Diabetes and Depression Text Messaging Intervention (DIAMANTE)
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|ClinicalTrials.gov Identifier: NCT03490253|
Recruitment Status : Not yet recruiting
First Posted : April 6, 2018
Last Update Posted : October 8, 2019
|Condition or disease||Intervention/treatment||Phase|
|Diabetes Depression Physical Activity||Behavioral: DIAMANTE Adaptive Behavioral: DIAMANTE Static||Not Applicable|
We utilized user-centered design (UCD) methods to iteratively develop the DIAMANTE content and text messaging system through three iterative phases of UCD with ten patients each (total n=30). The first phase consisted of 1.5-hour individual semi-structured interviews. Findings from phase 1 were used to inform content and information delivery decisions of the final intervention, including selecting the thematic message categories and the design. In the second phase, patients tested out an early prototype of the mobile application through usability testing. Patients tested the final DIAMANTE intervention including thematic message content and the application in the third, final UCD phase, in order to address any user-related issues prior to launching the randomized control trial.
In the DIAMANTE Randomized Controlled Trial, we aim to examine the effect of a smartphone app that uses reinforcement learning to predict the most effective messages for increasing physical activity. We will recruit 276 low-income minority patients with depression and diabetes within he San Francisco Health Network. We will compare this intervention to static messages with health management content, and a control group that only receives a weekly mood message.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||276 participants|
|Intervention Model:||Parallel Assignment|
|Intervention Model Description:||This study is a randomized, controlled, one-center superiority trial with three-arm groups and a primary endpoint of increase in daily steps during a 6 month intervention by a smartphone app. Randomization will be performed as block randomization with a 1:1:1 allocation. Patients will be automatically randomized into groups trough our secure server during onboarding of the app. Patients need to be informed of the nature and frequency of the messages they will be receiving and discuss this with investigators during the course of the study. Therefore, investigators and patients will not be blinded. We used the SPIRIT checklist when writing our report.|
|Masking:||Single (Outcomes Assessor)|
|Masking Description:||The investigators and those completing the statistical analyses will not be aware of the true study assignments|
|Official Title:||Improving Diabetes and Depression Self-management Via Adaptive Mobile Messaging|
|Estimated Study Start Date :||December 2019|
|Estimated Primary Completion Date :||March 2021|
|Estimated Study Completion Date :||September 2021|
Active Comparator: Static Messaging
We will send patients a total of seven messages per week (one per day) at 10.00 am. For the physical health management messages we use messages from established topics in the Diabetes Prevention Program(23) content with the emphasis on physical activity and stress management. The final message, on the seventh day will ask patients to rate their mood on a scale from 1 to 9. Physical activity (step-count/day) will be passively monitored via the app on their smartphone.
Behavioral: DIAMANTE Static
The static intervention group receives health information text-messages, typical of existing text-messaging interventions for diabetes and depression.
Other Name: Static Mobile Messaging
Experimental: Adaptive Messaging
Patients in the adaptive messaging arm will receive the daily messages of the static arm, and additionally receive daily messages within different categories of feedback and motivational messages that are chosen using a reinforcement learning (RL) algorithm. Physical activity (step-count/day) will be actively monitored via the app on their smartphone.
Behavioral: DIAMANTE Adaptive
In a three arm randomized controlled trial we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen by a reinforcement learning algorithm.
Other Name: Adaptive Mobile Messaging
No Intervention: Control Condition
Control patients will only install the app on their phone and will not receive any feedback messages. They will receive one message a week, on a fixed day, asking them to assess their mood in the previous week on a scale of 1 to 9. The message will be sent daily at 10:00 am. Non-responders will receive reminders to submit their mood self-assessments in two hour intervals.
- Physical Activity [ Time Frame: 6 months ]Our primary outcome, change in daily step counts, will be passively collected by a mobile phone application during the time that patients remain in the intervention.
- Hemoglobin A1c [ Time Frame: 6 months ]We will derive HbA1c, the average plasma glucose over the previous eight to 12 weeks, recommended as a means to diagnose diabetes (20), from patients' electronic health records (EHR). We will use the most recent, available measurement from a maximum of 12 months before participating in the study. After 6 months, we will again assess the most recent HbA1c (pulling from patients EHR), ensuring that at least 3 months elapsed between baseline and follow-up HbA1c levels.
- Patient Health Questionnaire-8 (PHQ-8) [ Time Frame: 6 months ]We will compare the self-reported PHQ-8 from medical records at baseline, intervention completion, and at the 6 month follow-up.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03490253
|Contact: Courtney Lyles, PhD||(628) firstname.lastname@example.org|
|Contact: Faviola Garcia, BA||(628) email@example.com|
|Principal Investigator:||Courtney Lyles, PhD||University of California, San Francisco|
|Principal Investigator:||Adrian Aguilera, PhD||UC Berkeley|