Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: J Intellect Disabil Res. 2020 Jan 23;64(6):426–433. doi: 10.1111/jir.12714

Physiological Arousal and Observed Behaviour in Parent-Child Interactions Involving Young Children with Down Syndrome

Emily Lorang 1,3, Sigan Hartley 2,3, Audra Sterling 1,3
PMCID: PMC7237288  NIHMSID: NIHMS1067941  PMID: 31971300

Abstract

Background:

Parents of children with Down syndrome (DS) play an important role in their child’s development. Physiological measures, such as electrodermal activity (EDA), can shed light on parent-child relations beyond the behavioural level. The goals of the current study were to assess the feasibility of collecting EDA data in preschool age children with DS, examine the association between parent and child EDA during play-based interactions, and investigate the relation between parent and child EDA and observed parent behaviours.

Method:

Two parents in fifteen families participated in dyadic free play interactions with their child with DS (i.e., 15 mother-child and 15 father-child interactions). The children with DS (aged 24–61 months) and both of their parents wore multisensory wristbands measuring EDA. Parent behaviours were coded as requests for behavioural complies, requests for verbal complies, or comments.

Results:

Usable EDA data were collected for 13/15 children and 11/15 mothers during the mother-child interactions, and 14/15 children and 12/15 fathers during the father-child interactions. Parent and child EDA variability were significantly positively related for father-child but not mother-child dyads. Maternal use of requests for behavioural complies was positively related to child EDA variability.

Conclusions:

The collection of EDA data through wristbands worn by young children with DS during early parent-child interactions was feasible. Preliminary findings indicated that some aspects of parent and child physiology in DS may be related in different ways for mother-child and father-child dyads.

Keywords: Down syndrome, electrodermal activity, parent-child interactions, language input

Parent-child interactions have marked effects on child development (Hart & Risley 1995), including in Down syndrome (DS; Warren & Brady, 2007; Yoder & Warren, 2004). To-date, research on DS has focused on the observable behavioural quality of these interactions (de Falco et al. 2009; Yoder & Warren 2004), and specifically, the maternal behaviours that facilitate or hinder high-quality parent-child interactions (e.g., Roach et al., 1998; Venuti et al., 2008). Recently, researchers have begun assessing physiological arousal in parents and children during interactions using indices of the sympathetic nervous system, such as electrodermal activity (EDA; Baker et al., 2015, 2018; Fenning et al., 2017). EDA provides a window into under-the-skin arousal. The current study examined the feasibility of assessing EDA in young children with DS during parent-child interactions and investigated the association between child and parent EDA and the behavioural quality of the interaction.

DS is the most common genetic cause of intellectual disability, occurring in one in 691 live births (Parker et al., 2010). Children with DS demonstrate a host of communication and cognitive deficits (Martin et al. 2009), and gains in these areas have been shown to rely on responsive and engaging parent-child interactions (Karaaslan & Mahoney, 2013; Venuti et al., 2008). Parental responsivity is defined as exhibiting warmth and stability, in addition to following the child’s lead to promote learning (Warren & Brady 2007). One way researchers measure parental responsivity in free play interactions is through the parents’ communication behaviours; specifically, studies have examined parent attempts to elicit a verbal (i.e., request for verbal comply) or behavioural response (i.e., request for behavioural comply) from the child, as well as parent use of comments (See Table 2 for definitions). Previous research indicates that these parent communication behaviours predict later language and cognitive growth in children with and without developmental disabilities (Landry et al. 2001; Landry et al. 2000; Warren et al. 2010). It is not known if these parent communication behaviours affect and/or are affected by underlying physiological arousal of the parent and young child with DS.

Table 2.

Maternal and Paternal Behavioural Codes

Behaviour Definition Examples
Request for verbal comply Question or statement used to directly elicit a verbal response Parent says, “Use your words,” or asks, “What’s this?”
Request for behavioural comply Question or directive intended to get the child to complete a specific action Parent says, “Come back here,” or asks, “Can you put the block in the bucket?”
Comment The parent comments or remarks on the interaction in some way Parent says, “Nice job,” or notes, “That ball is red.”
Open in a new tab

Recent research has shown that children with other developmental disabilities, such as autism spectrum disorder (ASD) and fragile X syndrome, evidence atypical physiological arousal (Hall et al. 2009; Kushki et al. 2013), which has implications for engagement and learning during parent-child interactions. Many of these studies have examined EDA variability, which has been operationalized as the standard deviation of EDA. Previous work has suggested that EDA variability as conceptualized by standard deviations may represent a meaningful variable for capturing individual variability in physiological arousal across an interaction (Baker et al. 2015; Fenning et al. 2017). Using this metric, one study found that child EDA variability during a problem-solving task was positively related to the severity of child ASD symptoms in children with ASD (Fenning et al. 2017). Moreover, children with ASD who had more variable EDA had parents who also had more variable EDA during interactions (Baker et al. 2015). Furthermore, parent-child synchrony (i.e., positive covariation in EDA across time) was lower for children with more severe ASD symptoms (Baker et al., 2015). To-date, nothing is known about EDA in young children with DS, the association with parent EDA, or the relation to observable parent behaviours during parent-child interactions.

The objectives of this study were to determine the feasibility of collecting EDA data during free play parent-child interactions in young children with DS, and examine the association between observed parent communication behaviours indicative of parental responsivity and child and parent EDA. Understanding EDA in parent-child interactions may offer information about how physiological arousal relates to parent-child interactions and provide insight into learning during parent-child free play, which is an important context for learning for young children with DS (for a review, see Warren & Brady 2007).

Study questions:

  1. Is it possible to collect EDA during free play parent-child interactions in the majority (defined as > 80%) of young children with DS?

  2. Is the EDA of young children with DS and parents related during interactions?

  3. Is the EDA of young children with DS and parents associated with observed parent behaviours during interactions?

Methods

Participants

Fifteen families of children with DS (children, mothers, and fathers; n=45) participated. Parents provided genetic documentation of trisomy 21. Both parents resided within the same home. All children had hearing and vision within or corrected to the normal range. Table 1 includes family descriptives (e.g., parent education, child developmental level). The study was approved by the University’s Institutional Review Board.

Table 1.

Participant Characteristics

Mean Standard Deviation Range
Parent variables
Maternal chronological age (years) 39.00 5.57 29–49
Paternal chronological age (years) 41.33 6.03 29–50
Maternal educationa 6.67 1.29 4–8
Paternal educationa 6.13 1.51 3–8
Child variables
Chronological age (months) 39.67 12.11 24–61
MSEL composite scoreb 52.47 5.68 49–66
Open in a new tab
a

Parent education was measured using a scale from one to eight (1=no high school education, 2=some high school education, 3=graduated high school/GED, 4=some college or technical school, 5=associates or technical college degree, 6=completed bachelor’s degree, 7=some graduate work, 8=completed graduate degree).

b

Measured from the Visual Reception, Fine Motor, Expressive Language, and Receptive Language subscales of the Mullen Scales of Early Learning (Mullen, 1995). The MSEL composite score is based on mean of 100 and a standard deviation of 15.

Procedure

Assessments took place in the participant’s home during two visits. The child with DS completed a developmental assessment, and each mother-child and father-child dyad participated in a seven-minute recorded free play activity. An examiner read instructions to “play as you typically would.” A standard set of toys was provided. The order for the parent-child interactions (i.e., mother-child vs. father-child) was randomized.

EDA.

EDA was measured using Empatica E4 wristbands during parent-child free play. The band was placed on children and parents’ wrist. If the child was reluctant and/or distressed by the placement of the band, it was placed on their lower calf. The non-dominant wrist/calf was selected when known (i.e., per parent report, some children did not yet have a dominant hand). Prior research has not found EDA differences between these placements (Baker et al. 2018). EDA was sampled at 4 Hz, with values ranging from 0.01μSiemens–100μSiemens. EDA provides a valid assessment of sweat gland activity (Sano et al., 2014). Prior to analyses, visual inspection and an algorithm was used to identify artifacts and detect EDA values outside of the valid range based on recommendations from the Empatica Software team and procedures from previous work (Fenning et al. 2017). Consistent with previous research, EDA data were reduced to two-second bins, and variability across the interaction was assessed through standard deviations (Baker et al. 2015). Standard deviations were selected for physiological arousal rather than both nonspecific skin conductance responses (NSCRs) and standard deviations due to the overlap identified between these two variables, the ability of standard deviations to capture the magnitude of changes in EDA, as well as concern regarding Type I error given the sample size (Fenning et al. 2017). The E4 also collected movement data via the tri-axal acceleration, which was processed in MATLAB and considered as a potential covariate in analyses.

Parent Communication Behaviour Coding

Two trained staff independently coded all of the parent-child interactions. Parent utterances were coded for request for verbal comply, request for behavioural comply, or comment using an adapted coding scheme (See Table 2 for definitions; Landry et al. 2001; Landry et al. 1998; Sterling & Warren 2014; Warren et al. 2010). When parents produced multiple utterances with less than a three-second pause between utterances, the last utterance was coded. Disagreements were discussed through consensus coding. Inter-rater reliability based on all 30 interactions was calculated prior to consensus coding. Cohen’s kappas for request for verbal comply, request for behavioural comply, and comment were .74, .91, and .93, respectively, indicating substantial to near perfect agreement (Hallgren 2012).

Data Analysis Plan

Histograms and descriptive statistics were used to examine data normality, outliers, linearity, and homoscedasticity. All variables met assumptions for parametric tests except EDA. Due to positively skewed EDA values, a square root transformation was performed. To assess feasibility, compliance wearing the wristband and the number of children with DS with useable data were examined. Independent samples t-tests were used to determine if EDA variability differed based on wristband placement (wrist vs. lower calf). Pearson correlations were used to investigate the association between parent and child EDA variability during the two interactions. Pearson correlations were also conducted to examine the association between parent and child EDA variability and the proportions of parent communication behaviours. Consistent with previous work in parent-child interactions, proportions rather than total numbers were used since parents communicated at different frequencies, and we were interested in the quality rather than quantity of parent behaviours (Bornstein et al. 1992; Bryce & Jahromi 2013; Peterson et al. 2007; Zampini et al. 2011). Movement data were considered but not included in the final analyses; including movement in the models did not change the results. The strength of the Pearson correlations were interpreted using previously established guidelines where r = .00 to .10 is a small effect, r = .30 to .49 is a moderate effect, and r ≥ .50 is a large effect (Cohen, 1994).

Results

Feasibility

Eight children wore the wristband on their wrists and seven on their lower calf. Data were considered usable if the wristband tracked for at least 90% of the seven-minute interaction. Usable data were collected for 13/15 (86.67%) and 14/15 (93.33%) children during the mother-child and father-child interactions, respectively. One child removed the wristband before the interactions. One child wore the wristband for the father-child interaction but removed it before the mother-child interaction. The children wearing the wristbands on their calves were younger (M=30.14 months, SD=3.89) than those wearing it on their wrists (M=48.00 months, SD=11.34). There was no significant difference in EDA variability based on wristband placement (mother-child: t (11) = −1.11, p = .289, d = 0.67; father-child: t (12) = −0.87, p = .403, d = 0.50. All parents tolerated the wristbands (on wrist). Useable data were collected for 11/15 mothers (73.33%) and 12/15 fathers (80.00%). One father had >50% usable data and was included in the analyses with the other 12 fathers as his data did not change the results. The missing parent EDA data occurred due to software malfunctions, such as the wristband turning off (n=1), not accurately tracking EDA (i.e., downloaded data included values near or at zero indicating inaccurate tracking; n=3), an uploading error (n=2), and a software update that resulted in an error when downloading the data (n=1).

Physiological Arousal

Table 3 includes averages and standard deviations for child and parent EDA variability. There was not a significant association between mother and child EDA variability during the mother-child interaction, r = −.02, p = .962. There was a significant positive relation, with a large effect size, between father and child EDA variability during the father-child interaction, r = .69, p = .009.

Table 3.

Averages and standard deviations for EDA variability and parent behaviours

Child EDA variability
M (SD) 		Parent EDA variability
M (SD) 		Proportion of requests for verbal complies
M (SD) 		Proportion of requests for behavioural complies
M (SD) 		Proportion of comments
M (SD)
Mother-child interaction .22 (.42) .68 (.85) .44 (.11) .14 (.07) .42 (.14)
Father-child interaction .26 (.61) 1.31 (1.56) .37 (.12) .19 (.12) .44 (.13)
Open in a new tab

Note. Reported EDA values are raw values rather than transformed values.

Physiological Arousal and Behaviour

Table 3 includes the average proportions of parent behaviours. Child EDA variability was significantly positively correlated with the proportion of maternal requests for behavioural complies but not the proportions of comments or requests for verbal complies (see Table 4). However, there was a moderate negative correlation between child EDA variability and the proportion of maternal comments. Maternal EDA variability was not significantly correlated with the proportions of maternal behaviours. Child and paternal EDA variability were not significantly correlated with the proportions of paternal behaviours (see Table 4).

Table 4.

Pearson correlations and p-values between parent and child behavioural and physiological variables

Proportion of requests for verbal complies Proportion of requests for behavioural complies Proportion of comments
Mother-child interaction Child EDA variability r = .23 r = .76 r = −.33
p = .443 p = .002 p = .266
Maternal EDA variability r = −.19 r = .09 r = .10
p = .545 p = .766 p = .757
Father-child interaction Child EDA variability r = .12 r = −.04 r = −.08
p = .689 p = .898 p = .793
Paternal EDA variability r = −.12 r = .23 r = −.05
p = .675 p = .421 p = .861
Open in a new tab

Discussion

This study is the first to examine physiological arousal in young children with DS and parents (both mothers and fathers) during parent-child interactions. The use of multisensory wristbands during naturalistic parent-child free play interactions was feasible in young children with DS. Greater variability in child EDA was associated with greater variability in paternal but not maternal EDA. Literature on typically developing children has noted behavioural differences between mothers and fathers during parent-child interactions. Feldman et al. (2003) found that mother-child dyads engaged in more cyclic instances of synchrony within social episodes whereas father-child dyads engaged in more frequent and consistent synchrony within play. The positive association between father and child EDA variability may mirror these findings.

The examination of parent-child physiological synchrony is an emerging area of research, and thus optimal levels of physiological synchrony are not yet clear (Baker et al., 2015; Fenning et al. 2017). On one hand, the higher level association of parent and child EDA variability in the father-child interaction may mean that the father and child are more often in-tune and responsive to one another. On the other hand, a child with highly variable physiological arousal may benefit from a parent with less variable physiological arousal. Thus, the lack of association in the mother-child interactions may signal a beneficial effect of mothers’ efforts to adjust child arousal. This study is not able to determine whether or not an association between parent and child arousal is advantageous or unfavorable. The biobehavioural model of synchrony and previous research examining maternal and paternal responses to infants suggest that mothers and fathers may have different physiological responses to children, and parents may be able to provide “external regulation” to their child (Feldman 2011; Feldman et al. 2012; Mosek-Eilon et al. 2013). The quality of the parent-child interaction (e.g., whether it is mostly positive or negative) may also shape the extent to which parent-child synchrony is optimizing child learning or conversely, if adjustments are needed. In this study, there was not a difference in communicative behaviours between mothers and fathers (ps>.095). However, it is possible that the mother-child and father-child interactions systematically differed in ways that were not captured by the coding system.

Greater variability in EDA in the young child with DS was also significantly associated with a greater proportion of maternal requests for child behavioural complies, with a large effect size. This finding may indicate that mothers attempted to regulate the arousal of the young children with DS. When children with DS have more variable physiological arousal, they may also have more problematic behaviours, and mothers may try to manage those behaviours given findings linking EDA variability and behaviours in children with ASD (Baker et al. 2018). Alternatively, the children with DS in this study may have become increasing emotionally dysregulated as mothers placed more behavioural demands on them, although the current study cannot tease apart directionality. There was also a moderate negative association between EDA variability and the proportion of maternal comments in young children with DS. It is possible that the mothers who engaged in a high proportion of comments aided in helping their young child with DS achieve more stable levels of arousal. It is also possible that when child arousal is stable, mothers engage in more commenting.

Limitations and Future Directions

This study provided important feasibility data and a preliminary examination of the association between child and parent physiological arousal and parent communication behaviours in parent-child interactions in DS. However, there were several study limitations. EDA data collection was limited to two interactions: mother-child and father-child free play interactions. Child EDA during the mother-child interaction was positively related to child EDA during the father-child interaction (r = .88, p < .001). However, reliability across time (e.g., from one day to the next) and/or stability across multiple mother-child or father-child interactions was not assessed; this is an important future direction. Our small sample limited our ability to fully test for potential third variables. For example, it is possible that child factors (e.g., cognitive ability, medical comorbidities, history of severe emotional and behavioural problems) shaped both child and parent physiological arousal and behaviours during the interaction. The nature of the interaction (i.e., free play) could have influenced our findings. Future research should examine these and other potential third variables, and examine parent-child physiological synchrony through alternative indices such as NSCRs, as this is another way of capturing EDA variability by examining the frequency of increases in EDA of a certain magnitude (at least 0.03μSiemens over 3 seconds) throughout an interaction (Fenning et al. 2017).

Finally, longitudinal research is needed to build on the current cross-sectional studies and tease apart directionality between child physiological arousal and parent psychological arousal and communication behaviours. Theoretical and empirical evidence from other populations suggests that there may be bi-directional pathways between behaviour and physiology (Baker et al. 2015; Beauchaine et al. 2015; Feldman 2003; Feldman 2012).

Conclusions

Findings suggest that aspects of parent communication behaviour and parent and child physiological arousal may be intertwined during parent-child interactions for young children with DS. Parent-child EDA associations, however, may differ for mothers versus fathers. Results should be interpreted as preliminary given the small sample size and need to test for potential third variables. Assessing physiology and its link to behaviour is an emerging research area. Parent-child interactions are a critical context for child learning (Karaaslan & Mahoney 2013; Venuti et al. 2008). Elucidating how under-the-skin experiences of children with DS shape and/or are shaped by parent under-the-skin experiences and communicative behaviours may offer new intervention targets for optimizing learning during parent-child interactions.

Acknowledgements

First and foremost, thank you to the families who participated in this study. Thank you to the Research in Developmental Disabilities Language Lab, especially Kallie Renfus, Sierra Polzin, Bianca Schroeder, and Julia Kennelly for assistance in data processing and coding. This project was supported by the following grants: T32 DC005359 (S. Ellis Weismer, PI), a UW-Madison Faculty Research Grant/Wisconsin Alumni Research Fund (A. Sterling, PI), a Vilas Life Cycle Award (A. Sterling, PI), a core grant to the Waisman Center from the National Institute of Child Health and Human Development (U54 HD090256), as well as start-up funds from the University of Wisconsin - Madison awarded to Audra Sterling.

Footnotes

Conflict of Interest

The authors report no conflict of interest.

References

  1. Baker JK et al. , 2015. Brief Report: A pilot study of parent-child biobehavioral synchrony in autism spectrum disorder. Journal of Autism and Developmental Disorders, 45, pp.4140–4146. [DOI] [PubMed] [Google Scholar]
  2. Baker JK et al. , 2018. Sympathetic under-arousal and externalizing behavior problems in children with autism spectrum disorder. Journal of Abnormal Child Psychology, 46, pp.895–906. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Beauchaine TP et al. , 2015. Electrodermal responding predicts responses to, and may be altered by, preschool intervention for ADHD. Journal of Consulting and Clinical Psychology, 83, pp.293–303. [DOI] [PubMed] [Google Scholar]
  4. Bornstein MH et al. , 1992. Maternal responsiveness to infants in three societies: The United States, France, and Japan. Child Development, 63, pp.808–821. [DOI] [PubMed] [Google Scholar]
  5. Bryce CI & Jahromi LB, 2013. Brief report: Compliance and noncompliance to parental control strategies in children with high-functioning autism and their typical peers. Journal of Autism and Developmental Disorders, 43, pp.236–243. [DOI] [PubMed] [Google Scholar]
  6. de Falco S et al. , 2009. Mother-child and father-child emotional availability in families of children with Down syndrome. Parenting: Science and Practice, 9, pp.198–215. [Google Scholar]
  7. E4 Wristband User’s Manual, 2015. Empatica. Milano Italy. [Google Scholar]
  8. Feldman R, 2012. Bio-behavioral synchrony: A model for integrating biological and microsocial behavioral processes in the study of parenting. Parenting, 12, pp.154–164. [Google Scholar]
  9. Feldman R, 2003. Infant-mother and infant-father synchrony: The coregulation of positive arousal. Infant Mental Health Journal, 24, pp.1–23. [Google Scholar]
  10. Feldman R et al. , 2011. Mother and infant coordinate heart rhythms through episodes of interaction synchrony. Infant Behavior and Development, 34, pp.569–577. [DOI] [PubMed] [Google Scholar]
  11. Fenning RM et al. , 2017. Electrodermal variability and symptom severity in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 47, pp.1062–1072. [DOI] [PubMed] [Google Scholar]
  12. Hall SS et al. , 2009. Physiological correlates of social avoidance behavior in children and adolescents with fragile X syndrome. Journal of the American Academy of Child and Adolescent Psychiatry, 48, pp.320–329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hallgren KA, 2012. Computing inter-rater reliability for observational data: An overview and tutorial. Tutorials in Quantitative Methods for Psychology, 8, pp.23–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hart B & Risley TR, 1995. Meaningful differences in the everyday experience of young American children, Baltimore, MD, US: Paul H Brookes Publishing. [Google Scholar]
  15. Karaaslan O & Mahoney G, 2013. Effectiveness of responsive teaching with children with Down syndrome. Intellectual and Developmental Disabilities, 51, pp.458–469. [DOI] [PubMed] [Google Scholar]
  16. Kushki A et al. , 2013. Investigating the autonomic nervous system response to anxiety in children with autism spectrum disorders. PLoS ONE, 8, pp.2–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Landry SH et al. , 2001. Does early responsive parenting have a special importance for children’s development or is consistency across early childhood necessary? Developmental psychology, 37, pp.387–403. [DOI] [PubMed] [Google Scholar]
  18. Landry SH et al. , 2000. Early maternal and child influences on children’s later independent cognitive and social functioning. Child Development, 71, pp.358–375. [DOI] [PubMed] [Google Scholar]
  19. Landry SH et al. , 1998. The relation of change in maternal interactive styles to the developing social competence of full-term and preterm children. Child development, 69, pp.105–123. [PubMed] [Google Scholar]
  20. Martin GE et al. , 2009. Language characteristics of individuals with Down syndrome. Topics in Language Disorders, 29, pp.112–132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mullen EM, 1995. Mullen Scales of Early Learning. Circle Pines, MN: American Guidance Service. [Google Scholar]
  22. Mosek-Eilon V et al. , 2013. Infant reminders alter sympathetic reactivity and reduce couple hostility at the transition to parenthood. Developmental Psychology, 49, pp.1385–1395. [DOI] [PubMed] [Google Scholar]
  23. Parker SE et al. , 2010. Updated national birth prevalence estimates for selected birth defects in the United States, 2004–2006. Birth Defects Research Part A - Clinical and Molecular Teratology, 88, pp.1008–1016. [DOI] [PubMed] [Google Scholar]
  24. Peterson C et al. , 2007. Enhancing parent-child interactions through home visiting: Promising practice or unfulfilled promise? Journal of Early Intervention, 29, pp.119–140. [Google Scholar]
  25. Roach MA et al. , 1998. The structure of mother-child play: Young children with Down syndrome and typically developing children. Developmental Psychology, 34, pp.77–87. [DOI] [PubMed] [Google Scholar]
  26. Sano A, Picard RW & Stickgold R, 2014. Quantitative analysis of wrist electrodermal activity during sleep. International Journal of Psychophysiology, 94, pp.382–389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Sterling A & Warren SF, 2014. Maternal responsivity in mothers of young children with Down syndrome. Developmental Neurorehabilitation, 17, pp.306–317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Venuti P et al. , 2008. Play and emotional availability in young children with Down syndrome. Infant Mental Health Journal, 29, pp.133–152. [DOI] [PubMed] [Google Scholar]
  29. Warren SF et al. , 2010. Maternal responsivity predicts language development in young children with fragile X syndrome. American Journal on Intellectual and Developmental Disabilities, 115, pp.54–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Warren SF & Brady NC, 2007. The role of maternal responsivity in the development of children with intellectual disabilities. Mental retardation and developmental disabilities research reviews, 13, pp.330–338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Yoder PJ & Warren SF, 2004. Early predictors of language in children with and without Down syndrome. American journal of mental retardation, 109, pp.285–300. [DOI] [PubMed] [Google Scholar]
  32. Zampini L, Fasolo M & D’Odorico L, 2011. Characteristics of maternal input to children with Down syndrome: A comparison with vocabulary size and chronological age-matched groups. First Language, 32, pp.324–342. [Google Scholar]

RESOURCES