Gamification in Rehabilitation of Patients With Musculoskeletal Diseases of the Shoulder: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Gamification has become increasingly important both in research and in practice. Particularly in long-term care processes, such as rehabilitation, playful concepts are gaining in importance to increase motivation and adherence. In addition to neurological diseases, this also affects the treatment of patients with musculoskeletal diseases such as shoulder disorders. Although it would be important to assist patients during more than one rehabilitation phase, it is hypothesized that existing systems only support a single phase. It is also unclear which game design elements are currently used in this context and how they are combined to achieve optimal positive effects on motivation. OBJECTIVE: This scoping review aims to identify and analyze information and communication technologies that use game design elements to support the rehabilitation processes of patients with musculoskeletal diseases of the shoulder. The state of the art with regard to fields of application, game design elements, and motivation concepts will be determined. METHODS: We conducted a scoping review to identify relevant application systems. The search was performed in 3 literature databases: PubMed, IEEE Xplore, and Scopus. Following the PICO (population, intervention, comparison, outcome) framework, keywords and Medical Subject Headings for shoulder, rehabilitation, and gamification were derived to define a suitable search term. Two independent reviewers, a physical therapist and a medical informatician, completed the search as specified by the search strategy. There was no restriction on year of publication. Data synthesis was done by deductive-inductive coding based on qualitative content analysis. RESULTS: A total of 1994 articles were screened; 31 articles in English, published between 2006 and 2019, were included. Within, 27 application systems that support patients with musculoskeletal diseases of the shoulder in exercising, usually at home but also in inpatient or outpatient rehabilitation clinics, were described. Only 2 application systems carried out monitoring of adherence. Almost all were based on in-house developed software. The most frequently used game components were points, tasks, and avatars. More complex game components, such as collections and teams, were rarely used. When selecting game components, patient-specific characteristics, such as age and gender, were only considered in 2 application systems. Most were described as motivating, though an evaluation of motivational effects was usually not conducted. CONCLUSIONS: There are only a few application systems supporting patients with musculoskeletal diseases of the shoulder in rehabilitation by using game design elements. Almost all application systems are exergames for supporting self-exercising. Application systems for multiple rehabilitation phases seem to be nonexistent. It is also evident that only a few complex game design elements are used. Patient-specific characteristic are generally neglected when selecting and implementing game components. Consequently, a holistic approach to enhance adherence to rehabilitation is required supporting patients during the entire rehabilitation process by providing motivational game design elements based on patient-specific characteristics.
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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 | 0,010 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».