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Record W4400439465 · doi:10.2196/56580

The Physical and Psychological Effects of Telerehabilitation-Based Exercise for Patients With Nonspecific Low Back Pain: Prospective Randomized Controlled Trial

2024· article· en· W4400439465 on OpenAlexvenueno aff
Weihong Shi, Yuhang Zhang, Yanyan Bian, Wangshu Yuan, Houqiang Zhang, Qiyang Feng, Huiling Zhang, D Liu, Ye Lin

Bibliographic record

VenueJMIR mhealth and uhealth · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsTelerehabilitationPhysical therapyMedicineRandomized controlled trialOswestry Disability IndexRehabilitationPopulationPsychological interventionTelemedicinePhysical medicine and rehabilitationLow back painQuality of life (healthcare)Health careAlternative medicineNursing

Abstract

fetched live from OpenAlex

Background: Physical therapy has demonstrated efficacy in managing nonspecific low back pain (NLBP) among patients. Nevertheless, the prevalence of NLBP poses a challenge, as the existing medical infrastructure may be insufficient to care for the large patient population, particularly in geographically remote regions. Telerehabilitation emerges as a promising method to address this concern by offering a method to deliver superior medical care to a greater number of patients with NLBP. Objective: The purpose of this study is to demonstrate the physical and psychological effectiveness of a user-centered telerehabilitation program, consisting of a smartphone app and integrated sensors, for patients with NLBP. Methods: This was a single-center, prospective, randomized controlled trial for individuals with NLBP for a duration exceeding 3 months. All participants were assigned randomly to either the telerehabilitation-based exercise group (TBEG) or the outpatient-based exercise group (OBEG). All participants completed a 30-minute regimen of strength and stretching exercises 3 times per week, for a total of 8 weeks, and were required to complete assessment questionnaires at 0, 2, 4, and 8 weeks. The TBEG completed home-based exercises and questionnaires using a telerehabilitation program, while the OBEG completed them in outpatient rehabilitation. The Oswestry Disability Index (ODI) served as the primary outcome measure, assessing physical disability. Secondary outcomes included the Numeric Pain Rating Scale, Fear-Avoidance Beliefs Questionnaire, and 36-item Short-Form Health Survey. Results: In total, 54 of 129 eligible patients were enrolled and randomly assigned to the study. The completion of all the interventions and assessments in the TBEG and OBEG was 89% (24/27) and 81% (22/27). The findings indicate that no statistical significance was found in the difference of ODI scores between the TBEG and the OBEG at 2 weeks (mean difference -0.91; odds ratio [OR] 0.78, 95% CI -5.96 to 4.14; P=.72), 4 weeks (mean difference -3.80; OR 1.33, 95% CI -9.86 to -2.25; P=.21), and 8 weeks (mean difference -3.24; OR 0.92, 95% CI -8.65 to 2.17; P=.24). The improvement of the ODI in the TBEG (mean -16.42, SD 7.30) and OBEG (mean -13.18, SD 8.48) was higher than 10 after an 8-week intervention. No statistically significant differences were observed between the 2 groups at the 8-week mark regarding the Fear-Avoidance Beliefs Questionnaire (mean difference 8.88; OR 1.04, 95% CI -2.29 to 20.06; P=.12) and Numeric Pain Rating Scale (mean difference -0.39; OR 0.44, 95% CI -2.10 to 1.31; P=.64). In the subgroup analysis, there was no statistically significant difference in outcomes between the 2 groups. Conclusions: Telerehabilitation interventions demonstrate comparable therapeutic efficacy for individuals with NLBP when compared to conventional outpatient-based physical therapy, yielding comparable outcomes in pain reduction and improvement in functional limitations.

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)0.003
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.012
GPT teacher head0.341
Teacher spread0.329 · 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 designRandomized trial
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

Citations14
Published2024
Admission routes1
Has abstractyes

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