Hassan Lamiece, Milton Alyssa, Sawyer Chelsea, Casson Alexander J, Torous John, Davies Alan, Ruiz-Yu Bernalyn, Firth Joseph
School for Health Sciences, University of Manchester, Manchester, United Kingdom.
Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
JMIR Ment Health. 2025 Jan 7;12:e65143. doi: 10.2196/65143.
Digital wearable devices, worn on or close to the body, have potential for passively detecting mental and physical health symptoms among people with severe mental illness (SMI); however, the roles of consumer-grade devices are not well understood.
This study aims to examine the utility of data from consumer-grade, digital, wearable devices (including smartphones or wrist-worn devices) for remotely monitoring or predicting changes in mental or physical health among adults with schizophrenia or bipolar disorder. Studies were included that passively collected physiological data (including sleep duration, heart rate, sleep and wake patterns, or physical activity) for at least 3 days. Research-grade actigraphy methods and physically obtrusive devices were excluded.
We conducted a systematic review of the following databases: Cochrane Central Register of Controlled Trials, Technology Assessment, AMED (Allied and Complementary Medicine), APA PsycINFO, Embase, MEDLINE(R), and IEEE XPlore. Searches were completed in May 2024. Results were synthesized narratively due to study heterogeneity and divided into the following phenotypes: physical activity, sleep and circadian rhythm, and heart rate.
Overall, 23 studies were included that reported data from 12 distinct studies, mostly using smartphones and centered on relapse prevention. Only 1 study explicitly aimed to address physical health outcomes among people with SMI. In total, data were included from over 500 participants with SMI, predominantly from high-income countries. Most commonly, papers presented physical activity data (n=18), followed by sleep and circadian rhythm data (n=14) and heart rate data (n=6). The use of smartwatches to support data collection were reported by 8 papers; the rest used only smartphones. There was some evidence that lower levels of activity, higher heart rates, and later and irregular sleep onset times were associated with psychiatric diagnoses or poorer symptoms. However, heterogeneity in devices, measures, sampling and statistical approaches complicated interpretation.
Consumer-grade wearables show the ability to passively detect digital markers indicative of psychiatric symptoms or mental health status among people with SMI, but few are currently using these to address physical health inequalities. The digital phenotyping field in psychiatry would benefit from moving toward agreed standards regarding data descriptions and outcome measures and ensuring that valuable temporal data provided by wearables are fully exploited.
PROSPERO CRD42022382267; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382267.
佩戴在身体上或靠近身体的数字可穿戴设备有潜力被动检测重度精神疾病（SMI）患者的心理和身体健康症状；然而，消费级设备的作用尚未得到充分理解。
本研究旨在探讨消费级数字可穿戴设备（包括智能手机或腕戴设备）的数据用于远程监测或预测精神分裂症或双相情感障碍成年人心理或身体健康变化的效用。纳入的研究需被动收集至少3天的生理数据（包括睡眠时间、心率、睡眠和清醒模式或身体活动）。排除研究级别的活动记录仪方法和侵入性身体设备。
我们对以下数据库进行了系统综述：Cochrane对照试验中央登记册、技术评估、AMED（联合与补充医学）、美国心理学会心理学文摘数据库、Embase、医学期刊数据库（MEDLINE®）和IEEE Xplore。检索于2024年5月完成。由于研究的异质性，结果采用叙述性综合，并分为以下表型：身体活动、睡眠和昼夜节律以及心率。
总体而言，纳入了23项研究，这些研究报告了来自12项不同研究的数据，大多使用智能手机，且主要围绕复发预防。只有1项研究明确旨在探讨SMI患者的身体健康结果。总共纳入了500多名SMI患者的数据，主要来自高收入国家。最常见的是，论文呈现了身体活动数据（n = 18），其次是睡眠和昼夜节律数据（n = 14）以及心率数据（n = 6）。8篇论文报告了使用智能手表支持数据收集；其余仅使用智能手机。有一些证据表明，活动水平较低、心率较高以及入睡时间较晚且不规律与精神疾病诊断或症状较差有关。然而，设备、测量方法、抽样和统计方法的异质性使解释变得复杂。
消费级可穿戴设备显示出能够被动检测表明SMI患者精神症状或心理健康状况的数字标记，但目前很少有人用这些设备来解决身体健康不平等问题。精神病学中的数字表型领域将受益于朝着关于数据描述和结果测量的商定标准发展，并确保充分利用可穿戴设备提供的有价值的时间数据。
PROSPERO CRD42022382267；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382267 。
