Acceptance of Unsupervised App-Based Cognitive Assessment in Outpatient Care: An Implementation Study
Bibliographic record
Abstract
Background: The use of unsupervised digital cognitive assessments provides considerable opportunities for early and comprehensive testing for Alzheimer disease, minimizing the demand on time and personnel resources in medical practices. However, the acceptance within health care has yet to be assessed. Objective: In this implementation study, the acceptance of an app-based, repeated cognitive assessment for early symptoms of Alzheimer disease in the outpatient care setting from both physicians' and patients' perspectives was examined. Methods: In total, 15 primary care practices participated, where patients with self- or relative-reported memory problems could be prescribed an app (neotivCare app [neotiv GmbH]) for comprehensive cognitive testing. Patients used the app to test their episodic memory function weekly for 12 weeks at home. After the testing period and the final consultation, physicians and patients received questionnaires to assess the app's acceptance. Results: We received completed questionnaires from physicians for 45 patients. In addition, we received 45 completed questionnaires from the patients themselves. The physicians reported that, for most patients, the app supported their decision-making in the diagnostic process (26/45, 58%). In addition, most physicians found the app's information dependable (34/45, 76%) and felt more certain in their decisions (38/45, 84%). From the patients' perspective, a majority felt thoroughly tested (34/45, 76%), and only a few considered the time commitment for the cognitive tests to be too burdensome (7/45, 16%). Furthermore, despite the weekly cognitive testing and the lengthy 12-week testing period, a majority of patients participated in all tests (39/54, 72%). Conclusions: Our results indicate a high level of acceptance by physicians and patients, suggesting significant potential for the implementation of unsupervised digital cognitive assessments into routine health care. In the future, acceptance should be assessed in large-scale studies, with a particular focus on the impact on health care delivery and patient outcomes.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".