YJDD 66 1522816

The relationship between sensory
integration challenges and the dietary
intake and nutritional status of children with
Autism Spectrum Disorders in
Mumbai, India
Pujitha S. Padmanabhan1 and Hemal Shroff2
1
School of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, India; 2Centre for Health &
Social Sciences, School of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, India
Objective: This study was conducted to assess the dietary intake, food refusal, and nutritional status of
younger and older children with Autism Spectrum Disorders (ASD) in Mumbai, India, and to understand the
relationships between these variables and mealtime behaviors and sensory integration.
Methods: This was an observational cross-sectional study. Convenience and snowball sampling was used.
Data were collected from 146 mother–children pairs, where children belonged to two age groups (3–6 years
and 7–11 years). Caregivers completed scales on mealtime behaviors, sensory integration, and the dietary
intake of their children. Nutritional status of the children was assessed by measuring their height and weight.
Based on BMI ‘z’ scores, children were classified as ‘Underweight’, ‘Mild Underweight’, ‘Normal BMI’,
‘Overweight’, or ‘Obese’.
Results: There was no significant difference in mealtime behavior and sensory integration scores between
the two age groups. There was a significant inverse relationship between the mealtime behavior and sensory
integration scores. There was no relationship between these two variables and the dietary intake of children.
However, there was a significant relationship between these two variables and the number of food groups
refused by children with ASD. Only 39.7% children had a normal BMI. There was a significant positive relationship between dietary intake and nutritional status of children.
Conclusion: Difficulties in sensory integration may contribute to mealtime behavioral problems and inadequate dietary intake in these children. At the same time, higher dietary intake (which may be poor in diversity) may be related with a higher likelihood of being overweight.
Keywords: Mealtime behavior; food refusal; dietary diversity; overweight; developmental disabilities
Introduction
The word Autism has originated from the Greek work
‘autos’ that means ‘self’. The term describes a condition where the person is removed from social interaction, i.e. ‘an isolated self’ (Vatanoglu-Lutz et al.
2014). As per the Diagnostic and Statistical Manual of
Mental Disorders-Fifth Revision (DSM-V), Autism
Spectrum Disorders (ASD) is now an umbrella term
used to collectively refer to a wide and heterogeneous
spectrum of neurobiological disorders namely: Autistic
disorders, Pervasive Development Disorders-Not
Otherwise
Specified
(PDD-NOS),
Asperger’s
Correspondence to: Pujitha S. Padmanabhan, School of Health Systems
Studies, Tata Institute of Social Sciences, V.N. Purav Marg, Deonar,
Mumbai 400088, India. Email: [email protected]
# The British Society of Developmental Disabilities 2018
DOI 10.1080/20473869.2018.1522816
Syndrome, and Childhood Disintegrative Disorder
(American Psychiatric Association 2013). Individuals
with ASD are required to present two types of symptoms to meet criteria: Deficits in social communication
and interaction, and restricted, repetitive patterns of
behavior, interests or activities. The current prevalence
of ASD in the U.S has been reported to be 1 in 59 children (Baio et al. 2018). However, there are no data
available from India to provide a country-specific estimate of the prevalence (Rudra et al. 2017). People with
ASD are at as much risk for health problems as the general population. They may also have specific health care
needs that are related to their existing condition of ASD
or due to other co-occurring conditions. They may be
more vulnerable to developing chronic noncommunicable
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P. Padmanabhan and H. Shroff
conditions because of the complex behavioral, physical,
and psychosocial difficulties that they experience (Curtin
et al. 2010).
Feeding problems occur in 60%–90% of young children with autism as against a stark contrast of
25%–35% in neurotypically developing children (Bruns
and Thompson 2011, Kodak and Piazza 2008).
Marshall et al. (2014) in their review of research studies (n ¼ 44) on feeding difficulties in children with
ASD, highlighted that the most common concerns in
this group of children were restricted dietary variety,
food neophobia, food-refusal, limiting diets based on
texture, and a susceptibility to being overweight. While
similar behaviors are reported in young, typically developing children and those with other developmental disorders, the high frequency and persistence of these
behaviors well into childhood appear to be uniquely
characteristic of ASD, where even the rate of occurrence and intensity of problematic eating behaviors
were higher in children with ASD than in other disability groups (Lane et al. 2014). There are contrasting
findings on the relationship between age and eating
problems. Nadon et al. (2011) found that older children
had fewer eating problems than younger children,
whereas Herndon et al. (2009) found more refusal
towards intake of different food groups in children aged
4–8 years as compared to children aged 1–3 years.
However, there are few studies that have examined the
relationship between age and change in eating habits (if
any) as children with ASD grow older.
As a way to understand the eating difficulties that
children with ASD have, researchers have examined if
there is a connection with sensory processing. The
results of some studies indicate that there is a link
between sensory processing problems and difficulties
with eating (Zobel-Lachiusa et al. 2015, TwachtmanReilly et al. 2008). In general, studies have reported a
high prevalence of sensory processing dysfunction in
children and adolescents with ASD (Posar and Visconti
2018, Shah et al. 2015) Lane et al. (2014) and Nadon
et al. (2011) have reported associations between sensory disturbances and food refusal in their studies.
Behavioral difficulties associated with autistic disorder
can interfere with typical feeding development and dietary intake as well. In a study conducted by Curtin et al.
(2015) comparing food-selectivity and mealtime behavioral problems in children aged 3–11 years with ASD
(n ¼ 53) and without ASD (n ¼ 58), it was found that
the frequency of mealtime behavioral problems was
higher in the group with ASD.
Bandini et al. (2010), in their study, noted that children with ASD had a significantly greater number of
nutrients for which their intake was inadequate, as compared to typically developing children. Johnson et al.
(2008) compared the eating habits and nutritional status
of young children (2–4 years) with (n ¼ 19), and
The relationship between sensory integration challenges
without ASD (n ¼ 15) and found that the former displayed more idiosyncratic feeding behaviors (mainly
refusal of food based on color, texture, and type) compared to the latter. However, this did not translate to
clear patterns of nutritional deficits in these children.
Similarly, Emond et al. (2010) reported that children
with ASD had net energy intake and growth (as
assessed by mean weight, height, and BMI) that was
not impaired. There have also been studies indicating
an increasing prevalence of overweight and obesity
among children with ASD (Gillette et al. 2015, Curtin
et al. 2014).
In his initial description of ASD, Leo Kanner cited
atypical eating patterns as prominent. In the past, diagnostic systems included feeding difficulties as a defining characteristic of ASD (Kanner 1943). Diets vary
across cultures and countries. Inspite of this, there is
insufficient research on feeding problems, nutrient
intake, and the nutritional status of children with ASD
in Asian countries. The bulk of the existing literature
on feeding difficulties and dietary intake in children
with ASD has been conducted in Western countries.
This paucity of research on feeding difficulties and their
impact on dietary intake and nutritional status of children with ASD in other parts of the world is a considerable gap that needs to be filled. Moreover, many Indian
families eat predominantly vegetarian diets as compared
to diets of other cultures which are primarily meatbased. In this context, it becomes important to examine
whether children with ASD are able to meet their basic
macronutrient requirements, especially protein, considering many non-vegetarian food sources have higher
protein content as compared to vegetarian sources.
Research in diverse populations would help to facilitate
and improve intervention strategies and parent education programs tailored to specific cultures and contexts
(Gray et al. 2016). Based on the previous studies and
findings, it is hypothesized that there will be a significant relationship between the variables of sensory integration and mealtime behavior, and dietary intake and
food refusal in children with ASD in a city in India. In
addition, the variables of dietary intake and nutritional
status of children with ASD would be positively related.
It is also hypothesized that there would be a difference
in these variables among younger (3–6 years) and older
(7–11 years) children with ASD.
Materials and methods
Study area
Data for this study were collected from respondents in
Mumbai, India. Data from mothers and children were
collected via referrals from several special schools,
non-governmental organizations (NGOs), and intervention centers in Mumbai.
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P. Padmanabhan and H. Shroff The relationship between sensory integration challenges
Research design
This was an observational cross-sectional study.
Mothers/Caregivers of children with ASD were the
respondents for basic demographic information for the
children, their eating/feeding behaviors, sensory processing, and dietary intake. Nutritional status was
assessed by measuring the height and weight of the
children. The inclusion criteria for the sample were that
the child was between 3 and 11 years of age, and they
had a formal diagnosis of ASD as determined by a
developmental pediatrician, psychologist/psychiatrist, or
any specialist with expertise in ASD. Children with a
comorbid chronic disorder that could affect dietary
intake (like Type 1 Diabetes, cleft lip/palate) and children with physical/locomotor disabilities, visual/hearing
disabilities and cerebral palsy were excluded from the
study. They were excluded as these conditions may
influence anthropometric indicators and/or food consumption, independent of ASD. Convenience and snowball sampling approaches were used to identify mothers
and children. The ideal sample size for this study was
calculated based on a power calculation and was estimated to be 96. But since the study didn’t employ simple random sampling method, a design effect of 1.5
was considered, and the total sample size was estimated
to be 144. The final sample size for the study was 146
respondents with 72 children in the 3–6 age category
and 74 children from the 7–11 years group.
Measures
A structured closed-ended interview schedule was used
to ask for the socio-demographic details and the child’s
medical history. A semi-structured interview schedule
was used to gather information on the child’s feeding
and nutrition in early infancy, and for information on
the child’s current dietary intake.
The Short Sensory Profile (SSP) is a 38-item parentrated tool that was used to evaluate functional behaviors
symptomatic of sensory processing disorders (McIntosh
et al.1999). This tool has questions on the sensory processing abilities of children across different domains like
tactile, taste, movement, auditory, and visual. Parents
rate their child on a scale of 1–5 for each item, where
1 ¼ Always,
2 ¼ Frequently,
3 ¼ Occasionally,
4 ¼ Seldom, and 5 ¼ Never, with higher scores representing more functional performance. This tool was
translated to Hindi to be used with parents who weren’t
familiar with English. Since this tool has not been used
with an Indian sample, the reliability was assessed by
computing the coefficient alpha. The internal consistency with this sample was found to be very good, with
a Cronbach’s alpha coefficient of 0.85.
Mealtime behaviors of children were evaluated using
the Brief Autism Mealtime Behavior Inventory
(BAMBI), which is an 18-item tool developed by
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Lukens and Linscheid (2008). It is the first standardized
measure to examine mealtime behaviors specifically in
children with ASD (Seiverling et al. 2010). This tool
assesses the child’s autism symptoms, food refusal, and
the variety of food items consumed. In this tool, parents
rate items using a 5-point frequency Likert scale, where
1 ¼ Never/Rarely,
2 ¼ Seldom,
3 ¼ Occasionally,
4 ¼ Often, and 5 ¼ At almost every meal. Higher scores
represent more disruptive mealtime behaviors. This tool
was also translated to Hindi. The internal consistency
for the tool with this sample was calculated, and was
adequate (a ¼ 0.74).
A 24-hour dietary recall was used to gather information on food and liquids (excluding water) consumed by
the child during the 24-hour period prior to the interview. Standardized measuring cups were used to collect
this data. Dietary diversity was assessed based on the
number of food groups consumed by the child, such
that, the more the number of food groups refused, lesser
the dietary diversity. Nutrient intake was estimated
based on the macronutrient consumption (energy, protein, and fat).
Anthropometric assessments were done for assessing
the height and weight of the child. Weight values for
the child were obtained using a digital weighing
machine. A non-stretchable tape was used to measure
the height of the children, where the children were
made to stand erect, with the heels, buttocks, shoulders
and head touching the wall, and the value was recorded
to the nearest centimeter. Body Mass Index (BMI) was
calculated based on the height and weight. The z scores
for BMI were calculated and descriptive statistics were
used to categorize the children as ‘Normal’, ‘Mildly
underweight’, ‘Underweight’, ‘Overweight’, and
‘Obese’. The nutritional status of the child was estimated by using the software WHO Anthro Version
3.2.2 (WHO 2010) for children less than 5 years of age,
and WHO Anthro Plus Version 1.0.4 for children older
than 5 years of age (WHO 2009). This software helps
to identify the child's nutritional status based on their
age and sex.
Data collection
Data collection commenced after receiving ethics
approval from the Institutional Review Board of the
Tata Institute of Social Sciences, Mumbai. The process
of data collection was begun by describing the study
and its aims to people working in NGOs, special
schools, intervention centers, and other professionals
working in the area of child health and disability in
Mumbai. The professionals working in these facilities
were requested to inform parents/caregivers about the
study. Research flyers were also put up in the facilities.
Using this approach, however, led to a low response
rate for participation. Hence, an additional snowball
sampling strategy was used to get in touch with more
NO. 2
P. Padmanabhan and H. Shroff
Table 1
Socio-demographic profile of the respondents.
Age (years)
25–29
30–34
35–39
40–44
45–49
Literacy levels
10 years of education
12 years of education
Graduate
Post graduate
Type of family
Joint
Nuclear
Employment status
Unemployed
Employed
Current marital status
Married
Divorced
Table 2
Number
(n ¼ 146)
Percentage
(%)
8
48
57
29
4
5.47
32.87
39.04
19.86
2.7
25
23
51
47
17.1
15.8
34.9
32.2
61
85
41.8
58.2
85
61
58.2
41.8
143
3
98
2
parents through referral networks (parent support
groups and referrals through providers). The interviews
were done at a time and location as per the convenience
of the caregivers.
Data were collected after taking informed consent
from the mothers of the children. Consent was sought
for conducting interviews with caregivers and for measuring the height and weight of the children. This meets
the guidelines for conducting research in India. Data
collection was done over a duration of eight months,
from March to November 2017.
Analysis
All the data were recorded in the software Statistical
Package for Social Sciences (SPSS) version 25.0 (IBM
Corp. 2017). Scores were calculated for the mealtime
behavior and sensory integration scales. Scores in both
age groups of children were compared using the
Independent samples ‘t’ test. Nutrient intake was computed and assessed by use of a nutrient calculation software called ‘Digest’ (Anand and Dharini 2003).
Pearson’s correlation test was used to check associations between scores on the mealtime behavior scale,
sensory integration measure, and dietary intake. The
associations between the above-mentioned variables
and the nutritional status of the children were assessed
using different bivariate analyses (Pearson’s correlation
or chi-square tests) as applicable. Scores in both age
groups of children were compared using the
Independent samples ‘t’ test.
Results
Socio-demographic characteristics of
the caregivers
All the caregivers were mothers. Majority (39%) of
them belonged within the age-group of 35–39 years.
The mean age of the women who participated in the
The relationship between sensory integration challenges
Socio-demographic profile of children
Number
(n ¼ 146)
Percentage
(%)
72
74
49.3
50.7
118
28
80.8
19.2
104
37
5
71.2
25.4
3.4
84
57
5
57.5
39
3.4
24
98
22
16.4
67.1
15.1
138
2
2
4
94.5
1.4
1.4
2.7
84
48
14
57.5
32.9
9.6
Age (years)
3–6
7–11
Sex
Male
Female
Birth order
First child
Second child
Third child
Number of siblings
0
1
2
Birth weight (kg)
< 2.5
2.5–3.5
> 3.5
Primary caregiver for the child
Mother
Father
Grandmother
Mother and Grandmother
Type of school
Special school
Mainstream school
Home-school
study was 34.19 years (S.D. ¼ 4.31). Majority of the
mothers (35%) who participated in the study were graduates (15–17 years of education), followed by 32.2% of
the mothers who were post-graduates (17–20 years of
education). Fifty eight percent of the women belonged
to nuclear families. Nuclear families are those where
only the parents and children live in the house and no
grandparents or other relatives live with the family.
Majority (58%) of the sample had been unemployed in
the last twelve months. Almost all the mothers studied
were currently married (98%) (Table 1).
Socio-demographic profile of the children
The mean age for the entire sample was 7.09 years
(S.D. ¼ 2.55). It was seen that 49.3% of the children
were in the younger age-group of 3–6 years, and the
remaining (50.7%) children were in the older age-group
of 7–11 years. Majority (81%) of the children in this
study were males. Most of the children (71.2%) in this
study were first-borns, and majority (57.5%) of them
did not have any siblings. Sixty seven percent of the
children in this study were born with normal birthweight. The mean birth weight of the children was
2.98 kg (S.D. ¼ 0.58). It was seen that for most of the
children in this study (94.5%), their mothers were the
primary caregivers. Only one child in this study was an
adopted child. More than half (57.5%) of the children
were enrolled in special schools. All children in this
study had undergone some form of therapy at some
point. For 29% of the children, the only therapy they
received was the one provided in their schools. The
most common forms of therapy were Occupational
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P. Padmanabhan and H. Shroff The relationship between sensory integration challenges
Table 3 Scores of children as assessed by Short
Sensory Profile
Domain
Score (Mean ± SD)
Tactile sensitivity
Taste/smell sensitivity
Movement sensitivity
Under-responsive/seeks sensation
Auditory filtering
Low energy/weak
Visual auditory
Total SSP scores
27.36 ± 4.98
12.89 ± 4.73
11.95 ± 3.02
21.77 ± 5.58
20.86 ± 4.95
23.17 ± 6.72
18.95 ± 4.46
136.93 ± 18.65
Table 4 Distribution of children based on %RDA met of
energy and protein
<50 %RDA met
<75% RDA met
75–100% RDA met
>100% RDA met
Total n ¼ 110
Energy n (%)
Protein n (%)
10 (9)
38 (34.54)
39 (35.45)
23 (20.9)
2 (1.8)
6 (5.4)
13 (11.8)
89 (80.9)
Note. RDA = Recommended Dietary Allowances.
therapy, followed by Speech therapy and Behavioral
therapy (Table 2).
The mealtime behaviors of the children were
assessed using the BAMBI measure. The mean BAMBI
score for the children was 41.56 (S.D. ¼ 9.07). An independent samples ‘t’ test indicated that there was no significant difference in the BAMBI scores between the
younger (M ¼ 42.61; S.D. ¼ 8.65) and the older
(M ¼ 40.55; S.D. ¼ 9.42) age groups.
Sensory integration in children was assessed with the
SSP. The mean scores obtained by the children on various domains of the SSP have been presented in Table
3. The mean total SSP score obtained by children was
136.93 (S.D. ¼ 18.65). Based on their SSP scores, children were accordingly classified as having ‘Typical
Performance’, ‘Probable difference’, and ‘Definite
Difference’ in sensory integration. It was seen that
majority (55.5%) of the children fell in the ‘Definite
Difference’ in sensory integration category, and 27.4%
children were in the ‘Probable Difference’ in sensory
integration category. Only 17.1% children were classified as having ‘Typical Performance’. An independent
samples ‘t’ test indicated that there was no significant
difference in sensory integration as assessed by the SSP
between the younger (M ¼ 137.39; S.D.=19.76) and
older age groups (M ¼ 136.5; S.D.=17.64).
A Pearson’s Correlation test was computed to assess
the relationship between children’s scores on the ‘Taste
and Smell Sensitivity’ domain of the SSP and their
mealtime behaviors (total BAMBI scores). There was a
significant negative relationship between these two variables (r ¼ 0.6, p < 0.01). Therefore, when children
have better taste and smell sensory integration (higher
score), they are less likely to have mealtime behavior
problems (lower score). A Pearson’s Correlation test
was also computed to assess the relationship between
the children’s sensory integration (total SSP scores) and
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their mealtime behaviors. There was a strong negative
correlation between the 2 variables (r ¼ 0.27,
p < 0.01). Thus, when children have fewer sensory integration difficulties (higher total SSP score), they have
more
positive
mealtime
behaviors
(lower
BAMBI scores).
Dietary/nutrient intake and associated factors
In this study, majority (59%) of the children belonged
to households that consumed non-vegetarian food.
Twenty five percent of them belonged to strictly vegetarian households, whereas 16% of them belonged to
households that consumed eggs along with vegetarian food.
The 24-hour dietary recalls could only be collected
from 110 mothers. The nutrient intake of children was
calculated from this and was compared with the
Recommended Dietary Allowance of nutrients for
Indian children as given by the Indian Council of
Medical Research (ICMR 2010). The mean energy consumption of children in this study was 1307 kcal. The
mean protein consumption was 39 grams, and the mean
fat consumption was 34 grams. It was seen that around
35% of the children met between 75 and100% of
energy requirement recommendations, and only 9% of
the children met less than 50% of the energy requirements recommended for age. Whereas, with respect to
protein consumption, it was seen that majority of the
children (81%) were able to meet more than what was
recommended for daily protein intake (Table 4).
Several mothers in this study described that their
children’s diets had very little variety, and that there
was frequent food refusal. In that context, it became
important to examine the extent of refusal of different
food groups in these children. Mothers were asked
about their child’s consumption of different food groups
namely: (i) Cereals, (ii) Pulses, (iii) Nuts and dry fruits,
(iv) Eggs, (v) Fruits, (vi) Vegetables, (vii) Green leafy
vegetables, (viii) Dairy & Dairy products, (ix) Fish, and
(x) Meat & Meat Products. These categories were created based on general consumption patterns in Indian
culture. The mean number of food groups refused by
children was 2.39 (S.D. ¼ 2.18). Data on food group
refusal in children are presented in Figure 1. Maximum
refusal was seen for fruits (50%), followed by nuts and
dry fruits (44.5%), and vegetables (30.8%).
A Pearson’s Correlation test was computed to assess
the relationship between children’s total energy intake
and their BAMBI scores, and the number of food
groups refusal and BAMBI scores. There was a strong
positive correlation between the number of food groups
refusal and BAMBI scores (r ¼ 0.55, p < 0.01). Refusal
of more number of food groups in this set of children
were correlated with higher mealtime behavioral issues.
There was a non-significant correlation between total
energy intake and BAMBI scores (r ¼ 0.14, p ¼ NS).
NO. 2
Percentage of children showing refusal
P. Padmanabhan and H. Shroff
The relationship between sensory integration challenges
Total n=146
60
50
44.5
50
40
30.8
30
13.7
20
10
28.8
17.8
15.8
19.2
17.8
3.4
0
Food groups
Figure 1 Food group refusal in children. Note: Total number of children for eggs, fish and meat is 114, 90, and 89,
respectively
A Pearson’s Correlation test was computed to assess
the relationship between children’s total energy intake
and their sensory integration, and the number of food
groups refusal and sensory integration. There was a
strong negative correlation between the number of food
groups refusal and sensory integration (r ¼ 0.26,
p < 0.01), implying that better sensory integration in
children (fewer difficulties with sensory processing)
was correlated with lesser food refusal. There was a
non-significant correlation between total energy intake
and sensory integration (r ¼ 0.06, p ¼ NS).
Nutritional status
In this sample of children with ASD, the mean BMI
was 16.21 kg/m2 (S.D ¼ 3.56). Based on the height-forage ‘z’ scores (HAZ), when stunting had to be assessed,
it was seen that only 6.16% of the children (n ¼ 9) had
HAZ less than 2 S.D.s, indicating that very few children in this study showed stunting or were chronically
undernourished.
Classification of the nutritional status of children
was done based on BMI z scores. Close to 40% children in this study were classified as having normal
BMI based on BMI z scores. There were 18.5% children in the underweight category (< 2 S.D.) and
16.4% children in the mild underweight category
(< 1 S.D.). On the other end of the nutrition spectrum,
12.3% children were in the overweight category
(>1 S.D.), and 13% of the children were seen to be falling in the obese category (>2 S.D.).
A chi-square analysis was done to see whether there
was an association between the BMI of the children
and age. Table 5 shows that there is a significant association between the two variables, and a higher proportion of older children fall in the overweight category, as
against children from the younger age-group
(v2 ¼ 10.83; p < 0.01). A Pearson’s Correlation test was
computed to assess the relationship between children’s
total energy and protein intake and their BMI. There
was a moderate positive correlation between total
energy intake and BMI (r ¼ 0.32, p < 0.01). There was
also a significant positive correlation between total protein intake and BMI (r ¼ 0.26, p < 0.01).
Discussion
This study was done to examine the relationships
between dietary intake, nutritional status, mealtime
behaviors, and sensory integration among children with
ASDs in a large metropolitan city in India.
In looking at dietary intake in this sample, it was
seen that most children (92.7%) were able to meet their
protein requirements, and around 55% of them were
able to meet more than 75% of the RDA of energy
requirements based on standard recommendations
(ICMR 2010). In this study, 25% of the children
belonged to vegetarian households. This is comparable
to the findings reported in the National Family Health
Survey (NFHS-4) of India, where vegetarianism was
reported in 22–30% of people surveyed (International
Institute for Population Sciences and ICF 2017), though
there are no data on vegetarianism in children specifically. In India, most of the vegetarian households consume milk, and milk products like curd or paneer
(cottage cheese), which are a source of high biological
value protein. This may be why meeting protein
requirements wasn’t a challenge even for children from
these households. There were also some families in this
study where the parents were vegetarians, but they tried
introducing eggs or non-vegetarian food to their children, as they were concerned about their child’s growth,
and non-vegetarian foods have higher protein content.
One of the hypotheses of this study was that there
would be a relationship between sensory integration
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P. Padmanabhan and H. Shroff The relationship between sensory integration challenges
Table 5
Younger
Older
Total
Relationship between age and BMI of children
Underweight n (%)
Normal n (%)
Overweight n (%)
Total n (%)
34 (47.2)
17 (23)
51 (34.9)
26 (36.1)
32 (43.2)
58 (39.7)
12 (16.7)
25 (33.8)
37 (25.3)
72 (100)
74 (100)
146 (100)
and mealtime behavior, dietary intake and food refusal
in these children with a diagnosis of ASD.
There was no significant association between sensory integration and total energy intake. There was,
however, a significant relationship between sensory
integration and food refusal, where children with higher
sensory integration refused a lesser number of food
groups. To the best of our knowledge, there is no published literature on the use of the SSP in a normative
sample from India. Our analysis indicates adequate reliability with the translated version of the measure,
although we were not able to assess validity. Sensory
integration in children of this study as assessed by
mean SSP scores, is comparable to mean SSP score
reported in the study by Lane et al. (2014). In the study
by Zobel-Lachiusa et al. (2015), the mean SSP score in
children with ASD was 113.34 (S.D. ¼ 28.71), which is
much lower than in this study. This is probably because
their study included children who were older and thus,
the children were more likely to have had some therapy/treatment prior to the study. In this study, it was
found that more than 50% of the children fell in the
‘Definite Difference’ category in sensory integration,
thus, indicating that sensory integration could indeed be
a challenge for children with ASD. For children in
whom food refusal was because of issues related to sensory integration, the same food could be offered by
changing one sensory attribute of the food (shape, flavor, texture, or color). For example, if the child refuses
to eat whole fruit, the same fruit could be offered in the
form of fruit pulp or milkshake, or if the child refuses
to drink milk because it is white in color, the same
could be offered as turmeric milk or chocolate milk.
Findings from some studies also suggest that certain
variations in food-presentation methods might facilitate
acceptance of previously rejected food (Bandini et al.
2010, Ahearn et al. 2001).
Anecdotally, textural sensitivity was the most commonly reported concern in sensory integration that was
reported to affect children’s dietary intake. Around 65%
of the children in this study were also reported to show
an aversion to chewing food, possibly due to some kind
of oral sensory sensitivity. In a study by Nadon et al.
(2011), 15% of the children were found to have oromotor difficulties with respect to chewing and swallowing. This perhaps explains maximum refusal being
shown for certain fruits and nuts/dry fruits, many of
which have a hard or chewy texture and need to be
chewed properly. Traditional Indian diets don’t
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2020
VOL. 66
Pearson’s
chi square (p)
10.83 (0.004)
routinely include vegetables in their raw forms, and are
usually eaten in the cooked forms as an accompaniment
with rotis (flatbread) or with rice and dal (lentil gravy).
However, even though cooked vegetables are likely to
be softer than raw vegetables, several mothers reported
that their children refused to consume them. Studies by
Malhi et al. (2017) and Emond et al. (2010) made similar observations, where they noted that children with
ASD ate fewer vegetables, salads, and fresh fruit. In
their study, Chistol et al. (2018) also found that among
children with ASD, those with atypical oral sensory
sensitivity refused more foods and ate fewer vegetables
than those with typical oral sensory sensitivity. Some
mothers who didn’t report fruit or vegetable refusal also
said that their children preferred to eat vegetables in the
mashed form only, or that the only fruit their children
ate were bananas. Many mothers also said that they had
to dip solid food like rotis and biscuits in liquids like
dal or milk to make it soggy, so that their children
would consume it. These findings are consistent with
findings from other studies indicating that children with
autism were selective about food texture, preferring to
eat only pureed or low textured food (Al-Kindi et al.
2016, Handayani et al. 2012, Schreck et al. 2004).
Another way in which sensory integration would be
interfering with dietary intake is via the nature of
Indian meals, where several dishes are eaten at the
same time. It is possible that a child with ASD could
get overwhelmed to see so many kinds of foods on a
plate, each with different textures, tastes, smells, and
colors. Thus, it might be helpful to have not more than
three things on the plate for a child with ASD. It may
also be useful to provide these children with divided
plates, or, gradually increase the number of foods on
the child’s plate as and when the child demonstrates
improved tolerance.
This study also highlighted that there was no association between the mealtime behavior score and total
energy intake. However, there was a significant relationship between mealtime behaviors and refusal of
food groups. This suggested that refusal of a greater
number of food groups was associated with higher
mealtime behavioral issues. There are no normative
data available for the BAMBI, which was used in this
study to assess mealtime behaviors in children with
ASD. With this sample, the coefficient alpha indicated
adequate reliability for the translated version of the
measure. The mean BAMBI score of children in this
study is higher as compared to the mean BAMBI score
NO. 2
P. Padmanabhan and H. Shroff
of 30.5 in a typically developing reference group provided by the authors of this tool (Lukens and Linscheid
2008), however, this was not statistically tested. In the
study by Crasta et al. (2014), that was conducted in
South India to assess feeding problems in children with
ASD in the age group of 3–10 years, the mean BAMBI
score was 39.64 (S.D. ¼ 10.12), which is very close to
what was seen in this sample of children. However,
they did not validate this measure in their study.
The most common mealtime behavioral concern that
mothers discussed were food refusal and resistance to
try new foods. Similar concerns have also been reported
in studies by Bandini et al. (2010) and Schreck et al.
(2004). Mothers in this study expressed that children
showed behaviors like getting up and walking away
midway without finishing their food, pushing food
away with hands, gagging on food, or spitting it out.
Many children with ASD have difficulties with communication and social interactions. Therefore, these behaviors may be their way of expressing their dislike for
certain foods or their unwillingness to try new foods. In
children whose diets had very restricted dietary variety,
dietary diversity could be encouraged in the child by
gradually adding new foods to the child’s diet, one at a
time. Another strategy that could be adopted to increase
dietary diversity in children with ASD is to expose the
child to a newly introduced food at regular intervals till
they accept it. Social modeling as a strategy may also
work with the child with ASD, where the behavior that
needs to be learned, is practiced in front of the child
consistently. For example, if the problem is that the
child keeps running away from the table during mealtime, to correct that, it could be emphasized to the child
that all members of the family sit together at the table
and eat their meals.
Many children with autism are extremely rigid in
their behavioral patterns, which could also translate into
specific mealtime behaviors like ‘need for sameness’,
and this could be another reason for them to resist trying new foods. This has been documented even by
Bruns and Thompson (2011). It was reported by Dickie
et al. (2009) that for children with ASD, issues related
to food were not limited to just sensory aspects like texture, taste, smell, visual aspects of the food itself, or
having the food on their hands or tongue, but were also
dependent on aspects outside of sensory domains such
as predictability, routine, and novelty. Some children in
this study were not just particular about eating the same
food every day or about the way of preparation of particular dishes, but also insisted on the same brand of
food, or the same utensil being used to serve their food.
Rogers et al. (2012) also documented this in their study
where children with ASD insisted on their food being
prepared and presented in a particular way. It is recommended that children with ASD be exposed to a range
of foods and an array of cups, plates and utensils right
The relationship between sensory integration challenges
from early childhood, and be encouraged to explore
these, even if not employed during the mealtime (Bruns
and Thompson 2011).
There was a significant negative correlation between
children’s scores on the ‘Taste and Smell Sensitivity’
domain of the SSP, and total BAMBI scores. This indicates that those having greater difficulties with Taste
and Smell Sensitivity (lower scores on this domain of
SSP) are more likely to demonstrate disruptive mealtime behaviors (higher BAMBI scores). There was also
a strong negative correlation between total SSP scores
and BAMBI scores implying that higher sensory integration in children (i.e. higher SSP scores) is correlated
with lower mealtime behavioral issues (i.e. lower
BAMBI scores). This could be because eating is a multisensory experience. Results of the study by Crasta
et al. (2014) showed that feeding problems in children
with autism are related to multi-sensory processing,
sensory processing related to endurance/tone, modulation of movement affecting activity level and emotional
and social responses. When there are difficulties with
these, it may manifest as food refusal, anxiety, meltdowns, or other behavioral issues during mealtimes,
because of difficulties with communicating or expressing distress. Although it was not possible to test a
causal relationship in this study, it is possible that children’s disruptive mealtime behaviors could be attributed
to their difficulties with sensory integration, particularly
at mealtime. This finding of our study suggests that
with therapy to improve sensory integration, there may
be a reduction in disruptive mealtime behaviors, and
possibly, a reduction in food refusal. This is an area of
future research. For parents, it may also be useful to
consider the effect of environmental factors such as
space around the meal area, lighting, noise level, and
even seating, for children with ASD during mealtimes.
These factors may also be responsible for sensory overload or disruptive mealtime behaviors in these children.
These could be addressed by making small modifications like: adjusting the height of the child’s chair to
the table, adjusting the brightness levels of the lighting
around the table such that it is comfortable for the
child, and/or not shouting at the child during times of
perceived eating problems.
Historically, childhood undernutrition has always
been a public health problem in India. However,
increasingly, even childhood obesity is becoming a
growing concern across socio-economic groups. India is
now posed with the unique problem of a ‘dual burden
of malnutrition’, where childhood undernutrition and
obesity co-exist (Ranjani et al. 2016). This study indicates that even in a clinical population like ASD, this
phenomenon is visible, where children with ASD tend
to be on either ends of the nutrition status scale –
Underweight or Overweight. Based on BMI z scores,
only 39.7% of the children were in the ‘normal’
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P. Padmanabhan and H. Shroff The relationship between sensory integration challenges
category. It was also seen in this study that a greater
percentage of children from the older age group were
overweight/obese, which is consistent with results of a
study done by Curtin et al. (2010). This could be
because with age, parental control over what children
eat reduces. As a result, children assert more choice
over the kind of food they want, which may lead them
to consume greater amounts of calorie-dense, unhealthy
food. In addition, it is possible that children engage in
less physical activity as they grow older, a trend seen
worldwide (Corder et al. 2016, Dobbins et al. 2013).
Unfortunately, physical activity was not assessed in
this study.
It was also hypothesized that there would be a relationship between dietary intake and nutritional status in
children with ASD. In this study, total energy intake
does show a clear positive association with BMI value.
However, these findings may have to be interpreted
with caution because diets could be calorie dense with
high carbohydrate or fat content, but still be inadequate
in terms of fiber, protein, vitamin or mineral content.
Though protein adequacy was not a concern for children in this study, several children showed refusal for
fruits, followed by vegetables and nuts and dry fruits,
all of which are a rich source of micronutrients
and fiber.
Low height-for-age z scores (HAZ) or stunting
reflects a failure to reach linear growth potential, and is
a key indicator of chronic undernutrition (Fenske et al.
2013). Majority of the children in this study were not
stunted (93.84%), based on the HAZ. This is an important clinical and research consideration as many caregivers and professionals express concern over the
'pickiness' and 'fussiness' of the food habits of children
with ASD under the assumption that they are inadequately nourished. However, there have been studies
that have also indicated that eating problems found
among children with ASD do not necessarily translate
into greater risk for compromised growth, because children with ASD may eat enough to meet their gross
energy needs (Sharp et al. 2013), and therefore relying
exclusively on anthropometric measures as a proxy for
health status may hide underlying nutritional deficits,
particularly for micronutrients. (Malhi et al. 2017). It
was beyond the scope of this study to look at intake
and assess if the children in this sample had deficiencies in micronutrients. That is an important research
gap that needs to be addressed.
In children having diets with extremely limited variety, some supplementation may be required. In this
study, 36% of the mothers were giving their children
some form of supplements. The most common supplements reported were Calcium, Vitamin-D, multivitamin
tonics/syrups, probiotics, and Omega-3 supplements. In
a study by Lane et al. (2014), consumption of dietary
supplements was seen in 45.8% of the children with
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ASD. The number may be less in this sample because a
smaller segment of the population (only people from
higher socio-economic groups) may be able to afford
different dietary supplements. Importantly, not all mothers in this study gave their children supplements under
professional guidance. There were some who gave their
children dietary supplements based on their own
research from the Internet or books, citing reasons like
wanting to improve immunity, gut health, or cognitive
function in their children with ASD. Some of the mothers were also concerned about their children’s
extremely limited food repertoire, and thereby, started
giving their children multivitamin syrups or tonics so as
to prevent nutritional deficiencies. However, consumption of dietary supplements without professional guidance is not recommended.
Although it was hypothesized that there would be
age-related differences, it was seen that there were no
differences in mealtime behavior scores and sensory
integration scores between the two age groups.
However, there are inconsistent findings in literature on
this. Some studies have reported that eating problems
due to disruptive mealtime behaviors and sensory integration difficulties decrease with age (Carruth et al.
1998, Pliner 1994), while Bandini et al. (2010) reported
that they are stable during childhood. One possible
explanation is that, a reduction in disruptive mealtime
behaviors and sensory integration difficulties are more
dependent on the types and intensity of therapies the
child receives, rather than being solely age-dependent.
However, this could be explored better by means of
longitudinal studies, and is an avenue for
future research.
A strength of this study is that it has a much larger
sample size compared to other studies in India. This is
also one of the first studies to assess and compare findings between two different age-groups, and to examine
multiple factors that might affect dietary intake using
validated scales. A limitation of this study is that
macronutrient intake was assessed only by means of
one 24-hour dietary recall, which may not accurately
represent average consumption. Also, a parameter like
weight depends on energy intake and energy expenditure, and in this study only the energy intake was
assessed. Some children in this study had been some
diagnosed with Attention Deficit Hyperactivity
Disorder, owing to which they may have higher energy
expenditure. There were also some children in this
study diagnosed with hypotonia who may have lower
energy expenditure. Energy expenditure was not
assessed because it was not feasible for this aspect to
be studied. Another limitation is that data was collected
solely by means of parent reports. Additional methods
like observation or weighment techniques to assess dietary intake would have given more insights. This study
was primarily clinic-based. But it would have been very
NO. 2
P. Padmanabhan and H. Shroff
difficult to conduct a community-based study with a
clinical population such as this. Further studies could
explore dietary intake of children with ASD on a longitudinal basis, to assess the changes that occur with age
in these group of children and to examine the impact of
various interventions on children's dietary intake and
food refusal.
Conclusion
This is one of the few studies where data on multiple
factors related to eating and weight were collected from
a moderately sized sample of children with ASD in
India. Although there are concerns about inadequate
dietary intake, majority of the Indian children with
ASD in this sample were meeting recommended guidelines for energy and protein intake. However, only a little over a third of the children were in the normal
weight range. Majority fell on either ends of the nutrition status categories – Underweight or Overweight,
with more children in the older age group being overweight and obese. Many mothers reported that their
children had difficulties with sensory integration. These
difficulties may contribute to mealtime behavioral problems and food refusal in children with ASD. More
empirical evidence is required to understand the extent
of eating problems in children with ASDs in India, so
that interventions can be planned accordingly. Since the
nature of feeding/eating problems in these children are
complex, a multi-disciplinary approach involving
speech and occupational therapists, nutritionists, psychologists and developmental pediatricians may be
most useful to ensure optimal nutrition in
these children.
Disclosure statement
No potential conflict of interest was reported by
the authors.
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