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Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life

Published online by Cambridge University Press:  20 October 2023

C. Ralph-Nearman Open the ORCID record for C. Ralph-Nearman [Opens in a new window] ,
L. E. Sandoval-Araujo Open the ORCID record for L. E. Sandoval-Araujo [Opens in a new window] ,
A. Karem Open the ORCID record for A. Karem [Opens in a new window] ,
C. E. Cusack Open the ORCID record for C. E. Cusack [Opens in a new window] ,
S. Glatt ,
M. A. Hooper Open the ORCID record for M. A. Hooper [Opens in a new window] ,
C. Rodriguez Pena ,
D. Cohen ,
S. Allen  and
E. D. Cash Open the ORCID record for E. D. Cash [Opens in a new window]
...Show all authors
C. Ralph-Nearman*
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
L. E. Sandoval-Araujo
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
A. Karem
Affiliation:
Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
C. E. Cusack
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
S. Glatt
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
M. A. Hooper
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA Department of Psychology, Vanderbilt University, Nashville, TN, USA
C. Rodriguez Pena
Affiliation:
Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
D. Cohen
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
S. Allen
Affiliation:
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
E. D. Cash
Affiliation:
Department of Otolaryngology-HNS and Communicative Disorders, University of Louisville School of Medicine, Louisville, KY, USA University of Louisville Healthcare-Brown Cancer Center, Louisville, KY, USA
K. Welch
Affiliation:
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
C. A. Levinson
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA Department of Pediatrics, Child and Adolescent Psychology and Psychiatry, University of Louisville, Louisville, KY, USA
*
Corresponding author: C. Ralph-Nearman; Email: Christina.Ralph-Nearman@Louisville.edu; ChristinaRalphNearman@gmail.com
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Abstract

Background

Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings.

Methods

From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature).

Results

Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.

Conclusions

This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.

Keywords

ambulatory assessmentbiomarkereating disordersjust-in-time interventionpersonalized medicinephysiologicalwearables

Information

Type
Original Article
Information
Psychological Medicine , Volume 54 , Issue 6 , April 2024 , pp. 1084 - 1090
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Alban, A. Q., Ayesh, M., Alhaddad, A. Y., Khalid Al-Ali, A., So, W. C., Connor, O., & Cabibihan, J. J. (2021). Detection of challenging behaviours of children with autism using wearable sensors during interactions with social robots. 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021. https://doi.org/10.1109/RO-MAN50785.2021.9515459CrossRefGoogle Scholar
Arcelus, J., Mitchell, A. J., Wales, J., & Nielsen, S. (2011). Mortality rates in patients with anorexia nervosa and other eating disorders: A meta-analysis of 36 studies. Archives of General Psychiatry, 68(7), 724–731. https://doi.org/10.1001/archgenpsychiatry.2011.74CrossRefGoogle ScholarPubMed
Barlow, D. H., & Hersen, M. (1973). Single-case experimental designs. Uses in applied clinical research. Archives of General Psychiatry, 29(3), 319–325.CrossRefGoogle ScholarPubMed
Bayer, M. (2012). SQLAlchemy. In Brown, A. & Wilson, G. (Eds.), The architecture of open source applications volume II: Structure, scale, and a Few more fearless hacks. aosabook.org. Retrieved from “http://aosabook.org/en/sqlalchemy.htmlGoogle Scholar
Berends, T., Boonstra, N., & Van Elburg, A. (2018). Relapse in anorexia nervosa: A systematic review and meta-analysis. Current Opinion in Psychiatry, 31(6), 445–455. https://doi.or/10.1097/YCO.0000000000000453CrossRefGoogle ScholarPubMed
Bidwell, J., Khuwatsamrit, T., Askew, B., Ehrenberg, J. A., & Helmers, S. (2015). Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies. Seizure, 32, 109–117. https://doi.org/10.1016/j.seizure.2015.09.006CrossRefGoogle ScholarPubMed
Bulik, C. M., Berkman, N. D., Brownley, K. A., Sedway, J. A., & Lohr, K. N. (2007). Anorexia nervosa treatment: A systematic review of randomized controlled trials. International Journal of Eating Disorders, 40(4), 310–320. https://doi.org/10.1002/eat.20367CrossRefGoogle ScholarPubMed
Christian, C., Cash, E., Cohen, D. A., Trombley, C. M., & Levinson, C. A. (2023). Electrodermal activity and heart rate variability during exposure fear scripts predict trait-level and momentary social anxiety and eating-disorder symptoms in an analogue sample. Clinical Psychological Science, 11(1), 134–148. https://doi.org/10.1177/21677026221083284CrossRefGoogle Scholar
Clifton, L., Clifton, D. A., Pimentel, M. A. F., Watkinson, P. J., & Tarassenko, L. (2014). Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors. IEEE Journal of Biomedical and Health Informatics, 18(3), 722–730. https://doi.org/10.1109/JBHI.2013.2293059CrossRefGoogle ScholarPubMed
Deloitte Access Economics. (2020). The social and economic cost of eating disorders in the United States of America: A report for the strategic training initiative for the prevention of eating disorders and the academy for eating disorders. Deloitte Access Economics. Retrieved from https://doi.org/https://www.hsph.harvard.edu/striped/reporteconomic-costs-of-eating-disorders/Google Scholar
First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured clinical interview for DSM-5 – Research version (SCID-5 for DSM-5, Research version; SCID-5-RV). Arlington: American Psychiatric Association.Google Scholar
Goodwin, M. S., Mazefsky, C. A., Ioannidis, S., Erdogmus, D., & Siegel, M. (2019). Predicting aggression to others in youth with autism using a wearable biosensor. Autism Research, 12(8), 1286–1296. https://doi.org/10.1002/aur.2151CrossRefGoogle ScholarPubMed
Hasselhorn, K., Ottenstein, C., & Lischetzke, T. (2022). The effects of assessment intensity on participant burden, compliance, within-person variance, and within-person relationships in ambulatory assessment. Behavior Research Methods, 54(4), 1541–1558. https://doi.org/10.3758/s13428-021-01683-6CrossRefGoogle ScholarPubMed
Hensen, M., & Barlow, D. H. (1984). Single case experimental designs. Pergamon, New York: GEN.Google Scholar
Jacobson, N. C., & Bhattacharya, S. (2022). Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy, 149, 104013. https://doi.org/10.1016/j.brat.2021.104013CrossRefGoogle ScholarPubMed
Juarascio, A. S., Parker, M. N., Lagacey, M. A., & Godfrey, K. M. (2018). Just-in-time adaptive interventions: A novel approach for enhancing skill utilization and acquisition in cognitive behavioral therapy for eating disorders. International Journal of Eating Disorders, 51(8), 826–830. https://doi.org/10.1002/eat.22924CrossRefGoogle Scholar
Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings (2nd ed.). New York, NY: Oxford University Press.Google Scholar
Keel, P. K., & Mitchell, J. E. (1997). Outcome in bulimia nervosa. In American Journal of Psychiatry, 154(3), 312–321). https://doi.org/10.1176/ajp.154.3.313Google ScholarPubMed
Krantz, M. J., Blalock, D. V., Tanganyika, K., Farasat, M., McBride, J., & Mehler, P. S. (2020). Is QTc-interval prolongation an inherent feature of eating disorders? A cohort study. The American Journal of Medicine, 133(9), 1088–1094. https://doi.org/10.1016/j.amjmed.2020.02.015CrossRefGoogle ScholarPubMed
Lavender, J. M., de Young, K. P., Wonderlich, S. A., Crosby, R. D., Engel, S. G., Mitchell, J. E., … le Grange, D. (2013). Daily patterns of anxiety in anorexia nervosa: Associations with eating disorder behaviors in the natural environment. Journal of Abnormal Psychology, 122(3), 672–683. https://doi.org/10.1037/A0031823CrossRefGoogle ScholarPubMed
Levinson, C. A., Christian, C., Shankar-Ram, S., Brosof, L. C., & Williams, B. (2019). Sensor technology implementation for research, treatment, and assessment of eating disorders. International Journal of Eating Disorders, 52(10), 1176–1180. https://doi.org/10.1002/eat.23120CrossRefGoogle ScholarPubMed
Levinson, C. A., Hunt, R. A., Christian, C., Williams, B. M., Keshishian, A. C., Vanzhula, I. A., … Ralph-Nearman, C. (2022). Longitudinal group and individual networks of eating disorder symptoms in individuals diagnosed with an eating disorder. Journal of Psychopathology and Clinical Science, 131(1), 58. https://doi.org/10.1037/abn0000727CrossRefGoogle ScholarPubMed
McCarthy, C., Pradhan, N., Redpath, C., & Adler, A. (2016, May). Validation of the Empatica E4 wristband. In 2016 IEEE EMBS international student conference (ISC) (pp. 1-4). IEEE.CrossRefGoogle Scholar
Northrup, J. B., Goodwin, M. S., Peura, C. B., Chen, Q., Taylor, B. J., Siegel, M. S., & Mazefsky, C. A. (2022). Mapping the time course of overt emotion dysregulation, self-injurious behavior, and aggression in psychiatrically hospitalized autistic youth: A naturalistic study. Autism Research, 15(10), 1855–1867. https://doi.org/10.1002/aur.2773CrossRefGoogle ScholarPubMed
Ortega-Roldan, B., Rodríguez-Ruiz, S., Perakakis, P., Fernandez-Santaella, M. C., & Vila, J. (2014). The emotional and attentional impact of exposure to one's own body in bulimia nervosa: A Physiological view. PloS One, 9(7), e102595. https://doi.org/10.1371/journal.pone.0102595CrossRefGoogle ScholarPubMed
Papežová, H., Yamamotova, A., & Uher, R. (2005). Elevated pain threshold in eating disorders: Physiological and psychological factors. Journal of Psychiatric Research, 39(4), 431–438. https://doi.org/10.1016/j.jpsychires.2004.10.006CrossRefGoogle ScholarPubMed
Park, J., Jeong, H., Park, J., & Lee, B. C. (2019). Relationships between cognitive workload and physiological response under reliable and unreliable automation. In Advances in Human Factors and Systems Interaction: Proceedings of the AHFE 2018 International Conference on Human Factors and Systems Interaction, July 21-25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA 9 (pp. 3-8). Springer International Publishing.Google Scholar
Presseller, E. K., Parker, M. N., Lin, M., Weimer, J., & Juarascio, A. S. (2020). The application of continuous glucose monitoring technology to eating disorders research: An idea worth researching. International Journal of Eating Disorders, 53(12), 1901–1905. https://doi.org/10.1002/eat.23404CrossRefGoogle ScholarPubMed
Presseller, E. K., Patarinski, A. G. G., Fan, S. C., Lampe, E. W., & Juarascio, A. S. (2022). Sensor technology in eating disorders research: A systematic review. International Journal of Eating Disorders, 55(5), 573–624. https://doi.org/10.1002/EAT.23715CrossRefGoogle ScholarPubMed
Ragot, M., Martin, N., Em, S., Pallamin, N., & Diverrez, J. M. (2018). Emotion recognition using physiological signals: laboratory vs. wearable sensors. In Advances in Human Factors in Wearable Technologies and Game Design: Proceedings of the AHFE 2017 International Conference on Advances in Human Factors and Wearable Technologies, July 17-21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA 8 (pp. 15-22). Springer International Publishing.Google Scholar
Ralph-Nearman, C., Osborn, K. D., Chang, R. S., & Barber, K. E.. (2023, under review). Wearable physiological indices related to eating disorders: A systematic and methodological reviewCrossRefGoogle Scholar
Ranzenhofer, L. M., Engel, S. G., Crosby, R. D., Haigney, M., Anderson, M., McCaffery, J. M., & Tanofsky-Kraff, M. (2016). Real-time assessment of heart rate variability and loss of control eating in adolescent girls: A pilot study. The International Journal of Eating Disorders, 49(2), 197–201. https://doi.org/10.1002/eat.22464CrossRefGoogle ScholarPubMed
Ravindran, K. K., Della Monica, C., Atzori, G., Lambert, D., Revell, V., & Dijk, D. J. (2022, July). Evaluating the Empatica E4 Derived Heart Rate and Heart Rate Variability Measures in Older Men and Women. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3370-3373). IEEE.CrossRefGoogle Scholar
Regalia, G., Onorati, F., Lai, M., Caborni, C., & Picard, R. W. (2019). Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Research, 153, 79–82. https://doi.org/10.1016/j.eplepsyres.2019.02.007CrossRefGoogle ScholarPubMed
Schaefer, L. M., Engel, S. G., & Wonderlich, S. A. (2020). Ecological momentary assessment in eating disorders research: Recent findings and promising new directions. Current Opinion in Psychiatry, 33(6), 528–533. https://doi.org/10.1097/YCO.0000000000000639CrossRefGoogle ScholarPubMed
Schuurmans, A. A., de Looff, P., Nijhof, K. S., Rosada, C., Scholte, R. H., Popma, A., & Otten, R. (2020). Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters: A comparison to electrocardiography (ECG). Journal of Medical Systems, 44, 1–11. https://doi.org/10.1007/s10916-020-01648-wCrossRefGoogle ScholarPubMed
Smith, K. E., & Juarascio, A. (2019). From ecological momentary assessment (EMA) to ecological momentary intervention (EMI): Past and future directions for ambulatory assessment and interventions in eating disorders. Current Psychiatry Reports, 21(7), 53. https://doi.org/10.1007/s11920-019-1046-8CrossRefGoogle ScholarPubMed
Smith, K. E., Mason, T. B., Juarascio, A., Schaefer, L. M., Crosby, R. D., Engel, S. G., & Wonderlich, S. A. (2019). Moving beyond self-report data collection in the natural environment: A review of the past and future directions for ambulatory assessment in eating disorders. International Journal of Eating Disorders, 52(10), 1157–1175. https://doi.org/10.1002/EAT.23124CrossRefGoogle ScholarPubMed
Steinhausen, H. C. (2009). Outcome of eating disorders. Child and Adolescent Psychiatric Clinics of North America, 18(1), 225–242. https://doi.org/10.1016/j.chc.2008.07.013CrossRefGoogle ScholarPubMed
Steinhausen, H. C., & Weber, S. (2009). The outcome of bulimia nervosa: Findings from one-quarter century of research. American Journal of Psychiatry 166(12), 1331–1341. https://doi.org/10.1176/appi.ajp.2009.09040582CrossRefGoogle ScholarPubMed
Stice, E., Fisher, M., & Martinez, E. (2004). Eating disorder diagnostic scale: Additional evidence of reliability and validity. Psychological Assessment, 16(1), 60–71. https://doi.org/10.1037/1040-3590.16.1.60CrossRefGoogle ScholarPubMed
Stice, E., Telch, C. F., & Rizvi, S. L. (2000). Development and validation of the Eating Disorder Diagnostic Scale: A brief self-report measure of anorexia, bulimia, and binge-eating disorder. Psychological Assessment, 12(2), 123–131. https://doi.org/10.1037//1040-3590.12.2.123CrossRefGoogle Scholar
Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16(3), 199–202. https://doi.org/10.1093/abm/16.3.199CrossRefGoogle Scholar
Swets, J., & Picket, R. (1982). Evaluation of diagnostic systems: Methods from signal detection theory. Academic Press: Cambridge, Massachusetts.Google Scholar
Thompson, D. A., Berg, K. M., & Shatford, L. A. (1987). The heterogeneity of bulimic symptomatology: Cognitive and behavioral dimensions. International Journal of Eating Disorders, 6(2), 215–234. https://doi.org/10.1002/1098-108X(198703)6:2<215::AID-EAT2260060206>3.0.CO;2-J3.0.CO;2-J>CrossRefGoogle Scholar
van den Berg, E., Houtzager, L., de Vos, J., Daemen, I., Katsaragaki, G., Karyotaki, E., & …Dekker, J. (2019). Meta-analysis on the efficacy of psychological treatments for anorexia nervosa. In European Eating Disorders Review, 27(4), 331–351. https://doi.org/10.1002/erv.2683CrossRefGoogle ScholarPubMed
van Lier, H. G., Pieterse, M. E., Garde, A., Postel, M. G., de Haan, H. A., Vollenbroek-Hutten, M. M., ... & Noordzij, M. L. (2020). A standardized validity assessment protocol for physiological signals from wearable technology: Methodological underpinnings and an application to the E4 biosensor. Behavior Research Methods, 52(2), 607–629. https://doi.org/10.3758/s13428-019-01263-9CrossRefGoogle Scholar
Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CA: CreateSpace.Google Scholar
Walsh, B. T., Xu, T., Wang, Y., Attia, E., & Kaplan, A. S. (2021). Time course of relapse following acute treatment for anorexia nervosa. The American Journal of Psychiatry, 178(9), 848–853. https://doi.org/10.1176/appi.ajp.2021.21010026CrossRefGoogle ScholarPubMed
Wang, S. B. (2021). Machine learning to advance the prediction, prevention and treatment of eating disorders. European Eating Disorders Review: the Journal of the Eating Disorders Association, 29(5), 683–691. https://doi.org/10.1002/erv.2850CrossRefGoogle ScholarPubMed
Welch, K. C., Pennington, R., Vanaparthy, S., Do, H. M., Narayanan, R., Popa, D., … Kuravackel, G. (2023). Using physiological signals and machine learning algorithms to measure attentiveness during robot-assisted social skills intervention: A case study of two children with autism spectrum disorder. IEEE Instrumentation & Measurement Magazine, 26(3), 39–45.CrossRefGoogle Scholar
Welch, K. C., Warning, N., Narayanan, R., Nethala, P., Do, H., Vanaparthy, S., … Daisey, S. (2022). First impressions of a NAO robot: evaluating emotions through E4 data and questionnaires. 2022 Conference of the International Society for Research on Emotion, ISRE 2022.Google Scholar
Wright, A. G. C., & Zimmermann, J. (2019). Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement. Psychological Assessment, 31(12), 1467. https://doi.org/10.1037/pas0000685CrossRefGoogle ScholarPubMed
Zarate, D., Stavropoulos, V., Ball, M., de Sena Collier, G., & Jacobson, N. C. (2022). Exploring the digital footprint of depression: A PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 22(1), 1–24. https://doi.org/10.1186/S12888-022-04013-YGoogle ScholarPubMed
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