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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".