Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut.
Yale University, New Haven, Connecticut.
Clin J Sport Med. 2023 Sep 1;33(5):512-520. doi: 10.1097/JSM.0000000000001078. Epub 2023 Jan 27.
Pilot-test personalized digital health information to substantiate human-delivered exercise support for adults with type 1 diabetes (T1D).
Single-group, 2-week baseline observation, then 10-week intervention with follow-up observation.
Community-based sample participating remotely with physician oversight.
Volunteers aged 18 to 65 years with T1D screened for medical readiness for exercise intervention offerings. N = 20 enrolled, and N = 17 completed all outcomes with 88% to 91% biosensor adherence.
Feedback on personalized data from continuous glucose monitoring (CGM), its intersection with other ecological data sets (exercise, mood, and sleep), and other informational and motivational elements (exercise videos, text-based exercise coach, and self-monitoring diary).
Feasibility (use metrics and assessment completion), safety (mild and severe hypoglycemia, and diabetic ketoacidosis), acceptability (system usability scale, single items, and interview themes), and standard clinical and psychosocial assessments.
Participants increased exercise from a median of 0 (Interquartile range, 0-21) to 64 (20-129) minutes per week ( P = 0.001, d = 0.71) with no severe hypoglycemia or ketoacidosis. Body mass index increased (29.5 ± 5.1 to 29.8 ± 5.4 kg/m 2 , P = 0.02, d = 0.57). Highest satisfaction ratings were for CGM use (89%) and data on exercise and its intersection with CGM and sleep (94%). Satisfaction was primarily because of improved exercise management behavioral skills, although derived motivation was transient.
The intervention was feasible, safe, and acceptable. However, there is a need for more intensive, sustained support. Future interventions should perform analytics upon the digital health information and molecular biomarkers (eg, genomics) to make exercise support tools that are more personalized, automated, and intensive than our present offerings.
为了证实个性化数字健康信息对 1 型糖尿病（T1D）成人运动支持的有效性，对其进行试点测试。
单组，2 周基线观察，然后进行 10 周干预，并进行后续观察。
社区为基础的样本，远程参与，由医生监督。
年龄在 18 至 65 岁之间的志愿者，他们接受了 T1D 运动干预服务的医学准备情况筛查。共招募了 20 名志愿者，其中 17 名完成了所有的结果，88%至 91%的生物传感器的使用率。
从连续血糖监测（CGM）中获取个性化数据的反馈，以及其与其他生态数据集（运动、情绪和睡眠）和其他信息和激励元素（运动视频、基于文本的运动教练和自我监测日记）的交叉反馈。
可行性（使用指标和评估完成情况）、安全性（轻度和重度低血糖、糖尿病酮症酸中毒）、可接受性（系统可用性量表、单项评估和访谈主题）以及标准的临床和心理社会评估。
参与者的运动时间中位数从每周 0（四分位距，0-21）增加到每周 64（20-129）分钟（P = 0.001，d = 0.71），且没有出现严重的低血糖或酮症酸中毒。体重指数增加（29.5 ± 5.1 至 29.8 ± 5.4 kg/m 2 ，P = 0.02，d = 0.57）。对 CGM 使用（89%）和关于运动及其与 CGM 和睡眠交叉的信息（94%）的满意度评价最高。满意度主要是因为改善了运动管理行为技能，尽管获得的动力是暂时的。
该干预措施是可行的、安全的、可接受的。然而，需要更强化、持续的支持。未来的干预措施应该对数字健康信息和分子生物标志物（如基因组学）进行分析，以提供比我们目前提供的更个性化、自动化和强化的运动支持工具。
