App characteristics and accuracy metrics of available digital biomarkers for autism: a scoping review

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Background: Diagnostic delays in autism are common, with time to diagnosis being up to three years from symptoms onset. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. Objective: The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. Methods: We conducted a structured literature search on databases and search engines aimed at identifying peer-reviewed studies and regulatory submissions describing app characteristics, validation study details and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. Results: We identified four studies evaluating four products, one commercial and three research apps. Accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5% respectively. PPV ranged from 20.3% to 76.6% and NPV fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants' demographics and, where these were reported, important imbalances in gender and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against DSM-5 criteria and others via self-reported measures. Further, whilst in most cases two classes were used for algorithm validation purposes, one of the studies reported a third category (“indeterminate”). These discrepancies significantly impact comparability and generalizability of the results, thus highlighting the need for standardized validation processes and reporting of findings. Conclusions: Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity as well as help researchers, clinicians and end users safely adopt novel tools within clinical and research practices.

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