McInerney A M, Schmitz N, Matthews M, Deschênes S S
School of Psychology, University College Dublin, Belfield, Dublin 4, Ireland.
Department of Population-Based Medicine, University of Tübingen, Tübingen, Germany.
BMC Digit Health. 2024;2(1):55. doi: 10.1186/s44247-024-00116-6. Epub 2024 Sep 12.
Digital phenotyping, the in-situ collection of passive (phone sensor) and active (daily surveys) data using a digital device, may provide new insights into the complex relationship between daily behaviour and mood for people with type 2 diabetes. However, there are critical knowledge gaps regarding its use in people with type 2 diabetes. This study assessed feasibility, tolerability, and user experience of digital phenotyping in people with and without type 2 diabetes after participation in a 2-month digital phenotyping study in Ireland. At study completion, participants rated methodology elements from "not a problem" to a "serious problem" on a 5-point scale and reported their comfort with the potential future use of digital phenotyping in healthcare, with space for qualitative expansion.
Eighty-two participants completed baseline. Attrition was 18.8%. Missing data ranged from 9-44% depending on data stream. Sixty-eight participants (82.9%) completed the user experience questionnaire (51.5% with type 2 diabetes; 61.8% female; median age-group 50-59). Tolerability of digital phenotyping was high, with "not a problem" being selected 76.5%-89.7% of the time across questions. People with type 2 diabetes (93.9%) were significantly more likely to be comfortable with their future healthcare provider having access to their digital phenotyping data than those without (53.1%), χ2 (1) = 14.01, = < .001. Free text responses reflected a range of positive and negative experiences with the study methodology.
An uncompensated, 2-month digital phenotyping study was feasible among people with and without diabetes, with low attrition and reasonable missing data rates. Participants found digital phenotyping to be acceptable, and even enjoyable. The potential benefits of digital phenotyping for healthcare may be more apparent to people with type 2 diabetes than the general population.
The online version contains supplementary material available at 10.1186/s44247-024-00116-6.
数字表型分析是指使用数字设备原位收集被动（手机传感器）和主动（日常调查）数据，它可能为2型糖尿病患者日常行为与情绪之间的复杂关系提供新的见解。然而，在2型糖尿病患者中使用数字表型分析存在关键的知识空白。本研究在爱尔兰进行了一项为期2个月的数字表型分析研究，评估了2型糖尿病患者和非2型糖尿病患者数字表型分析的可行性、耐受性和用户体验。在研究结束时，参与者对方法学要素从“不是问题”到“严重问题”进行5分制评分，并报告他们对数字表型分析在未来医疗保健中潜在应用的接受程度，同时留有定性扩展的空间。
82名参与者完成了基线评估。损耗率为18.8%。缺失数据因数据流而异，范围在9%-44%之间。68名参与者（82.9%）完成了用户体验问卷（2型糖尿病患者占51.5%；女性占61.8%；年龄中位数组为50-59岁）。数字表型分析的耐受性较高，在所有问题中，“不是问题”的选择率在76.5%-89.7%之间。与非2型糖尿病患者（53.1%）相比，2型糖尿病患者（93.9%）更有可能对未来的医疗保健提供者能够获取他们的数字表型分析数据感到满意，χ2(1)=14.01，P<0.001。自由文本回复反映了对研究方法的一系列积极和消极体验。
一项为期2个月、无补偿的数字表型分析研究在糖尿病患者和非糖尿病患者中都是可行的，损耗率低，缺失数据率合理。参与者认为数字表型分析是可以接受的，甚至是令人愉快的。数字表型分析对医疗保健的潜在益处对2型糖尿病患者可能比对普通人群更明显。
在线版本包含可在10.1186/s44247-024-00116-6获取的补充材料。
