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Purpose: To test the effectiveness and feasibility of a remotely delivered intervention to increase physical activity (walking) in middle-aged and older adults., Design: This study used a personalized (N-of-1) trial design., Setting: This study took place at a major healthcare system from November 2021 to February 2022., Subjects: Sixty adults (45-75 years, 92% female, 80% white) were recruited., Intervention: A 10-week study comprising a 2-week baseline, followed by four 2-week periods where four behavior change techniques (BCTs) - self-monitoring, goal setting, action planning, and feedback - were delivered one at a time in random order., Measures: Activity was measured by a Fitbit, and intervention components delivered by email/text. Average daily steps were compared between baseline and intervention. Participants completed satisfaction items derived from the System Usability Scale and reported attitudes and opinions about personalized trials., Results: Participants rated personalized trial components as feasible and acceptable. Changes in steps between baseline and intervention were not significant, but a large heterogeneity of treatment effects existed, suggesting some participants significantly increased walking while others significantly decreased., Conclusions: Our intervention was well-accepted but use of BCTs delivered individually did not result in a significant increase in steps. Feasibility and heterogeneity of treatment effects support adopting a personalized trial approach to optimize intervention results., (© 2024. International Society of Behavioral Medicine.)

Purpose: To test the feasibility of a remotely-delivered intervention to increase low-intensity physical activity (walking) in middle-aged and older adults., Design: This study used a Personalized (N-of-1) trial design., Setting: This study took place at a major healthcare system from November 2021 to February 2022., Subjects: Sixty adults (45-75 years, 92% female, 80% white) were recruited., Intervention: A 10-week study comprising a 2-week baseline, followed by four 2-week periods where 4 Behavior Change Techniques (BCTs) - self-monitoring, goal setting, action planning and feedback - were delivered one at a time in random order., Measures: Activity was measured by a Fitbit, and intervention components delivered by email/text. Average daily steps were compared between baseline and intervention. Participants completed satisfaction items derived from the System Usability Scale and reported attitudes and opinions about personalized trials., Results: Participants rated personalized trial components as feasible and acceptable. Changes in steps between baseline and intervention were not significant, but a large heterogeneity of treatment effects existed, suggesting some participants significantly increased walking while others significantly decreased., Conclusions: Our intervention was well-accepted but use of BCTs delivered individually did not result in a significant increase in steps. Feasibility and heterogeneity of treatment effects support adopting a personalized trial approach to optimize intervention results., Competing Interests: Conflict of Interest: Authors report no disclosures and no conflicts of interest reported.

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., (©Ciaran P Friel, Patrick L Robles, Mark Butler, Challace Pahlevan-Ibrekic, Joan Duer-Hefele, Frank Vicari, Thevaa Chandereng, Ken Cheung, Jerry Suls, Karina W Davidson. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 14.06.2023.)

Background: Differentiating activities that are research or quality improvement (QI) is challenging., Purpose: Compare tools that distinguish research from QI and evaluate the utility of tools to determine whether institutional review board (IRB) approval is required for a test-project., Methods: Scoping review of the literature to identify tools that distinguish QI from research. Two reviewers independently screened records in PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science and Google Scholar and extracted information from tools. Inclusion criteria were English language peer-reviewed publications or publicly available tools with scoring systems to differentiate between research and QI. The reporting of this review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We then applied a test-project to evaluate the utility of the tools., Findings: One-hundred forty sources were reviewed; 13 met inclusion criteria. Tools consistently used project intent/purpose, design and intervention as differentiating criteria; additional criteria varied. Five studies described tool development, and one reported that the tool had been tested. Our application of a test-project proved challenging as tools commonly presented research and QI as discrete activities., Discussion: Based on the core criteria common across tools to distinguish research from QI, we propose a simple four-criteria decision tool for assessing the need for IRB submission., (© 2021 John Wiley & Sons Ltd.)