Mobile-Based Digital Rehabilitation Program for Patients After Anterior Cervical Discectomy and Fusion: Prospective Cohort Study
Bibliographic record
Abstract
Background: Anterior cervical discectomy and fusion (ACDF) is a common treatment for degenerative cervical spine disease, yet its frequent postoperative follow-up places substantial demands on both patients and health care systems. A digital program integrating computer-vision-guided exercise, wearable posture monitoring, and cognitive behavioral therapy (CBT) could provide remote monitoring and rehabilitation to alleviate this burden. Objective: This study aims to evaluate the clinical effectiveness and compliance of a 12-week digital rehabilitation program after ACDF compared with conventional in-person therapy. Methods: In this prospective cohort study, 336 postoperative patients self-selected either a 12-week mobile-based program incorporating computer-vision-guided exercises, wearable posture sensors, and cognitive behavioral therapy (n=270), or in-person rehabilitation group (IRG, n=66) involving weekly therapist-supervised sessions and paper-based home exercises. Digital users were stratified into a digital rehabilitation completion group (DCG, n=192) and a digital rehabilitation noncompletion group (DNG, n=78). All participants were recruited at a single tertiary hospital and returned to the clinic for outcome assessments at 0, 12, and 24 weeks postoperatively. Outcomes-primarily pain (visual analog scale [VAS]) and disability (neck disability index [NDI]), as well as 36-item short form survey mental component summary (SF-36 MCS), 3-plane cervical range of motion (ROM), muscle endurance, and patient satisfaction-were recorded at 0, 12, and 24 weeks postoperatively. All statistical analyses were performed using SPSS (version 29.0; IBM Corporation). Results were reported as means, SDs, and 95% CIs. Results: Both the DCG (n=192, who completed all digital sessions) and IRG (who completed 12 weeks of weekly in-person sessions and home exercises) demonstrated significant improvements in pain and disability at weeks 12 and 24, with no significant differences between groups (P>.05). At Week 12, VAS decreased by -2.5 (95% CI -3.0 to -2.0) in the DCG and -2.8 (-3.7 to -1.9) in the IRG; NDI decreased by -6.8 (-10.3 to -3.3) and -8.1 (-14.3 to -1.9), respectively. At Week 24, VAS and NDI reductions reached -4.0 (-4.5 to -3.5) and -13.3 (-17.4 to -9.2) in the DCG, and -4.1 (-5.0 to -3.2) and -14.2 (-21.3 to -7.1) in the IRG. In contrast, the DNG showed minimal improvements: VAS changes were -0.8 (-1.6 to 0.0) at week 12 and -1.3 (-2.1 to -0.5) at week 24; NDI changes were -2.2 (-8.2 to 3.8) and -6.4 (-13.0 to 0.2), respectively (P<.05 compared to DCG and IRG). Conclusions: The digital rehab program led to comparable improvements in pain, function, and mental health as conventional in-person rehab. Higher adherence was linked to better outcomes, supporting digital rehab as an effective, patient-centered approach after ACDF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".