MedElevator Войти в исследования
← Поиск публикаций

DOI 10.2196/83144

Медицинская библиотека · JMIR formative research · 2026

Developing a Customizable Texting Intervention for Diabetes Self-Management: Participatory Design Approach.

Robinson Stephanie A, Shell Popy, Am Linda, Bilodeau Courtney L, Gordon Howard S, Uphold Constance R, Vimalananda Varsha G, Cutrona Sarah L, Hogan Timothy P, Smith Bridget, Shimada Stephanie L

СтатьяПоддержка правительства СШАПеревод в очереди

Аннотация

BACKGROUND

Uncontrolled diabetes contributes to serious comorbidities and mortality. Effective self-management can improve outcomes, though barriers such as limited education and support often prevent patients from engaging in such behaviors. Automated texting systems show promise to deliver diabetes self-management education as they are accessible and scalable. Furthermore, customizing these systems may further enhance patient engagement compared to standard, one-size-fits-all approaches. However, such customization is more resource-intensive, and it remains unclear whether the added effort meaningfully enhances diabetes self-management and outcomes.

OBJECTIVE

This study aimed to describe the development of 2 versions of an automated texting system intervention for diabetes self-management: (1) a standard, education-only intervention (Diabetes Self-Management Support; DSMS) and (2) an interactive, customizable intervention (Diabetes Self-Management Support + Interactive and Customizable Messages; DSMS+).

METHODS

Two versions of an automated texting system intervention were developed using a participatory design approach that incorporated input from veterans and expert clinicians. Message content was refined through feedback from a multidisciplinary team, veteran coinvestigators, national surveys, interviews, clinical expert panel reviews, and beta testing. Surveys were mailed to 1000 potential participants, oversampling rural, low-income, minority, and female participants. Respondents rated message relevance and provided preferences for content, timing, and frequency. Interviews provided customization preferences. A clinical expert panel reviewed all messages for safety and appropriateness. Beta testing informed final refinements.

RESULTS

Ninety-two surveys were completed (9.2% response rate). Respondents rated 62% of the messages as personally relevant and 61% confidence-enhancing. Interviews with 23 respondents revealed a preference for 1-2 texts per day, emphasizing topics such as healthy eating and weight management. The clinical expert panel reviewed 536 messages, flagging 81 for revision. Beta testing confirmed feasibility and informed refinements to clarity and timing. The 2 resulting interventions were built in the US Department of Veterans Affairs' automated texting system, Annie.

CONCLUSIONS

Two text messaging interventions, DSMS and DSMS+, were developed to support diabetes self-management among US veterans. DSMS delivers standard educational content, while DSMS+ incorporates interactive features and personalization. The subsequent clinical trial will assess whether customization enhances engagement and improves diabetes outcomes, providing insights into the potential of tailored mobile health interventions for chronic disease management.

Для цитирования: Robinson Stephanie A, Shell Popy, Am Linda, Bilodeau Courtney L, Gordon Howard S, Uphold Constance R, Vimalananda Varsha G, Cutrona Sarah L, Hogan Timothy P, Smith Bridget, Shimada Stephanie L. Developing a Customizable Texting Intervention for Diabetes Self-Management: Participatory Design Approach.. JMIR formative research. 2026. DOI: 10.2196/83144.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.