A German Smartphone-Based Self-management Tool for Psoriasis: Community-Driven Development and Evaluation of Quality-of-Life Effects
Bibliographic record
Abstract
Background Psoriasis is a chronic disease characterized by inflammation, increased scaling, itching, and other symptoms. Psoriasis is not contagious, but patients have often felt shunned. Therefore, in addition to psoriasis symptoms, stress, anxiety, and depression can also affect quality of life (QoL). Surveys show that only a quarter of patients are satisfied with the success of their therapy. However, in addition to medical therapy, self-management can also make it easier to deal with chronic diseases like psoriasis. Objective The aim of this project was to develop a smartphone-based self-management tool (SMT) specifically for patients with psoriasis using a community-driven process. The impact of the SMT on QoL as well as its acceptance and usability were evaluated. Methods In collaboration with an internet-based self-help community, 2 user surveys were conducted to determine the requirements for a smartphone-based SMT. The surveys consisted of semistructured questionnaires asking for desired features in an SMT for psoriasis. A pilot study was conducted to evaluate QoL, acceptance, and usability. Community users were recruited to use the app for 21 days and complete the Dermatology Life Quality Index (DLQI) questionnaire at the beginning (T0) and end (T1). Afterward, participants were asked to complete another questionnaire on usability and ease of use. Results SMT requirements were collected from 97 members of an internet-based community. The SMT was built as a progressive web app that communicates with a server back end and an Angular web app for content management. The app was used by 15 participants who also provided qualitative feedback, and 10 participants answered all questionnaires. The average DLQI score was 7.1 (SD 6.2) at T0 and 6.9 (SD 6.6) at T1. The minimal required sample size of 27 was not reached. Conclusions The high degree of community participation in the development process and the responses during the requirement engineering process indicated that there is a general need for an independently developed SMT for patients with psoriasis. However, the feedback received after app use shows that the SMT does not meet the needs of the community. It can be concluded that a more customizable app is needed. The focus and needs of the users were very heterogeneous. Similar developments and research could benefit from the findings of this project.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".