micro-Stress EMA: A Passive Sensing Framework for Detecting in-the-wild Stress in Pregnant Mothers
Open accessAbstract
High levels of stress during pregnancy increase the chances of having a premature or low-birthweight baby. Perceived self-reported stress does not often capture or align with the physiological and behavioral response. But what if there was a self-report measure that could better capture the physiological response? Current perceived stress self-report assessments require users to answer multi-item scales at different time points of the day. Reducing it to one question, using microinteraction-based ecological momentary assessment (micro-EMA, collecting a single in situ self-report to assess behaviors) allows us to identify smaller or more subtle changes in physiology. It also allows for more frequent responses to capture perceived stress while at the same time reducing burden on the participant. We propose a framework for selecting the optimal micro-EMA that combines unbiased feature selection and unsupervised Agglomerative clustering. We test our framework in 18 women performing 16 activities in-lab wearing a Biostamp, a NeuLog, and a Polar chest strap. We validated our results in 17 pregnant women in real-world settings. Our framework shows that the question "How worried were you?" results in the highest accuracy when using a physiological model. Our results provide further in-depth exposure to the challenges of evaluating stress models in real-world situations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A daily perceived stress score greater than 4.7 is considered stressful among perinatal individuals.
“with a score > 4.7 considered to be stressful among perinatal individuals (King et al. 2019).”
From Effects of a Personalized Stress Management Intervention on Maternal Mental Health: A Randomized Clinical Trial. (2025) · cites this paper as King et al. 2019
The Study’s Self-Reporting Measures section describes thresholds for classifying activities as stressful, many of them personalized to each participant. It does not give a 4.7 cutoff for a daily perceived-stress score. Searches for “4.7” and daily stress-score thresholds found no passage establishing the claimed cutoff for perinatal individuals. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
A Global Measure of Perceived Stress
Cohen, S., Kamarck, T., & Mermelstein, R. (1983) · Journal of Health and Social Behavior · doi:10.2307/2136404 · Cited by 32,807 · see citing works
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855 · see citing works
Kirschbaum, C., Pirke, K.-M., & Hellhammer, D. H. (2008) · Neuropsychobiology · doi:10.1159/000119004 · Cited by 6,093 · see citing works
Dickerson, S. S., & Kemeny, M. E. (2004) · Psychological Bulletin · doi:10.1037/0033-2909.130.3.355 · Cited by 5,881 · see citing works
(1998) · Cited by 3,476 · see citing works
Psychological stress and the human immune system: a meta-analytic study of 30 Years of inquiry
Segerstrom, S. C., & Miller, G. E. (2004) · Psychological Bulletin · doi:10.1037/0033-2909.130.4.601 · Cited by 3,334 · see citing works
The HPA axis in major depression: classical theories and new developments
Pariante, C. M., & Lightman, S. L. (2008) · Trends in Neurosciences · doi:10.1016/j.tins.2008.06.006 · Cited by 2,009 · cited 1× · see citing works
“The TSST is known to produce a consistent hypothalamic-pituitary-adrenal (HPA) axis response in humans, and dysfunction of this axis is associated with physical and mental health disorders [41].”
Heron, K. E., & Smyth, J. M. (2010) · British Journal of Health Psychology · doi:10.1348/135910709x466063 · Cited by 1,438 · see citing works
Dunkel Schetter, C., & Tanner, L. (2012) · Current Opinion in Psychiatry · doi:10.1097/yco.0b013e3283503680 · Cited by 1,268 · cited 1× · see citing works
“Pregnant women are also known to be a high-stress population worthy of dedicated study [48].”
Van den Bergh, B. R. H., Mulder, E. J. H., Mennes, M., & Glover, V. (2005) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2004.10.007 · Cited by 1,104 · see citing works
Understanding Self-Report Bias in Organizational Behavior Research
Donaldson, S. I., & Grant-Vallone, E. J. (2002) · Journal of Business and Psychology · doi:10.1023/a:1019637632584 · Cited by 1,068 · see citing works
Maternal Stress and Preterm Birth
Dole, N. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwf176 · Cited by 848 · see citing works
Emotional intelligence moderates the relationship between stress and mental health
Ciarrochi, J., Deane, F. P., & Anderson, S. (2002) · Personality and Individual Differences · doi:10.1016/s0191-8869(01)00012-5 · Cited by 820 · see citing works
SenseCam: A Retrospective Memory Aid
Hodges, S. E., Williams, L., Berry, E., Izadi, S., Srinivasan, J., Butler, A., Smyth, G., Kapur, N., & Wood, K. R. (2006) · Lecture notes in computer science · doi:10.1007/11853565_11 · Cited by 767 · see citing works
Davis, E. P., & Sandman, C. A. (2010) · Child Development · doi:10.1111/j.1467-8624.2009.01385.x · Cited by 704 · see citing works
HPA axis activation by a socially evaluated cold-pressor test
Schwabe, L., Haddad, L., & Schächinger, H. (2008) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2008.03.001 · Cited by 698 · see citing works
Interference Effects of Stroop Color-Word Test in Childhood, Adulthood, and Aging
Comalli, P. E., Wapner, S., & Werner, H. (1962) · The Journal of Genetic Psychology · doi:10.1080/00221325.1962.10533572 · Cited by 594 · see citing works
What is stress, and how does it affect reproduction?
Dobson, H., & Smith, R. F. (2000) · Animal Reproduction Science · doi:10.1016/s0378-4320(00)00080-4 · Cited by 549 · see citing works
Stress Recognition Using Wearable Sensors and Mobile Phones
Sano, A., & Picard, R. W. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.117 · Cited by 523 · see citing works
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gática-Pérez, D., & Choudhury, T. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370270 · Cited by 511 · see citing works
van den Bos, R., Harteveld, M., & Stoop, H. (2009) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2009.04.016 · Cited by 369 · cited 2× · see citing works
“Previous studies have demonstrated that men and women differ on how they respond to stress physiologically [52], behaviorally [56], and in self-reports [11].”
Sex differences in emotional and physiological responses to the Trier Social Stress Test
Kelly, M. M., Tyrka, A. R., Anderson, G. M., Price, L. H., & Carpenter, L. L. (2007) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2007.02.003 · Cited by 346 · see citing works
Hovsepian, K., Al’Absi, M., Ertin, E., Kamarck, T. W., Nakajima, M., & Kumar, S. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807526 · Cited by 341 · see citing works
Sex Differences in Physiological Responses to Stress and in Coronary Heart Disease: A Causal Link?
Stoney, C. M., Davis, M. C., & Matthews, K. A. (1987) · Psychophysiology · doi:10.1111/j.1469-8986.1987.tb00264.x · Cited by 339 · see citing works
Analyzing features for activity recognition
Huỳnh, T., & Schiele, B. (2005) · Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies · doi:10.1145/1107548.1107591 · Cited by 336 · see citing works
Prenatal stress and cognitive development and temperament in infants
Buitelaar, J. K., Huizink, A. C., Mulder, E. J. H., de Medina, P. R., & Visser, G. H. A. (2003) · Neurobiology of Aging · doi:10.1016/s0197-4580(03)00050-2 · Cited by 332 · see citing works
Work stress, smoking status, and smoking intensity: an observational study of 46 190 employees
Kouvonen, A. M., Mika Kivimaki, B., Virtanen, M., Pentti, J., & Vahtera, J. (2004) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2004.019752 · Cited by 311 · see citing works
Activity-Aware Mental Stress Detection Using Physiological Sensors
Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., & Griss, M. (2012) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-29336-8_12 · Cited by 292 · see citing works
Stress, Endocrine Physiology and Pathophysiology -- Endotext
DeGroot, L. J., Chrousos, G. P., Dungan, K., Grossman, A. B., Jm, H., Koch, C., Korbonits, M., McLachlan, R., New, M., Purnell, J. Q., et al. (2000) · Europe PMC (PubMed Central) · Cited by 260 · see citing works
A social stress model of substance abuse.
Rhodes, J., & Jason, L. (1990) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.58.4.395 · Cited by 252 · see citing works
Plarre, K., Raij, A. B., Hossain, S. M., Ali, A. A., Nakajima, M., Al’Absi, M., Ertin, E., Kamarck, T. W., Kumar, S., Scott, M. S., et al. (2011) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · Cited by 244 · see citing works
Entringer, S., Buß, C., & Wadhwa, P. D. (2010) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0b013e3283405921 · Cited by 236 · see citing works
The stress-eating paradox: Multiple daily measurements in adult males and females
Stone, A. A., & Brownell, K. D. (1994) · Psychology and Health · doi:10.1080/08870449408407469 · Cited by 233 · see citing works
In situ with bystanders of augmented reality glasses
Denning, T., Dehlawi, Z., & Kohno, T. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557352 · Cited by 228 · see citing works
Stress, Overeating, and Obesity: Insights from Human Studies and Preclinical Models
Razzoli, M., Pearson, C., Crow, S., & Bartolomucci, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.01.026 · Cited by 228 · see citing works
Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors
Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2169804 · Cited by 221 · see citing works
A social stress model of substance abuse.
Rhodes, J. E., & Jason, L. A. (1990) · Journal of Consulting and Clinical Psychology · doi:10.1037//0022-006x.58.4.395 · Cited by 211 · see citing works
Detecting emotional stress from facial expressions for driving safety
Gao, H., Yüce, A., & Thiran, J. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026203 · Cited by 197 · see citing works
Peirce, R. S., Frone, M. R., Russell, M., & Cooper, M. L. (1996) · Health Psychology · doi:10.1037//0278-6133.15.1.38 · Cited by 194 · see citing works
Peirce, R. S., Frone, M. R., Russell, M., et al. (1996) · Health Psychology · doi:10.1037/0278-6133.15.1.38 · Cited by 138 · see citing works
μEMA: Microinteraction-based ecological momentary assessment (EMA) using a smartwatch
Intille, S., Haynes, C., Maniar, D., Ponnada, A., & Manjourides, J. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971717 · Cited by 130 · see citing works
A new paradigm to induce mental stress: the Sing-a-Song Stress Test (SSST)
Brouwer, A.-M., & Hogervorst, M. A. J. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00224 · Cited by 117 · see citing works
Physiological reactivity to infant crying: a behavioral genetic study
Out, D., Pieper, S., Bakermans‐Kranenburg, M. J., & van IJzendoorn, M. H. (2010) · Genes Brain & Behavior · doi:10.1111/j.1601-183x.2010.00624.x · Cited by 85 · see citing works
Prototyping and sampling experience to evaluate ubiquitous computing privacy in the real world
Iachello, G., Truong, K. N., Abowd, G. D., Hayes, G. R., & Stevens, M. (2006) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124923 · Cited by 83 · see citing works
UStress: Understanding college student subjective stress using wrist-based passive sensing
Egilmez, B., Poyraz, E., Zhou, W., Memik, G., Dinda, P. A., & Alshurafa, N. I. (2017) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2017.7917644 · Cited by 80 · see citing works
Self-citationMicrointeraction Ecological Momentary Assessment Response Rates
Ponnada, A., Haynes, C., Maniar, D., Manjourides, J., & Intille, S. S. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130957 · Cited by 59 · see citing works
Dinges, D., Venkataraman, S., McGlinchey, E., & Metaxas, D. N. (2007) · Acta Astronautica · doi:10.1016/j.actaastro.2006.09.003 · Cited by 43 · see citing works
Developmental pathways from prenatal tobacco and stress exposure to behavioral disinhibition
Clark, C. A. C., Espy, K. A., & Wakschlag, L. (2016) · Neurotoxicology and Teratology · doi:10.1016/j.ntt.2015.11.009 · Cited by 43 · cited 1× · see citing works
“Indeed, stress exposure in utero has been linked to deleterious outcomes for the child including impaired motor development, lower mental development, heightened behavioral disinhibition, and associated clinical patterns such as attention-deficit/hyperactivity disorder and disruptive behavior[5, 9, 14, 37].”
Self-citationChang, K.-H., Fisher, D. G., Canny, J., & Hartmann, B. (2011) · International Conference on Body Area Networks · doi:10.5555/2318776.2318794 · Cited by 39 · see citing works
Melillo, P., Formisano, C., Bracale, U. M., & Pecchia, L. (2013) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-29305-4_126 · Cited by 20 · see citing works
Alharbi, R., Pfammatter, A. F., Spring, B. J., & Alshurafa, N. I. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053271 · Cited by 13 · see citing works
Self-citationMeasuring fine-grained heart-rate using a flexible wearable sensor in the presence of noise
Zhang, L., King, Z., Egilmez, B., Reeder, J. T., Ghaffari, R., Rogers, J. A., Rosen, K. D., Bass, M., Moskowitz, J. T., Tandon, S. D., et al. (2018) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2018.8329683 · Cited by 10 · see citing works
Self-citationDoes MAOA increase susceptibility to prenatal stress in young children?
Massey, S. H., Hatcher, A. E., Clark, C. A. C., Burns, J. L., Pine, D. S., Skol, A. D., Mroczek, D. K., Espy, K. A., Goldman, D. A., Cook, E. H., et al. (2017) · Neurotoxicology and Teratology · doi:10.1016/j.ntt.2017.01.005 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- [1]Alharbi Rawan, Pfammatter Angela, Spring Bonnie, and Alshurafa Nabil. 2017 WillSense: Adherence Barriers for Passive Sensing Systems That Track Eating Behavior In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2329– 2336.
- [3]Bersak Daniel, McDarby Gary, Augenblick Ned, McDarby Phil, McDonnell Daragh, McDonald Brian, and Karkun Rahul. 2001 Intelligent biofeedback using an immersive competitive environment. Paper at the Designing Ubiquitous Computing Games Workshop at UbiComp.
- [6]Chang Keng-hao, Fisher Drew, and Canny John. 2011 Ammon: A speech analysis library for analyzing affect, stress, and mental health on mobile phones. Proceedings of PhoneSense 2011 (2011).
- [36]Lu Hong, Frauendorfer Denise, Rabbi Mashfiqui, Mast Marianne Schmid, Chittaranjan Gokul T, Campbell Andrew T, Gatica-Perez Daniel, and Choudhury Tanzeem. 2012 Stresssense: Detecting stress in unconstrained acoustic environments using smartphones In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 351– 360.
Cited by
Sorted by citations
Delmastro, F., Martino, F. D., & Dolciotti, C. (2020) · IEEE Access · doi:10.1109/access.2020.2985301 · Cited by 114 · see citing works
Detection and monitoring of stress using wearables: a systematic review
Pinge, A., Gad, V. R., Jaisighani, D., Ghosh, S., & Sen, S. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1478851 · Cited by 104 · see citing works
Evaluating the Reproducibility of Physiological Stress Detection Models
Mishra, V., Sen, S., Chen, G., Tian, H., Rogers, J., Chen, C.-H., & Kotz, D. F. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3432220 · Cited by 84 · see citing works
Self-citationBooth, B. M., Vrzáková, H., Mattingly, S. M., Martinez, G. J., Faust, L. J., & D’Mello, S. K. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3188006 · Cited by 66 · see citing works
Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3411813 · Cited by 61 · see citing works
Artificial Intelligence in Pregnancy: A Scoping Review
Oprescu, A. M., Amarante, G. M., García‐Díaz, L., Beltrán, L. M., Rey, V. E., & Ternero, M. D. C. R. (2020) · IEEE Access · doi:10.1109/access.2020.3028333 · Cited by 57 · see citing works
Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review
Ahmad, S. G., Iqbal, T., Javaid, A., Munir, E. U., Kirn, N., Jan, S. U., & Ramzan, N. (2022) · Sensors · doi:10.3390/s22124362 · Cited by 57 · see citing works
Ha, U., Madani, S., & Adib, F. M. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478121 · Cited by 32 · see citing works
Dai, R., Lu, C., Yun, L., Lenze, E. J., Avidan, M. S., & Kannampallil, T. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106207 · Cited by 47 · see citing works
Ponnada, A., Wang, S. D., Chu, D., Do, B., Dunton, G. F., & Intille, S. S. (2021) · JMIR Formative Research · doi:10.2196/32772 · Cited by 38 · see citing works
Ponnada, A., Thapa-Chhetry, B., Manjourides, J., & Intille, S. S. (2021) · JMIR mhealth and uhealth · doi:10.2196/23391 · Cited by 34 · see citing works
K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels
Kang, S., Choi, W., Park, C. Y., Cha, N., Kim, A., Khandoker, A. H., Hadjileontiadis, L. J., Kim, H., Jeong, Y., & Lee, U. (2023) · Scientific Data · doi:10.1038/s41597-023-02248-2 · Cited by 33 · see citing works
n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the WildPreprint
Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04831 · Cited by 31 · see citing works
Neupane, S., Saha, M., Ali, N., Hnat, T. W., Samiei, S. A., Nandugudi, A., Almeida, D. M., & Kumar, S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642662 · Cited by 27 · see citing works
Investigating the Reliability of Self-report Data in the Wild: The Quest for Ground Truth
Gao, N., Rahaman, M. S., Shao, W., & Salim, F. D. (2021) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3460418.3479338 · Cited by 27 · see citing works
Ng, A., Wei, B., Jain, J., Ward, E. A., Tandon, S. D., Moskowitz, J. T., Krogh‐Jespersen, S., Wakschlag, L. S., & Alshurafa, N. I. (2022) · JMIR mhealth and uhealth · doi:10.2196/33850 · Cited by 27 · see citing works
Self-citationLiang, K., Zhou, A., Zhang, Z., Zhou, H., Ma, H., & Wu, C. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3610926 · Cited by 25 · see citing works
Semi-Supervised Learning for Wearable-based Momentary Stress Detection in the Wild
Yu, H., & Sano, A. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3596246 · Cited by 33 · see citing works
Wakschlag, L. S., Tandon, D., Krogh‐Jespersen, S., Petitclerc, A., Nielsen, A., Ghaffari, R., Mithal, L., Bass, M., Ward, E., Berken, J., et al. (2021) · Developmental Psychobiology · doi:10.1002/dev.22057 · Cited by 21 · see citing works
Self-citationWearable network for multilevel physical fatigue prediction in manufacturing workers
Mohapatra, P., Aravind, V., Bisram, M., Lee, Y.-J., Jeong, H., Jinkins, K. R., Gardner, R., Streamer, J., Bowers, B., Cavuoto, L. A., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae421 · Cited by 20 · see citing works
Self-citationContextual Biases in Microinteraction Ecological Momentary Assessment (μEMA) Non-response
Ponnada, A., Li, J., Wang, S. D., Wang, W., Do, B., Dunton, G. F., & Intille, S. S. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3517259 · Cited by 20 · see citing works
Toward Lightweight In-situ Self-reporting
Yan, X.-H., Raj, S., Huang, B., Park, S., & Newman, M. W. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3432212 · Cited by 27 · see citing works
Understanding Emotion Changes in Mobile Experience Sampling
Kang, S., Park, C. Y., Kim, A., Cha, N., & Lee, U. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501944 · Cited by 18 · see citing works
Detecting Mental Disorders with Wearables: A Large Cohort Study
Dai, R., Kannampallil, T., Kim, S., Thornton, V., Bierut, L. J., & Lu, C. (2023) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3576842.3582389 · Cited by 20 · see citing works
SOSW: Stress Sensing With Off-the-Shelf Smartwatches in the Wild
Toshnazarov, K. E., Lee, U., Kim, B. H., Mishra, V., Najarro, L. A. C., & Noh, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3375299 · Cited by 18 · see citing works
A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection
Pinge, A., Bandyopadhyay, S., Ghosh, S., & Sen, S. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668342 · Cited by 15 · see citing works
Recent Progress in Biosensors for Depression Monitoring—Advancing Personalized Treatment
Yin, J., Jia, X., Li, H., Zhao, B., Yang, Y. A., & Ren, T. (2024) · Biosensors · doi:10.3390/bios14090422 · Cited by 14 · see citing works
Stress prediction using micro-EMA and machine learning during COVID-19 social isolation
Li, H., Zheng, E., Zhong, Z., Xu, C., Roma, N., Lamkin, S., Visger, T. V., Chang, Y., & Xu, W. (2021) · Smart Health · doi:10.1016/j.smhl.2021.100242 · Cited by 14 · see citing works
King, Z., Yu, H., Vaessen, T., Myin‐Germeys, I., & Sano, A. (2023) · JMIR mhealth and uhealth · doi:10.2196/46347 · Cited by 13 · see citing works
Self-citationWearable sensor for psychological stress monitoring of pregnant woman – State of the art
Kuncoro, C. B. D., Efendi, A., & Sakanti, M. M. (2023) · Measurement · doi:10.1016/j.measurement.2023.113556 · Cited by 12 · see citing works
Haldar, S., Studd, H., Wong, N., Mohr, D. C., Reddy, M. C., & Miller, E. S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555614 · Cited by 11 · see citing works
Analysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy
Paula, L. D. S., Dias, L. P. S., Francisco, R., & Barbosa, J. L. V. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2132361 · Cited by 10 · see citing works
Systematic Evaluation of Personalized Deep Learning Models for Affect Recognition
Han, Y., Zhang, P., park, M., & Lee, U. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3699724 · Cited by 10 · see citing works
Yan, X., Li, Y., Huang, B., Park, S. Y., & Newman, M. (2021) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3447526.3472046 · Cited by 8 · see citing works
Cao, R., Rahmani, A. M., & Lindsay, K. L. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274298 · Cited by 9 · see citing works
Feasibility and Utility of Multimodal Micro Ecological Momentary Assessment on a Smartwatch
Le, H., Potter, V., Lakshminarayanan, R., Mishra, V., & Intille, S. S. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714086 · Cited by 7 · see citing works
Extending Stress Detection Reproducibility to Consumer Wearable Sensors
Amin, O. B., Mishra, V., Tapera, T. M., Volpe, R. J., & Sathyanarayana, A. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252853 · Cited by 7 · see citing works
A deep learning method for contactless emotion recognition from ballistocardiogram
Yu, X., Zou, Y., Mou, X., Li, S., Bai, Z., Du, L., Li, Z., Wang, P., Chen, X., Li, X., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106891 · Cited by 7 · see citing works
Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA)
Ponnada, A., Wang, S. D., Li, J., Wang, W., Dunton, G. F., Hedeker, D., & Intille, S. S. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749541 · Cited by 6 · see citing works
Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing
Xu, Z., Zhang, J., Greenberg, J. K., Frumkin, M. R., Javeed, S., Zhang, J. K., Benedict, B. C., Botterbush, K. S., Rodebaugh, T. L., Ray, W. Z., et al. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3659628 · Cited by 6 · see citing works
Zhao, Y., & Choudhury, T. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678373 · Cited by 5 · see citing works
Balderas-Díaz, S., Rodríguez‐Fórtiz, M. J., Garrido, J. L. G., Bellido‐González, M., & Guerrero-Contreras, G. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02036-9 · Cited by 6 · see citing works
The Work of Digital Social Re-entry in Substance Use Disorder Recovery
Phelan, C., Heyer, J., Pfafman, R., Kerrigan, C., Wernette, G. K. T., Dombrowski, L. S., Miller, A., & Pater, J. A. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555658 · Cited by 5 · see citing works
Cummings, P., Petitclerc, A., Moskowitz, J. T., Tandon, S. D., Zhang, Y., MacNeill, L. A., Alshurafa, N. I., Krogh‐Jespersen, S., Hamil, J. L., Nili, A., et al. (2022) · Affective Science · doi:10.1007/s42761-022-00147-0 · Cited by 5 · see citing works
Self-citationA Contactless and Non-Intrusive System for Driver's Stress Detection
Salman, M., Jang, H., Noh, Y., Jin, S., Jeong, D., Choi, H., Han, K., & Kim, H. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3610691 · Cited by 4 · see citing works
Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks
Mehta, C., Ananthoju, P. S., Swarubini, P. J., & Ganapathy, N. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3444810 · Cited by 4 · see citing works
HealthSense: Unobtrusive Continuous Stress Monitoring Using a Novel Dual ECG-PPG Patch
Fernandes, G. J., Wei, B., Romano, C., Ulusel, D., Dambanemuya, H. K., Gao, Y., Ghaffari, R., Rogers, J. A., & Alshurafa, N. I. (2024) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn63547.2024.10780566 · Cited by 4 · see citing works
Self-citationHossain, T., Rahaman, M. S., Yamashita, K., Kono, A., Guowei, M., Oyama, S., & Yonezawa, T. (2025) · ACM International Symposium on Wearable Computers (ISWC) · doi:10.1145/3715071.3750406 · Cited by 3 · see citing works
Ecological momentary assessment in maternal health management: A scoping review
Huo, K., Wu, N. Q., Yao, X., Xu, Y., Yang, D., Ge, Y., & Huang, R. (2025) · Midwifery · doi:10.1016/j.midw.2025.104568 · Cited by 3 · see citing works
Balderas-Díaz, S., Rodríguez‐Fórtiz, M. J., Garrido, J. L. G., Bellido‐González, M., & Guerrero-Contreras, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88358-4_11 · Cited by 3 · see citing works
Tandon, S. D., Moskowitz, J. T., Edwards, R. C., Zhang, Y., Giase, G., Sinche, B., Blum, A. L., Krislov, S., Reynolds, H. M., Rangarajan, A., et al. (2025) · Archives of Women's Mental Health · doi:10.1007/s00737-025-01619-5 · Cited by 3 · see citing works
“Perceived stress data from a given day was averaged to generate a daily perceived stress score, with a score > 4.7 considered to be stressful among perinatal individuals (King et al. 2019 ).”
InfluentialSelf-citationIbrahim, S. B., Dabrowski, J., Antle, A. N., Kientz, J. A., Chesters, A., & Slovák, P. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3762810 · Cited by 1 · see citing works
PuffEM: An E-cigarette Sleeve for Estimating User Nicotine Intake
Wang, Y., Goel, R., Hassan, S., Doscher, T. J., Wang, S., Whalen, L. A., Gandhi, A. S., Sangar, Y., Cabral, A., Xu, X., et al. (2025) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3721201.3721393 · Cited by 1 · see citing works
Lee, H., Park, T., Koh, Y., Lee, J.-G., & Lee, U. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770644 · Cited by 2 · see citing works
Stress Mindset Matters: Rethinking Mental Stress Detection with Multimodal Wearable Sensors
Meegahapola, L., Constantinides, M., Radivojević, Z., Li, H., Eggleston, M. S., & Quercia, D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791340 · Cited by 2 · see citing works
Koh, Y., Kim, J., Lee, K., Jung, Y., Hong, H., & Lee, U. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791194 · Cited by 1 · see citing works
Ulloa, M., Kamali, N., Fernandes, G. J., Soyemi, E., Beltzer, M., Alshurafa, N. I., & Jacobs, M. L. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3808695 · Cited by 1 · see citing works
Self-citationHernandez, R., Jin, H., Pyatak, E. A., Roll, S. C., Gonzalez, J. S., & Schneider, S. (2022) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2022.2149878 · Cited by 1 · see citing works
Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and RecoveryPreprint
Yarosh, S., Henry, S. B., Borning, A. H., Choudhury, M. D., Lampe, C. A. C., Mynatt, E. D., Schueller, S. M., & Veinot, T. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13259 · Cited by 4 · see citing works
Investigating the Reliability of Self-report Survey in the Wild: The Quest for Ground TruthPreprint
Gao, N., Rahaman, M. S., Shao, W., & Salim, F. D. (2021) · arXiv (Cornell University)
Ng, A., Wei, B., Jain, J., Ward, E. A., Tandon, S. D., Moskowitz, J. T., Krogh‐Jespersen, S., Wakschlag, L. S., & Alshurafa, N. I. (2021) · doi:10.2196/preprints.33850
Self-citationSolutions for Complications in Pregnant Women
Charanya, J., Renugadevi, A. S., & Vennila, A. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-8974-1.ch017
Unraveling Temporal Dependencies in Stress: Insights from Self-Reported Stress Data
Lim, H., Lee, D., & Lee, U. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678421
PUREmotion: Understanding the Impact of Highway Construction on People’s Wellbeing
Hammad, O., Hollo, A. K., Clements, N. S., Miller, S. L., Mishra, S., & Sullivan, E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78548-1_5
Balderas-Díaz, S., Fórtiz, M. J. R., Garrido, J. L., Bellido‐González, M., & Guerrero-Contreras, G. (2022) · Figshare · doi:10.6084/m9.figshare.21542131
PostMom: An AI-Driven and Culturally-Tailored Persuasive Application for Postnatal Care in Nigeria
Ogbonnaya-Okafor, C., Ataguba, G., Chan, G., & Orji, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-19687-3_17
From Ephemeral to Actionable: Parent Perspectives on Speculative Family Speech Tracking
Ibrahim, S. B., Constantinides, M., Ppali, S., & Slovák, P. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790844
Understanding Behind the Smile of Emotion Workers: Detecting After-Call Stress in Call Agents
Lee, D., Lim, H., Swain, V. D., & Lee, U. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790803
Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review
Sun, Y., Che, K., Lantta, T., Axelin, A., Azimi, I., & Liljeberg, P. (2026) · JMIR Mental Health · doi:10.2196/101677
Yang, Z., Lu, Y., Bagdasarian, J. L., Swain, V. D., Agarwal, R., Campbell, C., Al‐Refaie, W. B., El-Bayoumi, J., Gao, G. G., Wang, D., et al. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3788068 · Cited by 3 · see citing works
Lucca, E. R.-D., Heavey, K., & Murnane, E. L. (2026) · Journal of Affective Disorders Reports · doi:10.1016/j.jadr.2026.101111
Hernandez, R., Schneider, S., Moore, R. C., Gonzalez, J. S., Hoogendoorn, C. J., Roll, S. C., Jin, H., Fanning, J. T., Ginsberg, J. P., & Pyatak, E. A. (2026) · Ergonomics · doi:10.1080/00140139.2026.2683140
Akanbi, O. M., Walsh, G., Kohl, D., & Blodgett, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30549-7_1
- Authors
- King, Z. D., Moskowitz, J., Egilmez, B., Zhang, S., Zhang, L., Bass, M., Rogers, J., Ghaffari, R., Wakschlag, L., & Alshurafa, N.
- Affiliations
- Northwestern University (US)
- Northwestern University, Chicago, IL, United States
- ORCID
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2019-09-09
- Volume
- 3
- Issue
- 3
- Pages
- 1-22
- ISSN
- 2474-9567 (ISSN-L)
- PMC
- PMC7236910
- OpenAlex
- W2972463583
- Cited by
- 73
- References
- 58
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 12 · 2023: 8 · 2022: 14 · 2021: 10 · 2020: 8
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Northwestern University's Institute for Innovations in Developmental Sciences
- Stanley Manne Research Institute Perinatal Origins of Disease Strategic Research Initiative
- The Robert H. & Ann Lurie Children's Hospital
- National Institute of Diabetes and Digestive and Kidney Diseases (K25DK113242)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- perceived stress, ecological momentary assessment, pregnancy, physiological stress measurement, wearable sensors, chest strap, agglomerative clustering, feature selection, preterm birth
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
