User Experiences Among Patients and Health Care Professionals Who Participated in a Randomized Controlled Trial of E-nergEYEze, a Vision-Specific eHealth Intervention to Reduce Fatigue in Adults With Visual Impairment: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Fatigue is a common symptom occurring in individuals with visual impairment (VI). Feeling fatigued has a strong impact on an individual's well-being, with profound consequences. Cognitive and emotional functioning, social roles, and participation are negatively affected in severely fatigued individuals with VI. Therefore, we developed E-nergEYEze, a blended vision-specific eHealth intervention based on cognitive behavioral therapy and self-management to reduce fatigue severity in adults with VI. OBJECTIVE: We aimed to report the experience of patients and professionals with E-nergEYEze. To complement cost-effectiveness outcomes, the user experiences from both perspectives were considered relevant for a better understanding of the intervention uptake. METHODS: E-nergEYEze was studied in a randomized controlled trial. User experiences of participants with VI and severe fatigue (51/98, 52%; median age 58.0, IQR 53.0-65.0 years; female participants: 32/51, 63%), who were randomized to the intervention group, and professionals (n=11), who provided blended support, were evaluated. The Dutch Mental Health Care Thermometer questionnaire and a therapist evaluation were used and analyzed using mixed methods. A focus group meeting with social workers (4/7, 57%), a computer trainer (1/7, 14%), and psychologists (2/7, 29%) was held for more in-depth information. The eHealth platform provided data on user engagement from both perspectives. RESULTS: E-nergEYEze was completed by 63% (32/51) of patients for more than 80% of the module steps. Overall, results on user engagement showed that a median 89% (IQR 45%-100%) of all assigned module steps were completed, with all modules being completed by at least 50% (37/51) of the patients. Completion of the intervention was related to the presence of digital proficiency; having the appropriate expectations; content that matches personal preferences and life context; and the absence of impeding personal circumstances, mental health issues, or other concurrent rehabilitation programs. The intervention was given a median grade of 7.0 out of 10.0 (IQR 6.0-8.0), and 87% (39/45) of the patients reported that they would recommend E-nergEYEze to others. However, improvements in the frequency and quality of guidance were considered highly relevant. Professionals reported that E-nergEYEze required patients' self-efficacy, motivation, and digital skills; therefore, preselection was seen as essential. Professionals' affinity with eHealth was considered important to provide appropriate remote support. CONCLUSIONS: eHealth provides treatment opportunities for individuals with VI for which guidance is considered highly relevant. During participation in E-nergEYEze, patients were engaged, internalized personally relevant topics, and made use of the benefits of eHealth. More attention to the suitability of patients and training of professionals for providing remote support is considered essential. These user experiences underlined the potential of E-nergEYEze to reduce fatigue severity in adults with VI and provided valuable insights to learn from and optimize E-nergEYEze. TRIAL REGISTRATION: International Clinical Trials Registry Platform (ICTRP) NL7764; https://tinyurl.com/32b3xt74.
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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 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».