Digital Health Technology to Support Health Care Professionals and Family Caregivers Caring for Patients With Cognitive Impairment: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital health technology is a promising way of supporting health care providers and family caregivers as they care for patients with cognitive impairment. OBJECTIVE: This scoping review aimed to portray the use of digital health technology to assist health care providers and family caregivers in caring for patients with cognitive impairment who live in the community or in a facility. METHODS: We conducted a scoping review of peer-reviewed scientific articles available in MEDLINE, PsycINFO, Scopus, and CINAHL with Full Text, as well as gray literature available in preprint servers, theses depositories, and various national and international dementia organizations' websites. The search yielded 975 articles, of which we included 7 (0.7%) in the review. RESULTS: Of the 7 interventions included in the retrieved manuscripts, 2 (29%) were digital calendar reminder systems to support activities of daily living and medication management; 2 (29%) were apps on tablet devices to simulate the presence of family before therapy interventions; 1 (14%) was a social robot used in therapeutic sessions to include elements of musicotherapy, reminiscence, cognitive games, and relaxation; 1 (14%) was a commercially available computer system that provides access to various recreational leisure activities; and 1 (14%) was a web-based self-management support system that helps family caregivers to deal with behavior changes in a relative with dementia. Of the 7 articles, only 1 (14%) reported on the use of a behavior change theory, namely a comprehensive process model of engagement coupled with cognitive stimulation therapy. CONCLUSIONS: Literature on the topic is scarce, recent, and heterogeneous. There is a clear need for a theoretical framework to conceptualize and govern the use of behavior change models that incorporate technology for patients with cognitive impairment.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".