Inclusion of Older Adults in Digital Health Technologies to Support Hospital-to-Home Transitions: Secondary Analysis of a Rapid Review and Equity-Informed Recommendations
Bibliographic record
Abstract
BACKGROUND: Digital health technologies have been proposed to support hospital-to-home transition for older adults. The COVID-19 pandemic and the associated physical distancing guidelines have propelled a shift toward digital health technologies. However, the characteristics of older adults who participated in digital health research interventions to support hospital-to-home transitions remain unclear. This information is needed to assess whether current digital health interventions are generalizable to the needs of the broader older adult population. OBJECTIVE: This rapid review of the existing literature aimed to identify the characteristics of the populations targeted by studies testing the implementation of digital health interventions designed to support hospital-to-home transitions, identify the characteristics of the samples included in studies testing digital health interventions used to support hospital-to-home transitions, and create recommendations for enhancing the diversity of samples within future hospital-to-home digital health interventions. METHODS: A rapid review methodology based on scoping review guidelines by Arksey and O'Malley was developed. A search for peer-reviewed literature published between 2010 and 2021 on digital health solutions that support hospital-to-home transitions for older adults was conducted using MEDLINE, Embase, and CINAHL databases. The data were analyzed using descriptive statistics and qualitative content analysis. The Sex- and Gender-Based Analysis Plus lens theoretically guided the study design, analysis, and interpretation. RESULTS: A total of 34 studies met the inclusion criteria. Our findings indicate that many groups of older adults were excluded from these interventions and remain understudied. Specifically, the oldest old and those living with cognitive impairments were excluded from the studies included in this review. In addition, very few studies have described the characteristics related to gender diversity, education, race, ethnicity, and culture. None of the studies commented on the sexual orientation of the participants. CONCLUSIONS: This is the first review, to our knowledge, that has mapped the literature focusing on the inclusion of older adults in digital hospital-to-home interventions. The findings suggest that the literature on digital health interventions tends to operationalize older adults as a homogenous group, ignoring the heterogeneity in older age definitions. Inconsistency in the literature surrounding the characteristics of the included participants suggests a need for further study to better understand how digital technologies to support hospital-to-home transitions can be inclusive.
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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 | 0.123 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.057 | 0.046 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".