Remote Self-Administration of Cognitive Screeners for Older Adults Prior to a Primary Care Visit: Pilot Cross-Sectional Study of the Reliability and Usability of the MyCog Mobile Screening App
Bibliographic record
Abstract
BACKGROUND: Routine cognitive screening is essential in the early detection of dementia, but time constraints in primary care settings often limit clinicians' ability to conduct screenings. MyCog Mobile is a newly developed cognitive screening system that patients can self-administer on their smartphones before a primary care visit, which can help save clinics' time, encourage broader screening practices, and increase early detection of cognitive decline. OBJECTIVE: The goal of this pilot study was to examine the feasibility, acceptability, and initial psychometric properties of MyCog Mobile. Research questions included (1) Can older adults complete MyCog Mobile remotely without staff support? (2) Are the internal consistency and test-retest reliability of the measures acceptable? and (3) How do participants rate the user experience of MyCog Mobile? METHODS: A sample of adults aged 65 years and older (N=51) self-administered the MyCog Mobile measures remotely on their smartphones twice within a 2- to 3-week interval. The pilot version of MyCog Mobile includes 4 activities: MyFaces measures facial memory, MySorting measures executive functioning, MySequences measures working memory, and MyPictures measures episodic memory. After their first administration, participants also completed a modified version of the Simplified System Usability Scale (S-SUS) and 2 custom survey items. RESULTS: All participants in the sample passed the practice items and completed each measure. Findings indicate that the Mobile Toolbox assessments measure the constructs well (internal consistency 0.73 to 0.91) and are stable over an approximately 2-week delay (test-retest reliability 0.61 to 0.71). Participants' rating of the user experience (mean S-SUS score 73.17, SD 19.27) indicated that older adults found the usability of MyCog Mobile to be above average. On free-response feedback items, most participants provided positive feedback or no feedback at all, but some indicated a need for clarity in certain task instructions, concerns about participants' abilities, desire to be able to contact a support person or use in-app technical support, and desire for additional practice items. CONCLUSIONS: Pilot evidence suggests that the MyCog Mobile cognitive screener can be reliably self-administered by older adults on their smartphones. Participants in our study generally provided positive feedback about the MyCog Mobile experience and rated the usability of the app highly. Based on participant feedback, we will conduct further usability research to improve support functionality, optimize task instructions and practice opportunities, and ensure that patients feel comfortable using MyCog Mobile. The next steps include a clinical validation study that compares MyCog Mobile to gold-standard assessments and tests the sensitivity and specificity of the measures for identifying dementia.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".