Changes in Person-reported Outcomes and Behavior Using the Accu-Chek SmartGuide CGM and Predict App
Changes in Person-reported Outcomes and Behavior Due to a Predictive Algorithm: A Pre-post Trial With the Accu-Chek SmartGuide CGM and the Predict App Solution
The goal of this controlled pre-post-trial is to test the Accu-Chek SmartGuide continuous glucose monitoring (CGM) system and whether its predictive features provide additional benefits for adults with type 1 diabetes or insulin- treated type 2 diabetes.
The main questions this study aims to answer are:
- Does using the Accu-Chek SmartGuide together with the Predict App change participants' fear of hypoglycemia?
- Does the Predict App affect other psychological and behavioral outcomes, such as sleep quality, confidence in preventing hypoglycemia, diabetes- related distress, and diabetes empowerment?
- Does the use of the predictive features affect glucose levels and the way participants use and respond to CGM alarms and trend information?
Participants will use the Accu-Chek SmartGuide for 6 weeks. During the first 2 weeks, participants will use the CGM system without the predictive features. During the following 4 weeks, participants will use the system together with the Accu-Chek SmartGuide Predict App. Participants will complete online questionnaires at two time points (after 2 and after 6 weeks) and answer short questions twice a day through a smartphone app during the first 2 weeks and the last 2 weeks of the study. CGM data will be collected throughout the study. A subgroup of 10 participants will also be invited to take part in an online interview about their experiences with the predictive features.
調査の概要
状態
条件
詳細な説明
Continuous glucose monitoring (CGM) is widely used by people with insulin- treated diabetes to monitor glucose levels and support daily diabetes management. The Accu-Chek SmartGuide CGM system provides continuous glucose measurements and includes predictive features that are intended to provide information about possible future glucose trends.
This study will investigate the use of the Accu-Chek SmartGuide with and without these predictive features in adults with type 1 diabetes or type 2 diabetes treated with intensified insulin therapy. The study will be conducted remotely in Germany through the dia*link online panel. No in-person study visits are planned.
Participants will use the Accu-Chek SmartGuide CGM system for a total of 6 weeks. The study consists of two consecutive phases. During the first 2 weeks, participants will use the Accu-Chek SmartGuide CGM without the Predict App and therefore without the predictive features. This phase provides a period in which CGM data and self-reported outcomes can be assessed before the predictive features are introduced. After 2 weeks, participants will receive training on the Predict App and begin a 4-week period using the Accu-Chek SmartGuide together with the Predict App.
Participants will complete online questionnaires at the end of the first 2 weeks and at the end of the 6-week study period. The questionnaires assess fear of hypoglycemia, sleep quality, confidence in preventing hypoglycemia, diabetes- related distress, and diabetes empowerment.
During the first 2 weeks and the last 2 weeks of the study, participants will also complete short ecological momentary assessment (EMA) questionnaires twice daily using a smartphone app. The morning assessment asks about sleep quality and confidence in preventing hypoglycemia. The evening assessment asks about the use and evaluation of CGM functions, including trend arrows, alarms, and predictive features. Participants will also be asked about possible therapy adjustments, satisfaction with the CGM functions, and diabetes- related distress, including distress related to hypoglycemia, hyperglycemia, and glucose variability.
CGM data will be collected throughout both study phases. These data will include measures such as time in glucose ranges, hypoglycemia, hyperglycemia, glucose variability, and mean glucose levels. The study will assess changes in these measures between the two study phases.
The primary outcome is the change in fear of hypoglycemia during the 4-week period in which participants use the Accu-Chek SmartGuide together with the Predict App. Secondary outcomes include changes in other psychosocial outcomes, diabetes-related behaviors, use and evaluation of CGM alarms and trend arrows, and CGM-derived glucose measures.
In addition, a subgroup of 10 participants will be invited to take part in a semi-structured online interview after completing the study. The interviews will explore participants' experiences with the predictive features, their confidence and trust in these features, perceived benefits in everyday diabetes management, and suggestions for improvement. Participants will be selected to achieve a balanced representation with regard to age, sex, and type of diabetes.
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前:Dominic Ehrmann, Prof. Dr.
- 電話番号:+49 7931 96192 42
- メール:ehrmann@fidam.de
研究場所
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Baden-Wurttemberg
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Bad Mergentheim、Baden-Wurttemberg、ドイツ、97980
- 募集
- Fidam Rdc
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コンタクト:
- Dominic Ehrmann, Prof. Dr.
- 電話番号:+49 7931 96192 42
- メール:ehrmann@fidam.de
-
-
参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
Inclusion Criteria:
- Type 1 diabetes or Type 2 diabetes with intensified insulin therapy (defined as basal-bolus therapy with self-adjustment of bolus doses)
- CGM use during the past 3 months (without predictive features)
- Age ≥ 18 years
- HbA1c < 10%
- Informed consent
Exclusion Criteria:
- Use of the SmartGuide CGM solution
- AID use
- Current treatment with psychiatric medications
- Diabetes duration < 3 months
- Current or planned pregnancy
- Peripheral neuropathy that will impact sleep quality according to the judgement of the participant
- Severe complications (e.g. kidney disease, cancer)
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:処理
- 割り当て:非ランダム化
- 介入モデル:単一グループの割り当て
- 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 |
|---|---|
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アクティブコンパレータ:SmartGuide CGM without Predict App
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SmartGuide CGM without Predict App Accu-Check SmartGuide CGM solution will be tested without the prediction function
他の名前:
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実験的:SmartGuide CGM with Predict App
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SmartGuide CGM + Predict App Accu-Check SmartGuide CGM solution will be tested with the added benefit of the prediction function
他の名前:
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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 |
|---|---|---|
|
Fear of hypoglycemia (HFS-II)
時間枠:At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Within-person change in fear of hypoglycemia from baseline to follow-up, assessed via the Hypoglycemia-Fear Survey II questionnaire
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At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Overall sleep quality (PSQI)
時間枠:At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Changes will be analyzed using a questionnaire
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At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Hypoglycemia confidence (HCS)
時間枠:At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Changes will be analyzed using a questionnaire
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At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Diabetes distress (DDS)
時間枠:At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Changes will be analyzed using a questionnaire: Diabetes distress (DDS: total score + subscale scores), Hypoglycemia-related distress, Powerlessness
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At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Diabetes Empowerment (DES-SF)
時間枠:At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Changes will be analyzed using a questionnaire
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At baseline (after 2 weeks from enrollment) and at follow-up (after 4 weeks from baseline)
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Daily sleep quality
時間枠:14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Changes will be analyzed on a daily level using Ecological Momentary Assessment (EMA)
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14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Avoidance of hypos
時間枠:14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Changes will be analyzed on a daily level using Ecological Momentary Assessment (EMA)
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14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Time with diabetes and glycemic-specific distress
時間枠:14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Changes will be analyzed on a daily level using Ecological Momentary Assessment (EMA)
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14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Changes in behavior
時間枠:14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Use of and reaction to trend arrows, Use of and reaction to glucose alarms, Settings of glucose alarms, Use of and reaction to predictive features
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14 days after enrollment and 14 days before study end with two prompts per day (morning & evening)
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Changes in glycemic outcomes
時間枠:From enrollment to the end of the study at 6 weeks
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% 70-180 mg/dl, % 70-140 mg/dl, % < 70 mg/dl, % > 180 mg/dl, % < 54 mg/dl, % > 250 mg/dl, Glucose variability (Coefficient of variation), Mean glucose
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From enrollment to the end of the study at 6 weeks
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Personal experiences
時間枠:After the study period of 6 weeks
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Qualitative interviews to gain deeper insights into the personal experiences, expectations, handling, perceived advantages and disadvantages, wishes, etc. of users
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After the study period of 6 weeks
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協力者と研究者
スポンサー
出版物と役立つリンク
一般刊行物
- Ehrmann D, Laviola L, Priesterroth LS, Hermanns N, Babion N, Glatzer T. Fear of Hypoglycemia and Diabetes Distress: Expected Reduction by Glucose Prediction. J Diabetes Sci Technol. 2024 Sep;18(5):1027-1034. doi: 10.1177/19322968241267886. Epub 2024 Aug 19.
- Glatzer T, Ehrmann D, Gehr B, Penalba Martinez MT, Onvlee J, Bucklar G, Hofer M, Stangs M, Wolf N. Clinical Usage and Potential Benefits of a Continuous Glucose Monitoring Predict App. J Diabetes Sci Technol. 2024 Sep;18(5):1009-1013. doi: 10.1177/19322968241268353. Epub 2024 Aug 19.
- Hussain S, Polonsky W, Scibilia R, Glatzer T. Beyond the Trend Arrow: Potential Value of Artificial Intelligence-Supported Glucose Predictions for People with Type 1 Diabetes Using Continuous Glucose Monitoring Systems. Diabetes Technol Ther. 2025 Nov;27(11):943-949. doi: 10.1089/dia.2025.0293. Epub 2025 May 29.
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
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
追加の関連 MeSH 用語
その他の研究ID番号
- FIDAM-IIS-2501
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?
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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