Developing feedback methods for caregivers of persons with cognitive impairment using a home‐based sensing and computing system
Bibliographic record
Abstract
Abstract Background Conventional methods assessing activities related to caregiving rely on self‐report with caregivers providing estimations of time and resources directed to these activities, which vary over time and can be difficult to reliably estimate retrospectively. Home‐based assessment platforms using sensors to collect objective information on daily activities represent a novel method to evaluate caregivers. Method This pilot study is an observational trial using the Collaborative Aging Research Using Technology (CART) sensor platform to assess cognitive and functional outcome measures. The study is enrolling individuals with cognitive impairment living with a caregiver. All participants’ complete questionnaires on functional status and behavioral symptoms, and undergo cognitive testing at baseline. These conventional measures are compared to the sensor‐based outcome measures. Caregivers complete an additional survey on burden level, the Zarit Burden Inventory (ZBI‐12). Additionally, focus group sessions will be held including caregivers enrolled in the pilot study and caregivers of individuals with cognitive impairment in the community. Result Data is currently being collected on daily functional activities in caregivers. Enrollment is ongoing for participant homes installing the sensor system. Three caregivers are enrolled, with an average age of 61.7 years (± 5.0), and 14 (± 3.4) years of education. Baseline mean ZBI‐12 scores were 15 (±4.6). Data has been collected for 9672 hours in total for the 3 participants. Caregivers slept on average 8.8 (±2.3) hrs/night and took 3868 (± 592) steps per day. Sensor data will be compared to ZBI‐12 scores to identify those activities correlated with higher levels of burden. Information from the focus group sessions will be used to guide development of a feedback system to inform caregivers of their activities related to time and effort spent in caregiving. Conclusion A home‐based sensing and computing system could provide objective information on activity and effort related to caregiving activities. The design of the feedback system is being developed using input from end‐users. The information provided by this system could help to inform caregivers of changes in their level of caregiving activities associated with higher levels of stress and more promptly identify whether they should seek increased external or community support.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".