Development and Validation of a System for the Anticipation of Challenging Behaviors
Condition: Autism Spectrum Disorder
MeSH terms: Autistic Disorder; Autism Spectrum Disorder; Child Development Disorders, Pervasive
Study type: Observational

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.
    

Intervention name(s): Smart, non-invasive shirt - Physiological parameters; Smart, non-invasive shirt - Relation with challenging behaviors; Smart, non-invasive shirt - Proof of Concept
Intervention type(s): Other; Other; Other
Intervention description(s): Observation Group One and Control Group - Participants in these groups will be asked to wear a smart, non-invasive shirt while watching two different 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 with the participant in a sitting position. The duration of the entire session will be approximately 15 minutes. For the whole duration of the session, the smart shirt will collect data on participants' physiological parameters (electrocardiogram, heart rate, heart rate variability, respiration rate, minute ventilation, and body movement). Collected data will be used to develop an ad-hoc algorithm to anticipate the challenging behavior of participants with autism spectrum disorder based on physiological data collected by the smart shirt.; Observation Group Two - Each participant in this group will be asked to wear the smart, non-invasive shirt 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 be asked to accurately report the participant's status in a behavioral diary. Each evening, the data collected by the smart shirt will be uploaded to an online cloud along with the behavioral diary of the day. Collected data will be used to develop an ad-hoc algorithm able to anticipate the challenging behavior of participants with autism spectrum disorder based on physiological data collected by the smart shirt.; Proof of Concept - The participant with autism spectrum disorder enrolled in the proof-of-concept phase of this study will wear the smart, non-invasive shirt for seven days during all hours of attendance at the special school. At the end of the seven days, the healthcare professional and teacher who interacted with the developed system at the special school will participate in a focus group to analyze their experiences and opinions related to the system use.
Other intervention name(s): Hexoskin Smart Garments; Hexoskin Smart Garments; Hexoskin Smart Garments
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.
      
Keywords: Autism Spectrum Disorder; Problem Behavior; Aggression; Self-Injurious Behavior; Telemedicine; Accident Prevention