Predicting obsessive-compulsive disorder episodes in adolescents using a wearable biosensor—A wrist angel feasibility study
Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may...
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| Vydáno v: | Frontiers in psychiatry Ročník 14; s. 1231024 |
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02.10.2023
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| Abstract | Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models.
Nine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients.
Eight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation.
Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes.
ClinicalTrials.gov: NCT05064527, registered October 1, 2021. |
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| AbstractList | Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models.
Nine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients.
Eight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation.
Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes.
ClinicalTrials.gov: NCT05064527, registered October 1, 2021. IntroductionObsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models.MethodsNine adolescents (ages 10–17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients.ResultsEight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation.ConclusionOur pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes.Clinical trial registrationClinicalTrials.gov: NCT05064527, registered October 1, 2021. Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models.IntroductionObsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models.Nine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients.MethodsNine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients.Eight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation.ResultsEight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation.Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes.ConclusionOur pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes.ClinicalTrials.gov: NCT05064527, registered October 1, 2021.Clinical trial registrationClinicalTrials.gov: NCT05064527, registered October 1, 2021. |
| Author | Lønfeldt, Nicole Nadine Das, Sneha Olesen, Kristoffer Vinther Mora-Jensen, Anna-Rosa Cecilie Pagsberg, Anne Katrine Clemmensen, Line Katrine Harder |
| AuthorAffiliation | 3 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen , Denmark 1 Child and Adolescent Mental Health Center, Copenhagen University Hospital—Mental Health Services Copenhagen (CPH) , Hellerup , Denmark 2 Department of Applied Mathematics and Computer Science, Technical University of Denmark , Kgs Lyngby , Denmark |
| AuthorAffiliation_xml | – name: 1 Child and Adolescent Mental Health Center, Copenhagen University Hospital—Mental Health Services Copenhagen (CPH) , Hellerup , Denmark – name: 2 Department of Applied Mathematics and Computer Science, Technical University of Denmark , Kgs Lyngby , Denmark – name: 3 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen , Denmark |
| Author_xml | – sequence: 1 givenname: Nicole Nadine surname: Lønfeldt fullname: Lønfeldt, Nicole Nadine – sequence: 2 givenname: Kristoffer Vinther surname: Olesen fullname: Olesen, Kristoffer Vinther – sequence: 3 givenname: Sneha surname: Das fullname: Das, Sneha – sequence: 4 givenname: Anna-Rosa Cecilie surname: Mora-Jensen fullname: Mora-Jensen, Anna-Rosa Cecilie – sequence: 5 givenname: Anne Katrine surname: Pagsberg fullname: Pagsberg, Anne Katrine – sequence: 6 givenname: Line Katrine Harder surname: Clemmensen fullname: Clemmensen, Line Katrine Harder |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37850105$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.2196/33560 10.1037/ccp0000315 10.3390/bios11040126 10.1186/1472-6947-12-8 10.1016/j.compbiomed.2022.105280 10.1176/appi.ajp.2008.08020320 10.1145/2968219.2968306 10.1002/cpp.2392 10.1176/ajp.154.7.911 10.1111/ijpo.12201 10.1016/j.biopsycho.2010.03.010 10.1159/000284691 10.1016/S0005-7967(99)00038-8 10.1097/00004583-199706000-00023 10.1002/aur.2151 10.1109/SIBGRAPI51738.2020.00053 10.1089/104454603322126359 10.1371/journal.pone.0231517 10.2196/45123 10.1016/j.ijpsycho.2012.10.012 10.1109/PERCOMW.2019.8730725 10.1016/j.psychres.2004.06.009 10.3233/978-1-61499-516-6-24110 10.1001/jamapsychiatry.2015.3105 10.1109/EMBC48229.2022.9871899 10.1080/0954026021000046146 10.1016/j.jbi.2008.08.010 10.1109/EMBC44109.2020.9176506 10.1109/TITS.2005.848368 |
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| Keywords | machine learning adolescents children signal processing obsessive-compulsive disorder wearable biosensor |
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| SubjectTerms | adolescents children machine learning obsessive-compulsive disorder Psychiatry signal processing wearable biosensor |
| Title | Predicting obsessive-compulsive disorder episodes in adolescents using a wearable biosensor—A wrist angel feasibility study |
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