The Relationship Between Group Belonging, Subclinical Autistic Traits and Mental Health

Background:

There is evidence that depression and anxiety is elevated in young adults with high levels of subclinical autistic traits (Kanne et al., 2009). Furthermore, identifying with multiple groups (e.g. family, local community and social groups) predicts lower levels of anxiety and depression in adults in the general population (Sani et al., 2015). Individuals with ASD experience difficulties with communication and social interaction, which may impact on forming relationships with other people. They also experience elevated levels of mental ill health (Simonoff et al., 2009). Therefore, it is anticipated that subclinical autistic traits and group belonging are predictive of symptoms of generalised anxiety and depression.

Objectives:

It was hypothesised that:

• People who report identifying with fewer groups (either zero or one) will have more autistic traits than people who report identifying with more groups (either two or three).
• Group identification and autistic traits will significantly predict generalised anxiety symptoms and depression symptoms. Gender and age were also included as predictor variables for both multiple regressions.

Methods:

The Autistic Spectrum Quotient (AQ; Baron-Cohen et al., 2001), Group Identification Scale (GIS: Sani et al., 2014), Generalised Anxiety Scale–7 (GAD-7; Spitzer et al., 2006) and Major Depression Inventory (MDI; Bech et al., 2001) were administered to 171 individuals. Data from 149 participants (Female: 69.8%; Male: 30.2%) were analysed after exclusion criteria (i.e. missing more than one item per measure) were applied. AQ scores ranged from 3 to 45 (M = 17.16; SD = 8.37) and number of groups identified with ranged from 0 to 3 (M = 1.85; SD = 0.898).

Results:

The autistic traits of people with high group identification (2 or 3 groups) were compared to individuals with low group identification (0 or 1 group). The low group-belonging group had significantly higher autistic traits (M: 23.64; SD: 10.19) than those in the high group (M: 14.54; 5.76), t(147) = 6.891, p<.001.

GAD-7: The overall model demonstrated that age, gender, autistic traits and group identification statistically significantly predicted generalised anxiety symptoms, F(4,144) = 15.741, p < .001, R²Adj. = .285.

MDI: The overall model demonstrated that age, gender, autistic traits and group identification statistically significantly predicted depression symptoms, F(4,144) = 13.970, p < .001, R²Adj. = .260.

Conclusions:

Autistic traits and group belonging significantly predict generalised anxiety scores and depression scores in adults in the general population. Furthermore, individuals with low levels of group belonging had significantly more autistic traits than those with high group belonging. Higher levels of group belonging are related to both better physical health (Sani et al., 2014) and mental health (Sani et al., 2012) outcomes. Based on the results from this study, we suggest it would be beneficial to explore group belonging in individuals with diagnosed ASD, in order to determine whether it is predictive of mental health outcomes. If found to be the case, there are important clinical implications (e.g. for interventions to manage depression and anxiety) that should be explored.