Clinical trial NCT05340608: Development and Validation of a System for the Anticipation of Challenging Behaviors
Official title: Development and Validation of a System for the Anticipation of Challenging Behaviors of People With Autism Spectrum Disorder Based on a Smart Shirt: a Mixed-method Design
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Autism Spectrum Disorder
Keywords: Autism Spectrum Disorder
Problem Behavior
Aggression
Self-Injurious Behavior
Telemedicine
Accident Prevention
Lead sponsor: Ariel University
Sponsor class: OTHER
First posted: 2022-04-22
Enrollment: 51
Sex: ALL
Minimum age (years): 20
Maximum age (years): 40
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The participants in the Observation Groups (One and Two) and Proof of Concept will be recruited from the Israeli adult population with Autism Spectrum Disorder through.

Brief summary: Most people with autism spectrum disorder (ASD) present at least one form of challenging behavior (CB). Self-injurious, aggressive, and disruptive CBs linked with social interaction, community-based service exclusion, and a life quality reduction for people with ASD, their caregivers, and health professionals. The current study has three objectives: 1) to assess the differences in the physiological reaction of high-functioning adults with ASD and typically developed peers, using bio-signal measurements such as heart rate derived from wearable Smart Shirt (SS), 2) to learn which physiological parameters can best predict the imminent onset of a CB, and 3) to develop a system able to predict the incoming occurrence of a CB in real-time and inform the caregiver through an alert notification sent on a smartphone application.

Methods and analysis: comparison between physiological parameters will carry out with two groups of 20 participants with and without ASD. Each participant will be asked to watch two five-minute videos while wearing the SS: one showing relaxing images and the other impressive human body deformities. To identify the matching between the physiological parameters variation collected by the SS and the CBs, ten participants with ASD and aggressive or disruptive CBs will be recruited. Each of these participants will wear the SS for seven consecutive days during waking hours, performing their usual daily activities. During the same seven days, the caregivers who care for the participant will fill a behavioral diary with the participant's status, reporting the times of the day in which he is quiet, agitated and the occurrence of CBs. A learning algorithm capable of predicting immediate CBs occurrence based on physiological parameter variations will be developed together with an ad hoc smartphone application. If the algorithm detects the possibility of an incoming CB, a notification will be sent to the caregiver's smartphone to inform of the possible advent of a CB, therefore enabling the implementation of the selected intervention strategy. After developing the algorithm and related smartphone application, a system efficiency proof of concept (POC) will be carried out with one participant with ASD and CB for seven days in a special school setting with healthcare professionals and teachers. A focus group including health professionals will be conducted after the POC to identify the strengths and weaknesses of the developed system.

Detailed description: Study goals

The current protocol consists of three phases, each with a specific goal. The first aim is to assess the differences in physiological reaction measured with a Smart Wearable Shirt (SWS) between adults with high-functioning Autism Spectrum Disorder (ASD) and typically developed peers. The second goal is to learn which physiological parameters can best predict the imminent onset of a Challenging Behavior (CB). The third aim is to develop a system able to predict the incoming occurrence of a CB in real-time and inform the caregiver through an alert notification sent on a smartphone application.

Study design

An observational study design will be implemented in the first two phases of the current research. In phase one, participants' physiological reactions to two visual stimuli will be collected and analyzed. In phase two, the physiological characteristics of CBs presented by people with ASD will be collected. A single case study with a mixed-method design will be implemented in phase three, where the system validity proof of concept will be performed.

Ethics and safety issues

The research proposal was approved by the Ariel University institutional review board (AU-HEA-ML-20201203). The study will be carried out following the Declaration of Helsinki principles. At the recruitment, written informed consent will be collected from all participants or their legal guardians. The SWS planned to use in the current protocol is a non-invasive medical device. However, if a participant does not tolerate SWS, he can withdraw from the study at any time without any repercussions.

Participants

According to a sample size calculation analysis, a group of 20 subjects diagnosed with high functioning ASD aged between 20 and 40 years residing at home (observation group - OG) along with an age and sex-matched control group (CG) of 20 typical developed peers will be enrolled in the first protocol phase. For the study's second phase, 10 people with ASD presenting aggressive or disruptive CBs aged between 20 and 40 years and their caregivers will be recruited. Finally, one participant with ASD, aged between 20 and 40 years attending a special school and living at home, which exhibits aggressive or disruptive CBs, will participate in the third phase of the research.

Procedure

Phase one - comparison between people with and without ASD physiological outcomes.

For the first phase of the current protocol, the physiological parameters of the people in the OG and CG will be acquired using the SWS while participants watch two five-minute videos. One video will show relaxing images and will emit relaxing music (relaxing video). The second video will present impressive human body deformities accompanied by anxious music (disturbing video). Both videos will be presented to the participant in a sitting position. Before starting the relaxing video, the participant will be invited to relax and lean back onto the chair's backrest. The participant can close his eyes or keep them open at his discretion to promote relaxation. Participants will be asked not to lean against the chair backrest and keep their eyes open for the duration of the disturbing video watching. The first video watched by each participant will be chosen randomly between the two videos. The duration of the entire session will be approximately 15 minutes.

Phase two - Classify the physiological parameters' variations in people with ASD.

Each participant enrolled in phase two of the protocol will wear the SWS for seven consecutive days during waking hours, performing his usual daily activities. During the same seven days, the caregivers who care for the participant will report the participant's status in the behavioral diary. Each evening, data collected by the SWS will be uploaded to an online cloud along with the behavioral diary of the day. Once the data from all the 10 participants have been collected, a Deep Learning (DL) algorithm will be developed to learn the variations in the individual's physiological parameters that occur before a CB and predict future CB. Moreover, a smartphone application will be developed to receive the SWS data in real-time and send it to a remote server where it is analyzed through the developed algorithm and the classified CB events will be extracted and presented on the applications' notification screen. In other words: if the algorithm detects the possibility of an incoming CB, a notification is sent to the caregiver's smartphone to inform of the possible advent of a CB, therefore enabling the implementation of the selected intervention strategy.

Phase three - System proof of concept.

For one week, the developed system prototype and its efficacy will be tested on one participant with ASD for seven days with healthcare professionals and teachers in a special school setting. The participant will wear the SWS during all hours of attendance at the special school. At the end of the seven days, the QUEST 2.0 will be administered to each professional and teacher who will interact with the system, and a focus group will be carried out with them to address the research questions mentioned above. The focus group will aim to answer the following research questions:

* Did wearing the smart-shirt upset the participants?
* Was the system able to detect all relevant CB?
* Was the system operation speed sufficient to allow the in-time application of appropriate prevention strategies?
* Has the use of the system reduced the amount of CB?
* What improvements can be applied to the system to increase its effectiveness?

Information obtained from the QUEST 2.0 administration will be discussed within the focus group. In the last part of the focus group, a summation of solutions to each research question will be proposed to the group, and the number of participants that agree or disagree with the proposed summation answers will be collected.

Data analyses

Phase one

Data collected by the SWS from participants in the OG and CG will be analyzed and compared. From ECG received data, heart rate (HR) will be calculated between two consecutive QRS complexes. Considering the time interval between two QRS complexes as "t", the corresponding temporal HR will be 60/t. A percentage threshold value will be set using the sliding window method, and the minimum allowed peak width will be identified to remove unwanted artifacts from HR. The removal process will be performed for positive and negative peaks in two rounds. A window will be slid over the HR signal, and its median value will be calculated. The maximum (positive and negative) allowed peak amplitude will be determined for every window by multiplying the window's median value for a threshold value. The threshold value for positive peaks was set at 30% (for the first removal round) and 25% (for the second removal round) of the window's mean value. The threshold value was set for negative peaks at 50% (for the first removal round) and 30% (for the second removal round) of the window's mean value. Then, all peaks with amplitude bigger than the allowed value will be identified from every window. If one of these peaks is narrower than the minimum allowed peak width, it will be replaced with the reference window median value. Otherwise, if an identified peak width is bigger than the allowed peak width, its value will be replaced with the maximal allowed HR (for positive peaks) or the minimal allowed HR (for negative peaks). The maximal allowed HR will be calculated with the following formula: "209-(0.7×(Participant age))". The minimal allowed HR will be 60 bpm. After removing abnormal peaks, the signal will be filtered with a Gaussian filter with a sigma equal to 1. After the HR signal filtering process, the obtained cleaned HR signal will be used to classify the participant stress within the following levels: "no stress", "mild stress", "moderate stress", and "high stress". Each stress level will refer to an HR signal positioned within a specific range of values. The "no stress" level will include the HR values below 90% of the cleaned HR signal's lowest peak. If this HR value is lower than The "high stress" level will comprise values above 90% of the cleaned HR signal's highest peak. If this value is above the maximal allowed HR, it is substituted with 90% of the maximal allowed HR value. The range left between these two thresholds will be divided into two equal parts (lower and upper half). The HR data positioned in the lower half of this range will be classified as "mild stress" and those positioned in the upper half as "moderate stress". Each HR value will be classified and assigned with a numerical value corresponding to a stress level ("no stress" = 0, "mild stress" = 1, "moderate stress" = 2, and "high stress" = 3). After acquiring the sequences of the stress levels of all participants of Phase one, the sequences of the subjects in the OG and CG will be compared using a version of the Smith-Waterman algorithm adapted for the analysis of the obtained data.

Phase two

The data gathered by the SWS from participants enrolled in phase two will be analyzed as described above, and a DL algorithm will be developed in order to predict the incoming participants' stress levels. To find CBs patterns among subjects, a classifier based on supervised learning to find anomalies in the subject's data that might indicate on CB that is about to occur will be constructed. A long-short term memory (LSTM) algorithm will be taught to recognize data patterns corresponding to CBs occurrence using the data collected by the participants' caregivers through the behavioral diary and the information collected in Phase one. LSTM is an extension of the recurrent neural network (RNN). In contrast to other applications of Machine Learning (ML) and DL, in the process of analyzing and predicting time-series information, each data point is based on previous information, which must be examined as well. RNN is the most used network for time series applications since it can form the target vector by observing the current input data history, using shared wights among the hiding units of the network across each time step of the data. The authors choose LSTM, not the RNN, because RNN has a significant vanishing gradient problem. The gradient of output error is based on previous inputs vanishes when time lags between inputs and errors increase. To overcome this problem, the LSTM is introduced. LSTM has a memory, which comes to practice by replacing the nonlinear units of RNN in the hidden layers with memory blocks. The network propagates errors throughout the entire network, and as a result, it can learn long-term dependencies and forget unnecessary information based on the data at hand. The accuracy of the prediction model will be calculated according to standard estimation methods such as the confusion matrix and the area under the curve (AUC) Values. These values range from 0.5 to 1, with 1 being perfect classification and 0.5 being no better than luck.

Phase three

The themes that will emerge from the focus group will be extracted from the discussion transcription. Axial coding strategy will be applied to calculate the extensiveness of each theme discussion. This qualitative data analysis consists of assigning a reference number to each theme and then marking any sentence related to that theme with that number. A reliability check for the code-to-sentence matches will be applied by giving the list of codes to an independent researcher experienced in qualitative analysis and asking him to identify the sentence that matches each code. The level of agreement to summation answers to the research questions will be obtained by calculating the percentage of participants that agree with the proposed statement. The authors will discuss the developed answers in the light of the relevant emerged themes and the level of agreement of the discussion group.

Eligibility criteria: Inclusion Criteria for Observation Group One:

* diagnosis of High Functioning Autism Spectrum Disorder;
* age between 20 and 40 years.

Exclusion Criteria for Observation Group One:

* the presence of heart disease;
* comorbidity with other neurodevelopmental disorders.

Inclusion Criteria for Observation Group Two:

* diagnosis of Autism Spectrum Disorder;
* frequent emission of aggressive and disruptive challenging behaviors (at least three times per week);
* age between 20 and 40 years.

Exclusion Criteria for Observation Group Two:

* the presence of heart disease;
* comorbidity with other neurodevelopmental disorders.

Inclusion Criteria for Proof of Concept:

* diagnosis of Autism Spectrum Disorder;
* frequent emission of aggressive and disruptive challenging behaviors (at least three times per week);
* attending a special school for at least eight hours a day;
* age between 20 and 40 years.

Exclusion Criteria for Proof of Concept:

* the presence of heart disease;
* comorbidity with other neurodevelopmental disorders.

Primary outcomes:
measure: Heart rate - Phase one (1)
timeFrame: Five-minute during the study Phase one.
measure: Heart rate - Phase one (2)
timeFrame: Five-minute during the study Phase one.
measure: Heart rate - Phase two
timeFrame: Seven consecutive days during study Phase two.
measure: Heart rate - Phase three
timeFrame: Seven consecutive days during study Phase three.
measure: Behavioral Diary
timeFrame: Seven consecutive days during study Phase two.
measure: Quebec User Evaluation of Satisfaction with Assistive Technology second edition (QUEST 2.0)
timeFrame: Phase three, at the end of the seven-day proof of concept trial - one administration.
measure: Focus Group
timeFrame: Phase three, at the end of the seven-day proof of concept trial - one three-hour session.
Secondary outcomes:
Other outcomes:
Locations:
city: Ariel
country: Israel
facility: Ariel University
Study officials:
affiliation: Department of Physiotherapy, School of Health Sciences, Ariel University
name: Meir Lotan, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Economics and Business Administration, Ariel University
name: Moti Zwilling, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Baron-Cohen S, Scott FJ, Allison C, Williams J, Bolton P, Matthews FE, Brayne C. Prevalence of autism-spectrum conditions: UK school-based population study. Br J Psychiatry. 2009 Jun;194(6):500-9. doi: 10.1192/bjp.bp.108.059345.
citation: American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington (DC): American Psychiatric Association; 2013.
citation: Knapp M, Romeo R, Beecham J. Economic cost of autism in the UK. Autism. 2009 May;13(3):317-36. doi: 10.1177/1362361309104246.
citation: Arnold S, Reed P. Reading assessments for students with ASD: a survey of summative reading assessments used in special educational schools in the UK. Br J Spec Educ. 2016;43(2):122-141.
citation: Chiang HM. Expressive communication of children with autism: the use of challenging behaviour. J Intellect Disabil Res. 2008 Nov;52(11):966-72. doi: 10.1111/j.1365-2788.2008.01042.x. Epub 2008 Jan 18.
citation: O'Donnell S, Deitz J, Kartin D, Nalty T, Dawson G. Sensory processing, problem behavior, adaptive behavior, and cognition in preschool children with autism spectrum disorders. Am J Occup Ther. 2012 Sep-Oct;66(5):586-94. doi: 10.5014/ajot.2012.004168.
citation: Baker AE, Lane A, Angley MT, Young RL. The relationship between sensory processing patterns and behavioural responsiveness in autistic disorder: a pilot study. J Autism Dev Disord. 2008 May;38(5):867-75. doi: 10.1007/s10803-007-0459-0.
citation: Murphy GH, Beadle-Brown J, Wing L, Gould J, Shah A, Holmes N. Chronicity of challenging behaviours in people with severe intellectual disabilities and/or autism: a total population sample. J Autism Dev Disord. 2005 Aug;35(4):405-18. doi: 10.1007/s10803-005-5030-2.
citation: Prizant BM, Wetherby AM. Critical Issues in Enhancing Communication Abilities for Persons with Autism Spectrum Disorders. In: Volkmar FR, Paul R, Klin A, Cohen D, editors. Handbook of Autism and Pervasive Developmental Disorders. Hoboken, NJ. John Wiley & Sons, Inc.; 2005. p. 925-945.
citation: Reese RM, Richman DM, Belmont JM, Morse P. Functional characteristics of disruptive behavior in developmentally disabled children with and without autism. J Autism Dev Disord. 2005 Aug;35(4):419-28. doi: 10.1007/s10803-005-5032-0.
citation: Emerson E. Challenging behaviour: Analysis and intervention in people with severe intellectual disabilities. 2nd ed. Cambridge: Cambridge University Press; 2001.
citation: Holden B, Gitlesen JP. A total population study of challenging behaviour in the county of Hedmark, Norway: prevalence, and risk markers. Res Dev Disabil. 2006 Jul-Aug;27(4):456-65. doi: 10.1016/j.ridd.2005.06.001. Epub 2005 Aug 30.
citation: Jang J, Dixon DR, Tarbox J, Granpeesheh D. Symptom severity and challenging behavior in children with ASD. Res Autism Spectr Disord. 2011;5(3):1028-1032. doi: 10.1016/j.rasd.2010.11.008.
citation: Matson JL, Wilkins J, Macken J. The relationship of challenging behaviors to severity and symptoms of autism spectrum disorders. J Ment Health Res Intellect Disabil. 2008;2(1):29-44. doi:10.1080/19315860802611415.
citation: Murphy O, Healy O, Leader G. Risk factors for challenging behaviors among 157 children with autism spectrum disorder in Ireland. Res Autism Spectr Disord. 2009;3(2):474-482. doi:10.1016/j.rasd.2008.09.008.
citation: Woodbury-Smith MR, Clare ICH, Holland AJ, Kearns A. High functioning autistic spectrum disorders, offending and other law-breaking: findings from a community sample. J Forensic Psychiatry Psychol. 2006;17(1):108-120. doi:10.1080/14789940600589464.
citation: Mukaddes NM, Topcu Z. Case report: homicide by a 10-year-old girl with autistic disorder. J Autism Dev Disord. 2006 May;36(4):471-4. doi: 10.1007/s10803-006-0087-0.
citation: Nissen JM, Haveman MJ. Mortality and avoidable death in people with severe self-injurious behaviour: results of a Dutch study. J Intellect Disabil Res. 1997 Jun;41 ( Pt 3):252-7.
citation: Blacher J, McIntyre LL. Syndrome specificity and behavioural disorders in young adults with intellectual disability: cultural differences in family impact. J Intellect Disabil Res. 2006 Mar;50(Pt 3):184-98. doi: 10.1111/j.1365-2788.2005.00768.x.
citation: Felce D, Perry J, Lowe K, Jones E. The Impact of Autism or Severe Challenging Behaviour on Lifestyle Outcome in Community Housing. J Appl Res Intellect Disabil. 2011;24(2):95-104. doi:10.1111/J.1468-3148.2010.00571.X.
citation: Hieneman M. Positive Behavior Support for Individuals with Behavior Challenges. Behav Anal Pract. 2015 Apr 23;8(1):101-108. doi: 10.1007/s40617-015-0051-6. eCollection 2015 May.
citation: Chadwick O, Piroth N, Walker J, Bernard S, Taylor E. Factors affecting the risk of behaviour problems in children with severe intellectual disability. J Intellect Disabil Res. 2000 Apr;44 ( Pt 2):108-23. doi: 10.1046/j.1365-2788.2000.00255.x.
citation: Sigafoos J, Arthur-Kelly M, O'Reilly MF. Challenging behaviour and developmental disability. Paul H, editor. Baltimore (MD): Brookes Publishing Co; 2003.
citation: Allen D. Positive behavioural support as a service system for people with challenging behaviour. Psychiatry. 2009;8(10):408-412.
citation: Scheifes A, Walraven S, Stolker JJ, Nijman HL, Egberts TC, Heerdink ER. Adverse events and the relation with quality of life in adults with intellectual disability and challenging behaviour using psychotropic drugs. Res Dev Disabil. 2016 Feb-Mar;49-50:13-21. doi: 10.1016/j.ridd.2015.11.017. Epub 2015 Nov 29.
citation: Berg WK, Peck S, Wacker DP, Harding J, McComas J, Richman D, Brown K. The effects of presession exposure to attention on the results of assessments of attention as a reinforcer. J Appl Behav Anal. 2000 Winter;33(4):463-77. doi: 10.1901/jaba.2000.33-463.
citation: Rose D, Horne S, Rose JL, Hastings RP. Negative emotional reactions to challenging behaviour and staff burnout: Two replication studies. J Appl Res Intellect Disabil. 2004;17(3):219-223. doi:10.1111/j.1468-3148.2004.00194.x.
citation: McPheeters ML, Warren Z, Sathe N, Bruzek JL, Krishnaswami S, Jerome RN, Veenstra-Vanderweele J. A systematic review of medical treatments for children with autism spectrum disorders. Pediatrics. 2011 May;127(5):e1312-21. doi: 10.1542/peds.2011-0427. Epub 2011 Apr 4.
citation: Sawyer A, Lake JK, Lunsky Y, Liu SK, Desarkar P. Psychopharmacological treatment of challenging behaviours in adults with autism and intellectual disabilities: A systematic review. Res Autism Spectr Disord. 2014;8(7):803-813. doi:10.1016/J.RASD.2014.03.021.
citation: Blankenship K, Erickson CA, McDougle CJ. Pharmacotherapy of Autism and related disorders. Psychiatr Ann. 2010;40(4):203-209. doi:10.3928/00485713-20100330-06.
citation: Malone RP, Gratz SS, Delaney MA, Hyman SB. Advances in drug treatments for children and adolescents with autism and other pervasive developmental disorders. CNS Drugs. 2005;19(11):923-34. doi: 10.2165/00023210-200519110-00003.
citation: Scotti JR, Ujcich KJ, Weigle KL, Holland CM, Kirk KS. Interventions with Challenging Behavior of Persons with Developmental Disabilities: A Review of Current Research Practices. J Assoc Pers with Sev Handicap. 1996;21(3):123-134. doi:10.1177/154079699602100303.
citation: Fettig A, Barton EE. Parent Implementation of Function-Based Intervention to Reduce Children's Challenging Behavior: A Literature Review. Topics Early Child Spec Educ. 2014;34(1):49-61. doi:10.1177/0271121413513037.
citation: Lydon S, Healy O, O'Reilly M, McCoy A. A systematic review and evaluation of response redirection as a treatment for challenging behavior in individuals with developmental disabilities. Res Dev Disabil. 2013 Oct;34(10):3148-58. doi: 10.1016/j.ridd.2013.06.010. Epub 2013 Jul 22.
citation: Machalicek W, O'Reilly MF, Beretvas N, Sigafoos J, Lancioni GE. A review of interventions to reduce challenging behavior in school settings for students with autism spectrum disorders. Res Autism Spectr Disord. 2007;1(3):229-246. doi:10.1016/J.RASD.2006.10.005.
citation: Didden R, Korzilius H, van Oorsouw W, Sturmey P. Behavioral treatment of challenging behaviors in individuals with mild mental retardation: meta-analysis of single-subject research. Am J Ment Retard. 2006 Jul;111(4):290-8. doi: 10.1352/0895-8017(2006)111[290:BTOCBI]2.0.CO;2.
citation: Inoue M. Assessments and Interventions to Address Challenging Behavior in Individuals with Intellectual Disability and Autism Spectrum Disorder in Japan: A Consolidated Review. Yonago Acta Med. 2019 Jun 20;62(2):169-181. doi: 10.33160/yam.2019.06.001. eCollection 2019 Jun.
citation: Machalicek W, Raulston T, Knowles C, Ruppert T, Carnett A, Alresheed F. Challenging behavior. In: Matson JL, editor. Comorbid Conditions Among Children with Autism Spectrum Disorders. Cham: Springer; 2016. p. 137-170.
citation: MacNaul HL, Neely LC. Systematic Review of Differential Reinforcement of Alternative Behavior Without Extinction for Individuals With Autism. Behav Modif. 2018 May;42(3):398-421. doi: 10.1177/0145445517740321. Epub 2017 Nov 8.
citation: Erturk B, Machalicek W, Drew C. Self-Injurious Behavior in Children With Developmental Disabilities: A Systematic Review of Behavioral Intervention Literature. Behav Modif. 2018 Jul;42(4):498-542. doi: 10.1177/0145445517741474. Epub 2017 Nov 27.
citation: Weston R, Hodges A, Davis TN. Differential Reinforcement of Other Behaviors to Treat Challenging Behaviors Among Children With Autism: A Systematic and Quality Review. Behav Modif. 2018 Jul;42(4):584-609. doi: 10.1177/0145445517743487. Epub 2017 Nov 24.
citation: O'Neil ME, Freeman M, Christensen V, Telerant R, Addleman A, Kansagara D. A Systematic Evidence Review of Non-pharmacological Interventions for Behavioral Symptoms of Dementia [Internet]. Washington (DC): Department of Veterans Affairs (US); 2011 Mar. Available from http://www.ncbi.nlm.nih.gov/books/NBK54971/
citation: Doyle KL, Morgan EE, Morris S, Smith DM, Little S, Iudicello JE, Blackstone K, Moore DJ, Grant I, Letendre SL, Woods SP; Translational Methamphetamine AIDS Research Center (TMARC) Group. Real-world impact of neurocognitive deficits in acute and early HIV infection. J Neurovirol. 2013 Dec;19(6):565-73. doi: 10.1007/s13365-013-0218-2. Epub 2013 Nov 26.
citation: Subramaniam P, Woods B. The impact of individual reminiscence therapy for people with dementia: systematic review. Expert Rev Neurother. 2012 May;12(5):545-55. doi: 10.1586/ern.12.35.
citation: Cotelli M, Manenti R, Zanetti O. Reminiscence therapy in dementia: a review. Maturitas. 2012 Jul;72(3):203-5. doi: 10.1016/j.maturitas.2012.04.008. Epub 2012 May 16.
citation: Zetteler J. Effectiveness of simulated presence therapy for individuals with dementia: a systematic review and meta-analysis. Aging Ment Health. 2008 Nov;12(6):779-85. doi: 10.1080/13607860802380631.
citation: Neal M, Barton Wright P. Validation therapy for dementia. Cochrane Database Syst Rev. 2003;(3):CD001394. doi: 10.1002/14651858.CD001394.
citation: Wan Yunus F, Liu KP, Bissett M, Penkala S. Sensory-Based Intervention for Children with Behavioral Problems: A Systematic Review. J Autism Dev Disord. 2015 Nov;45(11):3565-79. doi: 10.1007/s10803-015-2503-9.
citation: Case-Smith J, Weaver LL, Fristad MA. A systematic review of sensory processing interventions for children with autism spectrum disorders. Autism. 2015 Feb;19(2):133-48. doi: 10.1177/1362361313517762. Epub 2014 Jan 29.
citation: Lang R, O'Reilly M, Healy O, Rispoli M, Lydon H, Streusand W, et al. Sensory integration therapy for autism spectrum disorders: A systematic review. Res Autism Spectr Disord. 2012;6(3):1004-1018. doi:10.1016/j.rasd.2012.01.006.
citation: Watling R, Hauer S. Effectiveness of Ayres Sensory Integration(R) and Sensory-Based Interventions for People With Autism Spectrum Disorder: A Systematic Review. Am J Occup Ther. 2015 Sep-Oct;69(5):6905180030p1-12. doi: 10.5014/ajot.2015.018051.
citation: Leong HM, Carter M, Stephenson J. Systematic review of sensory integration therapy for individuals with disabilities: Single case design studies. Res Dev Disabil. 2015 Dec;47:334-51. doi: 10.1016/j.ridd.2015.09.022. Epub 2015 Oct 23.
citation: Barton EE, Reichow B, Schnitz A, Smith IC, Sherlock D. A systematic review of sensory-based treatments for children with disabilities. Res Dev Disabil. 2015 Feb;37:64-80. doi: 10.1016/j.ridd.2014.11.006. Epub 2014 Nov 25.
citation: Gold C, Wigram T, Elefant C. Music therapy for autistic spectrum disorder. Cochrane Database Syst Rev. 2006 Apr 19;(2):CD004381. doi: 10.1002/14651858.CD004381.pub2.
citation: Simpson K, Keen D. Music interventions for children with autism: narrative review of the literature. J Autism Dev Disord. 2011 Nov;41(11):1507-14. doi: 10.1007/s10803-010-1172-y.
citation: Fakhoury N, Wilhelm N, Sobota KF, Kroustos KR. Impact of Music Therapy on Dementia Behaviors: A Literature Review. Consult Pharm. 2017 Oct 1;32(10):623-628. doi: 10.4140/TCP.n.2017.623.
citation: Stephenson J. Music therapy and the education of students with severe disabilities. Educ Train Dev Disabil. 2006;41(3):290-299.
citation: James R, Sigafoos J, Green VA, Lancioni GE, O'Reilly MF, Lang R, et al. Music Therapy for Individuals with Autism Spectrum Disorder: a Systematic Review. Rev J Autism Dev Disord. 2015;2:39-54. doi: 10.1007/s40489-014-0035-4.
citation: Reichow B, Servili C, Yasamy MT, Barbui C, Saxena S. Non-specialist psychosocial interventions for children and adolescents with intellectual disability or lower-functioning autism spectrum disorders: a systematic review. PLoS Med. 2013 Dec;10(12):e1001572; discussion e1001572. doi: 10.1371/journal.pmed.1001572. Epub 2013 Dec 17.
citation: Lim JM. Emotion regulation and intervention in adults with autism spectrum disorder: a synthesis of the literature. Adv Autism. 2019;6(1):48-62. doi:10.1108/AIA-12-2018-0050.
citation: Bishop-Fitzpatrick L, Minshew NJ, Eack SM. A systematic review of psychosocial interventions for adults with autism spectrum disorders. J Autism Dev Disord. 2013 Mar;43(3):687-94. doi: 10.1007/s10803-012-1615-8.
citation: Vanderkerken L, Heyvaert M, Maes B, Onghena P. Psychosocial interventions for reducing vocal challenging behavior in persons with autistic disorder: a multilevel meta-analysis of single-case experiments. Res Dev Disabil. 2013 Dec;34(12):4515-33. doi: 10.1016/j.ridd.2013.09.030. Epub 2013 Oct 31.
citation: Seida JK, Ospina MB, Karkhaneh M, Hartling L, Smith V, Clark B. Systematic reviews of psychosocial interventions for autism: an umbrella review. Dev Med Child Neurol. 2009 Feb;51(2):95-104. doi: 10.1111/j.1469-8749.2008.03211.x.
citation: Mirenda P. Supporting individuals with challenging behavior through functional communication training and AAC: Research review. Augment Altern Commun. 1997;13(4):207-225. doi:10.1080/07434619712331278048.
citation: Walker VL, Snell ME. Effects of augmentative and alternative communication on challenging behavior: a meta-analysis. Augment Altern Commun. 2013 Jun;29(2):117-31. doi: 10.3109/07434618.2013.785020.
citation: Lequia J, MacHalicek W, Rispoli MJ. Effects of activity schedules on challenging behavior exhibited in children with autism spectrum disorders: A systematic review. Res Autism Spectr Disord. 2012;6(1):480-492. doi:10.1016/j.rasd.2011.07.008.
citation: Gerow S, Hagan-Burke S, Rispoli M, Gregori E, Mason R, Ninci J. A Systematic Review of Parent-Implemented Functional Communication Training for Children With ASD. Behav Modif. 2018 May;42(3):335-363. doi: 10.1177/0145445517740872. Epub 2017 Dec 3.
citation: Gregori E, Wendt O, Gerow S, Peltier C, Genc-Tosun D, Lory C, et al. Functional Communication Training for Adults with Autism Spectrum Disorder: A Systematic Review and Quality Appraisal. J Behav Educ. 2020;29:42-63. doi:10.1007/s10864-019-09339-4.
citation: Goldstein H. Communication intervention for children with autism: a review of treatment efficacy. J Autism Dev Disord. 2002 Oct;32(5):373-96. doi: 10.1023/a:1020589821992.
citation: Forbes D, Forbes SC, Blake CM, Thiessen EJ, Forbes S. Exercise programs for people with dementia. Cochrane Database Syst Rev. 2015 Apr 15;2015(4):CD006489. doi: 10.1002/14651858.CD006489.pub4.
citation: Eggermont LHP, Scherder EJA. Physical activity and behaviour in dementia: A review of the literature and implications for psychosocial intervention in primary care. Dementia. 2006;5(3):411-428. doi:10.1177/1471301206067115.
citation: Ogg-Groenendaal M, Hermans H, Claessens B. A systematic review on the effect of exercise interventions on challenging behavior for people with intellectual disabilities. Res Dev Disabil. 2014 Jul;35(7):1507-17. doi: 10.1016/j.ridd.2014.04.003. Epub 2014 Apr 21.
citation: Sorensen C, Zarrett N. Benefits of Physical Activity for Adolescents with Autism Spectrum Disorders: A Comprehensive Review. Rev J Autism Dev Disord. 2014;1:344-353. doi:10.1007/S40489-014-0027-4/TABLES/4.
citation: Bremer E, Crozier M, Lloyd M. A systematic review of the behavioural outcomes following exercise interventions for children and youth with autism spectrum disorder. Autism. 2016 Nov;20(8):899-915. doi: 10.1177/1362361315616002. Epub 2016 Jan 28.
citation: Ferguson J, Craig EA, Dounavi K. Telehealth as a Model for Providing Behaviour Analytic Interventions to Individuals with Autism Spectrum Disorder: A Systematic Review. J Autism Dev Disord. 2019 Feb;49(2):582-616. doi: 10.1007/s10803-018-3724-5.
citation: Tanner K, Hand BN, O'Toole G, Lane AE. Effectiveness of Interventions to Improve Social Participation, Play, Leisure, and Restricted and Repetitive Behaviors in People With Autism Spectrum Disorder: A Systematic Review. Am J Occup Ther. 2015 Sep-Oct;69(5):6905180010p1-12. doi: 10.5014/ajot.2015.017806.
citation: Walker VL, Carpenter ME, Lyon KJ, Button L. A Meta-Analysis of Paraprofessional-Delivered Interventions to Address Challenging Behavior Among Students With Disabilities. J Posit Behav Interv. 2021;23(2):68-80. doi:10.1177/1098300720911147.
citation: Lindgren S, Wacker D, Suess A, Schieltz K, Pelzel K, Kopelman T, Lee J, Romani P, Waldron D. Telehealth and Autism: Treating Challenging Behavior at Lower Cost. Pediatrics. 2016 Feb;137 Suppl 2(Suppl 2):S167-75. doi: 10.1542/peds.2015-2851O.
citation: McDonnell A, Sturmey P, Oliver C, Cunningham J, Hayes S, Galvin M, et al. The effects of staff training on staff confidence and challenging behavior in services for people with autism spectrum disorders. Res Autism Spectr Disord. 2008,2(2):311-319. doi:10.1016/J.RASD.2007.08.001.
citation: Wahman CL, Pustejovsky JE, Ostrosky MM, Santos RM. Examining the Effects of Social StoriesTM on Challenging Behavior and Prosocial Skills in Young Children: A Systematic Review and Meta-Analysis. Topics Early Child Spec Educ. 2022,41(4):267-279. doi:10.1177/0271121419855692.
citation: Kinsbourne M. Do repetitive movement patterns in children and animals serve a dearousing function? J Dev Behav Pediatr. 1980 Mar;1(1):39-42. No abstract available.
citation: Ornitz EM, Ritvo ER. Perceptual inconstancy in early infantile autism. The syndrome of early infant autism and its variants including certain cases of childhood schizophrenia. Arch Gen Psychiatry. 1968 Jan;18(1):76-98. doi: 10.1001/archpsyc.1968.01740010078010. No abstract available.
citation: Hutt C, Hutt SJ. Effects of environmental complexity on stereotyped behaviours of children. Anim Behav. 1965;13(1):1-4. doi:10.1016/0003-3472(65)90064-3.
citation: Klusek J, Roberts JE, Losh M. Cardiac autonomic regulation in autism and Fragile X syndrome: a review. Psychol Bull. 2015 Jan;141(1):141-75. doi: 10.1037/a0038237. Epub 2014 Nov 24.
citation: Levine TP, Conradt E, Goodwin MS, Sheinkopf SJ, Lester B. Psychophysiological Arousal to Social Stress in Autism Spectrum Disorders. In: Patel V, Preedy V, Martin C, editors. Comprehensive Guide to Autism. New York: Springer, New York; 2014. p. 1177-1193.
citation: Lydon S, Healy O, Reed P, Mulhern T, Hughes BM, Goodwin MS. A systematic review of physiological reactivity to stimuli in autism. Dev Neurorehabil. 2016 Dec;19(6):335-355. doi: 10.3109/17518423.2014.971975. Epub 2014 Oct 30.
citation: Cohen IL, Yoo JH, Goodwin MS, Moskowitz L. Assessing Challenging Behaviors in Autism Spectrum Disorders: Prevalence, Rating Scales, and Autonomic Indicators. In: Matson J, Sturmey P, editors. International Handbook of Autism and Pervasive Developmental Disorders. New York: Springer, New York; 2011. p. 247-270.
citation: Hirstein W, Iversen P, Ramachandran VS. Autonomic responses of autistic children to people and objects. Proc Biol Sci. 2001 Sep 22;268(1479):1883-8. doi: 10.1098/rspb.2001.1724.
citation: Schoen SA, Miller LJ, Brett-Green B, Hepburn SL. Psychophysiology of children with autism spectrum disorder. Res Autism Spectr Disord. 2008;2(3):417-429. doi:10.1016/J.RASD.2007.09.002.
citation: 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. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5745-5748. doi: 10.1109/EMBC.2018.8513524.
citation: Goodwin MS, Mazefsky CA, Ioannidis S, Erdogmus D, Siegel M. Predicting aggression to others in youth with autism using a wearable biosensor. Autism Res. 2019 Aug;12(8):1286-1296. doi: 10.1002/aur.2151. Epub 2019 Jun 21.
citation: Goodwin MS, Ozdenizci O, Cumpanasoiu C, Tian P, Guo Y, Stedman A, Peura C, Mazefsky C, Siegel M, Erdogmus D, Ioannidis S. Predicting Imminent Aggression Onset in Minimally-Verbal Youth with Autism Spectrum Disorder Using Preceding Physiological Signals. Int Conf Pervasive Comput Technol Healthc. 2018 May;2018:201-207. doi: 10.1145/3240925.3240980.
citation: Nuske HJ, Finkel E, Hedley D, Parma V, Tomczuk L, Pellecchia M, Herrington J, Marcus SC, Mandell DS, Dissanayake C. Heart rate increase predicts challenging behavior episodes in preschoolers with autism. Stress. 2019 May;22(3):303-311. doi: 10.1080/10253890.2019.1572744. Epub 2019 Mar 1.
citation: 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). 2018 Dec 4;18(12):4271. doi: 10.3390/s18124271.
citation: Banaee H, Ahmed MU, Loutfi A. Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors (Basel). 2013 Dec 17;13(12):17472-500. doi: 10.3390/s131217472.
citation: Koo SH, Gaul K, Rivera S, Pan T, Fong D. Wearable technology design for autism spectrum disorders. Arch Des Res. 2018;31(1):37-55. doi:10.15187/adr.2018.02.31.1.37.
citation: Black MH, Milbourn B, Chen NTM, McGarry S, Wali F, Ho ASV, Lee M, Bolte S, Falkmer T, Girdler S. The use of wearable technology to measure and support abilities, disabilities and functional skills in autistic youth: a scoping review. Scand J Child Adolesc Psychiatr Psychol. 2020 Jul 2;8:48-69. doi: 10.21307/sjcapp-2020-006. eCollection 2020.
citation: Murphy KP. Machine learning: a probabilistic perspective. Cambridge: MIT press; 2012.
citation: Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
citation: Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: A review. Neurocomputing. 2016;187:27-48. doi:10.1016/j.neucom.2015.09.116.
citation: Munla N, Khalil M, Shahin A, Mourad A. Driver stress level detection using HRV analysis. ICABME 2015: International Conference on Advances in Biomedical Engineering, 2015 Sept 16-18; Beirut, Lebanon. IEEE; 2015. p. 61-64. doi:10.1109/ICABME.2015.7323251.
citation: Wu M, Cao H, Nguyen HL, Surmacz K, Hargrove C. Modeling perceived stress via HRV and accelerometer sensor streams. Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1625-8. doi: 10.1109/EMBC.2015.7318686.
citation: Coutts LV, Plans D, Brown AW, Collomosse J. Deep learning with wearable based heart rate variability for prediction of mental and general health. J Biomed Inform. 2020 Dec;112:103610. doi: 10.1016/j.jbi.2020.103610. Epub 2020 Oct 31.
citation: Goldberger AL, Goldberger ZD, Shvilkin A. Goldberger's Clinical Electrocardiography: A Simplified Approach. 9th ed. Philadelphia: Elsevier; 2017.
citation: Peltola MA. Role of editing of R-R intervals in the analysis of heart rate variability. Front Physiol. 2012 May 23;3:148. doi: 10.3389/fphys.2012.00148. eCollection 2012.
citation: Demers L, Weiss-Lambrou R, Ska B. Item analysis of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST). Assist Technol. 2000;12(2):96-105. doi: 10.1080/10400435.2000.10132015.
citation: Scherer MJ. Living in the State of Stuck: How Technology Impacts the Lives of Persons with Disabilities. 4th ed. Brookline: Brookline Books; 2005.
citation: Demers L, Weiss-Lambrou R, Ska B. The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0): An overview and recent progress. Technol Disabil. 2002;14(3):101-105. doi:10.3233/TAD-2002-14304.
citation: Kitzinger J. The methodology of Focus Groups: the importance of interaction between research participants. Sociol Health Illn. 1994;16(1):103-121. doi:10.1111/1467-9566.ep11347023.
citation: Flores JG, Alonso CG. Using Focus Groups in Educational Research: Exploring Teachers' Perspectives on Educational Change. Eval Rev. 1995;19(1):84-101. doi:10.1177/0193841X9501900104.
citation: Breen RL. A practical guide to focus-group research. J Geogr High Educ. 2006;30(3):463-475. doi:10.1080/03098260600927575.
citation: Becker DE. Fundamentals of electrocardiography interpretation. Anesth Prog. 2006 Summer;53(2):53-63; quiz 64. doi: 10.2344/0003-3006(2006)53[53:FOEI]2.0.CO;2.
citation: Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 2001 Jan;37(1):153-6. doi: 10.1016/s0735-1097(00)01054-8.
citation: Sidhu S, Marine JE. Evaluating and managing bradycardia. Trends Cardiovasc Med. 2020 Jul;30(5):265-272. doi: 10.1016/j.tcm.2019.07.001. Epub 2019 Jul 9.
citation: El Boujnouni I, Tali A. Heart Rate Variability Prediction based on the combination of Wavelet Decomposition and LSTM Networks. Int J Sci Eng Res. 2019;10(9):61-65.