App-Based Relaxation Exercises for Patients With Chronic Neck Pain: Pragmatic Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Chronic neck pain is a highly prevalent condition. Learning a relaxation technique is recommended by numerous guidelines for chronic neck pain. Smartphone apps can provide relaxation exercises; however, their effectiveness, especially in a self-care setting, is unclear. OBJECTIVE: The aim of this pragmatic randomized trial is to evaluate whether app-based relaxation exercises, including audio-based autogenic training, mindfulness meditation, or guided imagery, are more effective in reducing chronic neck pain than usual care alone. METHODS: Smartphone owners aged 18 to 65 years with chronic (>12 weeks) neck pain and the previous week's average neck pain intensity ≥4 on the Numeric Rating Scale (0=no pain to 10=worst possible pain) were randomized into either an intervention group to practice app-based relaxation exercises or a control group (usual care and app for data entry only). For both groups, the follow-up data were collected using app-based diaries and questionnaires. The primary outcome was the mean neck pain intensity during the first 3 months based on daily measurements. Secondary outcomes included neck pain based on weekly measurements, pain acceptance, neck pain-related stress, sick-leave days, pain medication intake, and adherence, which were all measured until the 6-month follow-up. For the primary analysis, analysis of covariance adjusted for baseline neck pain intensity was used. RESULTS: We screened 748 participants and enrolled 220 participants (mean age 38.9, SD 11.3 years; mean baseline neck pain 5.7, SD 1.3 points). The mean neck pain intensity in both groups decreased over 3 months; however, no statistically significant difference between the groups was found (intervention: 4.1 points, 95% CI 3.8-4.4; control: 3.8 points, 95% CI 3.5-4.1; group difference: 0.3 points, 95% CI -0.2 to 0.7; P=.23). In addition, no statistically significant between-group differences regarding neck pain intensity after 6 months, responder rate, pain acceptance, pain medication intake, or sick-leave days were observed. There were no serious adverse events that were considered related to the trial intervention. In week 12, only 40% (44/110) of the participants in the intervention group continued to practice the exercises with the app. CONCLUSIONS: The study app did not effectively reduce chronic neck pain or keep the participants engaged in exercising in a self-care setting. Future studies on app-based relaxation interventions should take into account the most recent scientific findings for behavior change techniques. TRIAL REGISTRATION: ClinicalTrials.gov NCT02019134; https://clinicaltrials.gov/ct2/show/NCT02019134. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/1745-6215-15-490.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".