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

34 Pages Posted: 10 Dec 2019

See all articles by Debarati Mukherjee

Debarati Mukherjee

Public Health Foundation of India - Centre for Chronic Conditions and Injuries

Supriya Bhavnani

Public Health Foundation of India - Centre for Chronic Conditions and Injuries

Akshay Swaminathan

Harvard University - Harvard Medical School

Deepali Verma

Sangath

Dhanya Parameshwaram

Sapien Labs

Gauri Divan

Sangath

Jayashree Dasgupta

Sangath

Kamalkant Sharma

Sangath

Tara Thiagarajan

Sapien Labs

Vikram Patel

Public Health Foundation of India - Centre for Chronic Conditions and Injuries; Harvard University - Harvard Medical School; Global Nature Care Sangathan Group of Institutions

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Abstract

Background: 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.

Methods: 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. 70% of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modelling, while 30% was assigned to the ‘test’ dataset to evaluate the algorithm’s accuracy on novel data.

Findings: 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.

Interpretation: Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP holds promise to serve as an acceptable, feasible and validated cognitive assessment tool to bridge the detection gap and support optimum child development.

Funding Statement: This work was funded by the Corporate Social Responsibility (CSR) initiative of Madura Microfinance Ltd.

Declaration of Interests: Dr Tara Thiagarajan, a collaborator from Sapien Labs in her scientific capacity, also holds the position of Chairperson of Madura Microfinance Ltd. The other authors declare no competing interests.

Ethics Approval Statement: This study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committees of the Public Health Foundation of India and Sangath. Prior to data collection, the objectives and methods of our study were explained to the parent and written informed consent was obtained from those who agreed to participate in this study.

Keywords: mHealth, digital assessment, serious game, India, preschool, child development, LMIC

Suggested Citation

Mukherjee, Debarati and Bhavnani, Supriya and Swaminathan, Akshay and Verma, Deepali and Parameshwaram, Dhanya and Divan, Gauri and Dasgupta, Jayashree and Sharma, Kamalkant and Thiagarajan, Tara and Patel, Vikram, Exploring the Potential of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children (November 22, 2019). Available at SSRN: https://ssrn.com/abstract=3491938 or http://dx.doi.org/10.2139/ssrn.3491938

Debarati Mukherjee

Public Health Foundation of India - Centre for Chronic Conditions and Injuries

India

Supriya Bhavnani

Public Health Foundation of India - Centre for Chronic Conditions and Injuries

India

Akshay Swaminathan

Harvard University - Harvard Medical School

25 Shattuck St
Boston, MA 02115
United States

Deepali Verma

Sangath

Goa
India

Dhanya Parameshwaram

Sapien Labs

Arlington, VA
United States

Gauri Divan

Sangath

Goa
India

Jayashree Dasgupta

Sangath

Goa
India

Kamalkant Sharma

Sangath

Goa
India

Tara Thiagarajan

Sapien Labs

Arlington, VA
United States

Vikram Patel (Contact Author)

Public Health Foundation of India - Centre for Chronic Conditions and Injuries ( email )

India

Harvard University - Harvard Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

Global Nature Care Sangathan Group of Institutions ( email )

India

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