Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children

Abstract

Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this "detection gap," we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child's cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34-40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. Seventy percent of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modeling, while 30% was assigned to the "test" dataset to evaluate the algorithm's accuracy on novel data. Of the 522 features that computationally described children's performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] > 0.6) and positively correlated (Pearson's r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (<10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP, pending further validation, holds promise to serve as an acceptable and feasible cognitive assessment tool to bridge the detection gap and support optimum child development.

Converging disciplines for assessing child development

“…We have demonstrated that our innovative tools allow us to take gamified assessments of child developmental capabilities into the hands of frontline workers and assess young children in the comfort of their own home while they interact with evidence-based engaging assessments (Bhavnani et al, 2019). We have also shown that these assessments fare well against the gold standards of child development assessments (Mukherjee et al, 2020). Our efforts have led to the creative integration of diverse assessment technologies, for example, designing the lead character of the DEEP games to be a child who wears a special cap has facilitated the child being assessed to agreeing to wear a portable EEG cap, thus allowing us to collect both the digital game data and EEG data simultaneously.…”

The association of a novel digital tool for assessment of early childhood cognitive development, ‘DEvelopmental assessment on an E-Platform (DEEP)’, with growth in rural India: A proof of concept study

“…Cognition at 3-years of age: The Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III), a developmental assessment for preschool children aged 0–42 months [24] , was administered on the 200 participants described above. A translated version of the BSID-III adapted for administration by non-specialists was used following a protocol described previously [ 15 , 22 ]. Raw scores were computed as per the manual, and used to generate age-adjusted composite scores.…”

“…DEEP has been piloted on a cohort of children in a rural north Indian region and demonstrated to be highly engaging for children across genders, acceptable to their parents and feasible for delivery by trained non-specialist personnel in the comfort of the child's home [14] . A proof-of-concept study has also demonstrated that it is possible to predict children's score on the cognitive domain of the Bayley's Scale for Infant and Toddler Development (BSID – IIIrd edition), using metrics captured by DEEP [15] . This study used a supervised machine learning approach benchmarked to the BSID-III cognitive score to develop an algorithm comprising a combination of features extracted from a child's performance on different DEEP games to derive the DEEP cognitive score.…”

Evolving Concepts of the Schizophrenia Spectrum: A Research Domain Criteria Perspective

“…For instance, a recent study followed an unselected sample of children from age 8 to late adolescence, collecting a large number of measures including neurocognitive tests and symptoms; children who developed psychotic symptoms later in adolescence were on average 1–2 years behind typically-developing children in cognitive growth, suggesting that early cognitive impairment could be a marker for psychosis risk and that growth charting may be an opportunity for early detection and prevention ( 24 ). Another group has independently begun to implement this concept with a developmental battery of “gamified” tasks (running on a mobile e-platform) that assesses six cognitive domains in young children in India as a first step to developing normative growth curves ( 25 ).…”