Technologies for monitoring activities of daily living in older adults: a systematic review
Bibliographic record
Abstract
PURPOSE: As the older adult population rise globally, technologies to monitoring activities of daily living (ADL) may have a role in supporting aging in place for older adults. The objective of this systematic literature review was to study the scope, diversity and readiness of technologies developed to monitor ADL in older adults. METHODS: We systematically searched two scientific databases (CINAHL and IEEE), following Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) guidelines. We included studies on technologies used to monitor older adults' ADL in the home but excluded studies focused on communication technologies (phone calls, text messages) or monitoring postures alone. The JBI checklist for case series was used for quality assessment. Extracted details included population characteristics, ADL assessment outcomes, types of monitoring technology, and technology readiness and usability. RESULTS: The search found 147 papers, with 16 papers included in the final analysis. The literature described 48 types of technologies. Of moderate quality studies, five studies used wearables at technology readiness level 4-6 to monitor basic ADL (walking, transfers and walking up stairs) and one used ambient sensors to detect urinary incontinence. CONCLUSIONS: Monitoring technologies remain at development stages. More research is needed to strengthen technologies that monitor activities of daily living.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".