Mood-Reactive Self-Esteem and Depression Vulnerability: Person-Specific Symptom Dynamics via Smart Phone Assessment

Heterogeneity in suicide risk: Evidence from personalized dynamic models

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

Complex Emotion Dynamics Contribute to the Prediction of Depression: A Machine Learning and Time Series Feature Extraction Approach

Emotion dynamics have demonstrated mixed ability to predict depressive symptoms and outperform traditional metrics like the mean and standard deviation of emotion reports. Here, we expand the types of emotion dynamic features used in prior work and apply a machine learning algorithm to predict depression symptoms. We obtained seven ecological momentary assessment (EMA) studies from previous work on depression and emotion dynamics (N = 890). These studies measured self-reported sadness, positive affect, and negative affect 5 to 10 times per day for 7 to 21 days (schedule varied across studies). These data were fed through a feature extraction routine to generate hundreds of emotion dynamic features. A gradient boosting machine (GBM) using all available emotion dynamics features was the best of all models assessed. This model's out-of-sample prediction (R 2 pred) for depression severity ranged from .20 to .44 depending on EMA interpolation method and samples included in the analysis. It also explained significantly more variance than a benchmark model of individuals' mean emotion ratings over the assessment period, R 2 pred = .089. Comprehensive feature mining of emotion dynamics obtained during EMA may be necessary to identify processes that predict depression symptoms beyond mean emotion ratings.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

Levels and Instability of Daily Self-Esteem in Adolescents: Relations to Depressive and Anxious Symptoms

The current study examined whether individual differences in depressive and anxious symptoms relate to level of daily self-esteem and instability of daily self-esteem in adolescence. Participants were a racially and ethnically diverse sample of adolescents (79 girls, 65 boys; M age = 13.53 years). Adolescents reported on their depressive and anxious symptoms during a baseline home visit. Then, adolescents reported on their daily self-esteem over the course of 12 consecutive days. Using hierarchical linear modeling analyses, level of daily self-esteem was negatively associated with depressive but not anxious symptoms. In addition, a positive relation emerged between instability of daily self-esteem and depressive symptoms when controlling for level of self-esteem; a similar relation did not emerge for anxious symptoms. The differential findings that emerged between both level and instability of daily self-esteem and depressive versus anxious symptoms may be linked to differences in the temporal orientation of these two types of internalizing symptoms; specifically, depressive symptoms result from backward-looking rumination over previous experiences, whereas anxious symptoms emerge from forward-looking worry about future events (Wenze et al., 2012).

Compliance With Mobile Ecological Momentary Assessment of Self-Reported Health-Related Behaviors and Psychological Constructs in Adults: Systematic Review and Meta-analysis

Background

Mobile ecological momentary assessment (mEMA) permits real-time capture of self-reported participant behaviors and perceptual experiences. Reporting of mEMA protocols and compliance has been identified as problematic within systematic reviews of children, youth, and specific clinical populations of adults.

Objective

This study aimed to describe the use of mEMA for self-reported behaviors and psychological constructs, mEMA protocol and compliance reporting, and associations between key components of mEMA protocols and compliance in studies of nonclinical and clinical samples of adults.

Methods

In total, 9 electronic databases were searched (2006-2016) for observational studies reporting compliance to mEMA for health-related data from adults (>18 years) in nonclinical and clinical settings. Screening and data extraction were undertaken by independent reviewers, with discrepancies resolved by consensus. Narrative synthesis described participants, mEMA target, protocol, and compliance. Random effects meta-analysis explored factors associated with cohort compliance (monitoring duration, daily prompt frequency or schedule, device type, training, incentives, and burden score). Random effects analysis of variance (P≤.05) assessed differences between nonclinical and clinical data sets.

Results

Of the 168 eligible studies, 97/105 (57.7%) reported compliance in unique data sets (nonclinical=64/105 [61%], clinical=41/105 [39%]). The most common self-reported mEMA target was affect (primary target: 31/105, 29.5% data sets; secondary target: 50/105, 47.6% data sets). The median duration of the mEMA protocol was 7 days (nonclinical=7, clinical=12). Most protocols used a single time-based (random or interval) prompt type (69/105, 65.7%); median prompt frequency was 5 per day. The median number of items per prompt was similar for nonclinical (8) and clinical data sets (10). More than half of the data sets reported mEMA training (84/105, 80%) and provision of participant incentives (66/105, 62.9%). Less than half of the data sets reported number of prompts delivered (22/105, 21%), answered (43/105, 41%), criterion for valid mEMA data (37/105, 35.2%), or response latency (38/105, 36.2%). Meta-analysis (nonclinical=41, clinical=27) estimated an overall compliance of 81.9% (95% CI 79.1-84.4), with no significant difference between nonclinical and clinical data sets or estimates before or after data exclusions. Compliance was associated with prompts per day and items per prompt for nonclinical data sets. Although widespread heterogeneity existed across analysis (I²>90%), no compelling relationship was identified between key features of mEMA protocols representing burden and mEMA compliance.

Conclusions

In this 10-year sample of studies using the mEMA of self-reported health-related behaviors and psychological constructs in adult nonclinical and clinical populations, mEMA was applied across contexts and health conditions and to collect a range of health-related data. There was inconsistent reporting of compliance and key features within protocols, which limited the ability to confidently identify components of mEMA schedules likely to have a specific impact on compliance.

Repeated measurement of implicit self-associations in clinical depression: Psychometric, neural, and computational properties

Implicit self-associations are theorized to be rigidly and excessively negative in affective disorders like depression. Such information processing patterns may be useful as an approach to parsing heterogeneous etiologies, substrates, and treatment outcomes within the broad syndrome of depression. However, there is a lack of sufficient data on the psychometric, neural, and computational substrates of Implicit Association Test (IAT) performance in patient populations. In a treatment-seeking, clinically depressed sample (n = 122), we administered five variants of the IAT-a dominant paradigm used in hundreds of studies of implicit cognition to date-at two repeated sessions (outside and inside a functional MRI scanner). We examined reliability, clinical correlates, and neural and computational substrates of IAT performance. IAT scores showed adequate (.67-.81) split-half reliability and convergent validity with one another and with relevant explicit symptom measures. Test-retest correlations (in vs. outside the functional MRI scanner) were present but modest (.15-.55). In depressed patients, on average, negative implicit self-representations appeared to be weaker or less efficiently processed relative to positive self-representations; elicited greater recruitment of frontoparietal task network regions; and, according to computational modeling of trial-by-trial data, were driven primarily by differential efficiency of information accumulation for negative and positive attributes. Greater degree of discrepancy between implicit and explicit self-worth predicted depression severity. Overall, these IATs show potential utility in understanding heterogeneous substrates of depression but leave substantial room for improvement. The observed clinical, neural, and computational correlates of implicit self-associations offer novel insights into a simple computer-administered task in a clinical population and point toward heterogeneous depression mechanisms and treatment targets. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

An idiographic statistical approach to clinical hypothesis testing for routine psychotherapy: A case study

In order to develop more targeted, efficient, and effective psychotherapeutic interventions, calls have been made in the literature for greater use of idiographic hypothesis testing. Idiographic analyses can provide useful information regarding mechanisms of change within individuals over time during treatment. However, it remains unclear how clinicians might utilize idiographic statistical analyses during routine treatment to test clinical hypotheses, and in turn, guide treatment. We present an idiographic statistical framework for clinical hypothesis testing with routine treatment data that enables clinicians to examine a) whether the client's symptoms and hypothesized mechanisms change over time, b) whether trajectories of change reflect the timing of interventions, c) whether mechanisms predict subsequent symptoms, and d) whether relationships exist between multiple mechanisms, symptoms, or other treatment-related constructs over time. We demonstrate the utility of the approach for clinical hypothesis testing by applying it to routine treatment data collected from a 56 year-old male who presented with a combination of anger problems, anxiety, and depressive symptoms. We discuss how results from analyses can inform the case-formulation and guide clinical decision-making. We aim to make these methods more accessible by providing an online platform where clinicians can enter client data, test their clinical hypotheses using idiographic analyses, and utilize the results to disseminate their findings.

Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data

Self-esteem is a crucial factor for an individual's well-being and mental health. Low self-esteem is associated with depression and anxiety. Data about self-esteem is oftentimes collected in Internet-based interventions through Ecological Momentary Assessments and is usually provided on an ordinal scale. We applied models for ordinal outcomes in order to predict the self-esteem of 130 patients based on diary data of an online depression treatment and thereby illustrated a path of how to analyze EMA data in Internet-based interventions. Specifically, we analyzed the relationship between mood, worries, sleep, enjoyed activities, social contact, and the self-esteem of patients. We explored several ordinal models with varying degrees of heterogeneity and estimated them using Bayesian statistics. Thereby, we demonstrated how accounting for patient-heterogeneity influences the prediction performance of self-esteem. Our results show that models that allow for more heterogeneity performed better regarding various performance measures. We also found that higher mood levels and enjoyed activities are associated with higher self-esteem. Sleep, social contact, and worries were significant predictors for only some individuals. Patient-individual parameters enable us to better understand the relationships between the variables on a patient-individual level. The analysis of relationships between self-esteem and other psychological factors on an individual level can therefore lead to valuable information for therapists and practitioners.

Ecological Momentary Assessment Using Smartphone-Based Mobile Application for Affect and Stress Assessment

Objectives

This study aimed to describe the process of utilizing a mobile application for ecological momentary assessment (EMA) to collect data on stress and mood in daily life setting.

Methods

A mobile application for the Android operating system was developed and installed with a set of questions regarding momentary mood and stress into a smartphone of a participant. The application sets alarms at semi-random intervals in 60-minute blocks, four times a day for 7 days. After obtaining all momentary affect and stress, the questions to assess the usability of the mobile EMA application were also administered.

Results

The data were collected from 97 police officers working in Gyeonggi Province of South Korea. The mean completion rate was 60.0% ranging from 3.5% to 100%. The means of positive and negative affect were 18.34 of 28 and 19.09 of 63. The mean stress was 17.92 of 40. Participants responded that the mobile application correctly measured their affect (4.34 ± 0.83) and stress (4.48 ± 0.62) of 5-point Likert scale.

Conclusions

Our study investigated the process of utilizing a mobile application to assess momentary affect and stress at repeated times. We found challenges regarding adherence to the research protocol, such as completion and delay of answering after alarm notification. Despite this inherent issue of adherence to the research protocol, the EMA still has advantages of reducing recall bias and assessing the actual moment of interest at multiple time points that improves ecological validity.

Influence of sad mood induction on implicit self-esteem and its relationship with symptoms of depression and anxiety

BACKGROUND AND OBJECTIVES

Implicit self-esteem (ISE) refers to the valence of triggered associations when the self is activated. Despite theories, previous studies often fail to observe low ISE in depression and anxiety. It is feasible that sad mood is required to activate dysfunctional self-associations. The present study tested the following hypotheses: i) ISE is lower following a sad mood induction (SMI); ii) the relationship between ISE and level of depression/anxiety symptoms is relatively strong when ISE is measured during sad mood; iii) individuals with higher levels of depression/anxiety symptoms will show a relatively large decrease in ISE following a SMI.

METHODS

In this mixed-designed study, university students completed the self-esteem implicit association test (IAT) either at baseline (control condition; n = 46) or following a SMI (experimental condition; n = 49). To test the third hypothesis, a SMI and IAT were also given in the control condition. Both conditions completed self-report measures of explicit self-esteem (ESE), and symptoms of depression and anxiety.

RESULTS

There was no support for the first two hypotheses, but some support that symptoms of anxiety correlated with larger decreases in ISE following a SMI which partly supported the third hypothesis. This disappeared when controlling for multiple testing.

LIMITATIONS

Results are limited to non-clinical participants.

CONCLUSIONS

While ISE was robust against increases in sad mood, there was some tentative support that symptoms of anxiety were related to larger decreases in ISE following a SMI.

Short-term escitalopram treatment normalizes aberrant self-referential processing in major depressive disorder

BACKGROUND

Increased self-focus and negative self-concept play an important role in depression. Antidepressants influence self-referential processing in healthy volunteers, but their function in self-processing of depressed patients remains unknown.

METHODS

Thirty-two depressed patients were randomly allocated to receive either escitalopram 10 mg or placebo for one week. After one week, neural responses to positive and negative self-referential adjectives and neutral control stimuli were assessed with functional magnetic resonance imaging. A group of matched healthy volunteers served as a control group.

RESULTS

Escitalopram decreased responses of medial fronto-parietal regions to self-referential words relative to non-emotional control stimuli, driven by increased responses to the control condition. Escitalopram also increased responses in the pre-defined region of the medial prefrontal cortex (MPFC) and the anterior cingulate cortex (ACC) to positive relative to negative words. Importantly, the changes in neural responses occurred before any effect on depressive symptoms, implying a direct effect of escitalopram. Furthermore, the placebo group had decreased responses of the MPFC and the ACC to positive self-referential processing relative to the matched healthy controls. However, neural responses of the escitalopram group and the healthy unmedicated controls were similar.

LIMITATIONS

Differences between the groups in self-reported depression symptoms and personality traits may have influenced the results.

CONCLUSION

One-week treatment with escitalopram normalized aberrant self-referential processing in depressed patients, shifting the focus from the self to the external environment and potentiating positive self-referential processing. This may be an important factor in mechanism of action of antidepressants.

Reactivity to smartphone-based ecological momentary assessment of depressive symptoms (MoodMonitor): protocol of a randomised controlled trial

BACKGROUND

Ecological momentary assessment (EMA) of mental health symptoms may influence the symptoms that it measures, i.e. assessment reactivity. In the field of depression, EMA reactivity has received little attention. We aim to investigate whether EMA of depressive symptoms induces assessment reactivity. Reactivity will be operationalised as an effect of EMA on depressive symptoms measured by a retrospective questionnaire, and, secondly, as a change in response rate and variance of the EMA ratings.

METHODS

This study is a 12-week randomised controlled trial comprising three groups: group 1 carries out EMA of mood and completes a retrospective questionnaire, group 2 carries out EMA of how energetic they feel and completes a retrospective questionnaire, group 3 is the control group, which completes only the retrospective questionnaire. The retrospective questionnaire (Centre for Epidemiologic Studies Depression scale; CES-D) assesses depressive symptoms and is administered at baseline, 6 weeks after baseline and 12 weeks after baseline. We aim to recruit 160 participants who experience mild to moderate depressive symptoms, defined as a Patient Health Questionnaire (PHQ-9) score of 5 to 15. This study is powered to detect a small between-groups effect, where no clinically relevant effect is defined as the effect size margin -0.25< d <0.25.

DISCUSSION

To our knowledge, this is the first study to investigate whether self-rated EMA of depressive symptoms could induce assessment reactivity among mildly depressed individuals.

TRIAL REGISTRATION

Netherlands Trial Register NTR5803. Registered 12 April 2016. http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5803 .

Phenomenological Characteristics of Autobiographical Memories: Responsiveness to an Induced Negative Mood State in Those With and Without a Previous History of Depression

In this study we investigated the relative accessibility of phenomenological characteristics in autobiographical memories of 104 students with and without a previous history of a depression. Participants recalled personal events that were elicited with cue words and then asked to rate these personal events for a number of phenomenological characteristics. The characteristics were typicality, rumination, valence, importance of others, expectancy, desirability, and personal importance. The effects of previous history of depression (without history or with previous history of depression) and self-reported mood (pre- and post-negative mood induction) on autobiographical recall was examined by employing a mixed factor design. Self-reported mood was measured as a manipulation check, before and after Mood Induction Procedure. Typicality, rumination and personal importance showed significant interaction effects in those with a history of depression. Ordinal regression supported the finding that those with a history of depression had a higher chance of typicality and personal importance than those without a history of depression. The results indicate that recall of autobiographical characteristics is in part dependent on induced negative mood state and on previous history of depression. The findings may prompt future research into targeted interventions that reduce individual tendencies for heightened cognitive reactivity in negative mood states for those with a history of depression.