Matthew David Nemesure

Passive data are increasingly used to capture continuous and unobtrusive information in psychological research. While digital markers based on step count, time at home, distance traveled, and sleep are frequently associated with depression-related and other psychological constructs, the reliability of passive data is rarely evaluated, potentially limiting credibility. In this paper, we evaluated the reliability of five depression-related constructs–sleep disturbance, activity level, affective dysregulation, behavioral inactivation, and social withdrawal– using three approaches: internal consistency, temporal stability, and inter-rater agreement. Analyses were conducted across daily, weekly, and monthly aggregation in 294 clinically depressed individuals over 90 days. Results revealed heterogeneity across constructs, measures, and timescales. Between-person internal consistency was generally high, indicating that passive measures can robustly differentiate individuals (mean consistency = .88, range = .32–1). Activity emerged as the most reliable construct, showing high between-person (mean = .92) and within-person (mean = .75) consistency, particularly at daily and weekly timescales. In contrast, constructs such as sleep, behavioral inactivation, and social withdrawal showed greater variability and often low within-person reliability, suggesting that their measures capture distinct constructs. Temporal stability increased with longer aggregation windows: while day-to-day behavior fluctuated considerably (mean intra class correlation (ICC) = .37), stability was moderate weekly (mean ICC = .64) and even higher monthly (mean ICC = .78). Lastly, device-specific differences highlight the need to account for measurement effects (Steps (Garmin vs phone): r = .52). These findings underscore a critical yet often overlooked aspect of passive sensing research: reliability should be systematically evaluated when constructing and interpreting passive measures.

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).

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).

Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression symptom measures are validated for EMA use in the manner expected for traditional questionnaires. Therefore, the current study examined the internal consistency, longitudinal stability, and convergent validity of the Mobile Patient Health Questionnaire-9 (MPHQ-9), a version of the PHQ-9 modified for EMA. 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 pre-study PHQ-9 and post-study PHQ-9 and Inventory of Depression and Anxiety Symptoms - Expanded Version (IDAS-II). The MPHQ-9 demonstrated fair to substantial adjusted item-total correlations (r = 0.42–0.83), often exceeding the PHQ-9 (r = 0.39–0.72), with Cronbach’s alpha coefficients of .91 and .81, respectively. Reliability analyses of the MPHQ-9 using generalizability theory (GT) and multilevel modeling (MLM) to account for repeated measures yielded substantial between-person reliability (~1.0) but mixed within-person reliability estimates of 0.81 (GT) and 0.44 (MLM). The MPHQ-9 showed moderate stability (r = .69, ICC = .58), compared to the slight stability of the PHQ-9 (r = .39, ICC = .37). There was moderate agreement between the MPHQ-9 and both the PHQ-9 (r = .71) and the IDAS-II General Depression subscale (r = .65). This study provides initial validation of the MPHQ-9 and compares its psychometric properties to the traditional PHQ-9.

Major Depressive Disorder (MDD) is a prevalent mental health disorder often identified by persistentlow mood, and a lack of motivation and energy. Persons with MDD often experience largefluctuations in their symptoms over hours and days, which can offer valuable clinical insights,highlighting potential targets for treatment and intervention strategies. However, there remains a gapin our understanding of how feasible it is to accurately predict fluctuations in depressive symptoms. Inthis preregistered study, we aim to explore the ability to predict acute fluctuations in depressivesymptoms using deep learning models among individuals diagnosed with MDD. Passively-collectedaccelerometry data, a ubiquitous, privacy-preserving data stream with empirical ties to MDDsymptomatology, was used to predict instability in depressive symptoms. Our sample containsclinically-depressed individuals per a structured clinical interview (N = 179, Mean Age = 38.2 ± 10.0,Women = 81.6%). Participants filled out their depressive symptom assessments three times a day overthe course of 90 days, resulting in an average of 214.49 EMA questionnaires (median: 237, min: 77,max: 270, SD: 54.27) per person. Although our results suggest a small-moderate correspondencebetween the predictions and observed outcomes of passively-collected movement alone in predictingfuture high/low depression instability (AUCTest = 0.59 ± 0.06, SensitivityTest = 0.30 ± 0.13, SpecificityTest = 0.81 ± 0.10), our model performed worse than a random intercept only baseline model(AUCTest = 0.83) and predicted intraindividual variability of instability in depressive symptoms belowchance (AUCTest = 0.46 ± 0.21). Thus, there is a need to improve predictive performance to enhanceclinical utility. This may require the inclusion of additional passive sensing modalities in futureresearch.

Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression symptom measures are validated for EMA use in the manner expected for traditional questionnaires. Therefore, the current study examined the internal consistency, longitudinal stability, and convergent validity of the Mobile Patient Health Questionnaire-9 (MPHQ-9), a version of the PHQ-9 modified for EMA. 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 pre-study PHQ-9 and post-study PHQ-9 and Inventory of Depression and Anxiety Symptoms - Expanded Version (IDAS-II). The MPHQ-9 demonstrated fair to substantial adjusted item-total correlations (r = 0.42–0.83), often exceeding the PHQ-9 (r = 0.39–0.72). Inter-item correlations ranged from slight to substantial for the MPHQ-9 (r = 0.26–0.87) and were typically larger than the correlations observed for the PHQ-9 (r = 0.11–0.71). Cronbach’s alpha coefficients were substantial for both the MPHQ-9 (.91) and PHQ-9 (.81). The MPHQ-9 showed moderate and superior stability (r = .69), compared to the slight stability of the PHQ-9 (r = .39). There was moderate agreement between the MPHQ-9 and both the PHQ-9 (r = .71) and the IDAS-II General Depression subscale (r = .65). This study provides initial validation of the MPHQ-9 and suggests it may be a better measure of depression symptomatology than the PHQ-9 in some contexts.