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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]

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]

Importance: Suicide rates among adolescents continue to rise, but there are a lack of clinical tools to predict when youths may be at risk for suicidal behaviors., Objective: To identify whether geolocation metrics, assessed through an app installed on adolescents' personal smartphones, could detect the risk of next-week suicidal events and clinically meaningful suicidal ideation., Design, Setting, and Participants: This case series study included high-risk adolescents aged 13 to 18 years reporting a current affective and/or substance use disorder, oversampled for suicidal thoughts and behaviors (STB). Participants were recruited from the greater New York City and Pittsburgh communities through psychiatric outpatient programs, emergency departments, medical center research registries, and social media. Participants installed the Effortless Assessment Research System (EARS) software application onto their personal smartphones, which obtained passive sensor data, including geolocation metrics (via the global positioning system [GPS]), as well as weekly experience sampling data probing STB for the duration of the 6-month study. Adolescents also completed clinical assessments at baseline as well as during the 1-, 3-, and 6-month follow-up assessments. Statistical analysis was performed from March 2023 to November 2024., Main Outcomes and Measures: Repeated measures mixed-effects logistic models estimated whether weekly aggregates of geolocation features (ie, entropy, homestay, distance traveled) were associated with next-week suicidal events (ie, suicide attempts, psychiatric hospitalizations, emergency department visits for suicide concerns) and clinically meaningful ideation (via weekly experience sampling)., Results: Overall, 186 participants were included in this study (148 [79.6%] female; 19 [10.2%] Asian, 23 [12.4%] Black, and 106 [57.0%] White), with a mean (SD) age of 16.4 (1.7) years. Greater homestay (amount of time spent at home) on a given week, relative to one's own mean, was associated with 2-fold greater odds of suicidal events during the subsequent week (odds ratio, 1.99 [95% CI, 1.15-3.45]). Results were not significant for entropy and distance traveled metrics. However, using leave-future-out validation, the accuracy of the homestay model was modest (area under the receiver operating characteristic curve, 0.64 [95% CI, 0.50-0.78])., Conclusions and Relevance: Advancements in smartphone technology afford unique opportunities to capture affective and behavioral dynamics that presage suicide risk. This case series study found that greater homestay obtained through smartphone GPS data over the course of a week, relative to one's own mean, was associated with greater odds of a suicidal event in the subsequent week. Although accuracy was modest, these findings offer a novel starting point for suicide prevention research, particularly as smartphone sensor data may have the capacity to identify who is at risk while also providing an opportunity to deliver clinical tools when that risk is greatest.