An Evaluation of a Mobile App for Chronic Low Back Pain Management: Prospective Pilot Study
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain is challenging to manage due to multidisciplinary considerations. It has substantial socioeconomic impacts and cannot be simply treated with pharmacotherapy, nonsurgical intervention, or spine surgery. Medical consensus recommends optimizing conservative self-management therapies (eg, home exercise, wellness strategies, yoga, etc) as first-line treatment options for chronic low back pain. However, access to these modalities is often limited and secondary to cost, convenience, and ease of use. Mobile health apps have emerged as a cost-effective and accessible option for chronic low back pain self-management. Established in-person pain programs can provide the structure for an optimal mobile app adaptation. PainNavigator (PainNavigator, Inc) is an example of a mobile app that is based on an Ascension-Illinois group-based pain program-Pain Rehabilitation Outpatient-Camp. OBJECTIVE: This was a prospective pilot clinical trial that evaluated the PainNavigator platform's utility in low back pain management to inform future trial development. METHODS: A total of 75 participants who used PainNavigator were studied. Pain, Enjoyment, and General Activity (PEG-3) scale scores and scores from a brief anxiety and depression scale based on the Patient Health Questionnaire-4 (PHQ-4) were obtained at baseline and following program completion. The PEG-3 total score was used, in addition to individual items-Average Pain, Pain Effect on Enjoyment, and Pain Effect on Activity. The PHQ-4 total score was also used, in addition to other individual items, including Felt Depressed, Loss of Interest, Felt Anxious, and Difficult to Control Worry. Paired sample t tests (2-tailed) compared mean differences in scores from before and after participants received the intervention. RESULTS: The analysis found that PEG-3 (n=27) and PHQ-4 (n=27) total scores were significantly lower upon the completion of PainNavigator (P<.001 and P=.001, respectively). The findings showed a 36% reduction in PEG-3 total scores, a 40% reduction in pain intensity, and a 40% reduction in PHQ-4 total scores. Scores for individual PEG-3 scale and PHQ-4 items also significantly decreased. All PEG-3 measures had large effect sizes. The PHQ-4 total score and Difficult to Control Worry item had large effect sizes, while the other three measures had medium effect sizes. CONCLUSIONS: These findings show that PainNavigator has clinical significance in managing chronic low back pain and can be easily utilized to improve patient care. All PEG-3 scale and PHQ-4 measures significantly improved following the use of the platform, supporting the multidimensional, biopsychosocial approach to low back pain management. Differences in effect sizes may inform quality improvement investigations, such as optimizing features that impact measures with only medium effect sizes. This feasibility study demonstrates an effective protocol, and it will inform future, more extensive randomized controlled trials.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".