User-Centered Development of a Mobile App to Assess the Quality of Life of Patients With Cancer: Iterative Investigation and Usability Testing
Bibliographic record
Abstract
BACKGROUND: The treatment for cancer can have a negative impact not only on physical well-being but also on mental health and the quality of life (QoL). Health apps enable the monitoring of different parameters, but to date, there are only few that support patients with cancer and none that focuses on the assessment of QoL. Furthermore, patients as stakeholders are often only integrated at the late stage of the development process, if at all. OBJECTIVE: The aim of this research was to develop and evaluate a smartphone app (Lion-App) to enable patients with cancer to autonomously measure the QoL with an iterative, user-centered approach. METHODS: Patients with cancer were involved in a 3-stage process from conceptualization to the point when the app was available on the tester's private device. First, focus groups with members (N=21) of cancer support groups were conducted to understand their expectations and needs. Thereafter, individual tests were performed. After developing a prototype that incorporated findings from the focus groups, a second test cycle was conducted, followed by a beta test lasting 2 months. In our app, the QoL can be assessed via a patient diary and an integrated questionnaire. Through all stages, usability was evaluated using the modular extended version of the User Experience Questionnaire (UEQ+), including the calculation of a key performance indicator (KPI). If possible, the impact of sex on the results was evaluated. As part of the beta test, usage rates as well as age-dependent differences were also assessed. RESULTS: A total of 21 participants took part in the initial 3 focus groups. In the subsequent usability testing (N=18), 17 (94%) participants rated their impression through the UEQ+, with a mean KPI of 2.12 (SD 0.64, range: -3 to 3). In the second usability test (N=14), the mean KPI increased to 2.28 (SD=0.49). In the beta test, the usage rate of 19 participants was evaluated, of whom 14 (74%) also answered the UEQ+ (mean KPI 1.78, SD 0.84). An influence of age on the number of questionnaire responses in Lion-App was observed, with a decrease in responses with increasing age (P=.02). Sex-dependent analyses were only possible for the first usability test and the beta test. The main adjustments based on user feedback were a restructuring of the diary as well as integration of a shorter questionnaire to assess the QoL. CONCLUSIONS: The iterative, user-centered approach for development and usability testing resulted in positive evaluations of Lion-App. Our app was rated as suitable for everyday use to monitor the QoL of patients with cancer. Initial results indicated that the sex and age of participants seem to play only a minor role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".