Exploring Laypersons’ Experiences With a Mobile Symptom Checker App as an Interface Between eHealth Literacy, Health Literacy, and Health-Related Behavior: Qualitative Interview Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».