Usage, Attitudes, Facilitators, and Barriers Toward Digital Health Technologies in Musculoskeletal Care: Survey Among Primary Care Physiotherapists in Norway
Bibliographic record
Abstract
Background Work burden increases for physiotherapists in the primary health care sector as the prevalence of musculoskeletal disorders (MSDs) increases. Digital health technologies (DHTs) are proposed as a viable solution to secure the sustainability of the health care system and have shown promising results in a range of conditions. However, little is known about use of DHTs among physiotherapists in the primary health care sector in Norway. Objective This study aimed to investigate the use of and attitudes toward DHTs among physiotherapists treating patients with MSDs in primary care, and potential facilitators or barriers for adopting DHTs in clinical practice. Methods An author-developed web-based questionnaire was distributed to physiotherapists in all Norwegian municipalities in March 2023. The questionnaire included items regarding use of technologies, attitudes, suitability, and factors influencing adoption of DHT. Suitability and agreement on statements were scored on an 11-point numeric rating scale (0=very unsuitable or strongly disagree, 10=very suitable or strongly agree). Differences across employment sites and users versus nonusers of DHT were analyzed using the χ2 test, Fisher exact test, Student t test, and Mann-Whitney U test. Results Approximately 5000 physiotherapists were invited to participate, of which 6.8% (338) completed the questionnaire. A total of 46.2% (156/338) offered DHTs in their practice, of which 53.2% (83/156) used it on a weekly basis, mostly telephone consultations (105/156, 67.3%). A higher proportion of physiotherapists in private practice offered DHT compared with those employed by municipalities (95/170, 55.9% vs 61/168, 36.3%; P<.001). A majority (272/335, 81.2%) were positive about recommending DHTs to their patients. Suitability of DHTs in physiotherapy was rated an average of 6 (SD 2.1). Apps for smartphones or tablets were rated most suitable (mean rating 6.8, SD 2.4). The most frequently reported advantages were flexibility in how physiotherapy is offered (278/338, 82.3%) and reduced travel time for the patient (235/338, 70%). The highest rated disadvantages were limited scope for physical examination (252/338, 74.6%) and difficulty in building rapport with the patient (227/338, 67.2%). The main facilitators and barriers included a functioning (median rating 10, IQR 8-10) or lack of functioning (median rating 9, IQR 8-10) internet connection, respectively. Lack of training in DHTs was prominent regarding evaluation, diagnosing, and treatment (median rating 0, IQR 0-2), with minor, but significant, differences between nonusers and users (median rating 0, IQR 0-1 vs median rating 1, IQR 0-4); P<.001). Conclusions Physiotherapists in Norwegian primary care treating patients with MSDs are positive about using DHTs, and almost 50% (156/338) have adopted them in clinical practice. Concerns are related to lack of a physical examination and technical aspects. Training in the use of DHTs should be addressed in implementation processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".