eHealth: Towards improving self-management of acute pain in older adults following a fracture
Notice bibliographique
Résumé
Pain is often poorly managed both in-hospital and post-hospital discharge and is associated with harmful outcomes such as increased falls, loss of autonomy and low quality of life and can, in certain instances, evolve into chronic pain. Mobile applications (apps) downloadable on devices such as smartphones and tablets can offer targeted interactive interventions to support patients in acute pain self-management following hospital discharge. However, little is known about the availability and quality of mobile apps for acute pain management in older adults, and the current levels of technology adoption and electronic health (eHealth) literacy in a population of older adults seen in orthopedic clinics. Furthermore, it is important to seek the voice of clinicians when developing a mobile app so that the content is evidence-based, credible and of high value. In this scholarly work, we sought to 1) identify mobile apps currently available for self-management of acute pain and characterize their features (content and functionalities), 2) identify the current level of technology adoption and eHealth literacy among older adults who recently suffered a fracture, to determine if use of mobile digital devices to optimize care interventions would be feasible, acceptable and 3) identify what clinicians believe are the most important content or functionalities to include in a mobile application for self-management of acute pain in older adults following a recent skeletal fracture. Following an environmental scan, where we were unable to identify high quality mobile apps for the self-management of acute pain, we conducted two surveys. In the first, we invited adults ≥50 years with a recent fracture to complete a self-administered survey composed of 21 closed-ended questions, including the eHealth literacy scale (eHEALS). A total of 401 participants completed the survey (women: 64%; ≥65 years old: 59%). Most respondents (81%) owned at least one mobile device: smartphone (49%), tablet (45%). The majority (65%) of older adults participating in this survey used the Internet in the 6 months prior to the survey (50-64 years: 84%; 65-74 years: 76%; ≥75 years: 61%) and approximately 69% of those who used the Internet had high eHealth literacy (eHEALS ≥26). Among adults ≥75 years who reported owning a smartphone and/or tablet, 60% had recently used the Internet and 64% indicated being interested in using technology to improve their health. Although the eHEALS scores and technology adoption in the ≥75 years group were significantly lower compared to younger age groups, our results do support the development of mobile applications for the management of acute pain in this patient population with recent fractures. In the second study, we surveyed clinicians across Canada with expertise in osteoporosis, fractures, rehabilitation and pain management using a snowball sampling method. The survey constituted of one question sent through email asking for recommendations for the most important content or functionalities to include in a mobile app for the self-management of acute pain following a recent fracture. Forty-two clinicians responded to our survey (response rate 1st wave 60%; 2nd wave 60%) and 230 references were extracted. Appropriate medical information, pain management modalities, pain self-management strategies were the most cited content recommendations while the primary app functions highlighted were the ability to receive direct feedback from the app (interactiveness), pain self-monitoring and access to healthcare providers. This work will support the development of a mobile app that will meet evidence-based standards and will support self-management of acute pain following a skeletal fracture in older individuals. This will improve quality of life by reducing pain levels while engaging in activities of daily living and promote healthy lifestyle behaviors to reduce the risk of subsequent injurious falls
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».