新加坡Nanyang Technological University（NTU）的研究人员开发了一项预测计算机程序，该程序使用可穿戴技术的数据来检测患有抑郁症风险增加的个人。
在使用来自沮丧和健康参与者组的数据的试验中，该计划在检测那些患有高抑郁症和没有风险的人的人方面达到了80％的准确性。
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Powered by machine learning, the program, named the Ycogni model, screens for the risk of depression by analyzing an individual’s physical activity, sleep patterns, and circadian rhythms derived from data from wearable devices that measure his or her steps, heart rate, energy expenditure, and sleep data.
据世界卫生组织称，抑郁症影响了全球1.64亿人，在所有情况下，一半的抑郁症未经诊断和未经治疗。在新加坡，Covid-19的大流行导致人们对心理健康的关注日益增加。新加坡心理健康研究所的一项新研究表明，心理健康问题可能会增加，包括与大流行有关的抑郁症。
据估计，活动跟踪器估计被近10亿人佩戴，高于2019年的7.22亿人。
为了开发Ycogni模型，科学家进行了一项研究，涉及新加坡290名工作成年人。参与者连续14天穿着Fitbit收费2个设备，并完成了两次健康调查，该调查在研究的开始和结束时筛选了抑郁症状，报告NTU。
参与者的平均年龄为33岁，样本密切反映了新加坡的民族人口。指示参与者始终佩戴追踪器，并仅在洗澡时或设备需要充电时才将其删除。
Professor Josip Car, Director, Centre for Population Health Sciences at NTU’s Lee Kong Chian School of Medicine (LKCMedicine), who co-led the study, said: “Our study successfully showed that we could harness sensor data from wearables to aid in detecting the risk of developing depression in individuals. By tapping on our machine learning program, as well as the increasing popularity of wearable devices, it could one day be used for timely and unobtrusive depression screening.”
Associate Professor Georgios Christopoulos, from NTU’s Nanyang Business School, who co-led the study, said: “This is a study that, we hope, can set up the basis for using wearable technology to help individuals, researchers mental health practitioners and policy makers to improve mental well-being. But on a more generic and futuristic application, we believe that such signals could be integrated with Smart Buildings or even Smart Cities initiatives: imagine a hospital or a military unit that could use these signals to identify people-at-risk.”
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该研究的结果发表在同行评审的学术期刊上Jmir Mhealth和uhealth十一月。
在明年，该团队希望通过通过智能手机使用的数据丰富模型来探索智能手机使用对抑郁症状的影响和抑郁症的风险。这包括个人使用手机的时间和经常时间，以及他们对社交媒体的依赖。
