The Use of Smartphone Serious Gaming Apps in the Treatment of Substance Use Disorders: Observational Study on Feasibility and Acceptability
Notice bibliographique
Résumé
Background Addiction is a worldwide problem with major health complications. Despite intensive treatment, relapse rates remain high. The prevalence of cognitive impairment is high in patients with substance use disorders (SUDs) and is associated with treatment dropout and relapse. Evidence indicates that cognitive function training in persons with SUDs may support treatment. Therefore, the use of web-based tools to test and train cognitive functions is of increasing interest. Objective The goal of this study was to determine the feasibility and acceptability of a serious gaming smartphone app to test and train cognitive functions in addition to the treatment of SUDs. Methods A prospective observational study was conducted with 229 patients seeking addiction treatment. The patients were offered 2 smartphone apps in addition to regular care: MyCognition Quotient (MyCQ) assessed cognitive functions and AquaSnap trained these functions. The feasibility was determined based on acceptance rates. The acceptability of the smartphone apps was qualitatively analyzed based on the answers to a questionnaire. Patient characteristics were compared between patients who played and did not play smartphone games. Explorative correlation analyses were performed between the playing time and cognitive assessment scores. Results Of the 229 patients who were offered the apps, 110 completed the MyCQ assessment, and 59 started playing AquaSnap, yielding acceptance rates of 48.0% and 25.8%, respectively. The group that completed the MyCQ assessment was significantly more educated than the group that did not download the apps (χ22=7.3; P=.03). The education level did not differ significantly between the group that played AquaSnap and the group that did not (P=.06). There were relatively more women in the AquaSnap playing group than in the nonplaying group (χ21=6.5; P=.01). The groups did not differ in terms of age, substance use, treatment setting, mood, or quality of life. With respect to acceptability, 83% (38/46) of the patients who filled out the questionnaire enjoyed taking the MyCQ measurement, whereas 41% (14/34) enjoyed playing the AquaSnap game. Furthermore, 76% (35/46) and 68% (23/34) rated the apps MyCQ and AquaSnap, respectively, as easy. More playing minutes was associated with decreased working memory reaction time and executive functioning accuracy. Conclusions Our study showed that the use of a smartphone app for cognitive assessment in patients with SUDs who are interested and highly educated is feasible and acceptable for the subgroup that was asked to fill out a perception questionnaire. However, the use of a smartphone app for cognitive training was less feasible for this group of patients. Improvement of the training application and enhancement of the motivation of clients are needed. Despite these limitations, the present results provide support for future research investigating the use of smartphone apps for cognitive assessment and training in relation to the treatment of SUDs.
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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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».