS) may be facilitated through mHealth solutions such as a mobile app. To date, there is limited information regarding preferred features of an app for people with MS. We explored desired features for an app that supports physical activity behaviour among persons with MS.

MATERIALS AND METHODS

Using a pluralistic analytical approach, we conducted a secondary qualitative analysis on a portion of data collected from an earlier study to explore (i) what persons with MS wanted in an app based on the PAGs and (ii) how the PAG-based app should facilitate behaviour change. The data were subjected to deductive, content analysis to identify populous mentions of desired PAG-based app elements. We then used inductive, semantic reflexive thematic analysis to further explore the opinions and evaluations of participants.

RESULTS

Participants (n = 16) perceived features such as activity tracking, incentives for completing milestones, and customization as both triggers for doing PA and supporting engagement with the app. Participants desired a personalized PA prescription based on mobility and fitness level, expert feedback based on data entered in the app, and an exercise library with a range of evidence-based content. Participants insisted the app be backed by a solid scientific foundation and that accessibility of personal data be controlled by the user.

CONCLUSIONS

This study identifies several design considerations for an app based on the PAGs. The results suggest a simple, trustworthy, and evidence-based app that focuses on helping persons with MS reach the PAGs.

Attraction and achievement as 2 attributes of gamification in healthcare: an evolutionary concept analysis

This study conducted a conceptual analysis of gamification in healthcare utilizing Rogers’ evolutionary concept analysis methodology to identify its attributes and provide a method for its applications in the healthcare field. Gamification has recently been used as a health intervention and education method, but the concept is used inconsistently and confusingly. A literature review was conducted to derive definitions, surrogate terms, antecedents, influencing factors, attributes (characteristics with dimensions and features), related concepts, consequences, implications, and hypotheses from various academic fields. A total of 56 journal articles in English and Korean, published between August 2 and August 7, 2023, were extracted from databases such as PubMed Central, the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery Digital Library, the Research Information Sharing Service, and the Korean Studies Information Service System, using the keywords “gamification” and “healthcare.” These articles were then analyzed. Gamification in healthcare is defined as the application of game elements in health-related contexts to improve health outcomes. The attributes of this concept were categorized into 2 main areas: attraction and achievement. These categories encompass various strategies for synchronization, enjoyable engagement, visual rewards, and goal-reinforcing frames. Through a multidisciplinary analysis of the concept’s attributes and influencing factors, this paper provides practical strategies for implementing gamification in health interventions. When developing a gamification strategy, healthcare providers can reference this analysis to ensure the game elements are used both appropriately and effectively.

Personalization Characteristics and Evaluation of Gamified Exercise for Middle-Aged and Older People: A Scoping Review

Many studies have shown that personalized exergames have a positive effect on promoting regular and proper exercise. However, there is no consensus on the design characteristics and evaluation of exergames. This systematic review of published research literature aimed to explore the general characteristics, personalization characteristics, and evaluation of personalized exergames for middle-aged and older people. We screened published studies in the Web of Science, Scopus, PubMed, ACM, and IEEE Xplore databases, extracted data, and performed a thematic analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. Three main themes and nine subthemes were generated from 24 included papers. Moreover, a personalization model (FACTS) and evaluation system (PMSS) of exergames were developed. Personalized exergames had potential positive effects on motivating middle-aged and older people to exercise and improve their health, particularly physical, mental, and social health. However, more fine-grained studies on personalized exergames are necessary in the future.

Exploring user engagement with real-time verbal feedback from an exoskeleton-based virtual exercise coach

Objective

Engaging users during physical exercise is crucial for fostering long-term commitment, however, sustaining that engagement remains a significant challenge. This study explores the design of a voice-enabled exoskeleton-based virtual exercise coach (VEC) that provides real-time verbal feedback to enhance user engagement. The objectives of this study are twofold: (i) to compare user engagement with real-time verbal feedback from both VEC and human exercise coach (HEC) during physical exercise, and (ii) to understand users' perceptions and gather their recommendations for improving future VEC technologies.

Methods

We developed an exoskeleton-based VEC that delivers real-time verbal feedback on users' exercise performance. To evaluate its impact on user engagement, we conducted a lab-based mixed-methods study ( N = 32 ) over a period of 6 weeks comparing users' engagement with the VEC and HEC using User Engagement Scale (UES) questionnaire and conducted semi-structured interviews to understand users' perceptions of the VEC.

Results

Participants in this study found the VEC more engaging than the HEC, in terms of focused attention ( Z = 156.5 , p < .001 ) and perceived usability ( Z = 32 , p < .001 ). Post-interaction interviews revealed that (i) users found the VEC to be engaging, intuitive, easy to use, and convenient; (ii) users perceived the VEC as a valuable training companion that could help reduce the emotional insecurities often associated with going to the gym; and (iii) users expressed a desire for the VEC to have a personality and embodiment that motivates and supports personalized interactions.

Conclusion

Based on our results, we discuss the challenges and implications for designing future voice-enabled VECs that support engaging physical exercises.

Designing personalised mHealth solutions: An overview

INTRODUCTION

Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation.

MATERIALS AND METHODS

We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques.

RESULTS

Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed.

DISCUSSION

Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it.

CONCLUSIONS

Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques.

A panoramic view of personalization based on individual differences in persuasive and behavior change interventions

Persuasive technologies are designed to change human behavior or attitude using various persuasive strategies. Recent years have witnessed increasing evidence of the need to personalize and adapt persuasive interventions to various users and contextual factors because a persuasive strategy that works for one individual may rather demotivate others. As a result, several research studies have been conducted to investigate how to effectively personalize persuasive technologies. As research in this direction is gaining increasing attention, it becomes essential to conduct a systematic review to provide an overview of the current trends, challenges, approaches used for developing personalized persuasive technologies, and opportunities for future research in the area. To fill this need, we investigate approaches to personalize persuasive interventions by understanding user-related factors considered when personalizing persuasive technologies. Particularly, we conducted a systematic review of 72 research published in the last ten years in personalized and adaptive persuasive systems. The reviewed papers were evaluated based on different aspects, including metadata (e.g., year of publication and venue), technology, personalization dimension, personalization approaches, target outcome, individual differences, theories and scales, and evaluation approaches. Our results show (1) increased attention toward personalizing persuasive interventions, (2) personality trait is the most popular dimension of individual differences considered by existing research when tailoring their persuasive and behavior change systems, (3) students are among the most commonly targeted audience, and (4) education, health, and physical activity are the most considered domains in the surveyed papers. Based on our results, the paper provides insights and prospective future research directions.

Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data

Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.

Exploring the coach-client interaction of virtual health coaching conducted in patients with type 2 diabetes mellitus: A scoping review

BACKGROUND AND AIMS

Recent studies reported that virtual health coaching (VHCs) had greater benefits on glycemic control compared to traditional diabetes care. However, VHCs are reported to lack real-time evaluations and personalized patient feedback. To support the intention of developing high quality VHC programs, this review aimed to describe characteristics of the coach-client interaction within VHC that had beneficial impacts on patients with type 2 diabetes mellitus (T2DM) patients.

METHODS

We conducted a comprehensive scoping review following the six steps of the framework developed by Arksey and O'Malley. Twelve articles that met the eligibility criteria were retrieved from Medline, ProQuest, Science Direct and Scopus.

RESULTS

We found five key concepts regarding the characteristics of coach-client interactions. First, the discussion through smartphones involved individualized feedback and insights, goals setting, barrier identification, facilitation to change behavior, and also clients' clinical, mental, and social conditions. Second, the interactions were supported by in-app features including in-app messaging, email, in-app live video consultation and in-app discussion forums. Third, the most used time of evaluation was 12 months. Fourth, the most commonly delivered topic was lifestyle changes which were predominantly focused on dietary patterns. Fifth, most of health coaches were health liaisons.

CONCLUSIONS

The findings highlight the discussion points within interaction through well-planned devices combining an appropriate in-app features contribute to an effective coach-client interactions of VHC. It is expected that future studies can apply these findings as the basis to develop a single set of standards for VHCs which refer to specific patterns of patient-oriented interaction.

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

Homecare interventions as a Service model for Obstructive sleep Apnea: Delivering personalised phone call using patient profiling and adherence predictions

BACKGROUND AND OBJECTIVE

Obstructive Sleep Apnea (OSA) is a sleep disorder that leads to different pathologies like depression and cardiovascular problems. The first-line medical treatment for OSA is Continuous Positive Airway Pressure (CPAP) therapy. However, this therapy has the lowest adherence level when compared to other homecare therapies. Consequently, the main objective of this paper is to increase this adherence level with methods that can be replicated in a large number of patients.

METHODS

The Homecare Intervention as a Service model can build, verify, and deliver per-sonalised home care interventions. With the Homecare Intervention as a Service model, we build and provide on-demand personalised interventions according to the patient's needs. The 2 core components of this model are patient clustering and CPAP adherence predictions. To define the patient profiles and predict the adherence level, we apply the K-means and the Logistic Regression algorithm respectively. To support these algorithms, we use the CPAP monitoring data and qualitative data on the patients.

RESULTS

We demonstrate that there are 3 patient profiles (non-adherent, attempter, and adherent). We draw a comparison with multiple machine learning algorithms to predict CPAP adherence at 30, 60 and 90 days. In this case, the Logistic Regression gives the best results with a f1-score of 0.84 for30 days, 0.79 for 60 days and 0.76 for 90 days. These newly build profiles were to be used to deliver personalised phone call interventions. The phone call intervention shows an increase in adherence by 1.02 h/night for non-adherent patients and 0.69 h/night for attempter patients.

CONCLUSIONS

This is the first study in CPAP therapy that formalises the process of transforming raw data into effective home care interventions that can be delivered directly to the patients. In fact,it is the first time that both patient characterisation and predictions based on data are used to provide personalised patient management for CPAP therapy. Our model is flexible to be extended to new types of interventions and other homecare therapies.

"You get what you need when you need it": A mixed methods examination of the feasibility and acceptability of a tailored digital tool to promote physical activity among women in midlife

During midlife (ages 40-60), women experience myriad changes that elevate their risk for cardiovascular disease (CVD), including decreased physical activity (PA). Women cite lack of social support for PA and lack of active peers who can serve as role models as key barriers. Digital tools such as web applications can provide exposure to these social inputs; they are also accessible in daily life and require modest time investment. However, as few tools have been designed to meet the unique needs of women in midlife with CVD risk, our research team previously built a web application that is tailored for this population. In the present study, we used a convergent mixed methods design to develop a deep understanding of the feasibility, usability, and acceptability of this web application in a sample of identified end users. Participants (N = 27, MAge = 53 years, MBMI = 32.6 kg/m2) used the web application at the start of each day for 7 days and completed a 1-hour qualitative interview at the end of this test period. Integration of findings from two-level multilevel models (quantitative) and thematic analysis (qualitative) indicated support for the feasibility, usability, and acceptability of the new web application among women in midlife with CVD risk conditions and identified critical opportunities for improving the user experience. Findings also speak to the utility of options for content selection that can meet women's needs in daily life and highlight women's desire for PA resources that prioritize their perspectives.

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

A health recommender system (HRS) provides a user with personalized medical information based on the user's health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

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.

Development and Initial Testing of a Personalized, Adaptive, and Socially Focused Web Tool to Support Physical Activity Among Women in Midlife: Multidisciplinary and User-Centered Design Approach

BACKGROUND

Women in midlife are vulnerable to developing cardiovascular disease, particularly those who have conditions such as hypertension. Physical activity (PA) can reduce risk, but efforts to promote PA in this population have been only modestly effective. More attention to social influences on PA behavior may be useful, particularly social support and social comparison processes. Activating these processes with digital tools can provide easy access that is flexible to the needs of women in midlife.

OBJECTIVE

This paper describes the user-centered design processes of developing and conducting initial evaluation of a personalized and adaptive web application, tailored to the social needs of women in midlife. The goal was to gather feedback from the population of interest, before and during the design process.

METHODS

This study was conducted in 4 stages. The first and second authors (DA and AFL) developed technical specifications, informed by their experience with the population of interest. We collected feedback on potential content for the web application with women in midlife using both interviews (5/10, 50%; mean age 47.4, SD 6.66 years; mean BMI 35.3, SD 9.55 kg/m2) and surveys (5/10, 50%; mean age 51, SD 6.60 years; mean BMI 32.7, SD 8.39 kg/m2). We used their feedback to inform support messages and peer profiles (ie, sources of social comparison information). Nine members of the behavioral science team and 3 testers unfamiliar with the web application completed internal testing. We conducted naturalistic functionality testing with a different group of women in midlife (n=5; mean age 50, SD 6.26 years; mean BMI 30.1, SD 5.83 kg/m2), who used the web application as intended for 4 days and provided feedback at the end of this period.

RESULTS

Iterative storyboard development resulted in programming specifications for a prototype of the web application. We used content feedback to select and refine the support messages and peer profiles to be added. The following 2 rounds of internal testing identified bugs and other problems regarding the web application's functioning and full data collection procedure. Problems were addressed or logged for future consideration. Naturalistic functionality testing revealed minimal further problems; findings showed preliminary acceptability of the web application and suggested that women may select different social content across days.

CONCLUSIONS

A multidisciplinary and user-centered design approach led to a personalized and adaptive web application, tailored to the social needs of women in midlife. Findings from testing with this population demonstrated the feasibility and acceptability of the new application and supported further development toward its use in daily life. We describe several potential uses of the web application and next steps for its development. We also discuss the lessons learned and offer recommendations for future collaborations between behavioral and computer scientists to develop similar tools.

Breast Cancer Physical Activity Mobile Intervention: Early Findings From a User Experience and Acceptability Mixed Methods Study

BACKGROUND

Physical activity (PA) is the most well-established lifestyle factor associated with breast cancer (BC) survival. Even women with advanced BC may benefit from moderate PA. However, most BC symptoms and treatment side effects are barriers to PA. Mobile health coaching systems can implement functionalities and features based on behavioral change theories to promote healthier behaviors. However, to increase its acceptability among women with BC, it is essential that these digital persuasive systems are designed considering their contextual characteristics, needs, and preferences.

OBJECTIVE

This study aimed to examine the potential acceptability and feasibility of a mobile-based intervention to promote PA in patients with BC; assess usability and other aspects of the user experience; and identify key considerations and aspects for future improvements, which may help increase and sustain acceptability and engagement.

METHODS

A mixed methods case series evaluation of usability and acceptability was conducted in this study. The study comprised 3 sessions: initial, home, and final sessions. Two standardized scales were used: the Satisfaction with Life Scale and the International Physical Activity Questionnaire-Short Form. Participants were asked to use the app at home for approximately 2 weeks. App use and PA data were collected from the app and stored on a secure server during this period. In the final session, the participants filled in 2 app evaluation scales and took part in a short individual interview. They also completed the System Usability Scale and the user version of the Mobile App Rating Scale. Participants were provided with a waist pocket, wired in-ear headphones, and a smartphone. They also received printed instructions. A content analysis of the qualitative data collected in the interviews was conducted iteratively, ensuring that no critical information was overlooked.

RESULTS

The International Physical Activity Questionnaire-Short Form found that all participants (n=4) were moderately active; however, half of them did not reach the recommended levels in the guidelines. System Usability Scale scores were all >70 out of 100 (72.5, 77.5, 95, and 80), whereas the overall user version of the Mobile App Rating Scale scores were 4, 4.3, 4.4, and 3.6 out of 5. The app was perceived to be nice, user-friendly, straightforward, and easy to understand. Recognition of achievements, the possibility of checking activity history, and the rescheduling option were positively highlighted. Technical difficulties with system data collection, particularly with the miscount of steps, could make users feel frustrated. The participants suggested improvements and indicated that the app has the potential to work well for survivors of BC.

CONCLUSIONS

Early results presented in this study point to the potential of this tool concept to provide a friendly and satisfying coaching experience to users, which may help improve PA adherence in survivors of BC.

A WeChat-Based Rehabilitation Platform for Children and Adolescents with Congenital Heart Disease to Promote Cardiac FITness (HeartFIT): Protocol for a Mixed-Methods Strategy from Evidence-Based Design to Pilot Study

Progress in medical and surgical care has tremendously improved the survival rates of children with congenital heart disease (CHD). However, reduced aerobic capacity and health-related issues remain a threaten to quality survival and prevention of related complications among children and adolescents with CHD. This research program aims to develop and evaluate a WeChat-based health platform (HeartFIT) to facilitate cardiac rehabilitation and promote physical fitness for this rapidly expanding young population. The study protocol describes the use of an iterative process of using a mixed-methods strategy to develop, refine, and pilot test the proposed HeartFIT platform. A sequential problem-solving process comprising four iterative phases with ongoing end-user input will be implemented. In phase 1, relevant literature was systematically reviewed (completed) and then child-parent dyads will be interviewed to understand the broad context and the requirements and considerations of the target population toward the WeChat-based rehabilitation platform. In phase 2, key features and priority functionalities for the platform will be ideated and refined, and a digital interactive prototype will be created. In phase 3, heuristic evaluation and three rounds of end-user testing will be conducted to ensure further refinement and usability of the prototype. In phase 4, a prospective pilot study will be performed to investigate the feasibility, acceptability, and preliminary efficacy of the developed platform over a 12-week intervention period. If HeartFIT intervention is feasible, acceptable, and demonstrates promising efficacy, an adequately powered randomized controlled trial (future work) will be deployed to test the real-world effectiveness of the intervention.

Mitigating Issues With/of/for True Personalization

A common but false perception persists about the level and type of personalization in the offerings of contemporary software, information systems, and services, known as Personalization Myopia: this involves a tendency for researchers to think that there are many more personalized services than there genuinely are, for the general audience to think that they are offered personalized services when they really are not, and for practitioners to have a mistaken idea of what makes a service personalized. And yet in an era, which mashes up large amounts of data, business analytics, deep learning, and persuasive systems, true personalization is a most promising approach for innovating and developing new types of systems and services—including support for behavior change. The potential of true personalization is elaborated in this article, especially with regards to persuasive software features and the oft-neglected fact that users change over time.

Mobile-Health based physical activities co-production policies towards cardiovascular diseases prevention: findings from a mixed-method systematic review

BACKGROUND

Cardiovascular disease (CVD) is the first cause of death globally, with huge costs worldwide. Most cases of CVD could be prevented by addressing behavioural risk factors. Among these factors, there is physical and amateur sports activity (PASA), which has a linear negative correlation with the risk of CVD. Nevertheless, attempts to encourage PASA, as exercise prescription programmes, achieved little impact at the community-wide level. A new frontier to promote PASA is represented by mobile health tools, such as exergaming, mobile device apps, health wearables, GPS/GIS and virtual reality. Nevertheless, there has not yet been any evident turnabout in patient active involvement towards CVD prevention, and inactivity rates are even increasing. This study aims at framing the state of the art of the literature about the use of m-health in supporting PASA, as a user-centric innovation strategy, to promote co-production health policies aiming at CVD prevention.

METHODS

A mixed-method systematic literature review was conducted in the fields of health and healthcare management to highlight the intersections between PASA promotion and m-health tools in fostering co-produced services focused on CVD prevention. The literature has been extracted by the PRISMA logic application. The resulting sample has been first statistically described by a bibliometric approach and then further investigated with a conceptual analysis of the most relevant contributions, which have been qualitatively analysed.

RESULTS

We identified 2,295 studies, on which we ran the bibliometric analysis. After narrowing the research around the co-production field, we found 10 papers relevant for the concept analysis of contents. The interest about the theme has increased in the last two decades, with a high prevalence of contributions from higher income countries and those with higher CVD incidence. The field of research is highly multi-disciplinary; most of documents belong to the medical field, with only a few interconnections with the technology and health policy spheres. Although the involvement of patients is recognized as fundamental for CVD prevention through PASA, co-design schemes are still lacking at the public management level.

CONCLUSIONS

While the link between the subjects of motor activity, medicine and technology is clear, the involvement of citizens in the service delivery process is still underinvestigated, especially the issue concerning how "value co-creation" could effectively be applied by public agencies. In synthesis, the analysis of the role of co-production as a system coordination method, which is so important in designing and implementing preventive care, is still lacking.

Involving Older Adults During COVID-19 Restrictions in Developing an Ecosystem Supporting Active Aging: Overview of Alternative Elicitation Methods and Common Requirements From Five European Countries

Background

Information and communication technology solutions have the potential to support active and healthy aging and improve monitoring and treatment outcomes. To make such solutions acceptable, all stakeholders must be involved in the requirements elicitation process. Due to the COVID-19 situation, alternative approaches to commonly used face-to-face methods must often be used. One aim of the current article is to share a unique experience from the Pharaon project where due to the COVID-19 outbreak alternative elicitation methods were used. In addition, an overview of common functional, quality, and emotional goals identified by six pilot sites is presented to complement the knowledge about the needs of older adults.

Methods

Originally planned face-to-face co-creation seminars were impossible to carry out, and all pilot sites chose alternative requirements elicitation methods that were most suitable in their situation. The elicited requirements were presented in the form of goal models. In one summary goal model, we provide an overview of common functional, quality, and emotional goals.

Results

Different elicitation methods were combined based on the digital literacy of the target group and their access to digital tools. Methods applied without digital technologies were phone interviews, reviews of literature and previous projects, while by means of digital technologies online interviews, online questionnaires, and (semi-)virtual co-creation seminars were conducted. The combination of the methods allowed to involve all planned stakeholders. Virtual and semi-virtual co-creation seminars created collaborative environment comparable to face-to-face situations, while online participation helped to save the time of the participants. The most prevalent functional goals elicited were “Monitor health,” “Receive advice,” “Receive information.” “Easy to use/comfortable,” “personalized/tailored,” “automatic/smart” were identified as most prevalent quality goals. Most frequently occurring emotional goals were “involved,” “empowered,” and “informed.”

Conclusion

There are alternative methods to face-to-face co-creation seminars, which effectively involve older adults and other stakeholders in the requirements elicitation process. Despite the used elicitation method, the requirements can be easily transformed into goal models to present the results in a uniform way. The common requirements across different pilots provided a strong foundation for representing detailed requirements and input for further software development processes.

A personalized mobile app for physical activity: An experimental mixed-methods study

Objectives

To investigate the feasibility of the be.well app and its personalization approach which regularly considers users’ preferences, amongst university students.

Methods

We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18–34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted.

Results

Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value <.001, 95% CI 782 to 3112).

Conclusions

Incorporating users’ preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.

Short-term effects of a social media-based intervention on the physical and mental health of remotely working young software professionals: A randomised controlled trial

The present study aimed to explore the short term effects of a social media-based intervention on the physical and mental health of the software professionals working remotely during the pandemic. Sixty software professionals with poor physical and mental health were randomised to Facebook-based intervention (FIIT) and a control (CONT) group for 2 months. Forty-six remote workers (26.25 ± 3.49 years) completed the study (FIIT = 22; CONT = 26). All the respondents had the median sitting time (7.07 ± 2.30 h/day) during office hours on workdays. We found a significant difference in the sitting time during office hours in workday within the subjects (F_1,46  = 4.66; p < .004; η_p ²  = .048) and between the subjects (F_1,46  = 3.81; p < .004; η_p ²  = .040). Post hoc analysis revealed participants in the FIIT group reduced their sitting time by 58 min during office hours during a typical workday compared with the control group. Nevertheless, we found a significant difference in the scores of stress, anxiety and depression within and between the groups. Short-term social media-based physical and mental health intervention may improve physical and mental health scores in the desk-based office workers working remotely.

Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.

Agent-Oriented Goal Models in Developing Information Systems Supporting Physical Activity Among Adolescents: Literature Review and Expert Interviews

Background

Information and communication technologies (ICTs) are becoming increasingly popular in supporting the fight against low physical activity (PA) levels among adolescents. However, several ICT solutions lack evidence-based content. Therefore, there is a need to identify important features that have the potential to efficiently and consistently support the PA of adolescents using ICT solutions.

Objective

This study aims to create evidence-based models of requirements for ICT solutions supporting PA by combining scientific evidence from literature and health experts. In addition, we test the suitability of agent-oriented goal models in this type of modeling process.

Methods

A literature search of PubMed, Web of Science, and Scopus databases was conducted to identify evidence-based functional, quality, and emotional goals that have previously been proven to be relevant in supporting PAs among youth using ICT solutions. The identified goals were presented in the form of goal models. These models were used to collaborate with health experts to receive their input on the topic and suggestions for improvement. The initial goal models were improved based on the feedback from the experts.

Results

The results indicated that agent-oriented goal modeling is a suitable method for merging information from the literature and experts. One strength of agent-oriented goal models is that they present emotional requirements together with quality and functional requirements. Another strength is the possibility of presenting results from a literature review in a systematic manner and using them thereafter in the communication process with stakeholders. Agent-oriented goal models that were created were easy to understand for health experts without previous experience in requirements engineering, which facilitates and supports collaboration with nontechnical stakeholders.

Conclusions

The proposed agent-oriented goal models effectively merged information from scientific literature and experts in the field and presented early functional, quality, and emotional requirements in a holistic and coherent manner. We believe that the created models have high potential to help requirements engineers and developers to provide more efficient ICT solutions that support PA among adolescents in the future.

Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making Framework

Despite the growing popularity of digital health interventions, limitations of traditional behavior change theories and a lack of theory integration hinder theory-driven behavior change applications. In this paper, we aim to review theories relevant to lifestyle behavior change from the broader psychology literature and then integrate these theories into a new theoretical framework called adaptive decision-making to address two specific problems. First, our framework represents lifestyle behaviors at two levels-one of individual daily decisions (action level) and one of larger behavioral episodes (reflection level)-to more closely match the temporal characteristics of lifestyle behaviors and their associated digital data. Second, the framework connects decision-making theories and learning theories to explain how behaviors and cognitive constructs dynamically influence each other, making it a suitable scaffold for building computational models. We map common digital intervention techniques onto the behavioral and cognitive processes in the framework and discuss possible contributions of the framework to both theory development and digital intervention design.

ESC working group on e-cardiology position paper: use of commercially available wearable technology for heart rate and activity tracking in primary and secondary cardiovascular prevention-in collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee

Commercially available health technologies such as smartphones and smartwatches, activity trackers and eHealth applications, commonly referred to as wearables, are increasingly available and used both in the leisure and healthcare sector for pulse and fitness/activity tracking. The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate (HR) and activity tracking, in clinical cardiovascular healthcare to support their implementation into clinical care. The widespread use of HR and fitness tracking technologies provides unparalleled opportunities for capturing physiological information from large populations in the community, which has previously only been available in patient populations in the setting of healthcare provision. The availability of low-cost and high-volume physiological data from the community also provides unique challenges. While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary and secondary prevention. Technical aspects of HR tracking especially during activity need to be further validated. How to analyse, translate, and interpret large datasets of information into clinically applicable recommendations needs further consideration. While the current users of wearable technologies tend to be young, healthy and in the higher sociodemographic strata, wearables could potentially have a greater utility in the elderly and higher-risk population. Wearables may also provide a benefit through increased health awareness, democratization of health data and patient engagement. Use of continuous monitoring may provide opportunities for detection of risk factors and disease development earlier in the causal pathway, which may provide novel applications in both prevention and clinical research. However, wearables may also have potential adverse consequences due to unintended modification of behaviour, uncertain use and interpretation of large physiological data, a possible increase in social inequality due to differential access and technological literacy, challenges with regulatory bodies and privacy issues. In the present position paper, current applications as well as specific barriers and gaps in knowledge are identified and discussed in order to support the implementation of wearable technologies from gadget-ology into clinical cardiology.

Breast Cancer Survivors' Perspectives on Motivational and Personalization Strategies in Mobile App-Based Physical Activity Coaching Interventions: Qualitative Study

Background

Despite growing evidence supporting the vital benefits of physical activity (PA) for breast cancer survivors, the majority do not meet the recommended levels of activity. Mobile app–based PA coaching interventions might be a feasible strategy to facilitate adherence of breast cancer survivors to the PA guidelines. To engage these individuals, PA apps need to be specifically designed based on their needs and preferences and to provide targeted support and motivation. However, more information is needed to understand how these technologies can provide individual and relevant experiences that have the ability to increase PA adherence and retain the individual’s interest in the long term.

Objective

The aim of this study is to explore insights from breast cancer survivors on motivational and personalization strategies to be used in PA coaching apps and interventions.

Methods

A qualitative study was conducted, using individual semistructured interviews, with 14 breast cancer survivors. The moderator asked open-ended questions and made use of a slideshow presentation to elicit the participants’ perspectives on potential mobile app–based intervention features. Transcribed interviews were evaluated by 3 reviewers using thematic content analysis.

Results

Participants (mean age 53.3, SD 8.7 years) were White women. In total, 57% (8/14) of the participants did not adhere to the PA guidelines. In general, participants had access to and were interested in using technology. The identified themes included (1) barriers to PA, (2) psychological mediators of PA motivation, (3) needs and suggestions for reinforcing motivation support, (4) personalization aspects of the PA coaching experience, and (5) technology trustworthiness. Motivational determinants included perceived control, confidence and perceived growth, and connectedness. Participants were interested in having a straightforward app for monitoring and goal setting, which would include a prescribed activity program and schedule, and positive communication. Opinions varied in terms of social and game-like system possibilities. In addition, they expressed a desire for a highly personalized coaching experience based on as much information collected from them as possible (eg, disease stage, physical limitations, preferences) to provide individualized progress information, dynamic adjustment of the training plan, and context-aware activity suggestions (eg, based on weather and location). Participants also wanted the app to be validated or backed by professionals and were willing to share their data in exchange for a more personalized experience.

Conclusions

This work suggests the need to develop simple, guiding, encouraging, trustworthy, and personalized PA coaching apps. The findings are in line with behavioral and personalization theories and methods that can be used to inform intervention design decisions. This paper opens new possibilities for the design of personalized and motivating PA coaching app experiences for breast cancer survivors, which might ultimately facilitate the sustained adherence of these individuals to the recommended levels of activity.

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

INTRODUCTION

Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.

METHODS AND ANALYSIS

In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.

ETHICS AND DISSEMINATION

The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.

TRIAL REGISTRATION NUMBER

NCT03490253; pre-results.

A Personalized Physical Activity Coaching App for Breast Cancer Survivors: Design Process and Early Prototype Testing

Background

Existing evidence supports the many benefits of physical activity (PA) in breast cancer survival. However, few breast cancer survivors adhere to the recommended levels of activity. A PA coaching app that provides personalized feedback, guidance, and motivation to the user might have the potential to engage these individuals in a more active lifestyle, in line with the general recommendations. To develop a successful tool, it is important to involve the end users in the design process and to make theoretically grounded design decisions.

Objective

This study aimed to execute the design process and early prototype evaluation of a personalized PA coaching app for posttreatment breast cancer survivors. In particular, the study explored a design combining behavioral theory and tailored coaching strategies.

Methods

The design process was led by a multidisciplinary team, including technical and health professionals, and involved input from a total of 22 survivors. The process comprised 3 stages. In stage 1, the literature was reviewed and 14 patients were interviewed to understand the needs and considerations of the target population toward PA apps. In stage 2, the global use case for the tool was defined, the features were ideated and refined based on theory, and a digital interactive prototype was created. In stage 3, the prototype went through usability testing with 8 patients and was subjected to quality and behavior change potential evaluations by 2 human-computer interaction experts.

Results

The design process has led to the conceptualization of a personalized coaching app for walking activities that addresses the needs of breast cancer survivors. The main features of the tool include a training plan and schedule, adaptive goal setting, real-time feedback and motivation during walking sessions, activity status through the day, activity history, weekly summary reports, and activity challenges. The system was designed to measure users’ cadence during walking, use this measure to infer their training zone, and provide real-time coaching to control the intensity of the walking sessions. The outcomes from user testing and expert evaluation of the digital prototype were very positive, with scores from the system usability scale, mobile app rating scale, and app behavior change scale of 95 out of 100, 4.6 out of 5, and 15 out of 21, respectively.

Conclusions

Implementing a user-centered design approach for the development and early evaluation of an app brings essential considerations to tailor the solution to the user’s needs and context. In addition, informing the design on behavioral and tailored coaching theories supports the conceptualization of the PA coaching system. This is critical for optimizing the usability, acceptability, and long-term effectiveness of the tool. After successful early in-laboratory testing, the app will be developed and evaluated in a pilot study in a real-world setting.

A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted. Thirdly, the extracted data were analyzed through the affinity diagram, noticing-collecting-thinking, and descriptive analysis. As outcomes, the review provides a state-of-the-art view of mobile apps developed for COVID-19 by revealing nine functionalities or features. It revealed ten factors related to information systems design characteristics that can guide future app design. The review outcome highlights the need for new development and further refinement of the existing applications considering not only the revealed objectives and their associated functionalities, but also revealed design characteristics such as reliability, performance, usefulness, supportive, security, privacy, flexibility, responsiveness, ease of use, and cultural sensitivity.