Evaluation of Digital Technologies Tailored to Support Young People’s Self-Management of Musculoskeletal Pain: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Digital technologies connect young people with health services and resources that support their self-care. The lack of accessible, reliable digital resources tailored to young people with persistent musculoskeletal pain is a significant gap in the health services in Australia. Recognizing the intense resourcing required to develop and implement effective electronic health (eHealth) interventions, the adaptation of extant, proven digital technologies may improve access to pain care with cost and time efficiencies. OBJECTIVE: This study aimed to test the acceptability and need for adaptation of extant digital technologies, the painHEALTH website and the iCanCope with Pain app, for use by young Australians with musculoskeletal pain. METHODS: A 3-phased, mixed methods evaluation was undertaken from May 2019 to August 2019 in Australia. Young people aged 15 to 25 years with musculoskeletal pain for >3 months were recruited. Phases were sequential: (1) phase 1, participant testing (3 groups, each of n=5) of co-designed website prototypes compared with a control website (painHEALTH), with user tasks mapped to eHealth quality and engagement criteria; (2) phase 2, participants' week-long use of the iCanCope with Pain app with engagement data captured using a real-time analytic platform (daily check-ins for pain, interference, sleep, mood, physical activity, and energy levels; goal setting; and accessing resources); and (3) phase 3, semistructured interviews were conducted to gain insights into participants' experiences of using these digital technologies. RESULTS: Fifteen young people (12/15, 80% female; mean age 20.5 [SD 3.3] years; range 15-25 years) participated in all 3 phases. The phase 1 aggregated group data informed the recommendations used to guide 3 rapid cycles of prototype iteration. Adaptations included optimizing navigation, improving usability (functionality), and enhancing content to promote user engagement and acceptability. In phase 2, all participants checked in, with the highest frequency of full check-ins attributed to pain intensity (183/183, 100.0%), pain interference (175/183, 95.6%), and mood (152/183, 83.1%), respectively. Individual variability was evident for monitoring progress with the highest frequency of history views for pain intensity (51/183, 32.3%), followed by pain interference (24/183, 15.2%). For the goals set feature, 87% (13/15) of participants set a total of 42 goals covering 5 areas, most frequently for activity (35/42, 83%). For phase 3, metasynthesis of qualitative data highlighted that these digital tools were perceived as youth-focused and acceptable. A total of 4 metathemes emerged: (1) importance of user-centered design to leverage user engagement; (2) website design (features) promoting user acceptability and engagement; (3) app functionality supporting self-management; and (4) the role of wider promotion, health professional digital prescriptions, and strategies to ensure longer-term engagement. CONCLUSIONS: Leveraging extant digital tools, with appropriate user-informed adaptations, can help to build capacity tailored to support young people's self-management of musculoskeletal pain.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,059 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
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