Protocol for randomized personalized trial for stress management compared to standard of care

Stress is a significant public health burden in the United States, with most Americans reporting unhealthy levels of stress. Stress management techniques include various evidence-based treatments shown to be effective but with heterogeneous treatment responses, indicating a lack of uniform benefits for all individuals. Designed to assess a participant's response to a specific intervention, personalized (N-of-1) trials provide guidance for which treatment (s) work (s) best for the individual. Prior studies examining the effects of mindfulness meditation, yoga, and walking for stress reduction found all three interventions to be associated with significant reductions in self-reported measures of stress. Delivering these treatments using a personalized trial approach has the potential to assist clinicians in identifying the best stress management techniques for individuals with persistently high stress while fostering treatment decisions that consider their personal condition/barriers. This trial will evaluate a personalized approach compared to standard of care for three interventions (guided mindfulness meditation; guided yoga; and guided brisk walking) to manage perceived stress. Participants will respond to daily surveys and wear a Fitbit device for 18 weeks. After a 2-week baseline period, participants in the personalized trial groups will receive 12 weeks of interventions in randomized order, while participants in the standard-of-care group will have access to all interventions for self-directed stress management. After intervention, all participants will undergo 2 weeks of observation, followed by two additional weeks of the stress management intervention of their choosing while continuing outcome measurement. At study completion, all participants will be sent a satisfaction survey. The primary analysis will compare perceived stress levels between the personalized and standard of care arms. The results of this trial will provide further support for the use of personalized designs for managing stress. Clinical Trial Registration: clinicaltrials.gov, NCT05408832. Protocol version: 9/14/2022, 21-0968-MRB.

Testing Behavior Change Techniques to Increase Physical Activity in Middle-Aged and Older Adults: Protocol for a Randomized Personalized Trial Series

BACKGROUND

Being physically active is critical to successful aging, but most middle-aged and older adults do not move enough. Research has shown that even small increases in activity can have a significant impact on risk reduction and improve quality of life. Some behavior change techniques (BCTs) can increase activity, but prior studies on their effectiveness have primarily tested them in between-subjects trials and in aggregate. These design approaches, while robust, fail to identify those BCTs most influential for a given individual. In contrast, a personalized, or N-of-1, trial design can assess a person's response to each specific intervention.

OBJECTIVE

This study is designed to test the feasibility, acceptability, and preliminary effectiveness of a remotely delivered personalized behavioral intervention to increase low-intensity physical activity (ie, walking) in adults aged 45 to 75 years.

METHODS

The intervention will be administered over 10 weeks, starting with a 2-week baseline period followed by 4 BCTs (goal-setting, self-monitoring, feedback, and action planning) delivered one at a time, each for 2 weeks. In total, 60 participants will be randomized post baseline to 1 of 24 intervention sequences. Physical activity will be continuously measured by a wearable activity tracker, and intervention components and outcome measures will be delivered and collected by email, SMS text messages, and surveys. The effect of the overall intervention on step counts relative to baseline will be examined using generalized linear mixed models with an autoregressive model that accounts for possible autocorrelation and linear trends for daily steps across time. Participant satisfaction with the study components and attitudes and opinions toward personalized trials will be measured at the intervention's conclusion.

RESULTS

Pooled change in daily step count will be reported between baseline and individual BCTs and baseline versus overall intervention. Self-efficacy scores will be compared between baseline and individual BCTs and between baseline and the overall intervention. Mean and SD will be reported for survey measures (participant satisfaction with study components and attitudes and opinions toward personalized trials).

CONCLUSIONS

Assessing the feasibility and acceptability of delivering a personalized, remote physical activity intervention for middle-aged and older adults will inform what steps will be needed to scale up to a fully powered and within-subjects experimental design remotely. Examining the effect of each BCT in isolation will allow for their unique impact to be assessed and support design of future behavioral interventions. In using a personalized trial design, the heterogeneity of individual responses for each BCT can be quantified and inform later National Institutes of Health stages of intervention development trials.

TRIAL REGISTRATION

clinicaltrials.gov NCT04967313; https://clinicaltrials.gov/ct2/show/NCT04967313.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/43418.

Introducing Data Sciences to N-of-1 Designs, Statistics, Use-Cases, the Future, and the Moniker 'N-of-1' Trial

This article, an introduction to HDSR's "Personalized (N-of-1) Trials: Methods, Applications, and Impact" special issue, describes the rationale for a primer of the methods, data types and management, designs, and use cases for personalized (N-of-1) trials. It explains that the design and implementation of personalized (N-of-1) trials is only useful if patients volunteer for research involving them and actively participate in clinical services that use them. However, 'N-of-1 trials' may be an inadequate name to enact such patient engagement. The authors briefly review what patients have reported about the 'N-of-1' label and propose a more consumer-friendly moniker for this type of research and clinical approach to improve evidence-based science.

Quantitative Synthesis of Personalized Trials Studies: Meta-Analysis of Aggregated Data Versus Individual Patient Data

We have entered an era in which scientific knowledge and evidence increasingly inform research practice and policy. As there is an exponential increase in the use of personalized trials, there is a remarkable growing interest in the quantitative synthesis of personalized trials. One technique that is developed and can be applied for this purpose is meta-analysis. Meta-analysis involves the quantitative integration of effect sizes from several personalized trials. In this study, aggregated data (AD) and individual patient data (IPD) methods for meta-analysis of personalized trials are discussed, together with an empirical demonstration using a subset of a real meta-analytic data set. For the empirical demonstration, 26 personalized trials received usual care and yoga intervention in a randomized sequence. Results show a general consensus between the AD and IPD approach in terms of conclusions-that both usual care and the yoga intervention are effective in reducing pain. However, the IPD approach provides more information about the intervention effectiveness and intervention heterogeneity. IPD is a more flexible modeling approach, allowing for a variety of modeling options.

Experimental Designs to Optimize Treatments for Individuals: Personalized N-of-1 Trials

Conventional randomized clinical trials (RCTs) compare treatment effectiveness to provide support for evidence-based treatments that can be generalized to the average patient. However, the information obtained from RCTs may not always be useful for selecting the best treatment for individual patients. This article presents a complementary approach to identifying optimized treatments using experimental designs that focus on individuals. Personalized, or N-of-1, designs provide both a comparative analysis of treatments and a functional analysis demonstrating that changes in patient symptoms are likely because of the treatment implemented. This approach contributes to the zeitgeist of personalized medicine and provides clinicians with a paradigm for investigating optimal treatments for rare diseases for which RCTs are not always feasible, identifying personally effective treatments for patients with comorbidities who have historically been excluded from most RCTs, handling clinical situations in which patients respond idiosyncratically (either positively or negatively) to treatment, and shortening the time lag between identification and implementation of an evidence-based treatment. These designs merge experimental analysis of behavior methods used for decades in psychology with new methodological and statistical advances to assess significance levels of changes in individual patients, and they can be generalized to larger populations for meta-analytic purposes. This article presents a case for why these models are needed, an overview of how to apply personalized designs for different types of clinical scenarios, and a brief discussion of challenges associated with interpretation and implementation of personalized designs. The goal is to empower pediatricians to take personalized trial designs into clinical practice to identify optimal treatments for their patients.