Applications of Digital Health Technologies in Knee Osteoarthritis: Narrative Review
Notice bibliographique
Résumé
BACKGROUND: With the increasing adoption of high-speed internet and mobile technologies by older adults, digital health is a promising modality to enhance clinical care for people with knee osteoarthritis (KOA), including those with knee replacement (KR). OBJECTIVE: This study aimed to summarize the current use, cost-effectiveness, and patient and clinician perspectives of digital health for intervention delivery in KOA and KR. METHODS: In this narrative review, search terms such as mobile health, smartphone, mobile application, mobile technology, ehealth, text message, internet, knee osteoarthritis, total knee arthroplasty, and knee replacement were used in the PubMed and Embase databases between October 2018 and February 2021. The search was limited to original articles published in the English language within the past 10 years. In total, 91 studies were included. RESULTS: Digital health technologies such as websites, mobile apps, telephone calls, SMS text messaging, social media, videoconferencing, and custom multi-technology systems have been used to deliver interventions in KOA and KR populations. Overall, there was significant heterogeneity in the types and applications of digital health used in these populations. Digital patient education improved disease-related knowledge, especially when used as an adjunct to traditional methods of patient education for both KOA and KR. Digital health that incorporated person-specific motivational messages, biofeedback, or patient monitoring was more successful at improving physical activity than self-directed digital interventions for both KOA and KR. Many digital exercise interventions were found to be as effective as in-person physical therapy for people with KOA. Many digital exercise interventions for KR incorporated both in-person and web-based treatments (blended format), communication with clinicians, and multi-technology systems and were successful in improving knee range of motion and self-reported symptoms and reducing the length of hospital stays. All digital interventions that incorporated cognitive behavioral therapy or similar psychological interventions showed significant improvements in knee pain, function, and psychological health when compared with no treatment or traditional treatments for both KOA and KR. Although limited in number, studies have indicated that digital health may be cost-effective for these populations, especially when travel costs are considered. Finally, although patients with KOA and KR and clinicians had positive views on digital health, concerns related to privacy and security and concerns related to logistics and training were raised by patients and clinicians, respectively. CONCLUSIONS: For people with KOA and KR, many studies found digital health to be as effective as traditional treatments for patient education, physical activity, and exercise interventions. All digital interventions that incorporated cognitive behavioral therapy or similar psychological treatments were reported to result in significant improvements in patients with KOA and KR when compared with no treatment or traditional treatments. Overall, technologies that were blended and incorporated communication with clinicians, as well as biofeedback or patient monitoring, showed favorable outcomes.
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 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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».