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Record W4409430666 · doi:10.2196/58142

Feasibility and Effects of a Gait Assistance and Gait Resistance Training Program Using a Walking-Assist Wearable Robot for Community-Dwelling Older Adults: Single-Group, Pre-, and Posttest Study

2025· article· en· W4409430666 on OpenAlexvenueno aff
Eunhee Cho, Sinwoo Hwang, Seok‐Jae Heo, Bokman Lim, J.H. Lee, Younbaek Lee

Bibliographic record

VenueJMIR mhealth and uhealth · 2025
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsGaitWearable computerPhysical medicine and rehabilitationPreprintGait trainingTest (biology)PsychologyWearable technologyPhysical therapyRobotMedicineComputer scienceRehabilitationArtificial intelligence

Abstract

fetched live from OpenAlex

Background: Two-thirds of people aged 65 years and older may require help with daily activities such as eating, bathing, and getting in and out of bed or a chair. Walking-assist wearable robots have shown significant improvements in physical function in controlled settings for patients. Objective: In this study, we aimed to assess the feasibility and the effect of a gait assistance and gait resistance training program using a walking-assist wearable robot for community-dwelling older adults. Methods: A total of 23 community-dwelling older adults aged 65 years and older (30 participants recruited, 7 dropped out) enrolled in a 12-session, 6-week gait assistance and gait resistance training program using a walking-assist wearable robot. A single-group, pre- and posttest design was employed to evaluate the feasibility based on program adherence and effectiveness. The primary and secondary outcomes for evaluating effectiveness were walking speed and functional performance, respectively. Results: Regarding the feasibility, the average number of sessions attended was 11.7 out of 12, indicating a mean adherence rate of 97.8%. Linear mixed model analysis revealed significant improvements in walking speed and functional performance at the end of the program compared with baseline. Specifically, the walking speed measured using the 10-Meter Walk Test, which includes self-selected velocity and fastest safe velocity, improved by a mean of 0.15 (SD 0.13) m/s (P<.001) and 0.15 (SD 0.17) m/s (P<.001), respectively. Functional performance also improved, with faster performance in Timed Up-and-Go (mean -0.63, SD 0.92 s; P=.003) and Four Square Step Test (mean -1.71, SD 1.64 s; P≤.001). Leg muscle strength increased across all measured domains, including plantarflexion (mean +7.29, SD 4.92; P=.004), hip adduction (mean +3.03, SD 2.73; P≤.001), hip extension (mean +2.63, SD 2.50; P≤.001), knee extension (mean +2.33, SD 3.12; P≤.001), knee flexion (mean +2.19, SD 2.17; P≤.001), dorsiflexion (mean +2.10, SD 3.06; P≤.001), hip abduction (mean +1.59, SD 1.92; P=.002), and hip flexion (mean +0.90, SD 1.56; P≤.001). Conclusions: This study stands out for applying gait assistance and resistance training across various terrains, unlike previous studies that only tested gait assistance in controlled environments. The results demonstrated significant improvements in walking speed and functional performance in older adults, suggesting the effectiveness of preventive health care services using a walking-assist wearable robot as an intervention that can contribute to improving independent functioning and frailty among community-dwelling older adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.341
Teacher spread0.303 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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