de Angel Valeria, Adeleye Fadekemi, Zhang Yuezhou, Cummins Nicholas, Munir Sara, Lewis Serena, Laporta Puyal Estela, Matcham Faith, Sun Shaoxiong, Folarin Amos A, Ranjan Yatharth, Conde Pauline, Rashid Zulqarnain, Dobson Richard, Hotopf Matthew
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom.
JMIR Ment Health. 2023 Jan 24;10:e42866. doi: 10.2196/42866.
Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment.
A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement.
A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device.
The overall retention rate was 60%. Higher-intensity treatment (χ=4.6; P=.03) and higher baseline anxiety (t=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list.
Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.
智能手机和可穿戴设备等远程测量技术（RMT）可通过提供有关情绪和行为的客观、连续且生态有效的见解，帮助改善抑郁症治疗。人们对RMT的参与情况各不相同，且高度依赖具体情境；然而，很少有研究在治疗背景下调查其可行性。
采用混合方法设计，评估接受心理治疗的抑郁症患者通过RMT进行主动和被动数据收集的参与情况。我们评估了治疗对两种不同类型参与度的影响：研究损耗（对研究方案的参与度）和缺失数据模式（对数字设备的参与度），我们将其称为数据可用性。进行定性访谈以帮助解释参与度的差异。
共有66名接受抑郁症心理治疗的患者被随访7个月。主动数据通过每周问卷以及言语和认知任务收集，被动数据通过智能手机传感器和Fitbit（Fitbit公司）可穿戴设备收集。
总体保留率为60%。高强度治疗（χ=4.6；P=0.03）和更高的基线焦虑水平（t=-2.80，双侧；P=0.007）与损耗相关，但抑郁严重程度无关（t=-0.18；P=0.86）。发现治疗时间越长与损耗增加之间的关联有显著趋势（U=339.5；P=0.05）。主动数据的初始数据可用性高于被动数据，但下降速度更快（7个月内从90%降至30%）。对于被动数据，可穿戴设备的数据可用性在7个月时从最高80%降至45%，但总体数据可用性高于基于智能手机的数据，后者在整个过程中保持在20%-40%的稳定范围内。GPS位置数据中的缺失数据更为普遍，其次是蓝牙数据，然后是加速度计数据。对于主动数据，言语和认知任务的完成率低于临床问卷。接受治疗的参与者提供的Fitbit数据较少，但主动数据比候补名单上的参与者多。
尽管从同一设备收集，但不同的数据流显示出不同的缺失数据模式。更长、更复杂的治疗以及更高基线焦虑等临床特征可能会降低对RMT的长期参与度，并且不同设备在治疗期间可能显示出相反的缺失模式。这对RMT在医疗保健环境中的可扩展性和采用情况、这些方法收集的数据的普遍性和准确性、特征构建以及RMT长期使用的适宜性都有影响。
