Effects of a Digital Functional Exercise Program on the Disease Activities and Physical Capabilities of Patients With Ankylosing Spondylitis: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Ankylosing spondylitis (AS), a chronic inflammatory disease, causes spinal stiffness, functional impairment, and reduced quality of life. While exercise is critical for managing AS, traditional home-based programs lack real-time supervision to ensure movement quality and adherence. Emerging digital tools like inertial sensors may address this gap, but their clinical impact remains unproven. OBJECTIVE: This study aims to evaluate the feasibility and efficacy of a Digital Functional Exercise Program (DFEP) using wearable sensors and real-time feedback for patients with AS. METHODS: This single-blind randomized controlled trial (ChiCTR2300068327) enrolled 80 adults with AS from the Chinese People's Liberation Army (PLA) General Hospital. Participants were randomized 1:1 to: DFEP Group: 24-week sensor-guided exercises via the Healbone Mini Program (Jiakangzhongzhi, Co), with real-time feedback and remote physiotherapist oversight. Control Group: standard home-based exercises with written instructions. The primary outcome is the change in Ankylosing Spondylitis Disease Activity Score with C-reactive protein (ASDAS-CRP) at 24 weeks. Secondary outcomes include Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Metrology Index (BASMI), Assessment of Spondyloarthritis International Society Health Index (ASAS HI), 36-Item Short Form Survey (SF-36), pain visual analog scale, Five-Times-Sit-to-Stand, 4-meter walk test, Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), and adherence. Analyses will follow an intention-to-treat approach with the Last Observation Carried Forward for missing data; continuous variables will be compared with t tests or ANOVA, and categorical variables with chi-square tests, using a 2-sided =.05. RESULTS: We have screened 216 outpatients who may be eligible subjects, among them 126 outpatients who initially met the inclusion criteria. After evaluating clinicians performed face-to-face assessments, 23 lacked a stable medication regimen, 5 presented with severe cervical vertebral bridges, 3 reported regular structured exercise, 2 could not be reached after prescreening, 10 had significant cardiovascular disease, and 3 declined participation due to time constraints. Ultimately, 80 eligible participants were enrolled, with 36 randomly allocated to the intervention group and 44 to the control group. Recruitment (February 2023-March 2024) and follow-up (concluding September 2024) are complete. Data analysis (November 2024) and result dissemination (April 2025) are pending. CONCLUSIONS: This trial is the first to test a fully digital, sensor-based exercise program for AS. If effective, DFEP could offer a scalable, cost-effective solution for home rehabilitation in AS and related conditions. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300068327; https://www.chictr.org.cn/showproj.html?proj=190897. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67556.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".