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Background: Cross sectional studies have identified linguistic correlates of major depressive disorder (MDD) in smartphone communication. However, it is unclear whether monitoring these linguistic characteristics can detect when an individual is experiencing MDD, which would facilitate timely intervention. Methods: Approximately 1.2 million messages typed into smartphone social communication apps (e.g. texting, social media) were passively collected from 90 adolescents with a range of depression severity over a 12‐month period. Sentiment (i.e. positive vs. negative valence of text), proportions of first‐person singular pronouns (e.g. 'I'), and proportions of absolutist words (e.g. 'all') were computed for each message and converted to weekly aggregates temporally aligned with weekly MDD statuses obtained from retrospective interviews. Idiographic, multilevel logistic regression models tested whether within‐person deviations in these linguistic features were associated with the probability of concurrently meeting threshold for MDD. Results: Using more first‐person singular pronouns in smartphone communication relative to one's own average was associated with higher odds of meeting threshold for MDD in the concurrent week (OR = 1.29; p =.007). Sentiment (OR = 1.07; p =.54) and use of absolutist words (OR = 0.99; p =.90) were not related to weekly MDD. Conclusions: Passively monitoring use of first‐person singular pronouns in adolescents' smartphone communication may help detect MDD, providing novel opportunities for early intervention. [ABSTRACT FROM AUTHOR]

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory‐based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real‐world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13–18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1‐week, resulting in indices of emotion (mean‐level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA‐reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real‐world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just‐in‐time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states. This study evaluated relations between a laboratory measure of socioemotional information processing and emotion dynamics captured using smartphone‐based ecological momentary assessment over 1 week in adolescents. Whereas heightened late positive potential (LPP) amplitude for happy faces was related to less happiness inertia, heightened LPP amplitude for angry faces was associated with greater anger inertia. Results highlight potentially important links between laboratory measures and ecologically sensitive measures of affective dynamics, as well as possible targets (e.g., just‐in‐time interventions) for novel treatment approaches. [ABSTRACT FROM AUTHOR]

Intensive longitudinal research—including experience sampling and smartphone sensor monitoring—has potential for identifying proximal risk factors for psychopathology, including suicidal thoughts and behaviors (STB). Yet, missing data can complicate analysis and interpretation. This study aimed to address whether clinical and study design factors are associated with missing data and whether missingness predicts changes in symptom severity or STB. Adolescents ages 13- to 18 years old (N = 179) reporting depressive, anxiety, and/or substance use disorders were enrolled; 65% reported current suicidal ideation and 29% indicated a past-year attempt. Passively acquired smartphone sensor data (e.g., global positioning system, accelerometer, and keyboard inputs), daily mood surveys, and weekly suicidal ideation surveys were collected during the 6-month study period using the effortless assessment research system smartphone app. First, acquisition of passive smartphone sensor data (with data on ∼80% of days across the whole sample) was strongly associated with survey data acquisition on the same day (∼44% of days). Second, STB and psychiatric symptoms were largely not associated with missing data. Rather, temporal features (e.g., length of time in study, weekends, and summer) explained more missingness of survey and passive smartphone sensor data. Last, within-participant changes in missing data over time neither followed nor predicted subsequent change in suicidal ideation and psychiatric symptoms. Findings indicate that considering technical and study design factors impacting missingness is critical and highlight several factors that should be addressed to maximize the validity of clinical interpretations in intensive longitudinal research. General Scientific Summary: Missing data poses substantial challenges to longitudinal research on mental health. This study worked to characterize patterns of missing data among adolescents with psychiatric disorders and at risk for suicide, in particular missingness of smartphone-based surveys and measurements passively collected from participants' smartphones (e.g., global positioning system, accelerometer, and keyboard). Findings highlight key technical and study design factors that should be considered to minimize missing data and maximize the validity of results in future longitudinal studies using smartphones. [ABSTRACT FROM AUTHOR]