Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study.
Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study.
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通过手机收集的个人附近蓝牙设备计数数据预测抑郁症状严重程度：初步纵向研究。
DOI：
10.2196/29840
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发表时间：
2021-07-30
影响因子：
5
通讯作者：
Dobson RJB
中科院分区：
文献类型：
--
作者：
Zhang Y;Folarin AA;Sun S;Cummins N;Ranjan Y;Rashid Z;Conde P;Stewart C;Laiou P;Matcham F;Oetzmann C;Lamers F;Siddi S;Simblett S;Rintala A;Mohr DC;Myin-Germeys I;Wykes T;Haro JM;Penninx BWJH;Narayan VA;Annas P;Hotopf M;Dobson RJB
心理健康研究发现，抑郁症与个人的行为和状态之间存在关联，如社会联系和互动，工作状态，流动性，社会孤立和孤独。这些行为和状态可以通过移动的电话中的蓝牙传感器检测到的附近蓝牙设备计数（NBDC）来近似。本研究旨在探讨NBDC数据在预测抑郁症状严重程度（通过8项患者健康问卷（PHQ-8）测量）中的价值。本文中使用的数据包括2886个双周PHQ-8记录，这些记录来自荷兰、西班牙和英国三个研究中心招募的316名参与者，作为欧盟疾病和复发-中枢神经系统远程评估（RADAR-CNS）研究的一部分。从每次PHQ-8评分前2周的NBDC数据中，我们提取了49个蓝牙特征，包括用于测量个人生活节奏的周期性和规律性的统计特征和非线性特征。使用线性混合效应模型来探索蓝牙功能与PHQ-8评分之间的关联。然后，我们应用分层贝叶斯线性回归模型来根据提取的蓝牙特征预测PHQ-8评分。蓝牙功能和抑郁症状严重程度之间存在一些显着的关联。一般来说，沿着抑郁症状的加重，在前2周的NBDC数据中发现以下一种或多种变化：（1）数量减少，（2）方差减少，（3）周期性（特别是昼夜节律）减少，（4）NBDC序列变得更加不规则。与常用的机器学习模型相比，所提出的分层贝叶斯线性回归模型实现了最佳的预测指标（R2=0.526）和均方根误差（RMSE）为3.891。相对于没有蓝牙功能的基线模型，蓝牙功能可以解释PHQ-8分数中额外18.8%的方差（R2=0.338，RMSE=4.547）。我们的统计结果表明，NBDC数据有可能反映个人的行为和状态的变化，同时在抑郁状态的变化。预测结果表明，NBDC数据在预测抑郁症状严重程度方面具有显著价值。这些发现可能对现实环境中的心理健康监测实践具有实用价值。
Research in mental health has found associations between depression and individuals’ behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals’ behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.
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影响因子：
6.6
作者：
Aluoja, A;Leinsalu, M;Luuk, K
通讯作者：
Luuk, K
影响因子：
--
作者：
Eagle, Nathan;Pentland, Alex (Sandy)
通讯作者：
Pentland, Alex (Sandy)
影响因子：
3.8
作者：
Burgess, P;Pirkis, J;Croke, E
通讯作者：
Croke, E
影响因子：
3.6
作者：
Akhtar-Danesh N;Landeen J
通讯作者：
Landeen J
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y