Gait Device Treatment Using Telehealth for Individuals With Stroke During the COVID-19 Pandemic: Nonrandomized Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, rehabilitation providers and consumers adopted telehealth practices at unprecedented rates. Multiple prepandemic studies demonstrate the feasibility and comparable efficacy between in-clinic and remote treatment for certain impairments caused by stroke, such as upper extremity weakness and impaired motor function. However, less guidance has been available regarding gait assessment and treatment. Despite this limitation, safe and effective gait treatment is fundamental to optimizing health and well-being after stroke and should be considered a treatment priority, including during the COVID-19 pandemic. OBJECTIVE: This study explores the feasibility of using telehealth to deliver gait treatment using a wearable gait device, the iStride device, to stroke survivors during the 2020 pandemic. The gait device is used to treat hemiparetic gait impairments caused by stroke. The device alters the user's gait mechanics and creates a subtle destabilization of the nonparetic limb; therefore, supervision is required during its usage. Before the pandemic, treatment with the gait device had been provided in person to appropriate candidates using a combination of physical therapists and trained personnel. However, upon the emergence of the COVID-19 pandemic, in-person treatment was halted in adherence to pandemic guidelines. This study investigates the feasibility of 2 remote delivery treatment models with the gait device for stroke survivors. METHODS: Participants were recruited during the first half of 2020 after the onset of the pandemic and included 5 individuals with chronic stroke (mean age 72 years; 84 months post stroke). Four participants were previous gait device users who transitioned to the telehealth delivery model to continue their gait treatment remotely. The fifth participant performed all study-related activities, from recruitment through follow-up, remotely. The protocol included virtual training for the at-home care partner, followed by 3 months of remote treatment with the gait device. Participants were instructed to wear gait sensors during all treatment activities. To assess feasibility, we monitored the safety of the remote treatment, compliance with protocol activities, acceptability of the telehealth treatment delivery, and preliminary efficacy of the gait treatment. Functional improvement was measured using the 10-Meter Walk Test, the Timed Up and Go Test, and the 6-Minute Walk Test, and quality of life was assessed using the Stroke-Specific Quality of Life Scale. RESULTS: No serious adverse events occurred, and participants rated high acceptance of the telehealth delivery. Protocol compliance averaged 95% of treatment sessions, 100% of assessments, and 85% of sensor usage during treatment. After 3 months of treatment, the average improvement in each functional outcome exceeded the minimal clinically important difference or minimal detectable change value. CONCLUSIONS: Remote treatment delivery with the gait device appeared feasible with care partner support. Gait treatment using telehealth may be useful to offset negative immobility impacts for those requiring or preferring remote care during the pandemic or otherwise. TRIAL REGISTRATION: ClinicalTrials.gov NCT04434313; https://clinicaltrials.gov/ct2/show/NCT04434313.
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".