JMIR糖尿病

改善循环用户的血糖和生活质量:来自单个中心的真实数据分析

2022 - 10 - 24 - t11:32:21内

尽管自助式自动胰岛素输送是一种未经批准的胰岛素输送方法，但全球越来越多的1型糖尿病(T1D)患者选择使用Loop，这是一种自助式自动胰岛素输送系统。在本研究中，我们旨在评估埃德蒙顿当地已知Loop用户的血糖结果、安全性和对生活质量(QOL)的感知影响。对使用Loop的成年T1D患者进行了观察性研究。对血糖和安全性结果、HbA<低于>1c、在范围内的时间、住院次数和低于范围的时间进行了评估，比较了用户最近6个月使用Loop的情况，以及他们之前监管机构批准的胰岛素给药方法。使用胰岛素剂量系统:感知、想法、反思和期望、糖尿病影响和设备满意度测量(最高分别为100分、10分和10分)和半结构化访谈评估生活质量结局。参与本研究的24名成年T1D患者16名(67%)为女性，中位年龄为33岁(IQR 28-45岁)，糖尿病的中位持续时间为22年(IQR 17-32年)，中位Loop前HbA_1c为7.9% (IQR 7.6%-8.3%)， Loop使用的中位持续时间为18个月(IQR 12-25个月)。在使用Loop期间，参与者的中位数(IQR)值为7.1%(6.5%-7.5%)，范围内HbA< >1c为54 mmol(48-58)，时间为76.5%(64.6%-81.9%)，这比之前的治疗有显著改善(P=。001和P=.005)，在区间以下时间无显著缩短;3.0到3.9更易/ L(<我> P。< / i > =) & lt; 3更易/ L(<我> < / i > = 53页)。总的来说，在总共470个月的Loop使用中发生了2例糖尿病酮症酸中毒，没有发生严重的低血糖。在定性分析中探索了Loop对生活质量的积极影响，并通过胰岛素给药系统:感知、想法、反思和期望的中位数得分为86 (IQR 79-95)，糖尿病影响的中位数得分为2.8 (IQR 2.1-3.9)，设备满意度中位数得分为9 (IQR 8.2-9.4)来证明。 Conclusions: This local cohort of people with T1D demonstrated a beneficial effect of Loop use on both glycemic control and QOL, with no safety concerns being highlighted.

通过基于web的英国糖尿病健康论坛探索胰岛素治疗2型糖尿病的经验和观点:线程的定性专题分析

2022 - 10 - 05 - t09:45:02内

背景:尽管出现了2型糖尿病(T2D)缓解策略和新型治疗药物，许多T2D患者仍需要胰岛素治疗来达到目标血糖，目的是预防或延迟糖尿病并发症。然而，胰岛素拒绝和停止治疗在这一群体中是常见的，他们的需求被低估和相对未被探索。目的:本研究旨在探讨在网络健康论坛上表达胰岛素治疗的T2D患者的经验和观点，以便为基于证据的结构化教育和支持策略的发展提供信息，并提高医疗保健提供者的意识。在12个月的时间内(2019年8月-2020年)，筛选了来自英国2个最大的、免费和公众可访问的糖尿病健康论坛的回顾性存档论坛帖子。在Diabetes UK和Diabetes.co. UK论坛上搜索相关主题。共有3名独立研究人员通过专题分析分析了论坛的主题和帖子。相关的主题通过转述成员的引用来确定和说明，以确保匿名性。在研究期间，研究人员分析了来自英国糖尿病网站29个帖子的299篇文章和来自糖尿病网站28个帖子的295篇文章。总共有57个线程符合纳入标准，并被纳入最终分析。结果:产生了四个总体主题，以说明未满足的需求，促使成员通过论坛寻求关于胰岛素治疗的信息、建议和支持，而不是通过他们通常的护理提供:通过分享自我管理策略赋予权力，寻求和提供扩展的生活方式建议，与卫生保健专业人员的关系，以及心理同伴支持的来源。 Conclusions: This is the first study to collect data from web-based health forums to characterize the experiences and perspectives of people with T2D for whom insulin therapy is indicated. The observed naturalistic conversations have generated useful insights; our findings suggest that there are significant unmet self-management and psychological needs within this group that are not being met elsewhere, prompting the seeking of information and support on the web. These include practical aspects such as insulin injection technique, storage and dose titration, driving and travel considerations, the emerging use of technology, and a strong interest in the effects of extended lifestyle (diet and activity) approaches to support insulin therapy. In addition, problematic relationships with health care professionals appear to be a barrier to effective insulin therapy for some. In contrast, seeking and offering mutually beneficial, practical, and psychological support from peers was viewed as enabling. The study results will help to directly inform insulin-focused self-management and support strategies to enable individuals in this group to achieve their best outcomes.

数字患者赋权和沟通工具对2型糖尿病患者代谢控制的影响:DeMpower多中心双向研究

2022 - 10 - 03 - t09:30:02内

背景:糖尿病是一个主要的卫生保健问题，在世界范围内都是流行病。将血红蛋白A_1c (HbA_1c)水平降低到推荐目标与显著降低2型糖尿病(T2DM)相关并发症的风险相关。新技术的实施，特别是远程医疗，可能有助于促进自我保健和增强2型糖尿病患者的能力，从而改善疾病的代谢控制。本研究旨在分析家庭数字患者赋权和沟通工具(DeMpower App)对控制不充分的2型糖尿病患者代谢控制的影响。DeMpower研究为多中心研究，采用回顾性(观察性:52周随访)和前瞻性(介入性:52周随访)设计，纳入年龄≥18岁且≤80岁的T2DM患者，HbA<亚>1c水平≥7.5%至≤9.5%，接受非胰岛素抗高血糖药物治疗，能够使用智能手机应用程序。患者被随机(2:1)分配到使用DeMpower应用程序的组或对照组。我们描述了赋权对达到研究血糖目标的患者比例的影响，定义为第24周时HbA< >以下1c以下≤7.5%，HbA< >以下1c以下降低≥0.5%。由于COVID-19大流行，研究过早停止，对50例患者(DeMpower应用程序授权组33例，对照组17例)进行了分析。达到研究血糖目标的患者比例呈上升趋势(46% vs 18%;P=.07)，当目标为HbA_1c≤7.5%时，具有统计学意义(64% vs 24%;<我> < / i > = .02页)或HbA <子> 1 c < /子>≤8% (85% vs 53%; P=.02). The mean HbA_1c was significantly reduced at week 24 (−0.81, SD 0.89 vs −0.15, SD 1.03; P=.03); trends for improvement in other cardiovascular risk factors, medication adherence, and satisfaction were observed. Conclusions: The results suggest that patient empowerment through home digital tools has a potential effect on metabolic control, which might be even more relevant during the COVID-19 pandemic and in a digital health scenario.

移动健康应用程序使用与2型糖尿病和前驱糖尿病成人体重减轻和血糖控制之间的关系(D’lite研究):前瞻性队列研究

2022 - 09 - 30 - t09:30:03内

移动健康应用程序越来越多地被用作早期干预，以支持糖尿病预防和控制的行为改变，其首要目标是降低整体疾病负担。这项在新加坡进行的前瞻性队列研究，旨在通过技术授权随机对照试验，研究糖尿病生活方式干预干预组的糖尿病和前驱糖尿病成年人的应用程序用户参与特征及其与减肥和改善血糖控制的关系。方法:糖尿病和前体糖尿病患者(N=171)，中位年龄为52岁，BMI为29.3 kg/m²，糖化血红蛋白(HbA_1c)水平为6.5%，并分配营养师Buddy糖尿病应用程序。在基线、3个月和6个月测量体重和HbA< >1c。通过后端仪表板和开发者报告，我们总共追踪了476,300个每日应用粘性数据点。应用粘性数据采用四分位数和周均值(以每周天数表示)进行分析。线性混合模型分析用于确定应用程序使用与百分比权重和HbA_1c变化之间的相关性。结果:6个月时，整体应用粘性中值维持在90%以上。积极参与≥5个应用程序功能的参与者总体体重下降幅度最大，较基线下降10.6%(平均差值- 6,95% CI - 8.9至- 3.2;P<.001)。 Adhering to the carbohydrate limit of >5.9 days per week and choosing healthier food options for >4.3 days per week had the most impact, eliciting weight loss of 9.1% (mean difference −5.2, 95% CI −8.2 to −2.2; P=.001) and 8.8% (mean difference −4.2, 95% CI −7.1 to −1.3; P=.005), respectively. Among the participants with diabetes, those who had a complete meal log for >5.1 days per week or kept within their carbohydrate limit for >5.9 days per week each achieved greater HbA_1c reductions of 1.2% (SD 1.3%; SD 1.5%), as compared with 0.2% (SD 1%; SD 0.6%). in the reference groups who used the features <1.1 or ≤2.5 days per week, respectively. Conclusions: Higher app engagement led to greater weight loss and HbA_1c reduction among adults with overweight or obesity with type 2 diabetes or prediabetes. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001112358; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617001112358

糖尿病自我管理应用程序:采用决定因素和未来研究议程的系统回顾

2022 - 07 - 28 - t09:45:02内

背景:大多数糖尿病的管理包括自我管理。有效的自我管理可以改善糖尿病的控制，降低并发症的风险，并改善患者的预后。糖尿病自我管理(DSM)手机应用程序可以增强患者的自我管理活动。然而，只有当临床医生推荐它们，患者使用它们时，它们才有效。目的:本研究旨在探讨患者使用DSM应用程序的决定因素以及卫生保健专业人员(HCPs)对其的建议。它还概述了在糖尿病护理中使用DSM应用程序的未来研究议程。我们系统地回顾了影响患者和HCPs采用DSM应用程序的因素。使用PubMed、Scopus、CINAHL、Cochrane Central、ACM和Xplore数字图书馆搜索2008年至2020年发表的文章。搜索词为糖尿病、移动应用程序、和自我管理。从纳入的研究中提取相关数据，并使用主题综合方法进行分析。 Results: A total of 28 studies met the inclusion criteria. We identified a range of determinants related to patients’ and HCPs’ characteristics, experiences, and preferences. Young female patients were more likely to adopt DSM apps. Patients’ perceptions of the benefits of apps, ease of use, and recommendations by patients and other HCPs strongly affect their intention to use DSM apps. HCPs are less likely to recommend these apps if they do not perceive their benefits and may not recommend their use if they are unaware of their existence or credibility. Young and technology-savvy HCPs were more likely to recommend DSM apps. Conclusions: Despite the potential of DSM apps to improve patients’ self-care activities and diabetes outcomes, HCPs and patients remain hesitant to use them. However, the COVID-19 pandemic may hasten the integration of technology into diabetes care. The use of DSM apps may become a part of the new normal.

以糖尿病为重点的电子出院令集和出院后护理支持在控制不良的住院患者中的有效性:随机对照试验

2022 - 07 - 26 - t10:00:04内

虽然使用电子订单集已成为住院糖尿病患者管理的标准做法，但出院时的决策支持有限。目的:在本研究中，我们评估了电子出院单集(DOS)加护士随访电话是否能改善2型糖尿病住院患者的出院单和出院后结局。这是一项随机、开放标签、单中心研究，比较了住院需要胰岛素的2型糖尿病患者的电子DOS和护士电话与强化标准护理(ESC)。主要转归为出院后24周糖化血红蛋白(HbA_1c)水平的变化。次要结果包括与糖尿病相关的出院处方的完整性和准确性。结果:由于长期随访的可行性问题，本研究提前终止。然而，158名参与者被登记(DOS: n=82;ESC: n=76)，其中155人有放电数据。DOS组有更高频率的胰岛素处方(78% vs 44%;P=.01)，针头或注射器(95% vs 63%; P=.03), and glucometers (86% vs 36%; P<.001). The clarity of the orders was similar. HbA_1c data were available for 54 participants in each arm at 12 weeks and for 44 and 45 participants in the DOS and ESC arms, respectively, at 24 weeks. The unadjusted difference in change in HbA_1c level (DOS – ESC) was −0.6% (SD 0.4%; P=.18) at 12 weeks and −1.1% (SD 0.4%; P=.01) at 24 weeks. The adjusted difference in change in HbA_1c level was −0.5% (SD 0.4%; P=.20) at 12 weeks and −0.7% (SD 0.4%; P=.09) at 24 weeks. The achievement of the individualized HbA_1c target was greater in the DOS group at 12 weeks but not at 24 weeks. Conclusions: An intervention that included a DOS plus a postdischarge nurse phone call resulted in more complete discharge prescriptions. The assessment of postdischarge outcomes was limited, owing to the loss of the long-term follow-up, but it suggested a possible benefit in glucose control. Trial Registration: ClinicalTrials.gov NCT03455985; https://clinicaltrials.gov/ct2/show/NCT03455985

1型糖尿病低血糖预测算法:系统综述

2022 - 07 - 21 - t09:15:03内

背景:糖尿病是一种慢性疾病，需要定期监测和自我管理患者的血糖水平。1型糖尿病(T1D)患者如果接受适当的糖尿病治疗，可以过上富有成效的生活。然而，血糖控制不严格可能会增加发生低血糖的风险。这种情况的发生可能是由于各种原因，如服用额外剂量的胰岛素，不吃饭，或过度运动。低血糖的症状主要从轻微的烦躁到更严重的情况，如果不及时发现。在这篇综述中，我们旨在报道识别和预防低血糖发作的创新检测技术和策略，重点是T1D。我们按照PRISMA(系统评价和元分析首选报告项目)指南进行了系统文献检索，重点是PubMed，谷歌 Scholar， IEEE Xplore，以及ACM数字图书馆，以查找与T1D患者低血糖检测相关技术的文章。结果:所提出的方法已被用于或设计用于加强血糖监测，并提高其预测未来血糖水平的有效性，这可能有助于预测未来低血糖发作。我们检测了19种低血糖预测模型，特别是在T1D上，使用了广泛的算法方法，从统计学(1.9/ 19,10%)到机器学习(9.88/ 19,52%)和深度学习(7.22/ 19,38%)。使用最多的算法是卡尔曼滤波和分类模型(支持向量机、k近邻和随机森林)。 The performance of the predictive models was found to be satisfactory overall, reaching accuracies between 70% and 99%, which proves that such technologies are capable of facilitating the prediction of T1D hypoglycemia. Conclusions: It is evident that continuous glucose monitoring can improve glucose control in diabetes; however, predictive models for hypo- and hyperglycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mobile health in T1D. Prospective studies are required to demonstrate the value of such models in real-life mobile health interventions.

机器学习衍生的产前预测风险模型，以指导干预和预防妊娠糖尿病向2型糖尿病的进展:预测模型开发研究

2022 - 07 - 05 - t09:00:04内

背景:妊娠期糖尿病(GDM)患病率的增加值得关注，因为患有GDM的女性在以后的生活中患2型糖尿病(T2D)的风险很高。这一风险的严重性凸显了早期干预预防GDM向T2D进展的重要性。产后筛查率并不理想，在亚洲国家通常只有13%。在一些卫生保健系统中，缺乏通过结构化产后筛查进行预防保健，公众意识不高是进行产后糖尿病筛查的主要障碍。在本研究中，我们开发了一种机器学习模型，用于常规产前GDM筛查后产后T2D的早期预测。在产前护理中早期预测产后T2D将有助于实施有效的糖尿病预防干预策略。据我们所知，这是第一个在亚洲裔产前人群中使用机器学习进行产后T2D风险评估的研究。在新加坡表型最深刻的母亲-后代队列研究中，来自561名孕妇的前瞻性多民族数据(中国、马来和印度民族)被用于预测建模。特征变量包括人口统计学、病史或产科史、体格测量、生活方式信息和GDM诊断。Shapley值与CatBoost树集合相结合进行特征选择。 Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features—midpregnancy BMI after gestational weight gain and diagnosis of GDM—was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P=.02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care. Trial Registration: ClinicalTrials.gov NCT01174875; https://clinicaltrials.gov/ct2/show/NCT01174875

评估GREAT4Diabetes WhatsApp聊天机器人在COVID-19大流行期间教育2型糖尿病患者的实施情况:聚合混合方法研究

2022 - 06 - 24 - t10:00:03内

背景:在南非，糖尿病是发病率和死亡率的主要原因，在2019冠状病毒病大流行期间病情加剧。封锁期间，大多数教育和咨询活动都停止了，GREAT4Diabetes WhatsApp聊天机器人被创新来填补这一空白。本研究旨在评估2021年5月至10月期间聊天机器人在南非开普敦的实施情况。方法:采用收敛混合方法评估实施结果:可接受性、采用性、适当性、可行性、保真度、成本、覆盖范围、效果和可持续性。从聊天机器人中获得定量数据，并使用SPSS进行分析。采用Atlas-ti辅助框架方法，从关键信息源收集定性数据并进行分析。该聊天机器人用英语、南非荷兰语或科萨语为用户提供了16条语音信息和图形。信息的重点是COVID-19感染和2型糖尿病的自我管理。该聊天机器人被地铁卫生服务部门采用，以帮助在封锁期间受到医疗限制、因感染COVID-19而住院和死亡风险较高的糖尿病患者。该聊天机器人通过初级保健机构和当地非营利组织的医护人员以及当地媒体和电视进行传播。 Two technical glitches interrupted the dissemination but did not substantially affect user behavior. Minor changes were made to the chatbot to improve its utility. Many patients had access to smartphones and were able to use the chatbot via WhatsApp. Overall, 8158 people connected with the chatbot and 4577 (56.1%) proceeded to listen to the messages, with 12.56% (575/4577) of them listening to all 16 messages, mostly within 32 days. The incremental setup costs were ZAR 255,000 (US $16,876) and operational costs over 6 months were ZAR 462,473 (US $30,607). More than 90% of the users who listened to each message found them useful. Of the 533 who completed the whole program, 351 (71.1%) said they changed their self-management a lot and 87.6% (369/421) were more confident. Most users changed their lifestyles in terms of diet (315/414, 76.1%) and physical activity (222/414, 53.6%). Health care workers also saw benefits to patients and recommended that the service continues. Sustainability of the chatbot will depend on the future policy of the provincial Department of Health toward mobile health and the willingness to contract with Aviro Health. There is the potential to go to scale and include other languages and chronic conditions. Conclusions: The chatbot shows great potential to complement traditional health care approaches for people with diabetes and assist with more comprehensive patient education. Further research is needed to fully explore the patient’s experience of the chatbot and evaluate its effectiveness in our context.

手机对糖尿病管理支持的可访问性和开放性:1型糖尿病患者使用先进糖尿病技术的调查研究

2022 - 06 - 24 - t09:30:41内

使用先进的糖尿病技术(包括持续葡萄糖监测(CGM)系统和混合闭环胰岛素泵(HCLs))对1型糖尿病(T1D)患者提供移动健康(mHealth)支持的可行性知之甚少。本研究旨在评估T1D患者使用CGM系统或hcl接受mHealth糖尿病支持的可及性和开放性。我们在使用CGM系统或学术医疗中心管理的HCLs的T1D患者中进行了横断面调查。参与者报告了他们使用移动设备的情况;手机通话、短信或互联网连接;开放各种移动健康通信渠道(智能手机应用程序、短信短信和交互式语音应答[IVR]呼叫)。参与者的人口学特征和CGM数据收集自医疗记录。分析的重点是根据人口统计学变量和血糖控制措施定义的不同群体对移动健康和移动健康沟通渠道的开放程度的差异。结果:在所有参与者中(N=310;女性:198人，63.9%; mean age 45, SD 16 years), 98.1% (n=304) reported active cellphone use and 80% (n=248) were receptive to receiving mHealth support to improve glucose control. Among participants receptive to mHealth support, 98% (243/248) were willing to share CGM glucose data for mHealth diabetes self-care assistance. Most (176/248, 71%) were open to receiving messages via apps, 56% (139/248) were open to SMS text messages, and 12.1% (30/248) were open to IVR calls. Older participants were more likely to prefer SMS text messages (P=.009) and IVR calls (P=.03) than younger participants. Conclusions: Most people with T1D who use advanced diabetes technologies have access to cell phones and are receptive to receiving mHealth support to improve diabetes control.