Reversing Frailty in Older Adults: A Scoping Review
Bibliographic record
Abstract
Abstract Background: Individuals 65 years or older are presumably more susceptible to becoming frail, which increases their risk of multiple adverse health outcomes. Reversing frailty has received recent attention; however, little is understood about what it means and how to achieve it. Thus, the purpose of this scoping review is to summarize and synthesize the evidence regarding the impact of interventions on frail older adults, to determine whether the intervention (s) resulted in frailty reversal. Methods: We followed Arksey and O’Malley’s five-stage scoping review approach and conducted searches in CINAHL, EMBASE, PubMed, and Web of Science. We hand-searched the reference list of included studies and conducted a grey literature search. Two independent reviewers completed the title, abstract screenings, and full-text review using the eligibility criteria, and independently extracted approximately 10% of the studies. We critically appraised studies using Joanna Briggs critical appraisal checklist/tool, and we used a descriptive and narrative method to synthesize and analyze data. Results: Of 7499 articles, thirty met the criteria and three studies were identified in the references of included studies. Seventeen studies (56.7%) framed frailty as a reversible condition, with 11 studies (36.7%) selecting it as their primary outcome. Reversing frailty varied from either frail to pre-frail, frail to non-frail, and severe to mild frailty. We identified different types of single and multi-component interventions each targeting various domains of frailty. The physical domain was most frequently targeted (n=32, 97%). Interventions also varied in their frequencies of delivery, intensities, and durations, and targeted participants from different settings, most commonly from community dwellings (n=23; 69.7%). Conclusion: Some studies indicated that it is possible to reverse frailty. However, this depended on how the researchers assessed or measured frailty. The current understanding of reverse frailty is a shift from a frail or severely frail state to at least a pre-frail or mildly frail state. To gain further insight into reversing frailty, we recommend a concept analysis. Furthermore, we recommend more primary studies considering the participant’s lived experiences to guide intervention delivery.
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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 | 0.024 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".