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Record W4413258562 · doi:10.2196/67297

Balance Improvement and Fall Risk Reduction in Stroke Survivors After Treatment With a Wearable Home-Use Gait Device: Single-Arm Longitudinal Study With 1-Year Follow-Up

2025· article· en· W4413258562 on OpenAlexvenueno aff
Brianne Darcy, Lauren Rashford, David Huizenga, Kyle B. Reed, Stacy Bamberg

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsGaitBalance (ability)Physical medicine and rehabilitationWearable computerStroke (engine)MedicineReduction (mathematics)Longitudinal studyPhysical therapyGait analysisComputer scienceEngineering

Abstract

fetched live from OpenAlex

Background: Falls are a common and serious problem after stroke, often leading to injuries, loss of independence, and increased health care usage. Functional balance, a primary risk factor for falls, is frequently impaired in individuals with hemiparetic gait impairments. Previous research with the iStride gait device (Moterum Technologies, Inc) showed that functional balance improved immediately following 4 weeks of treatment. However, the long-term retention of these effects remains unknown and could improve the management of balance and mobility impairments after stroke. Objective: This study aimed to determine the long-term functional balance effects of treatment with the gait device for individuals with hemiparetic gait impairments from stroke. Methods: Eighteen individuals with chronic stroke (9 male, 9 female, mean age 57 years, and 60 months post stroke) participated in twelve 30-minute treatment sessions with the gait device. During each treatment session, the device was worn on the less affected lower extremity during overground ambulation in the participant's home. All treatment and assessments were overseen by licensed physical therapists. Functional balance was evaluated using the Berg Balance Scale (BBS), the Timed Up and Go (TUG) test, and the Functional Gait Assessment (FGA) at baseline and 5 posttreatment follow-ups: 1 week, 1 month, 3 months, 6 months, and 12 months after treatment. Balance improvement was analyzed using repeated-measures ANOVA from baseline to each follow-up time frame, correlation analysis, comparison to each outcome's minimal detectable change (MDC) value, evaluation of fall risk classification changes, and subjective questionnaires. Results: Participants retained statistically significant improvements on the BBS, TUG, and FGA compared with baseline at all posttreatment time frames (P<.05). All participants initially identified as being at risk for falls reduced their fall risk on at least one outcome during one or more follow-up assessments. At 12 months post treatment, the average improvement on all 3 outcomes remained above their respective MDC thresholds, demonstrated by a 5.9-point improvement on the BBS, a 4.9-second improvement on the TUG, and a 34.6% (3.8-point) improvement on the FGA. At least 72% of participants exceeded the MDC of BBS, at least 44% exceeded the MDC of TUG, and at least 66% exceeded the MDC of FGA at every posttreatment time point. Subjective questionnaire responses indicated that 88% of participants perceived functional balance improvement following treatment with the gait device. Conclusions: The findings of this study indicate that treatment with the gait device may result in long-term functional balance improvement for individuals with hemiparetic gait impairments from stroke. Larger, controlled studies are recommended to confirm these findings.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.048
GPT teacher head0.400
Teacher spread0.351 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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