Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Online model selection by learning how compositional kernels evolve

Motivated by the need for efficient, personalized learning in mobile health, we investigate the problem of online compositional kernel selection for multi-task Gaussian Process regression. Existing composition selection methods do not satisfy our strict criteria in health; selection must occur quickly, and the selected kernels must maintain the appropriate level of complexity, sparsity, and stability as data arrives online. We introduce the Kernel Evolution Model (KEM), a generative process on how to evolve kernel compositions in a way that manages the bias-variance trade-off as we observe more data about a user. Using pilot data, we learn a set of kernel evolutions that can be used to quickly select kernels for new test users. KEM reliably selects high-performing kernels for a range of synthetic and real data sets, including two health data sets.

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.

Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health

Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual's changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant public health concern. Valid experimental designs and analysis methods are required to inform the development of JITAIs. Here, we briefly review the cutting-edge design of microrandomized trials (MRTs), covering both the classical MRT design and its outcome-adaptive counterpart. Associated statistical challenges related to the design and analysis of MRTs are also discussed. Two case studies are provided to illustrate the aforementioned concepts and designs throughout the article. We hope our work leads to better design and application of JITAIs, advancing public health research and practice. (Am J Public Health. 2023;113(1):60-69. https://doi.org/10.2105/AJPH.2022.307150).

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)-a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).

Identifying conducive contexts and working mechanisms of sedentary behaviour interventions in older adults: a realist review protocol as part of the 'Stand UP Seniors' project

INTRODUCTION

Lifestyle behaviours, including sedentary behaviour, have been listed as key modifiable factors to promote healthy ageing. Sedentary behaviour is ubiquitous in older adults and has a strong link with age-related functional declines and chronic health conditions. Although several interventions have been developed aimed at the reduction of sedentary behaviour in older adults, little in-depth information is available on how these complex interventions work in different contexts. Therefore, the aim of our study was to unpack the mechanisms of how existing interventions aimed at the reduction of older adults' sedentary behaviour work or fail to work in particular contexts in order to optimise the development and implementation of future sedentary behaviour interventions.

METHODS AND ANALYSIS

A realist review will be conducted as a first part of the Stand UP Seniors (SUPS) project and will be structured as follows: (1) defining the scope of the review, (2) searching and appraising the evidence, (3) extracting data and synthesising the results, and (4) drawing conclusions and formulating recommendations. The result of this iterative process will be a final programme theory that can be used to identify which context triggers which mechanism, and in turn might elicit which outcome. The final programme theory will be used to inform the second and the third parts of the SUPS project, which are, respectively, the development and evaluation of a sedentary behaviour intervention in older adults.

ETHICS AND DISSEMINATION

Ethical approval is not required for the review. Dissemination of the realist review results, including the final programme theory, will occur through peer-reviewed publications and presentations at relevant conferences. The peer-reviewed realist review will be prepared according to the Realist and Meta-narrative Evidence Synthesis: Evolving Standards publication standards for realist syntheses.

PROSPERO REGISTRATION NUMBER

CRD42021248795.

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

BACKGROUND

Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT.

OBJECTIVE

The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions.

METHODS

Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment.

RESULTS

The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing.

CONCLUSIONS

This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss).

TRIAL REGISTRATION

ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/33568.

Consensus Statement on Ethical & Safety Practices for Conducting Digital Monitoring Studies with People at Risk of Suicide and Related Behaviors

Objective

Digital monitoring technologies (e.g., smart-phones and wearable devices) provide unprecedented opportunities to study potentially harmful behaviors such as suicide, violence, and alcohol/substance use in real-time. The use of these new technologies has the potential to significantly advance the understanding, prediction, and prevention of these behaviors. However, such technologies also introduce myriad ethical and safety concerns, such as deciding when and how to intervene if a participant's responses indicate elevated risk during the study?

Methods

We used a modified Delphi process to develop a consensus among a diverse panel of experts on the ethical and safety practices for conducting digital monitoring studies with those at risk for suicide and related behaviors. Twenty-four experts including scientists, clinicians, ethicists, legal experts, and those with lived experience provided input into an iterative, multi-stage survey, and discussion process.

Results

Consensus was reached on multiple aspects of such studies, including: inclusion criteria, informed consent elements, technical and safety procedures, data review practices during the study, responding to various levels of participant risk in real-time, and data and safety monitoring.

Conclusions

This consensus statement provides guidance for researchers, funding agencies, and institutional review boards regarding expert views on current best practices for conducting digital monitoring studies with those at risk for suicide-with relevance to the study of a range of other potentially harmful behaviors (e.g., alcohol/substance use and violence). This statement also highlights areas in which more data are needed before consensus can be reached regarding best ethical and safety practices for digital monitoring studies.

Microrandomized Trial Design for Evaluating Just-in-Time Adaptive Interventions Through Mobile Health Technologies for Cardiovascular Disease

Smartphone and wearable device use is rising broadly and can be leveraged for chronic disease management. Just-in-time adaptive interventions promise to deliver personalized, dynamic interventions directly to patients through use of push notifications from mobile devices. Although just-in-time adaptive interventions are a powerful tool for shaping health behavior, their application to cardiovascular disease management has been limited as they can be challenging to design. Herein, we provide a general overview and conceptual framework for microrandomized trials, a novel experimental study design that can be used to optimize just-in-time adaptive interventions. Microrandomized trials leverage mobile devices to sequentially randomize participants to types or levels of an intervention to determine the effectiveness of an intervention and time-varying moderators of those effects. Microrandomized trials are an efficient study design that can be used to determine which intervention components to include in just-in-time adaptive interventions and to optimize their decision rules while maintaining the strength of causal inference associated with traditional randomized controlled trials.

Designing m-Health interventions for precision mental health support

Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers.