Predicting Emotional Exhaustion with Multimodal Sensor Data During Return‑to‑Work Trajectories: A 6‑Month Longitudinal Study

Lea Berkemeier; Wim  Kamphuis; Hilbrand Oldenhuis; Robin N Kok; Ruud Verdaasdonk; Lisette van Gemert-Pijnen

doi:10.1007/s10926-026-10395-7

Predicting Emotional Exhaustion with Multimodal Sensor Data During Return‑to‑Work Trajectories: A 6‑Month Longitudinal Study

Vertaalde titel van de bijdrage: Emotionele uitputting tijdens re-integratietrajecten voorspellen met behulp van multimodale sensordata: Een 6-maanden lange longitudinale studie

Lea Berkemeier (First author)
, Wim Kamphuis
, Hilbrand Oldenhuis
, Robin N Kok
, Ruud Verdaasdonk
, Lisette van Gemert-Pijnen

Digitale Transformatie

University of Twente, Psychology, Health & Technology, Centre for eHealth Research and Wellbeing
TNO
Human Total Care
University of Twente - Faculty of Science and Technology (S&T)

Onderzoeksoutput: Article › Academic › peer review

Samenvatting

Purpose: Emotional exhaustion is a core component of burnout and impacts return-to-work trajectories following sick leave
due to burnout or stress. Previous research identified links between emotional exhaustion and sleep, physical activity, mobility, and smartphone usage, yet little is known about how these associations vary within and between individuals over time.
This study examined how emotional exhaustion can be monitored and predicted using multimodal data from smartphones
and smart rings during return-to-work trajectories.
Methods: Eighteen employees on sick leave in the Netherlands due to stress or burnout symptoms were recruited via occupational physicians. For six months, participants completed daily Ecological Momentary Assessments (EMAs) of emotional
exhaustion and provided multimodal sensor data on sleep and physical activity (Oura ring), mobility, and phone usage patterns (Avicenna app). Between- and within-person associations were examined using correlational analyses and linear mixed
models. Subject-dependent Random Forest (RF) models were trained to assess the predictive performance of multimodal
features.
Results: Subject-dependent RF models achieved an average Spearman’s ρ̄ of 0.38 (range: 0.10–0.72). Sleep and physical
activity features showed more consistent associations with emotional exhaustion than mobility and smartphone usage patterns, which were more heterogeneous. The strength and direction of associations, as well as the most predictive features,
varied substantially between participants.
Conclusions: Multimodal device data can modestly predict emotional exhaustion, with performance varying by individual.
Results indicate that emotional exhaustion patterns are highly variable among employees, necessitating individualized
approaches to return-to-work counselling. Future research should incorporate longer monitoring periods and examine interand intra-individual variability

Vertaalde titel van de bijdrage	Emotionele uitputting tijdens re-integratietrajecten voorspellen met behulp van multimodale sensordata: Een 6-maanden lange longitudinale studie
Originele taal-2	English
Pagina's (van-tot)	1-13
Aantal pagina's	13
Tijdschrift	Journal of Occupational Rehabilitation
DOI's	https://doi.org/10.1007/s10926-026-10395-7
Status	Published - 22 apr. 2026

Duurzame ontwikkelingsdoelstellingen van de VN

Deze output draagt bij aan de volgende duurzame ontwikkelingsdoelstelling(en)

SDG 03 – Goede gezondheid en welzijn

Keywords

emotionele uitputting
werknemer
longitudionaal
werkhervatting
arbeidsongeschikt
draagbare sensoren

Research Focus Areas Hanze University of Applied Sciences

Ondernemerschap
Healthy Ageing

Research Focus Areas Research Centre or Centre of Expertise

Digitale Transformatie

Publinova thema's

Techniek
Gezondheid
Mens en Maatschappij
Taal, Cultuur & Kunsten
Economie en Management
Recht

Toegang tot document

10.1007/s10926-026-10395-7Licentie: CC BY

https://link.springer.com/article/10.1007/s10926-026-10395-7Licentie: CC BY

5 Article
1 Contribution to conference proceeding
1 Paper
1 Poster

Does Wearable-Measured Heart Rate Variability During Sleep Predict Perceived Morning Mental and Physical Fitness?
de Vries, H., Oldenhuis, H., van der Schans, C., Sanderman, R. & Kamphuis, W., 9 jan. 2023, In: Applied Psychophysiology Biofeedback. 48, 2, blz. 247-257 11 blz.
Onderzoeksoutput: Article › Academic › peer review

Open Access
Predicting subjective stress using wearable-measured sleep and resting HRV
Vertaalde titel van de bijdrage: Het voorspellen van subjectieve gevoelens van stress met behulp van door draagbare technologie gemeten slaap en hartritme-variabiliteitde Vries, H., Pennings, H. J. M., van der Schans, C., Sanderman, R., Oldenhuis, H. & Kamphuis, W., 20 apr. 2023. 1 blz.
Onderzoeksoutput: Poster › Academic

Open Access
Bestand
Towards continuous mental state detection in everyday settings: investigating between-subjects variations in a longitudinal study
Berkemeier, L. (First author), Kamphuis, W., de Vries, H., Brouwer, A.-M., van Baardewijk, J. U., Schadd, M., Oldenhuis, H., Verdaasdonk, R. & van Gemert-Pijnen, L., 1 jan. 2023, Towards continuous mental state detection in everyday settings: investigating between-subjects variations in a longitudinal study. AHFE International, Vol. 102. blz. 131-140 9 blz. 10.54941/ahfe1003013
Onderzoeksoutput: Contribution to conference proceeding › Academic › peer review

Open Access
Bestand
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Citeer dit

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title = "Predicting Emotional Exhaustion with Multimodal Sensor Data During Return‑to‑Work Trajectories: A 6‑Month Longitudinal Study",

abstract = "Purpose: Emotional exhaustion is a core component of burnout and impacts return-to-work trajectories following sick leave due to burnout or stress. Previous research identified links between emotional exhaustion and sleep, physical activity, mobility, and smartphone usage, yet little is known about how these associations vary within and between individuals over time. This study examined how emotional exhaustion can be monitored and predicted using multimodal data from smartphones and smart rings during return-to-work trajectories. Methods: Eighteen employees on sick leave in the Netherlands due to stress or burnout symptoms were recruited via occupational physicians. For six months, participants completed daily Ecological Momentary Assessments (EMAs) of emotional exhaustion and provided multimodal sensor data on sleep and physical activity (Oura ring), mobility, and phone usage patterns (Avicenna app). Between- and within-person associations were examined using correlational analyses and linear mixed models. Subject-dependent Random Forest (RF) models were trained to assess the predictive performance of multimodal features. Results: Subject-dependent RF models achieved an average Spearman{\textquoteright}s {\=ρ} of 0.38 (range: 0.10–0.72). Sleep and physical activity features showed more consistent associations with emotional exhaustion than mobility and smartphone usage patterns, which were more heterogeneous. The strength and direction of associations, as well as the most predictive features, varied substantially between participants. Conclusions: Multimodal device data can modestly predict emotional exhaustion, with performance varying by individual. Results indicate that emotional exhaustion patterns are highly variable among employees, necessitating individualized approaches to return-to-work counselling. Future research should incorporate longer monitoring periods and examine inter- and intra-individual variability.",

keywords = "emotionele uitputting, werknemer, longitudionaal, werkhervatting, arbeidsongeschikt, draagbare sensoren, Emotional exhaustion, Employee, Longitudinal, Return-to-work, Sick-listed, Wearable sensors",

author = "Lea Berkemeier and Wim Kamphuis and Hilbrand Oldenhuis and Kok, \{Robin N\} and Ruud Verdaasdonk and \{van Gemert-Pijnen\}, Lisette",

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month = apr,

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doi = "10.1007/s10926-026-10395-7",

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pages = "1--13",

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T2 - A 6‑Month Longitudinal Study

AU - Berkemeier, Lea

AU - Kamphuis, Wim

AU - Oldenhuis, Hilbrand

AU - Kok, Robin N

AU - Verdaasdonk, Ruud

AU - van Gemert-Pijnen, Lisette

PY - 2026/4/22

Y1 - 2026/4/22

N2 - Purpose: Emotional exhaustion is a core component of burnout and impacts return-to-work trajectories following sick leave due to burnout or stress. Previous research identified links between emotional exhaustion and sleep, physical activity, mobility, and smartphone usage, yet little is known about how these associations vary within and between individuals over time. This study examined how emotional exhaustion can be monitored and predicted using multimodal data from smartphones and smart rings during return-to-work trajectories. Methods: Eighteen employees on sick leave in the Netherlands due to stress or burnout symptoms were recruited via occupational physicians. For six months, participants completed daily Ecological Momentary Assessments (EMAs) of emotional exhaustion and provided multimodal sensor data on sleep and physical activity (Oura ring), mobility, and phone usage patterns (Avicenna app). Between- and within-person associations were examined using correlational analyses and linear mixed models. Subject-dependent Random Forest (RF) models were trained to assess the predictive performance of multimodal features. Results: Subject-dependent RF models achieved an average Spearman’s ρ̄ of 0.38 (range: 0.10–0.72). Sleep and physical activity features showed more consistent associations with emotional exhaustion than mobility and smartphone usage patterns, which were more heterogeneous. The strength and direction of associations, as well as the most predictive features, varied substantially between participants. Conclusions: Multimodal device data can modestly predict emotional exhaustion, with performance varying by individual. Results indicate that emotional exhaustion patterns are highly variable among employees, necessitating individualized approaches to return-to-work counselling. Future research should incorporate longer monitoring periods and examine inter- and intra-individual variability.

AB - Purpose: Emotional exhaustion is a core component of burnout and impacts return-to-work trajectories following sick leave due to burnout or stress. Previous research identified links between emotional exhaustion and sleep, physical activity, mobility, and smartphone usage, yet little is known about how these associations vary within and between individuals over time. This study examined how emotional exhaustion can be monitored and predicted using multimodal data from smartphones and smart rings during return-to-work trajectories. Methods: Eighteen employees on sick leave in the Netherlands due to stress or burnout symptoms were recruited via occupational physicians. For six months, participants completed daily Ecological Momentary Assessments (EMAs) of emotional exhaustion and provided multimodal sensor data on sleep and physical activity (Oura ring), mobility, and phone usage patterns (Avicenna app). Between- and within-person associations were examined using correlational analyses and linear mixed models. Subject-dependent Random Forest (RF) models were trained to assess the predictive performance of multimodal features. Results: Subject-dependent RF models achieved an average Spearman’s ρ̄ of 0.38 (range: 0.10–0.72). Sleep and physical activity features showed more consistent associations with emotional exhaustion than mobility and smartphone usage patterns, which were more heterogeneous. The strength and direction of associations, as well as the most predictive features, varied substantially between participants. Conclusions: Multimodal device data can modestly predict emotional exhaustion, with performance varying by individual. Results indicate that emotional exhaustion patterns are highly variable among employees, necessitating individualized approaches to return-to-work counselling. Future research should incorporate longer monitoring periods and examine inter- and intra-individual variability.

KW - emotionele uitputting

KW - werknemer

KW - longitudionaal

KW - werkhervatting

KW - arbeidsongeschikt

KW - draagbare sensoren

KW - Emotional exhaustion

KW - Employee

KW - Longitudinal

KW - Return-to-work

KW - Sick-listed

KW - Wearable sensors

U2 - 10.1007/s10926-026-10395-7

DO - 10.1007/s10926-026-10395-7

M3 - Article

SN - 1053-0487

SP - 1

EP - 13

JO - Journal of Occupational Rehabilitation

JF - Journal of Occupational Rehabilitation

ER -

Predicting Emotional Exhaustion with Multimodal Sensor Data During Return‑to‑Work Trajectories: A 6‑Month Longitudinal Study

Samenvatting

Duurzame ontwikkelingsdoelstellingen van de VN

Keywords

Research Focus Areas Hanze University of Applied Sciences

Research Focus Areas Research Centre or Centre of Expertise

Publinova thema's

Toegang tot document

Vingerafdruk

Onderzoekersoutput

Does Wearable-Measured Heart Rate Variability During Sleep Predict Perceived Morning Mental and Physical Fitness?

Predicting subjective stress using wearable-measured sleep and resting HRV

Towards continuous mental state detection in everyday settings: investigating between-subjects variations in a longitudinal study

Citeer dit