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Enregistrement W3009109143 · doi:10.2196/16831

Mobile App to Help People With Chronic Illness Reflect on Their Strengths: Formative Evaluation and Usability Testing

2020· article· en· W3009109143 sur OpenAlexvenueno aff
Ólöf Birna Kristjánsdóttir, Elin Børøsund, Marianne Westeng, Cornelia M. Ruland, Una Stenberg, Heidi A. Zangi, Kurt C. Stange, Jelena Mirković

Notice bibliographique

RevueJMIR Formative Research · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueDiabetes Management and Education
Établissements canadiensnon disponible
Organismes subventionnairesHelse Sør-Øst RHFDiakonhjemmet
Mots-clésUsabilityFormative assessmentEmpowermentHealth carePsychologyMedicineeHealthHealth literacyTest (biology)NursingMedical educationComputer science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Supporting patient engagement and empowerment is increasingly seen as essential in providing person-centered health care to people with chronic illness. Mobile apps helping patients reflect on their concerns as preparation for consultations with their health care providers can have beneficial effects on the consultation quality. However, apps focusing on empowerment and personal strengths are still scarce. OBJECTIVE: This study aimed to (1) develop a mobile app to support patients with rheumatic diseases in reflecting on their strengths in preparation for consultations with health care providers and (2) explore patients' perceived usability of the app in a nonclinical test setting. METHODS: A prototype app was developed based on input from patients and health care providers, as reported in previous studies. The app was designed for use in self-management support settings aiming to promote awareness of strengths and to focus attention on strengths in the patient-health care provider dialogue. The features included in the prototype were as follows: (1) introduction to the topic of strengths, (2) list of examples of strengths to promote reflection and registration of own strengths, (3) summary of registered strengths, (4) value-based goal setting, (5) linking of strengths to goals, (6) summary of all registrations, and (7) options to share summary digitally or as a print version. In this study, the app was refined through a formative evaluation with patients and health care providers recruited from a specialized rheumatology hospital unit. Patients' perceptions of the app's usability were explored in a test setting with self-report measurements and semistructured interviews. The interviews were audiotaped, transcribed, and analyzed with directed content analysis. Data from questionnaires were analyzed with descriptive statistics. RESULTS: Developmental and formative evaluation included 18 patients and 7 health care providers. The evaluation resulted in minor adjustments to the prototype but no major changes in features. The usability testing included 12 patients. All participants found the usability acceptable; the median score on the System Usability Scale was 86.3 (range 70-100). All reported that it was meaningful and relevant to use the app. Out of 12 participants, 9 (75%) reported becoming more aware of their own strengths by using the app; 1 (8%) disagreed and 2 (17%) provided a neutral response. The results on the goal-related feature were mixed, with half of the patients finding it useful to link strengths to concrete goals. A statistically significant positive change from pre- to postintervention was identified on measures of self-efficacy and negative emotions. CONCLUSIONS: In this formative evaluation of a mobile app to promote patients' reflections on their strengths, patients perceived the app as meaningful and supporting awareness. The results suggest the usefulness of building in functionality to support use of strengths and goal attainment. Further studies on efficacy and usability in a clinical setting, including health care providers, are needed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,724
Score d'incertitude au seuil0,454

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,089
Tête enseignante GPT0,435
Écart entre enseignants0,346 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations16
Publié2020
Routes d'admission1
Résumé présentoui

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