Investigating Changes in the Brain After Using a Wearable Healthy Lifestyle Intervention: A Mixed-Methods Protocol (Preprint)
Bibliographic record
Abstract
BACKGROUND Wearable technologies, such as the Apple Watch, are rapidly transforming the healthcare landscape by enabling the tracking of real-time physiological and behavioral data used in healthy lifestyle interventions. Despite their widespread adoption in the consumer market, scientific validation of their impact on the brain remains limited, and factors influencing adherence and effects on cognition are not fully understood. This gap is critical as consumers, healthcare providers, and researchers depend on this data to unlock the potential of wearables for cognitive support. OBJECTIVE In this study, we aim to assess the cognitive and behavioral effect of a commercially available wearable technology, the Apple Watch, on the brain, while exploring user experience (UX) factors influencing adherence, engagement, and acceptability of wearable healthy lifestyle interventions. Our goal is to provide comprehensive guidelines to evaluate the effect of wearable technologies on the brain, which could potentially shape the future of brain health interventions. METHODS Healthy adults aged 18-40 with a sedentary lifestyle (n=72) will be recruited in the Vancouver Metropolitan Area, BC, Canada. Participants will be randomized into an intervention group following a healthy lifestyle intervention using an Apple Watch or a control group following standard physical activity guidelines without an Apple Watch over a period of four weeks. We will employ a convergent parallel mixed methods design to investigate three constructs of a wearable healthy lifestyle intervention: effectiveness, engagement, and acceptability. This design integrates pre-, during, and post-intervention assessments combining quantitative and qualitative approaches. Effectiveness will be evaluated with quantitative assessments including perceived health (SF-36), behavioral performance (Go/No-Go task) and cognitive activity (EEG-based ERPs and resting-state activity). Engagement and acceptability will be assessed using questionnaires (goals completion and System Usability Scale, respectively). Qualitative assessments will include weekly check-ins and semi-structured interviews to capture UX factors influencing the acceptability of the intervention. Secondary outcomes include descriptions of possible interactions between the three constructs. RESULTS The study received funding by the AGE-WELL Network Centres of Excellence (April 2020–March 2023) and the Pacific Economic Development Agency of Canada (April 2022–March 2025) and was approved by Simon Fraser University’s Research Ethics Board. Enrollment for the study is ongoing. The data collected from participants who have successfully completed the intervention is currently being processed and analyzed. The first results are expected to be published by the end of 2025. CONCLUSIONS This study outlines a foundational framework for evaluating the effect of wearable health interventions on the brain. Further, we hope our mixed methods approach will inform the design and development of effective, engaging, and friendly wearable brain health technologies.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".