Leaning Imogen E, Costanzo Andrea, Jagesar Raj, Reus Lianne M, Visser Pieter Jelle, Kas Martien J H, Beckmann Christian F, Ruhé Henricus G, Marquand Andre F
Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
Department for Medical Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
J Med Internet Res. 2025 Apr 28;27:e64007. doi: 10.2196/64007.
Brain-related disorders are characterized by observable behavioral symptoms, for example, social withdrawal. Smartphones can passively collect behavioral data reflecting digital activities such as communication app usage and calls. These data are collected objectively in real time, avoiding recall bias, and may, therefore, be a useful tool for measuring behaviors related to social functioning. Despite promising clinical utility, analyzing smartphone data is challenging as datasets often include a range of temporal features prone to missingness.
Hidden Markov models (HMMs) provide interpretable, lower-dimensional temporal representations of data, allowing for missingness. This study aimed to investigate the HMM as a method for modeling smartphone time series data.
We applied an HMM to an aggregate dataset of smartphone measures designed to assess phone-related social functioning in healthy controls (HCs) and participants with schizophrenia, Alzheimer disease (AD), and memory complaints. We trained the HMM on a subset of HCs (91/348, 26.1%) and selected a model with socially active and inactive states. Then, we generated hidden state sequences per participant and calculated their "total dwell time," that is, the percentage of time spent in the socially active state. Linear regression models were used to compare the total dwell time to social and clinical measures in a subset of participants with available measures, and logistic regression was used to compare total dwell times between diagnostic groups and HCs. We primarily reported results from a 2-state HMM but also verified results in HMMs with more hidden states and trained on the whole participant dataset.
We identified lower total dwell times in participants with AD (26/257, 10.1%) versus withheld HCs (156/257, 60.7%; odds ratio 0.95, 95% CI 0.92-0.97; false discovery rate [FDR]-corrected P<.001), as well as in participants with memory complaints (57/257, 22.2%; odds ratio 0.97, 95% CI 0.96-0.99; FDR-corrected P=.004). The result in the AD group was very robust across HMM variations, whereas the result in the memory complaints group was less robust. We also observed an interaction between the AD group and total dwell time when predicting social functioning (FDR-corrected P=.02). No significant relationships regarding total dwell time were identified for participants with schizophrenia (18/257, 7%; P>.99).
We found the HMM to be a practical, interpretable method for digital phenotyping analysis, providing an objective phenotype that is a possible indicator of social functioning.
脑部相关疾病具有可观察到的行为症状，例如社交退缩。智能手机可以被动收集反映数字活动（如通信应用使用情况和通话记录）的行为数据。这些数据是实时客观收集的，避免了回忆偏差，因此可能是测量与社会功能相关行为的有用工具。尽管具有潜在的临床应用价值，但分析智能手机数据具有挑战性，因为数据集通常包含一系列容易出现缺失值的时间特征。
隐马尔可夫模型（HMM）能够提供可解释的低维数据时间表示，允许存在缺失值。本研究旨在探讨将HMM作为一种对智能手机时间序列数据进行建模的方法。
我们将HMM应用于一个智能手机测量的汇总数据集，该数据集旨在评估健康对照者（HC）以及患有精神分裂症、阿尔茨海默病（AD）和有记忆障碍主诉的参与者的与手机相关的社会功能。我们在一部分HC（91/348，26.1%）上训练HMM，并选择一个具有社交活跃和不活跃状态的模型。然后，我们为每个参与者生成隐藏状态序列，并计算他们的“总停留时间”，即处于社交活跃状态的时间百分比。线性回归模型用于在一部分有可用测量值的参与者中比较总停留时间与社会和临床测量值，逻辑回归用于比较诊断组与HC之间的总停留时间。我们主要报告来自双状态HMM的结果，但也在具有更多隐藏状态并在整个参与者数据集上训练的HMM中验证了结果。
我们发现AD患者（26/257，10.1%）的总停留时间低于未参与训练的HC（156/257，60.7%；优势比0.95，95%置信区间0.92 - 0.97；错误发现率[FDR]校正P <.001），有记忆障碍主诉的参与者（57/257，22.2%；优势比0.97，95%置信区间0.96 - 0.
