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Yu X, Cheng P, Yang Z, Fan H, Wang Q, Xu J, Zhu H, Gao Q. A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study. J Psychiatr Res 2024; 177:420-428. [PMID: 39098285 DOI: 10.1016/j.jpsychires.2024.07.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/22/2024] [Accepted: 07/27/2024] [Indexed: 08/06/2024]
Abstract
BACKGROUND Accurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. METHODS Cox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. RESULTS Research findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86-0.98), single (HR = 0.11, 95% CI: 0.02-0.68), having children (one child, HR = 0.07, 95%CI: 0.01-0.87; more than one child, HR = 0.33, 95%CI: 0.04-2.48), living in dormitory (HR = 0.25, 95%CI: 0.08-0.77), non-student (employee, HR = 0.24, 95% CI: 0.07-0.88; non-employee, HR = 0.09, 95% CI: 0.02-0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82-0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23-22.48), living alone (HR = 4.37, 95% CI: 1.60-11.94), having suicide history (HR = 2.51, 95% CI: 1.06-5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00-1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00-1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. CONCLUSIONS A novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures.
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Affiliation(s)
- Xinyi Yu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, PR China
| | - Peixia Cheng
- Department of Maternal and Child Health, School of Public Health, Capital Medical University, Beijing, PR China; Laboratory for Gene-Environment and Reproductive Health, Laboratory for Clinical Medicine, Capital Medical University, Beijing, PR China
| | - Zexi Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, PR China
| | - Hua Fan
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, PR China
| | - Qian Wang
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, PR China
| | - Jiaying Xu
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, PR China
| | - Huiping Zhu
- Department of Maternal and Child Health, School of Public Health, Capital Medical University, Beijing, PR China; Laboratory for Gene-Environment and Reproductive Health, Laboratory for Clinical Medicine, Capital Medical University, Beijing, PR China
| | - Qi Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, PR China.
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Ralph-Nearman C, Sandoval-Araujo LE, Karem A, Cusack CE, Glatt S, Hooper MA, Rodriguez Pena C, Cohen D, Allen S, Cash ED, Welch K, Levinson CA. Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life. Psychol Med 2024; 54:1084-1090. [PMID: 37859600 PMCID: PMC10939805 DOI: 10.1017/s003329172300288x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
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.
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Affiliation(s)
- C. Ralph-Nearman
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - L. E. Sandoval-Araujo
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - A. Karem
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
| | - C. E. Cusack
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - S. Glatt
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - M. A. Hooper
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - C. Rodriguez Pena
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
| | - D. Cohen
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
| | - S. Allen
- Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - E. D. Cash
- 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
- Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - C. A. Levinson
- 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
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Autism Across the Lifespan. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2024; 22:194-195. [PMID: 38680978 PMCID: PMC11046723 DOI: 10.1176/appi.focus.24022001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
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Ferina J, Kruger M, Kruger U, Ryan D, Anderson C, Foster J, Hamlin T, Hahn J. Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data. J Pers Med 2023; 13:1513. [PMID: 37888124 PMCID: PMC10608042 DOI: 10.3390/jpm13101513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.
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Affiliation(s)
- Jennifer Ferina
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
| | - Melanie Kruger
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
- Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Uwe Kruger
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
| | - Daniel Ryan
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Conor Anderson
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Jenny Foster
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Theresa Hamlin
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Frazier TW, Crowley E, Shih A, Vasudevan V, Karpur A, Uljarevic M, Cai RY. Associations between executive functioning, challenging behavior, and quality of life in children and adolescents with and without neurodevelopmental conditions. Front Psychol 2022; 13:1022700. [PMID: 36337537 PMCID: PMC9632446 DOI: 10.3389/fpsyg.2022.1022700] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/06/2022] [Indexed: 11/25/2022] Open
Abstract
The present study sought to clarify the impact of executive and social functioning on challenging behavior and the downstream influence of challenging behavior on quality of life and functioning in a large transdiagnostic sample. Understanding these relationships is crucial for developing and designing tailored intervention strategies. In a cross-sectional study, parent informants of 2,004 children completed measures of executive and social functioning, challenging behavior, child and family quality of life, and reported on functional impacts of challenging behavior. Using structural (path) modeling, analyses evaluated the associations between executive and social functioning, including emotion regulation and risk avoidance, with overall and specific types of challenging behavior. Structural models also examined the influence of challenging behavior on child and family quality of life, including measures of the immediate and extended environment, and functional impacts on the parent/child as well as interactions with the medical/legal systems. Finally, mediational models explored the direct and indirect effects of executive and social functioning on quality of life and impact measures via challenging behavior. Results indicated that executive functioning accounts for substantial variance (R2 = 0.47) in challenging behavior. In turn, challenging behavior accounts for substantial variance in child and family quality of life (R2 = 0.36) and parent/child impacts (R2 = 0.31). Exploratory mediational models identified direct effects from executive and social functioning measures on quality of life and functional impacts and indirect effects for executive functioning via challenging behavior. These findings support the development of new intervention strategies and suggest the need to measure executive functioning when assessing and tailoring the treatment of challenging behavior in clinical practice.
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Affiliation(s)
- Thomas W. Frazier
- Department of Psychology, John Carroll University, University Heights, OH, United States
- *Correspondence: Thomas W. Frazier,
| | - Ethan Crowley
- Department of Psychology, John Carroll University, University Heights, OH, United States
| | - Andy Shih
- Science and Public Health Department, Autism Speaks, New York, NY, United States
| | - Vijay Vasudevan
- Science and Public Health Department, Autism Speaks, New York, NY, United States
| | - Arun Karpur
- Science and Public Health Department, Autism Speaks, New York, NY, United States
| | - Mirko Uljarevic
- The School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Ru Ying Cai
- Aspect Research Centre for Autism Practice, French’s Forest, NSW, Australia
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Zerman N, Zotti F, Chirumbolo S, Zangani A, Mauro G, Zoccante L. Insights on dental care management and prevention in children with autism spectrum disorder (ASD). What is new? FRONTIERS IN ORAL HEALTH 2022; 3:998831. [PMID: 36238091 PMCID: PMC9551997 DOI: 10.3389/froh.2022.998831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
Autistic subjects represent a severe concern to dentistry due to the considerable difficulty in managing their oral health, maintaining routine toothbrushing, and preventing dental and periodontal problems. The social and economic burden of managing dental care in autism spectrum disorder (ASD) children is particularly cumbersome for families and public and private health expenditure, especially when children reach the dentist following a late diagnosis with evident oral health problems. An early diagnosis of ASD helps dentists better address these children's oral health. Unfortunately, insufficient attention is paid to the training and education of general pediatricians, dentists, and dental hygienists, allowing them to get to approach the different clinical aspects of ASD. Usually, children diagnosed with ASD are scheduled for dental appointments like their neurotypical peers, whereas their needs are typically complex and personalized. Scant attention is also devoted to these patients by commercial manufacturers of dental products and devices for oral hygiene and prevention of caries and periodontal diseases, leaving parents without the support and often failing when they address the oral health of autistic children. The difficulties of oral care do not derive simply from the behavior of ASD patients, as is commonly assumed, and therefore cannot be overcome solely by the patience and attention of parents and dentists. Genetics, dietary habits, sensory impairments, and cognition disorders are other causes contributing in various degrees to the impact on the mood and psychological reactions of autistic children towards dentists. How can we prevent teeth caries, periodontal disorders, and other oral health impairments by properly managing ASD children? This manuscript gives an up-to-date overview of these problems and helps to provide good remarks.
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Affiliation(s)
- Nicoletta Zerman
- Department of Surgery, Dentistry, Paediatrics and Gynecology, University of Verona, Verona, Italy
| | - Francesca Zotti
- Department of Surgery, Dentistry, Paediatrics and Gynecology, University of Verona, Verona, Italy
| | - Salvatore Chirumbolo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Alessandro Zangani
- Department of Surgery, Dentistry, Paediatrics and Gynecology, University of Verona, Verona, Italy
| | | | - Leonardo Zoccante
- Autism Veneto Region Center, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
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