Daily Functioning of Veterans With Type 2 Diabetes: Protocol for an Ambulatory Assessment Study
Notice bibliographique
Résumé
BACKGROUND: Diabetes impacts nearly 25% of veterans. Many veterans do not engage in recommended physical activity and other diabetes self-management behaviors. Type 2 diabetes is generally asymptomatic; as such, the long-term consequences of inadequate self-management and benefits of consistent self-management are not salient in the short term. Furthermore, self-management behaviors typically take place outside of medical visits; however, self-management-related factors are only assessed during medical visits, likely missing large amounts of variability. Thus, ambulatory assessment methods such as ecological momentary assessment (EMA), accelerometry, and continuous glucose monitoring are needed to understand the dynamics of daily self-management and identify potential intervention targets. OBJECTIVE: The overarching goal of this study is to understand daily, time-varying factors (comorbid affective symptoms and social context) that influence physical activity, diabetes self-management, glycemic management, daily functioning, and quality of life in participants' natural environments. METHODS: We are recruiting veterans with type 2 diabetes (target N=100). Participants are required to complete a battery of baseline assessments related to mental health, psychosocial factors, and self-management behaviors. Participants then receive 5 momentary EMA surveys and 1 daily EMA survey per day, in which veterans report comorbid affective symptoms (mood, stress, and pain), social support, social interactions, physical activity, and other self-management behaviors. Momentary surveys are delivered randomly during daily preprogrammed intervals over a 14-day sampling period. Accelerometry and continuous glucose monitoring are also used to assess physical activity and blood glucose, respectively. The first 6 participants also completed interviews assessing their experience in the study and barriers to participation. These test participants informed modifications to the protocol for the remaining participants. RESULTS: The project received funding in April of 2023. Enrollment began in March of 2023 and is planned to be completed in April 2025. Among the 6 test participants, the overall EMA response rate was 87% (range 74%-95%). The response rate for the EMA survey including daily items (67%, range 21%-93%) was lower than the earlier shorter EMA surveys (89%, range 81%-96%). The mean rate of valid accelerometer wear of at least 20 hours per day was 93% (SD 11%), and continuous glucose monitoring data were available for 91% (SD 17%) of days on average. Participants reported few barriers to completing EMA surveys but noted the random timing of questions made it difficult to plan around, and the end-of-day survey was long. Two participants reported survey items reminded or motivated them to engage in diabetes self-management behaviors. CONCLUSIONS: Assessment tools developed from this study can inform clinical decision-making by considering barriers to self-management that occur in daily life. Clinical applications include tailored, adaptive technology-supported interventions to improve self-management that provide the right type and amount of support at the right time by adapting to an individual's changing internal and contextual state. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53874.
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 | 0,016 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,007 |
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 ».