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Record W4415647968 · doi:10.1101/2025.10.23.25338670

Moving towards precision post-stroke rehabilitation: A systematic review and meta-analysis of wearable sensor-derived walking activities in daily living

2025· preprint· W4415647968 on OpenAlexaff
Joy Ezeugwa, Aiza Khan, Deborah Okunsanya, Liz Dennett, Brian Buck, Patricia J. Manns, Victor E. Ezeugwu

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Language
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychological interventionRehabilitationWearable computerGaitActivities of daily livingStroke (engine)Preferred walking speedRandomized controlled trial

Abstract

fetched live from OpenAlex

Abstract Background Physical rehabilitation interventions can enhance functional capacity after stroke, but improved ability doesn’t always lead to better real-world performance. Understanding this gap is key to optimizing post-stroke recovery strategies. Objectives This research aimed to evaluate the effectiveness of physical rehabilitation interventions specifically exercise, behavior change techniques (BCTs), or their combination on real-world walking measured using wearable sensors and capacity (gait speed and walking endurance) outcomes in stroke survivors. Methods This systematic review and meta-analysis followed PRISMA guidelines. Comprehensive searches were conducted in Medline, Embase, CINAHL, and Scopus up to January 2025. Randomized controlled trials involving stroke survivors receiving physical rehabilitation interventions—exercise, BCTs, or both—compared to exercise-only or usual care were included. Outcomes assessed were daily steps, gait speed (comfortable and fastest), and endurance (6-minute walk test). Meta-analyses using random-effects models (STATA 18) reported standardized mean differences (SMDs) and 95% confidence intervals (CIs). Heterogeneity was evaluated using I² statistics. Results Of 1,782 references screened, 28 studies met the inclusion criteria, and 23 were included in meta-analyses comprising 2,327 participants. Exercise-only interventions produced a small but significant improvement in daily steps (SMD = 0.23; 95% CI: 0.03 to 0.44; I 2 = 36.6%; moderate certainty ), and moderate improvements in both comfortable gait speed (SMD = 0.38; 95% CI: 0.19 to 0.57; I 2 = 35.5%; moderate certainty ) and endurance (SMD = 0.39; 95% CI: 0.26 to 0.52; I 2 = 0%; moderate certainty ). BCT-only interventions demonstrated a larger effect on daily steps (SMD = 0.41; 95% CI: 0.19 to 0.63; I 2 = 0%; moderate certainty). In contrast, combined exercise and BCT interventions did not yield significant improvements in any outcomes and were supported by very low to low certainty of evidence. Conclusion Exercise-only interventions improve gait speed and endurance after stroke, with small gains in daily steps. BCT-only interventions yield greater improvements in daily walking activity. Combined interventions show limited added benefit. Protocol registration This study has been registered in PROSPERO (No. CRD42023411679)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.076
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.038
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.028
GPT teacher head0.306
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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