Long-term Effectiveness of mHealth Physical Activity Interventions: Systematic Review and Meta-analysis of Randomized Controlled Trials

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review

BACKGROUND

Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.

OBJECTIVE

This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA.

METHODS

We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation).

RESULTS

A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence.

CONCLUSIONS

Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

BACKGROUND

Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

OBJECTIVE

This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

METHODS

A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

RESULTS

The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

CONCLUSIONS

PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/39389.

Digital Peer-Supported App Intervention to Promote Physical Activity Among Community-Dwelling Older Adults: Nonrandomized Controlled Trial

BACKGROUND

The use of mobile apps has promoted physical activity levels. Recently, with an increasing number of older adults accessing the internet, app-based interventions may be feasible in older populations. Peer support-based interventions have become a common method for promoting health-related behavior change. To our knowledge, the feasibility of using digital peer support apps (DPSAs) to increase physical activity among older adults and its impact on physical activity and physical function have not been investigated.

OBJECTIVE

This study aims to assess the feasibility of using DPSAs in older adults and to assess changes in physical activity and physical function in DPSA users.

METHODS

We conducted a nonrandomized controlled trial of older adults aged ≥65 years. We recruited participants for 2 distinct 12-week programs designed to increase physical activity. Participants could choose between an intervention group (app program and exercise instruction) or a control group (exercise instruction only). DPSA creates a group chat for up to 5 people with a common goal, and participants anonymously post to each other in the group. Once a day, participants posted a set of their step counts, photos, and comments on a group chat box. The intervention group used the DPSA after receiving 2 face-to-face lectures on its use. The participants were characterized using questionnaires, accelerometers, and physical function assessments. The feasibility of the DPSA was assessed using retention and adherence rates. Physical activity was assessed using accelerometers to measure the daily step count, light intensity physical activity, moderate to vigorous intensity physical activity (MVPA), and sedentary behavior. Physical function was assessed using grip strength and the 30-second chair-stand test.

RESULTS

The participants in the intervention group were more frequent users of apps, were more familiar with information and communication technology, and had a higher baseline physical activity level. The retention and adherence rates for the DPSA intervention were 88% (36/41) and 87.7%, respectively, indicating good feasibility. Participants in the intervention group increased their step count by at least 1000 steps and their MVPA by at least 10 minutes using the DPSA. There was a significant difference in the interaction between groups and intervention time points in the daily step count and MVPA (step count, P=.04; duration of MVPA, P=.02). The DPSA increased physical activity, especially in older adults with low baseline physical activity levels.

CONCLUSIONS

The feasibility of DPSA was found to be good, with the intervention group showing increases in daily steps and MVPA. The effects of DPSA on step count, physical activity, and physical function in older adults with low baseline physical activity should be investigated using randomized controlled trials.

A randomized controlled mHealth trial that evaluates social comparison-oriented gamification to improve physical activity, sleep quantity, and quality of life in young adults

INTRODUCTION

The integration of gamification in mHealth interventions presents a novel approach to enhance user engagement and health outcomes. This study aims to evaluate whether comparison-oriented gamification can effectively improve various aspects of health and well-being, including physical activity, sedentary behavior, sleep, and overall quality of life among young adults.

METHODS

Potential 107 young adults (from 19 to 28 years old) participated in an 8-week trial. Participants were assigned to either a gamified mHealth intervention (LevantApp) with daily leaderboards and progress bars (n = 53, 26 % dropped-out), or a control condition without gamification (n = 52, 29 % dropped-out). Physical activity (number of steps, moderate and moderate-to-vigorous physical activity -MVPA-) and sleep quantity were measured objectively via accelerometry and subjectively using the International Physical Activity Questionnaire(IPAQ), Pittsburgh Sleep Quality Index(PSQI), Sedentary Behavior Questionnaire(SBQ), and Short Form Health Survey(SF-36).

RESULTS

This mHealth intervention with social comparison-oriented gamification significantly improved moderate physical activity to a greater extent than the control group. Additionally, the intervention group showed improvements in the number of steps, moderate physical activity, sedentary time, emotional wellbeing, and social functioning. However, no significant group by time interaction was observed. No significant differences were observed in sleep quality or quantity.

CONCLUSION

s: The LevantApp gamified mHealth intervention was effective in improving moderate physical activity, physical functioning, and role-emotional in young adults. No significant effects were found on step counts, MVPA or sleep, suggesting that while gamification can enhance specific aspects of physical activity and quality of life, its impact may vary across different outcomes.

Effect of Physical Exercise Programs Based on Mobile Health and Ecological Momentary Assessment on the Physical and Mental Health, Cognitive Functions, and Social Environment of Adults in Developing Countries: A Systematic Review

Background and Objectives: Although there is strong evidence of the positive effects of physical exercise on health, adherence to face-to-face exercise programs in the adult population is low, identifying several barriers that hinder their practice. There is research that demonstrates the viability of physical exercise programs with the use of Mobile Health in Ecological Momentary Assessment (EMA) mode, which contributes to overcoming many reported barriers. To synthesize the methodological characteristics and health effects of physical exercise programs based on mobile health in EMA modality in adults in developing countries. Materials and Methods: This systematic review was conducted according to guidelines established by the PRISMA statement in APA PsycArticles and CINAHL databases by EBSCOhost, Cochrane Library, PubMed, and Web of Science for articles published between 2008 and March 2024. Results: Telephone counseling on clinical-behavioral factors is believed to reduce morbidity and mortality in developed countries, but this aspect is not explored in developing countries. We included nine randomized controlled trials with a total of 4394 male and female participants aged 18 to 60 years. The interventions were mainly carried out by text messages, lasting between 20 to 80 min per session, 3 to 5 days per week, and most were carried out over 12 months. The interventions on the variables of physical activity, nutrition, and medical assessments showed significant effects, and variables such as quality of life and anthropometric measurements were not significant in most studies. Conclusions: This systematic review included studies from different developing countries, the most common diseases being diabetes, overweight, obesity, and hypertension. All the studies used mobile devices as the technology, finding a profile of the adults studied, as well as the characteristics of exercise programs based on mobile health in EMA modality.

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED)

BACKGROUND

The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour.

METHODS

The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form.

RESULTS

The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence.

CONCLUSIONS

The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Identifying app components that promote physical activity: a group concept mapping study

Background

Digital interventions are a promising avenue to promote physical activity in healthy adults. Current practices recommend to include end-users early on in the development process. This study focuses on the wishes and needs of users regarding an a mobile health (mHealth) application that promotes physical activity in healthy adults, and on the differences between participants who do or do not meet the World Health Organization's recommendation of an equivalent of 150 minutes of moderate intensity physical activity.

Methods

We used a mixed-method design called Group Concept Mapping. In a first phase, we collected statements completing the prompt "In an app that helps me move more, I would like to see/ do/ learn the following…" during four brainstorming sessions with physically inactive individuals (n = 19). The resulting 90 statements were then sorted and rated by a new group of participants (n = 46). Sorting data was aggregated, and (dis)similarity matrices were created using multidimensional scaling. Hierarchical clustering was applied using Ward's method. Analyses were carried out for the entire group, a subgroup of active participants and a subgroup of inactive participants. Explorative analyses further investigated ratings of the clusters as a function of activity level, gender, age and education.

Results

Six clusters of statements were identified, namely 'Ease-of-use and Self-monitoring', 'Technical Aspects and Advertisement', 'Personalised Information and Support', 'Motivational Aspects', 'Goal setting, goal review and rewards', and 'Social Features'. The cluster 'Ease-of-use and Self-monitoring' was rated highest in the overall group and the active subgroup, whereas the cluster 'Technical Aspects and Advertisement' was scored as most relevant in the inactive subgroup. For all groups, the cluster 'Social Features' was scored the lowest. Explorative analysis revealed minor between-group differences.

Discussion

The present study identified priorities of users for an mHealth application that promotes physical activity. First, the application should be user-friendly and accessible. Second, the application should provide personalized support and information. Third, users should be able to monitor their behaviour and compare their current activity to their past performance. Fourth, users should be provided autonomy within the app, such as over which and how many notifications they would like to receive, and whether or not they want to engage with social features. These priorities can serve as guiding principles for developing mHealth applications to promote physical activity in the general population.

Effectiveness of a Smartphone App to Promote Physical Activity Among Persons With Type 2 Diabetes: Randomized Controlled Trial

BACKGROUND

Physical activity is well known to have beneficial effects on glycemic control and to reduce risk factors for cardiovascular disease in persons with type 2 diabetes. Yet, successful implementation of lifestyle interventions targeting physical activity in primary care has shown to be difficult. Smartphone apps may provide useful tools to support physical activity. The DiaCert app was specifically designed for integration into primary care and is an automated mobile health (mHealth) solution promoting daily walking.

OBJECTIVE

This study aimed to investigate the effect of a 3-month-long intervention promoting physical activity through the use of the DiaCert app among persons with type 2 diabetes in Sweden. Our primary objective was to assess the effect on moderate to vigorous physical activity (MVPA) at 3 months of follow-up. Our secondary objective was to assess the effect on MVPA at 6 months of follow-up and on BMI, waist circumference, hemoglobin A1c, blood lipids, and blood pressure at 3 and 6 months of follow-up.

METHODS

We recruited men and women with type 2 diabetes from 5 primary health care centers and 1 specialized center. Participants were randomized 1:1 to the intervention or control group. The intervention group was administered standard care and access to the DiaCert app at baseline and 3 months onward. The control group received standard care only. Outcomes of objectively measured physical activity using accelerometers, BMI, waist circumference, biomarkers, and blood pressure were assessed at baseline and follow-ups. Linear mixed models were used to assess differences in outcomes between the groups.

RESULTS

A total of 181 study participants, 65.7% (119/181) men and 34.3% (62/181) women, were recruited into the study and randomized to the intervention (n=93) or control group (n=88). The participants' mean age and BMI were 60.0 (SD 11.4) years and 30.4 (SD 5.3) kg/m2, respectively. We found no significant effect of the intervention (group by time interaction) on MVPA at either the 3-month (β=1.51, 95% CI -5.53 to 8.55) or the 6-month (β=-3.53, 95% CI -10.97 to 3.92) follow-up. We found no effect on any of the secondary outcomes at follow-ups, except for a significant effect on BMI at 6 months (β=0.52, 95% CI 0.20 to 0.84). However, mean BMI did not differ between the groups at the 6-month follow-up.

CONCLUSIONS

We found no evidence that persons with type 2 diabetes being randomized to use an app promoting daily walking increased their levels of MVPA at 3 or 6 months' follow-up compared with controls receiving standard care. The effect of the app on BMI was unclear, and we found nothing to support an effect on secondary outcomes. Further research is needed to determine what type of mHealth intervention could be effective to increase physical activity among persons with type 2 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03053336; https://clinicaltrials.gov/study/NCT03053336.

The Use of Mobile Technologies to Promote Physical Activity and Reduce Sedentary Behaviors in the Middle East and North Africa Region: Systematic Review and Meta-Analysis

BACKGROUND

The Middle East and North Africa (MENA) region faces unique challenges in promoting physical activity and reducing sedentary behaviors, as the prevalence of insufficient physical activity is higher than the global average. Mobile technologies present a promising approach to delivering behavioral interventions; however, little is known about the effectiveness and user perspectives on these technologies in the MENA region.

OBJECTIVE

This study aims to evaluate the effectiveness of mobile interventions targeting physical activity and sedentary behaviors in the MENA region and explore users' perspectives on these interventions as well as any other outcomes that might influence users' adoption and use of mobile technologies (eg, appropriateness and cultural fit).

METHODS

A systematic search of 5 databases (MEDLINE, Embase, CINAHL, Scopus, and Global Index Medicus) was performed. Any primary studies (participants of all ages regardless of medical condition) conducted in the MENA region that investigated the use of mobile technologies and reported any measures of physical activity, sedentary behaviors, or user perceptions were included. We conducted a narrative synthesis of all studies and a meta-analysis of randomized controlled trials (RCTs). The Cochrane risk-of-bias tool was used to assess the quality of the included RCTs; quality assessment of the rest of the included studies was completed using the relevant Joanna Briggs Institute critical appraisal tools.

RESULTS

In total, 27 articles describing 22 interventions (n=10, 37% RCTs) and 4 (15%) nonexperimental studies were included (n=6141, 46% women). Half (11/22, 50%) of the interventions included mobile apps, whereas the other half examined SMS. The main app functions were goal setting and self-monitoring of activity, whereas SMS interventions were primarily used to deliver educational content. Users in experimental studies described several benefits of the interventions (eg, gaining knowledge and receiving reminders to be active). Engagement with the interventions was poorly reported; few studies (8/27, 30%) examined users' perspectives on the appropriateness or cultural fit of the interventions. Nonexperimental studies examined users' perspectives on mobile apps and fitness trackers, reporting several barriers to their use, such as perceived lack of usefulness, loss of interest, and technical issues. The meta-analysis of RCTs showed a positive effect of mobile interventions on physical activity outcomes (standardized mean difference=0.45, 95% CI 0.17-0.73); several sensitivity analyses showed similar results. The trim-and-fill method showed possible publication bias. Only 20% (2/10) of the RCTs measured sedentary behaviors; both reported positive changes.

CONCLUSIONS

The use of mobile interventions for physical activity and sedentary behaviors in the MENA region is in its early stages, with preliminary evidence of effectiveness. Policy makers and researchers should invest in high-quality studies to evaluate long-term effectiveness, intervention engagement, and implementation outcomes, which can inform the design of culturally and socially appropriate interventions for countries in the MENA region.

TRIAL REGISTRATION

PROSPERO CRD42023392699; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=392699.

User engagement with organizational mHealth stress management intervention - A mixed methods study

Mobile health (mHealth) demonstrates great promise for providing effective and accessible interventions within an organizational context. Compared with traditional workplace interventions, mHealth solutions may be significantly more scalable and easier to standardize. However, inadequate user engagement is a major challenge with mHealth solutions that can negatively impact the potential benefits of an intervention. More research is needed to better understand how to ensure sufficient engagement, which is essential for designing and implementing effective interventions. To address this issue, this study employed a mixed methods approach to investigate what factors influence user engagement with an organizational mHealth intervention. Quantitative data were collected using surveys (n = 1267), and semi-structured interviews were conducted with a subset of participants (n = 17). Primary findings indicate that short and consistent interactions as well as user intention are key drivers of engagement. These results may inform future development of interventions to increase engagement and effectiveness.

Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.

Real-world nudging, pricing, and mobile physical activity coaching was insufficient to improve lifestyle behaviours and cardiometabolic health: the Supreme Nudge parallel cluster-randomised controlled supermarket trial

BACKGROUND

Context-specific interventions may contribute to sustained behaviour change and improved health outcomes. We evaluated the real-world effects of supermarket nudging and pricing strategies and mobile physical activity coaching on diet quality, food-purchasing behaviour, walking behaviour, and cardiometabolic risk markers.

METHODS

This parallel cluster-randomised controlled trial included supermarkets in socially disadvantaged neighbourhoods across the Netherlands with regular shoppers aged 30-80 years. Supermarkets were randomised to receive co-created nudging and pricing strategies promoting healthier purchasing (N = 6) or not (N = 6). Nudges targeted 9% of supermarket products and pricing strategies 3%. Subsequently, participants were individually randomised to a control (step counter app) or intervention arm (step counter and mobile coaching app) to promote walking. The primary outcome was the average change in diet quality (low (0) to high (150)) over all follow-up time points measured with a validated 40-item food frequency questionnaire at baseline and 3, 6, and 12 months. Secondary outcomes included healthier food purchasing (loyalty card-derived), daily step count (step counter app), cardiometabolic risk markers (lipid profile and HbA1c via finger prick, and waist circumference via measuring tape), and supermarket customer satisfaction (questionnaire-based: very unsatisfied (1) to very satisfied (7)), evaluated using linear mixed-models. Healthy supermarket sales (an exploratory outcome) were analysed via controlled interrupted time series analyses.

RESULTS

Of 361 participants (162 intervention, 199 control), 73% were female, the average age was 58 (SD 11) years, and 42% were highly educated. Compared to the control arm, the intervention arm showed no statistically significant average changes over time in diet quality (β - 1.1 (95% CI - 3.8 to 1.7)), percentage healthy purchasing (β 0.7 ( - 2.7 to 4.0)), step count (β - 124.0 (- 723.1 to 475.1), or any of the cardiometabolic risk markers. Participants in the intervention arm scored 0.3 points (0.1 to 0.5) higher on customer satisfaction on average over time. Supermarket-level sales were unaffected (β - 0.0 (- 0.0 to 0.0)).

CONCLUSIONS

Co-created nudging and pricing strategies that predominantly targeted healthy products via nudges were unable to increase healthier food purchases and intake nor improve cardiometabolic health. The mobile coaching intervention did not affect step count. Governmental policy measures are needed to ensure more impactful supermarket modifications that promote healthier purchases.

TRIAL REGISTRATION

Dutch Trial Register ID NL7064, 30 May 2018, https://www.onderzoekmetmensen.nl/en/trial/20990.

"Simply complicated": Uncovering the processes of lifestyle behavior change among college and university students with access to a digital multiple lifestyle intervention

Background

One approach to promoting healthy lifestyle behaviors is to target students with digital interventions. One of these is the digital intervention Buddy. This study aimed to understand why college and university students' chose to participate in a digital multiple lifestyle behavior intervention trial (Buddy), and their subsequent experiences of the behavior-change process.

Methods

College and university students taking part in a trial of the Buddy intervention were individually interviewed after completing the 4-month intervention. Participants were guided to narrate their experiences and actions that followed signing up. Altogether, 50 interviews were conducted via telephone. The verbatim transcribed texts were analyzed qualitatively.

Results

The analysis generated seven personas, which illustrated the students' different levels of engagement with the intervention and the behavior-change process. These were: the Occupied, the Kickstarter, the Aimless, the Reflective, the Goal-oriented, the Compliant, and the Personally developed. Buddy worked best for students who had clear ideas about what they wanted to change and why, and who were aware of their needs, and those who could translate information and reflection into action and had the mental and physical energy needed to make changes.

Conclusions

The progress of behavior change depends on the interaction between the digital mode of delivery, the intervention materials of Buddy, the individual's expectations, needs, and skills, and their current life situation. This suggests that designing lifestyle interventions could benefit from more often considering the various personas' different intentions, knowledge, and contexts. By doing so, interventions are likely to emerge that can better match different needs in the target population.

Promoting Physical Activity Among Workers for Better Mental Health: An mHealth Intervention With Deep Learning

There is clear scientific evidence that physical activity helps to prevent depression and anxiety. Utilizing mobile health (mHealth) technologies to enable physical activity is promising, but the evidence of the effectiveness of mHealth interventions on physical activity and mental health is inconsistent. We recently developed a native smartphone app to prevent depression and anxiety by promoting physical activity. One of the app's strengths is that it adopts a deep-learning model and automatically estimates psychological distress from users' physical activity patterns. We conducted a single-arm, 1-month feasibility trial to examine the implementation of the app and its effectiveness in promoting physical activity and improving depression and anxiety. As a result, we did not observe any significant improvement in physical activity or psychological distress. For implementation aspects, the participants used the app less. The conclusion of the presentation is that mHealth interventions are promising for the improvement of physical activity and mental health among workers, but, at this stage, their effectiveness is unclear. There are challenges to be addressed, especially in implementation.

User characteristics associated with use of wrist-worn wearables and physical activity apps by adults with and without impaired speech-in-noise recognition: a cross-sectional analysis

OBJECTIVE

To study weekly use of smartwatches, fitness watches and physical activity apps among adults with and without impaired speech-in-noise (SIN) recognition, to identify subgroups of users.

DESIGN

Cross-sectional study.

STUDY SAMPLE

Adults (aged 28-80 years) with impaired (n = 384) and normal SIN recognition (n = 341) as measured with a web-based digits-in-noise test, from the Netherlands Longitudinal Study on Hearing. Multiple logistic regression analyses were used to study differences and to build an association model.

RESULTS

Employed adults in both groups are more likely to use each type of fitness technology (all ORs >3.4, all p-values < 0.004). Specific to fitness watch use, adults living with others use it more (OR 2.5, 95%CI 1.1;5.8, p = 0.033) whereas those abstaining from alcohol (OR 0.3, 95%CI 0.1;0.6) or consuming >2 glasses/week (OR 0.4, 95%CI 0.2;0.81, overall p = 0.006) and hearing aid users (OR 0.5, 95%CI 0.2;0.9, p = 0.024) make less use.

CONCLUSIONS

Subgroups of adults more and less likely to use fitness technology exist, but do not differ between adults with and without impaired SIN recognition. More research is needed to confirm these results and to develop interventions to increase physical activity levels among adults with hearing loss.

Comparative effectiveness of interventions on promoting physical activity in older adults: A systematic review and network meta-analysis

Background

Despite the well-established health benefits of physical activity, a large population of older adults still maintain sedentary life style or physical inactivity. This network meta-analysis (NMA) aimed to compare the effectiveness of wearable activity tracker-based intervention (WAT), electronic and mobile health intervention (E&MH), structured exercise program intervention (SEP), financial incentive intervention (FI) on promoting physical activity and reducing sedentary time in older adults.

Methods

The systematic review based on PRISMA guidelines, a systematic literature search of PubMed, Web of Science, Google Scholar, EMbase, Cochrane Library, Scopus were searched from inception to December 10th 2022. The randomized controlled trials (RCT) were included. Two reviewers independently conducted study selection, data extraction, risk of bias and certainty of evidence assessment. The effect measures were standard mean differences (SMD) and 95% confidence interval (CI) in daily steps, moderate-to-vigorous physical activity (MVPA) and sedentary time.

Results

A total of 69 studies with 14,120 participants were included in the NMA. Among these included studies, the results of daily steps, MVPA and sedentary time was reported by 55, 25 and 15 studies, respectively. The NMA consistency model analysis suggested that the following interventions had the highest probability (surface under the cumulative ranking, SUCRA) of being the best when compared with control: FI + WAT for daily steps (SUCRA = 96.6%; SMD = 1.32, 95% CI:0.77, 1.86), WAT + E&MH + SEP for MVPA (SUCRA = 91.2%; SMD = 0.94, 95% CI: 0.36, 1.52) and WAT + E&MH + SEP for sedentary time (SUCRA = 80.3%; SMD = -0.50, 95% CI: -0.87, -0.14). The quality of the evidences of daily steps, MVPA and sedentary time was evaluated by very low, very low and low, respectively.

Conclusions

In this NMA, there's low quality evidence that financial incentive combined with wearable activity tracker is the most effective intervention for increasing daily steps of older adults, wearable activity tracker combined with electronic and mobile health and structured exercise program is the most effective intervention to help older adults to increase MVPA and reduce sedentary time.

Effectiveness of holistic mobile health interventions on diet, and physical, and mental health outcomes: a systematic review and meta-analysis

Background

Good physical and mental health are essential for healthy ageing. Holistic mobile health (mHealth) interventions-including at least three components: physical activity, diet, and mental health-could support both physical and mental health and be scaled to the population level. This review aims to describe the characteristics of holistic mHealth interventions and their effects on related behavioural and health outcomes among adults from the general population.

Methods

In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure, and Google Scholar (first 200 records). The initial search covered January 1, 2011, to April 13, 2022, and an updated search extended from April 13, 2022 to August 30, 2023. Randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) were included if they (i) were delivered via mHealth technologies, (ii) included content on physical activity, diet, and mental health, and (iii) targeted adults (≥18 years old) from the general population or those at risk of non-communicable diseases (NCDs) or mental disorders. Studies were excluded if they targeted pregnant women (due to distinct physiological responses), individuals with pre-existing NCDs or mental disorders (to emphasise prevention), or primarily utilised web, email, or structured phone support (to focus on mobile technologies without exclusive human support). Data (summary data from published reports) extraction and risk-of-bias assessment were completed by two reviewers using a standard template and Cochrane risk-of-bias tools, respectively. Narrative syntheses were conducted for all studies, and random-effects models were used in the meta-analyses to estimate the pooled effect of interventions for outcomes with comparable data in the RCTs. The study was registered in PROSPERO, CRD42022315166.

Findings

After screening 5488 identified records, 34 studies (25 RCTs and 9 pre-post NRSIs) reported in 43 articles with 5691 participants (mean age 39 years, SD 12.5) were included. Most (91.2%, n = 31/34) were conducted in high-income countries. The median intervention duration was 3 months, and only 23.5% (n = 8/34) of studies reported follow-up data. Mobile applications, short-message services, and mobile device-compatible websites were the most common mHealth delivery modes; 47.1% (n = 16/34) studies used multiple mHealth delivery modes. Of 15 studies reporting on weight change, 9 showed significant reductions (6 targeted on individuals with overweight or obesity), and in 10 studies reporting perceived stress levels, 4 found significant reductions (all targeted on general adults). In the meta-analysis, holistic mHealth interventions were associated with significant weight loss (9 RCTs; mean difference -1.70 kg, 95% CI -2.45 to -0.95; I2 = 89.00%) and a significant reduction in perceived stress levels (6 RCTs; standardised mean difference [SMD] -0.32; 95% CI -0.52 to -0.12; I2 = 14.52%). There were no significant intervention effects on self-reported moderate-to-vigorous physical activity (5 RCTs; SMD 0.21; 95%CI -0.25 to 0.67; I2 = 74.28%) or diet quality scores (5 RCTs; SMD 0.21; 95%CI -0.47 to 0.65; I2 = 62.27%). All NRSIs were labelled as having a serious risk of bias overall; 56% (n = 14/25) of RCTs were classified as having some concerns, and the others as having a high risk of bias.

Interpretation

Findings from identified studies suggest that holistic mHealth interventions may aid reductions in weight and in perceived stress levels, with small to medium effect sizes. The observed effects on diet quality scores and self-reported moderate-to-vigorous physical activity were less clear and require more research. High-quality RCTs with longer follow-up durations are needed to provide more robust evidence. To promote population health, future research should focus on vulnerable populations and those in middle- and low-income countries. Optimal combinations of delivery modes and components to improve efficacy and sustain long-term effects should also be explored.

Funding

National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.

Promoting physical activity and a healthy active lifestyle in community-dwelling older adults: a design thinking approach for the development of a mobile health application

Background

Physical activity (PA) has wide-ranging, and well documented benefits for older adults, encompassing physical, cognitive, and mental well-being. The World Health Organization advocates for a minimum of 150-300 min of moderate intensity PA per week, supplemented by muscle-strengthening exercises. However, the rates of PA among older adults remain a concern. While portable technologies hold promises in promoting PA, sustaining long-term engagement continues to be a challenge.

Objective

The aims of this study are to identify barriers and facilitators to PA in older adults, to develop an mHealth app promoting PA and an active healthy lifestyle in collaboration with community-dwelling older adults guided by the design thinking process, and to test it.

Methods

A co-creative process was used, employing design thinking. Interviews were conducted to understand the needs of the target population and identify the problem of insufficient PA. Two cocreation sessions involving older adults and experts were conducted to generate innovative ideas. Participants were selected based on age (≥65 years), no severe illness, Dutch language proficiency, and active participation ability. Results were qualitatively analyzed and coded. Finally a prototype was developed and tested.

Results

Interviews with older adults highlighted diverse perceptions of PA but unanimous agreement on its importance. They recognized health benefits such as improved mobility, balance, and reduced fall risk, while emphasizing the social and mental aspects. Barriers included poor health, time constraints, weather conditions and fear of falling. Cocreation sessions identified key topics: perception of a healthy lifestyle, coping strategies, mHealth App features, screen visualization, and tailored notifications, which led to the development of a mobile app promoting PA and an active lifestyle. The app was stepwise prototyped.

Conclusion

This study emphasizes the importance of promoting PA among older adults through a collaborative design thinking approach. However, the implementation of mHealth apps faces obstacles due to the digital divide, necessitating personalized solutions to bridge the gap. Moreover, it calls for further research to investigate the long-term impact of such interventions and explore behavior change patterns in this population.

Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey

Background

Physical inactivity is a global health issue, and mobile health (mHealth) apps are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity.

Objective

This study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample.

Methods

We recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used.

Results

Approximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders.

Conclusions

Regardless of the current trend in app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Rates of compliance and adherence to high-intensity interval training: a systematic review and Meta-analyses

BACKGROUND

To determine rates of compliance (i.e., supervised intervention attendance) and adherence (i.e., unsupervised physical activity completion) to high-intensity interval training (HIIT) among insufficiently active adults and adults with a medical condition, and determine whether compliance and adherence rates were different between HIIT and moderate-intensity continuous training (MICT).

METHODS

Articles on adults in a HIIT intervention and who were either insufficiently active or had a medical condition were included. MEDLINE, EMBASE, PsychINFO, SPORTDiscus, CINAHL, and Web of Science were searched. Article screening and data extraction were completed by two independent reviewers. Risk of bias was assessed using RoB 2.0 or ROBINS-I. Meta-analyses were conducted to discern differences in compliance and adherence between HIIT vs. MICT. Sensitivity analyses, publication bias, sub-group analyses, and quality appraisal were conducted for each meta-analysis.

RESULTS

One hundred eighty-eight unique studies were included (n = 8928 participants). Compliance to HIIT interventions averaged 89.4% (SD:11.8%), while adherence to HIIT averaged 63% (SD: 21.1%). Compliance and adherence to MICT averaged 92.5% (SD:10.6%) and 68.2% (SD:16.2%), respectively. Based on 65 studies included in the meta-analysis, compliance rates were not different between supervised HIIT and MICT interventions [Hedge's g = 0.015 (95%CI: - 0.088-0.118), p = .78]. Results were robust and low risk of publication bias was detected. No differences were detected based on sub-group analyses comparing medical conditions or risk of bias of studies. Quality of the evidence was rated as moderate over concerns in the directness of the evidence. Based on 10 studies, adherence rates were not different between unsupervised HIIT and MICT interventions [Hedge's g = - 0.313 (95%CI: - 0.681-0.056), p = .096]. Sub-group analysis points to differences in adherence rates dependent on the method of outcome measurement. Adherence results should be interpreted with caution due to very low quality of evidence.

CONCLUSIONS

Compliance to HIIT and MICT was high among insufficiently active adults and adults with a medical condition. Adherence to HIIT and MICT was relatively moderate, although there was high heterogeneity and very low quality of evidence. Further research should take into consideration exercise protocols employed, methods of outcome measurement, and measurement timepoints.

REGISTRATION

This review was registered in the PROSPERO database and given the identifier CRD42019103313.

mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial

BACKGROUND

Physical activity effectively prevents depression and anxiety. Although mobile health (mHealth) technologies offer promising results in promoting physical activity and improving mental health, conflicting evidence exists on their effectiveness, and employees face barriers to using mHealth services. To address these problems, we recently developed a smartphone app named ASHARE to prevent depression and anxiety in the working population; it uses a deep learning model for passive monitoring of depression and anxiety from information about physical activity.

OBJECTIVE

This study aimed to preliminarily investigate (1) the effectiveness of the developed app in improving physical activity and reducing depression and anxiety and (2) the app's implementation outcomes (ie, its acceptability, appropriateness, feasibility, satisfaction, and potential harm).

METHODS

We conducted a single-arm interventional study. From March to April 2023, employees aged ≥18 years who were not absent were recruited. The participants were asked to install and use the app for 1 month. The ideal usage of the app was for the participants to take about 5 minutes every day to open the app, check the physical activity patterns and results of an estimated score of psychological distress, and increase their physical activity. Self-reported physical activity (using the Global Physical Activity Questionnaire, version 2) and psychological distress (using the 6-item Kessler Psychological Distress Scale) were measured at baseline and after 1 month. The duration of physical activity was also recorded digitally. Paired t tests (two-tailed) and chi-square tests were performed to evaluate changes in these variables. Implementation Outcome Scales for Digital Mental Health were also measured for acceptability, appropriateness, feasibility, satisfaction, and harm. These average scores were assessed by comparing them with those reported in previous studies.

RESULTS

This study included 24 employees. On average, the app was used for 12.54 days (44.8% of this study's period). After using the app, no significant change was observed in physical activity (-12.59 metabolic equivalent hours per week, P=.31) or psychological distress (-0.43 metabolic equivalent hours per week, P=.93). However, the number of participants with severe psychological distress decreased significantly (P=.01). The digitally recorded duration of physical activity increased during the intervention period (+0.60 minutes per day, P=.08). The scores for acceptability, appropriateness, and satisfaction were lower than those in previous mHealth studies, whereas those for feasibility and harm were better.

CONCLUSIONS

The ASHARE app was insufficient in promoting physical activity or improving psychological distress. At this stage, the app has many issues that are to be addressed in terms of both implementation and effectiveness. The main reason for this low effectiveness might be the poor evaluation of the implementation outcomes by app users. Improving acceptability, appropriateness, and satisfaction are identified as key issues to be addressed in future implementation.

TRIAL REGISTRATION

University Hospital Medical Information Network Clinical Trials Registry UMIN000050430; https://tinyurl.com/mrx5ntcmrecptno=R000057438.

Personalized Coaching via Texting for Behavior Change to Understand a Healthy Lifestyle Intervention in a Naturalistic Setting: Mixed Methods Study

BACKGROUND

Digital health interventions, such as personalized SMS coaching, are considered affordable and scalable methods to support healthy lifestyle changes. SMS, or texting, is a readily available service to most people in Sweden, and personalized SMS coaching has shown great promise in supporting behavior changes.

OBJECTIVE

This study aims to explore the effectiveness of highly personalized SMS coaching for behavior change according to the Capability, Opportunity, Motivation-Behavior (COM-B) model on a sample of physically inactive adults in a nonprofit fitness organization in Sweden.

METHODS

The study used a mixed methods design in which clients acted as their own controls. The participants were clients (n=28) and fitness consultants (n=12). Three types of data were collected: (1) quantitative data at baseline and after the SMS intervention and the waitlist from the clients, (2) qualitative data from semistructured interviews with the fitness consultants, and (3) pseudonymized texting conversations between the fitness consultants and clients.

RESULTS

Overall, the results showed that personalized SMS coaching was effective in supporting the clients' behavior changes. The quantitative analysis showed how the clients' capabilities (Cohen d=0.50), opportunities (Cohen d=0.43), and relationship with the fitness consultants (Cohen d=0.51) improved during the SMS intervention in comparison with baseline. Furthermore, the qualitative analysis revealed how personalized texts added value to existing work methods (eg, increasing continuity and flexibility) and how the relationship between the clients and fitness consultants changed during the intervention, which helped motivate the clients.

CONCLUSIONS

Personalized SMS coaching is an effective method for supporting healthy behavior changes. The human connection that emerged in this study needs to be further explored to fully understand the effectiveness of a digital health intervention.

"Weight loss is my goal and being healthy is my goal… I can get over the hot flashes": a qualitative exploration of menopausal transition experiences and preferences for weight management among White women

OBJECTIVES

A qualitative research study design was used to (1) describe experiences of White women during the menopausal transition, and (2) identify barriers and facilitators for participating in a lifestyle program targeting weight management.

METHODS

Perimenopausal and postmenopausal White women who self-reported a desire to lose or maintain weight participated in focus groups. Women were queried about their past diet, exercise, and weight management practices; menopausal transition; and specific components and considerations for developing a lifestyle program for weight management. Thematic analysis was conducted on coded transcripts and four main themes emerged, each containing three to six subthemes.

RESULTS

Twenty-eight White women (age 54 ± 3 y, body mass index 31.4 ± 9.5 kg/m 2 ) were enrolled. Overall, women felt menopause was a major life event that coincided with weight gain and frustrating body changes. Women already engaged in many different types of exercises and diets to lose weight. Women also talked to healthcare professionals about menopause but were disappointed in the support they received. Women were interested in a lifestyle program that included menopause-specific education, which focused on results beyond weight, which was flexible to their busy lifestyle, and which provided opportunities to build camaraderie among other women experiencing menopause.

CONCLUSIONS

This cohort of White women were interested in receiving menopause information and improving their overall health as part of a lifestyle program targeting weight management during this transition. Building camaraderie with other women affected by menopause is important to women, as is creating a lifestyle program that is flexible with daily life.

Can mobile-health applications contribute to long-term increase in physical activity after medical rehabilitation?-A pilot-study

Due to the positive effects of rehabilitation declining over time, the aim of this study was to investigate the long-term physical activity level (PAL) following inpatient rehabilitation in relation to the use of a smartphone-based after-care program. 202 patients (mean Body Mass Index (BMI): 30,8 kg/m2; 61% female) with chronic diseases (e.g., diabetes mellitus, obesity, chronic low back pain, depression) were recruited between 08/2020 and 08/2021 in this single-arm observational study. All patients underwent a 3-week inpatient rehabilitation program. PAL (in total activity minutes/week) was measured with a validated (online) questionnaire (Freiburger Questionnaire on PA) after 3, 6, 9, and 12 months. App usage (online time, completion of a course) was recorded automatically and used to evaluate the app user behavior (adherence). A variety of socio-economic factors (age, sex, education level, income etc.) were collected to identify possible barriers of app use. Except for sex, no significant difference was observed for socio-economic factors regarding app usage behavior. Median PAL significantly increased after rehabilitation in the total cohort from 360 min/week (before rehabilitation) to 460 min/week 6 months after rehabilitation, then declined to 420 min/week 9 months after rehabilitation before falling below baseline level after 12 months. There was no significant difference in PAL between app users (45%, 91/202) and non-users (55%, 111/202), although app users tended to retain higher activity levels after 3 and 6 months, respectively. Overall, our study emphasizes the effectiveness of a 3-week rehabilitation program on PAL and the acceptance and usability of a smartphone-based after-care program in this patient group. The adherence to this 3-months after-care app program was acceptable (30%), with modest evidence supporting the effectiveness of app use to sustain PAL in the short term.

Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study

Aims

Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.

Methods and results

We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.

Conclusion

Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.

Development and feasibility of a theory-guided and evidence-based physical activity intervention in pregnant women with high risk for gestational diabetes mellitus: a pilot clinical trial

BACKGROUND

Physical activity has been utilized as an effective strategy to prevent gestational diabetes mellitus (GDM). However, most pregnant women with high risk for GDM did not achieve the recommended physical activity level. Furthermore, relevant physical activity protocols have varied without theory-guided and evidence-based tailored to pregnant women with high risk for GDM. This study aimed to develop and pilot test a theory-guided and evidence-based physical activity intervention protocol for pregnant women with high risk for GDM.

METHODS

The study design was guided by the Medical Research Council Framework for Developing and Evaluating Complex Intervention (the MRC framework). The preliminary protocol for physical activity intervention was developed based on self-efficacy theory, research evidence identified from systematic reviews and clinic trials, stakeholder engagement, context, and economic considerations. The preliminary intervention protocol was validated through a content validity study by an expert panel of 10 experts. A single-blinded randomized controlled trial (RCT) was designed to test the feasibility and acceptability of the intervention.

RESULTS

The validity of the preliminary intervention protocol was excellent as consensus was achieved. The final 13 sessions of self-efficacy enhancing physical activity intervention protocol were developed, including knowledge education, exercise clinic visits and video, and group discussions with face-to-face and online blended sessions. In the feasibility study, 34 pregnant women with high risk for GDM were randomized for the intervention (n = 17) or the control group (n = 17). The recruitment and retention rates were 82.9% and 58.9%, respectively. Women in the intervention group had a lower incidence of GDM (26.7% vs. 36.5%) than the control group (P >0.05). All participants were satisfied with the intervention and agreed that the intervention was helpful.

CONCLUSIONS

The developed self-efficacy-enhancing physical activity intervention is a feasible and acceptable intervention for enhancing physical activity among pregnant women with high risk for GDM and is ready to be tested in a more extensive RCT study.

TRIAL REGISTRATION

The study was registered on 4 February 2022 (ChiCTR2200056355) by the Chinese Clini Trial Registry (CHiCTR).

Improving Predictability and Effectiveness in Preventive Digital Health Interventions: Scoping Review

BACKGROUND

Lifestyle-related diseases caused by inadequate diet and physical activity cause premature death, loss of healthy life years, and increased health care costs. Randomized controlled trial (RCT) studies indicate that preventive digital health interventions (P-DHIs) can be effective in preventing these health problems, but the results of these studies are mixed. Adoption studies have identified multiple factors related to individuals and the context in which they live that complicate the transfer of positive results from RCT studies to practical use. Implementation studies have revealed barriers to the large-scale implementation of mobile health (mHealth) solutions in general. Consequently, there is no clear path to delivering predictable outcomes from P-DHIs and achieving effectiveness when scaling up interventions to reduce health problems in society.

OBJECTIVE

This research aimed to expand our understanding of how to increase the outcome predictability of P-DHIs by focusing on physical activity and diet behaviors and amplify our understanding of how to improve effectiveness in large-scale implementations.

METHODS

The research objective was pursued through a multidisciplinary scoping review. This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) as a guide. A comprehensive search of Web of Science and PubMed limited to English-language journal articles published before January 2022 was conducted. Google Scholar was used for hand searches. Information systems theory was used to identify key constructs influencing outcomes of IT in general. Public health and mHealth literature were used to identify factors influencing the adoption of, outcomes from, and implementation of P-DHIs. Finally, the P-DHI investment model was developed based on information systems constructs and factors from the public health and mHealth literature.

RESULTS

In total, 203 articles met the eligibility criteria. The included studies used a variety of methodologies, including literature reviews, interviews, surveys, and RCT studies. The P-DHI investment model suggests which constructs and related factors should be emphasized to increase the predictability of P-DHI outcomes and improve the effectiveness of large-scale implementations.

CONCLUSIONS

The research suggests that outcome predictability could be improved by including descriptions of the constructs and factors in the P-DHI investment model when reporting from empirical studies. Doing so would increase our understanding of when and why P-DHIs succeed or fail. The effectiveness of large-scale implementations may be improved by using the P-DHI investment model to evaluate potential difficulties and possibilities in implementing P-DHIs to create better environments for their use before investing in them and when designing and implementing them. The cost-effectiveness of large-scale implementations is unknown; implementations are far more complicated than just downloading and using apps, and there is uncertainty accompanying implementations given the lack of coordinated control over the constructs and factors that influence the outcome.

Clinicians' Perspectives of Wearable Technology to Detect and Monitor Exacerbations of Chronic Obstructive Pulmonary Disease: Mixed-Method Survey

Objective

To investigate clinicians' perspectives on the current use of wearable technology for detecting COPD exacerbations, and to identify potential facilitators and barriers to its adoption in clinical settings.

Methods

A mixed-method survey was conducted through an online survey platform involving clinicians working with COPD patients. The questionnaires were developed by an expert panel specialising in respiratory medicine at UCL. The questionnaire evaluated clinicians' perspectives on several aspects: the current extent of wearable technology utilisation, the perceived feasibility, and utility of these devices, as well as the potential facilitators and barriers that hinder its wider implementation.

Results

Data from 118 clinicians were included in the analysis. Approximately 80% of clinicians did not currently use information from wearable devices in routine clinical care. A majority of clinicians did not have confidence in the effectiveness of wearables and their consequent impact on health outcomes. However, clinicians highlighted the potential value of wearables in helping deliver personalised care and more rapid assistance. Ease of use, technical support and accessibility of data were considered facilitating factors for wearable utilisation. Costs and lack of technical knowledge were the most frequently reported barriers to wearable utilisation.

Conclusion

Clinicians' perspectives of the use of wearable technology to detect and monitor COPD exacerbations are variable. While accessibility and technical support facilitate wearable implementation, cost, technical issues, and knowledge act as barriers. Our findings highlight the facilitators and barriers to using wearables in patients with COPD and emphasise the need to assess patients' perspectives on wearable acceptability.

Identifying Predictors of Adherence to the Physical Activity Goal: A Secondary Analysis of the SMARTER Weight Loss Trial

INTRODUCTION/PURPOSE

Research is needed to inform tailoring supportive strategies for promoting physical activity (PA) in the context of behavioral treatment of obesity. We aimed to identify baseline participant characteristics and short-term intervention response predictors associated with adherence to the study-defined PA goal in a mobile health (mHealth) weight loss trial.

METHODS

A secondary analysis was conducted of a 12-month weight loss trial (SMARTER) that randomized 502 adults with overweight or obesity to either self-monitoring of diet, PA, and weight with tailored feedback messages ( n = 251) or self-monitoring alone ( n = 251). The primary outcome was average adherence to the PA goal of ≥150 min·wk -1 of moderate- and vigorous-intensity aerobic activities (MVPA) from Fitbit Charge 2™ trackers over 52 wk. Twenty-five explanatory variables were considered. Machine learning methods and linear regression were used to identify predictors of adherence to the PA goal.

RESULTS

The sample ( N = 502) was mostly female (80%), White (82%) with the average age of 45 ± 14.4 yr and body mass index of 33.7 ± 4.0 kg·m -2 . Machine learning methods identified PA goal adherence for the first week as the most important predictor of long-term PA goal adherence. In the parsimonious linear regression model, higher PA goal adherence for the first week, greater PA FB messages opened, older age, being male, higher education, being single and not having obstructive sleep apnea were associated with higher long-term PA goal adherence.

CONCLUSIONS

To our knowledge, this is the first study using machine learning approaches to identify predictors of long-term PA goal adherence in a mHealth weight loss trial. Future studies focusing on facilitators or barriers to PA among young and middle-age adults and women with low PA goal adherence are warranted.

Efficacy of an mHealth Behavior Change Intervention for Promoting Physical Activity in the Workplace: Randomized Controlled Trial

BACKGROUND

Insufficient physical activity (PA) is a well-established risk factor for several noncommunicable diseases such as cardiovascular diseases, cancer, diabetes, depression, and dementia. The World Health Organization (WHO) advises that individuals engage in 150 minutes of moderate PA per week or 75 minutes of intense PA per week. According to the WHO's latest report, 23% of adults fail to meet the minimum recommended level of PA. The percentage was even higher in a recent global study that showed 27% of adults were insufficiently active and reported a 5% increase in the prevalence trend of insufficient PA between 2001 and 2016. The study also showed the rate of insufficient PA among countries varied significantly. For instance, it was estimated that 40% were insufficiently active in the United States, and the percentage was even higher in Saudi Arabia (more than 50%). Governments are actively developing policies and methods to successfully establish a PA-inducing environment that encourages a healthy lifestyle in order to address the global steady decline in PA.

OBJECTIVE

The purpose of this study was to determine the effectiveness of mobile health (mHealth) interventions, particularly SMS text messaging interventions, to improve PA and decrease BMI in healthy adults in the workplace.

METHODS

In this parallel, 2-arm randomized controlled trial, healthy adults (N=327) were randomized to receive an mHealth intervention (tailored text messages combined with self-monitoring (intervention; n=166) or no intervention (control; n=161). Adults who were fully employed in an academic institution and had limited PA during working hours were recruited for the study. Outcomes, such as PA and BMI, were assessed at baseline and 3 months later.

RESULTS

Results showed significant improvement in PA levels (weekly step counts) in the intervention group (β=1097, 95% CI 922-1272, P<.001). There was also a significant reduction in BMI (β=0.60, 95% CI 0.50-0.69, P<.001).

CONCLUSIONS

Combining tailored text messages and self-monitoring interventions to improve PA and lower BMI was significantly effective and has the potential to leverage current methods to improve wellness among the public.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial

BACKGROUND

The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking.

METHODS

We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months.

DISCUSSION

The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05351359, 28/04/2022).

Efficacy of Individualized Sensory-Based mHealth Interventions to Improve Distress Coping in Healthcare Professionals: A Multi-Arm Parallel-Group Randomized Controlled Trial

Detrimental effects of chronic stress on healthcare professionals have been well-established, but the implementation and evaluation of effective interventions aimed at improving distress coping remains inadequate. Individualized mHealth interventions incorporating sensor feedback have been proposed as a promising approach. This study aimed to investigate the impact of individualized, sensor-based mHealth interventions focusing on stress and physical activity on distress coping in healthcare professionals. The study utilized a multi-arm, parallel group randomized controlled trial design, comparing five intervention groups (three variations of web-based training and two variations of an app training) that represented varying levels of individualization to a control group. Both self-reported questionnaire data (collected using Limesurvey) as well as electrocardiography and accelerometry-based sensory data (collected using Mesana Sensor) were assessed at baseline and post-intervention (after eight weeks). Of the 995 eligible participants, 170 (26%) completed the post-intervention measurement (Group 1: N = 21; Group 2: N = 23; Group 3: N = 7; Group 4: N = 34; Group 5: N = 16; Control Group: N = 69). MANOVA results indicated small to moderate time-by-group interaction effects for physical activity-related outcomes, including moderate to vigorous physical activity (F(1,5) = 5.8, p = ≤0.001, η2p = 0.057) and inactivity disruption (F(1,5) = 11.2, p = <0.001, η2p = 0.100), in the app-based intervention groups, but not for step counts and inactivity. No changes were observed in stress-related heart rate variability parameters over time. Despite a high dropout rate and a complex study design, the individualized interventions showed initial positive effects on physical activity. However, no significant changes in stress-related outcomes were observed, suggesting that the intervention duration was insufficient to induce physiological adaptations that would result in improved distress coping.

mHealth to Support Outdoor Gym Resistance Training: The ecofit Effectiveness RCT

INTRODUCTION

In Australia, 45% of adults meet the aerobic recommendations, and only 9%-30% meet the resistance training guidelines. Given the lack of at-scale community-based interventions promoting resistance training, the aim of this study was to assess the impact of an innovative mHealth intervention on upper- and lower-body muscular fitness, cardiorespiratory fitness, physical activity, and social-cognitive mediators among a sample of community-dwelling adults.

STUDY DESIGN

Researchers evaluated the community-based ecofit intervention using a cluster RCT from September 2019 to March 2022 in 2 regional municipalities of New South Wales, Australia.

SETTING/PARTICIPANTS

Researchers recruited a sample of 245 participants (72% female, aged 53.4±13.9 years) who were randomized to the ecofit intervention group (n=122) or waitlist control (n=123) group.

INTERVENTION

The intervention group received access to a smartphone application with standardized workouts tailored to 12 outdoor gym locations and an introductory session. Participants were encouraged to perform at least 2 ecofit workouts per week.

MAIN OUTCOME MEASURES

Primary and secondary outcomes were assessed at baseline, 3 months, and 9 months. The coprimary muscular fitness outcomes were evaluated using the 90-degree push-up and the 60-second sit-to-stand test. Intervention effects were estimated using linear mixed models accounting for group-level clustering (participants could enroll in groups of up to 4). Statistical analysis was conducted in April 2022.

RESULTS

Statistically significant improvements were observed in upper (1.4 repetitions, 95% CI=0.3, 2.6, p=0.018) and lower (2.6 repetitions, 95% CI=0.4, 4.8, p=0.020) body muscular fitness at 9 months but not at 3 months. Increases in self-reported resistance training, resistance training self-efficacy, and implementation intention for resistance training were statistically significant at 3 and 9 months.

CONCLUSION

This study has shown that a mHealth intervention promoting resistance training using the built environment can improve muscular fitness, physical activity behavior, and related cognitions in a community sample of adults.

TRIAL REGISTRATION

This trial was preregistered with the Australian and New Zealand Clinical Trial Registry (ACTRN12619000868189).

A Contactless App-Based Intervention to Improve Health Behaviors in Airline Pilots: A Randomized Trial

INTRODUCTION

There is a need for enhanced preventive health care among airline pilots to mitigate the prevalence of cardiometabolic health risk factors.

DESIGN

A randomized, waitlist-controlled trial was utilized to evaluate the effectiveness of a smartphone-based app intervention for improving health behaviors and cardiometabolic health parameters.

SETTING/PARTICIPANTS

A total of 186 airline pilots (aged 43.2±9.1 years; male, 64%) were recruited and participated in the trial during 2022.

INTERVENTION

This intervention was a personalized, 16-week smartphone-based app multicomponent physical activity, healthy eating, and sleep hygiene intervention.

MAIN OUTCOME MEASURES

Outcome measures of objective health (Cooper's 12-minute exercise test, resting heart rate, push ups, plank isometric hold, body mass), subjective health (self-rated health, perceived psychological stress and fatigue), and health behaviors (weekly physical activity, sleep quality and duration, fruit and vegetable intake) were collected at baseline and after intervention. The waitlist control completed the same measures.

RESULTS

Significant interactions for time Χ group from baseline to 16 weeks were found for all outcome measures (p<0.001). Significant between-group differences for positive health changes in favor of the intervention group were found after intervention for all outcome measures (p<0.05, d=0.4-1.0) except for self-rated health, body mass, and Pittsburgh Sleep Quality Index score.

CONCLUSIONS

Study findings show that an app-based health behavior intervention can elicit positive cardiometabolic health changes among airline pilots over 16 weeks, associated with trivial to large effect sizes.

TRIAL REGISTRATION

The trial protocol was prospectively registered at The Australian New Zealand Clinical Trials Registry (ACTRN12622000288729).

Smart Walk: A Culturally Tailored Smartphone-Delivered Physical Activity Intervention for Cardiometabolic Risk Reduction among African American Women

This article reports the results of Smart Walk: a randomized pilot trial of an 8-month culturally tailored, smartphone-delivered physical activity (PA) intervention for African American women with obesity. Sixty participants (age range = 24−49 years; BMI range = 30−58 kg/m2) were randomized to the Smart Walk intervention (n = 30) or a wellness comparison intervention (n = 30). Results supported the acceptability and feasibility of the intervention, as demonstrated by participant retention (85% at 4 months and 78% at 8 months), Smart Walk app use, and intervention satisfaction (i.e., 100% of PA participants completing the intervention [n = 24] reported they would recommend it to friend). Smart Walk participants also reported greater increases in moderate-to-vigorous PA (4-month between-arm difference in change [b] = 43.3 min/week; p = 0.018; Cohen’s d = 0.69; 8-month b = 56.6 min/week; p = 0.046; d = 0.63) and demonstrated clinically relevant, although not statistically significant (p-values > 0.05), baseline to 4 months improvements in cardiorespiratory fitness (b = 1.67 mL/kg/min; d = 0.40), systolic blood pressure (b = −3.33 mmHg; d = 0.22), diastolic blood pressure (b = −4.28 mmHg; d = 0.37), and pulse wave velocity (b = −0.46 m/s; d = 0.33). Eight-month cardiometabolic outcomes followed similar trends, but had high rates of missing data (45−53%) due to COVID-19 restrictions. Collectively, findings demonstrated favorable outcomes for acceptability and feasibility, while also highlighting key areas for refinement in future research.

The Effectiveness of E-Health Interventions Promoting Physical Activity and Reducing Sedentary Behavior in College Students: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Insufficient physical activity (PA) and excessive sedentary behavior (SB) are detrimental to physical and mental health. This systematic review and meta-analysis aimed to identify whether e-health interventions are effective for improving PA and SB in college students. Five electronic databases, including Medline, Web of Science, Embase, Cochrane Library, and ProQuest, were searched to collect relevant randomized controlled trials up to 22 June 2022. In total, 22 trials (including 31 effects) with 8333 samples were included in this meta-analysis. The results showed that e-health interventions significantly improved PA at post-intervention (SMD = 0.32, 95% CI: 0.19, 0.45, p < 0.001) compared with the control group, especially for total PA (SMD = 0.34, 95% CI: 0.10, 0.58, p = 0.005), moderate to vigorous PA (SMD = 0.17, 95% CI: 0.01, 0.32, p = 0.036), and steps (SMD = 0.75, 95% CI: 0.23, 1.28, p < 0.001. There were no significant effects for both PA at follow-up (SMD = 0.24, 95% CI: − 0.01, 0.49, p = 0.057) and SB (MD = −29.11, 95% CI: −70.55, 12.32, p = 0.17). The findings of subgroup analyses indicated that compared to the control group, interventions in the group of general participants (SMD = 0.45, 95% CI: 0.27, 0.63, p < 0.001), smartphone apps (SMD = 0.46, 95% CI: 0.19, 0.73, p = 0.001), and online (SMD = 0.23, 95% CI: 0.04, 0.43, p < 0.001) can significantly improve PA at post-intervention. Moreover, the intervention effects were significant across all groups of theory, region, instrument, duration, and female ratio. At follow-up, interventions in groups of developing region (SMD = 1.17, 95% CI: 0.73, 1.62, p < 0.001), objective instrument (SMD = 0.83, 95% CI: 0.23, 1.42, p = 0.007), duration ≤ 3-month (SMD = 1.06, 95% CI: 0.72, 1.39, p < 0.001), and all female (SMD = 0.79, 95% CI: 0.02, 1.56, p = 0.044) can significantly improve PA. The evidence of this meta-analysis shows that e-health interventions can be taken as promising strategies for promoting PA. The maintenance of PA improvement and the effect of interventions in reducing SB remain to be further studied. Educators and health practitioners should focus on creating multiple e-health interventions with individualized components.

Ethical Issues in the Use of Smartphone Apps for HIV Prevention in Malaysia: Focus Group Study With Men Who Have Sex With Men

BACKGROUND

The use of smartphone apps can improve the HIV prevention cascade for key populations such as men who have sex with men (MSM). In Malaysia, where stigma and discrimination toward MSM are high, mobile health app-based strategies have the potential to open new frontiers for HIV prevention. However, little guidance is available to inform researchers about the ethical concerns that are unique to the development and implementation of app-based HIV prevention programs.

OBJECTIVE

This study aimed to fill this gap by characterizing the attitudes and concerns of Malaysian MSM regarding HIV prevention mobile apps, particularly regarding the ethical aspects surrounding their use.

METHODS

We conducted web-based focus group discussions with 23 MSM between August and September 2021. Using in-depth semistructured interviews, participants were asked about the risks and ethical issues they perceived to be associated with using mobile apps for HIV prevention. Each session was digitally recorded and transcribed. Transcripts were inductively coded using the Dedoose software (SocioCultural Research Consultants) and analyzed to identify and interpret emerging themes.

RESULTS

Although participants were highly willing to use app-based strategies for HIV prevention, they raised several ethical concerns related to their use. Prominent concerns raised by participants included privacy and confidentiality concerns, including fear of third-party access to personal health information (eg, friends or family and government agencies), issues around personal health data storage and management, equity and equitable access, informed consent, and regulation.

CONCLUSIONS

The study's findings highlight the role of ethical concerns related to the use of app-based HIV prevention programs. Given the ever-growing nature of such technological platforms that are intermixed with a complex ethical-legal landscape, mobile health platforms must be safe and secure to minimize unintended harm, safeguard user privacy and confidentiality, and obtain public trust and uptake.

Older adults as designers of behavior change strategies to increase physical activity-Report of a participatory design process

Background

Despite the significant value of physical activity for the health of older adults, this population often fails to achieve recommended activity levels. Digital interventions show promise in providing support for self-managed physical activity. However, more information is needed about older adults' preferences for digital support to change physical activity behaviors as well as the process of designing them. The aim of this paper was to describe the participatory design process in which older adults were involved in the co-creation of digitally supported behavioral change strategies to support self-managed physical activity, and how the results were integrated in a prototype.

Methods

The participatory design process involved with nine older adults and two researchers. The participants were divided in two groups, and each group participated in three workshops and completed home tasks in between workshops. Following an iterative design process influenced by theories of behavior change, the workshops and home tasks were continuously analyzed, and the content and process were developed between groups and the next set of workshops. Prototypes of a mobile health (mHealth) solution for fall preventive exercise for older adults were developed in which the conceptualized strategies were integrated. To support coherence in reporting and evaluation, the developed techniques were mapped to the Behavior Change Technique Taxonomy v1 and the basic human psychosocial needs according to the Self-determination Theory.

Results

The results highlight different preferences of older adults for feedback on physical activity performance, as well as the importance of transparency regarding the identification of the sender of feedback. Preferences for content and wording of feedback varied greatly. Subsequently, the design process resulted in a virtual health coach with three different motivational profiles and tools for goal setting and self-monitoring. These behavior change strategies were integrated in the exercise application Safe Step v1. The conformity of the design concepts with the needs of Self-determination Theory and Behavior Change Technique Taxonomy v1 are presented.

Conclusion

The participatory design process exemplifies how older adults successfully contributed to the design of theory-based digital behavior change support, from idea to finished solution. Tailoring feedback with a transparent sender is important to support and not undermine motivation.

mHealth App Barriers, Usability, and Personalization: A Cross-Sectional Study from Egypt and Saudi Arabia

Mobile health (mHealth) has emerged as a substantial segment of eHealth. Herein, we conducted a cross-sectional study to investigate mHealth app barriers, usability, and personalization in Egypt and Saudi Arabia. We used a Google survey to recruit participants from both countries between the 15th of September and the 15th of October 2022. Among 299 participants (247 from Egypt and 52 from Saudi Arabia), aged ≥ 18 years, 27.4% reported mHealth app use. In the age-, sex-, and country-adjusted regression models, age > 25 years: OR (95% CI) = 1.98 (1.11, 3.54), residing in Saudi Arabia: OR (95% CI) = 4.33 (2.22, 8.48), and physical activity: OR (95% CI) = 2.53 (1.44, 4.44) were associated with mHealth app use. The main mHealth app purposes were lifestyle promotion (35.4%), diet and nutrition (30.5%), and administrative services (13.4%). On a scale from 20 to 100, mHealth app usability scores were 46.3% (20−40), 7.3% (41−60), 31.7% (61−80), and 14.7% (81−100). According to 93.9% of users, mHealth app features were modifiable to meet personal health goals, while 37% stated that mHealth apps helped them set new personal health goals. In conclusion, age, residing in Saudi Arabia (compared to Egypt), and physical activity were positively associated with mHealth app use. mHealth app feature personalization and helping users set new personal health goals were largely reported, suggesting that mHealth has the potential to help put personalized healthcare into practice.

Preliminary Use and Outcome Data of a Digital Home Exercise Program for Back, Hip, and Knee Pain: Retrospective Observational Study With a Time Series and Matched Analysis

BACKGROUND

Musculoskeletal conditions are among the main contributors to the global burden of disease. International guidelines consider patient education and movement exercises as the preferred therapeutic option for unspecific and degenerative musculoskeletal conditions. Innovative and decentralized therapeutic means are required to provide access to and availability of such care to meet the increasing therapeutic demand for this spectrum of conditions.

OBJECTIVE

This retrospective observational study of preliminary use and outcome data explores the clinical outcomes of Vivira (hereafter referred to as "program"), a smartphone-based program for unspecific and degenerative pain in the back, hip, and knee before it received regulatory approval for use in the German statutory health insurance system.

METHODS

An incomplete matched block design was employed to assess pain score changes over the intended 12-week duration of the program. Post hoc analyses were performed. In addition, a matched comparison of self-reported functional scores and adherence rates is presented.

RESULTS

A total of 2517 participants met the inclusion criteria and provided sufficient data to be included in the analyses. Overall, initial self-reported pain scores decreased significantly from an average of 5.19 out of 10 (SD 1.96) to an average of 3.35 out of 10 (SD 2.38) after 12 weeks. Post hoc analyses indicate a particularly emphasized pain score reduction over the early use phases. Additionally, participants with back pain showed significant improvements in strength and mobility scores, whereas participants with hip or knee pain demonstrated significant improvements in their coordination scores. Across all pain areas and pain durations, a high yet expected attrition rate could be observed.

CONCLUSIONS

This observational study provides the first insights into the clinical outcomes of an exercise program for unspecific and degenerative back, hip, and knee pain. Furthermore, it demonstrates a potential secondary benefit of improved functionality (ie, strength, mobility, coordination). However, as this study lacks confirmatory power, further research is required to substantiate the clinical outcomes of the program assessed.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00021785; https://drks.de/search/en/trial/DRKS00021785.

Framing physical literacy for aging adults: an integrative review

PURPOSE

Physical literacy is an important component for improving functional health for adults. However, little is known how physical literacy can be framed to support the rehabilitation needs of aging adults.

METHODS

An integrative review was conducted to understand what components are used to frame physical literacy for adults. Electronic databases were searched from 2000-2021 for eligibility criteria including: 1) adults ≥45 years of age, 2) addressed physical literacy OR physical literacy components AND referred to outcomes assessing either mobility, physical function, rehabilitation, health promotion, health prevention, public health, or physical activity.

RESULTS

A total of 22 articles met the inclusion criteria. The varied methodological quality, including grey literature (50%) to systematic reviews (14%), indicates that physical literacy for adults is a novel topic. When defining physical literacy for adults, 13 new constructs emerged with purposeful activities, knowledge of age-related changes and social interaction as the top three. Physical literacy interventions demonstrated improvements in self-efficacy, physical function, and exercise behaviour.

CONCLUSION

Findings from the current literature indicate that engagement in purposeful, social, and diverse activities, obtaining knowledge of age-related changes and being able to self-adapt to mobility fluctuations is the foundation to becoming a physically literate adult.Implications for RehabilitationPhysical literacy is emerging as a promising health strategy for adults and older adults.Reconceptualization of the physical literacy construct to include rehabilitation approaches is needed to address the mobility challenges associated with aging.The foundation to becoming a physically literate adult involves monitoring one's ability for movement and having the knowledge to identify and remediate changes in function while engaging in purposeful movements.Physical literacy programs for adults and older adults have potential to positively impact important health outcomes equitably for all individuals.

Factors related to moderate exercise during COVID-19 for overweight and obese individuals: A secondary analysis of HINTS data

Objective

COVID-19 has exacerbated pre-existing rates of overweight and obesity in the United States. mHealth technologies are gaining in popularity for its potential to reduce obesity, if facilitated by patient-centered communication. This study explores predictors of overweight and obese individuals' exercise levels during COVID-19.

Methods

2191 respondents who visited a doctor in the past year and self-reported being overweight were selected from the 2020 Health Information National Trends Survey (HINTS). Respondents reported their physical activity, beliefs about obesity, health tracking behaviors, and communication with providers during the pandemic. Structural equation modeling was used to explore connections among the variables.

Results

Patient-provider (e-)communication was significantly associated with changes in people's obesity-related beliefs and mHealth tracking usage, predicting moderate exercise during the pandemic.

Conclusion

The findings illustrate the need for patient-centered communication encounters to include discussions on mHealth technologies and accessible methods of engaging in physical activity.Innovation: This study examined secondary data provided by overweight and obese individuals from the early days of the COVID-19 pandemic; this population may benefit from targeted health interventions using mHealth technologies. Our findings suggest that healthcare providers should engage patients through mHealth technology and seek to improve digital health literacy to progress physical activity nationwide.

Implementation of a text messaging intervention to patients on warfarin therapy in Brazilian primary care units: a quasi-experimental study

Background

Warfarin remains the most affordable oral anticoagulant in many countries. However, it may have serious side effects, and the success of the therapy depends on the patient’s understanding of the medication and their adherence to treatment. The use of short messages services (SMS) is a strategy that can be used to educate patients, but there are no studies evaluating this intervention in patients taking warfarin. Therefore, we aimed to develop, implement, and assess the feasibility of an intervention using SMS to primary care patients taking warfarin in a medium-sized Brazilian city.

Methods

A bank of 79 SMS was drafted and validated by an expert panel. During 6 months, three times a week, patients received messages about anticoagulation with warfarin. At baseline and after 3 months, we assessed their knowledge and adherence with validated instruments. At the end of the follow-up, participants answered a satisfaction questionnaire. Subsequently, a scale-up phase was conducted, with another round of the intervention including 82 participants (29 from the first phase and 53 newly recruited). Seven months after the end of the scale-up, we asked the patients for their insights about the long-term effects of this program. All patients signed informed consent. The study was approved by the Research and Ethics committee of the Universidade Federal de Minas Gerais.

Results

In the pilot, 33 (89.2%) patients completed the follow-up. Among the participants who answered the satisfaction questionnaire (n = 29), 86.2% considered that the intervention motivated a healthy lifestyle and improved their understanding of warfarin therapy. All patients were willing to continue receiving the messages. Adherence measured by the Measure of Adherence to Treatment (MAT) test was high in the pre-intervention assessment and remained high (96.7% vs. 93.3%; p = 1.0000). The proportion of patients who achieved > 75% correct answers on the Oral Anticoagulation Knowledge (OAK) test increased from 6.5% to 25.6, p = 0.0703. In the scale-up, 23 patients answered the long-term assessment questionnaire. The main long-term knowledge reported was dietary information. Nine patients received the messages but did not remember their content.

Conclusion

The intervention was well-accepted and had a positive impact on patient’s knowledge about oral anticoagulation therapy. The scale-up assessment reinforced the need to constantly monitor digital interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-022-01647-5.

Physical Activity Surveillance in Children and Adolescents using Smartphone Technology: Systematic Review (Preprint)

BACKGROUND

Self-reported physical activity (PA) questionnaires have traditionally been used for PA surveillance in children and adolescents, especially in free-living conditions. Objective measures are more accurate at measuring PA, but high cost often creates a barrier for their use in low- and middle-income settings. The advent of smartphone technology has greatly influenced mobile health and has offered new opportunities in health research, including PA surveillance.

OBJECTIVE

This review aimed to systematically explore the use of smartphone technology for PA surveillance in children and adolescents, specifically focusing on the use of smartphone apps.

METHODS

A literature search was conducted using 5 databases (PubMed, Scopus, CINAHL, MEDLINE, and Web of Science) and Google Scholar to identify articles relevant to the topic that were published from 2008 to 2023. Articles were included if they included children and adolescents within the age range of 5 to 18 years; used smartphone technology as PA surveillance; had PA behavioral outcomes such as energy expenditure, step count, and PA levels; were written in English; and were published between 2008 and 2023.

RESULTS

We identified and analyzed 8 studies (5 cross-sectional studies and 3 cohort studies). All participants were aged 12-18 years, and all studies were conducted in high-income countries only. Participants were recruited from schools, primary care facilities, and voluntarily. Five studies used mobile apps specifically and purposely developed for the study, whereas 3 studies used mobile apps downloadable from the Apple App Store and Android Play Store. PA surveillance using these apps was conducted from 24 hours to 4 weeks.

CONCLUSIONS

Evidence of PA surveillance using smartphone technology in children and adolescents was insufficient, which demonstrated the knowledge gap. Additional research is needed to further study the feasibility and validity of smartphone apps for PA surveillance among children and adolescents, especially in low- and middle-income countries.

A “No-Code” App Design Platform for Mobile Health Research: Development and Usability Study

Background

A challenge facing researchers conducting mobile health (mHealth) research is the amount of resources required to develop mobile apps. This can be a barrier to generating relevant knowledge in a timely manner. The recent rise of “no-code” software development platforms may overcome this challenge and enable researchers to decrease the cost and time required to develop mHealth research apps.

Objective

We aimed to describe the development process and the lessons learned to build Pathverse, a no-code mHealth app design platform.

Methods

The study took place between November 2019 and December 2021. We used a participatory research framework to develop the mHealth app design platform. In phase 1, we worked with researchers to gather key platform feature requirements and conducted an exploratory literature search to determine needs related to this platform. In phase 2, we used an agile software framework (Scrum) to develop the platform. Each development sprint cycle was 4 weeks in length. We created a minimum viable product at the end of 7 sprint cycles. In phase 3, we used a convenience sample of adults (n=5) to gather user feedback through usability and acceptability testing. In phase 4, we further developed the platform based on user feedback, following the V-model software development process.

Results

Our team consulted end users (ie, researchers) and utilized behavior change technique taxonomy and behavior change models (ie, the multi-process action control framework) to guide the development of features. The first version of the Pathverse platform included features that allowed researchers to (1) design customized multimedia app content (eg, interactive lessons), (2) set content delivery logic (eg, only show new lessons when completing the previous lesson), (3) implement customized participant surveys, (4) provide self-monitoring tools, (5) set personalized goals, and (6) customize app notifications. Usability and acceptability testing revealed that researchers found the platform easy to navigate and that the features were intuitive to use. Potential improvements include the ability to deliver adaptive interventions and add features such as community group chat.

Conclusions

To our knowledge, Pathverse is the first no-code mHealth app design platform for developing mHealth interventions for behavior. We successfully used behavior change models and the behavior change technique taxonomy to inform the feature requirements of Pathverse. Overall, the use of a participatory framework, combined with the agile and hybrid-agile software development process, enabled our team to successfully develop the Pathverse platform.

Going digital - a commentary on the terminology used at the intersection of physical activity and digital health

In recent years digital technologies have become a major means for providing health-related services and this trend was strongly reinforced by the current Coronavirus disease 2019 (COVID-19) pandemic. As it is well-known that regular physical activity has positive effects on individual physical and mental health and thus is an important prerequisite for healthy aging, digital technologies are also increasingly used to promote unstructured and structured forms of physical activity. However, in the course of this development, several terms (e.g., Digital Health, Electronic Health, Mobile Health, Telehealth, Telemedicine, and Telerehabilitation) have been introduced to refer to the application of digital technologies to provide health-related services such as physical interventions. Unfortunately, the above-mentioned terms are often used in several different ways, but also relatively interchangeably. Given that ambiguous terminology is a major source of difficulty in scientific communication which can impede the progress of theoretical and empirical research, this article aims to make the reader aware of the subtle differences between the relevant terms which are applied at the intersection of physical activity and Digital Health and to provide state-of-art definitions for them.

The Effect of a Future-Self Avatar Mobile Health Intervention (FutureMe) on Physical Activity and Food Purchases: Randomized Controlled Trial

BACKGROUND

Insufficient physical activity and unhealthy diets are contributing to the rise in noncommunicable diseases. Preventative mobile health (mHealth) interventions may help reverse this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence of whether such interventions can change health behavior is lacking.

OBJECTIVE

We aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs.

METHODS

We conducted a 12-week parallel randomized controlled trial (RCT), followed by semistructured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention, a physical activity and food purchase tracking mobile phone app that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition by leveraging digital receipts from loyalty card data and analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals, and nonparametric tests were conducted to test for within- and between-group differences.

RESULTS

We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44 years (IQR 19), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power. The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median -1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system.

CONCLUSIONS

Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04505124; https://clinicaltrials.gov/ct2/show/NCT04505124.

mHealth Interventions to Reduce Physical Inactivity and Sedentary Behavior in Children and Adolescents: Systematic Review and Meta-analysis of Randomized Controlled Trials

Background

Children and adolescents increasingly do not meet physical activity (PA) recommendations. Hence, insufficient PA (IPA) and sedentary behavior (SB) among children and adolescents are relevant behavior change domains for using individualized mobile health (mHealth) interventions.

Objective

This review and meta-analysis investigated the effectiveness of mHealth interventions on IPA and SB, with a special focus on the age and level of individualization.

Methods

PubMed, Scopus, Web of Science, SPORTDiscus, and Cochrane Library were searched for randomized controlled trials published between January 2000 and March 2021. mHealth interventions for primary prevention in children and adolescents addressing behavior change related to IPA and SB were included. Included studies were compared for content characteristics and methodological quality and summarized narratively. In addition, a meta-analysis with a subsequent exploratory meta-regression examining the moderating effects of age and individualization on overall effectiveness was performed.

Results

On the basis of the inclusion criteria, 1.3% (11/828) of the preliminary identified studies were included in the qualitative synthesis, and 1.2% (10/828) were included in the meta-analysis. Trials included a total of 1515 participants (mean age (11.69, SD 0.788 years; 65% male and 35% female) self-reported (3/11, 27%) or device-measured (8/11, 73%) health data on the duration of SB and IPA for an average of 9.3 (SD 5.6) weeks. Studies with high levels of individualization significantly decreased insufficient PA levels (Cohen d=0.33; 95% CI 0.08-0.58; Z=2.55; P=.01), whereas those with low levels of individualization (Cohen d=−0.06; 95% CI −0.32 to 0.20; Z=0.48; P=.63) or targeting SB (Cohen d=−0.11; 95% CI −0.01 to 0.23; Z=1.73; P=.08) indicated no overall significant effect. The heterogeneity of the studies was moderate to low, and significant subgroup differences were found between trials with high and low levels of individualization (χ21=4.0; P=.04; I2=75.2%). Age as a moderator variable showed a small effect; however, the results were not significant, which might have been because of being underpowered.

Conclusions

Evidence suggests that mHealth interventions for children and adolescents can foster moderate reductions in IPA but not SB. Moreover, individualized mHealth interventions to reduce IPA seem to be more effective for adolescents than for children. Although, to date, only a few mHealth studies have addressed inactive and sedentary young people, and their quality of evidence is moderate, these findings indicate the relevance of individualization on the one hand and the difficulties in reducing SB using mHealth interventions on the other.

Trial Registration

PROSPERO CRD42020209417; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209417

What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review

Background: Lifestyle Medicine (LM) aims to address six main behavioral domains: diet/nutrition, substance use (SU), physical activity (PA), social relationships, stress management, and sleep. Digital Health Interventions (DHIs) have been used to improve these domains. However, there is no consensus on how to measure lifestyle and its intermediate outcomes aside from measuring each behavior separately. We aimed to describe (1) the most frequent lifestyle domains addressed by DHIs, (2) the most frequent outcomes used to measure lifestyle changes, and (3) the most frequent DHI delivery methods. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) Extension for Scoping Reviews. A literature search was conducted using MEDLINE, Cochrane Library, EMBASE, and Web of Science for publications since 2010. We included systematic reviews and meta-analyses of clinical trials using DHI to promote health, behavioral, or lifestyle change. Results: Overall, 954 records were identified, and 72 systematic reviews were included. Of those, 35 conducted meta-analyses, 58 addressed diet/nutrition, and 60 focused on PA. Only one systematic review evaluated all six lifestyle domains simultaneously; 1 systematic review evaluated five lifestyle domains; 5 systematic reviews evaluated 4 lifestyle domains; 14 systematic reviews evaluated 3 lifestyle domains; and the remaining 52 systematic reviews evaluated only one or two domains. The most frequently evaluated domains were diet/nutrition and PA. The most frequent DHI delivery methods were smartphone apps and websites. Discussion: The concept of lifestyle is still unclear and fragmented, making it hard to evaluate the complex interconnections of unhealthy behaviors, and their impact on health. Clarifying this concept, refining its operationalization, and defining the reporting guidelines should be considered as the current research priorities. DHIs have the potential to improve lifestyle at primary, secondary, and tertiary levels of prevention-but most of them are targeting clinical populations. Although important advances have been made to evaluate DHIs, some of their characteristics, such as the rate at which they become obsolete, will require innovative research designs to evaluate long-term outcomes in health.

mHealth interventions targeting movement behaviors in Asia: A scoping review

mHealth interventions can promote healthy movement behaviors (physical activity, sedentary behavior, and sleep). However, recent reviews include few studies from Asia, despite it being home to over 60% of the world population. The aim is to map the current evidence for mHealth interventions targeting movement behaviors in Asia. Six databases were searched up until August 2021. Included studies described an mHealth intervention targeting one or more movement behaviors, delivered in a country/territory in Asia, to a general population. A total of 3986 unique records were screened for eligibility in duplicate. Eighty studies with 1,413,652 participants were included. Most were randomized (38.8%) or quasi-experimental (27.5%) trials. Studies were from 17 countries/territories (out of 55); majority were high- (65.0%) or upper middle-income (28.7%). Physical activity was targeted most often (93.8%), few targeted sedentary behavior (7.5%), or sleep (8.8%). Most targeted one movement behavior (90.0%), and none targeted all three together. Interventions typically incorporated a single mHealth component (70.0%; app, pedometer, text messages, wearable) and were delivered remotely (66.3%). The average intervention length was 121.8 (SD 127.6) days. mHealth interventions in Asia have primarily targeted physical activity in high- and upper middle-income countries. There are few interventions targeting sedentary behavior or sleep, and no interventions in low-income countries.