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The Role of Novel Research Technologies in Autism Spectrum Disorders

Mobile devices are woven into the fabric of our typical day. Portable technologies such as the iPhone, iPad and wearable activity trackers like Fitbit have significantly transformed not only how we communicate but also enable us to collect an enormous amount of health relevant data and information (Kumar et al., 2013). The majority of parents and clinical caregivers who interact with children with Autism Spectrum Disorder (ASD) have mobile devices and children with ASD find smartphone technologies particularly engaging (Mazurek & Wenstrup, 2013; Shane & Albert, 2008). Thus, there seems to be a clear opportunity to harness the flexibility and convenience of portable technologies to conduct research in ASD (Shic & Goodwin, 2015) and provide meaningful feedback to families and clinicians that may expand treatment options for the ASD population.

In this article, I will discuss some of the most exciting mobile and wearable technologies that are being used in research settings, which aspects of ASD research these technologies are well suited to target, and future directions for the potential of wearable devices in ASD. It is exciting to consider how both mainstream commercially available devices as well as the development of new devices may change how we study ASD and ultimately help those with ASD and their families.

Since the introduction of the iPhone in 2007, iPad in 2011 and Fitbit Flex (wrist wearable device) in 2013 there has been an explosion in demand for mobile devices. At the end of 2013, 1 in 10 Americans over the age of 18 owned an activity tracker (Endeavor report). These devices are accessible and affordable to the public and there is acceptance for using them regularly in practically all settings. Thus it is part of our common landscape to see children and adults engaging with some type of portable technology on a regular basis. As long as there is demand, there will be motivation to keep updating and refining these technologies to be applicable to a broad population of users, including those who are on the spectrum and their caregivers.

There are countless applications for these devices for the health sector, and there is a growing response from the scientific and medical communities about what data to collect and how it should be processed and interpreted. The introduction of ResearchKit, Apple’s new Health application in 2015, made it clear that research with mobile devices is going mainstream. A recent collaboration between researchers at Duke University and the developers at ResearchKit, called “Autism and Beyond”, is designed to study facial emotion abnormalities associated with ASD using the outward facing camera of a smartphone. The collaboration suggests that there is going to be an increasing crosstalk between both academic researchers and commercial entities.

One significant area for portable devices in ASD research is a wearable wrist sensor that can typically track arousal through a sweat response (electrodermal activity – EDA) as well as movement (acceleration). Newer models also include the ability to track heart rate and temperature. While classic studies of arousal and sweat response have been in a laboratory setting, what is appealing about these wrist sensors is that they are mobile and can be worn during daily activities. Some children and adults with ASD have tantrums or disruptive behavior and it is the hope that these sensors will be able to identify these events in their early stages or before they begin. In other words, if these devices can detect precipitous changes in arousal that foreshadow a tantrum, perhaps caregivers or clinicians could be alerted in advance to these events through mobile technology connected to the wrist sensor in order to minimize the behavior. This area of research is interesting but in nascent stages. What is most exciting is being able to understand how to use the various readouts from these wrist sensors as a way to better understand the internal state of an individual with ASD who may not be able to communicate as well about how they are feeling (either in a positive or negative manner). If we can continue to study how physiological measurements correspond to different moods, emotions, attitudes and health conditions, we will be better equipped to understand individuals who have difficulty expressing how they are feeling.

Another significant area of expansion is developing novel applications for smartphones that better track an individual’s daily behavior, particularly tailored for those who are on the spectrum or a caregiver of someone on the spectrum. Traditionally, information about behavior such as language, social skills, restricted interests or tantrums are collected via paper and pencil. When there is an interest from either a clinician, school and/or research study to measure changes in behavior over time, these paper and pencil questionnaires are administered repeatedly. Collecting data in this fashion is tedious and redundant both for those completing the forms as well as those who are interpreting the data. They are also potentially more prone to human error. Methods that rely on electronic entering and storage of data facilitate all aspects of the data process and have the potential to be less time intensive.

Smartphones also offer flexibility and immediacy in data collection since caregivers can answer various questions about their child, student or patient in real-time. This means not only ease but also speed in general data collection and the ability to provide real-time reports about a child’s behavior. Research questions can focus on ‘what is happening right now’ or ‘what happened today’, versus static questions that target a general trend about behavior. Mobile devices may be able to provide a more complete picture of how behavior changes across time and also how it may vary in different contexts. Lastly, caregivers may be more motivated to complete data collection on a mobile device because of its constant availability. The ability to offer an adaptable time window is especially important and appealing to those who are taking care of children with ASD. Lastly, in cases where individuals with ASD provide self-report, they may be more motivated to do so on a mobile device versus the traditional paper and pencil methods.

Quick automated analysis algorithms are tackling the high volumes of data that are generated by portable technologies. For example, recent work that has designed automated tools to measure eye contact from video cameras embedded into commercially available eye-glasses (Pivothead glasses) (Ye et al., 2012). In order to generate information on the frequency and duration of eye contact from videos, a researcher typically needs to manually code the video frame by frame. This process is time consuming and prone to human error. The automated algorithms are more efficient and are equipped to handle the large amounts of data that are generated by the video camera. These algorithms developed for wearable eye glasses is just one example of the many different computer algorithms that are being developed to process data generated from wearable devices including wrist sensors, devices that record spoken language and information from smartphones. This is an exciting area where research and the technology industry intersect and it is clearly mutually beneficial.

The types of devices as well as the capacity of existing devices to collect real-time information is growing and I expect that we will continue to see innovative tools that are adapted specifically to the needs of individuals with ASD. For example, there are wearable vests that measure electrodermal activity (EDA), acceleration electrocardiography (ECG) and temperature (Fletcher, Amemori, Goodwin, & Graybiel, 2012) and these might become tools that are particularly helpful when an individual with ASD has sensory sensitivities and will not tolerate a wrist sensor.

It is likely that wearable technologies will become more commonplace for individuals with ASD and that, increasingly, families and schools will want the data from these devices to be processed in a way that provides them with real-time feedback that they can use to change how they engage with the child or adult with ASD. While there are many privacy concerns that are beyond the scope of this article, the more data that is generated about the child or adult with ASD, when used in conjunction with clinical observations and recommendations, the more we can optimize treatment for those with ASD. Ultimately, portable devices may help us to understand the everyday fabric of living with ASD.

For more information, please visit www.nyp.org/autism.

 

References

Fletcher, R. R., Amemori, K., Goodwin, M., & Graybiel, A. M. (2012). Wearable wireless sensor platform for studying autonomic activity and social behavior in non-human primates. Conf Proc IEEE Eng Med Biol Soc, 2012, 4046-4049.

Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., … Swendeman, D. (2013). Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med, 45(2), 228-236.

Mazurek, M. O., & Wenstrup, C. (2013). Television, video game and social media use among children with ASD and typically developing siblings. J Autism Dev Disord, 43(6), 1258-1271.

Shane, H. C., & Albert, P. D. (2008). Electronic screen media for persons with autism spectrum disorders: results of a survey. J Autism Dev Disord, 38(8), 1499-1508.

Shic, F., & Goodwin, M. (2015). Introduction to Technologies in the Daily Lives of Individuals with Autism. J Autism Dev Disord, 45(12), 3773-3776.

Ye, Z., Li, Y., Fathi, A., Han, Y., Rozga, A., Abowd, G. D., & Rehg, J. (2012). Detecting eye contact using wearable eye-tracking glasses. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 699-704.

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