User Engagement With and Perceived Impact of a Digital Cognitive Training App on Cognition, Daily Functioning, and Mental Fitness: Secondary Analysis of Cross-Sectional Survey Data
Bibliographic record
Abstract
BACKGROUND: Cognitive difficulties are common and can interfere with daily functioning. While digital cognitive training apps are widely used, few studies have examined whether personalized tools support perceived improvements in cognitive functioning, daily functioning, and overall mental fitness among general adult users. OBJECTIVE: The purpose of this secondary analysis was to explore the self-reported cognitive benefits of Elevate, a commercial, personalized cognitive training app developed to support cognitive functioning, as well as engagement with the app. We aimed to (1) describe demographics, engagement metrics, and self-reported improvements; (2) examine associations between app engagement and self-reported improvements in cognitive functioning skills directly targeted by the app; and (3) examine associations between app engagement and self-reported improvements in daily functioning and overall mental fitness as potential transfer effects of cognitive training. METHODS: Adult Elevate users (aged ≥18 years) who used the app at least twice in the previous 30 days completed a brief web-based survey on perceived cognitive, functional, and mental fitness improvements. Responses were linked to objective app use data, including total active weeks, mean active days per week, and mean time per day. Ordinal logistic regressions tested associations between engagement metrics and self-reported outcomes controlling for demographic variables. A Bonferroni correction was applied to adjust for multiple comparisons. RESULTS: A total of 3367 adult Elevate users were included. Participants were primarily middle-aged (mean 55, SD 16 y), White (2557/3336, 76.65%), and female (2184/3362, 64.96%), with 67.72% (2274/3358) holding at least a college degree. Using the app across more weeks was associated with a greater likelihood of reporting improvements in all cognitive skills (odds ratios [ORs] 1.0014-1.0027, 95% CI 1.0006-1.0036), several areas of daily functioning (eg, motivation and task efficiency; ORs 1.0014-1.0017, 95% CI 1.0006-1.0026), and overall mental fitness (OR 1.0021, 95% CI 1.0012-1.0031). More days of use per week were linked to improvement in math only (OR 1.15, 95% CI 1.09-1.22), whereas spending more time per day was associated with improvements in speaking, reading, math, motivation, personal progress, and mental fitness (ORs 1.02-1.04, 95% CI 1.01-1.06). CONCLUSIONS: Greater use of the Elevate app was linked to self-reported improvements in cognitive skills, daily functioning, and overall mental fitness. These findings suggest that personalized, adaptive cognitive training apps such as Elevate may serve as scalable tools for enhancing everyday cognitive and functional well-being. Future research should use rigorous, longitudinal methods to confirm these effects and clarify which app features drive meaningful outcomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".