To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App

Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation

BACKGROUND

Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system's decision-making.

OBJECTIVE

This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems.

METHODS

To inform the design of such dynamic adaptation, we analyzed data from the implementation and deployment of a system that incorporates passively sensed data across everyday work devices to send just-in-time stress reduction microinterventions in the workplace to 43 participants during a 4-week deployment. We evaluated 27 trait-based factors (ie, individual characteristics), state-based factors (ie, workplace contextual and behavioral signals and momentary stress), and intervention-related factors (ie, location and function) across 1585 system-initiated interventions. We built logistical regression models to identify the factors contributing to momentary engagement, the choice of interventions, the engagement given an intervention choice, the user rating of interventions engaged, and the stress reduction from the engagement.

RESULTS

We found that women (odds ratio [OR] 0.41, 95% CI 0.21-0.77; P=.03), those with higher neuroticism (OR 0.57, 95% CI 0.39-0.81; P=.01), those with higher cognitive reappraisal skills (OR 0.69, 95% CI 0.52-0.91; P=.04), and those that chose calm interventions (OR 0.43, 95% CI 0.23-0.78; P=.03) were significantly less likely to experience stress reduction, while those with higher agreeableness (OR 1.73, 95% CI 1.10-2.76; P=.06) and those that chose prompt-based (OR 6.65, 95% CI 1.53-36.45; P=.06) or video-based (OR 5.62, 95% CI 1.12-34.10; P=.12) interventions were substantially more likely to experience stress reduction. We also found that work-related contextual signals such as higher meeting counts (OR 0.62, 95% CI 0.49-0.78; P<.001) and higher engagement skewness (OR 0.64, 95% CI 0.51-0.79; P<.001) were associated with a lower likelihood of engagement, indicating that state-based contextual factors such as being in a meeting or the time of the day may matter more for engagement than efficacy. In addition, a just-in-time intervention that was explicitly rescheduled to a later time was more likely to be engaged with (OR 1.77, 95% CI 1.32-2.38; P<.001).

CONCLUSIONS

JITAI systems have the potential to integrate timely support into the workplace. On the basis of our findings, we recommend that individual, contextual, and content-based factors be incorporated into the system for tailoring as well as for monitoring ineffective engagements across subgroups and contexts.

Deceptively Simple yet Profoundly Impactful: Text Messaging Interventions to Support Health

This paper examines the use of text message (SMS) interventions for health-related behavioral support. It first outlines the historical progress in SMS intervention research publications and the variety of funds from US government agencies. A narrative review follows, highlighting the effectiveness of SMS interventions in key health areas, such as physical activity, diet and weight loss, mental health, and substance use, based on published meta-analyses. It then outlines advantages of text messaging compared to other digital modalities, including the real-time capability to collect information and deliver microdoses of intervention support. Crucial design elements are proposed to optimize effectiveness and longitudinal engagement across communication strategies, psychological foundations, and behavior change tactics. We then discuss advanced functionalities, such as the potential for generative artificial intelligence to improve user interaction. Finally, major challenges to implementation are highlighted, including the absence of a dedicated commercial platform, privacy and security concerns with SMS technology, difficulties integrating SMS interventions with medical informatics systems, and concerns about user engagement. Proposed solutions aim to facilitate the broader application and effectiveness of SMS interventions. Our hope is that these insights can assist researchers and practitioners in using SMS interventions to improve health outcomes and reducing disparities.

Service Users' Experiences of a Nationwide Digital Type 2 Diabetes Self-Management Intervention (Healthy Living): Qualitative Interview Study

BACKGROUND

Diabetes Self-Management Education and Support programs for people living with type 2 diabetes mellitus (T2DM) can increase glycemic control and reduce the risk of developing T2DM-related complications. However, the recorded uptake of these programs is low. Digital self-management interventions have the potential to overcome barriers associated with attendance at face-to-face sessions. Healthy Living is an evidence-based digital self-management intervention for people living with T2DM, based on the Healthy Living for People with Type 2 Diabetes (HeLP-Diabetes) intervention, which demonstrated effectiveness in a randomized controlled trial. NHS England has commissioned Healthy Living for national rollout into routine care. Healthy Living consists of web-based structured education and Tools components to help service users self-manage their condition, including setting goals. However, key changes were implemented during the national rollout that contrasted with the trial, including a lack of facilitated access from a health care professional and the omission of a moderated online support forum.

OBJECTIVE

This qualitative study aims to explore service users' experiences of using Healthy Living early in the national rollout.

METHODS

A total of 19 participants were interviewed via telephone or a videoconferencing platform. Topics included users' experiences and views of website components, their understanding of the intervention content, and the overall acceptability of Healthy Living. Transcripts were analyzed thematically using a framework approach.

RESULTS

Participants valued having trustworthy information that was easily accessible. The emotional management content resonated with the participants, prompting some to book an appointment with their general practitioners to discuss low mood. After completing the structured education, participants might have been encouraged to continue using the website if there was more interactivity (1) between the website and other resources and devices they were using for self-management, (2) with health professionals and services, and (3) with other people living with T2DM. There was consensus that the website was particularly useful for people who had been newly diagnosed with T2DM.

CONCLUSIONS

Digital Diabetes Self-Management Education and Support programs offering emotional aspects of self-management are addressing an unmet need. Primary care practices could consider offering Healthy Living to people as soon as they are diagnosed with T2DM. Participants suggested ways in which Healthy Living could increase interaction with the website to promote continued long-term use.

The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study

Background: Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Method: Data were derived from two iterations of a four-week study exploring app engagement in college students (n = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Results: Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. Conclusions: While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

Guided web app intervention for reducing symptoms of depression in postpartum women: Results of a feasibility randomized controlled trial

Background

Chile faces a significant postpartum depression prevalence and treatment gap, necessitating accessible interventions. While cognitive-behavioral internet-based interventions have proven effective in high-income countries, this field is underdeveloped in Chile. Based on the country's widespread use of digital technology, a guided 8-week cognitive-behavioral web app intervention named "Mamá, te entiendo" was developed.

Objective

This study aimed to assess the acceptability and feasibility of "Mamá, te entiendo", for reducing depressive symptomatology in postpartum women.

Methods

Sixty-five postpartum women with minor or major depression were randomly assigned to either intervention or waitlist. Primary outcomes centered on study feasibility, intervention feasibility, and acceptability. Semi-structured interviews with a sub-sample enriched the understanding of participants' experiences. Secondary outcomes included mental health variables assessed at baseline, post-intervention, and 1-month follow-up.

Results

Chilean women displayed great interest in the intervention. 44.8 % of participants completed the intervention. Participants reported high satisfaction and engagement levels, with interviewees highlighting the value of the intervention's content, exercises, and therapist's feedback. However, preliminary efficacy analysis didn't reveal a significant interaction between group and time for outcome measures.

Discussion

This research represents a pioneering effort in Chile to evaluate an internet-based intervention for postpartum depression symptoms. The demonstrated feasibility and acceptability highlight the potential of integrating technology-driven approaches into mental health interventions. However, the intervention did not demonstrate superiority, as both groups exhibited similar positive progress in several outcomes. Therefore, the following research phase should involve a larger and more diverse sample to assess the intervention's effectiveness, identify influencing factors, and determine the individuals who benefit the most.

Preferences of people with chronic kidney disease regarding digital health interventions that promote healthy lifestyle: qualitative systematic review with meta-ethnography

OBJECTIVES

Diet and physical activity are crucial for people with chronic kidney disease (CKD) to maintain good health. Digital health interventions can increase access to lifestyle services. However, consumers' perspectives are unclear, which may reduce the capacity to develop interventions that align with specific needs and preferences. Therefore, this review aims to synthesise the preferences of people with CKD regarding digital health interventions that promote healthy lifestyle.

DESIGN

Qualitative systematic review with meta-ethnography.

DATA SOURCES

Databases Scopus, CENTRAL, MEDLINE, CINAHL and SPORTDiscus were searched between 2000 and 2023.

ELIGIBILITY CRITERIA

Primary research papers that used qualitative exploration methods to explore the preferences of adults with CKD (≥18 years) regarding digital health interventions that promoted diet, physical activity or a combination of these health behaviours.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened title, abstract and full text. Discrepancies were resolved by a third reviewer. Consumers' quotes were extracted verbatim and synthesised into higher-order themes and subthemes.

RESULTS

Database search yielded 5761 records. One record was identified following communication with a primary author. 15 papers were included. These papers comprised 197 consumers (mean age 51.0±7.2), including 83 people with CKD 1-5; 61 kidney transplant recipients; 53 people on dialysis. Sex was reported in 182 people, including 53% male. Five themes were generated regarding consumers' preferences for digital lifestyle interventions. These included simple instruction and engaging design; individualised interventions; virtual communities of care; education and action plans; and timely reminders and automated behavioural monitoring.

CONCLUSION

Digital health interventions were considered an important mechanism to access lifestyle services. Consumers' preferences are important to ensure future interventions are tailored to specific needs and goals. Future research may consider applying the conceptual framework of consumers' preferences in this review to develop and evaluate the effect of a digital lifestyle intervention on health outcomes.

PROSPERO REGISTRATION NUMBER

CRD42023411511.

Mobile health technologies for workers' health and wellbeing: A systematic search of mHealth applications in Brazil

BACKGROUND

Inadequate working conditions and sedentary work can exert a negative impact on workers' health and wellbeing, leading to musculoskeletal disorders and disability. Mobile health (mHealth) applications (apps) have high potential for the self-management of workers' health.

OBJECTIVE

To identify mHealth apps aimed at promoting workers' health and wellbeing available in Brazilian online stores and assess these apps in terms of engagement, functionality, aesthetics and information quality.

METHODS

A systematic search for apps was conducted in the Brazilian online App Store and Play Store in December 2022. Only smartphone apps in Brazilian Portuguese directed at workers' health were assessed. The appraisal of the quality of the applications was performed using the Mobile App Rating Scale (MARS).

RESULTS

Among the 3449 mHealth apps found, ten were eligible for inclusion. The mean overall score was 3.15 ± 0.91 on a scale of 1-5. The lowest score was found for the "credibility" item. Exercises and breaks were the most frequent strategies. Most apps provided low-quality information from questionable sources and therefore received a mean score of 2.1 ± 1.5 on the MARS information subscale.

CONCLUSION

Ten relevant mHealth apps were eligible for inclusion. The mHealth apps for the promotion of workers' health and wellbeing currently available in Brazil exhibited moderate quality and limited functionality.

Integration of Patient-Reported Outcome Data Collected Via Web Applications and Mobile Apps Into a Nation-Wide COVID-19 Research Platform Using Fast Healthcare Interoperability Resources: Development Study

BACKGROUND

The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher.

OBJECTIVE

Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required.

METHODS

We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability.

RESULTS

The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers.

CONCLUSIONS

This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

Development of a digital intervention for psychedelic preparation (DIPP)

Psychedelic substances induce profound alterations in consciousness. Careful preparation is therefore essential to limit adverse reactions, enhance therapeutic benefits, and maintain user safety. This paper describes the development of a self-directed, digital intervention for psychedelic preparation. Drawing on elements from the UK Medical Research Council (MRC) framework for developing complex interventions, the design was informed by a four-factor model of psychedelic preparedness, using a person-centred approach. Our mixed-methods investigation consisted of two studies. The first involved interviews with 19 participants who had previously attended a 'high-dose' psilocybin retreat, systematically exploring their preparation behaviours and perspectives on the proposed intervention. The second study engaged 28 attendees of an ongoing psilocybin retreat in co-design workshops, refining the intervention protocol using insights from the initial interviews. The outcome is a co-produced 21-day digital course (Digital Intervention for Psychedelic Preparation (DIPP)), that is organised into four modules: Knowledge-Expectation, Psychophysical-Readiness, Safety-Planning, and Intention-Preparation. Fundamental components of the course include daily meditation practice, supplementary exercises tied to the weekly modules, and mood tracking. DIPP provides a comprehensive and scalable solution to enhance psychedelic preparedness, aligning with the broader shift towards digital mental health interventions.

Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

BACKGROUND

As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants' responses when implementing new survey delivery methods.

OBJECTIVE

This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants' reported emotional state. We examine the factors that affect participants' receptivity to EMAs in a 10-day wearable and EMA-based emotional state-sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs.

METHODS

We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity.

RESULTS

Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions.

CONCLUSIONS

Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

Using Digital Health Technologies to Monitor Pain, Medication Adherence and Physical Activity in Young People with Juvenile Idiopathic Arthritis: A Feasibility Study

Juvenile idiopathic arthritis can be influenced by pain, medication adherence, and physical activity. A new digital health intervention, InteractiveClinics, aims to monitor these modifiable risk factors. Twelve children, aged 10 to 18 years, received daily notifications on a smartwatch to record their pain levels and take their medications, using a customised mobile app synchronised to a secure web-based platform. Daily physical activity levels were automatically recorded by wearing a smartwatch. Using a quantitative descriptive research design, feasibility and user adoption were evaluated. The web-based data revealed the following: Pain: mean app usage: 68% (SD 30, range: 28.6% to 100%); pain score: 2.9 out of 10 (SD 1.8, range: 0.3 to 6.2 out of 10). Medication adherence: mean app usage: 20.7% (SD, range: 0% to 71.4%), recording 39% (71/182) of the expected daily and 37.5% (3/8) of the weekly medications. Pro-re-nata (PRN) medication monitoring: 33.3% (4/12), one to six additional medications (mean 3.5, SD 2.4) for 2-6 days. Physical activity: watch wearing behaviour: 69.7% (439/630), recording low levels of moderate-to-vigorous physical activity (mean: 11.8, SD: 13.5 min, range: 0-47 min). To conclude, remote monitoring of real-time data is feasible. However, further research is needed to increase adoption rates among children.

Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial

BACKGROUND

Multiparametric remote measurement technologies (RMTs), which comprise smartphones and wearable devices, have the potential to revolutionize understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the utmost importance for the validity of predictive analytical methods and long-term use and can be conceptualized as both objective engagement (data availability) and subjective engagement (system usability and experiential factors). Positioning the design of user interfaces within the theoretical framework of the Behavior Change Wheel can help maximize effectiveness. In-app components containing information from credible sources, visual feedback, and access to support provide an opportunity to promote engagement with RMTs while minimizing team resources. Randomized controlled trials are the gold standard in quantifying the effects of in-app components on engagement with RMTs in patients with MDD.

OBJECTIVE

This study aims to evaluate whether a multiparametric RMT system with theoretically informed notifications, visual progress tracking, and access to research team contact details could promote engagement with remote symptom tracking over and above the system as usual. We hypothesized that participants using the adapted app (intervention group) would have higher engagement in symptom monitoring, as measured by objective and subjective engagement.

METHODS

A 2-arm, parallel-group randomized controlled trial (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms used the RADAR-base system, comprising a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50, 50%) also had access to additional in-app components. The primary outcome was objective engagement, measured as the percentage of weekly questionnaires completed during follow-up. The secondary outcomes measured subjective engagement (system engagement, system usability, and emotional self-awareness).

RESULTS

The levels of completion of the Patient Health Questionnaire-8 (PHQ-8) were similar between the control (67/97, 69%) and intervention (66/97, 68%) arms (P value for the difference between the arms=.83, 95% CI -9.32 to 11.65). The intervention group participants reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19), and system usability (2.29, 95% CI -5.93 to 10.52) scores than the control group participants at follow-up; however, all CIs were wide and included 0. Process evaluation suggested that participants saw the in-app components as helpful in increasing task completion.

CONCLUSIONS

The adapted system did not increase objective or subjective engagement in remote symptom tracking in our research cohort. This study provides an important foundation for understanding engagement with RMTs for research and the methodologies by which this work can be replicated in both community and clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/32653.

Designing and Developing a Mobile App for Management and Treatment of Gestational Diabetes in Nepal: User-Centered Design Study

BACKGROUND

Mobile apps can aid with the management of gestational diabetes mellitus (GDM) by providing patient education, reinforcing regular blood glucose monitoring and diet/lifestyle modification, and facilitating clinical and social support.

OBJECTIVE

This study aimed to describe our process of designing and developing a culturally tailored app, Garbhakalin Diabetes athawa Madhumeha-Dhulikhel Hospital (GDM-DH), to support GDM management among Nepalese patients by applying a user-centered design approach.

METHODS

A multidisciplinary team of experts, as well as health care providers and patients in Dhulikhel Hospital (Dhulikhel, Nepal), contributed to the development of the GDM-DH app. After finalizing the app's content and features, we created the app's wireframe, which illustrated the app's proposed interface, navigation sequences, and features and function. Feedback was solicited on the wireframe via key informant interviews with health care providers (n=5) and a focus group and in-depth interviews with patients with GDM (n=12). Incorporating their input, we built a minimum viable product, which was then user-tested with 18 patients with GDM and further refined to obtain the final version of the GDM-DH app.

RESULTS

Participants in the focus group and interviews unanimously concurred on the utility and relevance of the proposed mobile app for patients with GDM, offering additional insight into essential modifications and additions to the app's features and content (eg, inclusion of example meal plans and exercise videos).The mean age of patients in the usability testing (n=18) was 28.8 (SD 3.3) years, with a mean gestational age of 27.2 (SD 3.0) weeks. The mean usability score across the 10 tasks was 3.50 (SD 0.55; maximum score=5 for "very easy"); task completion rates ranged from 55.6% (n=10) to 94.4% (n=17). Findings from the usability testing were reviewed to further optimize the GDM-DH app (eg, improving data visualization). Consistent with social cognitive theory, the final version of the GDM-DH app supports GDM self-management by providing health education and allowing patients to record and self-monitor blood glucose, blood pressure, carbohydrate intake, physical activity, and gestational weight gain. The app uses innovative features to minimize the self-monitoring burden, as well as automatic feedback and data visualization. The app also includes a social network "follow" feature to add friends and family and give them permission to view logged data and a progress summary. Health care providers can use the web-based admin portal of the GDM-DH app to enter/review glucose levels and other clinical measures, track patient progress, and guide treatment and counseling accordingly.

CONCLUSIONS

To the best of our knowledge, this is the first mobile health platform for GDM developed for a low-income country and the first one containing a social support feature. A pilot clinical trial is currently underway to explore the clinical utility of the GDM-DH app.

Dissemination Strategies for mHealth Apps: Systematic Review

BACKGROUND

Among the millions of mobile apps in existence, thousands fall under the category of mobile health (mHealth). Although the utility of mHealth apps has been demonstrated for disease diagnosis, treatment data management, and health promotion strategies, to be effective they must reach and be used by their target audience. An appropriate marketing strategy can ensure that apps reach potential users and potentially convert them to actual users. Such a strategy requires definitions of target end users, communication channels, and advertising content, as well as a timeline for effectively reaching and motivating end users to adopt and maintain engagement with the mHealth app.

OBJECTIVE

The aim of this study was to identify strategies and elements that ensure that end users adopt and remain engaged with mHealth apps.

METHODS

A systematic search of the PubMed, PsycINFO, Scopus, and CINAHL databases was conducted for suitable studies published between January 1, 2018, and September 30, 2022. Two researchers independently screened studies for inclusion, extracted data, and assessed the risk of bias. The main outcome was dissemination strategies for mHealth apps.

RESULTS

Of the 648 papers retrieved from the selected databases, only 10 (1.5%) met the inclusion criteria. The marketing strategies used in these studies to inform potential users of the existence of mHealth apps and motivate download included both paid and unpaid strategies and used various channels, including social media, emails, printed posters, and face-to-face communication. Most of the studies reported a combination of marketing concepts used to advertise their mHealth apps. Advertising messages included instructions on where and how to download and install the apps. In most of the studies (6/10, 60%), instructions were oriented toward how to use the apps and maintain engagement with a health intervention. The most frequently used paid marketing platform was Facebook Ads Manager (2/10, 20%). Advertising performance was influenced by many factors, including but not limited to advertising content. In 1 (10%) of the 10 studies, animated graphics generated the greatest number of clicks compared with other image types. The metrics used to assess marketing strategy effectiveness were number of downloads; nonuse rate; dropout rate; adherence rate; duration of app use; and app usability over days, weeks, or months. Additional indicators such as cost per click, cost per install, and clickthrough rate were mainly used to assess the cost-effectiveness of paid marketing campaigns.

CONCLUSIONS

mHealth apps can be disseminated via paid and unpaid marketing strategies using various communication channels. The effects of these strategies are reflected in download numbers and user engagement with mHealth apps. Further research could provide guidance on a framework for disseminating mHealth apps and encouraging their routine use.

An umbrella review of effectiveness and efficacy trials for app-based health interventions

Health interventions based on mobile phone or tablet applications (apps) are promising tools to help patients manage their conditions more effectively. Evidence from randomized controlled trials (RCTs) on efficacy and effectiveness of such interventions is increasingly available. This umbrella review aimed at mapping and narratively summarizing published systematic reviews on efficacy and effectiveness of mobile app-based health interventions within patient populations. We followed a pre-specified publicly available protocol. Systematic reviews were searched in two databases from inception until August 28, 2023. Reviews that included RCTs evaluating integrated or stand-alone health app interventions in patient populations with regard to efficacy/effectiveness were considered eligible. Information on indications, outcomes, app characteristics, efficacy/effectiveness results and authors' conclusions was extracted. Methodological quality was assessed using the AMSTAR2 tool. We identified 48 systematic reviews published between 2013 and 2023 (35 with meta-analyses) that met our inclusion criteria. Eleven reviews included a broad spectrum of conditions, thirteen focused on diabetes, five on anxiety and/or depression, and others on various other indications. Reported outcomes ranged from medication adherence to laboratory, anthropometric and functional parameters, symptom scores and quality of life. Fourty-one reviews concluded that health apps may be effective in improving health outcomes. We rated one review as moderate quality. Here we report that the synthesized evidence on health app effectiveness varies largely between indications. Future RCTs should consider reporting behavioral (process) outcomes and measures of healthcare resource utilization to provide deeper insights on mechanisms that make health apps effective, and further elucidate their impact on healthcare systems.

Self-Care Mental Health App Intervention for Post-Intensive Care Syndrome-Family: A Randomized Pilot Study

BACKGROUND

Post-intensive care syndrome-family (PICS-F) is a constellation of adverse psychological symptoms experienced by family members of critically ill patients during and after acute illness. Cognitive behavioral therapy delivered using smartphone technology is a novel approach for PICS-F symptom self-management.

OBJECTIVE

To determine the efficacy of smartphone delivery of cognitive behavioral therapy in reducing the prevalence and severity of PICS-F symptoms in family members of critically ill patients.

METHODS

The study had a randomized controlled longitudinal design with control and intervention groups composed of family members of patients admitted to 2 adult intensive care units. The intervention consisted of a mental health app loaded on participants' personal smartphones. The study time points were upon enrollment (within 5 days of intensive care unit admission; time 1), 30 days after enrollment (time 2), and 60 days after enrollment (time 3). Study measures included demographic data, PICS-F symptoms, mental health self-efficacy, health-related quality of life, and app use.

RESULTS

The study sample consisted of 60 predominantly White (72%) and female (78%) family members (30 intervention, 30 control). Anxiety and depression symptom severity decreased significantly over time in the intervention group but not in the control group. Family members logged in to the app a mean of 11.4 times (range, 1-53 times) and spent a mean of 50.16 minutes (range, 1.87-245.92 minutes) using the app.

CONCLUSIONS

Delivery of cognitive behavioral therapy to family members of critically ill patients via a smartphone app shows some efficacy in reducing PICS-F symptoms.

Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners

BACKGROUND

Mobile health (mHealth) interventions can deliver personalized behavioral support to users in daily contexts. These interventions have been increasingly adopted to support individuals who require low-cost and low-burden support. Prior research has demonstrated the feasibility and acceptability of an mHealth intervention app (CareQOL) designed for use with informal care partners. To further optimize the intervention delivery, we need to investigate how care partners, many of whom lack the time for self-care, react and act in response to different behavioral messages.

OBJECTIVE

The goal of this study was to understand the factors that impact care partners' decision-making and actions in response to different behavioral messages. Insights from this study will help optimize future tailored and personalized behavioral interventions.

METHODS

We conducted semistructured interviews with participants who had recently completed a 3-month randomized controlled feasibility trial of the CareQOL mHealth intervention app. Of the 36 participants from the treatment group of the randomized controlled trial, 23 (64%) participated in these interviews. To prepare for each interview, the team first selected representative behavioral messages (eg, targeting different health dimensions) and presented them to participants during the interview to probe their influence on participants' thoughts and actions. The time of delivery, self-reported perceptions of the day, and user ratings of a message were presented to the participants during the interviews to assist with recall.

RESULTS

The interview data showed that after receiving a message, participants took various actions in response to different messages. Participants performed suggested behaviors or adjusted them either immediately or in a delayed manner (eg, sometimes up to a month later). We identified 4 factors that shape the variations in user actions in response to different behavioral messages: uncertainties about the workload required to perform suggested behaviors, concerns about one's ability to routinize suggested behaviors, in-the-moment willingness and ability to plan for suggested behaviors, and overall capability to engage with the intervention.

CONCLUSIONS

Our study showed that care partners use mHealth behavioral messages differently regarding the immediacy of actions and the adaptation to suggested behaviors. Multiple factors influence people's perceptions and decisions regarding when and how to take actions. Future systems should consider these factors to tailor behavioral support for individuals and design system features to support the delay or adaptation of the suggested behaviors. The findings also suggest extending the assessment of user adherence by considering the variations in user actions on behavioral support (ie, performing suggested or adjusted behaviors immediately or in a delayed manner).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/32842.

Evaluating the Impact of an mHealth Platform for Managing Acute Postoperative Dental Pain: Randomized Controlled Trial

Background

Postoperative dental pain is pervasive and can affect a patient's quality of life. Adopting a patient-centric approach to pain management involves having contemporaneous information about the patient's experience of pain and using it to personalize care.

Objective

In this study, we evaluated the use of a mobile health (mHealth) platform to collect pain-related patient-reported outcomes over 7 days after the patients underwent pain-inducing dental procedures; we then relayed the information to the dentist and determined its impact on the patient's pain experience.

Methods

The study used a cluster-randomized experimental study design with an intervention arm where patients were prompted to complete a series of questions relating to their pain experience after receiving automated text notifications on their smartphone on days 1, 3, 5, and 7, with the resulting information fed back to dentists, and a control arm where patients received usual care. Providers were randomized, and patients subsequently assumed the enrollment status of their providers. Providers or their staff identified eligible patients and invited them to participate in the study. Provider interviews and surveys were conducted to evaluate acceptance of the mHealth platform.

Results

A total of 42 providers and 1525 patients participated. For the primary outcome (pain intensity on a 1 to 10 scale, with 10 being the most painful), intervention group patients reported an average pain intensity of 4.8 (SD 2.6), while those in the control group reported an average pain intensity of 4.7 (SD 2.8). These differences were not significant. There were also no significant differences in secondary outcomes, including pain interference with activity or sleep, patient satisfaction with pain management, or opioid prescribing. Patient surveys revealed reluctance to use the app was mostly due to technological challenges, data privacy concerns, and a preference for phone calls over texting. Providers had high satisfaction with the app and suggested integrating additional features, such as an in-system camera for patients to upload pictures and videos of the procedural site, and integration with the electronic health record system.

Conclusions

While the mHealth platform did not have a significant impact on acute postoperative pain experience, patients and providers indicated improvement in patient-provider communication, patient-provider relationship, postoperative complication management, and ability to manage pain medication prescribing. Expanded collaboration between mHealth developers and frontline health care providers can facilitate the applicability of these platforms, further help improve its integration with the normal clinic workflow, and assist in moving toward a more patient-centric approach to pain management.

Refocusing of Attention on Positive Events Using Monitoring-Based Feedback and Microinterventions for Patients With Chronic Musculoskeletal Pain in the PerPAIN Randomized Controlled Trial: Protocol for a Microrandomized Trial

BACKGROUND

Chronic musculoskeletal pain (CMSP) affects between 13% and 47% of the population, with a global growth rate of 20.3% within the last 15 years, suggesting that there is a high need for effective treatments. Pain diaries have long been a common tool in nonpharmacological pain treatment for monitoring and providing feedback on patients' symptoms in daily life. More recently, positive refocusing techniques have come to be used, promoting pain-free episodes and positive outcomes rather than focusing on managing the pain.

OBJECTIVE

This study aims to evaluate the feasibility (ie, acceptability, intervention adherence, and fidelity) and initial signals of efficacy of the PerPAIN app, an ecological momentary intervention for patients with CMSP. The app comprises digitalized monitoring using the experience sampling method (ESM) and feedback. In addition, the patients receive 3 microinterventions targeted at refocusing of attention on positive events.

METHODS

In a microrandomized trial, we will recruit 35 patients with CMSP who will be offered the app for 12 weeks. Participants will be prompted to fill out 4 ESM monitoring questionnaires a day assessing information on their current context and the proximal outcome variables: absence of pain, positive mood, and subjective activity. Participants will be randomized daily and weekly to receive no feedback, verbal feedback, or visual feedback on proximal outcomes assessed by the ESM. In addition, the app will encourage participants to complete 3 microinterventions based on positive psychology and cognitive behavioral therapy techniques. These microinterventions are prompts to report joyful moments and everyday successes or to plan pleasant activities. After familiarizing themselves with each microintervention individually, participants will be randomized daily to receive 1 of the 3 exercises or none. We will assess whether the 2 feedback types and the 3 microinterventions increase proximal outcomes at the following time point. The microrandomized trial is part of the PerPAIN randomized controlled trial (German Clinical Trials Register DRKS00022792) investigating a personalized treatment approach to enhance treatment outcomes in CMSP.

RESULTS

Approval was granted by the Ethics Committee II of the University of Heidelberg on August 4, 2020. Recruitment for the microrandomized trial began in May 2021 and is ongoing at the time of submission. By October 10, 2022, a total of 24 participants had been enrolled in the microrandomized trial.

CONCLUSIONS

This trial will provide evidence on the feasibility of the PerPAIN app and the initial signals of efficacy of the different intervention components. In the next step, the intervention would need to be further refined and investigated in a definitive trial. This ecological momentary intervention presents a potential method for offering low-level accessible treatment to a wide range of people, which could have substantial implications for public health by reducing disease burden of chronic pain in the population.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43376.

The engagement problem: A review of engagement with digital mental health interventions and recommendations for a path forward

Purpose of the review

Digital mental health interventions (DMHIs) are an effective and accessible means of addressing the unprecedented levels of mental illness worldwide. Currently, however, patient engagement with DMHIs in real-world settings is often insufficient to see clinical benefit. In order to realize the potential of DMHIs, there is a need to better understand what drives patient engagement.

Recent findings

We discuss takeaways from the existing literature related to patient engagement with DMHIs and highlight gaps to be addressed through further research. Findings suggest that engagement is influenced by patient-, intervention- and systems-level factors. At the patient-level, variables such as sex, education, personality traits, race, ethnicity, age and symptom severity appear to be associated with engagement. At the intervention-level, integrating human support, gamification, financial incentives and persuasive technology features may improve engagement. Finally, although systems-level factors have not been widely explored, the existing evidence suggests that achieving engagement will require addressing organizational and social barriers and drawing on the field of implementation science.

Summary

Future research clarifying the patient-, intervention- and systems-level factors that drive engagement will be essential. Additionally, to facilitate improved understanding of DMHI engagement, we propose the following: (a) widespread adoption of a minimum necessary 5-element engagement reporting framework; (b) broader application of alternative clinical trial designs; and (c) directed efforts to build upon an initial parsimonious conceptual model of DMHI engagement.

A flexible micro-randomized trial design and sample size considerations

Technological advancements have made it possible to deliver mobile health interventions to individuals. A novel framework that has emerged from such advancements is the just-in-time adaptive intervention, which aims to suggest the right support to the individuals when their needs arise. The micro-randomized trial design has been proposed recently to test the proximal effects of the components of these just-in-time adaptive interventions. However, the extant micro-randomized trial framework only considers components with a fixed number of categories added at the beginning of the study. We propose a more flexible micro-randomized trial design which allows addition of more categories to the components during the study. Note that the number and timing of the categories added during the study need to be fixed initially. The proposed design is motivated by collaboration on the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation study, which learns to deliver effective text messages to encourage physical activity among patients with diabetes and depression. We developed a new test statistic and the corresponding sample size calculator for the flexible micro-randomized trial using an approach similar to the generalized estimating equation for longitudinal data. Simulation studies were conducted to evaluate the sample size calculators and an R shiny application for the calculators was developed.

Patients' Experiences Using a Mobile Health App for Self-Care of Heart Failure in a Real-World Setting: Qualitative Analysis

BACKGROUND

Publicly available patient-focused mobile health (mHealth) apps are being increasingly integrated into routine heart failure (HF)-related self-care. However, there is a dearth of research on patients' experiences using mHealth apps for self-care in real-world settings.

OBJECTIVE

The purpose of this study was to explore patients' experiences using a commercially available mHealth app, OnTrack to Health, for HF self-care in a real-world setting.

METHODS

Patient satisfaction, measured with a 5-point Likert scale, and an open-ended survey were used to gather data from 23 patients with HF who were provided the OnTrack to Health app as a part of routine HF management. A content analysis of patients' responses was conducted with the qualitative software Atlas.ti (version 8; ATLAS.ti Scientific Software Development GmbH).

RESULTS

Patients (median age 64, IQR 57-71 years; 17/23, 74% male) used OnTrack to Health for a median 164 (IQR 51-640) days before the survey. All patients reported excellent experiences related to app use and would recommend the app to other patients with HF. Five themes emerged from the responses to the open-ended questions: (1) features that enhanced self-care of HF (medication tracker, graphic performance feedback and automated alerts, secured messaging features, and HF self-care education); (2) perceived benefits (provided assurance of safety, improved HF self-care, and decreased hospitalization rates); (3) challenges with using apps for self-care (giving up previous self-care strategies); (4) facilitators (perceived ease of use and availability of technical support); and (5) suggested improvements (streamlining data entry, integration of apps with an electronic medical record, and personalization of app features).

CONCLUSIONS

Patients were satisfied with using OnTrack to Health for self-care. They perceived the features of the app as valuable tools for improving self-care ability and decreasing hospitalization rates. The development of apps in collaboration with end users is essential to ensure high-quality patient experiences related to app use for self-care.

A micro-randomized pilot study to examine the impact of just-in-time nudging on after-dinner snacking in adults with type 2 diabetes: A study protocol

AIM

To determine whether a digital nudge soon after dinner reduces after-dinner snacking events as measured objectively by continuous glucose monitoring (CGM) in patients with type 2 diabetes (T2D).

METHODS

This is a single-site micro-randomized trial (MRT). People with T2D, aged 18-75 years, managed with diet or a stable dose of oral antidiabetic medications for at least 3 months, and who habitual snack after dinner at least 3 nights per week, will be recruited. Picto-graphic nudges were designed by mixed research methods. After a 2-week lead-in phase to determine eligibility and snacking behaviours by a CGM detection algorithm developed by the investigators, participants will be micro-randomized daily (1:1) to a second 2-week period to either a picto-graphic nudge delivered-in-time (Intui Research) or no nudge. During lead-in and MRT phases, 24-hour glucose will be measured by CGM, sleep will be tracked by an under-mattress sleep sensor, and dinner timing will be captured daily by photographing the evening meal.

RESULTS

The primary outcome is the difference in the incremental area under the CGM curve between nudging and non-nudging days during the period from 90 minutes after dinner until 04:00 AM. Secondary outcomes include the effect of baseline characteristics on treatment, and comparisons of glucose peaks and time-in-range between nudging and non-nudging days. The feasibility of 'just-in-time' messaging and nudge acceptability will be evaluated, along with the analysis of sleep quality measures and their night-to-night variability.

CONCLUSIONS

This study will provide preliminary evidence of the impact of appropriately timed digital nudges on 24 -hour intertitial glucose levels resulting from altered after-dinner snacking in people with T2D. An exploratory sleep substudy will provide evidence of a bidirectional relationship between after-dinner snacking behaviour, glycaemia and sleep. Ultimately, this study will allow for the design of a future confirmatory study of the potential for digital nudging to improve health related behaviours and health outcomes.

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial

BACKGROUND

Drink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to "Please complete your drinks and mood diary," yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users' reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement.

OBJECTIVE

Our objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy.

METHODS

We conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement.

RESULTS

Receiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different.

CONCLUSIONS

We found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase "in-the-moment" engagement. Further optimization is required to improve the long-term engagement.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/18690.

Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial

OBJECTIVE

While self-monitoring can help mitigate alcohol misuse in young adults, engagement with digital self-monitoring is suboptimal. The present study investigates the utility of two types of digital prompts (reminders) to encourage young adults to self-monitor their alcohol use. These prompts leverage information that is self-relevant (i.e., represents and is valuable) to the person.

METHOD

Five hundred ninety-one college students (Mage = 18; 61% = female, 76% = White) were enrolled in an 8-week intervention study involving biweekly digital self-monitoring of their alcohol use. At baseline, participants selected an item they would like to purchase for themselves and their preferred charitable organization. Then, biweekly, participants were microrandomized to a prompt highlighting the opportunity to either (a) win their preferred item (self-interest prompt); or (b) donate to their preferred charity (prosocial prompt). Following self-monitoring completion, participants allocated reward points toward lottery drawings for their preferred item or charity.

RESULTS

The self-interest (vs. prosocial) prompt was significantly more effective in promoting proximal self-monitoring at the beginning of the study, Est = exp(.14) = 1.15; 95% confidence interval (CI) [1.01, 1.29], whereas the prosocial (vs. self-interest) prompt was significantly more effective at the end, Est = exp(-.17) = 0.84; 95% CI [0.70, 0.98]. Further, the prosocial (vs. self-interest) prompt was significantly more effective among participants who previously allocated all their reward points to drawings for their preferred item, Est = exp(-.15) = 0.86; 95% CI [.75, .97].

CONCLUSIONS

These results suggest that the advantage of prompts that appeal to a person's self-interest (vs. prosocial) motives varies over time and based on what reward options participants prioritized in previous decisions. Theoretical and practical implications for intervention design are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies

Objective

Insufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies.

Method

Data from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response.

Results

Although prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time.

Conclusion

Our study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies.

Predictors for response to electronic patient-reported outcomes in routine care in patients with rheumatoid arthritis: a retrospective cohort study

Routine collection of electronic patient-reported outcomes (ePROs) can improve clinical care. However, a low response rate may counteract the benefits. To optimize adoption, the aim of this study was to investigate which patient factors and/or timing of the invitation predicted response to ePROs sent prior to consultations in patients with rheumatoid arthritis. We performed a retrospective database study with clinical data collected as part of usual care from the electronic medical records at Reade Amsterdam. The dataset comprised the email invitations to complete the ePRO sent prior to consultation. Multiple patient factors and factors defining the timing of the invitation were investigated if they predicted response to the ePRO through a multivariable logistic generalized estimating equation analysis. In total, 17.070 ePRO invitations were sent to 3194 patients (mean age 60 (SD 14), 74% female), of which 40% was completed. Patients between 55 and 73 years (OR 1.39, 95%CI 1.09-1.77) and with higher social economic status (SES) (OR 1.51, 95%CI 1.22-1.88) had significantly higher odds for completing the ePRO, while patients living in an urban area had lower odds (OR 0.69, 95% CI 0.62-0.76). In year 4 after implementation, the OR was increased to 3.69 (95% CI 2.91-4.90). The implementation of ePROs in daily clinical practice needs improvement since 40% of the ePROs sent prior to consultations were completed. Patients that had higher odds to report the next ePRO were between the age of 55-73, had a higher socio-economic status, and were residents in a rural area. The adoption of reporting the PRO increased over time, but the timing of the prompt did not predict response. Additional research is needed to understand ePRO completion, especially for patients with lower socio-economic status.

A systematic review of remotely delivered contingency management treatment for substance use

BACKGROUND

Substance use and related consequences (e.g., impaired driving, injuries, disease transmission) continue to be major public health concerns. Contingency management (CM) is a highly effective treatment for substance use disorders. Yet CM remains vastly underutilized, in large part due to implementation barriers to in-person delivery. If feasible and effective, remote delivery of CM may reduce barriers at both the clinic- and patient-level, thus increasing reach and access to effective care. Here, we summarize data from a systematic review of studies reporting remote delivery of CM for substance use treatment.

METHODS

We conducted a systematic review, reported according to PRISMA guidelines. The study team identified a total of 4358 articles after deduplication. Following title and abstract screening, full-text screening, and reference tracking, 39 studies met the eligibility criteria. We evaluated the methodological quality of the included studies using the Effective Public Health Practice Project Quality tool.

RESULTS

Of 39 articles included in the review, most (n = 26) targeted cigarette smoking, with others focusing on alcohol (n = 9) or other substance use or targeting multiple substances (n = 4). Most remotely delivered CM studies focused on abstinence (n = 29), with others targeting substance use reduction (n = 2), intervention engagement (n = 5), and both abstinence and intervention engagement (n = 3). CM was associated with better outcomes (either abstinence, use reduction, or engagement), with increasingly more remotely delivered CM studies published in more recent years. Studies ranged from moderate to strong quality, with the majority (57.5 %) of studies being strong quality.

CONCLUSIONS

Consistent with in-person CM, remotely delivered CM focusing on abstinence or use reduction from substances or engagement in substance use treatment services improves outcomes at the end of treatment compared to control conditions. Moreover, remotely delivered CM is feasible across a variety of digital delivery platforms (e.g., web, mobile, and wearable), with acceptability and reduced clinic and patient burden as technological advancements streamline monitoring and reinforcer delivery.

The Relationship Between Broadband Speeds, Device Type, Demographic Characteristics, and Care-Seeking Via Telehealth

Background: This study sought to examine the complex relationship between individual and environmental characteristics, broadband access, device type (computer or smartphone), and telehealth utilization as it relates to the digital divide. Methods: We analyzed a combination of electronic health record and publicly available zip code-level data for 2,770 men seeking treatment on a large, nationally available, direct-to-consumer telehealth platform. Using logistic regression, we determined the likelihood of accessing the platform through a smartphone (vs. a computer) based on key features of the environment, including broadband access and income, and demographic characteristics, including age and race. Results: We found that living in areas with higher rates of broadband adoption significantly decreased the likelihood of accessing virtual care using a smartphone (odds ratio [OR] = 0.17, p < 0.001). Compared with the 18-29 age category, the odds of accessing virtual care using a smartphone decreased for men between the age categories of 40-59 (OR = 0.63, p < 0.01) and over 60 (OR = 0.29, p < 0.001) years. Belonging to historically marginalized communities of color (Black, Hispanic, and Native American) almost doubled the odds of using a smartphone to access the platform (OR = 1.8, p < 0.001). Broadband availability and median area income were not significantly associated with mobile use. Conclusions: Telehealth platform design and policy solutions intended to expand access to virtual care should be flexible enough to accommodate the sometimes competing needs of patients who are at the greatest risk of being left behind.

Supporting Autonomous Motivation for Physical Activity With Chatbots During the COVID-19 Pandemic: Factorial Experiment

BACKGROUND

Although physical activity can mitigate disease trajectories and improve and sustain mental health, many people have become less physically active during the COVID-19 pandemic. Personal information technology, such as activity trackers and chatbots, can technically converse with people and possibly enhance their autonomous motivation to engage in physical activity. The literature on behavior change techniques (BCTs) and self-determination theory (SDT) contains promising insights that can be leveraged in the design of these technologies; however, it remains unclear how this can be achieved.

OBJECTIVE

This study aimed to evaluate the feasibility of a chatbot system that improves the user's autonomous motivation for walking based on BCTs and SDT. First, we aimed to develop and evaluate various versions of a chatbot system based on promising BCTs. Second, we aimed to evaluate whether the use of the system improves the autonomous motivation for walking and the associated factors of need satisfaction. Third, we explored the support for the theoretical mechanism and effectiveness of various BCT implementations.

METHODS

We developed a chatbot system using the mobile apps Telegram (Telegram Messenger Inc) and Google Fit (Google LLC). We implemented 12 versions of this system, which differed in 3 BCTs: goal setting, experimenting, and action planning. We then conducted a feasibility study with 102 participants who used this system over the course of 3 weeks, by conversing with a chatbot and completing questionnaires, capturing their perceived app support, need satisfaction, physical activity levels, and motivation.

RESULTS

The use of the chatbot systems was satisfactory, and on average, its users reported increases in autonomous motivation for walking. The dropout rate was low. Although approximately half of the participants indicated that they would have preferred to interact with a human instead of the chatbot, 46.1% (47/102) of the participants stated that the chatbot helped them become more active, and 42.2% (43/102) of the participants decided to continue using the chatbot for an additional week. Furthermore, the majority thought that a more advanced chatbot could be very helpful. The motivation was associated with the satisfaction of the needs of competence and autonomy, and need satisfaction, in turn, was associated with the perceived system support, providing support for SDT underpinnings. However, no substantial differences were found across different BCT implementations.

CONCLUSIONS

The results provide evidence that chatbot systems are a feasible means to increase autonomous motivation for physical activity. We found support for SDT as a basis for the design, laying a foundation for larger studies to confirm the effectiveness of the selected BCTs within chatbot systems, explore a wider range of BCTs, and help the development of guidelines for the design of interactive technology that helps users achieve long-term health benefits.

Increasing Engagement in the Electronic Framingham Heart Study: Factorial Randomized Controlled Trial

BACKGROUND

Smartphone apps and mobile health devices offer innovative ways to collect longitudinal cardiovascular data. Randomized evidence regarding effective strategies to maintain longitudinal engagement is limited.

OBJECTIVE

This study aimed to evaluate smartphone messaging interventions on remote transmission of blood pressure (BP) and heart rate (HR) data.

METHODS

We conducted a 2 × 2 × 2 factorial blinded randomized trial with randomization implemented centrally to ensure allocation concealment. We invited participants from the Electronic Framingham Heart Study (eFHS), an e-cohort embedded in the FHS, and asked participants to measure their BP (Withings digital cuff) weekly and wear their smartwatch daily. We assessed 3 weekly notification strategies to promote adherence: personalized versus standard; weekend versus weekday; and morning versus evening. Personalized notifications included the participant's name and were tailored to whether or not data from the prior week were transmitted to the research team. Intervention notification messages were delivered weekly automatically via the eFHS app. We assessed if participants transmitted at least one BP or HR measurement within 7 days of each notification after randomization. Outcomes were adherence to BP and HR transmission at 3 months (primary) and 6 months (secondary).

RESULTS

Of the 791 FHS participants, 655 (82.8%) were eligible and randomized (mean age 53, SD 9 years; 392/655, 59.8% women; 596/655, 91% White). For the personalized versus standard notifications, 38.9% (126/324) versus 28.8% (94/327) participants sent BP data at 3 months (difference=10.1%, 95% CI 2.9%-17.4%; P=.006), but no significant differences were observed for HR data transmission (212/324, 65.4% vs 209/327, 63.9%; P=.69). Personalized notifications were associated with increased BP and HR data transmission versus standard at 6 months (BP: 107/291, 36.8% vs 66/295, 22.4%; difference=14.4%, 95% CI 7.1- 21.7%; P<.001; HR: 186/281, 66.2% vs 158/281, 56.2%; difference=10%, 95% CI 2%-18%; P=.02). For BP and HR primary or secondary outcomes, there was no evidence of differences in data transmission for notifications sent on weekend versus weekday or morning versus evening.

CONCLUSIONS

Personalized notifications increased longitudinal adherence to BP and HR transmission from mobile and digital devices among eFHS participants. Our results suggest that personalized messaging is a powerful tool to promote adherence to mobile health systems in cardiovascular research.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03516019; https://clinicaltrials.gov/ct2/show/NCT03516019.

Malthusian Trajectory for Heart Failure and Novel Translational Ambulatory Technologies

INTRODUCTION

It has been estimated that congestive heart failure (CHF) will reach epidemic proportions and contribute to large unsustainable impacts on health budgets for any cardiovascular condition. Against other major trends in cardiovascular outcomes, readmission and disease burden continue to rise as the demographics shift.

METHODS

The rise in heart failure with preserved ejection fraction (HFpEF) among elderly women will present new challenges. Gold standard care delivers sustainable and cost-effective health improvements using organised care programs. When coordinated with large hospitals, this can be replicated universally.

RESULTS

A gradient of outcomes and ambulatory care needs to be shifted from established institutions and shared with clients and community health services, being a sizeable proportion of CHF care.

CONCLUSION

In this review, we explore health technologies as an emerging opportunity to address gaps in CHF management.

HIV self-test reporting using mHealth platforms: A pilot study in Johannesburg, South Africa

Background

The main impediment to operational scale-up of HIV self-testing (HIVST) and counselling, is a dearth of information on utilisation, reporting, and linkage to care for HIV-positive individuals. To inform solutions to this issue, this study investigated the utility of self-testers reporting their results using a mobile-health (mHealth) platform, and whether seropositive users linked into care.

Method

Candidates who met the recruitment criteria across multiple sites within inner-city Johannesburg each received an HIVST kit. Using short message service (SMS) reminders (50% standard and 50% behavioural science), participants were prompted to self-report results on provided platforms. On the seventh day, users who did not make contact, were called, and surveyed via an interactive voice response system (IVRS). Multivariable regression was used in reporting by age and sex.

Results

Of the 9,505 participants, 2,467 (25.9%) participants answered any survey question, and of those, 1,933 (78.4%) were willing to self-report their HIV status. Men were more likely than women to make an inbound call (10.2% vs. 9.1%, p = 0.06) however, women were significantly more likely to self-report their test result (AOR = 1.12, 95%CI = 1.01-1.24, p = 0.025). Overall, self-reporting a test result was predicted by being younger and female. In addition, reporting HIV results was associated with age, 25-35 (AOR = 1.58, 95% CI = 1.24-2.02) and above 35 years (AOR = 2.12, 95% CI = 1.61-2.80). Out of 1,933 participants willing to report their HIV status, 314 reported a positive test, indicating a HIV prevalence of 16.2% (95% CI: 14.6%-18.0%) and of those 204 (65.0%) reported inclination to link to care.

Conclusion

While self-reporting HIVST results via an IVRS system yielded a higher response rate, behavioural SMSs were ineffective in increasing self-reporting.

Evaluating User Preferences, Comprehension, and Trust in Apps for Environmental Health Hazards: Qualitative Case Study

BACKGROUND

Climate change is projected to increase environmental health hazard risks through fire-related air pollution and increased airborne pollen levels. To protect vulnerable populations, it is imperative that evidence-based and accessible interventions are available. The environmental health app, AirRater, was developed in 2015 in Australia to provide information on multiple atmospheric health hazards in near real time. The app allows users to view local environmental conditions, and input and track their personal symptoms to enable behaviors that protect health in response to environmental hazards.

OBJECTIVE

This study aimed to develop insights into users' perceptions of engagement, comprehension, and trust in AirRater to inform the future development of environmental health apps. Specifically, this study explored which AirRater features users engaged with, what additional features or functionality needs users felt they required, users' self-perception of understanding app information, and their level of trust in the information provided.

METHODS

A total of 42 adult AirRater users were recruited from 3 locations in Australia to participate in semistructured interviews to capture location- or context-specific experiences. Participants were notified of the recruitment opportunity through multiple avenues including newsletter articles and social media. Informed consent was obtained before participation, and the participants were remunerated for their time and perspectives. A preinterview questionnaire collected data including age range, any preexisting conditions, and location (postcode). All participant data were deidentified. Interviews were recorded, transcribed, and analyzed using thematic analysis in NVivo 12 (QSR International).

RESULTS

Participants discussed app features and functionality, as well as their understanding of, and trust in, the information provided by the app. Most (26/42, 62%) participants used and valued visual environmental hazard features, especially maps, location settings, and hazard alerts. Most (33/42, 78%) found information in the app easy to understand and support their needs, irrespective of their self-reported literacy levels. Many (21/42, 50%) users reported that they did not question the accuracy of the data presented in the app. Suggested enhancements include the provision of meteorological information (eg, wind speed or direction, air pressure, UV rating, and humidity), functionality enhancements (eg, forecasting, additional alerts, and the inclusion of health advice), and clarification of existing information (eg, symptom triggers), including the capacity to download personal summary data for a specified period.

CONCLUSIONS

Participants' perspectives can inform the future development of environmental health apps. Specifically, participants' insights support the identification of key elements for the optimal development of environmental health app design, including streamlining, capacity for users to customize, use of real time data, visual cues, credibility, and accuracy of data. The results also suggest that, in the future, iterative collaboration between developers, environmental agencies, and users will likely promote better functional design, user trust in the data, and ultimately better population health outcomes.

Evaluation of the communication strategy for promoting physical activity in a cross-company network in Germany: A mixed-methods analysis

Introduction

The workplace is considered a promising setting for reaching physically inactive adults, but participation quotes in workplace health promotion (WHP) remain low. Regarding the low participation in WHP, the question emerges concerning the importance of health communication strategies. This paper presents the results from the evaluation of the communication strategy of a cross-company network for promoting physical activity and derives findings for the successful communication of measures.

Materials and methods

Quantitative and qualitative data sources were used to evaluate the communication strategy. The methods applied included individual semi-structured interviews (n = 14) and the monitoring of the usage of digital communication channels.

Results

The analysis revealed that the usage of the digital communication channels within this study was subjected to major fluctuations and a variety of factors must be considered when communicating physical activity measures in a cross-company network. It is important to engage in appropriate communication management that explicitly takes the interpersonal communication and the organizational circumstances into account.

Conclusion

This study revealed which factors may have an influence on the successful communication of physical activity measures in the context of WHP in cross-company networks. Thus, it makes an important contribution to the transfer of science and practice as it captured relevant questions from the field of WHP.

Trial registration

German Clinical Trials Register (DRKS)-ID: DRKS00020956; Date of registration: 18 June 2020, https://drks.de/search/de/trial/DRKS00020956.

The feasibility of using Apple's ResearchKit for recruitment and data collection: Considerations for mental health research

In 2015, Apple launched an open-source software framework called ResearchKit. ResearchKit provides an infrastructure for conducting remote, smartphone-based research trials through the means of Apple's App Store. Such trials may have several advantages over conventional trial methods including the removal of geographic barriers, frequent assessments of participants in real-life settings, and increased inclusion of seldom-heard communities. The aim of the current study was to explore the feasibility of participant recruitment and the potential for data collection in the non-clinical population in a smartphone-based trial using ResearchKit. As a case example, an app called eMovit, a behavioural activation (BA) app with the aim of helping users to build healthy habits was used. The study was conducted over a 9-month period. Any iPhone user with access to the App Stores of The Netherlands, Belgium, and Germany could download the app and participate in the study. During the study period, the eMovit app was disseminated amongst potential users via social media posts (Twitter, Facebook, LinkedIn), paid social media advertisements (Facebook), digital newsletters and newspaper articles, blogposts and other websites. In total, 1,788 individuals visited the eMovit landing page. A total of 144 visitors subsequently entered Apple's App Store through that landing page. The eMovit product page was viewed 10,327 times on the App Store. With 79 installs, eMovit showed a conversion rate of 0.76% from product view to install of the app. Of those 79 installs, 53 users indicated that they were interested to participate in the research study and 36 subsequently consented and completed the demographics and the participants quiz. Fifteen participants completed the first PHQ-8 assessment and one participant completed the second PHQ-8 assessment. We conclude that from a technological point of view, the means provided by ResearchKit are well suited to be integrated into the app process and thus facilitate conducting smartphone-based studies. However, this study shows that although participant recruitment is technically straightforward, only low recruitment rates were achieved with the dissemination strategies applied. We argue that smartphone-based trials (using ResearchKit) require a well-designed app dissemination process to attain a sufficient sample size. Guidelines for smartphone-based trial designs and recommendations on how to work with challenges of mHealth research will ensure the quality of these trials, facilitate researchers to do more testing of mental health apps and with that enlarge the evidence-base for mHealth.

Appreciating the Good Things in Life During the Covid-19 Pandemic: A Randomized Controlled Trial and Evaluation of a Gratitude App

The Covid-19 pandemic has had many negative consequences on the general public mental health. The aim of this study was to test the effectiveness of and satisfaction with an app with gratitude exercises to improve the mental health of people with reduced mental well-being due to the Covid-19 pandemic, as well as potential mechanisms of well-being change and dose-response relationships. A two-armed randomized controlled trial design was used, with two groups receiving the 6-week gratitude intervention app either immediately (intervention group, n = 424) or after 6 weeks (waiting list control group, n = 425). Assessments took place online at baseline (T0), six weeks later (T1) and at 12 weeks (T2), measuring outcomes (i.e., mental well-being, anxiety, depression, stress), and potential explanatory variables (i.e., gratitude, positive reframing, rumination). Linear mixed models analyses showed that when controlled for baseline measures, the intervention group scored better on all outcome measures compared to the control group at T1 (d = .24-.49). These effects were maintained at T2. The control group scored equally well on all outcome measures at T2 after following the intervention. Effects of the intervention on well-being were partially explained by gratitude, positive reframing, and rumination, and finishing a greater number of modules was weakly related to better outcomes. The intervention was generally appealing, with some room for improvement. The results suggest that a mobile gratitude intervention app is a satisfactory and effective way to improve the mental health of the general population during the difficult times of a pandemic.

A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training

Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants' adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants' overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

Mobile health technologies for the management of spine disorders: A systematic review of mHealth applications in Brazil

BACKGROUND

Spine disorders are conditions that affect a growing number of individuals, and mobile health (mHealth) applications (apps) offer potential to assist the self-management of these conditions.

OBJECTIVES

To perform a systematic review of the availability of mHealth apps for patients with spine disorders at Brazilian online stores and evaluate the apps in terms of engagement, user interface, experience, and quality of the information.

DESIGN

Systematic review.

METHOD

A search for spine disorders mHealth apps from the Google Play Store and AppStore in Brazil was performed by two independent reviewers on June 2021. Only smartphone apps in Brazilian Portuguese directed at spine disorders that provided information about education, counseling, exercise, or monitoring of patient health were included. The quality of eligible mHealth apps was assessed using the Mobile App Rating Scale (MARS).

RESULTS

Of the 2775 mHealth apps found, 10 were eligible for inclusion. All apps offered exercise programs. Three apps also offered tools to track patient-reported symptoms, nutritional orientation, or educational content in addition to the exercise program. Using MARS, the apps scored poorly in terms of quality, with an overall mean score ±standard deviation of 2.75 ± 0.63 on a scale of 1-5 points. Most apps scored poorly for credibility, user interface, and engagement.

CONCLUSIONS

The mHealth apps for spine disorders currently available in Brazil are of poor quality and limited functionality. Effective collaboration between industry and researchers is needed to develop better user-centered mHealth apps that can empower patients with these conditions.

Examining weekly facilitated group sessions and counselor-crafted self-monitoring feedback on treatment outcome in digital weight control: A pilot factorial study

Objective

Weight control programs that incorporate group sessions produce greater weight losses, but this has not been explored in the context of online programs. Further, counselor-crafted self-monitoring feedback is a core element of lifestyle interventions, although pre-scripted, modular feedback which does not require detailed counselor review may adequately promote weight loss. The current study explored the weight losses achieved in an online program that included facilitated group sessions, as well as outcomes when counselor-crafted self-monitoring feedback was provided.

Methods

A 2 × 2 pilot factorial randomized participants (90% women) with overweight/obesity (N = 73) to facilitated group sessions (yes/no) and type of feedback (counselor-crafted/pre-scripted, modular) within a 16-week online behavioral weight control program. Weight change outcomes were collected digitally. Treatment engagement and intervention delivery time were also tracked.

Results

Individuals offered weekly facilitated online group sessions lost more weight (-5.3% ± 4.9%) than those receiving the same digital program without group sessions (-3.1% ± 4.0%; p = 0.04). Those receiving group sessions also demonstrated significantly greater treatment engagement. Individuals receiving pre-scripted, modular feedback lost significantly more weight (-5.3% ± 4.8%) than those receiving the more traditional counselor-crafted feedback (-3.1% ± 4.1%; p = 0.04), but treatment engagement did not differ between conditions. However, interventionist time required to provide feedback was markedly lower for pre-scripted than counselor-crafted feedback (1.4 vs. 3.5 h per participant over 16 weeks, respectively, p = 0.01).

Conclusions

Incorporating weekly facilitated online group sessions significantly increased weight losses achieved in a digital lifestyle program. Further, pre-scripted, modular feedback required significantly less staff time than counselor-crafted feedback without diminishing weight losses. Thus, group sessions and pre-scripted feedback warrant consideration when designing digital lifestyle programs.

Recruitment & Retention in Remote Research: Learnings from a Large Decentralized Real-World Study (Preprint)

BACKGROUND

Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner.

OBJECTIVE

We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated.

METHODS

We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression.

RESULTS

We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants' sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence.

CONCLUSIONS

Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.

Data-driven Interpretable Policy Construction for Personalized Mobile Health

To promote healthy behaviors, many mobile health applications provide message-based interventions, such as tips, motivational messages, or suggestions for healthy activities. Ideally, the intervention policies should be carefully designed so that users obtain the benefits without being overwhelmed by overly frequent messages. As part of the HeartSteps physical-activity intervention, users receive messages intended to disrupt sedentary behavior. HeartSteps uses an algorithm to uniformly spread out the daily message budget over time, but does not attempt to maximize treatment effects. This limitation motivates constructing a policy to optimize the message delivery decisions for more effective treatments. Moreover, the learned policy needs to be interpretable to enable behavioral scientists to examine it and to inform future theorizing. We address this problem by learning an effective and interpretable policy that reduces sedentary behavior. We propose Optimal Policy Trees + (OPT+), an innovative batch off-policy learning method, that combines a personalized threshold learning and an extension of Optimal Policy Trees under a budget-constrained setting. We implement and test the method using data collected in HeartSteps V2/V3. Computational results demonstrate a significant reduction in sedentary behavior with a lower delivery budget. OPT+ produces a highly interpretable and stable output decision tree thus enabling theoretical insights to guide future research.

Electronic Health Record-Based Recruitment and Retention and Mobile Health App Usage: Multisite Cohort Study

BACKGROUND

To address the obesity epidemic, there is a need for novel paradigms, including those that address the timing of eating and sleep in relation to circadian rhythms. Electronic health records (EHRs) are an efficient way to identify potentially eligible participants for health research studies. Mobile health (mHealth) apps offer available and convenient data collection of health behaviors, such as timing of eating and sleep.

OBJECTIVE

The aim of this descriptive analysis was to report on recruitment, retention, and app use from a 6-month cohort study using a mobile app called Daily24.

METHODS

Using an EHR query, adult patients from three health care systems in the PaTH clinical research network were identified as potentially eligible, invited electronically to participate, and instructed to download and use the Daily24 mobile app, which focuses on eating and sleep timing. Online surveys were completed at baseline and 4 months. We described app use and identified predictors of app use, defined as 1 or more days of use, versus nonuse and usage categories (ie, immediate, consistent, and sustained) using multivariate regression analyses.

RESULTS

Of 70,661 patients who were sent research invitations, 1021 (1.44%) completed electronic consent forms and online baseline surveys; 4 withdrew, leaving a total of 1017 participants in the analytic sample. A total of 53.79% (n=547) of the participants were app users and, of those, 75.3% (n=412), 50.1% (n=274), and 25.4% (n=139) were immediate, consistent, and sustained users, respectively. Median app use was 28 (IQR 7-75) days over 6 months. Younger age, White race, higher educational level, higher income, having no children younger than 18 years, and having used 1 to 5 health apps significantly predicted app use (vs nonuse) in adjusted models. Older age and lower BMI predicted early, consistent, and sustained use. About half (532/1017, 52.31%) of the participants completed the 4-month online surveys. A total of 33.5% (183/547), 29.3% (157/536), and 27.1% (143/527) of app users were still using the app for at least 2 days per month during months 4, 5, and 6 of the study, respectively.

CONCLUSIONS

EHR recruitment offers an efficient (ie, high reach, low touch, and minimal participant burden) approach to recruiting participants from health care settings into mHealth research. Efforts to recruit and retain less engaged subgroups are needed to collect more generalizable data. Additionally, future app iterations should include more evidence-based features to increase participant use.

A Theory-Informed, Personalized mHealth Intervention for Adolescents (Mobile App for Physical Activity): Development and Pilot Study

Background

Evidence suggests that physical activity (PA) during childhood and adolescence is crucial as it usually results in adequate PA levels in adulthood. Given the ubiquitous use of smartphones by adolescents, these devices may offer feasible means to reach young populations and deliver interventions aiming to increase PA participation and decrease sedentary time. To date, very few studies have reported smartphone-based interventions promoting PA for adolescents. In addition, most available fitness apps do not include the latest evidence-based content.

Objective

This paper described the systematic development of a behavior change, theory-informed Mobile App for Physical Activity intervention with personalized prompts for adolescents aged 16 to 18 years. The within-subject trial results provided the first evidence of the general effectiveness of the intervention based on the outcomes step count, sedentary time, and moderate to vigorous PA (MVPA) minutes. The effectiveness of the intervention component personalized PA prompt was also assessed.

Methods

A 4-week within-subject trial with 18 healthy adolescents aged 16 to 18 years was conducted (mean age 16.33, SD 0.57 years). After the baseline week, the participants used the Mobile App for Physical Activity intervention (Fitbit fitness tracker+app), which included a daily personalized PA prompt delivered via a pop-up notification. A paired 1-tailed t test was performed to assess the effectiveness of the intervention. Change-point analysis was performed to assess the effectiveness of a personalized PA prompt 30 and 60 minutes after prompt delivery.

Results

The results showed that the intervention significantly reduced sedentary time in adolescents during the first week of the trial (t₁₇=−1.79; P=.04; bootstrapped P=.02). This trend, although remaining positive, diminished over time. Our findings indicate that the intervention had no effect on metabolic equivalent of task–based MVPA minutes, although the descriptive increase may give reason for further investigation. Although the results suggested no overall change in heart rate–based MVPA minutes, the results from the change-point analyses suggest that the personalized PA prompts significantly increased heart rate per minute during the second week of the study (t₁₆=1.84; P=.04; bootstrapped P=.04). There were no significant increases in participants’ overall step count; however, the personalized PA prompts resulted in a marginally significant increase in step counts per minute in the second week of the study (t₁₇=1.35; P=.09; bootstrapped P=.05).

Conclusions

The results of the trial provide preliminary evidence of the benefit of the Mobile App for Physical Activity intervention for modest yet significant reductions in participants’ sedentary time and the beneficial role of personalized PA prompts. These results also provide further evidence of the benefits and relative efficacy of personalized activity suggestions for inclusion in smartphone-based PA interventions. This study provides an example of how to guide the development of smartphone-based mobile health PA interventions for adolescents.

Can Personalization Persuade? Study of Notification Adaptation in Mobile Behavior Change Intervention Application

The growing ubiquity of smartphones and the ease of creating and distributing applications render the mobile platform an attractive means for facilitating positive behavior change at scale. Within the smartphone as a behavior change support system, mobile notifications play a critical role as they enable timely and relevant information distribution. In this paper we describe our preliminary investigation of the persuasiveness of mobile notifications delivered within a real-world behavior change intervention mobile app, which enabled users to set goals and define tasks related to those goals. The application aimed to motivate the users with notifications belonging to one of two groups—tailored and non-tailored, seeing them as sparks in the Fogg Behavior Model and personalizing them according to the users’ Big Five personality traits. Results indicate that customized messages may work for some individuals while working poorly for others. When analyzing users as a single group, no significant differences were observed, but when proceeding with the analysis on the individual level we found seven users whose personality traits notifications interact with in interesting ways. Our results offer two general insights: (1) Using personality-tailored messaging in a dynamic mobile domain as opposed to a static domain leads to different outcomes, and it seems that there is no one-to-one mapping between domains; (2) A major reason for most of our hypotheses being false may be that messages that are deemed as persuasive on their own are not what persuades people to perform an action. Unlike the clear-cut findings observed in other domains, we discover a rather nuanced relationship between the personalization and persuasiveness that calls for further exploration at the individual participant level.

Implementation of a Personalized Digital Application for Pediatric Pre-Anesthesia Evaluation and Education: An Ongoing Usability Analysis and Dynamic Improvement Scheme

BACKGROUND

Pre-anesthesia evaluation session is a basic practice preceding any surgical procedure, aimed at tailoring individualized anesthetic plan per patient, improving safety, and providing patients with educational knowledge and tools in preparation for the surgery day. In the last two decades, electronic health (eHealth) and mobile health (mHealth) settings gradually replaced part of the face-to-face encounters as the platform for pre-anesthesia communication between doctor and patient, yielding a range of benefits as demonstrated in recent publications. Yet, there is a lack of studies examining the effectiveness of surgical mHealth applications focusing on the pediatric preanesthetic setting and addressing their usability among families.

OBJECTIVE

This study describes a dynamic approach for the development process of GistMD's pre-anesthesia mHealth system, a mobile-based educational and management system designed for the pediatric setting.

METHODS

The study was conducted in four departments in a 1500-beds quaternary, academic medical center in Tel Aviv, Israel. During the study period, pre-anesthesia system was sent via text message to families whose children were about to undergo surgery. The system included pre-anesthesia questionnaires, educational videos, downloadable instructions, and consent forms. Ongoing collection and examination of usability data were conducted during the implementation term including responsiveness, effectiveness, and satisfaction indicators. The information collected in each stage was used to draw conclusions regarding potential usability gaps of the system and to plan product adjustments for the following period.

RESULTS

In a period of 141 days of implementation, GistMD pre-anesthesia management system was sent to 769 families. Three product fit actions were applied during this term: (1) Change of text message scheduling, aimed at addressing learnability and accessibility, resulted in a significant increase of 27% (χ2 [1] = 12.65, P<.001) in view rates and 27.4% (χ2 [1] = 30.01, P<.001) in satisfaction rates; (2) Reduce the number of screens, aimed at increasing efficiency and operability, resulted in a significant decrease of 8.6% of cases in which users did not perform any activity on the system after logging in (χ2 [1] = 6.18, P=.02); (3) Patient-focused campaign in two departments aimed at addressing memorability, resulted in significant increases in eight of twelve usability indicators.

CONCLUSIONS

Our results indicate that mHealth product-fit decisions derived from theory-based approach and ongoing usability data analysis allow tailoring of most appropriate responses for usability gaps, as reflected in increasing use rates and satisfaction. In the case of the pre-anesthesia management system in the pediatric setting, increased usability conveyed important benefits for patients and families. This work suggests a framework and study methods that may also be applicable in other mHealth settings and domains.

CLINICALTRIAL

The Effect of Tailored, Daily Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: The SMARTER Randomized Clinical Trial (Preprint)

Background

Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials.

Objective

The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m².

Methods

All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants’ dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal).

Results

Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m². The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: –2.12%, 95% CI –3.04% to –1.21%, P<.001; SM: –2.39%, 95% CI –3.32% to –1.47%; P<.001), but no difference between the groups (–0.27%; 95% CI –1.57% to 1.03%; t =–0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=–0.10; 95% CI –0.13 to –0.07; t =–5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001).

Conclusions

There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.

Trial Registration

Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936

Feature-level analysis of a smoking cessation smartphone app that uses a positive psychology approach (Preprint)

Background

Smoking cessation smartphone apps have emerged as highly accessible tools to support smoking cessation efforts. It is unknown how specific app features contribute to user engagement over time and relate to smoking outcomes.

Objective

To provide a feature-level analysis of the Smiling Instead of Smoking app (version 2) and to link feature use to subsequent smoking cessation.

Methods

Nondaily smokers (N=100) used the app for a period of 49 days (1 week before quitting and 6 weeks after quitting). Participants self-reported 30-day point-prevalence abstinence at the end of this period and at a 6-month follow up (the survey response rate was 94% and 89% at these points, respectively). Self-reported 30-day point prevalence abstinence rates were 40% at the end of treatment and 56% at the 6-month follow up. The app engaged users in both positive psychology content and traditional behavioral smoking cessation content. The app sent push notifications to prompt participants to complete prescribed content (ie, a “happiness exercise” every day and a “behavioral challenge” to use the app’s smoking cessation tools on 15 out of 49 days). Actions that participants took within the app were timestamped and recorded.

Results

Participants used the app on 24.7 (SD 13.8) days out of the 49 prescribed days, interacting with the happiness content on more days than the smoking content (23.8, SD 13.8 days vs 17.8, SD 10.3 days; t₉₉=9.28 [2-tailed]; P<.001). The prescribed content was frequently completed (45% of happiness exercises; 57% of behavioral challenges) and ad libitum tools were used on ≤7 days. Most participants used each ad libitum smoking cessation tool at least once, with higher use of personalized content (≥92% used “strategies,” “cigarette log,” “smoke alarms,” and “personal reasons”) than purely didactic content (79% viewed “benefits of quitting smoking”). The number of days participants used the app significantly predicted 30-day point-prevalence abstinence at the end of treatment (odds ratio [OR] 1.05, 95% CI 1.02-1.09; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.008-1.07; P=.01). The number of days participants engaged with the happiness content significantly predicted smoking abstinence at the end of treatment (OR 1.05, 95% CI 1.02-1.08; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.007-1.07; P=.02). This effect was not significant for the number of days participants engaged with the smoking cessation content of the app, either at the end of treatment (OR 1.04, 95% CI 0.996-1.08, P=.08) or at the 6-month follow up (OR 1.02, 95% CI 0.98-1.06; P=.29).

Conclusions

Greater app usage predicted greater odds of self-reported 30-day point-prevalence abstinence at both the end of treatment and over the long term, suggesting that the app had a therapeutic benefit. Positive psychology content and prescriptive clarity may promote sustained app engagement over time.

Trial Registration

ClinicalTrials.gov NCT03951766; https://clinicaltrials.gov/ct2/show/NCT03951766

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study

Background

Given the interrelated health of children and parents, strategies to promote stress regulation are critically important in the family context. However, the uptake of preventive mental health is limited among parents owing to competing family demands.

Objective

In this study, we aim to determine whether it is feasible and acceptable to randomize digital prompts designed to engage parents in real-time brief mindfulness activities guided by a commercially available app.

Methods

We conducted a 30-day pilot microrandomized trial among a sample of parents who used Android smartphones. Each day during a parent-specified time frame, participants had a 50% probability of receiving a prompt with a message encouraging them to engage in a mindfulness activity using a commercial app, Headspace. In the 24 hours following randomization, ecological momentary assessments and passively collected smartphone data were used to assess proximal engagement (yes or no) with the app and any mindfulness activity (with or without the app). These data were combined with baseline and exit surveys to determine feasibility and acceptability.

Results

Over 4 months, 83 interested parents were screened, 48 were eligible, 16 were enrolled, and 10 were successfully onboarded. Reasons for nonparticipation included technology barriers, privacy concerns, time constraints, or change of mind. In total, 80% (8/10) of parents who onboarded successfully completed all aspects of the intervention. While it is feasible to randomize prompt delivery, only 60% (6/10) of parents reported that the timing of prompts was helpful despite having control over the delivery window. Across the study period, we observed higher self-reported engagement with Headspace on days with prompts (31/62, 50% of days), as opposed to days without prompts (33/103, 32% of days). This pattern was consistent for most participants in this study (7/8, 87%). The time spent using the app on days with prompts (mean 566, SD 378 seconds) was descriptively higher than on days without prompts (mean 225, SD 276 seconds). App usage was highest during the first week and declined over each of the remaining 3 weeks. However, self-reported engagement in mindfulness activities without the app increased over time. Self-reported engagement with any mindfulness activity was similar on days with (40/62, 65% of days) and without (65/103, 63% of days) prompts. Participants found the Headspace app helpful (10/10, 100%) and would recommend the program to others (9/10, 90%).

Conclusions

Preliminary findings suggest that parents are receptive to using mindfulness apps to support stress management, and prompts are likely to increase engagement with the app. However, we identified several implementation challenges in the current trial, specifically a need to optimize prompt timing and frequency as a strategy to engage users in preventive digital mental health.