Feasibility of cognitive testing and ecological momentary assessments using smartphones in middle aged and older adults with insomnia
Bibliographic record
Abstract
Background: Older adults with insomnia who use benzodiazepine receptor agonists (BZAs) may be at increased risk of cognitive impairment. Cognitive testing outside of clinical settings may yield results that are more reflective of individuals' cognition in their natural environment, where they experience fluctuations in mental state (e.g. drowsiness). We assessed the feasibility of self-administered cognitive testing via smartphone apps for collecting in-moment, in-context data about a person's current state (ecological momentary assessment, EMA). Methods: = 20; median age 66 years; 14 females, 18 white) aged ≥ 55 years who were recruited from a BZA deprescribing trial were invited to complete (over a 28 day period) daily drowsiness assessments on an EMA app (cued by smartwatch alerts) and weekly self-administered digit span (DGS) forward/backward (2 [minimum] - 9 [maximum]), verbal paired associates (VPA; 0 [best]-24 [worst] total errors), and cued delayed recall of VPA (VPA-DR; 0 [best] - 8 [worst] errors) tests on a cognitive app. We assessed the completion of EMA (0-28 days) and cognitive sessions (# of participants per # sessions completed). We performed thematic analysis of the participant interviews. Results: The median number of days that EMA was completed was 24.5. Twelve (60%) individuals participated in 4 sessions; 2 (10%) individuals participated in 3 sessions; 2 (10%) individuals participated in 2 sessions; and 4 (20%) individuals participated in 1 session. No drowsiness was reported 36% of the time, whereas 38% of the responses reflected feeling "a little bit" drowsy and 26% at least "somewhat" drowsy. Mean cognitive test scores were DGS-Forward = 7 (SD 1.3), DGS-Backward = 5.6 (SD 1.0), VPA total errors = 9.9 (SD 3.7), and VPA-DR = 2.2 (SD 1.9). Three themes emerged from the participant interviews: 1) concern for one's own cognitive abilities, 2) strategies employed for optimizing scores (including strategies that would invalidate results), and 3) ease of use of the applications. Conclusions: Our findings indicate that mobile cognitive tests and EMAs are feasible in this older population. Further work is needed to understand how scores are influenced by the setting, mood, and behaviors. Supplementary Information: The online version contains supplementary material available at 10.1186/s44247-025-00158-4.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".