Just-in-time Adaptive Interventions Research Articles

Just-in-Time Adaptive Interventions for Depression

Mental problems are disorders whose incidence is increasing with the effect of the global crises experienced in the world today and which significantly reduce the functionality of the individual. Depression draws attention as the most common mental problem. An average of two-thirds of individuals diagnosed with depression cannot receive treatment due to treatment cost, transportation, stigma, lack of information, low perceived need for treatment, and barriers to seeking mental health help.Internet-based interventions can offer highly effective and advantageous suggestions to overcome the disadvantages created by these barriers. As an internet-based intervention, Just-in-Time Adaptive Interventions (JITAIs) is an intervention design that aims to provide the right type and intensity of support at the right time by adapting to the changing internal and contextual situation of the individual. This intervention has emerged from the need to use mobile health in general, to address situations of vulnerability for adverse health outcomes, and to take advantage of rapid, unexpected, ecologically emerging situations of opportunity. In general, the mechanisms of JITAIs include 6 key elements: vulnerability/opportunity situation, distal outcome, proximal outcomes, decision points, intervention options, adaptation of variables and decision rules. Considering the potential rise of depression, especially in relation to new global events (e.g., pandemics and economic downturns), this application, which can be considered as a scalable, fully automated self-administered biopsychosocial transdiagnostic digital intervention, can provide widespread benefits. In this study, we focus on the working principles and advantages of JITAIs in general.

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

Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility. This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content. During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases. In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention. ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input.

Mobile-based ecological momentary assessment and intervention: bibliometric analysis.

The aim of this article was to review existing documents in the field of mobile-based EMA and EMI, provide an overview of current hot topics, and predict future development trends. We conducted a bibliometric study on mobile-based EMA and EMI publications that were collected from the Web of Science Core Collection database. Biblioshiny and CiteSpace were utilized to analyze scientific productions, leading sources, authors, affiliations, documents, research hot topics, keywords, and trend topics. A total of 2222 documents related to EMA and EMI published between 1992 and 2023 were retrieved. In recent years, scholarly publications have generally increased in mobile-based EMA and EMI research, particularly in the last decade. JMIR mHealth and uHealth (n=86), as well as JMIR (n=73), showed the highest number of publications. The United States (n=1038), Germany (n=218) and Netherlands (n=175) were leading countries. Regarding keyword co-occurrence and trend topics analysis, mental health, health behaviors, and feasibility were hot topics in mobile-based EMA and EMI research. Future research trends included using EMA for tailoring EMI, just-in-time adaptive interventions (JITAI), and digital phenotyping. This bibliometric study on mobile-based EMA and EMI is a valuable resource for understanding the field's evolution and future trends. Our analysis indicates that EMA and EMI have great potential in health behaviors and mental health, but implementation should consider feasibility and reactivity issues carefully. Emerging trends include EMA-tailored EMI, JITAI, and digital phenotyping. In the future, strengthening multidisciplinary cooperation will be necessary to promote the continued development of the field.

The Real-Time Application of an Additional HRV Reduction Algorithm to Detect Negative Psychosocial States

Abstract: Unobtrusively identifying adverse psychosocial states in everyday life would offer intriguing possibilities to trigger just-in-time adaptive interventions (JITAIs). Previously, we simulated algorithms to predict psychosocial states by means of cardiac data (so-called, additional, nonmetabolic heart rate variability reductions; AddHRVr). For the first time, this study implemented the AddHRVr algorithm in real-time mode to evaluate whether we could predict stress, perseverative cognition, or low-quality social interactions. We applied an ecological momentary assessment (EMA) in a sample of 36 participants for five consecutive days. The functioning of the AddHRVr algorithm did not translate into real-world application, and higher stress, perseverative cognition, or low-quality social interactions following the AddHRVr trigger could not be confirmed. Further data simulations were conducted to evaluate the reasons for the algorithm’s malfunctioning. We provide recommendations for future studies and call for further research and technical refinements to better align simulation approaches with real-time applications.

The Promise of Just-in-Time Adaptive Interventions for Organizational Scholarship and Practice: Conceptual Development and Research Agenda

Organizational researchers are now making widespread use of ecological momentary assessments but have not yet taken the logical next step to ecological momentary interventions, also called Just-in-Time Adaptive Interventions (JITAIs). JITAIs have the potential to test within-person causal theories and maximize practical benefits to participants through two developmental phases: The microrandomized trial and the randomized controlled trial, respectively. In the microrandomized trial design, within-person randomization and experimental manipulation maximize internal validity at the within-person level. In the randomized controlled trial design, interventions are delivered in a timely and ecological manner while avoiding unnecessary and ill-timed interventions that potentially increase participant fatigue and noncompliance. Despite these potential advantages, the development and implementation of JITAIs require consideration of many conceptual and methodological factors. Given the benefits of JITAIs, but also the various considerations involved in using them, this review introduces organizational behavior and human resources researchers to JITAIs, provides guidelines for JITAI design, development, and evaluation, and describes the extensive potential of JITAIs in organizational behavior and human resources research.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions.

Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437. DERR1-10.2196/52161.

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study.

Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers' health-related quality of life (HRQOL), their effectiveness for caregivers remains poorly understood. The primary objective of this study is to evaluate the dose-response relationship of a fully automated JITAI-based self-management intervention involving personalized mobile app notifications targeted at decreasing the level of caregiver strain, anxiety, and depression. The secondary objective is to investigate whether the effectiveness of this mobile health intervention was moderated by the caregiver group. We also explored whether the effectiveness of this intervention was moderated by (1) previous HRQOL measures, (2) the number of weeks in the study, (3) step count, and (4) minutes of sleep. We examined 36 caregivers from 3 disease groups (10 from spinal cord injury, 11 from Huntington disease, and 25 from allogeneic hematopoietic cell transplantation) in the intervention arm of a larger randomized controlled trial (subjects in the other arm received no prompts from the mobile app) designed to examine the acceptability and feasibility of this intensive type of trial design. A series of multivariate linear models implementing a weighted and centered least squares estimator were used to assess the JITAI efficacy and effect. We found preliminary support for a positive dose-response relationship between the number of administered JITAI messages and JITAI efficacy in improving caregiver strain, anxiety, and depression; while most of these associations did not meet conventional levels of significance, there was a significant association between high-frequency JITAI and caregiver strain. Specifically, administering 5-6 messages per week as opposed to no messages resulted in a significant decrease in the HRQOL score of caregiver strain with an estimate of -6.31 (95% CI -11.76 to -0.12; P=.046). In addition, we found that the caregiver groups and the participants' levels of depression in the previous week moderated JITAI efficacy. This study provides preliminary evidence to support the effectiveness of the self-management JITAI and offers practical guidance for designing future personalized JITAI strategies for diverse caregiver groups. ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591.

A randomized trial of a mobile health intervention to augment cardiac rehabilitation

Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, −7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users.

Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app

BackgroundAdvances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups. ObjectiveHere, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended. MethodsTo do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning. ResultsWe demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout. ConclusionsOverall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.

Applying the Just-In-Time Adaptive Intervention Framework to the Development of Gambling Interventions.

Just-In-Time Adaptive Interventions (JITAIs) are emerging "push" mHealth interventions that provide the right type, timing, and amount of support to address the dynamically-changing needs for each individual. Although JITAIs are well-suited to the delivery of interventions for the addictions, few are available to support gambling behaviour change. We therefore developed GamblingLess: In-The-Moment and Gambling Habit Hacker, two smartphone-delivered JITAIs that differ with respect to their target populations, theoretical underpinnings, and decision rules. We aim to describe the decisions, methods, and tools we used to design these two treatments, with a view to providing guidance to addiction researchers who wish to develop JITAIs in the future. Specifically, we describe how we applied a comprehensive, organising scientific framework to define the problem, define just-in-time in the context of the identified problem, and formulate the adaptation strategies. While JITAIs appear to be a promising design in addiction intervention science, we describe several key challenges that arose during development, particularly in relation to applying micro-randomised trials to their evaluation, and offer recommendations for future research. Issues including evaluation considerations, integrating on-demand intervention content, intervention optimisation, combining active and passive assessments, incorporating human facilitation, adding cost-effectiveness evaluations, and redevelopment as transdiagnostic interventions are discussed.

Investigating two mobile just-in-time adaptive interventions to foster psychological resilience: research protocol of the DynaM-INT study.

Stress-related disorders such as anxiety and depression are highly prevalent and cause a tremendous burden for affected individuals and society. In order to improve prevention strategies, knowledge regarding resilience mechanisms and ways to boost them is highly needed. In the Dynamic Modelling of Resilience - interventional multicenter study (DynaM-INT), we will conduct a large-scale feasibility and preliminary efficacy test for two mobile- and wearable-based just-in-time adaptive interventions (JITAIs), designed to target putative resilience mechanisms. Deep participant phenotyping at baseline serves to identify individual predictors for intervention success in terms of target engagement and stress resilience. DynaM-INT aims to recruit N = 250 healthy but vulnerable young adults in the transition phase between adolescence and adulthood (18-27years) across five research sites (Berlin, Mainz, Nijmegen, Tel Aviv, and Warsaw). Participants are included if they report at least three negative burdensome past life events and show increased levels of internalizing symptoms while not being affected by any major mental disorder. Participants are characterized in a multimodal baseline phase, which includes neuropsychological tests, neuroimaging, bio-samples, sociodemographic and psychological questionnaires, a video-recorded interview, as well as ecological momentary assessments (EMA) and ecological physiological assessments (EPA). Subsequently, participants are randomly assigned to one of two ecological momentary interventions (EMIs), targeting either positive cognitive reappraisal or reward sensitivity. During the following intervention phase, participants' stress responses are tracked using EMA and EPA, and JITAIs are triggered if an individually calibrated stress threshold is crossed. In a three-month-long follow-up phase, parts of the baseline characterization phase are repeated. Throughout the entire study, stressor exposure and mental health are regularly monitored to calculate stressor reactivity as a proxy for outcome resilience. The online monitoring questionnaires and the repetition of the baseline questionnaires also serve to assess target engagement. The DynaM-INT study intends to advance the field of resilience research by feasibility-testing two new mechanistically targeted JITAIs that aim at increasing individual stress resilience and identifying predictors for successful intervention response. Determining these predictors is an important step toward future randomized controlled trials to establish the efficacy of these interventions.

Detecting Receptivity for mHealth Interventions

Just-In-Time Adaptive Interventions (JITAI) have the potential to provide effective support for health behavior by delivering the right type and amount of intervention at the right time. The timing of interventions is crucial to ensure that users are receptive and able to use the support provided. Previous research has explored the association of context and user-specific traits on receptivity and built machine-learning models to detect receptivity after the study was completed. However, for effective intervention delivery, JITAI systems need to make in-the-moment decisions about a user's receptivity. In this study, we deployed machinelearning models in a chatbot-based digital coach to predict receptivity for physical-activity interventions. We included a static model that was built before the study and an adaptive model that continuously updated itself during the study. Compared to a control model that sent intervention messages randomly, the machine-learning models improved receptivity by up to 36%. Receptivity to messages from the adaptive model increased over time.

Zoom-Based Mindfulness-Oriented Recovery Enhancement Plus Just-in-Time Mindfulness Practice Triggered by Wearable Sensors for Opioid Craving and Chronic Pain.

The opioid crisis in the USA remains severe during the COVID-19 pandemic, which has reduced access to evidence-based interventions. This Stage 1 randomized controlled trial (RCT) assessed the preliminary efficacy of Zoom-based Mindfulness-Oriented Recovery Enhancement (MORE) plus Just-in-Time Adaptive Intervention (JITAI) prompts to practice mindfulness triggered by wearable sensors (MORE + JITAI). Opioid-treated chronic pain patients (n = 63) were randomized to MORE + JITAI or a Zoom-based supportive group (SG) psychotherapy control. Participants completed ecological momentary assessments (EMA) of craving and pain (co-primary outcomes), as well as positive affect, and stress at one random probe per day for 90days. EMA probes were also triggered when a wearable sensor detected the presence of physiological stress, as indicated by changes in heart rate variability (HRV), at which time participants in MORE + JITAI were prompted by an app to engage in audio-guided mindfulness practice. EMA showed significantly greater reductions in craving, pain, and stress, and increased positive affect over time for participants in MORE + JITAI than for participants in SG. JITAI-initiated mindfulness practice was associated with significant improvements in these variables, as well as increases in HRV. Machine learning predicted JITAI-initiated mindfulness practice effectiveness with reasonable sensitivity and specificity. In this pilot trial, MORE + JITAI demonstrated preliminary efficacy for reducing opioid craving and pain, two factors implicated in opioid misuse. MORE + JITAI is a promising intervention that warrants investigation in a fully powered RCT. This study is registered on ClinicalTrials.gov (NCT04567043).

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

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

Usability Testing of a Multi-Level Modeling Framework for Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health.

The JITAI is an intervention design to support health behavior change. We designed a multi-level modeling framework for JITAIs and developed a proof-of-concept prototype (POC). This study aimed at investigating the usability of the POC by conducting two usability tests with students. We assessed the usability and the students' workload and success in completing tasks. In the second usability test, however, they faced difficulties in completing the tasks. We will work on hiding the complexity of the framework as well as improving the frontend and the instructions.

Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations.

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual's state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

Just-in-time adaptive ecological momentary assessment (JITA-EMA)

Interest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tailoring variables that are used to inform the timing of momentary intervention delivery. Ecological momentary assessment (EMA) is often used for this purpose, even though EMA in its traditional form was not designed specifically to facilitate momentary interventions. In this article, we introduce just-in-time adaptive EMA (JITA-EMA) as a strategy to reduce participant response burden and decrease measurement error when EMA is used as a tailoring variable in JITAI. JITA-EMA builds on computerized adaptive testing methods developed for purposes of classification (computerized classification testing, CCT), and applies them to the classification of momentary states within individuals. The goal of JITA-EMA is to administer a small and informative selection of EMA questions needed to accurately classify an individual’s current state at each measurement occasion. After illustrating the basic components of JITA-EMA (adaptively choosing the initial and subsequent items to administer, adaptively stopping item administration, accommodating dynamically tailored classification cutoffs), we present two simulation studies that explored the performance of JITA-EMA, using the example of momentary fatigue states. Compared with conventional EMA item selection methods that administered a fixed set of questions at each moment, JITA-EMA yielded more accurate momentary classification with fewer questions administered. Our results suggest that JITA-EMA has the potential to enhance some approaches to mobile health interventions by facilitating efficient and precise identification of momentary states that may inform intervention tailoring.

The association between physical behavior, affective states, and contextual factors: Using ambulatory assessment to characterize moderating effects

Introduction
 Physical behavior is a key lifestyle factor in regulating and preventing diseases across the lifespan. Researchers identified affective, cognitive, and contextual factors like weather conditions, as significant contributors in determining if individuals are physically active. However, there is scarce empirical evidence about potential associations between physical behavior and affective states influenced by weather conditions in daily life. Therefore, we investigated the within-subject association between momentary affective states and physical behavior, and weather conditions and physical behavior. Further, we additionally explored if weather conditions moderated the within-subject association between momentary affective states and subsequent physical behavior.
 Methods
 Utilizing ambulatory assessment, 79 participants completed electronic diaries about their affective states (i.e., valence, energetic arousal, and calmness) up to six times a day over five days, and their physical behavior (i.e., physical activity and sedentariness) was simultaneously recorded via accelerometers. Weather conditions (i.e., temperature and precipitation) recorded near participants’ locations served as moderators in the multilevel analyses.
 Results
 We showed, that the association of affective states and physical activity was moderated by temperature. Higher temperatures enhanced the positive effects of valence on physical activity (β = .001, p = .023) and attenuated the negative effects of calmness on physical activity (β = .001, p = .021). Moreover, higher temperatures enhanced the positive effects of valence on reduced sedentary behavior (β = -0.011, p = .043). In addition, we revealed a significant positive association between temperature, as a momentary weather condition, and physical activity (β = 0.025; p = .015). Furthermore, we confirmed earlier findings associating affective states with physical behavior. Increased valence and energetic arousal were positively associated with physical activity (β = 0.007; p < .001), whereas calmness predicted lower levels of physical activity (β = -0.006; p < .001). Higher levels of calmness showed a positive association with sedentary behavior (β = 0.054; p = .003).
 Conclusion
 Moderating effects, such as higher temperatures enhancing the positive effects of valence on PA, provide mechanistic real-life insights as a basis for future health technologies. Just-in-time adaptive interventions (JITAI) offer the possibility to incorporate weather conditions and allow triggering individuals within their preferred contextual condition to promote physical activity. Such personalized real-time systems have recently been promoted by the WHO as most promising.

Using Digital Health Technology to Prevent and Treat Diabetes.

Diabetes Technology & TherapeuticsVol. 25, No. S1 Original ArticlesFree AccessUsing Digital Health Technology to Prevent and Treat DiabetesMark Clements, Neal Kaufman, and Eran MelMark ClementsChildren's Mercy Hospital, Kansas City, MO, USA.University of Missouri-Kansas City, Kansas City, MO, USA.Search for more papers by this author, Neal KaufmanFielding School of Public Health, Geffen School of Medicine, University of California, Los Angeles, CA, USA.Canary Health Inc., Los Angeles, CA, USA.Search for more papers by this author, and Eran MelJesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Search for more papers by this authorPublished Online:20 Feb 2023https://doi.org/10.1089/dia.2023.2506AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionThis article is our annual attempt to highlight the key papers written in the prior 12 months (i.e., between July 1, 2021, and June 30, 2022) in which digital health technologies were used to provide digitally driven care for patients with diabetes mellitus or prediabetes. This article addresses both digital therapeutics and digital care solutions. Digital therapeutics deliver evidence-based interventions to an individual, either independently or in combination with other therapeutics (e.g., drugs, devices, and behavioral interventions). In contrast, digital care solutions may combine a suite of digital therapeutic strategies with a telemedical or remote care program, as well as with provider-facing population health management software to aid in identifying at-risk individuals or identifying the right patient and/or right time for a remote interaction.We selected papers that exemplified emerging themes related to digital care that deserve highlighting. First, there is an increasing focus on translating evidence-based diabetes prevention programs and similar strategies into validated digital delivery formats. With the increasing prevalence of connected glucose monitoring devices, fitness wearables, and smart scales, it has become possible for clinicians to remotely monitor individuals seeking to reverse dysglycemia, weight gain, insulin resistance, and other associated metabolic perturbations. With chatbots to provide guidance and artificial intelligence (AI) to predict short-term glycemic changes from self-monitored blood glucose data in an open-loop setting, it is becoming possible to automate at least some of the coaching that might typically be performed by a human. (AI to predict glycemia using continuous glucose monitor–derived data is already deployed across many closed-loop insulin delivery systems.) This trend is exciting, because coaches and healthcare professionals are expensive and time is a limited resource. And with the increasing use of text messaging, patient portals, digital social networks, and video telehealth, it has become easier for clinicians to interact remotely with patients. These trends also have the potential to positively impact access to care, although we will have to wait for new studies to be published before drawing a conclusion. Another theme relates to the power of digital therapeutics and digital care ecosystems to drive population health management processes and improve overall population health. Population health management operates on the underlying assumption (and fact) that many individuals with diabetes or prediabetes achieve suboptimal outcomes under a one-size-fits-all model. In other words, delivering the same “dose” and “type” of health care to all patients contributes to health inequity. In contrast, using population health approaches to tailor the dose and type of care to individuals—a strategy called risk-based management—has the potential to improve health equity and improve outcomes for individuals and for the population as a whole.Key Articles ReviewedEffects of a Digital Diabetes Prevention Program: An RCTKatula JA, Dressler EV, Kittel CA, Harvin LN, Almeida FA, Wilson KE, Michaud TL, Porter GC, Brito FA, Goessl CL, Jasik CB, Sweet CMC, Schwab R, Estabrooks PAAm J Prev Med 2022;62: 567–577Evaluating the Implementation of the GREAT4Diabetes WhatsApp Chatbot To Educate People with Type 2 Diabetes During the Covid-19 Pandemic: Convergent Mixed Methods StudyMash R, Schouw D, Fischer AEJMIR Diabetes 2022;7: e37882Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market AnalysisKeller R, Hartmann S, Teepe GW, Lohse KM, Alattas A, Tudor Car L, Müller-Riemenschneider F, von Wangenheim F, Mair JL, Kowatsch TJ Med Internet Res 2022;24: e33348Implementation of an Intensive Telehealth Intervention for Rural Patients with Clinic-Refractory DiabetesKobe EA, Lewinski AA, Jeffreys AS, Smith VA, Coffman CJ, Danus SM, Sidoli E, Greck BD, Horne L, Saxon DR, Shook S, Aguirre LE, Esquibel MG, Evenson C, Elizagaray C, Nelson V, Zeek A, Weppner WG, Scodellaro S, Perdew CJ, Jackson GL, Steinhauser K, Bosworth HB, Edelman D, Crowley MJJ Gen Intern Med 2022 Jan 3; 1–9. doi: 10.1007/s11606-021-07281-8. Online ahead of print.Personalized Glycemic Response Led Digital Therapeutics Program Improves Time in Range in a Period of 14 DaysVerma R, Bhardwaj S, Lathia T, Kalra S, Ranadive R, Tanna S, Padsalge M, Juneja A, Samundra K, Thakkar PB, Jain V, Kini V, Kothari S, Guntur S, Joshi S, Singal AInt J Diabetes Dev Ctries 2022 Jul 22;1–8. doi: 10.1007/s13410-022-01111-1. Online ahead of print.A Randomized Controlled Trial of an Innovative, User-Friendly, Interactive Smartphone App-Based Lifestyle Intervention for Weight LossVaz CL, Carnes N, Pousti B, Zhao H, Williams KJObes Sci Pract 2021;7: 555–568New Digital Health Technologies for Insulin Initiation and Optimization for People with Type 2 DiabetesKerr D, Edelman S, Vespasiani G, Khunti KEndocr Prac 2022;28: 811–821Digital Health Coaching for Type 2 Diabetes: Randomized Controlled Trial of Healthy at HomeAzelton KR, Crowley AP, Vence N, Underwood K, Morris G, Kelly J, Landry MJFront Digit Health 2021;3: 764735Improvement in Glucose Regulation Using a Digital Tracker and Continuous Glucose Monitoring in Healthy Adults and Those with Type 2 DiabetesDehghani Zahedani A, Shariat Torbaghan S, Rahili S, Karlin K, Scilley D, Thakkar R, Saberi M, Hashemi N, Perelman D, Aghaeepour N, McLaughlin T, Snyder MP, January AI, Menlo Park, CADiabetes Ther 2021;12: 1871–1886Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults with Type 2 Diabetes Using One Drop: Retrospective Cohort StudyImrisek SD, Lee M, Goldner D, Nagra H, Lavaysse LM, Hoy-Rosas J, Dachis J, Sears LEJMIR Diabetes 2022;7: e34624Education at Scale: Improvements in Type 1 Diabetes Self-Management Following a Massive Open Online CourseMackenzie SC, Cumming KM, Mehar S, Wilson L, Cunningham SG, Bickerton A, Wake DJDiabet Med 2022;39: e14842Gamification for the Improvement of Diet, Nutritional Habits, and Body Composition in Children and Adolescents: A Systematic Review and Meta-AnalysisSuleiman-Martos N, García-Lara RA, Martos-Cabrera MB, Albendín-García L, Romero-Béjar JL, Cañadas-De la Fuente GA, Gómez-Urquiza JLNutrients 2021;13: 2478Efficacy of Personalized Diabetes Self-Care Using an Electronic Medical Record-Integrated Mobile App in Patients with Type 2 Diabetes: 6-Month Randomized Controlled TrialLee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, Yoon KH, Hyun MK, Ko SHJ Med Internet Res 2022;24: e37430On the Efficacy of Behavior Change Techniques in mHealth for Self-Management of Diabetes: A Meta-AnalysisEl-Gayar O, Ofori M, Nawar NJ Biomed Infor 2021;119: 103839Continuous Glucose Monitoring Data Sharing in Older Adults with Type 1 Diabetes: Pilot Intervention StudyAllen NA, Litchman ML, Chamberlain J, Grigorian EG, Iacob E, Berg CAJMIR Diabetes 2022;7: e35687Promoting Self-Management Behaviors in Adolescents with Type 1 Diabetes, Using Digital Storytelling: A Pilot Randomized Controlled TrialZarifsaniey N, Shirazi MO, Mehrabi M, Bagheri ZBMC Endocr Disord 2022;22: 74Effect of Digital Lifestyle Management on Metabolic Control and Quality of Life in Patients with Well-Controlled Type 2 DiabetesDwibedi C, Abrahamsson B, Rosengren AHDiabetes Ther 2022;13: 423–439Uptake and Impact of the English National Health Service Digital Diabetes Prevention Programme: Observational StudyRoss JAD, Barron E, McGough B, Valabhji J, Daff K, Irwin J, Henley WE, Murray EBMJ Open Diab Res Care 2022;10: e002736The Use of Artificial Intelligence-Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical RecommendationsChew HSJJMIR Med Inform 2022;10: e32578A 12-Month Follow-Up of the Effects of a Digital Diabetes Prevention Program (VP Transform for Prediabetes) on Weight and Physical Activity Among Adults with Prediabetes: Secondary AnalysisBatten R, Alwashmi MF, Mugford G, Nuccio M, Besner A, Gao ZMIR Diabetes 2022;7: e23243Population-Level Management of Type 1 Diabetes via Continuous Glucose Monitoring and Algorithm-Enabled Patient Prioritization: Precision Health Meets Population HealthFerstad JO, Vallon JJ, Jun D, Gu A, Vitko A, Morales DP, Leverenz J, Lee MY, Leverenz B, Vasilakis C, Osmanlliu E, Prahalad P, Maahs DM, Johari R, Scheinker DPediatr Diabetes 2021;22: 982–991Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: The Pilot 4T StudyPrahalad P, Ding VY, Zaharieva DP, Addala A, Johari R, Scheinker D, Desai M, Hood K, Maahs DMJ Clin Endocrinol Metab 2022;107: 998–1008Effects of a Digital Diabetes Prevention Program: An RCTKatula JA1, Dressler EV2, Kittel CA2, Harvin LN2, Almeida FA3, Wilson KE4, Michaud TL3, Porter GC3, Brito FA3, Goessl CL3, Jasik CB5, Sweet CMC5, Schwab R6, Estabrooks PA31Department of Health & Exercise Science, Wake Forest University, Winston-Salem, NC; 2Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC; 3Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha, NE; 4Department of Kinesiology and Health, College of Education & Human Development, Georgia State University, Atlanta, GA; 5Medical Affairs, Omada Health, Inc., San Francisco, CA; 6Department of Internal Medicine, College of Medicine, University of Nebraska Medical Center, Omaha, NEAm J Prev Med 2022;62: 567−577IntroductionAlthough many digital diabetes prevention programs (DDPPs) have been clinically proven, data from rigorous studies are still needed to demonstrate DDPP effectiveness. Furthermore, these programs must be made more accessible. Hence, the aim of this study was to compare a DDPP with enhanced standard care plus waitlist control for effectiveness in improving weight, HbA1c, and cardiovascular risk factors among people with prediabetes.Study DesignThis was a single-blind RCT among participants at risk of developing type 2 diabetes and included 12 months of follow-up.Setting/ParticipantsA total of 599 volunteer patients with prediabetes were recruited primarily through electronic medical records and primary care practices.InterventionParticipants were randomized to either a DDPP (n = 299) or a single-session small-group diabetes-prevention education class (n = 300) focused on action planning for weight loss. The DPP regimen consisted of 52 weekly sessions, lifestyle coaching, virtual peer support, and behavior-tracking tools.Main Outcome MeasuresThe primary outcome was a change in HbA1c from baseline to 12 months using intent-to-treat analyses. On the basis of multiple comparisons of endpoints, 95% CIs are presented, and two-sided P<.025 was required for statistical significance. Secondary outcomes included body weight and cardiovascular disease risk factors.ResultsAmong 599 randomized participants (mean age, 55.4 years; 61.4% women), 483 (80%) completed the study. The DDPP regimen produced significantly greater reductions in HbA1c (0.08%; 95% CI, −0.12 to −0.03) and percentage change in body weight (−5.5% vs −2.1%; P<.001) at 12 months. A greater proportion of the DDPP group achieved clinically significant weight loss ≥5% (43% vs 21%, P<.001), and more participants shifted from prediabetes to normal HbA1c range (58% vs 48%; P = .04). Engagement in a DDPP was significantly related to improved HbA1c and weight loss.ConclusionsThis DDPP demonstrated clinical effectiveness and has significant potential for widespread dissemination and impact, particularly considering the growing demand for telemedicine in preventive health-care services.CommentsLowering the rate of progression from prediabetes to type 2 diabetes is a noble cause and one that has eluded nearly all who have attempted to accomplish it at scale. One promising trend is the ability for digital interventions to be successful. This randomized controlled trial adds to the literature by demonstrating the positive impact of Omada's digital intervention. The major limitation of the study is the relatively short timeline for measuring outcomes (1 year). Also, beyond the scope of this study are the methods used to recruit participants to the study and the generalizability of that approach to overcome the main challenge to these programs: the ability to cost-effectively recruit large proportions of the target population to yield population-based impact.Evaluating the Implementation of the GREAT4Diabetes WhatsApp Chatbot to Educate People with Type 2 Diabetes During the Covid-19 Pandemic: Convergent Mixed Methods StudyMash R1, Schouw D1, Fischer AE21Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; 2Aviro Health, Cape Town, South AfricaJMIR Diabetes 2022;7: e37882BackgroundIn South Africa, one of the leading causes of morbidity and mortality is diabetes, a problem that became even worse during the COVID-19 pandemic. As a result of the lockdown, most diabetes-related education and counseling were discontinued. To help compensate for the loss of these services, the GREAT4Diabetes WhatsApp Chatbot was created.ObjectiveThis study aimed to evaluate the implementation of the chatbot in Cape Town, South Africa, between May 2021 and October 2021.MethodsConvergent mixed methods were used to evaluate the implementation outcomes: acceptability, adoption, appropriateness, feasibility, fidelity, cost, coverage, effects, and sustainability. Quantitative data were derived from the chatbot and analyzed using the SPSS. Qualitative data were collected from key informants and analyzed using the framework method assisted by ATLAS.ti. The chatbot provided users with 16 voice messages and graphics in English, Afrikaans, or Xhosa. Messages focused on COVID-19 infection and self-management of type 2 diabetes.ResultsThe chatbot was adopted by the Metro Health Services to assist people with diabetes who had restricted health care during the lockdown and were at a higher risk of hospitalization and death from COVID-19 infection. The chatbot was disseminated via health-care workers in primary care facilities and local nonprofit organizations and via local media and television. Two technical glitches interrupted the dissemination but did not substantially affect user behavior. Minor changes were made to the chatbot to improve its utility. Many patients had access to smartphones and were able to use the chatbot via WhatsApp. Overall, 8158 people connected with the chatbot and 4577 (56.1%) proceeded to listen to the messages, with 12.56% (575/4577) of them listening to all 16 messages, mostly within 32 days. The incremental setup costs were ZAR 255,000 (US $16,876) and operational costs over 6 months were ZAR 462,473 (US $30,607). More than 90% of the users who listened to each message found the messages useful. Of the 533 who completed the whole program, 351 (71.1%) said they changed their self-management a lot, and 87.6% (369/421) were more confident. Most users changed their lifestyles in terms of diet (315/414, 76.1%) and physical activity (222/414, 53.6%). Health-care workers also saw benefits to patients and recommended that the service continue. Sustainability of the chatbot will depend on the future policy of the provincial Department of Health toward mobile health and the willingness to contract with Aviro Health. There is the potential to go to scale and include other languages and chronic conditions.ConclusionsBenefits were seen with the chatbot. This promising tool may complement traditional health care for people with diabetes and may help more extensively educate patients. Additional research is needed to further understand patients' experience with the chatbot and determine its effectiveness.CommentsThis convergent mixed-methods study evaluated implementation outcomes for the GREAT4Diabetes WhatsApp Chatbot, which was initially implemented in Cape Town, South Africa from May 2021 through Oct 2021 with 8158 people who have type 2 diabetes and restricted health access due to the pandemic. The chatbot delivered short audio education, sometimes along with a still picture. Users answered a question about the usefulness of the education in order to access the next education module. Results showed that 56.1% of those who registered for the program listened to the messages and 71.1% of those who completed the program made significant changes to self-management. This study is an excellent example of using qualitative methods for implementation science. Implementation outcomes include acceptability, adoption, appropriateness, feasibility, fidelity, cost, coverage, effects, and sustainability. We should hope to see more implementation science studies on digital applications for diabetes in the future.Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market AnalysisKeller R1,2, Hartmann S3, Teepe GW4, Lohse KM4, Alattas A1, Tudor Car L5,6, Müller-Riemenschneider F2,7, von Wangenheim F1,4, Mair JL1,2, Kowatsch T1,3,41Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore; 2Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; 3Centre for Digital Health Interventions, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland; 4Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland; 5Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; 6Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK; 7Yong Loo Lin School of Medicine, National University of Singapore, SingaporeJ Med Internet Res 2022;24: e33348BackgroundAs technology evolves, new methods arise for preventing and managing type 2 diabetes. Exemplifying such advancements are digital behavioral change interventions (DCBIs), which are being developed by digital diabetes companies and funded by venture capitalists. However, the scientific evidence behind DCBIs has not been closely examined, nor has the use of conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches.ObjectiveOur objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms.MethodsA systematic search was conducted using two venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs' websites; then data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs' publications, websites, and mobile apps were reviewed with regard to the intervention characteristics.ResultsThe 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only four out of the 50 identified publications associated with these DBCIs were about fully powered randomized controlled trials (RCTs). Further, one of those four RCTs showed a significant difference in glycated hemoglobin A1c (HbA1c) outcomes between the intervention and control groups. However, all the studies reported HbA1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, six interventions were fully recognized by the DPRP to deliver evidence-based programs, and two interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs.ConclusionsIn this study, the amount of funding for a DCBI did not correlate with the strength of the scientific evidence supporting a DCBI's effectiveness in preventing and managing type 2 diabetes. Overall, more rigorous trials are needed to determine effectiveness, and quality assurance authorities need to make reporting more transparent. As of now, automated approaches such as CAs and JITAIs are not used by many DCBIs, so the scalability and reach of these solutions are limited.CommentsFor more than 20 years, numerous emerging digital companies have attempted to improve outcomes for people with or at risk for diabetes. Many have been extraordinarily well funded, and the authors of this paper analyzed the published outcomes from the top 16 funded digital diabetes companies (ranging from US $15.5 million to $657.3 million, for a total of $2.4 billion). The majority of the interventions were for the prevention of type 2 diabetes. While the 16 companies published 50 papers, only four studies were randomized controlled trials, the gold standard of intervention assessment. On the promising side are the number of well-funded companies, the number of participants served, and the maturing of the marketplace and of the vendors. On the negative side are the less than stellar study designs and outcomes. The methodologies to structure these types of studies are also evolving, so we can expect many more in the near future.Implementation of an Intensive Telehealth Intervention for Rural Patients with Clinic-Refractory DiabetesKobe EA1, Lewinski AA2,3, Jeffreys AS2, Smith VA2,4,5, Coffman CJ2,6, Danus SM2, Sidoli E7, Greck BD7, Horne L8, Saxon DR9,10, Shook S11, Aguirre LE11, Esquibel MG11, Evenson C12, Elizagaray C12, Nelson V13, Zeek A13, Weppner WG14,15, Scodellaro S15, Perdew CJ,15 Jackson GL2,4,5,16, Steinhauser K2,4, Bosworth HB17, Edelman D2,15, Crowley MJ2,181Duke University School of Medicine, Durham, NC; 2Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC; 3School of Nursing, Duke University School of Medicine, Durham, NC; 4Department of Population Health Sciences, Duke University School of Medicine, Durham, NC; 5Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC; 6Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC; 7Western North Carolina Veteran Affairs Health Care System, Asheville, NC; 8VISN 19 Rocky Mountain Regional, Denver, CO; 9Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO; 10Division of Endocrinology, Rocky Mountain Veterans Affairs Medical Center, Aurora, CO; 11New Mexico Veteran Affairs Health Care System, University of New Mexico School of Medicine, Albuquerque, NM; 12Montana Veteran Affairs Health Care System, Kalispell, MT; 13Veterans Affairs Central Ohio Healthcare System, Columbus, OH; 14Division of General Internal Medicine, University of Washington School of Medicine, Seattle, WA; 15Boise Veteran Affairs Medical Center, Boise, ID; 16Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, NC; 17Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC; 18Division of Endocrinology, Department of Medicine, Duke University School of Medicine, Durham, NCJ Gen Intern Med 2022 Jan 3; 1–9. doi: 10.1007/s11606-021-07281-8. Online ahead of print.BackgroundPatients with type 2 diabetes (T2D) who live in rural areas may not be able to control their condition well because they may not be able to fully access specialty care and self-management support. Although telehealth can be used to deliver comprehensive care to T2D patients who live too far for in-person access, implementing this option can be difficult in clinical practice.ObjectiveTo examine the implementation of Advanced Comprehensive Diabetes Care (ACDC), an evidence-based, comprehensive telehealth intervention for clinic-refractory, uncontrolled T2D. ACDC leverages existing Veterans Health Administration (VHA) Home Telehealth (HT) infrastructure, making delivery practical in rural areas.DesignMixed-methods implementation study.ParticipantsA total of 230 patients with clinic-refractory, uncontrolled T2D.InterventionACDC bundles telemonitoring, self-management support, and specialist-guided medication management and is delivered over 6 months using existing VHA HT clinical staffing/equipment. Patients may continue in a maintenance protocol after the initial 6-month intervention period.Main MeasuresImplementation was evaluated using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. The primary effectiveness outcome was hemoglobin A1c (HbA1c).Key ResultsFrom 2017 to 2020, ACDC was delivered to 230 patients across seven geographically diverse VHA sites; on average, patients were 59 years of age, 95% male, 80% white, and 14% Hispanic/Latinx. Patients completed an average of 10.1 of 12 scheduled encounters during the 6-month intervention period. Model-estimated mean baseline HbA1c was 9.56% and improved to 8.14% at 6 months (−1.43%; 95% CI, −1.64 to −1.21; P<.001). Benefits persisted at 12 months (−1.26%, 95% CI, −1.48 to −1.05; P<.001) and 18 months (−1.08%; 95% CI, −1.35 to −0.81; P<.001). Patients reported increased engagement in self-management and awareness of glycemic control, while clinicians and HT nurses reported a moderate workload increase. As of this submission, some sites have maintained delivery of ACDC for up to 4 years.ConclusionsComprehensive care can be delivered to rural areas through telehealth when telehealth systems are designed to take advantage of existing infrastructure. Through ACDC, participants with clinic-refractory diabetes achieved sustained improvements in glycemic control.CommentsThis study represents another example of implementation science, this time using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. The authors studied Advanced Comprehensive Diabetes Care (ACDC), which is essentially an evidence-based remote patient monitoring program for clinic-refractory, uncontrolled type 2 diabetes. ACDC bundles telemonitoring, self-management support, and medication management and is delivered over 6 months. Prior randomized controlled trial data have demonstrated that ACDC improves A1c by 1.0% at 6 months, while in the present implementation study, A1c improved by 1.43%, with the effect being significantly maintained at 12 and 18 months. This study provides yet another example of the positive benefits of remote patient monitoring. How long will it take the field to release new consensus statements that recognize a new reality, that “connected care” adds significant value to the few in-person clinic visits a year to which we are all accustomed?Personalized Glycemic Response Led Digital Therapeutics Program Improves Time in Range in a Period of 14 DaysVerma R1, Bhardwaj S1, Lathia T2, Kalra S3, Ranadive R1, Tanna S4, Padsalge M5, Juneja A6, Samundra K7, Thakkar PB8, Jain V9, Kini V10, Kothari S11, Guntur S1, Joshi S1, Singal A11Fitterfly Healthtech Pvt Ltd, Akshar Blue Chip Corporate Park Turbhe MIDC, Navi Mumbai, India; 2Apollo Hospital, Navi Mumbai, India; 3Bharti Research Institute of Diabetes & Endocrinology, Haryana, India; 4Jupiter Hospital, Thane, India; 5Diabecare Diabetes & Thyroid Clinic, Navi Mumbai, India; 6Kokilaben Hospital, Mumbai, India; 7Diabetes Care Clinic, Navi Mumbai, India; 8Bombay Hospital, Marine Lines, Mumbai, India; 9Advanced Eye Hospital and Institute, Navi Mumbai, India; 10Care n Cure Speciality Clinic, Navi Mumbai, India; 11Global Hospitals, Mumbai, IndiaInt J Diabetes Dev Ctries 2022 Jul 22;1–8. doi: 10.1007/s13410-022-01111-1. Online ahead of print.BackgroundFor individuals with type 2 diabetes (T2D), making lifestyle changes is critical for managing the disease. However, it is often difficult to make sure that personalized lifestyle advice is accurate. Diabefly-Pro is a digital therapeutic system providing personalized advice based on an individual's glycemic responses, In this study, Diabefly-Pro is analyzed for its real-world effectiveness in improving glycemic control.MethodsData from continuous glucose monitoring (CGM) of 64 participants with T2D were analyzed. All participants were provided with modified lifestyle plan based on their personalized glycemic response. The CGM data were analyzed for a period of 7 days before

A Community Participatory Approach to Creating Contextually Tailored mHealth Notifications: myBPmyLife Project.

Just-in-time adaptive interventions (JITAIs) are a novel approach to mobile health (mHealth) interventions, sending contextually tailored behavior change notifications to participants when they are more likely to engage, determined by data from wearable devices. We describe a community participatory approach to JITAI notification development for the myBPmyLife Project, a JITAI focused on decreasing sodium consumption and increasing physical activity to reduce blood pressure. Eighty-six participants were interviewed, 50 at a federally qualified health center (FQHC) and 36 at a university clinic. Participants were asked to provide encouraging physical activity and low-sodium diet notifications and provided feedback on researcher-generated notifications to inform revisions. Participant notifications were thematically analyzed using an inductive approach. Participants noted challenging vocabulary, phrasing, and culturally incongruent suggestions in some of the researcher-generated notifications. Community-generated notifications were more direct, used colloquial language, and contained themes of grace. The FQHC participants' notifications expressed more compassion, religiosity, and addressed health-related social needs. University clinic participants' notifications frequently focused on office environments. In summary, our participatory approach to notification development embedded a distinctive community voice within our notifications. Our approach may be generalizable to other communities and serve as a model to create tailored mHealth notifications to their focus population.