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Background There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. Main body The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. Conclusion Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.

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.

Despite the positive health effect of physical activity, one third of the world’s population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at “high-resolution” and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants’ median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant’s individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of “just-in-time adaptive” behavioral interventions based on the detection of early warning signals for sudden behavioral losses.

Traditional mobile health interventions for physical activity (PA) primarily rely on reflective self-regulatory processes, often neglecting the role of affective associations in sustaining long-term engagement. The WalkToJoy intervention addresses this gap by applying the affective-reflective theory to enhance intrinsic motivation for PA among adults aged ≥40 years through affective message framing, evaluative conditioning, and belief updating. This proof-of-concept study evaluated the feasibility of the message-based WalkToJoy intervention package and examined the impact of its 3 components-walking suggestion prompts, salience messages, and planning prompts-on affective and behavioral outcomes related to walking. We conducted a fully remote, 6-week full factorial experiment with an embedded microrandomized trial (MRT) involving 49 adults aged ≥40 years. Statistical analyses, including paired t tests and generalized estimating equations, assessed pretest-posttest changes and the effects of smile-inducing walking suggestion prompts with short animated images (GIF images), salience messages, and planning prompts on weekly affective measures and daily step counts. In addition, MRT analyses evaluated the proximal effects of these components. Poststudy interviews were thematically analyzed to contextualize participants' experiences and engagement with the intervention. Significant pretest-posttest improvements were observed across affective outcomes on a 7-point Likert scale-affective attitudes improved by 0.547 points (P<.001), affective valuations improved by 0.718 points (P<.001), affective reflection improved by 0.692 points (P<.001), and anticipated affect improved by 0.692 points (P<.001). While the average daily steps showed a nonsignificant pretest-posttest increase of 80 steps (P=.79), further analysis revealed an increase of 506 steps (P=.07) when comparing baseline to the average of weeks 4 to 6. Among the intervention components, GIF prompts significantly increased anticipated affect by 0.345 points (P=.046) and average daily step count by 1834 steps (P=.05) compared to identical text-only prompts. However, MRT analysis found no significant increase in 4-hour step counts following the walking suggestion prompts (P=.55), which was explained by qualitative findings suggesting that participants interpreted messages as flexible day-long reminders rather than immediate calls to action. Salience and planning prompts did not yield substantial quantitative effects but were positively received by participants for promoting mindfulness and personalized engagement. The WalkToJoy intervention is a feasible and promising approach for improving affective associations with walking. Walking suggestion prompts were particularly effective in boosting engagement and mitigating message fatigue, highlighting the potential of affect-driven interventions to enhance PA motivation and adherence.

Mobile Health (mHealth) interventions are behavioral interventions that are accessible to individuals in their daily lives via a mobile device. Most mHealth interventions consist of multiple intervention components. Some of the components are “pull” components, which require individuals to access the component on their mobile device at moments when they decide they need help. Other intervention components are “push” components, which are initiated by the intervention, not the individual, and are delivered via notifications or text messages. Micro-randomized trials (MRTs) have been developed to provide data to assess the effects of push intervention components on subsequent emotions and behavior. In this paper, we review the micro-randomized trial design and provide an approach to computing a standardized effect size for these intervention components. This effect size can be used to compare different push intervention components that may be included in an mHealth intervention. In addition, a standardized effect size can be used to inform sample size calculations for future MRTs. Here, the standardized effect size is a function of time because the push notifications can occur repeatedly over time. We illustrate this methodology using data from an MRT involving HeartSteps, an mHealth intervention for physical activity as part of the secondary prevention of heart disease.

In the context of digital health, just-in-time adaptive interventions (JITAIs) are nascent precision medicine systems that can extend personalized health care support to everyday life. A challenge in designing JITAIs is that personalized support often involves sophisticated decision-making algorithms. These decision-making algorithms can require numerous nontrivial design decisions that must be made between successive JITAI deployments (eg, hyperparameter selection for an artificial intelligence algorithm). Making design decisions between deployments—rather than during deployment—ensures intervention fidelity and enhances the ability to replicate results. Yet, each deployment can be costly, precluding the use of A/B testing for every design decision. How should design decisions be made strategically between JITAI deployments? This paper introduces “digital twins for just-in-time adaptive interventions (JITAI-Twins)” to address this question. JITAI-Twins are “digital twins of a subpopulation” (term used in the 2023 National Academies workshop proceedings on digital twins). JITAI-Twins are used to virtually simulate the potential outcomes of a JITAI’s design decisions for an upcoming deployment. Based on simulation results, design decisions are made for the deployed JITAI. To continually improve the JITAI, data collected during deployment are used to update the JITAI-Twin—and this bidirectional feedback between deployments and simulation environments continues. JITAI-Twins are thus “fit-for-purpose” (term used in the National Academies 2024 consensus report on digital twins) instantiations of the digital twin concept. In this paper, we elucidate the specifics and design process of JITAI-Twins, with examples of prior use in clinical settings. JITAI-Twins highlight continuity over the course of a JITAI’s optimization and continual improvement, emphasizing the need for bidirectional feedback between versions of a simulation environment and a JITAI’s deployments.

Background There is a fundamental gap in understanding the causal mechanisms by which strategies for implementing evidence-based practices address local barriers to effective, appropriate service delivery. Until this gap is addressed, scientific knowledge and practical guidance about which implementation strategies to use in which contexts will remain elusive. This research project aims to identify plausible strategy-mechanism linkages, develop causal models for mechanism evaluation, produce measures needed to evaluate such linkages, and make these models, methods, and measures available in a user-friendly website. The specific aims are as follows: (1) build a database of strategy-mechanism linkages and associated causal pathway diagrams, (2) develop psychometrically strong, pragmatic measures of mechanisms, and (3) develop and disseminate a website of implementation mechanisms knowledge for use by diverse stakeholders. Methods For the first aim, a combination of qualitative inquiry, expert panel methods, and causal pathway diagramming will be used to identify and confirm plausible strategy-mechanism linkages and articulate moderators, preconditions, and proximal and distal outcomes associated with those linkages. For the second aim, rapid-cycle measure development and testing methods will be employed to create reliable, valid, pragmatic measures of six mechanisms of common strategies for which no high-quality measures exist. For the third aim, we will develop a user-friendly website and searchable database that incorporates user-centered design, disseminating the final product using social marketing principles. Discussion Once strategy-mechanism linkages are identified using this multi-method approach, implementation scientists can use the searchable database to develop tailored implementation strategies and generate more robust evidence about which strategies work best in which contexts. Moreover, practitioners will be better able to select implementation strategies to address their specific implementation problems. New horizons in implementation strategy development, optimization, evaluation, and deployment are expected to be more attainable as a result of this research, which will lead to enhanced implementation of evidence-based interventions for cancer control, and ultimately improvements in patient outcomes.

Achieving adequate blood pressure control is challenging for patients and clinicians. Digital hypertension management solutions that use push notifications to promote lifestyle management have been proposed as an approach, but their effectiveness remains unknown. This analysis was designed to interrogate the independent and short-term effects of push notifications, tailored to participant and environmental factors, and on physical activity levels and sodium intake among individuals with hypertension. The myBPmyLife study was a 6-month randomized controlled trial of participants with self-reported hypertension recruited from an academic medical center and federally qualified health centers. A core component of the intervention consisted of microrandomized push notifications promoting lifestyle modifications that were randomly delivered at 4 daily time points and focused on physical activity and dietary sodium intake. Our primary outcome for this secondary analysis was the step count 60 minutes after a physical activity notification and lower-sodium food choices 24 hours after a dietary notification. This analysis focuses on the results of the microrandomized trial and used a centered and weighted least squares method adapted for 2 or more treatments. A total of 298 participants were randomized to the intervention arm, of whom 287 had data available for analysis. Participants' mean age was 59.5 (SD 13.1) years, 138 (48.1%) were women, and 206 (71.8%) were White. Participants were randomized at 187,517 time points over 6 months, which led to 0.96 (SD 0.86) push notifications per day divided between activity (50.4%; SD 0.4) and dietary (49.8%; SD 0.4) notifications. Activity notifications did not increase step count in the 60 minutes after a notification (estimate 1.01, 95% CI 0.98-1.04; P=.40). Similarly, dietary notifications did not impact the number of lower-sodium food choices in the subsequent 24 hours (estimate 0.93, 95% CI 0.83-1.04; P=.23), but in exploratory post hoc analyses, did increase mobile app use by 95.5% (95% CI 1.81-2.10; P<.001), mobile app clicks or searches by 93.7% (95% CI 1.72%-2.16%; P<.001), and low sodium searches by 113.0% (95% CI 1.73-2.53; P<.001), all within 60 minutes. In patients with hypertension, push notifications did not impact short-term physical activity levels or dietary sodium intake but did improve intervention engagement.

Background:Physical activity is a critical target for health interventions, but effective interventions remain elusive. A growing body of work suggests that interventions targeting affective attitudes toward physical activity may be more effective for sustaining activity long term than those that rely on cognitive constructs alone, such as goal setting and self-monitoring. Anticipated affective response in particular is a promising target for intervention.Objective:We will evaluate the efficacy of an SMS text messaging intervention that manipulates anticipated affective response to exercise to promote physical activity. We hypothesize that reminding users of a positive postexercise affective state before their planned exercise sessions will increase their calories burned during this exercise session. We will deploy 2 forms of affective SMS text messages to explore the design space: low-reflection messages written by participants for themselves and high-reflection prompts that require users to reflect and respond. We will also explore the effect of the intervention on affective attitudes toward exercise.Methods:A total of 120 individuals will be enrolled in a 9-week microrandomized trial testing affective messages that remind users about feeling good after exercise (40% probability), control reminders (30% probability), or no message (30% probability). Two types of affective SMS text messages will be deployed: one requiring a response and the other in a read-only format. Participants will write the read-only messages themselves to ensure that the messages accurately reflect the participants’ anticipated postexercise affective state. Affective attitudes toward exercise and intrinsic motivation for exercise will be measured at the beginning and end of the study. The weighted and centered least squares method will be used to analyze the effect of delivering the intervention versus not on calories burned over 4 hours around the time of the planned activity, measured by the Apple Watch. Secondary analyses will include the effect of the intervention on step count and active minutes, as well as an investigation of the effects of the intervention on affective attitudes toward exercise and intrinsic motivation for exercise. Participants will be interviewed to gain qualitative insights into intervention impact and acceptability.Results:Enrollment began in May 2023, with 57 participants enrolled at the end of July 2023. We anticipate enrolling 120 participants.Conclusions:This study will provide early evidence about the effect of a repeated manipulation of anticipated affective response to exercise. The use of 2 different types of messages will yield insight into optimal design strategies for improving affective attitudes toward exercise.Trial Registration:ClinicalTrials.gov NCT05582369; https://classic.clinicaltrials.gov/ct2/show/NCT05582369International Registered Report Identifier (IRRID):PRR1-10.2196/46560

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. 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. 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. The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. 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). ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585. DERR1-10.2196/33568.