Evidence map›Paper›PMID 42867469›Full record

ArticleJMIR formative research2026

Associations Between Daily EMA of Emotional Exhaustion and Monthly Burnout Symptoms During Work Reintegration: Longitudinal Multilevel Study.

Lea Berkemeier, Wim Kamphuis, Hilbrand Oldenhuis, Rudolf Verdaasdonk, Lisette van Gemert-Pijnen

Abstract read
In one paragraph

Article in JMIR formative research, 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Lea BerkemeierDepartment of Psychology, Health and Technology, Behavioral, Management and Social Sciences (BMS), University of Twente, Drienerlolaan 5, Enschede, Overijssel, 7522 NB, The Netherlands, 31 53 489 9111.ORCID http://orcid.org/0000-0001-7065-5158
Wim KamphuisDepartment of Learning and Workforce Development, TNO, Netherlands Organisation for Applied Scientific Research, Soesterberg, Utrecht, The Netherlands.ORCID http://orcid.org/0000-0002-8363-9277
Hilbrand OldenhuisResearch Group Digital Transformation, Hanze University of Applied Sciences, Groningen, Groningen, The Netherlands.ORCID http://orcid.org/0000-0001-6493-5030
Rudolf VerdaasdonkTechMed Center, University of Twente, Enschede, Overijssel, The Netherlands.ORCID http://orcid.org/0000-0002-3115-637X
Lisette van Gemert-PijnenDepartment of Psychology, Health and Technology, Behavioral, Management and Social Sciences (BMS), University of Twente, Drienerlolaan 5, Enschede, Overijssel, 7522 NB, The Netherlands, 31 53 489 9111.ORCID http://orcid.org/0000-0001-6511-7240

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Burnout is increasingly conceptualized as comprising both stable, slowly changing features and more dynamic, fluctuating features, with emotional exhaustion widely regarded as one of burnout's core components. Traditionally, burnout has been assessed using retrospective self-report questionnaires, such as the Burnout Assessment Tool (BAT), administered at sporadic intervals. In contrast, ecological momentary assessment (EMA) enables high-frequency assessment of daily experiences while reducing recall bias. However, it is not well understood how EMA-based measures fluctuate and develop longitudinally within individuals or how they relate to retrospective burnout measures. Objective: This paper examined the relationship between 30-day aggregates of daily EMA-assessed emotional exhaustion, specifically the within-person mean level and variability (SD), and monthly burnout symptoms measured with the BAT. Methods: Eighteen participants undergoing work reintegration after stress- or burnout-related sick leave completed daily EMAs of emotional exhaustion (N=3157) and monthly BAT questionnaires over 6 months (N=94). For each monthly BAT assessment, EMA data from the preceding 30 days were aggregated into within-person mean and SD values. Linear mixed-effects models with random intercepts and time effects were fitted separately for each BAT subscale. Additionally, intraclass correlation coefficients (ICCs) were calculated to quantify the proportion of variance attributable to between-person differences. Results: ICCs indicated that a large proportion of variance in the BAT subscales was attributable to between-person differences, whereas daily EMA emotional exhaustion showed relatively greater within-person variability. Significant time-related changes were observed for the BAT subscales of Cognitive Impairment, Mental Distance, and Emotional Impairment. Within-person increases in the EMA mean of emotional exhaustion were significantly associated with higher scores on BAT Exhaustion ( Conclusions: EMA-based aggregates showed partial correspondence with monthly burnout scores. Within-person increases in mean daily emotional exhaustion were associated with higher subsequent scores on the BAT Exhaustion and Cognitive Impairment subscales, but EMA-based variability was not associated with any BAT subscale. Together with the ICC results, these findings suggest that EMA-based aggregates of emotional exhaustion can reflect short-term fluctuations and longer-term changes in symptom severity of exhaustion and cognitive impairment. Retrospective questionnaires remain useful for global symptom assessment or diagnoses at specific time points but appear less well suited for tracking within-person changes and supporting personalization. These results highlight the importance of aligning the choice of assessment with temporal and clinical goals.

Indexed as

Burnout, ProfessionalEcological Momentary AssessmentEmotional ExhaustionAdultFemaleHumansLongitudinal StudiesMaleMiddle AgedMultilevel AnalysisSurveys and Questionnairesburnout symptomsecological momentary assessmentemotional exhaustionlongitudinal monitoringmultilevel modelingwithin-person change

Identifiers

PMID42867469
PMCPMC13660457

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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.