Reversing frailty in older adults: a scoping review
Bibliographic record
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 synthesize the evidence regarding the impact of frail-related interventions on older adults living with frailty, identify what interventions resulted in frailty reversal and clarify the concept of reverse frailty. 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 | 0.025 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".