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Challenges in the Use of Technology for Autism Spectrum Disorder in Low- and Middle-Income Countries
Efforts to utilize technology to address the practical challenges people affected by ASD face every day, are laudable and important. However, the global ASD community is culturally, linguistic, socio-economically and geographically immensely diverse. If our overarching goal is to address the disparities in access to ASD screening, diagnosis, and treatment in low- income country (LMIC) settings using technology, these technologies need to be designed from the outset with this specific context, community and end user in mind. Many reviews have been performed of a range of technologies for ASD. However, the feasibility of the use of such technologies will depend on the ability to implement them including their accessibility, affordability, appropriateness, and scalability.
Objectives:
Here we set out to examine the feasibility of a range of ASD-related technologies for low- and middle-income settings.
Methods:
Six classes of ASD-related technologies were selected: 1) Personal Computers (PC) 2) Sensing Technologies (ST), 3) Robotics (RT); 4) Virtual Reality (VR), 5) Shared Active Surface (SAS), and 6) Mobile Technologies (MT). Technologies were evaluated for accessibility, affordability, appropriateness, and scalability. Two independent raters reviewed each methodology for each element of feasibility selected, and then provided an overall feasibility rating for implementation in LMIC. Ratings were then compared and consensus reached on all categories.
Results:
RT, VR and SAS were found to be the least affordable, required highly skilled technicians to operate and maintain the equipment and their use limited to research facilities and clinics therefore rating lowest in all categories evaluated. Although people in LMICs have access to PCs they typically are young, educated males living in cities. Furthermore, technologies using photography or video of people are forbidden in some cultures and therefore not universally appropriate.MT scored highest for relative cost (affordability), range of users, settings and purpose (accessibility and scalability).
Conclusions:
Our findings suggest that the feasibility of the majority of existing technologies are very limited in the context of affordability, accessibility, acceptability, cultural appropriateness and scalability. Given the widespread usage of mobile and smartphones and the increasing availability of affordable high speed mobile internet access in the majority of LMIC, mobile health (mHealth) seems to have the greatest potential to increase access to ASD screening, diagnosis and treatment in LMIC settings. There is, however, a striking difference between the large number of Apps available for public download and the small number of tested, evidence-based Apps. To our knowledge, no Apps for ASD have been evaluated specifically for a LMIC setting to date. To address the disparities in access to ASD screening, diagnosis, and treatment in low-resource settings using technology, research will be required to establish the feasibility of mHealth to provide access to quality metal health services and care to the culturally, linguistic, socio-economically and geographically diverse global ASD community.