Impact of Nutrition, Sleep and Physical Activity on Cognitive Function in Older Frailty Adults: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background As the global population ages, there is an increasing demand for effective strategies to maintain and improve health among older adults. Wearable technology presents a promising tool for health monitoring and management, yet its effectiveness in comprehensive health improvement for older adults remains uncertain. Objective This study aimed to evaluate the effectiveness of personalized lifestyle notifications, based on wearable device recorded data, in improving health outcomes, specifically cognitive and physical function, among older adults, compared to usual care. Methods In a 6-month randomized controlled trial, 355 older adults (aged 65+), including those with frailty, were randomly assigned to an intervention group ( n =178) or a control group ( n =177). The intervention group wore Fitbit Charge 5 devices and received personalized lifestyle alerts with rule-based personalization, using thresholds derived by human experts, throughout the 6-month period. The control group received no such notifications and were instructed not to use wearable devices. Some opt-in subjects, an intervention group ( n =128) or a control group ( n =116), were requested to record all meals using the application to deliver nutritional alerts. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Physical function was evaluated using Fried Frailty Phenotype criteria. Measurements were conducted at baseline and after 6 months. Results In the intention-to-treat analysis, the intervention group showed significant improvements in general cognitive function (MoCA scores increased by 1.0 (95% CI: 0.6 to 1.3) vs 0.2 (−0.2 to 0.5) in the control group, p =0.011) and frailty status (Fried frailty phenotype index change: −0.3 (−0.5 to −0.2) vs −0.1 (−0.2 to 0.1) in the control group, p =0.029). Subgroup analysis of participants with nutritional tracking showed significant improvements in MoCA scores (1.2 (0.8 to 1.6) vs 0.2 (−0.2 to 0.5), p =0.0004) and frailty status (−0.3 (−0.5 to −0.2) vs 0.0 (−0.2 to 0.1) in the control group, p =0.009). The per-protocol analysis showed similar results. Conclusion This study provides evidence that personalized, multifaceted Fitbit-based interventions can effectively enhance cognitive function, with notable improvements specifically in MoCA scores, and mitigate frailty progression in older adults as expected. These findings suggest that comprehensive lifestyle interventions including exercise, sleep and nutrition using wearable technology may be valuable for promoting healthy aging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".