Mean level of positive affect moderates associations between volatility in positive affect, mental health, and alcohol consumption among mothers

Day-to-day affect fluctuations in adults with childhood trauma history: a two-week ecological momentary assessment study

BACKGROUND

Childhood trauma (CT) may increase vulnerability to psychopathology through affective dysregulation (greater variability, autocorrelation, and instability of emotional symptoms). However, CT associations with dynamic affect fluctuations while considering differences in mean affect levels across CT status have been understudied.

METHODS

346 adults (age = 49.25 ± 12.55, 67.0% female) from the Netherlands Study of Depression and Anxiety participated in ecological momentary assessment. Positive and negative affect (PA, NA) were measured five times per day for two weeks by electronic diaries. Retrospectively-reported CT included emotional neglect and emotional/physical/sexual abuse. Linear regressions determined associations between CT and affect fluctuations, controlling for age, sex, education, and mean affect levels.

RESULTS

Compared to those without CT, individuals with CT reported significantly lower mean PA levels (Cohen's d = -0.620) and higher mean NA levels (d = 0.556) throughout the two weeks. CT was linked to significantly greater PA variability (d = 0.336), NA variability (d = 0.353), and NA autocorrelation (d = 0.308), with strongest effects for individuals reporting higher CT scores. However, these effects were entirely explained by differences in mean affect levels between the CT groups. Findings suggested consistency of results in adults with and without lifetime depressive/anxiety disorders and across CT types, with sexual abuse showing the smallest effects.

CONCLUSIONS

Individuals with CT show greater affective dysregulation during the two-week monitoring of emotional symptoms, likely due to their consistently lower PA and higher NA levels. It is essential to consider mean affect level when interpreting the impact of CT on affect dynamics.

There is no happiness in positive affect: the pervasive misunderstanding of the rotated circumplex model

Research on positive affect (PA) and negative affect (NA) is often guided by the rotational variant of the circumplex model of affect (RCMA). According to the RCMA, PA and NA are posited to be orthogonal, with PA ranging from the union of positive valence and high activation (e.g., excited) to the union of negative valence and low activation (e.g., sluggish), and NA ranging from the union of negative valence and high activation (e.g., distressed) to the union of positive valence and low activation (e.g., relaxed). However, many authors incorrectly interpret the RCMA as positing that positively valenced affect (i.e., pleasure) and negatively valenced affect (i.e., displeasure)-rather than PA and NA, as defined in the RCMA-are orthogonal. This "received view" of the RCMA has led to significant confusion in the literature. The present paper articulates the "received view" of the RCMA and characterizes its prevalence in psychological research. A random sample of 140 empirical research articles on affect published in 14 high-impact journals covering a range of psychological subdisciplines were reviewed. Over half of the articles subscribing to the RCMA showed evidence of the "received view," demonstrating that misuse of the terms PA and NA in the context of the RCMA is rampant in the psychological literature. To reduce continued confusion in the literature, we recommend abandoning use of the terms positive affect and negative affect. We further recommend referring to the two dimensions of the RCMA as positive activation and negative activation, and the two poles of the valence dimension as positive valence and negative valence (or pleasure and displeasure).

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.

Developing Digital Tools for Remote Clinical Research: How to Evaluate the Validity and Practicality of Active Assessments in Field Settings

The ability of remote research tools to collect granular, high-frequency data on symptoms and digital biomarkers is an important strength because it circumvents many limitations of traditional clinical trials and improves the ability to capture clinically relevant data. This approach allows researchers to capture more robust baselines and derive novel phenotypes for improved precision in diagnosis and accuracy in outcomes. The process for developing these tools however is complex because data need to be collected at a frequency that is meaningful but not burdensome for the participant or patient. Furthermore, traditional techniques, which rely on fixed conditions to validate assessments, may be inappropriate for validating tools that are designed to capture data under flexible conditions. This paper discusses the process for determining whether a digital assessment is suitable for remote research and offers suggestions on how to validate these novel tools.