Experiences of Older Adults With Mobile Phone Text Messaging as Reminders of Home Exercises After Specialized Manual Therapy for Recurrent Low Back Pain: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Clinical experience of manual therapy for musculoskeletal pain is that patients often suffer from recurrent pain and disorders, but that they do not continue to perform their physical home exercises when they are free from symptoms. The chance of positive long-term effects of manual therapy would probably increase if patients were reminded that they are to continue to perform their exercises. Mobile phone text messaging (short messaging service, SMS) is increasingly used as an innovative intervention to remind patient to exercise. However, there are only a few studies on such interventions in the field of low back pain (LBP). Qualitative studies of patients' experiences of receiving text messages as reminders of home exercises after manual treatment for recurrent LBP have to the best of our knowledge never been published. OBJECTIVES: The aim of this study was to explore older persons' common experiences of receiving reminders of home exercises through mobile phone text messaging after specialized manual therapy for recurrent LBP. METHODS: A total of 7 men and 8 women (67-86 years), who had sought specialized manual therapy (Naprapathic manual therapy) for recurrent LBP were included in the study. Individual one-way text messages as reminders of home exercises (to be performed on a daily basis) were sent to each patient every third day for 3 weeks, then once a week for another 2 weeks. Semistructured interviews with 2 broad, open-ended questions were held and data were analyzed with systematic text condensation, based on Giorgi's principles of psychological phenomenological analysis. RESULTS: The participants appreciated the messages, which were perceived as timely and usable, and also stimulated memorizing. The messages made the participants reflect on the aim of the exercise, value of being reminded, and on their improvement in pain. During the interviews, the participants created their own routines for continued adherence to the exercises. CONCLUSIONS: It seems plausible that mobile phone text messaging may serve as a useful tool for patient empowerment with regard to recurrent LBP in older persons. Further studies are needed to explore whether future compliance with the exercises will be as large if the participants are not being interviewed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".