Asthma and Technology in Emerging African American Adults (The ATHENA Project): Protocol for a Trial Using the Multiphase Optimization Strategy Framework
Notice bibliographique
Résumé
BACKGROUND: Asthma causes substantial morbidity and mortality in the United States, particularly among African American emerging adults (AAEAs; aged 18-30 years), but very few asthma programs have targeted this population. Interventions that provide education and address underlying motivation for managing asthma may be the most effective. However, intensive face-to-face interventions are often difficult to implement in this population. OBJECTIVE: The purpose of this study is to develop an effective mobile asthma management intervention to improve control among AAEAs. METHODS: We will assess the ability of multiple technologic components to assist and improve traditional asthma education. The first component is the Motivational Enhancement System for asthma management. It is a mobile 4-session intervention using supported self-regulation and motivational interviewing. Personalized content is based on each participant's activity level, daily experiences, and goals. The second component is supportive accountability. It is administered by asthma nurses using targeted mobile support (Skype/voice calls) to provide education, promote self-efficacy, and overcome barriers through a motivational interviewing-based framework. The third component is SMS text messaging. It provides reminders for asthma education, medication adherence, and physical activity. The fourth component is physical activity tracking. It uses wearable technology to help meet user-defined physical activity goals. Using a multiphase optimization strategy (MOST) framework, we will test intervention components and combinations of components to identify the most effective mobile intervention. The MOST framework is an innovative, and cost- and time-effective framework that uses engineering principles to produce effective behavioral interventions. We will conduct a component selection experiment using a factorial research design to build an intervention that has been optimized for maximum efficacy, using a clinically significant improvement in asthma. Participants (N=180) will be randomized to 1 of 6 intervention arms. Participants will be recruited from multiple sites of the American Lung Association-Airway Clinical Research Centers network and ambulatory care clinics at the Detroit Medical Center. Data collections will occur at baseline, and 3, 6, and 12 months. RESULTS: At study completion, we will have an empirically supported optimized mobile asthma management intervention to improve asthma control for AAEAs. We hypothesize that postintervention (3, 6, and 12 months), participants with uncontrolled asthma will show a clinically significant improvement in asthma control. We also hypothesize that improvements in asthma management behaviors (including physical activity), quality of life, symptoms, adherence, and exacerbation (secondary outcomes) will be observed. CONCLUSIONS: AAEAs are disproportionately impacted by asthma, but have been underrepresented in research. Mobile asthma management interventions may help improve asthma control and allow people to live healthier lives. During this project, we will use an innovative strategy to develop an optimized mobile asthma management intervention using the most effective combination of nurse-delivered asthma education, a smartphone app, and text messaging. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37946.
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 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 | 0,036 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,013 |
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 ».