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McCabe LH, Greer BD. Evaluations of heart rate during functional analyses of destructive behavior. J Appl Behav Anal 2023; 56:777-786. [PMID: 37608399 PMCID: PMC10696488 DOI: 10.1002/jaba.1019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023]
Abstract
Previous research has examined the predictive validity of heart rate on destructive behavior; however, such research has yet to improve clinical practice or enhance our understanding of the relation between physiology and destructive behavior. The purpose of this study was to examine the predictive validity of heart rate on varying topographies and functions of destructive behavior while controlling antecedent and consequent events through functional analysis. We first demonstrated the reliability of the Polar H10 heart rate monitor and assessed the feasibility of its use in simulated functional analysis sessions. However, across four consecutively enrolled patients, heart rate was not found to be a reliable predictor of destructive behavior, regardless of its topography or function. Instead, functional reinforcer presence and absence was sufficient to predict socially reinforced destructive behavior. This study may provide a framework for the future assessment of other biological measures in relation to destructive behavior occurrence and nonoccurrence.
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Affiliation(s)
- Liam H. McCabe
- Children’s Specialized Hospital–Rutgers University Center for Autism Research, Education, and Services (CSH–RUCARES), Somerset, NJ, USA
- Department of Applied Psychology, Rutgers University, Piscataway, NJ, USA
| | - Brian D. Greer
- Children’s Specialized Hospital–Rutgers University Center for Autism Research, Education, and Services (CSH–RUCARES), Somerset, NJ, USA
- Rutgers Brain Health Institute, Piscataway, NJ, USA
- Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
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2
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Melanson IJ, Fahmie TA. Functional analysis of problem behavior: A 40-year review. J Appl Behav Anal 2023; 56:262-281. [PMID: 36892835 DOI: 10.1002/jaba.983] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/19/2023] [Indexed: 03/10/2023]
Abstract
Extensive reviews of functional analysis literature were conducted 10 (Beavers et al., 2013) and 20 (Hanley et al., 2003) years ago; we expanded this review to capture the vast and innovative functional analysis research that has occurred over the past decade. Our review produced 1,333 functional analysis outcomes from 326 studies on the functional analysis of problem behavior between June 2012 and May 2022. Some characteristics of functional analysis studies were similar across the current and previous two reviews (e.g., child participants, developmental disability diagnosis, use of line graphs depicting session means, differentiated response outcomes). Other characteristics deviated from the previous two reviews (e.g., increase in autistic representation, outpatient settings, use of supplementary assessments, the inclusion of tangible conditions, and multiple function outcomes; decrease in session durations). We update previously reported participant and methodological characteristics, summarize outcomes, comment on recent trends, and propose future directions in the functional analysis literature.
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Affiliation(s)
- Isaac J Melanson
- University of Nebraska Medical Center's Munroe-Meyer Institute, Severe Behavior Department, Omaha, United States
| | - Tara A Fahmie
- University of Nebraska Medical Center's Munroe-Meyer Institute, Severe Behavior Department, Omaha, United States
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3
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Zwilling M, Romano A, Hoffman H, Lotan M, Tesler R. Development and validation of a system for the prediction of challenging behaviors of people with autism spectrum disorder based on a smart wearable shirt: A mixed-methods design. Front Behav Neurosci 2022; 16:948184. [DOI: 10.3389/fnbeh.2022.948184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
BackgroundMost people with autism spectrum disorder (ASD) present at least one form of challenging behavior (CB), causing reduced life quality, social interactions, and community-based service inclusion.ObjectivesThe current study had two objectives: (1) to assess the differences in physiological reaction to stressful stimuli between adults with and without high-functioning ASD; (2) to develop a system able to predict the incoming occurrence of a challenging behaviors (CBs) in real time and inform the caregiver that a CB is about to occur; (3) to evaluate the acceptability and usefulness of the developed system for users with ASD and their caregivers.MethodsComparison between physiological parameters will be conducted by enrolling two groups of 20 participants with and without ASD monitored while watching a relaxing and disturbing video. To understand the variations of the parameters that occur before the CB takes place, 10 participants with ASD who have aggressive or disruptive CBs will be monitored for 7 days. Then, an ML algorithm capable of predicting immediate CB occurrence based on physiological parameter variations is about to be developed. After developing the application-based algorithm, an efficient proof of concept (POC) will be carried out on one participant with ASD and CB. A focus group, including health professionals, will test the POC to identify the strengths and weaknesses of the developed system.ResultsHigher stress level is anticipated in the group of people with ASD looking at the disturbing video than in the typically developed peers. From the obtained data, the developed algorithm is used to predict CBs that are about to occur in the upcoming 1 min. A high level of satisfaction with the proposed technology and useful consideration for further developments are expected to emerge from the focus group.Clinical trial registration[https://clinicaltrials.gov/], identifier [NCT05340608].
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Posar A, Visconti P. Update about "minimally verbal" children with autism spectrum disorder. REVISTA PAULISTA DE PEDIATRIA 2021; 40:e2020158. [PMID: 34495269 PMCID: PMC8432069 DOI: 10.1590/1984-0462/2022/40/2020158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 09/12/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To review clinical and neurobiological features of minimally verbal children with autism spectrum disorder. DATA SOURCE We carried out a narrative review using the PubMed database. We considered the following search terms combined through the Boolean operator "AND": "autism spectrum disorder"; "minimally verbal." DATA SYNTHESIS To date, there is no shared definition of minimally verbal children with autism spectrum disorder. The heterogeneity in intellectual functioning and in linguistic abilities among these individuals suggests there is no single mechanism underlying their difficulties in learning to speak. However, the reasons why these children do not speak and the biological markers that can identify them are still unknown. Language impairment in these children can lead to several unfavorable consequences, including behavior problems (such as self-aggression, hetero-aggression, and property destruction), poorer daily living and social skills. Psychiatric comorbidities (including attention deficit/hyperactivity disorder, specific phobias, and compulsions) consist in a serious problem related to the lack of verbal language in individuals with autism spectrum disorder. Although in the literature there are very few evidence-based results, several findings suggest that an alternative and augmentative communication intervention, creating an extra-verbal communication channel, may be effective in these individuals. CONCLUSIONS The exact definition, clinical characteristics, associated disorders, etiology, and treatment of minimally verbal subjects with autism spectrum disorder must still be further studied and understood.
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Affiliation(s)
- Annio Posar
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Infantile, Bologna, Italia
| | - Paola Visconti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Infantile, Bologna, Italia
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5
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Zheng ZK, Staubitz JE, Weitlauf AS, Staubitz J, Pollack M, Shibley L, Hopton M, Martin W, Swanson A, Juárez P, Warren ZE, Sarkar N. A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC). SENSORS 2021; 21:s21020370. [PMID: 33430371 PMCID: PMC7826816 DOI: 10.3390/s21020370] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/17/2020] [Accepted: 01/04/2021] [Indexed: 11/16/2022]
Abstract
Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.
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Affiliation(s)
- Zhaobo K. Zheng
- Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37240, USA;
- Correspondence:
| | - John E. Staubitz
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Amy S. Weitlauf
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
| | - Johanna Staubitz
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Marney Pollack
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Lauren Shibley
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Michelle Hopton
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - William Martin
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Amy Swanson
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Pablo Juárez
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Zachary E. Warren
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Nilanjan Sarkar
- Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37240, USA;
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37240, USA
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Redd CB, Silvera-Tawil D, Hopp D, Zandberg D, Martiniuk A, Dietrich C, Karunanithi MK. Physiological Signal Monitoring for Identification of Emotional Dysregulation in Children. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4273-4277. [PMID: 33018940 DOI: 10.1109/embc44109.2020.9176506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Children, particularly those with atypical or delayed development, have a reduced ability to self-regulate their emotions and behaviour. After a number of anxiety or stress provoking events, this reduced regulatory ability can result in a meltdown. Extrinsic signals of an impending meltdown are often recognised and acted on by clinicians or parents. These external indications are also accompanied by internal physiological changes, such as increase in heart rate, skin electrodermal activity, and skin temperature. These physiological signals may be used to predict impending meltdown events and facilitate earlier and effective carer intervention, especially in complex management cases. We present a preliminary study using a wearable sensor system for continuous monitoring of physiological signals to measure and predict emotional changes in school-aged children. Our models are able to correctly classify the behavioural state of a child with 68% mean global model accuracy and up to 85% for person-dependent models. Prediction of emotion and identification of impending meltdowns will potentially assist parents, carers, teachers and clinicians to manage stress and problem behaviours before they escalate, and support self-management strategies throughout the variety of normal daily life.
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Ozdenizci O, Cumpanasoiu C, Mazefsky C, Siegel M, Erdoggmus D, Ioannidis S, Goodwin MS. Time-Series Prediction of Proximal Aggression Onset in Minimally-Verbal Youth with Autism Spectrum Disorder Using Physiological Biosignals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:5745-5748. [PMID: 30441641 DOI: 10.1109/embc.2018.8513524] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
It has been suggested that changes in physiological arousal precede potentially dangerous aggressive behavior in youth with autism spectrum disorder (ASD) who are minimally verbal (MV-ASD). The current work tests this hypothesis through time-series analyses on biosignals acquired prior to proximal aggression onset. We implement ridge-regularized logistic regression models on physiological biosensor data wirelessly recorded from 15 MV-ASD youth over 64 independent naturalistic observations in a hospital inpatient unit. Our results demonstrate proof-of-concept, feasibility, and incipient validity predicting aggression onset 1 minute before it occurs using global, person-dependent, and hybrid classifier models.
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8
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Ness SL, Bangerter A, Manyakov NV, Lewin D, Boice M, Skalkin A, Jagannatha S, Chatterjee M, Dawson G, Goodwin MS, Hendren R, Leventhal B, Shic F, Frazier JA, Janvier Y, King BH, Miller JS, Smith CJ, Tobe RH, Pandina G. An Observational Study With the Janssen Autism Knowledge Engine (JAKE ®) in Individuals With Autism Spectrum Disorder. Front Neurosci 2019; 13:111. [PMID: 30872988 PMCID: PMC6402449 DOI: 10.3389/fnins.2019.00111] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 01/30/2019] [Indexed: 11/13/2022] Open
Abstract
Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was "easy" (69%, 74/108) or "very easy" (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991.
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Affiliation(s)
- Seth L. Ness
- Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, FL, United States
| | - Abigail Bangerter
- Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, FL, United States
| | - Nikolay V. Manyakov
- Computational Biology, Discovery Sciences, Janssen Research & Development, Beerse, Belgium
| | - David Lewin
- Statistically Speaking Consulting, LLC, Chicago, IL, United States
| | - Matthew Boice
- Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, FL, United States
| | - Andrew Skalkin
- Informatics, Janssen Research & Development, Spring House, PA, United States
| | - Shyla Jagannatha
- Statistical Decision Sciences, Janssen Research & Development, Titusville, NJ, United States
| | - Meenakshi Chatterjee
- Computational Biology, Discovery Sciences, Janssen Research & Development, Spring House, PA, United States
| | - Geraldine Dawson
- Departments of Psychiatry and Behavioral Sciences, Duke Center for Autism and Brain Development, Duke University School of Medicine, Durham, NC, United States
| | - Matthew S. Goodwin
- Department of Health Sciences, Northeastern University, Boston, MA, United States
| | - Robert Hendren
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Bennett Leventhal
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Frederick Shic
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, United States
- Department of Pediatrics, University of Washington, Seattle, WA, United States
| | - Jean A. Frazier
- Eunice Kennedy Shriver Center and Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, United States
| | - Yvette Janvier
- Department of Developmental-Behavioral Pediatrics, Children's Specialized Hospital, Toms River, NJ, United States
| | - Bryan H. King
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Judith S. Miller
- Center for Autism Research, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | | | - Russell H. Tobe
- Department of Outpatient Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Gahan Pandina
- Neuroscience Therapeutic Area, Janssen Research & Development, Pennington, NJ, United States
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9
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Ferguson BJ, Hamlin T, Lantz JF, Villavicencio T, Coles J, Beversdorf DQ. Examining the Association Between Electrodermal Activity and Problem Behavior in Severe Autism Spectrum Disorder: A Feasibility Study. Front Psychiatry 2019; 10:654. [PMID: 31572238 PMCID: PMC6749070 DOI: 10.3389/fpsyt.2019.00654] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 08/14/2019] [Indexed: 12/28/2022] Open
Abstract
Background: Many individuals with autism spectrum disorder (ASD) engage in problem behavior, presenting significant challenges for those providing care and services for this population. Psychophysiological measures of arousal, such as electrodermal activity (EDA), may provide an early indication of subsequent problem behavior. However, variability in EDA patterns associated with behaviors may limit this predictive ability. Methods: EDA data was sampled from eight individuals with severe ASD in a naturalistic setting, while participating in educational programming in a school setting at a residential facility for severely affected individuals with developmental disabilities, to examine variability in EDA patterns. Results: An anticipatory rise in EDA only occurred 60% of the time prior to the problem behavior. Additionally, EDA after a problem behavior returned to median baseline levels only 45% of the time. Conclusions: Heterogeneity of EDA responses in those with the most severe forms of ASD will be an important consideration in future studies utilizing psychophysiological tools such as EDA to anticipate problem behavior, including the need for monitoring of return to baseline after problem behaviors. Incorporation of this consideration may lead to greater reliability of these approaches to help anticipate and manage problem behaviors.
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Affiliation(s)
- Bradley J Ferguson
- Department of Health Psychology, University of Missouri School of Health Professions, Columbia, MO, United States.,Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, Columbia, MO, United States.,Department of Radiology, University of Missouri School of Medicine, Columbia, MO, United States
| | | | | | | | - John Coles
- The Center for Discovery, Harris, NY, United States.,CUBRC, Inc. Information Exploitation Sector, Buffalo, NY, United States.,Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, United States
| | - David Q Beversdorf
- Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, Columbia, MO, United States.,Department of Radiology, University of Missouri School of Medicine, Columbia, MO, United States.,University of Missouri Departments of Neurology & Psychological Sciences, Columbia, MO, United States
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10
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Taj-Eldin M, Ryan C, O'Flynn B, Galvin P. A Review of Wearable Solutions for Physiological and Emotional Monitoring for Use by People with Autism Spectrum Disorder and Their Caregivers. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4271. [PMID: 30518133 PMCID: PMC6308558 DOI: 10.3390/s18124271] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 11/30/2018] [Accepted: 12/01/2018] [Indexed: 12/28/2022]
Abstract
The goal of real-time feedback on physiological changes, stress monitoring and even emotion detection is becoming a technological reality. People in their daily life experience varying emotional states, some of which are negative and which can lead to decreased attention, decreased productivity and ultimately, reduced quality of life. Therefore, having a solution that continuously monitors the physiological signals of the person and assesses his or her emotional well-being could be a very valuable tool. This paper aims to review existing physiological and motional monitoring devices, highlight their features and compare their sensing capabilities. Such technology would be particularly useful for certain populations who experience rapidly changing emotional states such as people with autism spectrum disorder and people with intellectual disabilities. Wearable sensing devices present a potential solution that can support and complement existing behavioral interventions. This paper presents a review of existing and emerging products in the market. It reviews the literature on state-of-the-art prototypes and analyzes their usefulness, clinical validity, and discusses clinical perspectives. A small number of products offer reliable physiological internal state monitoring and may be suitable for people with Autism Spectrum Disorder (ASD). It is likely that more promising solutions will be available in the near future. Therefore, caregivers should be careful in their selection of devices that meet the care-receiver's personal needs and have strong research support for reliability and validity.
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Affiliation(s)
- Mohammed Taj-Eldin
- Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland.
| | - Christian Ryan
- School of Applied Psychology, University College Cork, T12 R5CP Cork, Ireland.
| | - Brendan O'Flynn
- Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland.
| | - Paul Galvin
- Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland.
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