Predictors of mental health and well‐being in employed adults with autism spectrum disorder at 12‐month follow‐up


First published: 24 January 2019

People with Autism Spectrum Disorder (ASD) commonly experience poor outcomes in adulthood. Previous research on adult outcomes has focused on negative aspects of health and well‐being, while positive well‐being remains understudied. The current study charted 12‐month change in daily living skills, job satisfaction, depression, anxiety, and positive well‐being in 36 (32 male) newly employed adults with ASD aged 18 to 57 years who were participating in a supported employment program. There was a small increase in daily living skills, and a slight decrease in job satisfaction, with all other measures remaining stable over time. Regression analyses revealed that, controlling for baseline depression, positive well‐being negatively predicted depression at follow‐up. No significant predictors of anxiety were identified. Social support and depression at baseline were associated with positive well‐being at follow‐up; however, they were no longer significant predictors after the effects of baseline positive well‐being were taken into account. The findings provide evidence that positive well‐being may buffer against depression in people with ASD. Our finding of stability of mental health and well‐being measures over time indicates more research is required to uncover the mechanisms underpinning mental health and well‐being outcomes in employed adults with ASD. Autism Research 2019. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.

People with autism commonly experience poor outcomes in adulthood. We studied mental health and well‐being in newly employed adults with autism who were participating in a supported employment program. Apart from a slight increase in daily living skills and a slight decrease in job satisfaction, other measures of mental health and well‐being remained stable over time. Our findings suggest that positive well‐being may protect against symptoms of depression in people with autism.

Individuals with Autism Spectrum Disorder (ASD) are recognized as having generally poor outcomes in adulthood [Howlin, 2000, 2013; Kirby, Baranek, & Fox, 2016; Levy & Perry, 2011; Magiati, Tay, & Howlin, 2014]. This is evidenced by a range of indices of negative well‐being including social isolation and lack of participation [Howlin, 2000, 2013; Howlin, Moss, Savage, & Rutter, 2013], high incidence of co‐occurring mental health problems [Croen et al., 2015; Eaves & Ho, 2008; Hofvander et al., 2009; Hudson, Hall, & Harkness, 2018; Simonoff et al., 2008; Uljarević et al., 2017; van Steensel, Bogels, & Perrin, 2011], including suicidal ideation and behavior [Cassidy et al., 2014; Hedley & Uljarević, 2018; Hedley, Uljarević, Foley, Richdale, & Trollor, 2018; Hirvikoski et al., 2016; Zahid & Upthegrove, 2017], and increased risk of premature mortality [Hirvikoski et al., 2016]. In addition, high rates of unemployment and underutilisation of skills and qualifications in the employment context have been consistently reported [Eaves & Ho, 2008; Holwerda, van der Klink, Groothoff, & Brouwer, 2012; Roux, Shattuck, Rast, Rava, & Anderson, 2015; Shattuck et al., 2012], and a significant portion of adults with ASD remain dependent on family or other support services [Howlin, Goode, Hutton, & Rutter, 2004]. In many cases, outcomes may be worse in individuals with ASD without co‐occurring intellectual disability as they receive lower levels of support compared to individuals with co‐occurring intellectual disability [Cimera & Cowan, 2009; Howlin, 2000].

Although the majority of research on health and well‐being in ASD has focused on the negative aspects such as poor mental and physical health, it is important to note that well‐being is a multifaceted construct which, in addition to the noted negative aspects, also encompasses positive aspects of cognitive, emotional, and psychological well‐being [Keyes, Shmotkin, & Ryff, 2002]. Consistent with Tennant et al. [Tennant et al., 2007; see also Waterman, 1993], positive well‐being can be understood to encompass the hedonic experience of subjective happiness and life satisfaction, and the eudaimonic perspective of psychological functioning and self‐realization. On the contrary, negative mental health refers to limitations to both hedonic and eudaimonic well‐being [Keyes et al., 2002; Tennant et al., 2007; Waterman, 1993]. Measures of positive well‐being focus on the three (i.e., emotional, cognitive, and psychological) components of well‐being and, unlike assessments of negative mental‐health, tend to be worded positively [Tennant et al., 2007]. Negative and positive facets of well‐being should be considered separately, as indicated by empirical evidence showing distinct predictors and different impacts on functioning [Jahoda, 1958; Keyes, 2005; Keyes et al., 2002; Ryff & Keyes, 1995; Teismann et al., 2018; Westerhof & Keyes, 2010]. Importantly, a wealth of research on non‐clinical populations has demonstrated that positive well‐being is essential for effective functioning, allowing individuals to better cope with life stress, to be productive, and to experience positive relationships [Huppert, 2009; Tennant et al., 2007]. Furthermore, positive well‐being in the typically developing population may foster resilience and buffer against negative mental health and other poor outcomes [Teismann, Brailovskaia, et al., 2018; Teismann et al., 2018; Wilhelm, Wedgwood, Parker, Geerligs, & Hadzi‐Pavlovic, 2010]. However, with the exception of the recent population based study from the United Kingdom which reported that children with ASD exhibit relatively high levels of traits and behaviors indicative of positive well‐being, such as self‐esteem and happiness [McChesney & Toseeb, 2018], positive well‐being remains understudied in ASD.

While it is clear that well‐being outcomes in ASD are suboptimal, predictors of both positive and negative aspects of well‐being remain poorly characterized, impacting our ability to provide adequate support or to develop effective mental health interventions. In addition, it is currently not clear how different facets of well‐being interact. As noted above, positive well‐being has been suggested to serve as a resilience factor against poor mental health; however, it remains unknown whether it serves a similar function in people with ASD. Therefore, it is important to move beyond research focused solely on psychopathology and investigate the role of positive well‐being in improved outcomes in ASD.

The presence and severity of ASD traits and social support are two factors shown to be related to well‐being across both ASD and non‐ASD clinical and normative samples. Although children with ASD in the study by McChesney and Toseeb [2018] showed relatively high levels of positive well‐being, overall levels were lower than for children without ASD. Furthermore, children with ASD who fared worse in terms of positive well‐being had more behavioral, communication, and social problems, suggesting symptom severity may have impacted well‐being. Furthermore, ASD trait severity has been found to predict elements of negative mental health in adults with ASD, including anxiety and depression [Hedley, Uljarević, Foley, et al., 2018; Uljarević et al., 2018; cf. Uljarević et al., 2017]. Using the Autism Spectrum Quotient (AQ), a self‐reported instrument for the assessment of ASD traits in adults with normal intelligence [Baron‐Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001], trait severity has also been identified as a predictor of negative mental health, including suicidal behavior [Pelton & Cassidy, 2017], poor coping and lower quality of life [Pisula, Danielewicz, Kawa, & Pisula, 2015], depression [Salehzadeh, Nasiri, Barin, & Roshan, 2017], and social anxiety [Lamport & Zlomke, 2014] in non‐ASD populations.

Another key predictor of mental health and well‐being is social support. In adults with ASD, social support has been identified as a protective factor against depression and suicidal ideation [Hedley, Uljarević, Foley, et al., 2018; Hedley, Uljarević, Wilmot, Richdale, & Dissanayake, 2017], and is a significant predictor of state and trait anxiety [Harkry, 2017]. In non‐ASD populations, social support has been shown to predict life satisfaction [Strizović & Mratinković, 2016] and positive well‐being [Wilhelm et al., 2010], improves long‐term depression trajectories [Heinz et al., 2018], and provides a protective effect during crisis [Henderson, 1981]. In adults with physical disabilities, perceived quality of social support is associated with depression and anxiety (negatively) and positive well‐being (positively) [Tough, Siegrist, & Fekete, 2017].

A further factor that might be an important contributor to mental health and well‐being outcomes in people with ASD, but remains largely unexplored, is employment status. Below, we review relevant literature on the relationship between employment and health and well‐being in the general and ASD populations.

Employment represents a milestone in human development, providing opportunities to expand social networks, develop financial independence, and to give life meaning. The association between employment and better mental health and well‐being outcomes is well established in the general population [Backhans & Hemmingsson, 2012; Creed & Macintyre, 2001; Feather & O'Brien, 1986; Heinz et al., 2018; Helliwell & Huang, 2014; McKee‐Ryan, Song, Wanberg, & Kinicki, 2005; Murphy & Athanasou, 1999; Paul & Moser, 2009; Voßemer et al., 2018; Wanberg, 2012; Wulfgramm, 2014], with meaningful employment identified as a protective factor against depression and other forms of psychological distress [van der Noordt, IJzelenberg, Droomers, & Proper, 2014]. However, employment may not be associated with improved mental health outcomes in people with ASD. For example, Bishop‐Fitzpatrick et al. [2016] used latent class analysis to characterize patterns of quality of life and a range of well‐being outcomes, including employment, in 180 adults with ASD. They found that although participants assigned to the “greater independence group” met more normative outcomes, including employment, than either the “greater dependence” or “good physical and mental health” groups, they were significantly more likely to experience poor mental health outcomes.

Relatedly, job satisfaction has been shown to be related to health and well‐being in the general population [Daniels & Harris, 2000; Dubin, 1956; Faragher, Cass, & Cooper, 2005; Near, Rice, & Hunt, 1978; Rice, Near, & Hunt, 1980; Steiner & Truxillo, 1987; Weaver, 1978]. Job satisfaction is strongly associated with burnout, self‐esteem, depression, and anxiety [Faragher et al., 2005], and predicts job performance and turnover [Daniels & Harris, 2000; Wright & Bonett, 2007; Wright & Cropanzano, 2000]. In one of the only studies examining job satisfaction in people with ASD, Harkry [2017] reported significant negative correlations between job satisfaction and anxiety. Although people with ASD are amongst the most disadvantaged in terms of employment, experiencing significantly higher levels of un‐ and underemployment compared to people with and without other disabilities [Eaves & Ho, 2008; Holwerda et al., 2012; Roux et al., 2015; Shattuck et al., 2012], surprisingly few empirical studies have examined the relationship between job satisfaction and mental health and well‐being in this population.

Three studies have examined changes in quality of life indices over time in employed adults with ASD. García‐Villamisar, Wehman, and Navarro [2002] reported significant improvements in quality of life amongst 25 individuals with ASD participating in a community‐based supported employment program, but no improvements were found for 26 individuals working in a sheltered workshop. However, these results should be interpreted with caution due to poor sample characterization and matching as well as measurement and reporting issues such as, for example, overall change on the primary measure was small (<5 points). Furthermore, despite the average length of employment being 30 months, follow‐up data were collected at 5‐years without reporting any post‐intervention or other interim data. Katz, Dejak, and Gal [2015] reported a small increase on the competency sub‐scale of a quality of life measure over 9‐months in 26 adults with ASD working within a supported work placement program in Israel; no other significant changes were identified on the measure. In an Israeli Defence Force supported employment program, a small but statistically significant increase in quality of life scores was reported for 21 young adults with ASD following 6‐months integration into a military unit [Gal, Selanikyo, Erez, & Katz, 2015]. However, the majority of domains on a personal well‐being index failed to show significant improvements over the same time period in a sub‐sample of 11 individuals. Finally, in a qualitative study on data collected at a single time point, Hedley et al. [2018] identified positive health and well‐being outcomes following transition to supported employment in nine adults with ASD which included reported improvements in self‐esteem, sense of purpose in life, mood, financial independence, social relationships, and opportunities for socializing.

As can be seen from the literature to date, only a small number of studies have examined aspects of health and well‐being in employed individuals with ASD, and these have been limited by methodological issues. It is therefore important to consider health and well‐being in employed individuals with ASD, along with potential associations with job related factors (e.g., job satisfaction).

The aim in the current study was to chart 12‐month change in job satisfaction, mental health and well‐being in newly employed adults with ASD who were participating in a supported employment program, and to identify potential predictors of negative mental health and positive well‐being. The program was based within several Australian government departments, where participants were employed by information and communications technology (ICT) company DXC Technology, and worked in small teams within the ICT sector. Job roles included software testing, data analytics, and cyber security; teams were provided with technical support as well as support to assist their integration into the workforce (more details of the program can be found in the Appendix; see also Hedley, Cai, Uljarević, et al., 2018; Hedley, Uljarević, & Hedley, 2017). Based on the reviewed literature, it was predicted that ASD trait severity, perceived social support, and job satisfaction at baseline (T1) would be significant predictors of symptoms of anxiety, depression, and positive well‐being, at follow‐up (T2). Given significant relationships between depression, anxiety, and positive well‐being in typical [Headey, Kelley, & Wearing, 1993] and ASD [Uljarević et al., 2017] populations, T1 depression and anxiety were also examined as potential predictors of T2 positive well‐being. Conversely, T1 positive well‐being was examined as a potential predictor of T2 depression and anxiety.

Forty‐three adults (39 male) with ASD aged 18–57 years (T1: Mage = 27.50, SD = 9.63 years) participated in the study. A further three individuals enrolled in the program declined to participate in the research. Each participant provided a letter from a medical doctor confirming their diagnosis in order to establish their eligibility for the supported employment program. Follow‐up (T2) data were available for 36 (32 male; Mage = 28.11, SD = 10.28, range 18–57 years) participants. Thus follow‐up data were available for 83% of the participants who provided baseline data, and 78% of the total number of individuals (n = 46) enrolled in the program. Included and excluded participants did not differ significantly on demographic variables including age, t(41) = 0.934, P > 0.05, ASD traits, t(40) = −0.102, P > 0.05, or daily living skills, t(40) = −1.45, P > 0.05.

The research was approved by the relevant human research ethics committees, with informed consent obtained from all participants. All applicants in the employment program were invited to participate in the research study. Applicants were contacted by the first author or a research assistant to discuss the study and answer questions shortly after applying for the employment program. Those who expressed interest in the study were sent the survey link via Qualtrics survey platform [Qualtrics, 2017], which included all relevant measures including the study information and consent forms. Participants could complete the surveys at work or at home. Data were collected across four sites, with T1 data collected after participants had completed 4‐weeks of on‐boarding and orientation. Due to staggered start dates for each site, T1 data were collected in October 2015, in March and October 2016, and March 2017. Follow‐up (T2) data were collected 12‐months later. The identity of study applicants was kept confidential from the employing organization. All surveys included an option to opt‐out, and participants who opted out or did not complete surveys were not sent subsequent invitations.

Waisman Activities of Daily Living (W‐ADL) [Maenner et al., 2013] is a 17‐item scale designed to assess daily living skills in individuals with developmental disability, including ASD. The W‐ADL is strongly associated with IQ [Maenner et al., 2013], and was used in the present study to indicate level of functioning. Items are rated from 0 to 2 (range 0–34), with higher scores indicating greater independence. Cronbach's α for the current sample was 0.88–0.89.

Abridged Version of the Autism Spectrum Quotient (AQ‐Short) [Hoekstra et al., 2011] is a 28 item measure designed to assess ASD trait severity, returning similar psychometric properties to the original 50‐item version [Baron‐Cohen et al., 2001]. In the present study, the AQ‐Short was used to provide a continuous assessment of ASD traits. Respondents answer statements on a 4‐point Likert scale, from 1 to 4 (range 28–112). Higher scores indicating increased ASD trait severity, and a cut‐off point >65 provides sensitivity and specificity to clinical diagnosis of 0.97 and 0.82, respectively [Hoekstra et al., 2011]. Cronbach's α in the current sample was 0.82–0.83.

Interpersonal Support Evaluation List‐12 (ISEL‐12) [Cohen & Hoberman, 1983; Cohen, Mermelstein, Kamarck, & Hoberman, 1985] is a 12‐item measure that assesses perceived social support. It provides a total score and three subscales (Appraisal, Belonging, and Tangible Support). Items are rated on a 4‐point Likert‐type scale ranging from 0 to 3 (range 0–36). Cronbach's α for the total score in the present study was 0.89–0.90.

Minnesota Satisfaction Questionnaire–Short Form (MSQ‐SF) is a 20‐item self‐report measure of job satisfaction, which has been used with employees with disabilities [Weiss, Dawis, England, & Lofquist, 1967,1977]. Respondents indicate how satisfied they are with particular aspects of their job. Items are rated on a 5‐point Likert‐type scale ranging from 1 to 5. The MSQ‐SF provides a General Satisfaction score (20–100) and two factor scales consisting of Intrinsic Satisfaction (job task; score range 12–60) and Extrinsic Satisfaction (working conditions; score range 6–30). Cronbach's α for the total score in the present sample was 0.92–0.93.

Patient Health Questionnaire‐9 (PHQ‐9) [Kroenke & Spitzer, 2002; Kroenke, Spitzer, & Williams, 2001; Kroenke et al., 2009] is a nine item questionnaire used to detect major and subthreshold depressive disorder. Respondents rate how much problems have affected them on a 4‐point Likert scale (range 0–27). Higher scores indicate a greater degree of depressive symptoms. A cut‐off score of ≥10 strongly indicates clinical caseness for depression in clinical and normative samples, returning reasonable sensitivity (0.70–1.00) and good specificity (0.95–0.98) [Kroenke et al., 2009]. Cronbach's α in the current sample was 0.84–0.89.

DSM‐5 Dimensional Anxiety Scale (DSM‐5 DAS) [Beesdo‐Baum et al., 2012; Knappe et al., 2013; Lebeau et al., 2012] is a 10‐item self‐report measure of anxiety symptoms. Respondents rate the degree to which anxiety symptoms have affected them on a 5‐point Likert scale (range 0–36), with higher scores indicating greater severity. Scores >14 are indicative of clinically significant levels of anxiety [Beesdo‐Baum et al., 2012]. Cronbach's α in the current sample was 0.91–0.95.

Warwick‐Edinburgh Mental Well‐being Scale (WEMWBS) [Tennant et al., 2007] is a 14‐item scale of mental well‐being that covers subjective well‐being and psychological functioning. All items are worded positively and address aspects of positive mental health and well‐being. The scale is completed using a 5‐point Likert scale; item scores are summed to derive a total score (range 14–70), with higher scores indicating higher levels of positive mental well‐being. Cronbach's α in the current sample was 0.85–0.92.

Tests of normality revealed the PHQ‐9 and DSM‐5 DAS were positively skewed, and the Waisman Activities of Daily Living (W‐ADL) was negatively skewed. Subsequent analyses were performed with 2,000 resamples bootstrapping to provide more robust statistics with statistical significance determined by 95% bias corrected and accelerated confidence intervals [Efron & Tibshirani, 1993; Tabachnick & Fidell, 2007]. Cohen's d effect size for repeated measures, including 95% confidence intervals for d, were reported where relevant [Morris & DeShon, 2002].

All statistical analyses were completed using SPSS version 25.0 [IBM Corp., 2017]. Changes in study variables (T1) to follow‐up (T2) were analyzed using bootstrapped paired‐samples t‐tests. Correlational analysis was used to explore the pattern of the relationships between the study variables and identify variables for model entry. Separate regression models were then run to identify factors contributing to T2 depression (PHQ‐9), anxiety (DSM‐5 DAS), and positive well‐being (WEMWBS). Predictor variables were assessed for multicollinearity by examining the variance inflation factor (VIF < 10) and the tolerance statistic (1/VIF > 0.2) [Bowerman & O'Connell, 1990; Menard, 1995]. Multicollinearity for all predictors was well within these parameters (VIF = 1.00–2.30, tolerance = 0.434–1.00). To check residuals for bias, casewise diagnostics were also reviewed [Field, 2018]. Two models each had one case (2.78%) and one model had two cases (5.5%) with standardized residuals greater than ±2, with only one case overall exceeding ±2.5 (standardized residual = 2.57). This suggested the models were reliable and unlikely to be unduly affected by any subset of cases [Field, 2018].

Descriptive statistics and T1–T2 bootstrapped paired‐samples t‐tests for each measure are presented in Table 1. Bootstrapped results indicated a small but significant increase in daily living skills from baseline to follow‐up, and a small decline in job satisfaction. No further statistically significant differences between time points were identified for any of the other measures.

Table 1. Descriptive (M, SD) and T1–T2 Bootstrapped Comparisonsa
T1 T2 Bootstrapped comparisons (T1–T2)
Variable M SD M SD t(35) BCa 95% CI d [95% CI]
W‐ADL 31.08 4.03 31.94 3.60 −1.96 −1.78, −0.114 0.31 [−0.15, 0.78]
AQ‐Short 76.36 9.68 75.25 9.89 1.17 −0.595, 3.03 −0.20 [−0.66, 0.27]
ISEL‐12 19.86 8.28 19.81 7.82 0.073 −1.54, 1.62 −0.01 [−0.47, 0.45]
MSQ‐SF 77.75 9.84 74.97 11.82 2.11 0.243, 5.36 −0.40 [−0.87, 0.07]
PHQ‐9 6.72 4.86 7.39 5.59 −0.952 −1.97, 0.632 0.17 [−0.29, 0.64]
DAS 11.42 7.77 12.11 9.16 −0.524 −3.36, 1.74 0.10 [−0.37, 0.56]
WEMWBS 44.36 6.95 42.67 9.60 1.32 −0.774, 4.17 −0.28 [−0.74, 0.19]
  • a Two thousand bootstrap samples; 95% BCa confidence intervals that do not cross zero are bolded. W‐ADL: Waisman Activities of Daily Living; AQ‐Short: Autism Spectrum Quotient‐Short; ISEL‐12: Interpersonal Support Evaluation List‐12; MSQ‐SF: Minnesota Satisfaction Questionnaire–Short Form; PHQ‐9: Patient Health Questionnaire‐9; DAS: DSM‐5 Dimensional Anxiety Scale; WEMWBS: Warwick‐Edinburgh Mental Well‐being Scale.

Table 2 provides correlations between all study variables (T1, T2). Significant P‐values (*) and bootstrapped confidence intervals (bolded) are both provided; for the predictor variables P‐values and bootstrapped significance results were in agreement. Age was first examined as a potential control variable. Although age was correlated with daily living skills and ASD traits, it was not found to be significantly correlated with any of the dependent variables (i.e., depression, anxiety, and well‐being) and was therefore not considered for further analysis. To identify predictors for model entry, significant correlations for the hypothesized variables were reviewed for depression (T2), anxiety (T2), and well‐being (T2). Social support (T1) and positive well‐being (T1) were both significantly correlated with depression (T2) and anxiety (T2). Significant correlates for positive well‐being (T2) were ASD traits (T1), social support (T1), and depression (T1). All correlations were in the expected directions.

Table 2. Pearson's Bootstrapped Correlations T1–T2a
Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
1. Age (T1)
2. W‐ADL (T1) 0.360 *
3. W‐ADL (T2) 0.279 0.766 ***
4. AQ‐Short (T1) 0.388* 0.249 0.159
5. AQ‐Short (T2) 0.388* 0.149 0.012 0.832 ***
6. ISEL‐12 (T1) −0.135 0.064 0.015 −0.193 −0.187
7. ISEL‐12 (T2) −0.175 0.065 0.076 −0.225 −0.290 0.840 ***
8. MSQ‐SF (T1) 0.012 −0.102 −0.113 −0.144 0.004 0.298 0.072
9. MSQ‐SF (T2) −0.232 −0.260 −0.283 −0.268 −0.180 0.376 * 0.332* 0.749 ***
10. PHQ‐9 (T1) −0.062 −0.229 −0.239 0.081 0.090 −0.464 ** −0.304 −0.254 −0.162
11. PHQ‐9 (T2) −0.141 −0.292 −0.371 * 0.222 0.283 −0.539 ** −0.487 ** −0.078 −0.134 0.685 ***
12. DAS (T1) −0.110 −0.097 −0.084 0.301 0.277 −0.289 −0.220 −0.266 −0.099 0.637 *** 0.523 **
13. DAS (T2) −0.094 −0.187 −0.182 0.278 0.392 * −0.549 ** −0.551 *** 0.019 −0.060 0.572 *** 0.871 *** 0.569 ***
14. WEMWBS (T1) 0.016 0.146 0.173 −0.275 −0.224 0.496 ** 0.250 0.578 *** 0.455 ** −0.626 *** −0.622 *** −0.466** −0.452 **
15. WEMWBS (T2) −0.141 0.108 0.154 −0.347 * −0.402 * 0.615 *** 0.614 *** 0.226 0.472 ** −0.500 *** −0.772 *** −0.190 −0.698 *** 0.611 ***
  • a Two thousand bootstrap samples; 95% BCa confidence intervals that do not cross zero are bolded (significance based on P‐value are also identified). W‐ADL: Waisman Activities of Daily Living; AQ‐Short: Autism Spectrum Quotient‐Short; ISEL‐12: Interpersonal Support Evaluation List‐12; MSQ‐SF: Minnesota Satisfaction Questionnaire–Short Form; PHQ‐9: Patient Health Questionnaire‐9; DAS: DSM‐5 Dimensional Anxiety Scale; WEMWBS: Warwick‐Edinburgh Mental Well‐being Scale.
  • *P < 0.05.
  • **P < 0.01.
  • ***P < 0.001.

Full regression models predicting positive well‐being and negative mental health are presented in Table 3. Linear multiple regression models using 2,000 bootstrapped samples (due to non‐normal distributions) were used, and only predictors that significantly correlated with the dependent variable were examined. Where appropriate, the AQ‐Short was entered in the first step and hypothesized predictors were entered in the next step. For all dependent variables, T1 data were entered in the final step to control for the effects of the baseline levels of the measure.

Table 3. Linear Regression Models of Predictors of T2 Negative Mental Health (PHQ‐9, DSM‐5 DAS) and Positive Well‐Being (WEMWBS). 95% Bias Corrected and Accelerated Confidence Intervals and Standard Errors Based on 2000 Bootstrap Samples
Depression (T2)a Anxiety (T2)b Positive well‐being (T2)c
b SEB β BCa 95% CI b SEB β BCa 95% CI b SEB β BCa 95% CI
Step 1 Step 1 Step 1
Constant 28.29 4.43 18.12, 36.76 Constant 35.54 7.37 20.25, 48.07 Constant 68.93 11.68 48.28, 90.83
ISEL‐12 (T1) −0.206 0.125 −0.305 −0.434, 0.086 ISEL‐12 (T1) −0.476 0.243 −0.431 −0.919, 0.106 AQ‐Short (T1) −0.344 0.152 −0.347 −0.687, −0.003
WEMWBS (T1) −0.379 0.115 −0.471 −0.605, −0.173 WEMWBS (T1) −0.315 0.191 −0.239 −0.745, 0.044 Step 2
Step 2 Step 2 Constant 54.27 9.91 34.92, 73.42
Constant 15.72 6.49 4.74, 30.34 Constant 18.36 7.73 3.75, 32.33 AQ‐Short (T1) −0.237 0.112 −0.239 −0.471, 0.013
ISEL‐12 (T1) −0.147 0.119 −0.217 −0.356, 0.125 ISEL‐12 (T1) −0.440 0.248 −0.398 −0.854, 0.220 ISEL‐12 (T1) 0.512 0.179 0.441 0.117, 0.810
WEMWBS (T1) −0.197 0.128 −0.245 −0.465, −0.002 WEMWBS (T1) −0.074 0.194 −0.056 −0.498, 0.225 PHQ‐9 (T1) −0.544 0.305 −0.275 −1.18, −0.101
PHQ‐9 (T1) 0.496 0.206 0.431 0.084, 0.896 DAS (T1) 0.504 0.167 0.428 1.99, 1.08 Step 3
Constant 31.37 15.85 1.35, 55.81
AQ‐Short (T1) −0.181 0.112 −0.182 −0.426, 0.103
ISEL‐12 (T1) 0.435 0.219 0.375 −0.042, 0.728
PHQ‐9 (T1) −0.248 0.311 −0.125 −0.771, 0.208
WEMWBS (T1) 0.409 0.247 0.296 −0.130, 1.11
  • a Step 1, R2 = 0.458, P < 0.001; Step 2, R2Δ = 0.107, P = 0.009.
  • b Step 1 R2 = 0.345, P = 0.001; Step 2, R2Δ = 0.142, P = 0.005.
  • c Step 1, R2 = 0.12, P = 0.038; Step 2 R2Δ = 0.372, P < 0.001; Step 3, R2Δ = 0.046, P = 0.09. 95% BCa confidence intervals that do not cross zero are bolded. AQ‐Short: Autism Spectrum Quotient‐Short; ISEL‐12: Interpersonal Support Evaluation List‐12; PHQ‐9: Patient Health Questionnaire‐9; DAS: DSM‐5 Dimensional Anxiety Scale; WEMWBS: Warwick‐Edinburgh Mental Well‐being Scale; SEB: the standard error for the unstandardized beta.

We examined the predictors of negative mental health first. For depression (T2), social support (T1) and positive well‐being (T1) were entered at Step 1, and depression (T1) was entered at Step 2. The first step accounted for 45.8% of the variance, F(2, 33) = 13.92, P < 0.001, with positive well‐being (T1) significantly and negatively predicting depressive scores (T2). The second step accounted for an additional 10.7% (P = 0.009) of variance, with the total model accounting for 56.5% of the variance, F(3, 32) = 13.83, P < 0.001. In the final model, positive well‐being (T1) remained an independent significant predictor of depression (T2), even when controlling for baseline depression scores.

Next, to examine predictors of anxiety (T2), social support (T1), and positive well‐being (T1) were entered at Step 1 and anxiety (T1) was entered at Step 2. The first step accounted for 34.5% of the variance, F(2, 33) = 8.67, P = 0.001; however neither variable independently predicted anxiety scores (T2). The second step accounted for an additional 14.2% (P = 0.005) of variance, with the total model accounting for 48.7% of the variance, F(3, 32) = 10.12, P < 0.001. In the final model, only baseline anxiety (T1) significantly predicted anxiety (T2) at follow‐up.

We then examined the predictors of positive mental health (T2). ASD traits (T1) were entered at Step 1, and social support (T1) and depression (T1) were entered in Step 2. Baseline positive well‐being (T1) was added to the model in Step 3. The first step accounted for 12% of the variance, F(1, 34) = 4.64, P = 0.038, with ASD traits significantly and negatively predicting positive well‐being at this step. The second step accounted for an additional 37.2% (P < 0.001) of variance, with the total model accounting for 49.2% of the variance, F(3, 32) = 10.33, P < 0.001. Bootstrapped results indicated that both social support (positively) and depression (negatively) were unique predictors of positive well‐being at this step. Baseline positive well‐being (T1) was entered in the final step; however this step accounted for only 4.6% (P = 0.09) of additional variance. The final model accounted for 53.8% of the overall variance, F(4, 31) = 9.02, P < 0.001. However, none of the predictors uniquely contributed to the variance in positive well‐being (T2) once baseline levels were controlled for, with all bootstrapped confidence intervals crossing zero.

Within the general population, being employed has consistently been linked to better mental health and well‐being [Backhans & Hemmingsson, 2012; Creed & Macintyre, 2001; Feather & O'Brien, 1986; Heinz et al., 2018; McKee‐Ryan et al., 2005; Murphy & Athanasou, 1999; Paul & Moser, 2009; Voßemer et al., 2018; Wanberg, 2012], with greater benefits associated with higher levels of job satisfaction [Faragher et al., 2005]. As long‐term outcomes in terms of mental health and well‐being in employed adults with ASD is poorly understood, we monitored changes in mental health and well‐being over time in newly employed adults with ASD, a population rarely represented in previous research. The aims were to (a) chart any changes in mental health and well‐being over their first 12‐months of employment and (b) identify significant predictors of mental health and well‐being outcomes amongst the study participants.

Apart from a small increase in daily living skills, and slight decrease in job satisfaction, participants' ratings on the main measures of depression, anxiety, and positive well‐being remained stable over time. The finding of a slight decrease in job satisfaction over time in newly employed individuals is consistent with previous research in the general population, which suggests that job satisfaction peaks following entry into an organization and subsequently decreases [e.g., Boswell, Shipp, Payne, & Culbertson, 2009]. These findings are consistent with previous research within an ASD sample [Harkry, 2017], where no improvements were found across similar constructs. Of interest, however, is that participants enrolled in the same program reported via qualitative interviews that supported employment led to benefits across aspects such as self‐esteem and sense of purpose in life, that are consistent with improved positive aspects of well‐being [Hedley, Cai, Uljarević, et al., 2018]. Nonetheless, these benefits were not captured in the current study using standardized tools. These results suggest that although tangible improvements may be reported in specific areas of positive well‐being, these improvements may not necessarily transfer to the broader construct of well‐being as measured here.

Despite stability in mental health and well‐being over their 12‐month period of employment, as a group, the findings reported here provide important insights into potential correlates and predictors of negative and positive well‐being of the employees with ASD. First, the correlational analyses revealed that baseline social support and positive well‐being were significantly associated with depression at follow‐up. With the inclusion of all predictors in the regression models, baseline positive well‐being was found to negatively predict depression at follow‐up, even when controlling for baseline depression. These results are consistent with research in the general population [Teismann, Brailovskaia, et al., 2018; Teismann, Forkmann, et al., 2018; Wilhelm et al., 2010] that shows well‐being functions as a buffer or protective factor against depression. With the dearth of positive well‐being research amongst the ASD population, this finding reveals the potential for positive well‐being to function as a resilience factor against depression, and potentially other associated negative mental health outcomes, for individuals with ASD. Turning to anxiety, while baseline social support and positive well‐being were both negatively correlated with anxiety at follow‐up, neither was identified as a significant independent predictor of anxiety at the 12‐month follow‐up.

These results are consistent with other research suggestive of a complex relationship between positive well‐being, anxiety, and depression. Headey et al. [1993] examined the relationships between life satisfaction, positive affect, anxiety, and depression in a quality of life study across two time points. They concluded that a multi‐dimensional model better accounted for the relationships between variables: while life satisfaction and depression were strongly negatively correlated, the relationship between life satisfaction and anxiety was less pronounced. The potential for positive well‐being to functioning as a buffer against depression in this study, as well as the broader benefits of positive well‐being reported in the general population (e.g., managing stress, positive relationships) [Huppert, 2009; Tennant et al., 2007], suggests that positive well‐being should be cultivated within the employment context.

When examining well‐being as an outcome variable, correlational analyses initially showed that perceived social support was positively associated with positive well‐being at baseline and follow‐up. However, regression results failed to identify social support as a significant predictor of positive well‐being at follow‐up when controlling for baseline (well‐being) scores. While the association between social support and well‐being in the typical population is well replicated [Turner, 1981; Wilhelm et al., 2010], and there is growing evidence that social support may be an important protective factor against poor negative mental health outcomes (e.g., depression, suicidal ideation) in people with ASD [Hedley, Uljarević, Foley, et al., 2018; Hedley, Uljarević, & Hedley, 2017; Hedley, Uljarević, Wilmot, et al., 2017], our results do not strongly support this interpretation in the present sample. More research into the role of social support in improving mental health and well‐being in people with ASD is therefore needed.

Although our baseline finding for ASD traits is consistent with previous studies that have failed to identify a relationship between ASD traits and depression [Hedley, Uljarević, & Hedley, 2017; Hedley, Uljarević, Wilmot, Richdale, & Dissanayake, 2018], or anxiety [Uljarević et al., 2017], it is important to note that a number of cross‐sectional studies have identified significant relationships between these factors amongst ASD [Hedley, Uljarević, Foley, et al., 2018; Uljarević et al., 2018] and non‐ASD [Lamport & Zlomke, 2014; Salehzadeh et al., 2017] populations. Findings from our study and those of others suggests the relationship between ASD traits and psychopathology is complex and likely non‐linear, and highlights the importance of longitudinal study designs to fully characterize the role of ASD trait severity in the development and maintenance of psychopathology [Tebartz van Elst, Pick, Biscaldi, Fangmeier, & Riedel, 2013]. Interestingly, baseline ASD traits were found to be negatively correlated with positive well‐being at follow‐up; however, once entered into the regression models with other predictors, ASD traits did not emerge as a significant predictor of positive well‐being. Our findings suggest that ASD traits may operate relatively independently from both negative mental health and positive well‐being. This conclusion is supported by the studies identified above and, furthermore, indicates that other factors aside from self‐reported ASD traits are more important in terms of mental health and well‐being outcomes.

Finally, we predicted that job satisfaction would be significantly related to the mental health and well‐being variables. Although correlational analyses revealed a significant positive relationship between baseline job satisfaction and baseline positive well‐being, and job satisfaction at follow‐up was significantly and positively associated with social support and positive well‐being (both at baseline and follow‐up), baseline job satisfaction was not significantly correlated with any of the main outcome variables at follow‐up. This result is inconsistent with literature from the general population, which suggests a stronger relationship between job satisfaction and mental health and well‐being [Dubin, 1956; Faragher et al., 2005]. It is important to note that different authors have formalized directionality of the relationship between job satisfaction and well‐being in different ways. There is some evidence in the general population of a causal relationship between psychological well‐being and job performance [Daniels & Harris, 2000]; however, others argue that job satisfaction influences (i.e., is predictive of) employee health and well‐being [Faragher et al., 2005], or quality of life (depending on the relative importance of work in the individual's life) [Rice et al., 1980; Steiner & Truxillo, 1987]. This suggests directions for future research where job satisfaction might be formalized as a dependent variable. Based on the correlations reported in the present study, both social support and positive well‐being could be examined as potential predictors of job satisfaction. More work is needed to examine the role of job satisfaction in the mental health and well‐being of employed adults with ASD.

Our findings need to be interpreted with respect to some limitations. First, as recruitment was limited to individuals who were participating in the employment program, the sample was relatively small and not representative of the general ASD population. Participants in this study represented only a select group of individuals with ASD who were successful, not only in the recruitment process, but also in maintaining their employment for 12‐months. Furthermore, 10 individuals in the program either chose not to participate in the research (n = 3), or did not provide follow‐up data (n = 7). Nevertheless, the availability of data from two time‐points, which allowed us to identify potential predictors of mental health and well‐being, is a significant strength of the study. Second, although we have selected measures that have a strong evidence base in clinical and general populations, or which have been shown to perform well psychometrically in adults with ASD [Hedley, Uljarević, Foley, et al., 2018], it is generally acknowledged that there is a lack of validated instruments to assess mental health difficulties in ASD [Uljarević et al., 2017; Wigham, Barton, Parr, & Rodgers, 2017]. Third, we did not include a comparative sample of adults with ASD engaged in open (i.e., unsupported) employment. It is possible that, without workplace supports, the trajectory of employed adults with ASD is poor [e.g., Baldwin, Costley, & Warren, 2014], a hypothesis that we were unable to test in the present study. The identification of predictors of mental health and well‐being in a larger, population based sample of employed adults with ASD using a longitudinal design is a worthy avenue for future research. Furthermore, the potential impact of supported versus open employment versus no employment, on mental health and well‐being in this population will also be important to determine the potential contribution of supported work environments to mental health and well‐being outcomes in people with ASD. Moreover, a larger study with more time‐points would also allow for the development and testing of more complex theoretical models using additional analyses such as mediational techniques. While this approach was beyond the scope of the present study due to the aforementioned limitations, it provides a direction for future research. Finally, the present study focused on defined aspects of positive and negative well‐being, as conceptualized by the measures used. Given the overlap between these domains and standard quality of life measures, we did not include quality of life as a separate outcome measure. Nonetheless, several aspects of quality of life, such as general stress, physical health, or more specific employment related indices of well‐being will be important to assess in future research [e.g., see Dagenais‐Desmarais & Savoie, 2012].

Despite the noted limitations, our study provides evidence of the potential buffering or protective role of positive well‐being against depression in people with ASD. We also found tentative support for social support and depression as potential protective and risk factors in positive well‐being; however, this finding was not strongly supported once baseline well‐being was factored in the regression models. Further research will be needed to confirm or reject this hypothesis.

We thank the individuals who participated in this study and all of the staff from DXC Technology who provided access to participants and supported data collection. We also thank Professor Timothy Bartram, Associate Professor Amanda Richdale, Dr. Simon Moss, and Dr. Jennifer Spoor for their guidance, and Alex Aulich, Alex Haschek, and Mathilda Wilmot for assistance with data collection and recruitment. This research was funded by DXC Technology and the Australian Government Department of Human Services and Department of Defence. The funders had no role in the study design, data collection, analysis, data interpretation, or writing of the report.

D.H., M.U., and C.D. conceived of the study. D.H. completed the literature review, recruited participants, collected the data, completed the data analysis and interpretation, and prepared the tables. D.H. led the writing of the manuscript with edits and contributions from all authors. All authors reviewed and approved the final submitted version.

D.H. and S.B. were supported by funding from DXC Technology and the Australian Government Department of Human Services and Department of Defence. The authors declare no other actual or potential conflict of interest.

The DXC Technology Dandelion Program uses revised human resource management protocols and procedures for assessing individuals with ASD that were initially developed by the Danish company, Specialisterne [Austin & Sonne, 2014; Austin, Wareham, & Busquets, 2008]. The changes to the recruitment and selection process were intended to address some of the challenges associated with the traditional job application process experienced by applicants with ASD [e.g., Müller, Schuler, Burton, & Yates, 2003]. Candidates who are successful through the competitive recruitment process are employed by DXC Technology at a level equivalent to graduate level trainees. These individuals are provided with job specific training and work as a team within various Australian government agencies including the Department of Human Services, Department of Defence, and Home Affairs. Ongoing support processes have been developed by DXC Technology. For example, each team is provided with a dedicated support person who has experience working with people with ASD, and who is available full‐time and on‐site to provide individual counseling, mediation, and advocacy. Teams also include technical support personnel provided by DXC Technology, and work alongside other information and communications technology (ICT) professionals.

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