Evidence map›Paper›PMID 42415071›Full record

ArticleBMC medicine2026

Complex affect dynamics offer limited incremental value for cross-sectional prediction of psychological and behavioural variables in cross-validated models.

Yinuo Shu, Priscila Thalenberg Levi, Jeggan Tiego, Toby Constable, James C Pang, Bree Hartshorn, Jessica Kwee, Kate Fortune, Mark Bellgrove, Alex Fornito

Abstract read
In one paragraph

Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yinuo ShuSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia. yinuo.shu@monash.edu.
Priscila Thalenberg LeviSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Jeggan TiegoSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Toby ConstableSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
James C PangSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Bree HartshornSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Jessica KweeSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Kate FortuneSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Mark BellgroveSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.
Alex FornitoSchool of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Victoria, Australia.

Funding

Australian Research Council FL220100184the National Health and Medical Research Council, Australia 1197431 and 3251549
6 · The paper itself

Abstract

backgroundThere is growing interest in the use of end-of-day dairies (EODD) as a means for quantifying how individuals' affective experiences fluctuate over time, and how such dynamics relate to mental health variables. A plethora of methods exist for precisely quantifying these affect dynamics, but recent work pooling data from multiple studies has suggested that most of the variance in outcome measures of depression, borderline symptoms, and life satisfaction is captured by simple measures, such as the mean (M) and standard deviation (SD) of affect ratings over time. Ever-more sophisticated approaches for measuring affect dynamics may offer little value for understanding mental health. Here, we examined a broad array of mental health variables and affect dynamic measures within a single cohort to comprehensively evaluate whether EODD-derived measures of affect dynamics are associated with specific psychopathological experiences.

methodsA total of 314 adults (97 males; aged 18-45 years) first completed a 2-hour online questionnaire comprising a comprehensive battery of psychometrically validated instruments assessing personality traits, mental health symptoms, life satisfaction, and various psychological factors, and subsequently completed 28 days of EODD assessments (M

resultsAcross all 117 variables, no complex affect dynamics measures improved cross-validated R² by more than 5.3 % beyond the M and SD of PA and NA.

conclusionsElaborate measures of affect dynamics, as indexed by the PANAS-10, offer minimal incremental explanatory power in predicting psychopathology beyond basic summary statistics of daily affect. These findings question the added value of increasingly complex measures of affect dynamics for predicting standard psychological and behavioral variables.

Indexed as

AffectMental HealthAdolescentAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and QuestionnairesYoung AdultAffect dynamicsDaily diariesIntensive longitudinal assessmentsPredictive modelsPsychological well-beingPsychopathology

Identifiers

PMID42415071
PMCPMC13637415

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.