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

The recent history and near future of digital health in the field of behavioral medicine: an update on progress from 2019 to 2024

The field of behavioral medicine has a long and successful history of leveraging digital health tools to promote health behavior change. Our 2019 summary of the history and future of digital health in behavioral medicine (Arigo in J Behav Med 8: 67-83, 2019) was one of the most highly cited articles in the Journal of Behavioral Medicine from 2010 to 2020; here, we provide an update on the opportunities and challenges we identified in 2019. We address the impact of the COVID-19 pandemic on behavioral medicine research and practice and highlight some of the digital health advances it prompted. We also describe emerging challenges and opportunities in the evolving ecosystem of digital health in the field of behavioral medicine, including the emergence of new evidence, research methods, and tools to promote health and health behaviors. Specifically, we offer updates on advanced research methods, the science of digital engagement, dissemination and implementation science, and artificial intelligence technologies, including examples of uses in healthcare and behavioral medicine. We also provide recommendations for next steps in these areas with attention to ethics, training, and accessibility considerations. The field of behavioral medicine has made meaningful advances since 2019 and continues to evolve with impressive pace and innovation.

Digital integrated interventions for comorbid depression and substance use disorder: narrative review and content analysis

BACKGROUND

Integrated digital interventions for the treatment of comorbid depression and substance use disorder have been developed, and evidence of their effectiveness is mixed.

OBJECTIVE

To better understand the potential underlying causes of these mixed findings, we described intervention characteristics, examined evidence-based treatment strategies within integrated digital treatments, reported the frequency of specific evidence-based strategies across different treatment modalities, and identified overlap between various treatment strategies and critical gaps in existing literature.

METHODS

In June 2024, a literature search was conducted in Google Scholar to identify digital integrated interventions for comorbid MDD and SUD. Articles were included if they described interventions targeting both conditions simultaneously, were grounded in CBT, MI, or MET, and were delivered at least in part via digital modalities. Fourteen studies meeting these criteria were coded using an open coding approach to identify treatment strategies. Statistical analyses summarized the number, frequency, and overlap of these strategies.

RESULTS

Half of studies (50.0%, n=7) included participants with mild to moderate depression symptom severity and hazardous substance use. Only 35.7% (n=5) of the studies required that participants meet the full diagnostic criteria for MDD, as assessed by the SCID or MINI, for inclusion and 21.4% (n=3) required a SUD diagnosis. Web-based (35.3%, n=6), computer-based (21.4%, n=3) and supportive text messaging interventions (21.4%, n=3) were included. Treatment duration averaged 10.3 weeks (SD=6.8). Common treatment strategies included self-monitoring (78.6%, n=11), psychoeducation (71.4%, n=10), and coping skills (64.3%, n=9). Interventions often combined therapeutic strategies, with psychoeducation frequently paired with self-monitoring (64.3%, n=9) and coping skills (50%, n=7).

CONCLUSIONS

Among integrated digital interventions for comorbid depression and substance use, there was significant variability in inclusion criteria, digital modalities, methodology, and treatment strategies, significant methodological challenges, and underrepresentation of evidence-based practices. Without standardized methodologies comparison of the clinical outcomes across studies is challenging. These results emphasize the critical need for future research to adopt standardized approaches, thereby facilitating more accurate comparisons and a deeper understanding of intervention efficacy.

CLINICALTRIAL

Latent transition analysis of time-varying cannabis use motives to inform adaptive interventions

OBJECTIVE

The rising prevalence of daily cannabis use among older adolescents and young adults in the United States has significant public health implications. As a result, more individuals may be seeking or in need of treatment for adverse outcomes (e.g., cannabis use disorder) arising from excessive cannabis use. Our objective was to explore the potential of self-reported motives for cannabis use as a foundation for developing adaptive interventions tailored to reduce cannabis consumption over time or in certain circumstances. We aimed to understand how transitions in these motives, which can be collected with varying frequencies (yearly, monthly, daily), predict the frequency and adverse outcomes of cannabis use.

METHOD

We conducted secondary analyses on data collected at different frequencies from four studies: the Medical Cannabis Certification Cohort Study (n = 801, biannually), the Cannabis, Health, and Young Adults Project (n = 359, annually), the Monitoring the Future Panel Study (n = 7,851, biennially), and the Text Messaging Study (n = 87, daily). These studies collected time-varying motives for cannabis use and distal measures of cannabis use from adolescents, young adults, and adults. We applied latent transition analysis with random intercepts to analyze the data.

RESULTS

We identified the types of transitions in latent motive classes that are predictive of adverse outcomes in the future, specifically transitions into or staying in classes characterized by multiple motives.

CONCLUSIONS

The identification of such transitions has direct implications for the development of adaptive interventions designed to prevent adverse health outcomes related to cannabis use. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A health-equity framework for tailoring digital non-pharmacological interventions in aging

If designed with health equity in mind, digital non-pharmacological interventions (NPIs) represent a cost-effective, scalable means of reducing health disparities associated with age-related mental health disorders in older adults in the USA. However, disparities in technological access, literacy and effectiveness can limit the impact of these interventions in older adults from disadvantaged groups. We present a health-equity-promoting framework for the development of digital NPIs for age-related mental health disorders and provide an example from the literature that highlights how interventions can be targeted at specific groups to increase technological access, literacy and effectiveness to ensure that these interventions can meet their potential of reducing health disparities.

Towards the Science of Engagement with Digital Interventions

Digital technologies, such as mobile devices and wearable sensors, are ingrained in daily life, making them a promising vehicle for delivering health behavior interventions. However, a critical challenge that undermines the utility of digital interventions is the suboptimal engagement of participants, where participant engagement is defined as the investment of physical, cognitive, and affective energies in a focal stimulus or task. Recent years have seen substantial growth in research aiming to understand how to increase engagement with digital interventions. This paper highlights several limitations of the existing evidence that restrict its scientific and practical utility and discusses opportunities for advancing the science of engagement with digital interventions. Synthesizing the current body of evidence, we call for conceptualizing digital interventions as a collection of stimuli (e.g., notifications, reminders) and tasks (e.g., open the mobile app, practice a relaxation technique) and considering engagement with digital interventions as a process rather than a state (i.e., momentary conditions/experiences) or trait (i.e., a relatively stable disposition). This approach has the potential to enhance scientific rigor and transparency in measuring, reporting, and interpreting engagement with digital interventions that would ultimately serve to bolster progress towards developing strategies for optimizing engagement.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how to sequence and match interventions to the unique, changing needs of individuals. A variety of sample size planning resources for SMART studies have been developed, enabling researchers to plan SMARTs for addressing different types of scientific questions. However, relatively limited attention has been given to planning SMARTs with binary (dichotomous) outcomes, which often require higher sample sizes relative to continuous outcomes. Existing resources for estimating sample size requirements for SMARTs with binary outcomes do not consider the potential to improve power by including a baseline measurement and/or multiple repeated outcome measurements. The current paper addresses this issue by providing sample size planning simulation procedures and approximate formulas for two-wave repeated measures binary outcomes (i.e., two measurement times for the outcome variable, before and after intervention delivery). The simulation results agree well with the formulas. We also discuss how to use simulations to calculate power for studies with more than two outcome measurement occasions. Results show that having at least one repeated measurement of the outcome can substantially improve power under certain conditions.

Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design

PURPOSE OF REVIEW

Recent advances in digital technologies can be leveraged to adapt HIV prevention and treatment services to the rapidly changing needs of individuals in everyday life. However, to fully take advantage of these technologies, it is critical to effectively integrate them with human-delivered components. Here, we introduce a new experimental approach for optimizing the integration and adaptation of digital and human-delivered behavioral intervention components for HIV prevention and treatment.

RECENT FINDINGS

Typically, human-delivered components can be adapted on a relatively slow timescale (e.g., every few months or weeks), while digital components can be adapted much faster (e.g., every few days or hours). Thus, the systematic integration of these components requires an experimental approach that involves sequential randomizations on multiple timescales. Selecting an experimental approach should be motivated by the type of adaptive intervention investigators would like to develop, and the scientific questions they have about its construction.

A pilot multiple-baseline study of a mobile cognitive behavioral therapy for the treatment of eating disorders in university students

OBJECTIVE

Eating disorders (EDs) are serious psychiatric disorders associated with substantial morbidity and mortality that are prevalent among university students. Because many students do not receive treatment due to lack of access on university campuses, mobile-health (mHealth) adaptations of evidence-based treatments represent an opportunity to increase treatment accessibility and engagement. The purpose of this study was to test the initial efficacy of Building Healthy Eating and Self-Esteem Together for University Students (BEST-U), which is a 10-week mHealth self-guided cognitive-behavioral therapy (CBT-gsh) app that is paired with a brief 25-30-min weekly telehealth coaching, for reducing ED psychopathology in university students.

METHOD

A non-concurrent multiple-baseline design (N = 8) was used to test the efficacy of BEST-U for reducing total ED psychopathology (primary outcome), ED-related behaviors and cognitions (secondary outcomes), and ED-related clinical impairment (secondary outcome). Data were examined using visual analysis and Tau-BC effect-size calculations.

RESULTS

BEST-U significantly reduced total ED psychopathology and binge eating, excessive exercise, and restriction (effect sizes ranged from -0.39 to -0.92). Although body dissatisfaction decreased, it was not significant. There were insufficient numbers of participants engaging in purging to evaluate purging outcomes. Clinical impairment significantly reduced from pre-to-post-treatment.

DISCUSSION

The current study provided initial evidence that BEST-U is a potentially efficacious treatment for reducing ED symptoms and ED-related clinical impairment. Although larger-scale randomized controlled trials are needed, BEST-U may represent an innovative, scalable tool that could reach greater numbers of underserved university students than traditional intervention-delivery models.

PUBLIC SIGNIFICANCE

Using a single-case experimental design, we found evidence for the initial efficacy of a mobile guided-self-help cognitive-behavioral therapy program for university students with non-low weight binge-spectrum eating disorders. Participants reported significant reductions in ED symptoms and impairment after completion of the 10-week program. Guided self-help programs show promise for filling an important need for treatment among university students with an ED.

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.

A systematic scoping review of just-in-time, adaptive interventions finds limited automation and incomplete reporting

OBJECTIVES

To describe the degree of automation in just-in-time, adaptive interventions (JITAIs) assessed in randomized controlled trials (RCTs) in any medical specialty, and to assess the completeness of intervention reporting.

STUDY DESIGN AND SETTING

Systematic scoping review-we searched PubMed, PsycINFO, and Web of Science, from 1 January 2019 to 2 March 2021, for reports of RCTs assessing JITAIs. We assessed whether study reports included the minimum information required to replicate the interventions based on JITAI frameworks. We described JITAIs according to their automation level using an established framework (partially, highly, or fully automated), and care workload distribution (requiring work from patients, health care professionals [HCPs], both, or neither).

RESULTS

We included 88 JITAIs (62%, n = 55 supported chronic illness management and 12%, n = 11 supported health behavior change). Overall, 77% (n = 68) of JITAIs were missing some information required to replicate the intervention (e.g., n = 38, 43% inadequately reported the algorithm used to select intervention components). Only fifteen (17%) JITAIs were fully automated and did not require additional work from HCPs nor patients. Of the remaining JITAIs, 36% required work from both patients and HCPs, and 47% required work from either patients or HCPs.

CONCLUSION

Most JITAIs are not fully automated and require work from the HCPs and patients.