Longitudinal Remote Sleep and Cognitive Research in Older Adults With Mild Cognitive Impairment and Dementia: Prospective Feasibility Cohort Study
Bibliographic record
Abstract
BACKGROUND: Sleep holds promise as a modifiable risk factor for neurodegenerative diseases and dementia. Clinical trials to modify sleep in people at risk of or in the early stages of dementia are needed. Monitoring natural sleep from home could support pragmatic and decentralized large-scale clinical trials. However, whether longitudinal sleep research can be successfully delivered remotely in this population has not been established yet. OBJECTIVE: We investigated the feasibility of remote longitudinal research using wearable devices, web-based cognitive tasks, and a smartphone app to record sleep and cognition in older adults with mild cognitive impairment (MCI) or dementia. METHODS: Older adults with MCI or dementia due to Alzheimer disease or Lewy body disease and cognitively healthy participants completed at-home sleep and circadian monitoring (digital sleep diaries, actigraphy, wearable sleep electroencephalography, and saliva samples) and digital cognitive assessments for 8 weeks. Feasibility outcomes included recruitment, retention, and data completeness. RESULTS: In total, 41 participants consented (n=10, 24% participants with Alzheimer disease; n=11, 27% participants with Lewy body disease; and n=20, 49% controls). There were predominantly male and White British participants, with a mean age of 70.9 (SD 5.9) years. Retention was very high, with 40 (98%) participants completing 8 weeks of remote monitoring. Data completeness for sleep electroencephalography was 91% and ranged from 79% to 97% for all remote tasks and was overall high across all participant subgroups. In total, 30% (12/40) of participants reported receiving external support with completing study tasks. CONCLUSIONS: High rates of retention, data completeness, and data quality suggested that longitudinal multimodal sleep and cognitive profiling using novel and remote monitoring technology is feasible in older adults with MCI and dementia and healthy older adults, even without study partner support. Remote monitoring should be considered for mechanistic and interventional trials. Careful consideration should be given to how to ensure remote monitoring technologies reduce burden and enhance inclusivity, particularly in communities traditionally underserved by research and those with lower digital literacy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/52652.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".