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Mobile Technology Usage By the Other Numbers: User Analytics for Assessing and Justifying Implementation of Mobile Applications
Internet based businesses routinely collect data regarding customer behavior in order to exploit such behavior to expand their business. However, such companies also use these data to understand their effectiveness in serving their customers. The translation of research efforts in autism technology demands these same types of user analytics as rationale for those who would invest in and seek to promote the commercial enterprises that are essential for the translation of such research into practice. Translation necessitates the confluence of at least 4 key elements: societal recognition of a problem, a common understanding of the economics involved in addressing the problem, proof that delivery of a solution to the problem is accepted in the marketplace, and protectable intellectual property that allows an enterprise to recover the costs of translating the research into practice in the market.
Objectives:
The objectives of our activity are to address the double bottom line problem: how to translate research into practice in a manner that is economically feasible and simultaneously provides societal benefit. The former activity is addressed by assessing the societal value of delivering more effective mobile technology solutions to the population affected by autism, while the latter focuses on measuring the degree to which this activity has the potential for impact.
Methods:
Autism tools created for today’s popular tablet architectures (e.g. iOS, Android) can be fitted with common usage analytics collection tools (e.g. Google Analytics) as a first order usage information collection mechanism. We illustrate this scenario using an application for augmentative and alternative communication (AAC) training in minimally-verbal autism: The SPEAKall!® tablet application was instrumented with the Google analytics package to begin measurement of the communication activities performed by the population using SPEAKall!. This instrumentation allows the collection of usage patterns from a large population of AAC users, which is distinct from the assessment of individual effectiveness in and immediately after therapy delivery sessions. While collecting such usage patterns does not suffice as evidence for the effectiveness of a therapeutic approach, it does demonstrate the market acceptance and employment of an approach in practice, and is a completely objective measure of mobile technology usage intensity.
Results:
We present results of a population of users with autism using the SPEAKall! application instrumented with usage analytics. Specifically, we examine the difference in usage behaviors between those who receive free applications versus those who pay for the application. We also review the intensity of usage across the user population by studying the length of messages produced with the AAC solution. Further, we examine vocabulary growth by studying the degree to which users create new symbol vocabulary for use in their AAC applications. Finally, we also present a study of session length that provides a view into the frequency and timespan of user interaction with the AAC tool.
Conclusions:
Analytic collection tools can be used to track detailed usage of nearly all application features and provide a population based view of how mobile applications are used in intervention.