A Personalized Smartphone-Delivered Just-in-time Adaptive Intervention (JitaBug) to Increase Physical Activity in Older Adults: Mixed Methods Feasibility Study

The influence of individual-level determinants on compliance with mHealth walking suggestions and older adults' experiences: A longitudinal exploratory mixed methods study

Promoting healthy aging through physical activity (PA) is crucial as the global population grows older. Traditional interventions often fail to engage older adults, underlining the need for personalized, timely approaches. Smartphone-delivered PA interventions can offer personalized support during opportune moments for behavioral change. The current study examined whether the receptivity of inactive older adults influences compliance with mHealth walking suggestions after inactivity, and explored their experiences with it. Thirty healthy older adults (mean age 73.9 years) participated in the study and answered event-based EMA questionnaires via HealthReact after each 30-minute inactivity period. Emotions, physical complaints, intention, self-efficacy, perceived walking, and environmental permissiveness were assessed. Walking suggestions followed each EMA, and semi-structured interviews were conducted post-study. Multilevel logistic regressions in R were applied, and qualitative data were thematically analyzed using NVivo. Results show that higher intention, self-efficacy, and environmental permissiveness positively correlated with compliance, while higher perceived walking negatively correlated. Participants generally found the suggestions motivating and well-timed, but some reported increased alertness and pressure. Consequently, tailoring interventions to individual needs and targeting receptive moments can enhance compliance and promote healthier aging through increased PA. Future mobile interventions should consider self-efficacy, intention, prior activity, and environmental conditions to improve effectiveness.

Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study

Background

While the health benefits of physical activity are well-known, adherence to regular physical activity remains a major challenge. Just-in-time adaptive intervention (JITAI) has been proposed as one method to increase physical activity by delivering an intervention at a time when individuals are more likely to make behavioral changes. However, most studies that have implemented JITAI have used uniform intervention criteria (UIC) across participants rather than personalized intervention criteria (PIC) for the individual.

Objective

The objective of this paper was to examine the effectiveness of using JITAI implemented with PIC to increase physical activity.

Methods

Healthy university students wore a wrist activity monitor for 2 weeks. Participants were divided into 2 groups, which received JITAI to promote physical activity according to either PIC or UIC. In the first week, the mean distance moved and sedentary time per hour for each participant were calculated to derive PIC. UIC was obtained from a 2-week study with a different sample (n=47) conducted under the same conditions. In the second week, JITAI prompts were sent every hour if both of the following criteria were met: the distance moved was shorter, and sedentary time was longer than PIC or UIC. Differences in changes in physical activity as a result of implementing interventions according to PIC and UIC were analyzed using multilevel models.

Results

We analyzed data from 28 healthy university students (18-23 y old, female n=12). Both PIC (P<.001) and UIC (P<.001) significantly increased physical activity in the first hour after JITAI was received. In that first hour, PIC increased physical activity more than UIC; more calories were burned (P=.02), more steps were taken (P=.007), and distance moved was increased (P=.003). However, over the course of the week, the use of JITAI did not significantly increase physical activity levels.

Conclusions

Our results appear to suggest that PIC-based JITAI is more effective than UIC-based JITAI, consistent with the idea of a need for precision health approaches. Further research is needed to develop effective long-term intervention designs with sustainable effects.

Digital, Personalized Clinical Trials Among Older Adults, Lessons Learned From the COVID-19 Pandemic, and Directions for the Future: Aggregated Feasibility Data From Three Trials Among Older Adults

BACKGROUND

The COVID-19 pandemic was extremely disruptive to clinical practice and research. Given older adults' increased likelihood of chronic health concerns, limited resources, and greater risk for adverse outcomes of COVID-19, access to research participation during this time was critical, particularly to interventions that may impact health conditions or behaviors. Fortunately, the implementation of personalized, digital research trials during the pandemic allowed for research and intervention delivery for older adults to continue remotely, resulting in feasibility findings that can benefit researchers, practitioners, and the broader older adult population.

OBJECTIVE

This study discusses 3 digital, remote, and personalized intervention trials implemented during the pandemic to increase physical activity (2 trials) or to reduce back pain (1 trial).

METHODS

We identified measures used for all 3 trials including Fitbit activity monitor use and self-reported participant satisfaction. Participant levels of Fitbit activity monitor use and satisfaction ratings of the digital trials were compared between younger (younger than 55 years) and older adults (older than 55 years). Differences between these cohorts were analyzed using chi-square tests for categorical outcomes and 2-tailed independent-sample t tests for continuous outcomes.

RESULTS

Across the 3 trials, the majority of participants reported high satisfaction with the usability of the trials' digital systems including SMS text message interventions and surveys (≥62% satisfied) and the use of wearable devices such as Fitbits (≥81% satisfied). In addition, the use of the Fitbit device was shown to be feasible, as older adults across all trials wore their Fitbits for the majority of the day (mean 20.3, SD 3.6 hours). Furthermore, consistent Fitbit wear was common; 100% of participants older than 55 years wore their Fitbit an average of 10 or more hours per day. These trials highlight that digital, remote intervention delivery may be successfully implemented among older adults by way of personalized trials. Across the 3 digital interventions, feasibility and acceptability were high among older adults, and comparable to younger adults.

CONCLUSIONS

Given the success of the current trials amid pandemic restrictions, we argue that these trials serve as a useful framework to aid in designing personalized, digital, remote interventions in other areas of clinical care among older adults and in planning for future disruptions including new pandemics.

A scoping review of the feasibility, usability, and efficacy of digital interventions in older adults concerning physical activity and/or exercise

Background

The global population is aging, leading to significant health challenges among older adults, such as reduced muscle mass, increased risks of dementias, and chronic diseases. Physical activity (PA) is crucial for maintaining health and wellbeing in this demographic, yet participation tends to decrease with age due to various barriers. Digital technologies, including mobile health (mHealth) interventions, show promise in promoting PA among older adults, though their adoption remains limited due to intrinsic and extrinsic challenges.

Objectives

This scoping review aimed to systematically map existing evidence on digital PA interventions for older adults, assessing feasibility, usability, and efficacy, whilst providing recommendations for future research and practice.

Eligibility criteria

Original investigations concerning digital interventions in older adults (≥60 years of age) focusing on physical activity and/or exercise were considered. Sources of evidence: Four electronic databases [MEDLINE, CINAHL Ultimate, Scopus and Cochrane Central Register of Controlled Trials (CENTRAL)] were searched.

Methods

A scoping review was conducted using the scoping review methodological framework. Review selection and characterisation were carried out by two independent reviewers.

Results

The 34 included studies were published between 2005 and 2023 across Europe, North America, Asia, and Oceania. Participants varied from healthy to frail individuals, with some diagnosed with dementia or cognitive impairment. Interventions were most commonly delivered via exergames, tablet apps, and videoconferencing. The most common exercise program type was multicomponent. Most studies assessed efficacy, feasibility, and usability, with many using a combination of these measures. Reminders were commonly utilised to enhance engagement through various digital and non-digital methods.

Conclusion

There was a notable lack of mobile health (mHealth) studies in the literature, with most research focusing on exergame and tablet interventions. More research on smartphone apps, particularly for muscle strengthening, is needed, and the growing ease of app development may drive innovation and research. Digital interventions are generally feasible, usable, and effective for older adults, offering a promising, scalable approach for promoting PA. This review identified several valuable lessons from the existent literature for future developments.

Understanding the Relationship Between Ecological Momentary Assessment Methods, Sensed Behavior, and Responsiveness: Cross-Study Analysis

Background

Ecological momentary assessment (EMA) offers an effective method to collect frequent, real-time data on an individual's well-being. However, challenges exist in response consistency, completeness, and accuracy.

Objective

This study examines EMA response patterns and their relationship with sensed behavior for data collected from diverse studies. We hypothesize that EMA response rate (RR) will vary with prompt time of day, number of questions, and behavior context. In addition, we postulate that response quality will decrease over the study duration and that relationships will exist between EMA responses, participant demographics, behavior context, and study purpose.

Methods

Data from 454 participants in 9 clinical studies were analyzed, comprising 146,753 EMA mobile prompts over study durations ranging from 2 weeks to 16 months. Concurrently, sensor data were collected using smartwatch or smart home sensors. Digital markers, such as activity level, time spent at home, and proximity to activity transitions (change points), were extracted to provide context for the EMA responses. All studies used the same data collection software and EMA interface but varied in participant groups, study length, and the number of EMA questions and tasks. We analyzed RR, completeness, quality, alignment with sensor-observed behavior, impact of study design, and ability to model the series of responses.

Results

The average RR was 79.95%. Of those prompts that received a response, the proportion of fully completed response and task sessions was 88.37%. Participants were most responsive in the evening (82.31%) and on weekdays (80.43%), although results varied by study demographics. While overall RRs were similar for weekday and weekend prompts, older adults were more responsive during the week (an increase of 0.27), whereas younger adults responded less during the week (a decrease of 3.25). RR was negatively correlated with the number of EMA questions (r=-0.433, P<.001). Additional correlations were observed between RR and sensor-detected activity level (r=0.045, P<.001), time spent at home (r=0.174, P<.001), and proximity to change points (r=0.124, P<.001). Response quality showed a decline over time, with careless responses increasing by 0.022 (P<.001) and response variance decreasing by 0.363 (P<.001). The within-study dynamic time warping distance between response sequences averaged 14.141 (SD 11.957), compared with the 33.246 (SD 4.971) between-study average distance. ARIMA (Autoregressive Integrated Moving Average) models fit the aggregated time series with high log-likelihood values, indicating strong model fit with low complexity.

Conclusions

EMA response patterns are significantly influenced by participant demographics and study parameters. Tailoring EMA prompt strategies to specific participant characteristics can improve RRs and quality. Findings from this analysis suggest that timing EMA prompts close to detected activity transitions and minimizing the duration of EMA interactions may improve RR. Similarly, strategies such as gamification may be introduced to maintain participant engagement and retain response variance.

Bridging Innovation and Equity: Advancing Public Health Through Just-in-Time Adaptive Interventions

This review explores the transformative potential of just-in-time adaptive interventions (JITAIs) as a scalable solution for addressing health disparities in underserved populations. JITAIs, delivered via mobile health technologies, could provide personalized, context-aware interventions based on real-time data to address public health challenges such as addiction treatment, chronic disease management, and mental health support. JITAIs can dynamically adjust intervention strategies, enhancing accessibility and engagement for marginalized communities. We highlight the utility of JITAIs in reducing opportunity costs associated with traditional in-person health interventions. Examples from various health domains demonstrate the adaptability of JITAIs in tailoring interventions to meet diverse needs. The review also emphasizes the need for community involvement, robust evaluation frameworks, and ethical considerations in implementing JITAIs, particularly in low- and middle-income countries. Sustainable funding models and technological innovations are necessary to ensure equitable access and effectively scale these interventions. By bridging the gap between research and practice, JITAIs could improve health outcomes and reduce disparities in vulnerable populations.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Personalized interventions for behaviour change: A scoping review of just-in-time adaptive interventions

PURPOSE

Examine the development, implementation and evaluation of just-in-time adaptive interventions (JITAIs) in behaviour change and evaluate the quality of intervention reporting.

METHODS

A scoping review of JITAIs incorporating mobile health (mHealth) technologies to improve health-related behaviours in adults. We searched MEDLINE, Embase and PsycINFO using terms related to JITAIs, mHealth, behaviour change and intervention methodology. Narrative analysis assessed theoretical foundations, real-time data capturing and processing methods, outcome evaluation and summarized JITAI efficacy. Quality of intervention reporting was assessed using the template for intervention description and replication (TIDieR) checklist.

RESULTS

Sixty-two JITAIs across physical activity, sedentary behaviour, dietary behaviour, substance use, sexual behaviour, fluid intake, treatment adherence, social skills, gambling behaviour and self-management skills were included. The majority (71%) aimed to evaluate feasibility, acceptability and/or usability. Supporting evidence for JITAI development was identified in 46 studies, with 67% applying this to develop tailored intervention content. Over half (55%) relied solely on self-reported data for tailoring, and 13 studies used only passive monitoring data. While data processing methods were commonly reported, 44% did not specify their techniques. 89% of JITAI designs achieved full marks on the TIDieR checklist and provided sufficient details on JITAI components. Overall, JITAIs proved to be feasible, acceptable and user-friendly across behaviours and settings. Randomized trials showed tailored interventions were efficacious, though outcomes varied by behaviour.

CONCLUSIONS

JITAIs offer a promising approach to developing personalized interventions, with their potential effects continuously growing. The recommended checklist emphasizes the importance of reporting transparency in establishing robust intervention designs.

Exploring the Feasibility of a 5-Week mHealth Intervention to Enhance Physical Activity and an Active, Healthy Lifestyle in Community-Dwelling Older Adults: Mixed Methods Study

BACKGROUND

Advancements in mobile technology have paved the way for innovative interventions aimed at promoting physical activity (PA).

OBJECTIVE

The main objective of this feasibility study was to assess the feasibility, usability, and acceptability of the More In Action (MIA) app, designed to promote PA among older adults. MIA offers 7 features: personalized tips, PA literacy, guided peer workouts, a community calendar, a personal activity diary, a progression monitor, and a chatbot.

METHODS

Our study used a mixed methods approach to evaluate the MIA app's acceptability, feasibility, and usability. First, a think-aloud method was used to provide immediate feedback during initial app use. Participants then integrated the app into their daily activities for 5 weeks. Behavioral patterns such as user session duration, feature use frequency, and navigation paths were analyzed, focusing on engagement metrics and user interactions. User satisfaction was assessed using the System Usability Scale, Net Promoter Score, and Customer Satisfaction Score. Qualitative data from focus groups conducted after the 5-week intervention helped gather insights into user experiences. Participants were recruited using a combination of web-based and offline strategies, including social media outreach, newspaper advertisements, and presentations at older adult organizations and local community services. Our target group consisted of native Dutch-speaking older adults aged >65 years who were not affected by severe illnesses. Initial assessments and focus groups were conducted in person, whereas the intervention itself was web based.

RESULTS

The study involved 30 participants with an average age of 70.3 (SD 4.8) years, of whom 57% (17/30) were female. The app received positive ratings, with a System Usability Scale score of 77.4 and a Customer Satisfaction Score of 86.6%. Analysis showed general satisfaction with the app's workout videos, which were used in 585 sessions with a median duration of 14 (IQR 0-34) minutes per day. The Net Promoter Score was 33.34, indicating a good level of customer loyalty. Qualitative feedback highlighted the need for improvements in navigation, content relevance, and social engagement features, with suggestions for better calendar visibility, workout customization, and enhanced social features. Overall, the app demonstrated high usability and satisfaction, with near-daily engagement from participants.

CONCLUSIONS

The MIA app shows significant potential for promoting PA among older adults, evidenced by its high usability and satisfaction scores. Participants engaged with the app nearly daily, particularly appreciating the workout videos and educational content. Future enhancements should focus on better calendar visibility, workout customization, and integrating social networking features to foster community and support. In addition, incorporating wearable device integration and predictive analytics could provide real-time health data, optimizing activity recommendations and health monitoring. These enhancements will ensure that the app remains user-friendly, relevant, and sustainable, promoting sustained PA and healthy behaviors among older adults.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05650515; https://clinicaltrials.gov/study/NCT05650515.

Understanding and overcoming barriers to digital health adoption: a patient and public involvement study

BACKGROUND

Digital health (DH) technologies provide scalable and cost-effective solutions to improve population health but face challenges of uneven adoption and high attrition, particularly among vulnerable and minority groups.

PURPOSE

This study explores factors influencing DH adoption in a multicultural population and identifies strategies to improve equitable access.

METHODS

Using a Patient and Public Involvement approach, lay facilitators engaged adults at public eateries in Singapore to discuss motivations and barriers to DH adoption. A semi-structured guide facilitated discussions, followed by an optional socio-demographic survey. Data were analyzed through inductive thematic analysis and mapped to behavior change theory to identify mechanisms of action (MoA) and behavior change techniques (BCTs) to support adoption.

RESULTS

Facilitators engaged 118 participants between November 2022 and February 2023. Five key themes were identified from the discussions: (a) awareness of DH solutions, (b) weighing benefits against burdens, (c) accessibility, (d) trust in DH developers and technology, and (e) the impact of user experience. These themes were mapped to 13 MoA and 26 BCTs, informing five key strategies to enhance DH adoption: community-based promotion of credible DH solutions and digital literacy training, brief counselling at opportune moments in healthcare settings, variable rewards tied to personal values, policies ensuring accessibility and regulation, and gamified, user-friendly designs emphasizing feedback and behavioral cues.

CONCLUSION

Designing and implementing DH solutions that are accessible, trustworthy, and motivating-integrated within healthcare services and promoted through community efforts-can address barriers to adoption by diverse communities and may help to narrow the digital divide.

Just-in-time adaptive interventions for adolescent and young adult health and well-being: protocol for a systematic review

INTRODUCTION

Health behaviours such as exercise and diet strongly influence well-being and disease risk, providing the opportunity for interventions tailored to diverse individual contexts. Precise behaviour interventions are critical during adolescence and young adulthood (ages 10-25), a formative period shaping lifelong well-being. We will conduct a systematic review of just-in-time adaptive interventions (JITAIs) for health behaviour and well-being in adolescents and young adults (AYAs). A JITAI is an emerging digital health design that provides precise health support by monitoring and adjusting to individual, specific and evolving contexts in real time. Despite demonstrated potential, no published reviews have explored how JITAIs can dynamically adapt to intersectional health factors of diverse AYAs. We will identify the JITAIs' distal and proximal outcomes and their tailoring mechanisms, and report their effectiveness. We will also explore studies' considerations of health equity. This will form a comprehensive assessment of JITAIs and their role in promoting health behaviours of AYAs. We will integrate evidence to guide the development and implementation of precise, effective and equitable digital health interventions for AYAs.

METHODS AND ANALYSIS

In adherence to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines, we will conduct a systematic search across multiple databases, including CENTRAL, MEDLINE and WHO Global Index Medicus. We will include peer-reviewed studies on JITAIs targeting health of AYAs in multiple languages. Two independent reviewers will conduct screening and data extraction of study and participant characteristics, JITAI designs, health outcome measures and equity considerations. We will provide a narrative synthesis of findings and, if data allows, conduct a meta-analysis.

ETHICS AND DISSEMINATION

As we will not collect primary data, we do not require ethical approval. We will disseminate the review findings through peer-reviewed journal publication, conferences and stakeholder meetings to inform participatory research.

PROSPERO REGISTRATION NUMBER

CRD42023473117.

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial

BACKGROUND

Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches.

METHODS

This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC's benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18-75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m2 (≥ 23 kg/m2 for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA.

DISCUSSION

Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376.

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.

Digital interventions for healthy ageing and cognitive health in older adults: a systematic review of mixed method studies and meta-analysis

BACKGROUND

Currently, there is no systematic review to investigate the effectiveness of digital interventions for healthy ageing and cognitive health of older adults. This study aimed to conduct a systematic review to evaluate the effectiveness of digital intervention studies for facilitating healthy ageing and cognitive health and further identify the considerations of its application to older adults.

METHODS

A systematic review and meta-analysis of literature were conducted across CINAHL, Medline, ProQuest, Cochrane, Scopus, and PubMed databases following the PRISMA guideline. All included studies were appraised using the Mixed Methods Appraisal Tool Checklist by independent reviewers. Meta-analyses were performed using JBI SUMARI software to compare quantitative studies. Thematic analyses were used for qualitative studies and synthesised into the emerging themes.

RESULTS

Thirteen studies were included. Quantitative results showed no statistically significant pooled effect between health knowledge and healthy behaviour (I2 =76, p=0.436, 95% CI [-0.32,0.74]), and between cardiovascular-related health risks and care dependency I2=0, p=0.426, 95% CI [0.90,1.29]). However, a statistically significant cognitive function preservation was found in older adults who had long-term use of laptop/cellphone devices and had engaged in the computer-based physical activity program (I2=0, p<0.001, 95% CI [0.01, 0.21]). Qualitative themes for the considerations of digital application to older adults were digital engagement, communication, independence, human connection, privacy, and cost.

CONCLUSIONS

Digital interventions used in older adults to facilitate healthy ageing were not always effective. Health knowledge improvement does not necessarily result in health risk reduction in that knowledge translation is key. Factors influencing knowledge translation (i.e., digital engagement, human coaching etc) were identified to determine the intervention effects. However, using digital devices appeared beneficial to maintain older adults' cognitive functions in the longer term. Therefore, the review findings suggest that the expanded meaning of a person-centred concept (i.e., from social, environmental, and healthcare system aspects) should be pursued in future practice. Privacy and cost concerns of technologies need ongoing scrutiny from policy bodies. Future research looking into the respective health benefits can provide more understanding of the current digital intervention applied to older adults.

STUDY REGISTRATION

PROSPERO record ID: CRD42023400707 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=400707 .

Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study

BACKGROUND

Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility.

OBJECTIVE

This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content.

METHODS

During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases.

RESULTS

In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention.

CONCLUSIONS

ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input.

Feasibility and acceptability of design and conduct of a registry-based randomised clinical trial evaluating eVIS as a digital support for physical activity in interdisciplinary pain rehabilitation programs: A randomised pilot study

Background

Patients with chronic pain often struggle to engage in physical activity despite its health benefits. The eVISualisation of physical activity and pain intervention (eVIS) was developed to support adherence to physical activity plans in Interdisciplinary Pain Rehabilitation Programs (IPRPs) by visualising activity, pain levels, pain interference, and pharmacological use. This pilot study assesses the feasibility and acceptability of trial design and trial conduct of a registry-based randomised clinical trial (R-RCT).

Method

This randomised clinical pilot study included the first 10% (n = 39, mean age 43.5, 74.4% females) of the R-RCT sample (n≈400). Participants with non-cancer chronic pain from six IPRP units were randomly assigned to either the intervention group (IPRP + eVIS, n = 19) or the control group (IPRP, n = 20). Feasibility and acceptability were evaluated using pre-defined criteria on recruitment- and data collection procedures (e.g., inclusion rates, representativeness, adverse events), physiotherapists' ratings of trial design and conduct (e.g., acceptability, feasibility), and outcome data characteristics and completeness (e.g., adherence, data accessibility).

Results

Recruitment was largely feasible, though attrition differences and the need for refined eligibility screening were noted. Physiotherapists cited time and implementation challenges. Both groups had satisfactory data completeness, but the control group showed lower adherence to daily reporting in the final third of the study. The intervention group had greater improvements in physical health, with 19.5% more participants achieving the minimum clinically important difference (≥3) on the physical component summary scale (PCS). No adverse events occurred.

Conclusion

With minor adjustments, the R-RCT design is mostly feasible, though some challenges to feasibility were identified and addressed.

The acceptability, usability, engagement and optimisation of a mHealth service promoting healthy lifestyle behaviours: A mixed method feasibility study

Objective

Mobile health (mHealth) services suffer from high attrition rates yet represent a viable strategy for adults to improve their health. There is a need to develop evidence-based mHealth services and to constantly evaluate their feasibility. This study explored the acceptability, usability, engagement and optimisation of a co-developed mHealth service, aiming to promote healthy lifestyle behaviours.

Methods

The service LongLife Active® (LLA) is a mobile app with coaching. Adults were recruited from the general population. Quantitative results and qualitative findings guided the reasoning for the acceptability, usability, engagement and optimisation of LLA. Data from: questionnaires, log data, eight semi-structured interviews with users, feedback comments from users and two focus groups with product developers and coaches were collected. Inductive content analysis was used to analyse the qualitative data. A mixed method approach was used to interpret the findings.

Results

The final sample was 55 users (82% female), who signed up to use the service for 12 weeks. Engagement data was available for 43 (78%). The action plan was the most popular function engaged with by users. The mean scores for acceptability and usability were 3.3/5.0 and 50/100, respectively, rated by 15 users. Users expressed that the service's health focus was unique, and the service gave them a 'kickstart' in their behaviour change. Many ways to optimise the service were identified, including to increase personalisation, promote motivation and improve usability.

Conclusion

By incorporating suggestions for optimisation, this service has the potential to support peoples' healthy lifestyle behaviours.

The Use of Wearable Devices to Measure Sedentary Behavior during COVID-19: Systematic Review and Future Recommendations

The SARS-CoV-2 pandemic resulted in approximately 7 million deaths and impacted 767 million individuals globally, primarily through infections. Acknowledging the impactful influence of sedentary behaviors, particularly exacerbated by COVID-19 restrictions, a substantial body of research has emerged, utilizing wearable sensor technologies to assess these behaviors. This comprehensive review aims to establish a framework encompassing recent studies concerning wearable sensor applications to measure sedentary behavior parameters during the COVID-19 pandemic, spanning December 2019 to December 2022. After examining 582 articles, 7 were selected for inclusion. While most studies displayed effective reporting standards and adept use of wearable device data for their specific research aims, our inquiry revealed deficiencies in apparatus accuracy documentation and study methodology harmonization. Despite methodological variations, diverse metrics, and the absence of thorough device accuracy assessments, integrating wearables within the pandemic context offers a promising avenue for objective measurements and strategies against sedentary behaviors.

A scoping review of 'Pacing' for management of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): lessons learned for the long COVID pandemic

BACKGROUND

Controversy over treatment for people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a barrier to appropriate treatment. Energy management or pacing is a prominent coping strategy for people with ME/CFS. Whilst a definitive definition of pacing is not unanimous within the literature or healthcare providers, it typically comprises regulating activity to avoid post exertional malaise (PEM), the worsening of symptoms after an activity. Until now, characteristics of pacing, and the effects on patients' symptoms had not been systematically reviewed. This is problematic as the most common approach to pacing, pacing prescription, and the pooled efficacy of pacing was unknown. Collating evidence may help advise those suffering with similar symptoms, including long COVID, as practitioners would be better informed on methodological approaches to adopt, pacing implementation, and expected outcomes.

OBJECTIVES

In this scoping review of the literature, we aggregated type of, and outcomes of, pacing in people with ME/CFS.

ELIGIBILITY CRITERIA

Original investigations concerning pacing were considered in participants with ME/CFS.

SOURCES OF EVIDENCE

Six electronic databases (PubMed, Scholar, ScienceDirect, Scopus, Web of Science and the Cochrane Central Register of Controlled Trials [CENTRAL]) were searched; and websites MEPedia, Action for ME, and ME Action were also searched for grey literature, to fully capture patient surveys not published in academic journals.

METHODS

A scoping review was conducted. Review selection and characterisation was performed by two independent reviewers using pretested forms.

RESULTS

Authors reviewed 177 titles and abstracts, resulting in 17 included studies: three randomised control trials (RCTs); one uncontrolled trial; one interventional case series; one retrospective observational study; two prospective observational studies; four cross-sectional observational studies; and five cross-sectional analytical studies. Studies included variable designs, durations, and outcome measures. In terms of pacing administration, studies used educational sessions and diaries for activity monitoring. Eleven studies reported benefits of pacing, four studies reported no effect, and two studies reported a detrimental effect in comparison to the control group.

CONCLUSIONS

Highly variable study designs and outcome measures, allied to poor to fair methodological quality resulted in heterogenous findings and highlights the requirement for more research examining pacing. Looking to the long COVID pandemic, our results suggest future studies should be RCTs utilising objectively quantified digitised pacing, over a longer duration of examination (i.e. longitudinal studies), using the core outcome set for patient reported outcome measures. Until these are completed, the literature base is insufficient to inform treatment practises for people with ME/CFS and long COVID.

Designing personalised mHealth solutions: An overview

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

DISCUSSION

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

CONCLUSIONS

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

Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health

Digital therapeutics (DTx, software as a medical device) provide personalized treatments for chronic diseases and expand precision medicine beyond pharmacogenomics-based pharmacotherapies. In this perspective article, we describe how DTx for chronic low back pain (CLBP) can be integrated with pharmaceutical drugs (e.g., NSAIDs, opioids), physical therapy (PT), cognitive behavioral therapy (CBT), and patient empowerment. An example of an FDA-authorized DTx for CLBP is RelieVRx, a prescription virtual reality (VR) app that reduces pain severity as an adjunct treatment for moderate to severe low back pain. RelieVRx is an immersive VR system that delivers at-home pain management modalities, including relaxation, self-awareness, pain distraction, guided breathing, and patient education. The mechanism of action of DTx is aligned with recommendations from the American College of Physicians to use non-pharmacological modalities as the first-line therapy for CLBP. Herein, we discuss how DTx can provide multimodal therapy options integrating conventional treatments with exposome-responsive, just-in-time adaptive interventions (JITAI). Given the flexibility of software-based therapies to accommodate diverse digital content, we also suggest that music-induced analgesia can increase the clinical effectiveness of digital interventions for chronic pain. DTx offers opportunities to simultaneously address the chronic pain crisis and opioid epidemic while supporting patients and healthcare providers to improve therapy outcomes.

Put your money where your feet are: The real-world effects of StepBet gamified deposit contracts for physical activity

Background

Gamification and deposit contracts (a financial incentive in which participants pledge their own money) can enhance effectiveness of mobile behavior change interventions. However, to assess their potential for improving population health, research should investigate implementation of gamified deposit contracts outside the research setting. Therefore, we analyzed data from StepBet, a smartphone application originally developed by WayBetter, Inc.

Objective

To perform a naturalistic evaluation of StepBet gamified deposit contracts, for whom they work best, and under which conditions they are most effective to help increase physical activity.

Methods

WayBetter provided data of StepBet participants that participated in a stepcount challenge between 2015 and 2020 (N = 72,974). StepBet challenges were offered on the StepBet smartphone application. The modal challenge consisted of a $40 deposit made prior to a 6-week challenge period during which participants needed to reach daily and weekly step goals in order to regain their deposit. Participants who met their goals also received additional earnings which were paid out from the money lost by those who failed their challenge. Challenge step goals were tailored on a 90-day historic step count retrieval that was also used as the baseline comparison for this study. Primary outcomes were increase in step count (continuous) and challenge success (dichotomous).

Results

Overall, average daily step counts increased by 31.2 % (2423 steps, SD = 3462) from 7774 steps (SD = 3112) at baseline to 10,197 steps (SD = 4162) during the challenge. The average challenge success rate was 73 %. Those who succeeded in their challenge (n = 53,281) increased their step count by 44.0 % (3465 steps, SD = 3013), while those who failed their challenge (n = 19,693) decreased their step count by -5.3 % (-398 steps, SD = 3013). Challenges started as a New Year's resolution were slightly more successful (77.7 %) than those started during the rest of the year (72.6 %).

Discussion

In a real-world setting, and among a large and diverse sample, participating in a gamified deposit contract challenge was associated with a large increase in step counts. A majority of challenges were successful and succeeding in a challenge was associated with a large and clinically relevant increase in step counts. Based on these findings, we recommend implementing gamified deposit contracts for physical activity where possible. An interesting avenue for future research is to explore possible setback effects among people who fail a challenge, and how setbacks can be mitigated.

Pre-registration

Open Science Framework (doi:10.17605/OSF.IO/D237C).

Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study

BACKGROUND

Physical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times.

OBJECTIVE

The aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants' previous 5 weeks' steps-per-minute data.

METHODS

We conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartSteps mobile health physical-activity intervention conducted in 2015. The prediction performance of 6 algorithms was evaluated, as follows: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. For the MLP, 90 random layer architectures were tested for optimization. Prior 5-week hourly walking data, including missingness, were used for predictors. Whether the participant walked during the next 3 hours was used as the outcome. K-fold cross-validation (K=10) was used for the internal validation. The primary outcome measures are classification accuracy, the Mathew correlation coefficient, sensitivity, and specificity.

RESULTS

The total sample size included 6 weeks of data among 44 participants. Of the 44 participants, 31 (71%) were female, 26 (59%) were White, 36 (82%) had a college degree or more, and 15 (34%) were married. The mean age was 35.9 (SD 14.7) years. Participants (n=3, 7%) who did not have enough data (number of days <10) were excluded, resulting in 41 (93%) participants. MLP with optimized layer architecture showed the best performance in accuracy (82.0%, SD 1.1), whereas XGBoost (76.3%, SD 1.5), random forest (69.5%, SD 1.0), support vector machine (69.3%, SD 1.0), and decision tree (63.6%, SD 1.5) algorithms showed lower performance than logistic regression (77.2%, SD 1.2). MLP also showed superior overall performance to all other tried algorithms in Mathew correlation coefficient (0.643, SD 0.021), sensitivity (86.1%, SD 3.0), and specificity (77.8%, SD 3.3).

CONCLUSIONS

Walking behavior prediction models were developed and validated. MLP showed the highest overall performance of all attempted algorithms. A random search for optimal layer structure is a promising approach for prediction engine development. Future studies can test the real-world application of this algorithm in a "smart" intervention for promoting physical activity.

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.