Usage, Attitudes, Facilitators, and Barriers Toward Digital Health Technologies in Musculoskeletal Care: Survey Among Primary Care Physiotherapists in Norway
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».