Health Behaviour Changes following Personalized Feedback Reports: A HANDDS-ONT Study.
Bibliographic record
Abstract
Paired with health feedback, wearable technologies can influence health behaviours in older adults and those with neurodegenerative diseases (NDD). However, our understanding of how feedback influences behaviour in these groups is limited. The author completed a secondary analysis of survey responses from 203 participants, 98 controls and 105 with NDD enrolled in the Health in Aging, Neurodegenerative Diseases and Dementias in Ontario Study. Participants received personalized health feedback reports generated from wearable sensor data. There were no group differences in the proportion of people endorsing (p = 0.086, η² = 0.12) or the number of health behaviour changes made (t(200.31) = 1.283, p = 0.201, d = 0.180). Two factors were influential: sharing reports with family/friends (OR = 2.258, p = 0.021) and perceived report helpfulness (OR = 0.527, p = 0.001). A thematic analysis revealed subthemes of contemplating change, seeking health information, and social support in behaviour change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".