The fitness of apps: a theory-based examination of mobile fitness app usage over 5 months

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.

Home-Based Intelligent Exercise System for Seniors' Healthcare: The Example of Golf Croquet

In 2020, the world experienced the threat of the COVID-19 epidemic, and seniors and chronic disease patients generally reduced their exercise and social activities to avoid increasing the risk of infection, which could lead to increased loneliness and even many diseases. Combining golf croquet games with a mobile application (App) and AIoT companion robots, this research constructs a home-based intelligent exercise system, uses the technology acceptance model (TAM), deduces users' intention to use this system based on perceived usefulness and perceived ease of use, and adds the needs of love and belonging, esteem, cognitive, aesthetic, and self-actualization in Maslow's hierarchy of needs theory (HNT) to conduct an analysis of system needs. This study collected empirical data, totaling 320 participants including seniors and their caregivers, from elderly care centers in northern Taiwan using a cross-sectional survey and purposive sampling. Based on regression and variance analysis, the results show that participants have a high level of acceptance of this system, believing that it is easy to learn and operate and can increase interaction with others, improve self-confirmation, satisfy the thirst for knowledge, increase the feeling of happiness, and fulfill self-actualization needs. In the future, by collecting and recording the process of seniors using the App, so as to find out their health problems as soon as possible, expand their daily life through this exercise, and achieve the goal of happy living and better healthcare.

The effectiveness of wearable activity trackers for increasing physical activity and reducing sedentary time in older adults: A systematic review and meta-analysis

Background

Traditional interventions such as education and counseling are successful in increasing physical activity (PA) participation, but are usually labor and resource intensive. Wearable activity trackers can objectively record PA and provide feedback to help users to achieve activity goals and are an increasingly popular tool among adults used to facilitate self-monitoring of PA. However, no reviews systematically explored the roles of wearable activity trackers in older populations.

Methods

We searched PubMed, Web of Science, Google Scholar, Embase, Cochrane Library, and Scopus from inception to September 10, 2022. Randomized controlled trials were included. Two reviewers independently conducted study selection, data extraction, risk of bias, and certainty of evidence assessment. A random-effects model was used to evaluate the effect size.

Results

A total of 45 studies with 7144 participants were included. A wearable activity tracker was effective in increasing daily steps (standard mean differences (SMD) = 0.59, 95% confidence interval (CI) (0.44, 0.75)), weekly moderate-to-vigorous PA (MVPA) (SMD = 0.54, 95% CI (0.36, 0.72)), and total daily PA (SMD = 0.21, 95% CI (0.01, 0.40)) and reducing sedentary time (SMD = -0.10, 95% CI (-0.19, -0.01)). Subgroup analysis showed that the effectiveness of wearable activity trackers for daily steps was not influenced by participants and intervention features. However, wearable activity trackers seemed more effective in promoting MVPA of participant's age <70 than participant's age ≥70. In addition, wearable activity trackers incorporated with traditional intervention components (e.g. telephone counseling, goal setting, and self-monitoring) could better promote MVPA than alone use. Short-term interventions potentially achieve better MVPA increase than long-term.

Conclusion

This review showed that wearable activity trackers are an effective tool to increase PA for the old population and also favor reducing sedentary time. When used together with other interventions, wearable activity trackers can achieve better MVPA increase, especially in the short term. However, how to more effectively improve the effectiveness of wearable activity trackers is an important direction of future research.

A Smartphone Physical Activity App for Patients in Alcohol Treatment: Single-Arm Feasibility Trial

BACKGROUND

Alcohol use disorder (AUD) is a significant public health concern worldwide. Alcohol consumption is a leading cause of death in the United States and has a significant negative impact on individuals and society. Relapse following treatment is common, and adjunct intervention approaches to improve alcohol outcomes during early recovery continue to be critical. Interventions focused on increasing physical activity (PA) may improve AUD treatment outcomes. Given the ubiquity of smartphones and activity trackers, integrating this technology into a mobile app may be a feasible, acceptable, and scalable approach for increasing PA in individuals with AUD.

OBJECTIVE

This study aims to test the Fit&Sober app developed for patients with AUD. The goals of the app were to facilitate self-monitoring of PA engagement and daily mood and alcohol cravings, increase awareness of immediate benefits of PA on mood and cravings, encourage setting and adjusting PA goals, provide resources and increase knowledge for increasing PA, and serve as a resource for alcohol relapse prevention strategies.

METHODS

To preliminarily test the Fit&Sober app, we conducted an open pilot trial of patients with AUD in early recovery (N=22; 13/22, 59% women; mean age 43.6, SD 11.6 years). At the time of hospital admission, participants drank 72% of the days in the last 3 months, averaging 9 drinks per drinking day. The extent to which the Fit&Sober app was feasible and acceptable among patients with AUD during early recovery was examined. Changes in alcohol consumption, PA, anxiety, depression, alcohol craving, and quality of life were also examined after 12 weeks of app use.

RESULTS

Participants reported high levels of satisfaction with the Fit&Sober app. App metadata suggested that participants were still using the app approximately 2.5 days per week by the end of the intervention. Pre-post analyses revealed small-to-moderate effects on increase in PA, from a mean of 5784 (SD 2511) steps per day at baseline to 7236 (SD 3130) steps per day at 12 weeks (Cohen d=0.35). Moderate-to-large effects were observed for increases in percentage of abstinent days (Cohen d=2.17) and quality of life (Cohen d=0.58) as well as decreases in anxiety (Cohen d=-0.71) and depression symptoms (Cohen d=-0.58).

CONCLUSIONS

The Fit&Sober app is an acceptable and feasible approach for increasing PA in patients with AUD during early recovery. A future randomized controlled trial is necessary to determine the efficacy of the Fit&Sober app for long-term maintenance of PA, ancillary mental health, and alcohol outcomes. If the efficacy of the Fit&Sober app could be established, patients with AUD would have a valuable adjunct to traditional alcohol treatment that can be delivered in any setting and at any time, thereby improving the overall health and well-being of this population.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02958280; https://www.clinicaltrials.gov/ct2/show/NCT02958280.

A Sensor-Controlled Digital Game for Heart Failure Self-Care Based on Behavioral Change Frameworks

Self-care behaviors are critical to manage the adverse impact of heart failure disease. However, engaging in self-care behaviors such as physical activity or daily weight-monitoring can be difficult due to lack of knowledge or motivation. Digital games can serve as an alternative to traditional patient education to provide information and motivate engagement in critical self-care behaviors. In this paper, we describe a sensor-controlled digital game (SCDG) in which game play is driven by the player's real life self-care behaviors. We also present the design and development of the next iteration of the SCDG based on playtesting results and behavioral theoretical frameworks.

A Comparative Utility Score for Digital Health Tools

Digital health tools (DHT) are increasingly poised to change healthcare delivery given the Coronavirus Disease 2019 (COVID-19) pandemic and the drive to telehealth. Establishing the potential utility of a given DHT could aid in identifying how it could be best used and further opportunities for healthcare improvement. We propose a metric, a Utility Factor Score, which quantifies the benefits of a DHT by explicitly defining adherence and linking it directly to satisfaction and health goals met. To provide data for how the comparative utility score can or should work, we illustrate in detail the application of our metrics across four DHTs with two simulated users. The Utility Factor Score can potentially facilitate integration of DHTs into various healthcare settings and should be evaluated within a clinical study.

Personalization of Intervention Timing for Physical Activity: Scoping Review

Background

The use of sensors in smartphones, smartwatches, and wearable devices has facilitated the personalization of interventions to increase users’ physical activity (PA). Recent research has focused on evaluating the effects of personalized interventions in improving PA among users. However, it is critical to deliver the intervention at an appropriate time to each user to increase the likelihood of adoption of the intervention. Earlier review studies have not focused on the personalization of intervention timing for increasing PA.

Objective

This review aims to examine studies of information technology–based PA interventions with personalized intervention timing (PIT); identify inputs (eg, user location) used by the system for generating the PIT, the techniques and methods used for generating the PIT, the content of the PA intervention, and delivery mode of the intervention; and identify gaps in existing literature and suggest future research directions.

Methods

A scoping review was undertaken using PsycINFO, PubMed, Scopus, and Web of Science databases based on a structured search query. The main inclusion criteria were as follows: the study aimed to promote PA, included some form of PIT, and used some form of information technology for delivery of the intervention to the user. If deemed relevant, articles were included in this review after removing duplicates and examining the title, abstract, and full text of the shortlisted articles.

Results

The literature search resulted in 18 eligible studies. In this review, 72% (13/18) of the studies focused on increasing PA as the primary objective, whereas it was the secondary focus in the remaining studies. The inputs used to generate the PIT were categorized as user preference, activity level, schedule, location, and predicted patterns. On the basis of the intervention technique, studies were classified as manual, semiautomated, or automated. Of these, the automated interventions were either knowledge based (based on rules or guidelines) or data driven. Of the 18 studies, only 6 (33%) evaluated the effectiveness of the intervention and reported positive outcomes.

Conclusions

This work reviewed studies on PIT for PA interventions and identified several aspects of the interventions, that is, inputs, techniques, contents, and delivery mode. The reviewed studies evaluated PIT in conjunction with other personalization approaches such as activity recommendation, with no study evaluating the effectiveness of PIT alone. On the basis of the findings, several important directions for future research are also highlighted in this review.

User Engagement and Abandonment of mHealth: A Cross-Sectional Survey

Mobile health (mHealth) apps have great potential to improve health outcomes. Given that mHealth apps have become ubiquitous, there is limited focus on their abandonment. Data concerning crucial metrics, including reasons for adoption and discontinued use, are limited. This study aims to gain broad insights into utilization of mHealth and game-like features promoting user engagement. We conducted a cross-sectional survey of 209 mHealth users worldwide. The 17-item survey assessed sociodemographics, as well as the key motivators for mHealth uptake and discontinued use. Our findings show that sports and fitness activity tracking were the most common categories of health apps, with most users engaging with them at least several times a week. Interestingly, the most downloaded mHealth apps among younger adults include MyFitnessPal, Fitbit, Nike Run Club, and Samsung Health. Critical drivers of abandonment of mHealth apps were amotivation, loss of interest, and experimenting with different apps to identify the most suitable tool. Additionally, the financial cost of mHealth apps is crucial, with most participants advocating for free or more affordable apps. The study findings suggest that while many individuals utilize mHealth, several factors drive their abandonment. Moreover, data indicate that mHealth developers need to consider gamification strategies to sustain user commitment, as well as psychological variables, such as intrinsic motivation.

Feasibility of a Sensor-Controlled Digital Game for Heart Failure Self-management: Randomized Controlled Trial

BACKGROUND

Poor self-management of heart failure (HF) contributes to devastating health consequences. Our innovative sensor-controlled digital game (SCDG) integrates data from sensors to trigger game rewards, progress, and feedback based on the real-time behaviors of individuals with HF.

OBJECTIVE

The aim of this study is to compare daily weight monitoring and physical activity behavior adherence by older adults using an SCDG intervention versus a sensors-only intervention in a feasibility randomized controlled trial.

METHODS

English-speaking adults with HF aged 55 years or older who owned a smartphone and could walk unassisted were recruited from Texas and Oklahoma from November 2019 to August 2020. Both groups were given activity trackers and smart weighing scales to track behaviors for 12 weeks. The feasibility outcomes of recruitment, retention, intervention engagement, and satisfaction were assessed. In addition to daily weight monitoring and physical activity adherence, the participants' knowledge, functional status, quality of life, self-reported HF behaviors, motivation to engage in behaviors, and HF-related hospitalization were also compared between the groups at baseline and at 6, 12, and 24 weeks.

RESULTS

A total of 38 participants with HF-intervention group (IG; 19/38, 50%) and control group (CG; 19/38, 50%)-were enrolled in the study. Of the 38 participants, 18 (47%) were women, 18 (47%) were aged 65 years or older, 21 (55%) had been hospitalized with HF in the past 6 months, and 29 (76%) were White. Furthermore, of these 38 participants, 31 (82%)-IG (15/19, 79%) and CG (16/19, 84%)-had both weight monitoring and physical activity data at the end of 12 weeks, and 27 (71%)-IG (14/19, 74%) and CG (13/19, 68%)-participated in follow-up assessments at 24 weeks. For the IG participants who installed the SCDG app (15/19, 79%), the number of days each player opened the game app was strongly associated with the number of days the player engaged in weight monitoring (r=0.72; P=.04) and the number of days with physical activity step data (r=0.9; P<.001). The IG participants who completed the satisfaction survey (13/19, 68%) reported that the SCDG was easy to use. Trends of improvement in daily weight monitoring and physical activity in the IG, as well as within-group improvements in HF functional status, quality of life, knowledge, self-efficacy, and HF hospitalization in both groups, were observed in this feasibility trial.

CONCLUSIONS

Playing an SCDG on smartphones was feasible and acceptable for older adults with HF for motivating daily weight monitoring and physical activity. A larger efficacy trial of the SCDG intervention will be needed to validate trends of improvement in daily weight monitoring and physical activity behaviors.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03947983; https://clinicaltrials.gov/ct2/show/NCT03947983.

Identifying the Antecedents of University Students’ Usage Behaviour of Fitness Apps

The purpose of the study is to explore the antecedents of university students’ fitness application usage behaviours by combining the theory of planned behaviour and the technology acceptance model. An anonymous questionnaire survey was adopted to address the objectives of the study. Purposive and snowball sampling was used to select eligible students from six universities in Zhanjiang City. An online survey was used to collect data from 634 eligible subjects, and partial least squares structural equation modelling was used to analyse the collected data. The results indicated that the students’ perceived usefulness (β = 0.17, p < 0.05) and perceived ease of use (β = 0.32, p < 0.05) concerning the application and their attitude (β = 0.31, p < 0.05) toward it significantly influenced their usage intentions. Furthermore, perceived usefulness (β = 0.11, p < 0.05) and perceived ease of use (β = 0.38, p < 0.05) fully mediated the relationship between subjective norms and usage intentions. However, subjective norms and perceived behavioural control did not enhance the students’ intentions to use fitness applications. That is, students’ attitudes and fitness application design are the determinants of usage intention. Accordingly, improving students’ fitness applications usage intention requires strategies that involve customised services, social networking, and collaboration with schools; this would further increase students’ engagement in physical exercise.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years

Background

Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.

Objective

The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.

Methods

We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.

Results

Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.

Conclusions

Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Acceptance of Technologies for Aging in Place: A Conceptual Model

BACKGROUND

Older adults want to preserve their health and autonomy and stay in their own home environment for as long as possible. This is also of interest to policy makers who try to cope with growing staff shortages and increasing health care expenses. Ambient assisted living (AAL) technologies can support the desire for independence and aging in place. However, the implementation of these technologies is much slower than expected. This has been attributed to the lack of focus on user acceptance and user needs.

OBJECTIVE

The aim of this study is to develop a theoretically grounded understanding of the acceptance of AAL technologies among older adults and to compare the relative importance of different acceptance factors.

METHODS

A conceptual model of AAL acceptance was developed using the theory of planned behavior as a theoretical starting point. A web-based survey of 1296 older adults was conducted in the Netherlands to validate the theoretical model. Structural equation modeling was used to analyze the hypothesized relationships.

RESULTS

Our conceptual model showed a good fit with the observed data (root mean square error of approximation 0.04; standardized root mean square residual 0.06; comparative fit index 0.93; Tucker-Lewis index 0.92) and explained 69% of the variance in intention to use. All but 2 of the hypothesized paths were significant at the P<.001 level. Overall, older adults were relatively open to the idea of using AAL technologies in the future (mean 3.34, SD 0.73).

CONCLUSIONS

This study contributes to a more user-centered and theoretically grounded discourse in AAL research. Understanding the underlying behavioral, normative, and control beliefs that contribute to the decision to use or reject AAL technologies helps developers to make informed design decisions based on users' needs and concerns. These insights on acceptance factors can be valuable for the broader field of eHealth development and implementation.

Exercise and Physical Activity eHealth in COVID-19 Pandemic: A Cross-Sectional Study of Effects on Motivations, Behavior Change Mechanisms, and Behavior

OBJECTIVES

The aims of this research were (1) to compare the levels of physical activity of eHealth users and non-users, (2) to determine the effects of these technologies on motivations, and (3) to establish the relationship that could exist between psychological constructs and physical activity behaviors.

METHODS

This cross-sectional study involved 569 adults who responded to an online questionnaire during confinement in France. The questions assessed demographics, usage of eHealth for exercise and physical activity, and behavioral levels. The questionnaire also measured the constructs of Social Cognitive Theory, the Theory of Planned Behavior, and automaticity facets toward eHealth for exercise and physical activity.

RESULTS

Participants who were users of eHealth for exercise and physical activity presented significantly higher levels of vigorous physical activity and total physical activity per week than non-users (p < 0.001). The chi-square test showed significant interactions between psychological constructs toward eHealth (i.e., self-efficacy, behavioral attitudes, intentions, and automaticity) and physical activity levels (all interactions were p < 0.05). Self-efficacy was significantly and negatively correlated with walking time per week. Concerning the automaticity facets, efficiency was positive and significantly correlated with vigorous physical activity levels per week (p < 0.05). Then, regressions analyses showed that self-efficacy and automaticity efficiency explained 5% of the variance of walking minutes per week (ß = -0.27, p < 0.01) and vigorous physical activity per week (ß = 0.20, p < 0.05), respectively.

CONCLUSION

This study has shown that people during confinement looked for ways to stay active through eHealth. However, we must put any technological solution into perspective. The eHealth offers possibilities to stay active, however its benefits and the psychological mechanisms affected by it remains to be demonstrated: eHealth could be adapted to each person and context.

Opportunities and Challenges of a Self-Management App to Support People With Spinal Cord Injury in the Prevention of Pressure Injuries: Qualitative Study

BACKGROUND

Mobile health applications can offer tailored self-management support to individuals living with chronic health conditions. However, there are several challenges to the adoption of these technologies in practice. Co-design is a promising approach to overcoming some of these challenges by enabling the development of solutions that meet the actual needs and preferences of the relevant stakeholder groups.

OBJECTIVE

Taking spinal cord injury as a case in point, the overall objectives of this study were to identify the perceived benefits of a co-designed self-management app that could promote its uptake and to explore the factors that may impede adoption.

METHODS

We adopted a qualitative research approach guided by the Technology Acceptance Model. Data were collected through semistructured interviews with individuals with spinal cord injury (n=15) and two focus groups with health care professionals specialized in spinal cord injury (n=7, n=5). Prior to the interviews and focus groups, study participants were given time to explore the app prototype. All interviews were transcribed verbatim and analyzed using inductive thematic analysis.

RESULTS

Findings of our analysis indicate that study participants perceived the app prototype as potentially useful for supporting individuals with spinal cord injury in preventing pressure injuries. In particular, we identified three concrete use cases highlighting the benefits of the app for different audiences: (1) a companion for newly injured individuals, (2) an emergency kit and motivational support, and 3) a guide for informal caregivers and family members. We also uncovered several challenges that might impede the adoption of the self-management app in practice, including (1) challenges in motivating individuals to use the app, (2) concerns about the misuse and abuse of the app, and (3) organizational and maintenance challenges.

CONCLUSIONS

This study adds to a growing body of research that investigates individuals' adoption and nonadoption behavior regarding mobile health solutions. Building on earlier work, we make recommendations on how to address the barriers to the adoption of mobile health solutions identified by this study. In particular, there is a need to foster trust in mobile health among prospective users, including both patients and health care professionals. Moreover, increasing personal relevance of mobile health solutions through personalization may be a promising approach to promote uptake. Last but not least, organizational support also plays an instrumental role in mobile health adoption. We conclude that even though co-design is promoted as a promising approach to develop self-management tools, co-design does not guarantee adoption. More research is needed to identify the most promising strategies to promote the adoption of evidence-based mobile health solutions in practice.

App-based supplemental exercise in rehabilitation, adherence, and effect on outcomes: a randomized controlled trial

QUESTION

To determine the uptake of an app-based supplemental exercise programme in a rehabilitation setting and the effect of such a programme on length of stay and function compared to usual care physiotherapy.

DESIGN

Randomized controlled trial with random allocation and assessor blinding.

PARTICIPANTS

A total of 144 individuals with mixed diagnoses (orthopaedic, neurological, reconditioning) admitted for inpatient sub-acute rehabilitation.

INTERVENTIONS

Participants were randomly allocated to usual care physiotherapy (control group) or usual care physiotherapy with the addition of an app-based supplemental exercise programme (intervention group).

OUTCOME MEASURES

The primary measure of interest was total supplementary exercise dosage completed by the intervention group. The primary between-group outcome measure was length of stay with secondary measures including walking endurance (Six-Minute Walk Test), walking speed (10-Metre Walk Test), functional mobility (Timed Up and Go Test) and level of disability (Functional Independence Measure).

RESULTS

Participants in the intervention group performed 7 minutes (SD: 9) or 49 repetitions (SD: 48) of supplementary exercise using the app each day. There were no differences between the groups for length of stay (mean difference (MD): -0.5 days, 95% confidence interval (CI): -3.2 to 2.2) or change in any secondary functional outcome measures, including walking speed (MD: -0.1 m/s, 95% CI: -0.2 to 0.0) and disability (MD: -0.9, 95% CI: -3.6 to 1.8).

CONCLUSION

A small supplementary exercise dose was achieved by participants in the intervention group. However, such a programme did not affect length of stay or functional outcomes when compared to usual care.

How Are Wearable Activity Trackers Adopted in Older Adults? Comparison between Subjective Adoption Attitudes and Physical Activity Performance

Wearable activity trackers can motivate older adults to engage in the recommended daily amount of physical activity (PA). However, individuals may not maintain their use of the trackers over a longer period. To investigate the attitudes of activity tracker adoption and their effects on actual PA performance, we conducted a three-month study. We gave activity trackers to 16 older adults and assessed attitudes on activity tracker adoption through a survey during the study period. We extracted participants’ PA measures, step counts, and moderate and vigorous physical activity (MVPA) times. We observed significant differences in adoption attitudes during the three different periods (χ²(2, 48) = 6.27, p < 0.05), and PA measures followed similar decreasing patterns (F(83, 1357) = 12.56, 13.94, p < 0.00001). However, the Pearson correlation analysis (r = 0.268, p = 0.284) and a Bland–Altman plot indicated a bias between two PA measures. Positive attitudes at the initial stage did not persist through the study period, and both step counts and length of MVPA time showed waning patterns in the study period. The longitudinal results from both measures demonstrated the patterns of old adults’ long-term use and adoption. Considering the accuracy of the activity tracker and older adults’ athletic ability, MVPA times are more likely to be a reliable measure of older adults’ long-term use and successful adoption of activity trackers than step counts. The results support the development of better activity tracker design guidelines that would facilitate long-term adoption among older adults.

Factors influencing user's adherence to physical activity applications: A scoping literature review and future directions

BACKGROUND

Although mobile app-delivered physical activity (PA) interventions have the potential to promote exercise, poor adherence to these apps is a common issue impeding their effectiveness. Gaining insights into the factors that influence PA app adherence is an important priority for app developers and intervention designers.

OBJECTIVE

The objective of this study is to perform a literature review to synthesize the factors influencing PA app adherence and to identify directions for future research in this area.

METHODS

A scoping review of prior research was conducted to uncover the factors influencing PA app adherence. Seven online journal databases were searched for relevant articles published from January 1, 2014, to December 31, 2018. The initial search identified 5,572 articles. After a screening and eligibility check based on inclusion criteria, 24 articles were finally selected. The definition of PA app adherence in this review could be categorized along four dimensions derived from previous studies: i.e., frequency of PA app usage, intention/motivation to sustain use of the PA app, degree of function use within the PA app, and the duration of PA app usage.

RESULTS

Of the 24 included studies (both qualitative and quantitative), 12 studies were conducted in the U.S. The methods and study designs varied considerably, with the study durations ranging from 2 weeks to 24 months. The synthesized evidence indicates that 89 distinct factors influenced PA app adherence, and these could be classified into three categories: Personal Factors (n = 28), Technology Features (n = 53), and Contextual Factors (n = 8). Nine more detailed sub-categories were also compiled. Factors in sub-categories, such as psychological factors, health-related factors and predefined goals, are essential for physical activity behavior change experts to implement interventions. Factors in technology features, including PA tracking, PA goal setting and customization of exercise, are specifically useful for PA app developers or PA intervention designers. Overall, evidence of causal factors was limited. Only 5 of the 24 articles explored causal factors that affect PA app adherence. Furthermore, longitudinal studies with long durations were also limited.

CONCLUSIONS

Uncovering the factors influencing PA app adherence is critical as it can expand our current knowledge and provide guidance for app-delivered PA interventions, as well as the design of PA apps. This scoping review identified and categorized factors that influence PA app adherence in prior studies. Based on the evidence synthesized, users are paying more attention to the "playfulness" and personalized features of PA apps, in addition to basic functional requirements. Also, app glitches are the most common factors found to negatively influence app adherence. Several important directions for future research are highlighted in this review, especially the design of studies to explore causality.

Lifestyle E-Coaching for Physical Activity Level Improvement: Short-Term and Long-Term Effectivity in Low Socioeconomic Status Groups

E-coaching applications can improve people's lifestyles; however, their impact on people from a lower socioeconomic status (low SES) is unknown. This study investigated the effectiveness of a lifestyle e-coaching application in encouraging people facing low SES disadvantages to engage in a more active lifestyle over a course of 19 weeks. In this bicountry study, 95 people with low activity level (GR: 50, NL: 45) used a mobile application linked to a wearable activity tracker. At the start and after 6 and 19 weeks, self-reported physical activity levels, attitudes, and intention towards increasing activity levels, perceived behavioral control, and wellbeing were measured. Results indicated that participants using the lifestyle e-coaching application reported significantly more often an increase in activity levels than a parallel control group. Additionally, the people using the application also more often reported increased levels of wellbeing and perceived behavioral control. Therefore, lifestyle e-coaching applications could be a cost-effective solution for promoting healthier lifestyles in low-SES populations.

A Mobile Application to Collect Daily Data on Preexposure Prophylaxis Adherence and Sexual Behavior Among Men Who Have Sex With Men: Use Over Time and Comparability With Conventional Data Collection

BACKGROUND

We studied the use of a mobile application (app) to measure human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP) adherence and sexual behavior, assessed determinants of app use, and we compared data in app and questionnaires.

METHODS

Men who have sex with men participating in the Amsterdam PrEP project (AMPrEP) on daily or event-driven PrEP at the Public Health Service of Amsterdam completed the data on sexual risk behavior and PrEP adherence through a standard questionnaire every 3 months and on a daily basis using the project's app. Regression analyses were used to assess factors associated with app use. Among those who reported 90% or greater of data in the app, the number of PrEP pills taken and number of unknown casual sex partners were compared between the app and the questionnaires by Wilcoxon signed-rank test.

RESULTS

Of all participants (n = 374), 94% (352 of 374) reported data in the app at least once; 72% (261 of 362) reported data ≥90% of the days in the sixth month and 62% (222 of 359) in the 12th month following PrEP initiation.Factors associated with reporting data in the app were using daily PrEP and recent initiation of PrEP. The reported numbers of pills taken and unknown sexual partners were comparable between app and questionnaires.

CONCLUSIONS

The AMPrEP app was used frequently, especially by those using a daily PrEP regimen. Data collected by app regarding adherence and sexual risk behavior were consistent with questionnaire data among those who used the app consistently. An app is a promising tool to measure PrEP adherence and sexual risk behavior.

Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review

BACKGROUND

Only a few telemedicine applications have made their way into regular care. One reason is the lack of acceptance of telemedicine by potential end users.

OBJECTIVE

The aim of this systematic review was to identify theoretical predictors that influence the acceptance of telemedicine.

METHODS

An electronic search was conducted in PubMed and PsycINFO in June 2018 and supplemented by a hand search. Articles were identified using predefined inclusion and exclusion criteria. In total, two reviewers independently assessed the title, abstract, and full-text screening and then individually performed a quality assessment of all included studies.

RESULTS

Out of 5917 potentially relevant titles (duplicates excluded), 24 studies were included. The Axis Tool for quality assessment of cross-sectional studies revealed a high risk of bias for all studies except for one study. The most commonly used models were the Technology Acceptance Model (n=11) and the Unified Theory of Acceptance and Use of Technology (n=9). The main significant predictors of acceptance were perceived usefulness (n=11), social influences (n=6), and attitude (n=6). The results show a superiority of technology acceptance versus original behavioral models.

CONCLUSIONS

The main finding of this review is the applicability of technology acceptance models and theories on telemedicine adoption. Characteristics of the technology, such as its usefulness, as well as attributes of the individual, such as his or her need for social support, inform end-user acceptance. Therefore, in the future, requirements of the target group and the group's social environment should already be taken into account when planning telemedicine applications. The results support the importance of theory-guided user-centered design approaches to telemedicine development.

Contradictions in digital health engagement: An activity tracker's ambiguous influence on vulnerable young adults' engagement in own health

OBJECTIVE

Activity trackers are designed to support individuals in monitoring and increasing their physical activity. The use of activity trackers among individuals diagnosed with depression and anxiety has not yet been examined. This pilot study investigates how this target group engages with an activity tracker during a 10-week health intervention aimed to increase their physical activity level and improve their physical and mental health.

METHODS

Two groups of 11 young adults (aged 18-29 years) diagnosed with depression or anxiety participated in the digital health intervention. The study used mixed methods to investigate the research question. Quantitative health data were used to assess the intervention's influence on the participants' health and qualitative data provided insights into the participants' digital health experience.

RESULTS

The study demonstrated an ambiguous influence from the use of an activity tracker with positive physical and mental health results, but a fading and even negative digital health engagement and counterproductive competition.

CONCLUSIONS

The ambiguous results identify a need for (1) developing strategies for health professionals to provide supervised use of activity trackers and support the target groups' abilities to convert health information about physical activity into positive health strategies, and (2) designing alternatives for health promoting IT targeted users who face challenges and need motivation beyond self-tracking and competition.