Engaging Older Adults With Cognitive Impairment in Digital Health Technologies: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The aging of the population is a major issue in Canada, particularly in Quebec. For people older than the age of 65 years, aging is often associated with both mild and severe cognitive impairment. The management of these disorders increases the pressure on health care systems. Digital health technologies could be used to promote the cognitive health of older people living with cognitive disorders. However, to reap the full benefits of using digital health technologies, it is critical that older people with cognitive disorders engage with these technologies. A dose-response relationship has been demonstrated between the level of engagement with digital health technologies and the effectiveness of interventions in older people. It is thus important to understand how older people with cognitive impairment engage with digital health technologies and how this engagement can influence the success of digital health interventions. OBJECTIVE: This study aims to describe how the engagement of older adults with cognitive impairment with digital health technologies is conceptualized and assessed, and how this engagement relates to the effectiveness of digital health interventions. METHODS: We will use the scoping review method outlined by Arksey and O'Malley. We will apply a systematic approach following the Joanna Briggs Institute guidelines and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist to ensure reproducibility of the scoping review. A search strategy, created with a medical information specialist, will be applied to MEDLINE, Embase, CINAHL, Web of Science, and Google Scholar, without time restrictions. Two reviewers will independently select titles, abstracts, and full texts. Data extraction will be conducted by the research team and validated by a senior member, resolving disagreements by consensus or a third party if necessary. Descriptive analyses will be done using concept mapping for a narrative synthesis of the results by themes related to the research questions. RESULTS: The development of the search strategy and the completion of the selection phases of the review were completed in July 2024. Data extraction and analysis began in August 2024, and results are expected to be available in November 2024. CONCLUSIONS: The results of this scoping review will provide a comprehensive overview of the different conceptualizations of engagement with digital health technologies in older people with cognitive impairment, as well as the tools to measure it. This will contribute to a better understanding of the relationships between levels of engagement and the effectiveness of digital health interventions in older people living with neurocognitive disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/65515.
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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 | 0.093 | 0.084 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.103 | 0.018 |
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".