Dynamically Tailored Behavioral Interventions in Diabetes
Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial (RCT)
調査の概要
詳細な説明
One of the main difficulties in managing diabetes is that each affected individual requires personally tailored combination of diet, exercise, and medication to effectively control their blood sugar. Rather than strictly following a doctor's prescription, individuals need to carefully examine their lifestyle choices and their impact on their health. Independent learning, experimentation and problem solving become of great importance. However, they can be challenging for individuals with diabetes. In this project, the investigators will refine and evaluate a novel intervention for diabetes self-management that uses computational analysis of self-monitoring data to help individuals with type 2 diabetes identify what daily activities, including consumption of meals, physical activity, and sleep, have impact on blood glucose levels, and suggest modifications to these daily activities to improve blood glucose levels.
Growing evidence highlights significant differences in glycemic function and cultural, social, and economical circumstances of individuals with type 2 diabetes (T2DM) that impact their self-management. Precision medicine strives to personalize medical treatment to an individual's genetic makeup, computationally discovered clinical phenotypes and lifestyle. Studies showed the benefits of tailoring not only medical treatment, but also behavioral interventions. Yet, currently, personalization of self-management in T2DM requires each individual to engage in discovery, reflection, and problem-solving-critical but cognitively demanding activities-or to rely on their healthcare providers. Both of these may present considerable barriers to individuals from medically under-served low income communities. Mobile health (mHealth) solutions in T2DM bring promise of reaching wider populations in need of self-management; however, few such solutions provide assistance with personalizing self-management behaviors. Ongoing efforts on personalizing behavioral interventions outside of T2DM focus on tailoring behavior modification techniques to individuals' psycho-social characteristics, such as self-efficacy ), and tailoring delivery of intervention to individuals' context rather than on personalizing self-management strategies.
The ongoing focus of this research is on developing informatics interventions for diabetes self-management, with a specific focus on discovery with self-monitoring data and on problem-solving for improving glycemic control. In the proposed research the investigators introduce T2.coach, an mHealth intervention that uses computational analysis of self-monitoring data to identify behavioral patterns associated with poor glycemic control and formulate personalized behavioral goals for changing problematic behaviors. This study will evaluate T2.coach's efficacy in a two-arm RCT with stratified randomization conducted with Clinical Directors Network (CDN), a well-recognized primary care practice-based research network (PBRN) of Federally Qualified Health Centers (FQHCs), and Agency for Healthcare Research and Quality (AHRQ)-designated Center of Excellence (P30) for Practice-based Research and Learning.
研究の種類
入学 (予想される)
段階
- 適用できない
連絡先と場所
研究場所
-
-
New York
-
New York、New York、アメリカ、10032
- 募集
- Columbia University Irving Medical Center
-
コンタクト:
- Olena Mamykina, PhD
- 電話番号:212-305-3923
- メール:om2196@cumc.columbia.edu
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New York、New York、アメリカ、10018
- 募集
- Clinical Directors Network
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コンタクト:
- Andrea Cassells, MPH
- 電話番号:212-382-0699
- メール:acass@cdnetwork.org
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副調査官:
- Andrea Cassells, MPH
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主任研究者:
- Jonathan Tobin, PhD
-
-
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
Inclusion Criteria:
- Patient of the health center for ≥ 6 months and a diagnosis of T2DM
- HbA1c ≥ 8.0,
- Aged 18 to 65 years
- Attends diabetes education program at the health center
- Owns a basic mobile phone
- Proficient in either English or Spanish
Exclusion Criteria:
- Pregnant
- Presence of severe cognitive impairment (recorded in patient chart),
- Existence of other serious illnesses (e.g. cancer diagnosis with active treatment, advanced stage heart failure, dialysis, multiple sclerosis, advanced retinopathy, recorded in patient chart),
- Plans for leaving the FQHC in the next 12 months,
- Participation in the previous trial of diabetes self-management technologies
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:他の
- 割り当て:ランダム化
- 介入モデル:並列代入
- マスキング:なし(オープンラベル)
武器と介入
参加者グループ / アーム |
介入・治療 |
---|---|
実験的:T2.coach
Participants receive standard care (diabetes self-management education provided by their Federally Qualified Community Health Center) and are asked to use T2.coach for 6 months.
|
T2.coach is a smartphone app for low-burden capture of diet and blood glucose (BG) levels and for reviewing past records, integrated with FitBit for captured of physical activity and sleep.
All captured data are sent to the computational inference engine that uses machine learning methods and expert system to formulate personalized behavioral goals.
Examples of behavioral goals include the following: "For high carbohydrate breakfasts, reduce your carbs to be about 1 carb choice.
Examples of 1 carb choice are 1 slice of whole wheat toast, 1 cup of oatmeal, or 1 apple."
The T2.coach chatbot companion uses text messages to help individuals set goals that are consistent with evidence based guidelines for diabetes self-management, inferences on data captured with T2.coach, and their own preferences, as well as send individuals goal reminders and prompts for reflection on goal achievement.
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介入なし:Control
Participants receive standard care (diabetes self-management education provided by their Federally Qualified Community Health Center).
|
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
---|---|---|
Change in HbA1c value
時間枠:Baseline, 6 months, 12 months
|
Hemoglobin A1c
|
Baseline, 6 months, 12 months
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
---|---|---|
DPSI Score
時間枠:Baseline, 6 months, 12 months
|
Diabetes Problem-Solving Inventory (DPSI) is a 9-item, open-ended questionnaire.
Answers are coded on a Likert 5-point scale (1-very poor strategy; 5-excellent strategy).
The final score ranges from 1 (lowest) to 5 (highest) and an overall score ≤3 indicates poor diabetes problem solving, so a higher score indicates a better outcome.
|
Baseline, 6 months, 12 months
|
SCA-I Score
時間枠:Baseline, 6 months, 12 months
|
Diabetes Self-Care Inventory (SCA-I) is a 15-item 5-point Likert scale (1-never engage; 5-always engage) for measuring different aspects of diabetes self-care.
The final score ranges from 1 (lowest) to 5 (highest) with a higher score indicating better self-care (better outcome).
|
Baseline, 6 months, 12 months
|
DSES Score
時間枠:Baseline, 6 months, 12 months
|
Diabetes Self-Efficacy Scale (DSES) is a 15-item 10-point Likert scale (1-not at all confident; 4-totally confident) that measures the belief that one can self-manage one's own health, adapted to diabetes.
The final score ranges from 1 (lowest) to 4 (highest) with a lower score indicating poor self-efficacy (worse outcome).
|
Baseline, 6 months, 12 months
|
PAID Score
時間枠:Baseline, 6 months, 12 months
|
Problem Areas in Diabetes (PAID) is a 20-item 5-point Likert scale (0-not a problem; 4-very serious problem) that measures the emotional aspect of living with diabetes.
The final score ranges from 0 (lowest) to 80 (highest), with a higher score indicating greater emotional discomfort (worse outcome).
|
Baseline, 6 months, 12 months
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協力者と研究者
スポンサー
協力者
捜査官
- 主任研究者:Olena Mamykina, PhD、Columbia University
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (予想される)
研究の完了 (予想される)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
- AAAS5528
- R01DK113189 (米国 NIH グラント/契約)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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