Assessment of Weekly Fluctuations in Zarit Burden Interview Scores in Caregivers of Individuals with Cognitive Impairment
Bibliographic record
Abstract
Abstract Background Current tools to assess caregiver burden in individuals caring for people living with dementia commonly involve the use of questionnaires administered at infrequent intervals. Caregiver burden can vary significantly between individuals and at different time points throughout the progression of the disease. There has been research investigating the determinants of caregiver burden, but less is known about what represents a clinically‐meaningful amount of intra‐individual change in caregiver burden. The objective of this preliminary analysis is to present data showing change in caregiver burden over time in a cohort with frequent collection of burden level. Method Data were derived from longitudinal studies involving the ORCATECH technology platform, consisting of ambient, wearable and other sensors deployed in participants’ homes collecting continuous data on daily activities. Participants were individuals with mild cognitive impairment (MCI) or dementia living with a caregiver (one dyad per home). Caregivers completed the Zarit Burden Interview Short Version (ZBI‐12, range 0‐48) weekly through the duration of their participation. ZBI‐12 scores and their distribution were analyzed. Result Data are presented from 47 dyads. Caregivers completed a total of 2949 weekly ZBI‐12 questionnaires (range 2‐118). Caregivers had a mean age of 71.3 and 68% were female. The mean ZBI‐12 score was 15.5 (SD 9.5, range 0‐44). The modal distribution of ZBI‐12 scores demonstrated peaks at 2‐4, 11‐12, and 22‐24, at low, moderate and high levels of burden. The variability in ZBI‐12 scores (standard deviation) increased with the total mean ZBI‐12 score (r2 = 0.23) per participant. Conclusion Caregiver burden ranged from very low to high and fluctuated week‐to‐week in the majority of caregivers of individuals with MCI and dementia. Caregivers who had a higher average level of burden also had more variability in their ZBI‐12 scores. More frequent assessments of caregiver burden could help to provide a more complete picture of the dementia care situation. Novel approaches, such as continuous monitoring using home sensors, may provide a method to assess variability in burden level. Future work will evaluate changes in ZBI‐12 scores that represent clinically meaningful differences in caregiver burden and sensor outcome measures that predict higher levels of burden.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".