Summary of ASD-ADHD MobileApp ICMR Proposal

Many children with ASD also experience clinically-significant Attention deficit hyperactivity disorder (ADHD) symptoms. The observed cognitive impairments in ASD may be related to comorbid ADHD symptoms rather than shared genetic liability or core mechanisms. Some evidence using continuous symptom measures supports that impairments in response inhibition (Corbett et al., 2009; Yerys, Kenworthy, Jankowski, Strang, & Wallace, 2013) and arousal (Corbett et al., 2009) in ASD may be driven by comorbid ADHD symptoms, whereas ASD symptoms may contribute unique variance to processing speed (Yerys et al., 2013). However, studies are small and have included tasks with emotional face stimuli (Yerys et al., 2013), which may be uniquely related to ASD traits in ways that non-emotional and non-face stimuli are not, thus, it remains unclear. Here, we examine overlapping and distinct neuropsychological profiles of children with ADHD and ASD and evaluate the effects of dimensional symptom measures on neuropsychological profiles for both groups through this mobile-App.

Developmental screening can be done by a number of professionals in health care, community, and school settings. However, primary health care providers are in a unique position to promote children’s developmental health. Keeping the constraints of trained manpower and resources in view, a technology based early diagnosis tool will be a smart solution to save time, effort, cost, and resources and to reach out all children in multiple settings.

An ideal technology based Indian diagnostic tool for Autism and ADHD requires to be at least in the national language. It needs to be highly scalable; inexpensive; accurate; psychometrically robust; easy to administer; no issues with portability, maintenance, and storing; and errorless in recording data. It also needs to be multi-purpose; multi-setting, multi-users, multi-language. This proposed MobileApp will serve the purpose. Therefore, we want to develop a technology based Indian tool for early screening of two major neurodevelopmental disorders namely ASD and ADHD. Keeping the huge population of India in view, the number is huge and the ratio of specialist: children is very poor. Moreover, specialists are only available in large hospitals. Many government district level hospital do not have specialists such as psychiatrist or clinical psychologists. Nurses and other regular non-specialist health care professionals are the first point of reporting in any hospital, and if screening is done at that point, it will save lot of time, effort, and resources of the patients and hospital. A quick and validated tool which is technology enabled will ensure less human error and better data recording.

Aim: to develop and test psychometric properties of a Bi-lingual Mobile-App based Neuro-Cognitive Screening Tool for Early Identification of Children aged 3-11 years with Autism and ASD co-morbid with ADHD

Primary Objectives are to:-

·   Develop a bi-lingual neuro-cognitive assessment Mobile-App to be used by the nurse/health workers/non-specialist physicians for the early identification of Autism and autism-comorbid with attention deficit hyperactive disorder.

·   Find out the overlapping and distinct neuro-cognitive deficits in children with Autism and ADHD.

·   Test the Psychometric properties of this new tool taking samples from multiple centers.

Secondary objective: -

To compare the emotion recognition ability of both groups through visual-eye- movement tracking.

Hypotheses:

·      The newly developed mobile-App will be a psychometrically robust tool with ability to distinguish between children with and without ASD.

·      ASD group would show greater impairment than the ADHD group on set-shifting and working memory, but would have similar or less impairment than children with ADHD on measures of arousal, inhibitory control, and reward discounting. We do not expect ASD symptoms to account for cognitive impairments in ADHD, but would like to clarify whether ADHD symptoms accounted for cognitive impairments in ASD. The tool will be able to distinguish between ASD with ADHD and ASD without ADHD symptoms.

·      The tool will have robust psychometric properties.

·       The cut off will automatically highlight the screened cases for detailed evaluation and treatment.

Methodology

Sample and Sampling: All children between 3-11 years will constitute the universe of sample. Sample collection settings will be from Pediatrics and Psychiatry OPDs of 3 hospitals in Delhi. A total of 150 children with ASD, 100 with ADHD, and 150 without any neurodevelopmental disorders will be recruited following consecutive sampling technique.

Tools: Apart from personal and clinical proforma, separate tools will be used to measure IQ screening (Vineland Social Maturity Scale; VSMS) (Doll, 1935; Malin, 1971); participants’ motor dexterity, visuo-motor coordination, spatial organization, and speed and accuracy of performance Seguine Form Board Test (SFBT: Seguine, 1856); tools for divergent validity and convergent validity (Indian Scale for Assessment of Autism: ISAA, International Clinical Epidemiology Network (INCLEN) Diagnostic Tool for Indian Children with ASD (INDT-ASD: Juneja et.al., 2014); neurocognitive profile (Cambridge Neuropsychological Test Automated Battery :CANTAB or similar digital tool); and psychophysiological protocol standardization (Visual Tracking System).  

The procedure of MobileApp will be done through FGDs/key interviews with 15 non-health care professionals working with children in a hospital set up will be done to generate items regarding their basic understanding of the illness and its symptom profile.  A panel of expert using the modified Delphi technique will select the pool of items (Fischer, 1978). From the pool of items, the symptoms will be rank-ordered by the panel members, and further reduced using endorsement rate approach (Fitzpatrick, Davey, Buxton, Jones, 1998). Face and content validity will be found out. Necessary modifications will be incorporated.

Comparison: The non-specialist healthcare professionals will be compared for their accuracy of training on pre and post- MobileApp use.