A sensor technology to continuously monitor changes in cognition
Bibliographic record
Abstract
Abstract Background Researchers are increasingly utilizing real‐time sensors with ultra‐wideband radio frequency identification device (UWB RFID) technology to continuously, automatically and unobtrusively track the behavior and movement of older adults across care settings (Bowen, Kearns, Crenshaw, & Stanhope, 2019; Bowen & Rowe, 2019; Bowen & Rowe, 2016). These systems can be integrated into the health care environment and are ecologically valid measures taken concurrently as adults perform everyday activities of daily living (ADL). The purpose of this study is to examine the use of a UWB RFID system for the early recognition of behaviors associated with cognitive decline among skilled nursing residents (N=23) of two health care facilities. Method Residents were assessed continuously for an average of 10 months (SD=32.16 weeks). Scoring algorithms from the UWB RFID system utilized 1663 data points developed from x, y coordinates to measure the number and distance (meters) of continuous walking activity in a path. A path is defined as at least 60 seconds of walking and ends with a 30‐second stop. Cognitive function was measured by the Montreal Cognitive Assessment (Nasreddine, et al., 2005) and the Physical and Cognitive Performance Test (Bowen, Rowe, Ersek, Ibrahim, & Shea, 2017). Result On average, residents walked 1522.8 meters/week (SD=1572.7) with an average of 23.2 paths/week (SD=25.6). In multilevel models, accounting for ADL ability, an increased average number of paths per week was associated with decreased cognitive functioning (β= ‐0.65; p≤0.01). Conclusion Continuous walking in a path may be an early indicator of cognitive decline. Using an UWB RFID system may aid in the early detection and recognition of cognitive changes in this patient population. Importantly, these changes are also associated with other health events (e.g., falls, delirium, urinary tract infection, hospitalization) that may be ameliorable to intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".