Telehealth interventions for mobility after lower limb loss: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Mobility is a crucial component for healthy aging after lower limb loss (LLL). Telehealth technologies, for example, smart devices, are novel approaches for health programs delivery regardless of geographical boundaries. OBJECTIVES: To assess the effect of telehealth interventions on mobility, quality of life, and antecedents of health behavior compared with a control condition (usual care or simpler telehealth interventions with fewer number of behavior change techniques [BCTs]) for community-dwelling adults (>50 years) with an LLL and the effect of mode of delivery and BCTs used in telehealth interventions on health outcomes. STUDY DESIGN: Systematic review and meta-analysis. METHODS: We systematically searched MEDLINE, PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane, PsycINFO, and SPORTDiscus on January 28, 2021, to identify relevant randomized controlled trials. Two authors independently screened records and assessed risk of bias. We conducted a narrative synthesis of evidence and, when appropriate, used the standardized mean difference (SMD) and mean difference for meta-analyses and the Grading Recommendations Assessment, Development, and Evaluation approach for practice recommendations. RESULTS: We identified six randomized controlled trials. Telephone was the most common delivery mode (n = 3), and "instructions for performing behaviors" was the most common BCT (n = 5). Very low certainty evidence showed no changes in mobility (six studies: SMD = 0.33 [95% confidence interval [CI] = -0.08, 0.75]), quality of life (two studies: mean difference = -0.08 [95% CI = -0.30, 0.15]), and antecedents of behavior (five studies: SMD = 0.04 [95% CI = -0.28, 0.36]). CONCLUSIONS: Our review highlights a knowledge gap for the effect of telehealth interventions for people with LLL. Although no promising effect was shown for telehealth interventions, very low certainty evidence precludes making a definitive clinical recommendation.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".