Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
McLean Hospital, Belmont, MA, USA.
Psychol Med. 2023 Aug;53(11):5146-5154. doi: 10.1017/S0033291722002161. Epub 2022 Jul 27.
Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question.
Adolescent participants ( = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2-3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage.
A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%).
To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief 'just-in-time' interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.
青春期的特点是深刻的变化，包括负面情绪的增加。大约 84%的美国青少年拥有智能手机，它可以持续且毫不显眼地跟踪可能预测负面情绪加剧的变量（例如活动水平、位置、手机使用模式）。内置智能手机传感器在多大程度上能够可靠地预测青少年情绪升高的状态是一个悬而未决的问题。
使用生态瞬间评估（EMA）和连续收集的被动智能手机传感器数据相结合，对抑郁症状低至高的青少年参与者（n=22；年龄 13-18 岁）进行了 15 周的随访。EMAs 每隔一周每天探测 2-3 次负面情绪状态（即愤怒、悲伤和焦虑），整个研究共进行了 1145 次 EMA 测量。收集智能手机加速度计、位置和设备状态数据，得出 14 种离散的行为估计值，包括活动水平、在家时间百分比、睡眠开始和持续时间以及手机使用情况。
从智能手机传感器数据中得出的个性化集成机器学习模型优于其他统计方法（例如线性混合模型），并以可接受的区分能力预测愤怒和焦虑的升高状态（曲线下面积（AUC）分别为 74%和 71%），但预测悲伤升高状态的区分能力稍逊一筹（AUC = 66%）。
在某种程度上，智能手机数据可以为青少年的高度负面情绪状态提供合理准确的实时预测，通过智能手机通知或心理健康应用程序可以立即部署简短的“即时”干预措施来缓解这些状态。
