Supporting Behavior Change in Sedentary Adults via Real-time Multidimensional Physical Activity Feedback: Mixed Methods Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Increasing physical activity (PA) behavior remains a public health priority, and wearable technology is increasingly being used to support behavior change efforts. Using wearables to capture and provide comprehensive, visually persuasive, multidimensional feedback with real-time support may be a promising way of increasing PA in inactive individuals. OBJECTIVE: This study aims to explore whether a 6-week self-monitoring intervention using composite web-based multidimensional PA feedback with real-time daily feedback supports increased PA in adults. METHODS: A 6-week, mixed methods, 2-armed exploratory randomized controlled trial with 6-week follow-up was used, whereby low to moderately active (PA level [PAL] <2.0) adults (mean age 51.3 years, SD 8.4 years; women 28/51, 55%) were randomly assigned to receive the self-monitoring intervention (36/51, 71%) or waiting list control (15/51, 29%). Assessment of PA across multiple health-harnessing PA dimensions (eg, PAL, weekly moderate to vigorous intensity PA, sedentary time, and steps), psychosocial cognitions (eg, behavioral regulation, barrier self-efficacy, and habit strength), and health were made at the prerandomization baseline at 6 and 12 weeks. An exploratory analysis of the mean difference and CIs was conducted using the analysis of covariance model. After the 12-week assessment, intervention participants were interviewed to explore their views on the program. RESULTS: There were no notable differences in any PA outcome immediately after the intervention; however, at 12 weeks, moderate-to-large effects were observed with a mean difference in PAL of 0.09 (95% CI 0.02-0.15; effect size [Hedges g] 0.8), daily moderate-intensity PA of 24 (95% CI 0-45; Hedges g=0.6) minutes, weekly moderate-to-vigorous intensity PA of 195 (95% CI 58-331; Hedges g=0.8) minutes, and steps of 1545 (95% CI 581-2553; Hedges g=0.7). Descriptive analyses suggested that the differences in PA at 12 weeks were more pronounced in women and participants with lower baseline PA levels. Immediately after the intervention, there were favorable differences in autonomous motivation, controlled motivation, perceived competence for PA, and barrier self-efficacy, with the latter sustained at follow-up. Qualitative data implied that the intervention was highly informative for participants and that the real-time feedback element was particularly useful in providing tangible, day-to-day behavioral support. CONCLUSIONS: Using wearable trackers to capture and present sophisticated multidimensional PA feedback combined with discrete real-time support may be a useful way of facilitating changes in behavior. Further investigation into the ways of optimizing the use of wearables in inactive participants and testing the efficacy of this approach via a robust study design is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT02432924; https://clinicaltrials.gov/ct2/show/NCT02432924.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".