Burnout is increasingly recognized as a dynamic state that fluctuates in daily life. Existing measures assess burnout as a stable trait, limiting their suitability for experience sampling (ESM) and daily diary studies. To address this gap, we developed short versions of the Burnout Assessment Tool (BAT) specifically for ecological momentary assessment (BAT-M) and daily diary designs (BAT-D). Based on employee data from a 2-week ESM study (N = 129; 3,087 observations), we applied Ant Colony Optimization to select eight momentary and eight daily items (two per core dimension) that maximize psychometric quality while minimizing participant burden. Both scales demonstrated excellent model fit and substantial within-person variability, correlated strongly with trait burnout, and demonstrated meaningful associations with job demands and stress. Latent Markov Factor Analysis indicated partial but non-negligible measurement noninvariance, providing valuable insights. Overall, these tools enable valid, low-burden assessment of burnout dynamics, offering opportunities for research and intervention development.
AholaK.HonkonenT.IsometsäE.KalimoR.NykyriE.AromaaA.LönnqvistJ. (2005). The relationship between job-related burnout and depressive disorders—Results from the Finnish Health 2000 Study. Journal of Affective Disorders, 88(1), 55–62. https://doi.org/10.1016/j.jad.2005.06.004
2.
AholaK.HonkonenT.PirkolaS.IsometsäE.KalimoR.NykyriE.AromaaA.LönnqvistJ. (2006). Alcohol dependence in relation to burnout among the Finnish working population. Addiction, 101(10), 1438–1443. https://doi.org/10.1111/j.1360-0443.2006.01539.x
3.
ArendM. G.SchäferT. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological Methods, 24(1), 1–19. https://doi.org/10.1037/met0000195
4.
BasinskaB. A.GruszczynskaE. (2020). Burnout as a state: Random-intercept cross-lagged relationship between exhaustion and disengagement in a 10-day study. Psychology Research and Behavior Management, Volume, 13, 267–278. https://doi.org/10.2147/PRBM.S244397
5.
BayesA.TavellaG.ParkerG. (2021). The biology of burnout: Causes and consequences. The World Journal of Biological Psychiatry, 22(9), 686–698. https://doi.org/10.1080/15622975.2021.1907713
6.
BianchiR.VerkuilenJ.SchonfeldI. S.HakanenJ. J.Jansson-FröjmarkM.Manzano-GarcíaG.LaurentE.MeierL. L. (2021). Is burnout a depressive condition? A 14-sample meta-analytic and bifactor analytic study. Clinical Psychological Science, 9(4), 579–597. https://doi.org/10.1177/2167702620979597
7.
BosleyH. G.SoysterP. D.FisherA. J. (2019). Affect dynamics as predictors of symptom severity and treatment response in mood and anxiety disorders: Evidence for specificity. Journal for Person-Oriented Research, 5(2), 101–113. https://doi.org/10.17505/jpor.2019.09
8.
BrandS.BeckJ.HatzingerM.HarbaughA.RuchW.Holsboer-TrachslerE. (2010). Associations between satisfaction with life, burnout-related emotional and physical exhaustion, and sleep complaints. The World Journal of Biological Psychiatry, 11(5), 744–754. https://doi.org/10.3109/15622971003624205
9.
CloosL.CeulemansE.KuppensP. (2023). Development, validation, and comparison of self-report measures for positive and negative affect in intensive longitudinal research. Psychological Assessment, 35(3), 189–204. (2023-26201-001). https://doi.org/10.1037/pas0001200
10.
CookeE. M.SchuurmanN. K.ZhengY. (2022). Examining the within- and between-person structure of a short form of the positive and negative affect schedule: A multilevel and dynamic approach. Psychological Assessment, 34(12), 1126–1137. https://doi.org/10.1037/pas0001167
11.
CranfordJ. A.ShroutP. E.IidaM.RafaeliE.YipT.BolgerN. (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably?Personality and Social Psychology Bulletin, 32(7), 917–929. https://doi.org/10.1177/0146167206287721
12.
CurranP. J.BauerD. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62(1), 583–619. https://doi.org/10.1146/annurev.psych.093008.100356
13.
De BeerL. T.HakanenJ. J.SchaufeliW. B.De WitteH.GlaserJ.KaltiainenJ.SeubertC.MorinA. J. S. (2025). The burnout-depression conundrum: Investigating construct-relevant multidimensionality across four countries and four patient samples. Psychology & Health, 40(8), 1358–1385. https://doi.org/10.1080/08870446.2024.2321358
14.
De BeerL. T.SchaufeliW. B.BakkerA. B. (2022). Investigating the validity of the short form Burnout Assessment Tool: A job demands-resources approach. African Journal of Psychological Assessment, 4. https://doi.org/10.4102/ajopa.v4i0.95
15.
De BeerL. T.SchaufeliW. B.De WitteH.HakanenJ. J.ShimazuA.GlaserJ.SeubertC.BosakJ.SinvalJ.RudnevM. (2020). Measurement invariance of the Burnout Assessment Tool (BAT) across seven cross-national representative samples. International Journal of Environmental Research and Public Health, 17(15), 5604. https://doi.org/10.3390/ijerph17155604
16.
DemeroutiE.BakkerA. B. (2008). The Oldenburg Burnout Inventory: A good alternative to measure burnout and engagement. In HalbeslebenJ. (Ed.), Handbook of stress and burnout in health care (pp. 65–78). Nova Science Publishers.
17.
DorigoM. (with Stützle, G.). (2004). Ant colony optimization. MIT Press.
18.
EiseleG.VachonH.LafitG.KuppensP.HoubenM.Myin-GermeysI.ViechtbauerW. (2022). The Effects of sampling frequency and questionnaire length on perceived burden, compliance, and careless responding in experience sampling data in a student population. Assessment, 29(2), 136–151. https://doi.org/10.1177/1073191120957102
ForkmannT.SpangenbergL.RathD.HallenslebenN.HegerlU.KerstingA.GlaesmerH. (2018). Assessing suicidality in real time: A psychometric evaluation of self-report items for the assessment of suicidal ideation and its proximal risk factors using ecological momentary assessments. Journal of Abnormal Psychology, 127(8), 758–769. https://doi.org/10.1037/abn0000381
21.
FrickS.Van Der MeijL.SmoldersK.DemeroutiE.De KortY. (2024). The effect of time and day of the week on burnout-related experiences: An experience sampling study. European Journal of Work and Organizational Psychology, 33(3), 276–293. https://doi.org/10.1080/1359432X.2023.2266874
22.
Fuller-TyszkiewiczM.Hartley-ClarkL.CumminsR. A.TomynA. J.WeinbergM. K.RichardsonB. (2017). Using dynamic factor analysis to provide insights into data reliability in experience sampling studies. Psychological Assessment, 29(9), 1120–1128. https://doi.org/10.1037/pas0000411
23.
GanderF.TrautA. C.UhlichM.HorstmannK. T.SteppanM.ZieglerM.GrobA. (2025). Assessing personality states–The development and validation of a five-factor model Personality States Inventory. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000906
24.
GeldhofG. J.PreacherK. J.ZyphurM. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91. https://doi.org/10.1037/a0032138
25.
GruszczynskaE.BasinskaB. A.SchaufeliW. B. (2021). Within- and between-person factor structure of the Oldenburg Burnout Inventory: Analysis of a diary study using multilevel confirmatory factor analysis. PLOS ONE, 16(5), Article e0251257. https://doi.org/10.1371/journal.pone.0251257
26.
GuthierC.DormannC.VoelkleM. C. (2020). Reciprocal effects between job stressors and burnout: A continuous time meta-analysis of longitudinal studies. Psychological Bulletin, 146(12), 1146–1173. https://doi.org/10/ghpbrx
27.
HadžibajramovićE.SchaufeliW.De WitteH. (2022). Shortening of the Burnout Assessment Tool (BAT)—From 23 to 12 items using content and Rasch analysis. BMC Public Health, 22(1), 560. https://doi.org/10.1186/s12889-022-12946-y
28.
HadžibajramovićE.SchaufeliW.WitteH. D. (2024). The ultra-short version of the Burnout Assessment Tool (BAT4)–development, validation, and measurement invariance across countries, age and gender. PLOS ONE, 19(2), Article e0297843. https://doi.org/10.1371/journal.pone.0297843
29.
HaehnerP.PfeiferL. S.JahreL. M.LuhmannM.WolfO. T.FrachL. (2023). Validation of a German Version of the Stress Overload Scale and comparison of different time frames in the instructions. Psychological Test Adaptation and Development, 4(1), 41–54. https://doi.org/10.1027/2698-1866/a000037
30.
HaightB. L.PeddieL.CrosswellA. D.HivesB. A.AlmeidaD. M.PutermanE. (2023). Combined effects of cumulative stress and daily stressors on daily health. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 42(5), 325–334. https://doi.org/10.1037/hea0001281
31.
HamakerE. L.SchuurmanN. K.ZijlmansE. A. O. (2017). Using a few snapshots to distinguish mountains from waves: Weak factorial invariance in the context of trait-state research. Multivariate Behavioral Research, 52(1), 47–60. https://doi.org/10.1080/00273171.2016.1251299
32.
HaneyA. M.FlemingM. N.WycoffA. M.GriffinS. A.TrullT. J. (2023). Measuring affect in daily life: A multilevel psychometric evaluation of the PANAS-X across four ecological momentary assessment samples. Psychological Assessment, 35(6), 469–483. https://doi.org/10.1037/pas0001231
33.
HorstmannK. T.ZieglerM. (2020). Assessing personality states: What to consider when constructing personality state measures. European Journal of Personality, 34(6), 1037–1059. https://doi.org/10.1002/per.2266
34.
JankowskyK.OlaruG.SchroedersU. (2020). Compiling measurement invariant short scales in cross–cultural personality assessment using ant colony optimization. European Journal of Personality, 34(3), 470–485. https://doi.org/10.1002/per.2260
35.
JanssenA. B.SchultzeM.GrötschA. (2017). Following the ants: Development of short scales for proactive personality and supervisor support by ant colony optimization. European Journal of Psychological Assessment, 33(6), 409–421. https://doi.org/10.1027/1015-5759/a000299
36.
JimenezA.McMahonT. P.WatsonD.Naragon-GaineyK. (2022). Dysphoria and well-being in daily life: Development and validation of ecological momentary assessment scales. Psychological Assessment, 34(6), 546–557. https://doi.org/10.1037/pas0001117
37.
KemperC. J.TrappS.KathmannN.SamuelD. B.ZieglerM. (2019). Short versus long scales in clinical assessment: Exploring the trade-off between resources saved and psychometric quality lost using two measures of obsessive–compulsive symptoms. Assessment, 26(5), 767–782. https://doi.org/10.1177/1073191118810057
38.
KleimanE. M.RizviS. L.DubersteinP. R. (2023). Initial validation of an ecological momentary assessment measure of self-efficacy to manage negative emotions. Assessment, 30(7), 2223–2233. https://doi.org/10.1177/10731911221146341
39.
KristensenT. S.HannerzH.HøghA.BorgV. (2005). The Copenhagen Psychosocial Questionnaire—A tool for the assessment and improvement of the psychosocial work environment. Scandinavian Journal of Work, Environment & Health, 31(6), 438–449. https://doi.org/10.5271/sjweh.948
40.
KungY.-T.ChiS.-C.ChenY.-C.ChangC.-M. (2021). Using residual dynamic structural equation modeling to explore the relationships among employees’ self-reported health, daily positive mood, and daily emotional exhaustion. Healthcare, 9(1), 93. https://doi.org/10.3390/healthcare9010093
41.
LeeR. T.AshforthB. E. (1996). A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of Applied Psychology, 81(2), 123–133. https://doi.org/10.1037/0021-9010.81.2.123
42.
LeinerD. J. (2019). Too fast, too straight, too weird: Non-reactive indicators for meaningless data in internet surveys. Survey Research Methods, 13, 229–248. https://doi.org/10.18148/SRM/2019.V13I3.7403
43.
LeiteW. L.HuangI.-C.MarcoulidesG. A. (2008). Item selection for the development of short forms of scales using an ant colony optimization algorithm. Multivariate Behavioral Research, 43(3), 411–431. https://doi.org/10.1080/00273170802285743
44.
MaslachC. (1982). Burnout: The cost of caring. Prentice Hall.
45.
MaslachC.JacksonS. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205
46.
McNeishD.MackinnonD. P.MarschL. A.PoldrackR. A. (2021). Measurement in intensive longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal, 28(5), 807–822. https://doi.org/10.1080/10705511.2021.1915788
47.
MeierS. T.KimS. (2022). Meta-regression analyses of relationships between burnout and depression with sampling and measurement methodological moderators. Journal of Occupational Health Psychology, 27(2), 195–206. https://doi.org/10.1037/ocp0000273
48.
MestdaghM.VerdonckS.PiotM.NiemeijerK.KilaniG.TuerlinckxF.KuppensP.DejonckheereE. (2023). m-Path: An easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice. Frontiers in Digital Health, 5, 1182175. https://doi.org/10.3389/fdgth.2023.1182175
49.
MillsapR. E. (2010). Testing measurement invariance using item response theory in longitudinal data: An introduction. Child Development Perspectives, 4(1), 5–9. https://doi.org/10.1111/j.1750-8606.2009.00109.x
50.
MurrayA. L.XiaoZ.ZhuX.SpeyerL. G.YangY.BrownR. H.KatusL.EisnerM.RibeaudD. (2023). Psychometric evaluation of an adapted version of the perceived stress scale for ecological momentary assessment research. Stress and Health, 39(4), 841–853. https://doi.org/10.1002/smi.3229
51.
Myin-GermeysI.OorschotM.CollipD.LatasterJ.DelespaulP.Van OsJ. (2009). Experience sampling research in psychopathology: Opening the black box of daily life. Psychological Medicine, 39(9), 1533–1547. https://doi.org/10.1017/S0033291708004947
52.
NelsonJ.KlumparendtA.DoeblerP.EhringT. (2020). Everyday emotional dynamics in major depression. Emotion, 20(2), 179–191. https://doi.org/10.1037/emo0000541
53.
O’ConnorK.Muller NeffD.PitmanS. (2018). Burnout in mental health professionals: A systematic review and meta-analysis of prevalence and determinants. European Psychiatry, 53, 74–99. https://doi.org/10.1016/j.eurpsy.2018.06.003
54.
OlaruG.DannerD. (2021). Developing cross-cultural short scales using ant colony optimization. Assessment, 28(1), 199–210. https://doi.org/10.1177/1073191120918026
55.
OlaruG.SchroedersU.HartungJ.WilhelmO. (2019). Ant Colony Optimization and Local Weighted Structural Equation Modeling. A Tutorial on Novel Item and Person Sampling Procedures for Personality Research. European Journal of Personality, 33(3), 400–419. https://doi.org/10.1002/per.2195
56.
OlthofM.HasselmanF.StrunkG.Van RooijM.AasB.HelmichM. A.SchiepekG.Lichtwarck-AschoffA. (2020). Critical fluctuations as an early-warning signal for sudden gains and losses in patients receiving psychotherapy for mood disorders. Clinical Psychological Science, 8(1), 25–35. https://doi.org/10.1177/2167702619865969
57.
R Core Team. (2024). R: A language and environment for statistical computing [Software]. R Foundation for Statistical Computing. https://www.R-project.org/
58.
ReinM. T.VogelsmeierL. V. D. E.BolsinovaM. (2023). Assessing life satisfaction in everyday life: An example of scale development for ecological momentary assessment. PsyArXiv. https://doi.org/10.31234/osf.io/47bry
59.
ReisD.HartA.KrautterK.PresteleE.LehrD.FrieseM. (2024). Mindfulness and cognitive–behavioral strategies for psychological detachment: Comparing effectiveness and mechanisms of change. Journal of Occupational Health Psychology, 29(4), 258–279. https://doi.org/10.1037/ocp0000381
60.
RiedlE. M.ThomasJ. (2019). The moderating role of work pressure on the relationships between emotional demands and tension, exhaustion, and work engagement: An experience sampling study among nurses. European Journal of Work and Organizational Psychology, 28(3), 414–429. https://doi.org/10.1080/1359432X.2019.1588251
61.
RushJ.HoferS. M. (2014). Differences in within- and between-person factor structure of positive and negative affect: Analysis of two intensive measurement studies using multilevel structural equation modeling. Psychological Assessment, 26(2), 462–473. https://doi.org/10.1037/a0035666
62.
SalvagioniD. A. J.MelandaF. N.MesasA. E.GonzálezA. D.GabaniF. L.AndradeS. M. D. (2017). Physical, psychological and occupational consequences of job burnout: A systematic review of prospective studies. PLOS ONE, 12(10), e0185781. https://doi.org/10.1371/journal.pone.0185781
63.
SchäferS. K.Von BorosL.GöritzA. S.BaumannS.WessaM.TüscherO.LiebK.MöhringA. (2023). The Perceived Stress Scale 2&2: A two-factorial German short version of the Perceived Stress Scale. Frontiers in Psychiatry, 14, 1195986. https://doi.org/10.3389/fpsyt.2023.1195986
64.
SchaufeliW. B.DesartS.De WitteH. (2020). Burnout Assessment Tool (BAT)—Development, Validity, and Reliability. International Journal of Environmental Research and Public Health, 17(24), 9495. https://doi.org/10.3390/ijerph17249495
65.
SchaufeliW. B.De WitteH. (2023). Burnout Assessment Tool (BAT): A fresh look at burnout. In KrägelohC. U.AlyamiM.MedvedevO. N. (Eds.), International Handbook of Behavioral Health Assessment (pp. 1–24). Springer. https://doi.org/10.1007/978-3-030-89738-3_54-1
66.
SchillingO.LückeA.KatzorreckM.KunzmannU.GerstorfD. (2021). Measurement invariance of negative affect in ambulatory assessments of young-old and old-old adults. Innovation in Aging, 5(Suppl. 1), 655–655. https://doi.org/10.1093/geroni/igab046.2477
67.
SchoeversR. A.van BorkuloC. D.LamersF.ServaasM. N.BastiaansenJ. A.BeekmanA. T. F.van HemertA. M.SmitJ. H.PenninxB. W. J. H.RieseH. (2021). Affect fluctuations examined with ecological momentary assessment in patients with current or remitted depression and anxiety disorders. Psychological Medicine, 51(11), 1906–1915. https://doi.org/10.1017/S0033291720000689
68.
ScholtenS.SchemerL.HerzogP.HaasJ. W.HeiderJ.WinterD.ReisD.GlombiewskiJ. A. (2024). Leveraging single-case experimental designs to promote personalized psychological treatment: Step-by-step implementation protocol with stakeholder involvement of an outpatient clinic for personalized psychotherapy. Administration and Policy in Mental Health and Mental Health Services Research, 51(5), 702–724. https://doi.org/10.1007/s10488-024-01363-5
69.
SchroedersU.WilhelmO.OlaruG. (2016). Meta-heuristics in short scale construction: Ant colony optimization and genetic algorithm. PLOS ONE, 11(11), e0167110. https://doi.org/10.1371/journal.pone.0167110
70.
SchuurmanN. K.HamakerE. L. (2019). Measurement error and person-specific reliability in multilevel autoregressive modeling. Psychological Methods, 24(1), 70–91. https://doi.org/10.1037/met0000188
71.
ShalomJ. G.AderkaI. M. (2020). A meta-analysis of sudden gains in psychotherapy: Outcome and moderators. Clinical Psychology Review, 76, 101827. https://doi.org/10.1016/j.cpr.2020.101827
SpectorP. E.JexS. M. (1998). Development of four self-report measures of job stressors and strain: Interpersonal Conflict at Work Scale, Organizational Constraints Scale, Quantitative Workload Inventory, and Physical Symptoms Inventory. Journal of Occupational Health Psychology, 3(4), 356–367. https://doi.org/10.1037/1076-8998.3.4.356
74.
StoneA. A.SchneiderS.SmythJ. M. (2023). Evaluation of pressing issues in ecological momentary assessment. Annual Review of Clinical Psychology, 19(1), 107–131. https://doi.org/10.1146/annurev-clinpsy-080921-083128
75.
VandenbergR. J.LanceC. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. https://doi.org/10.1177/109442810031002
Villacura-HerreraC.Acosta-AntognoniH.MaldonadoJ.ArriazaF.Cancino-LetelierN.Nvo-FernándezM.SchaufeliW. B. (2025). Burnout Assessment Tool: A reliability generalisation meta-analysis. Work & Stress, 39(2), 169–196. https://doi.org/10.1080/02678373.2025.2484758
78.
VogelsmeierL. V. D. E.JongerlingJ.MaassenE. (2023). Assessing and accounting for measurement in intensive longitudinal studies: Current practices, considerations, and avenues for improvement. PsyArXiv. https://doi.org/10.31234/osf.io/uat5r
79.
VogelsmeierL. V. D. E.VermuntJ. K.De RooverK. (2022). How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa. Behavior Research Methods, 55(5), 2387–2422. https://doi.org/10.3758/s13428-022-01898-1
80.
VogelsmeierL. V. D. E.VermuntJ. K.Van RoekelE.De RooverK. (2019). Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies. Structural Equation Modeling: A Multidisciplinary Journal, 26(4), 557–575. https://doi.org/10.1080/10705511.2018.1554445
81.
WeigeltO.SiestrupK.PremR. (2021). Continuity in transition: Combining recovery and day-of-week perspectives to understand changes in employee energy across the 7-day week. Journal of Organizational Behavior, 42(5), 567–586. https://doi.org/10.1002/job.2514
XanthopoulouD.MeierL. L. (2014). Daily burnout experiences: Critical events and measurement challenges. In LeiterM. P.BakkerA. B.MaslachC. (Eds.), Burnout at work: A psychological perspective (pp. 80–97). Taylor & Francis.
