Arvind Pillai

Ecological momentary assessment is well-suited for capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression measures are validated for ecological momentary assessment use in the manner expected for traditional questionnaires. Therefore, this study examined the internal consistency, longitudinal stability, and convergent validity of the Mobile Patient Health Questionnaire-9 (MPHQ-9), a version of the Patient Health Questionnaire-9 (PHQ-9) modified for ecological momentary assessment. Depressed participants (N = 280; female = 83.93%; White = 79.29%) completed the MPHQ-9 three times daily for 90 days. Data from the first and last 2 weeks were analyzed to align with a prestudy PHQ-9 and poststudy PHQ-9 and Inventory of Depression and Anxiety Symptoms-II. The MPHQ-9 demonstrated fair to substantial adjusted item-total correlations (r = .42-.83), often exceeding the PHQ-9 (r = .39-.72), with Cronbach's α coefficients of .91 and .81, respectively. Reliability analyses of the MPHQ-9 using generalizability theory and multilevel modeling to account for repeated measures yielded substantial between-person reliability (∼1.0) but mixed within-person reliability estimates of .81 (generalizability theory) and .44 (multilevel modeling). The MPHQ-9 showed moderate stability (r = .69, intraclass correlation coefficient = .58), compared to the slight stability of the PHQ-9 (r = .39, intraclass correlation coefficient = .37). There was moderate agreement between the MPHQ-9 and both the PHQ-9 (r = .71) and the Inventory of Depression and Anxiety Symptoms-II General Depression subscale (r = .65). Supplementary analyses identified short forms with similar convergent validity but reduced symptom-level information. This study provides initial validation of the MPHQ-9 and compares its psychometric properties to the traditional PHQ-9. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Despite major strides in conceptualizing and modeling the multifaceted nature of suicidal thought and behavior (STB) over the past few decades, the overall predictability of STB has not improved. This may be partly due to the dynamic nature of suicidal ideation (SI), which often fluctuates over hours, yet is largely overlooked in studies. Bolstered by the application and promise of natural language processing (NLP) across the mental health field, efforts toward richer operationalization of acute SI may include analyses on written data that occur alongside changes in SI, thus offering a better understanding of STB as it unfolds. Ecological momentary assessment (EMA) data from 268 participants with major depressive disorder (MDD) were utilized to investigate acute changes in SI. Data consisted of thrice-daily SI severity scores measured through self-report responses to item 9 of the Patient Health Questionnaire mobile version (MPHQ-9) as well as free-form diary text. Using difference scores and probability of acute change thresholds, eleven acute SI phase trajectory types were defined to label change in SI over three consecutive EMAs. In total, 5,938 acute SI trajectories were paired with the temporally centered diary entries. The Sentiment Analysis and Cognition Engine (SEANCE) tool was applied to quantify the written content of each diary entry across eight established lexica. Entry results were grouped based on phase trajectory type, and the Kruskal-Wallis test was employed with post-hoc multiple hypothesis correction to statistically compare SEANCE features between all group pairs. There were 131 statistically significant (adjusted p-value < 0.05) pairwise differences between acute SI phase trajectory groups, implicating 31 NLP features. Consistent with the literature, results highlighted qualities of writing that are generally associated with heightened SI, including personal pronoun usage, passivity, and negative valence. Patterns of significance also uncovered novel contextual nuance in terms of how characteristics such as verbosity, hostility, anger, and pleasantness present in relation to SI over short change trajectories. This work provides an accessible exploratory framework that capitalizes on the benefits of dense EMA sampling and NLP to profile and quantify acute SI trajectories. The use of the MPHQ’s item 9 to quantify SI is an important limitation as it is designed to also capture precursory SI, passive SI, and SI-adjacent behaviors, potentially overestimating the SI expressed by participants. Nonetheless, future research should continue to focus on short timeframes as there are likely important signals and interpretative nuances to SI expression that have yet to be fully detailed.

Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing techniques have been used to predict depression, with pronoun and emotion usage being identified as important features. However, it is unclear how depressed individuals use positive and negative words when writing about themselves. Individuals with MDD (N = 258) completed ecological momentary assessments three times a day (including the Patient Health Questionnaire-9 [PHQ-9] and a free-text diary entry) and weekly ecological momentary assessments (including a free-text response to a life events prompt) over a 90-day study period. Using natural language processing techniques, we generated 20 model features to detect and predict averages of and changes in weekly depression from diary entries. Four regression models detected and predicted total PHQ-9 and changes in PHQ-9, and two classification models detected and predicted moderate to severe depression. The models classified current (area under the receiver operating curve [AUC] = 0.68) and future depression (AUC = 0.63), and suggest that lower valence increased usage of "I"/"me"/"my," and lower valence of passages with "I"/"me" as the subject, influenced model predictions toward more severe depression, supporting prior research. These findings highlight that depressed individuals use less positive and more negative words when referring to themselves. Treatments targeting positive affect and digital interventions with written components may be beneficial for targeting MDD. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.