Benefits, Recruitment, Dropout, and Acceptability of the Strength Back Digital Health Intervention for Patients Undergoing Spinal Surgery: Nonrandomized, Qualitative, and Quantitative Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Patients undergoing spinal surgery report high levels of insecurity, pain, stress, and anxiety before and after surgery. Unfortunately, there is no guarantee that surgery will resolve all issues; postsurgical recovery often entails moderate to severe postoperative pain, and some patients undergoing spinal surgery do not experience (long-term) pain relief after surgery. Therefore, focusing on sustainable coping skills and resilience is crucial for these patients. A digital health intervention based on acceptance and commitment therapy (ACT) and positive psychology (PP) was developed to enhance psychological flexibility and well-being and reduce postsurgical pain. OBJECTIVE: The objective of this study was 3-fold: to explore the potential benefits for patients undergoing spinal surgery of the digital ACT and PP intervention Strength Back (research question [RQ] 1), explore the feasibility of a future randomized controlled trial in terms of recruitment and dropout (RQ 2), and assess the acceptability of Strength Back by patients undergoing spinal surgery (RQ 3). METHODS: We used a nonrandomized experimental design with an intervention group (n=17) and a control group (n=20). To explore the potential benefits of the intervention, participants in both groups filled out questionnaires before and after surgery. These questionnaires included measurements of pain intensity (Numeric Pain Rating Scale), pain interference (Multidimensional Pain Inventory), anxiety and depression (Hospital Anxiety and Depression Scale), valued living (Engaged Living Scale), psychological flexibility (Psychological Inflexibility in Pain Scale), and mental well-being (Mental Health Continuum-Short Form). Semistructured interviews combined with log data and scores on the Twente Engagement With eHealth Technologies Scale were used to assess the acceptability of the intervention. RESULTS: A significant improvement over time in emotional (V=99; P=.03) and overall (V=55; P=.004) well-being (Mental Health Continuum-Short Form) was observed only in the intervention group. In addition, the intervention group showed a significantly larger decline in pain intensity (Numeric Pain Rating Scale) than did the control group (U=75; P=.003). Of the available weekly modules on average 80% (12/15) was completed by patients undergoing spinal fusion and 67% (6/9) was completed by patients undergoing decompression surgery. A total of 68% (17/25) of the participants used the intervention until the final interview. Most participants (15/17, 88%) in the intervention group would recommend the intervention to future patients. CONCLUSIONS: This pilot feasibility study showed that combining ACT and PP in a digital health intervention is promising for patients undergoing spinal surgery as the content was accepted by most of the participants and (larger) improvements in pain intensity and well-being were observed in the intervention group. A digital intervention for patients undergoing (spinal) surgery can use teachable moments, when patients are open to learning more about the surgery and rehabilitation afterward. A larger randomized controlled trial is now warranted.
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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 | 0.056 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".