Health Behaviour Changes driven by Personalized Feedback Reports from wearables data: A HANDDS‐ONT Study
Bibliographic record
Abstract
BACKGROUND: Wearable technologies combined with health feedback can influence health behaviours in older adults and individuals with neurodegenerative diseases (NDD). Despite their potential, limited research explores how health feedback from wearables affects behaviour change in these populations. This study used data from the Health in Aging, Neurodegenerative Diseases and Dementias in Ontario (HANDDS-ONT) study to examine behaviour changes following the delivery of personalized health feedback reports. Reports were co-designed with input from clinicians, community stakeholders, and individuals with NDD (Van Ooteghem et al., 2023). METHOD: Participants wore wearable devices for 7-10 days, collecting data on physical activity, sedentary behaviour, and sleep. Personalized feedback reports were generated and reviewed in a guided session. After four weeks, participants completed an online survey and interview with a research coordinator assessing feedback utility and behaviour changes. This prospective observational study included participants with at least four days of device wear and survey/interview data (n = 203; 98 controls, 105 NDD) (Table 1). Statistical analyses examined group differences (NDD/controls) and predictors of behaviour change, complemented by a thematic analysis of open-ended survey responses. RESULTS: = 0.12) or in the number of changes reported (t(200.31) = 1.283, p = 0.201, d = 0.180) (Figure 1). A key predictor of behaviour change included sharing the report with family/friends (OR = 2.258, p = 0.021). Participants who rated the report as less helpful were less likely to change behaviour (OR = -0.527, p = 0.001) (Figure 2). Thematic analysis identified subthemes of contemplating change, seeking health information, and social support in behaviour change. CONCLUSION: Personalized health feedback reports can encourage behaviour change in individuals with NDD. Perceived helpfulness and social sharing play critical roles. Findings highlight the potential of wearable technologies and tailored feedback in promoting self-management and health optimization. Future research should explore long-term behaviour change and the factors influencing sustainability over time.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".