Using a Digital Approach to Improving Mental Health in Adults With Self-reported Psoriasis: An Analysis of Real-world User Data
Bibliographic record
Abstract
Background Visible scales and perceived stigma often lead to feelings of embarrassment, shame, low self-esteem, and self-consciousness among people with psoriasis. Beyond the negative effects of this distress on mental health, some researchers also argue that the inflammatory response caused by psoriasis predisposes patients with psoriasis to mental health conditions like depression. Given that anxiety and depression are linked to higher disease activity and pain in patients with psoriasis, researchers have argued that an important component of any psoriasis management plan should include addressing mental health. However, a shortage of mental health professionals, particularly in low income and rural areas, makes access to mental health care more challenging than many other common referrals—a problem that has been exacerbated in light of the COVID-19 pandemic. Objective This study aims to explore the feasibility of using a digital mental health intervention to help improve subjective well-being and anxiety among adults with self-reported psoriasis. Methods Real-world users who signed up for the digital wellness program, Happify, between January 1, 2017, and June 10, 2021, and who reported having psoriasis during onboarding were included in this analysis. To qualify, users had to complete at least two in-app assessments (which include a proprietary measure of subjective well-being, the Happify Scale, and the Generalized Anxiety Disorder 2 scale to measure anxiety), complete at least 1 Happify activity, complete no more than 3 activities before taking their first assessment, and had to have at least 42 days between their first and last assessment. We examined changes in well-being and anxiety among these participants based on Happify use (recommended vs less than recommended). Results Users who engaged with the program at the recommended level experienced significantly greater improvements in both well-being (P<.001) and anxiety (P=.01). More specifically, users who completed the recommended number of activities improved their well-being scores by 26.8%, whereas users who completed fewer activities improved their well-being scores by only 4.11%. Similarly, users who completed the recommended number of activities improved their anxiety scores by 26.64%, compared to 8.15% among those who engaged below the recommended level. Conclusions These data suggest that a digital mental health intervention can effectively improve both subjective well-being and anxiety among patients with psoriasis when used at the recommended level. Although future research is required to better understand whether this subsequently impacts disease-specific outcomes, such as disease activity and pain, our data suggest that a digital approach may be one method of providing greater access to mental health support for patients with psoriasis, increasing the likelihood that it can be incorporated into their treatment plan. Conflicts of Interest None declared.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".