Psychometric Properties of Apathy Scales in Dementia: A Systematic Review
Bibliographic record
Abstract
Apathy is a prevalent and problematic neuropsychiatric symptom in those with dementia that is emerging as a treatment target, necessitating accurate assessment. While many apathy scales are available, not all have been developed for use exclusively in dementia, and psychometric properties may vary across different populations. This systematic review aimed to provide an overview of the psychometric properties of apathy scales used in Alzheimer's disease (AD) and related dementias, as well as rate the methodological quality of supporting studies. In addition, for those scales identified, performance in clinical trials was reviewed. A search was conducted through Medline, Psychinfo, Embase, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews. Articles that reported psychometric properties of an apathy scale in an AD or mixed dementia population were included. Of 15 articles, the methodological quality ratings of the studies ranged from adequate to excellent. Three clinical trials and two pooled analyses of clinical trials were included that used apathy scales evaluated in this review. Three scales emerged. The Neuropsychiatric Inventory apathy subscale (NPI-apathy) and the Apathy Evaluation Scale (AES) had the greatest number of studies evaluating psychometric properties and were also used in the clinical trials and have shown sensitivity to change. The Dementia Apathy Interview and Rating demonstrated excellent values of internal consistency, validity, and reliability for use in an AD population. Future research should address comparative scale performance and assess ability to distinguish subtypes of apathy. Validation may include evaluation of performance against specific imaging defined deficits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| 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) | 0.003 | 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".