Background: Assessment of cognitive development is essential to identify children with faltering developmental attainment and monitor the impact of interventions. A key barrier to achieving these goals is the lack of standardized, scalable tools to assess cognitive abilities.Objective: This study aimed to develop a tablet-based gamified assessment of cognitive abilities of 3-year-old children which can be administered by non-specialist field workers.Methods: Workshops among domain experts, literature search for established and gamified paradigms of cognitive assessments and rapid review of mobile games for 3-year-old children was done to conceptualize games for this study. Formative household visits (N = 20) informed the design and content of the games. A cross-sectional pilot study (N = 100) was done to assess feasibility of the tool and check if increasing levels of difficulty and the expected variability between children were evident in game metrics. In-depth interviews (N = 9) were conducted with mothers of participating children to assess its acceptability.Results: Six cognitive domains were identified as being integral to learning – divided attention, response inhibition, reasoning, visual form perception and integration and memory. A narrative, musical soundtrack and positive reinforcement were incorporated into the tool to enhance participant engagement. Child performance determined level timers and difficulty levels in each game. Pilot data indicate that children differ in their performance profile on the tool as measured by the number of game levels played and their accuracy and completion time indicating that it might be possible to differentiate children based on these metrics. Qualitative data suggest high levels of acceptability of the tool amongst participants.Conclusions: A DEvelopmental assessment on an E-Platform (DEEP) has been created comprising distinct games woven into a narrative, which assess six cognitive domains, and shows high levels of acceptability and generates metrics which may be used for validation against gold standard cognitive assessments.

Green space exposure has been positively correlated with better mental-health indicators in several high income countries, but has not been examined in low- and middle-income countries undergoing rapid urbanization. Building on a study of mental health in adults with a pre-existing chronic condition, we examined the association between park availability and major depression among 1208 adults surveyed in Delhi, India. Major depression was measured using the Mini International Neuropsychiatric Interview. The ArcGIS platform was used to quantify park availability indexed as (i) park distance from households, (ii) area of the nearest park; and within one km buffer area around households - the (iii) number and (iv) total area of all parks. Mixed-effects logistic regression models adjusted for socio-demographic characteristics indicated that relative to residents exposed to the largest nearest park areas (tertile 3), the odds [95% confidence interval] of major depression was 3.1 [1.4-7.0] times higher among residents exposed to the smallest nearest park areas (tertile 1) and 2.1 [0.9-4.8] times higher in residents with mid-level exposure (tertile 2). There was no statistically significant association between other park variables tested and major depression. We hypothesized that physical activity in the form of walking, perceived stress levels and satisfaction with the neighbourhood environment may have mediating effects on the association between nearest park area and major depression. We found no significant mediation effects for any of our hypothesized variables. In conclusion, our results provide preliminary and novel evidence from India that availability of large parks in the immediate neighborhood positively impacts mental well-being of individuals with pre-existing chronic conditions, at the opportune time when India is embarking on the development of sustainable cities that aim to promote health through smart urban design – one of the key elements of which is the inclusion of urban green spaces.

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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.

Autism Spectrum Disorders, hereafter referred to as autism, emerge early and persist throughout life, contributing significantly to global years lived with disability. Typically, an autism diagnosis depends on clinical assessments by highly trained professionals. This high resource demand poses a challenge in resource-limited areas where skilled personnel are scarce and awareness of neurodevelopmental disorder symptoms is low. We have developed and tested a novel app, START, that can be administered by non-specialists to assess several domains of the autistic phenotype (social, sensory, motor functioning) through direct observation and parent report. N=131 children (2-7 years old; 48 autistic, 43 intellectually disabled, and 40 typically developing) from low-resource settings in the Delhi-NCR region, India were assessed using START in home settings by non-specialist health workers. We observed a consistent pattern of differences between typically and atypically developing children in all three domains assessed. The two groups of children with neurodevelopmental disorders manifested lower social preference, higher sensory sensitivity, and lower fine-motor accuracy compared to their typically developing counterparts. Parent-report further distinguished autistic from non-autistic children. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups (ASD, ID, TD). Qualitative analysis of the interviews with health workers and families (N= 15) of the participants suggest high acceptability and feasibility of the app. These results provide proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental disorders in low-resource settings.

Background There is an urgent need to fill the gap of scalable cognitive assessment tools for preschool children to enable identification of children at-risk of sub-optimal development and to support their timely referral into interventions. We present the associations between growth in early childhood, a well-established marker of cognitive development, and scores on a novel digital cognitive assessment tool called DEvelopmental Assessment on an E-Platform (DEEP) on a sample of 3-year old pre-schoolers from a rural region in north India. Methods Between February 2018 and March 2019, 1359 children from the Sustainable Programme Incorporating Nutrition and Games (SPRING) programme were followed up at 3-years age and data on DEEP, anthropometry and a clinical developmental assessment, the Bayley's Scale of Infant and Toddler Development, 3rd edition (BSID-III) was collected. DEEP data from 200 children was used to train a machine learning algorithm to predict their score on the cognitive domain of BSID-III. The DEEP score of the remaining 1159 children was then predicted using this algorithm to examine the cross-sectional and prospective association of growth with the DEEP score. Findings The magnitude of the concurrent positive association between height-for-age and cognitive z -scores in 3-year olds was similar when cognition was measured by BSID-III (0.20 standard deviations increase for every unit change in specifically age-adjusted height (HAZ), 95% CI = 0.06–0.35) and DEEP (0.26 CI, 0.11–0.41). A similar positive prospective relationship was found between growth at 18 (0.21 CI, 0.17–0.26) and 12-months (0.18 CI, 0.13–0.23) and DEEP score measured at 3-years. Additionally, the relationship between growth and cognitive development was found to be dependant on socioeconomic status (SES). Interpretation In this study, we suggest the utility of DEEP, a scalable, digital cognitive assessment tool, to measure cognition in preschool children. Further validation in different and larger datasets is necessary to confirm our findings. Funding The SPRING Programme was funded through a Wellcome Trust programme grant and the follow-up study by the Corporate Social Responsibility initiative grant from Madura Microfinance Ltd.

Background: Early adversities negatively impact children’s growth and development, putatively mediated by chronic physiological stress resulting from these adverse experiences. We aimed to estimate the associations between prospectively measured cumulative early adversities with growth and cognition outcomes in rural Indian preschool children and to explore if hair cortisol concentration (HCC), a measure of chronic physiological stress, mediated the above association. Methods: Participants were recruited from the SPRING cRCT in rural Haryana, India. Adversities experienced through pregnancy and the first year of life were measured in 1304 children at 12-months. HCC was measured at 12-months in 845 of them. Outcome measures were height-for-age-z-score (HAZ), weight-for-age-z-score (WAZ) and cognition, measured in 1124 children followed up at 3-years. Cognition was measured using a validated tablet-based gamified tool named DEEP. Results: Cumulative adversities at 12-months were inversely associated with all outcomes measures at 3-years. Each unit increase in adversity score led to a decrease of 0·08 units [95% confidence interval (CI):-0·11,-0·06] in DEEP-z-score; 0·12 units [-0·14,-0·09] in HAZ and 0·11 units [-0·13,-0·09] in WAZ. 12-month HCC was inversely associated with DEEP-z-score (-0·09 [-0·16,-0·01]) and HAZ (-0·12 [-0·20,-0·04]), but the association with WAZ was not significant (p = 0·142). HCC marginally mediated the association between cumulative adversities and HAZ (proportion mediated = 0·06, p = 0·014). No evidence of mediation was found for the cognition outcome. Conclusions: Cumulative early adversities and HCC measured at 12-months have persistent negative effects on child growth and cognition at 3-years. The association between adversities and these two child outcomes were differentially mediated by HCC, with no evidence of mediation observed for the cognitive outcome. Future studies should focus on other stress biomarkers, and alternate pathways such as the immune, inflammation and cellular ageing pathways, to unpack key mechanisms underlying the established relationship between early adversities and poor child outcomes.

Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children’s natural environments by non-specialists. The purpose of this systematic review is to identify and characterise potentially scalable digital tools for direct assessment of autism spectrum disorder risk in early childhood. In total, 51,953 titles, 6884 abstracts and 567 full-text articles from four databases were screened using predefined criteria. Of these, 38 met inclusion criteria. Tasks are presented on both portable and non-portable technologies, typically by researchers in laboratory or clinic settings. Gamified tasks, virtual-reality platforms and automated analysis of video or audio recordings of children’s behaviours and speech are used to assess autism spectrum disorder risk. Tasks tapping social communication/interaction and motor domains most reliably discriminate between autism spectrum disorder and typically developing groups. Digital tools employing objective data collection and analysis methods hold immense potential for early identification of autism spectrum disorder risk. Next steps should be to further validate these tools, evaluate their generalisability outside laboratory or clinic settings, and standardise derived measures across tasks. Furthermore, stakeholders from underserved communities should be involved in the research and development process. Lay abstract The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children’s homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the ‘proof-of-concept’ stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children’s behaviours or speech. Computerised analysis of children’s interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.