Matthew D. Nemesure’s research while affiliated with Harvard Medical School and other places

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Publications (28)


Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults
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November 2024

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37 Reads

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Tess Griffin

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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.

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Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults

November 2024

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15 Reads

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1 Citation

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.


Table 2
Anhedonia in flux: Understanding the associations of emotion regulation and anxiety with anhedonia dynamics in a sample with major depressive disorder

November 2024

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103 Reads

Journal of Affective Disorders

Negative rumination and emotion regulation difficulties have been consistently linked with depression. Despite anhedonia—the lack of interest in pleasurable experiences—being a cardinal symptom of depression, emotion regulation of positive emotions, including dampening, are considered far less in the literature. Given that anhedonia may manifest through blunted responses to previously positive or enjoyable experiences, it is vital to understand how different positive emotion regulation strategies impact anhedonia symptom severity and how it can vary or change over time. Moreover, understanding the detrimental or protective nature of positive emotion regulation on anhedonia can aid with future anhedonia-focused treatments. Therefore, the current study examined the temporal association between anhedonia dynamics and two different emotion rumination strategies in response to positive emotions: dampening and positive rumination. Depressed persons (N = 137) completed baseline measures of positive emotion regulation, difficulties regulating negative emotions, and anxiety, and completed ecological momentary assessments three times per day for 90 days regarding their depressive symptoms, including anhedonia. We assessed baseline dampening and amplifying scores to predict anhedonia dynamics through four linear models with interactions. Providing partial support for our hypotheses, results indicate that amplifying positivity is positively associated with fluctuations, instability, and acute changes in anhedonia over the course of 90 days; however, neither dampening, difficulties regulating negative emotions, nor anxiety were related to anhedonia dynamics. The current findings suggest that amplifying positivity may be able to predict changes in anhedonia over time and should further be examined as a potential protective factor of anhedonia.


From Mood to Use: Using Ecological Momentary Assessments to Examine how Anhedonia and Depressed Mood Impact Cannabis Use in a Depressed Sample

July 2024

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77 Reads

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1 Citation

Psychiatry Research

Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2) how these symptoms predict CU over more proximal periods of time, including the next day or week (rather than proceeding weeks or months). The current study used data collected from ecological momentary assessment (EMA) in a sample with MDD (N = 55) and employed mixed effects models to detect and predict weekly and daily CU from anhedonia and depressed mood over 90 days. Results indicated that anhedonia and depressed mood were significantly associated with CU, yet varied at daily and weekly scales. Moreover, these associations varied in both strength and directionality. In weekly models, less anhedonia and greater depressed mood were associated with greater CU, and directionality of associations were reversed in the models looking at any CU (compared to none). Findings provide evidence that anhedonia and depressed mood demonstrate complex associations with CU and emphasize leveraging EMA-based studies to understand these associations with more fine-grained detail.


The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults

July 2024

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27 Reads

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3 Citations

Journal of Affective Disorders

Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs). The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period. Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time. Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only. The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.



Figure 3 Changes in Symptom Dynamics for Participant 3
Figure 4 Changes in Symptom Dynamics for Participant 4
Figure 5
Depressive Symptoms as a Heterogeneous and Constantly Evolving Dynamical System: Idiographic Depressive Symptom Networks of Rapid Symptom Changes Among Persons With Major Depressive Disorder

February 2024

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360 Reads

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13 Citations

Journal of Psychopathology and Clinical Science

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time.


Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones

January 2024

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78 Reads

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16 Citations

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset, making their performance unknown under distribution shifts. In this paper, we investigate the generalizability of speech-based suicidal ideation detection using mobile phones through cross-dataset experiments using four datasets with N=786 individuals experiencing major depressive disorder, auditory verbal hallucinations, persecutory thoughts, and students with suicidal thoughts. Our results show that machine and deep learning methods generalize poorly in many cases. Thus, we evaluate unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) to mitigate performance decreases owing to distribution shifts. While SSDA approaches showed superior performance, they are often ineffective, requiring large target datasets with limited labels for adversarial and contrastive training. Therefore, we propose sinusoidal similarity sub-sampling (S3), a method that selects optimal source subsets for the target domain by computing pair-wise scores using sinusoids. Compared to prior approaches, S3 does not use labeled target data or transform features. Fine-tuning using S3 improves the cross-dataset performance of deep models across the datasets, thus having implications in ubiquitous technology, mental health, and machine learning.


A flow diagram representing the selection and exclusion of participants, which led to the 939-participant sample in the present work
From top to bottom: Headers at the top of the diagram reflect projects, with citations, from which the data originated. Below the headers, we show the absolute numbers of participants, changing with further exclusion, during each stage of the project. Dialogue bubbles provide detail at a stage where participants were excluded. Large rectangular dialogue boxes contain high-level detail regarding features included at each stage. Gradient arrows indicate feature change or subsetting that occurred to produce the feature set used in the present work.
Model(s) actual versus predicted values plotted with respective correlative strength and the top five most influential features for the models’ predictions
In the respective SHAP plots, the individual dot color corresponds to the value of the variable, and location on the plot’s x axis corresponds to that point’s relative impact on the model output (e.g., a high-feature value (red) with a corresponding high x axis value (SHAP value) represents a point that strongly, positively influences the model’s prediction of depression symptom variability). A Baseline biodemographic variables. B Passively collected movement and sleep variables. C A composite model, using biodemographic and passively collected movement and sleep variables. r = Pearson’s correlation coefficient. For binary features, the presence of comorbid migraines, male sex, required financial assistance, and white race represented a higher feature value (red SHAP value color).
Comparative analysis incorporating or transforming all originally collected variables for the three respective models
Model(s) actual versus predicted values plotted with respective correlative strength and the top five most influential features for the models’ predictions. In the respective SHAP plots, the individual dot color corresponds to the value of the variable, and location on the plot’s x axis corresponds to that point’s relative impact on the model output (e.g., a high-feature value (red) with a corresponding high x axis value (SHAP value) represents a point that strongly, positively influences the model’s prediction of depression symptom variability). A Baseline biodemographic variables. B Passively collected movement and sleep variables. C A composite model, using biodemographic and passively collected movement and sleep variables. r = Pearson’s correlation coefficient.
Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data

December 2023

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96 Reads

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10 Citations

Translational Psychiatry

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included ( N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability ( r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.


Cohort Sociodemographics
NLP Features for Modeling
Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

March 2023

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248 Reads

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2 Citations

Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing (NLP) 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 (EMAs) three times a day (including the Patient Health Questionnaire-9 (PHQ-9) and a free-text diary entry) and weekly EMAs (including a free-text response to a life events prompt) over a 90-day study period. Using NLP techniques, we generated 20 model features to detect and predict average weekly depression from diary entries. Two regression models detected and predicted total PHQ-9 and two classification models detected and predicted moderate to severe depression. The models classified current (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 towards 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 that written components may be beneficial for targeting MDD.


Citations (21)


... Changes in anhedonia symptoms over time can be measured through a variety of related, yet unique change metrics. Past studies have used dynamic metrics to examine changes in depression and affect (Bos et al., 2019;Kline et al., 2024;Koval et al., 2013). ...

Reference:

Anhedonia in flux: Understanding the associations of emotion regulation and anxiety with anhedonia dynamics in a sample with major depressive disorder
The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults
  • Citing Article
  • July 2024

Journal of Affective Disorders

... A decade of research in human-computer interaction, ubiquitous computing, and digital mental health has studied how a combination of active and passive data can be used to measure behavioral and physiological signals associated with symptoms of mental illness. This research has focused on conditions including depression [2,105,147], anxiety [32], bipolar disorder [49], and schizophrenia [138,139]. Our findings advocate for continuing this behavioral tracking work, centering how passive and active data can measure functional and engagement outcomes contextualized to specific interventions in care. ...

MoodCapture: Depression Detection Using In-the-Wild Smartphone Images

... While the PHQ-9 can summarize depression symptoms from the previous 2 weeks, it cannot capture the short-term symptom dynamics that depressed people experience (Crowe et al., 2019). Recent research has demonstrated that these symptom dynamics are crucial for understanding depression as it is lived-a constantly changing, complex system unique to each patient (Nemesure & Collins et al., 2024). Additionally, reliance on retrospective recall may limit the accuracy of reporting, introducing bias into the measurement (Ben-Zeev et al., 2009;Horwitz et al., 2023). ...

Depressive Symptoms as a Heterogeneous and Constantly Evolving Dynamical System: Idiographic Depressive Symptom Networks of Rapid Symptom Changes Among Persons With Major Depressive Disorder

Journal of Psychopathology and Clinical Science

... Class attention is introduced for efficient spoken language understanding [66]. Data-driven techniques could improve word recognition, and phone-based applications could address speechbased emotion detection, suicidal tendencies [67], dysarthric speech, and stroke detection [68]. Further exploration is needed to develop a promising system. ...

Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

... Using commercial smartwatches, previous research has explored the feasibility of individual trait assessment based on daily heart rate recordings, including personality, mental stress, etc. [13,14]. Furthermore, measurements from wearable devices can provide insights into individuals' daily states, offering a more comprehensive view of the daily states of depression patients [15]. The latest studies have extended the wearable computing method to clinical use to distinguish patients with depression from those with other diseases [16]. ...

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data

Translational Psychiatry

... Borgogna et al., 2019). Additionally, the PHQ-9 has demonstrated validity when modified for EMA research; in a previous study, scores were associated with the negative emotional intensity of text-based responses (Collins et al., 2023). In the current study, reliability was acceptable (Level 1 Ω = .76 ...

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

... People with limited health literacy may respond differently to anxiety and depression questions compared to those with higher health literacy[34]. In 2023, a study with 30 adolescents (aged 15 to 17) who completed the GAD-7 scale every three weeks over a period of year found that individuals with identical GAD-7 scores experienced different dynamics of symptoms[35]. This suggests that symptom patterns are variable and dynamic, and that to gain a full understanding of a patient's clinical presentation, it is important to consider both the progression of symptoms over time and consult specialists to verify the diagnoses. ...

Leveraging deep learning models to understand the daily experience of anxiety in teenagers over the course of a year
  • Citing Article
  • February 2023

Journal of Affective Disorders

... In terms of intervention modality, digital modalities such as online topical groups surfaced as a preferred modality, and findings underscored the strengths of anonymous online spaces for enabling people to connect, share a more authentic self, and feel supported by like-minded peers. This aligns well with the recent growth of research and testing of digital interventions, including for ED prevention (Kass et al., 2017;Nemesure et al., 2023;Ong & Sündermann, 2022). At the same time, safety-related considerations in online spaces must be considered, underscoring the need for strong moderation and attention to creating protected spaces for exploration. ...

Evaluating change in body image concerns following a single session digital intervention
  • Citing Article
  • December 2022

Body Image

... This recall bias may therefore lead to an inaccurate representation of symptom severity that has been averaged over a long period over time and potentially distorted by any underlying cognitive dysfunction, which is highly prevalent in mood and anxiety disorders (Balderston et al., 2017;Gadassi Polack et al., 2020;Khdour et al., 2016;LeMoult and Gotlib, 2019;Onnela, 2021). Determining diagnosis and symptom severity in this way overlooks the intricacies and nuances of symptom profiles that can fluctuate on a day-to-day basis within patients (Nemesure et al., 2022;van Eeden et al., 2019). ...

Depressive Symptoms as a Heterogeneous and Constantly Evolving Dynamical System: Idiographic Depressive Symptom Networks of Rapid Symptom Changes among Persons with Major Depressive Disorder
  • Citing Preprint
  • October 2022

... For example, Li et al. (2024) used machine learning to predict naturalistic onset of any anxiety disorder in participants with no anxiety disorders at baseline. Other researchers applied machine learning models to understand the importance of clinical biomarkers for the concurrent diagnosis of anxiety disorders (Calderon et al., 2024;Sharma & Verbeke, 2021) or to predict response to specific GAD treatments (Gyorda et al., 2023;Lueken et al., 2016;Zainal & Newman, 2024;Zainal & Newman, in press). Also, in the prior studies listed above, baseline predictors were examined for more short-term outcomes (i.e. 6 days to 3 years) than the current study. ...

Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention
  • Citing Article
  • September 2022

Journal of Affective Disorders