Exploring Laypersons’ Experiences With a Mobile Symptom Checker App as an Interface Between eHealth Literacy, Health Literacy, and Health-Related Behavior: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Symptom checkers aim to help users recognize medical symptoms and recommend actions. However, they are not yet reliable for self-triage or diagnostics. Health literacy plays a role in their use, but the process from symptom recognition to health care consultation remains unclear. OBJECTIVE: This qualitative observatory study explored how laypersons use symptom checkers, focusing on the process of use, entry points and outcomes, and the role of health literacy. Laypersons are defined as individuals who are neither medical professionals nor developers of such apps. Three research questions were addressed: (1) How do such users describe the process of using symptom checkers? (2) What are entry points and possible outcomes of symptom checker app use? (3) How are health literacy and eHealth literacy expressed during the use of symptom checker apps? METHODS: As part of the Ethical, Legal, and Social Implications of Symptom Checker Apps in Primary Health Care project, 15 laypersons (n=9, 60% female and n=6, 40% male; mean age 30.7, SD 13.6 years) were interviewed about their experiences with the symptom checker Ada. The interviews were analyzed using an integrative approach combining social positioning, agency, and the Rubicon model as a heuristic framework. RESULTS: App use follows a cyclic process comprising 4 steps: motivation (influenced by biography and context), intention formation (assigning a purpose), intention implementation (recruiting resources), and evaluation (transforming interactions into health-related insights). Biographical, social, and contextual factors shape process initiation. Users use symptom checkers for 3 main purposes: understanding their condition, receiving recommendations for action, and documenting or communicating health-related information. Each purpose requires specific planning and integration into health-related behaviors drawing on personal, social, and technological resources. Evaluation depends on contextual factors, app outputs, and the outcomes of users' health-related actions. Users assess whether the app aligns with their expectations, condition severity, and previous experiences, with health literacy playing a critical role in validation processes. CONCLUSIONS: Symptom checker use is a complex, cyclic process shaped by context, biography, and health literacy. Users are motivated by health concerns influenced by personal, social, and contextual factors, with trust and attitudes impacting initial engagement. Intention formation reflects a balance between user skills and context, where app outputs inform decisions but may not always lead to action, especially in ambiguous situations. Users rely on personal resources and social networks to integrate app use into health-related behaviors, highlighting the limitations of symptom checkers in providing social or empathetic support. Symptom checkers have the potential to serve as an interface between users and health care, but future development must address the complexity of their use to unlock this potential. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/34026.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".