Barriers to and Facilitators of Engaging With and Adhering to Guided Internet-Based Interventions for Depression Prevention and Reduction of Pain-Related Disability in Green Professions: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Internet-based interventions (IBIs) are effective for the prevention and treatment of mental disorders and are valuable additions for improving routine care. However, the uptake of and adherence to IBIs are often limited. To increase the actual use of IBIs, it is important to identify factors for engaging with and adhering to IBIs. OBJECTIVE: We qualitatively evaluated barriers and facilitators regarding a portfolio of guided IBIs in green professions (farmers, gardeners, and foresters). METHODS: Interview participants were selected from 2 randomized controlled trials for either the prevention of depression (Prevention of Depression in Agriculturists [PROD-A]) or the reduction of pain interference (Preventive Acceptance and Commitment Therapy for Chronic Pain in Agriculturists [PACT-A]) in green professions. The intervention group in PROD-A (N=180) participated in an IBI program, receiving access to 1 of 6 symptom-tailored IBIs. The intervention group in PACT-A (N=44) received access to an IBI for chronic pain. Overall, 41 semistructured qualitative interviews were conducted and transcribed verbatim. Barriers and facilitators were identified via inductive qualitative content analysis, with 2 independent coders reaching almost perfect intercoder reliability (Cohen κ=0.92). A quantitative follow-up survey (30/41, 73%) was conducted to validate the results. Subgroup analyses were performed based on intervention characteristics. RESULTS: We identified 42 barriers and 26 facilitators, which we assigned to 4 superordinate categories related to the intervention (20 barriers; 17 facilitators), work (4 barriers; 1 facilitator), individual (13 barriers; 8 facilitators), and technical (5 barriers; 0 facilitators) aspects. Key barriers (identified by at least 50% of the interviewees) were time-consuming work life (29/40, 73%) and time-consuming private life (23/40, 58%). Similarly, the most frequently identified facilitators included presence of motivation, curiosity, interest and perseverance (30/40, 75%), flexible time management at work (25/40, 63%), and support from family and friends (20/40, 50%). Although agreement with barriers in the quantitative follow-up survey was rather low (mean 24%, SD 11%), agreement with facilitators was substantially higher (mean 80%, SD 13%). Differences in agreement rates were found particularly between intervention completers and noncompleters. Completers agreed significantly more often that perceived IBI success; being motivated, curious, interested, and perseverant; and having a persisting level of psychological strain have been facilitating. Noncompleters agreed more often with experiencing the e-coach contact as insufficient and technical problems as hindering for intervention completion. CONCLUSIONS: Based on these results, strategies such as customization of modules for more flexible and adaptive use; video chat options with the e-coach; options to facilitate social support by family, friends, or other participants; or using prompts to facilitate training completion can be derived. These approaches could be evaluated in further quantitative research designs in terms of their potential to enhance intervention use in this occupational group. TRIAL REGISTRATION: German Clinical Trials Register DRKS00014000, https://tinyurl.com/3bukfr48; German Clinical Trials Register DRKS0001461, https://tinyurl.com/ebsn4sns.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".