Fatigue,wellbeing and parental self-efficacy in mothers of children with an Autism Spectrum Disorder

Abstract

Raising a child with an Autism Spectrum Disorder (ASD) presents significant challenges for parents that potentially have a impact on their health and wellbeing. The current study examined the extent to which parents experience fatigue and its relationship to other aspects of wellbeing and parenting. Fifty mothers of children with an ASD aged 2–5 years participated in the study. Compared with mothers of typically developing children, mothers of children with an ASD reported significantly higher fatigue, with overall scores in the moderate range. Factors associated with high levels of fatigue were poor maternal sleep quality, a high need for social support and poor quality of physical activity. Fatigue was also significantly related to other aspects of wellbeing, including stress, anxiety and depression, and lower parenting efficacy and satisfaction. The need for interventions to specifically target parental fatigue and its impact on families affected by ASDs both in the short and long term is clearly indicated.

Keywords

Mothers wellbeing fatigue parenting parental self-efficacy autism

There is much research indicating that parents of children with an Autism Spectrum Disorder (ASD) experience wellbeing difficulties, including stress and depression (Carter, et al., 2009; Glasberg, et al., 2007; Lloyd and Hastings, 2008). These difficulties can have a significant impact on parenting experiences and behaviour (Hastings and Brown, 2002; Rutgers, et al., 2007). Another aspect of health and wellbeing that has the potential to affect the functioning of parents of children with an ASD is exhaustion or fatigue. Although several qualitative studies have documented that exhaustion is common among parents of children with an ASD and other disabilities (Benderix, et al., 2006; Lopez-Wagner, et al., 2008; Vickers, et al., 2004), no known studies have explored the extent to which parents of children with an ASD experience fatigue, and its potential impact on their wellbeing and parenting.

Unlike tiredness, which is typically alleviated by rest, fatigue has been defined as an enduring sense of physical and mental exhaustion not easily relieved by rest (North American Nursing Diagnosis Association, 2001; Ream and Richardson, 1996). Fatigue has been conceptualized as a health outcome that can have an impact on cognitive functions, such as concentration, memory and decision-making (Hockey, et al., 2000; van der Linden, et al., 2003). It is viewed as a serious health concern for parents of typically developing (TD) children (Fisher, et al., 2004; Ward and Giallo, 2008) and has been associated with reduced daytime functioning and impaired clarity of thinking (Fisher, et al., 2004). It has also been linked to wellbeing difficulties, including depression, anxiety and stress (Ward and Giallo, 2008; Giallo, et al., 2011).

Fatigue also has the potential to affect parenting experiences and practices. For instance, parents of young TD children have reported that exhaustion affects their patience and ability to cope with their children (Nyberg and Sternhufvud, 2000). More recently, in a large survey of 1387 parents of young children (aged 0–5 years), high levels of fatigue were associated with low parental self-efficacy, high parenting stress and irritability (e.g. losing temper and raising voice) (Ward and Giallo, 2008). Given the high caregiving demands and challenges for parents of children with an ASD, the current study aimed to extend previous research to examine the extent to which these parents experience fatigue. Fatigue was conceptualized as a health and wellbeing outcome for parents. Its relationship as a predictor of parent wellbeing and parenting was also explored.

Although fatigue in parents of children with an ASD has not been researched specifically, exhaustion has emerged as predominant theme in several qualitative studies on parental experiences (Benderix, et al., 2006; Lopez-Wagner, et al., 2008; Vickers, et al., 2004). Parents of children with a range of disabilities and chronic health conditions have described burnout, emotional exhaustion and physical fatigue (Kennedy and Llewelyn, 2003; Vickers, et al., 2004). In a more recent study, parents of children with an ASD (aged 10–11 years) living in a group home described a constant state of exhaustion. They reported that tiredness contributed to cognitive deterioration at work and home and was the main reason why they did not socialize and why, ultimately, they found a place for their child in residential care (Benderix et al., 2006). One quantitative study found that fathers of children with Down’s Syndrome reported significantly lower mean scores on the Vitality subscale of the SF-36 health scale than fathers of children without a disability, indicating that they tended to feel tired and worn out more often (Hedov, et al., 2000). Taken together, these findings highlight that fatigue is a serious problem that has the potential to have an impact on parents’ daily functioning and, in some cases, their ability to care for their children.

Parents of children with an ASD are becoming increasingly involved in the provision of early intervention and learning activities to promote positive outcomes for their children (Kuhn and Carter, 2006). Research shows that time-intensive interventions involving positive behaviour support, play, social skills, language and communication skills have the greatest outcomes for children’s intellectual and emotional functioning, social skills and adaptive behaviours (Osborne, et al., 2008). However, several studies have documented that parental stress as well as a lack of time and energy are barriers to providing early intervention activities within the home learning environment (Benson, et al., 2008; Johnson and Hastings, 2002; Osborne, et al., 2008). Understanding factors, such as lack of energy or fatigue that may limit the capacity of the parent to assist in promoting their child’s development is critical for this group.

This study was also interested in understanding what factors are associated with fatigue for parents of children with an ASD. Research with parents of young TD children have identified that poor sleep quality (Elek, et al., 1997), marital dissatisfaction (Elek, et al., 2002) and poor nutrition (Lee and Zaffke, 1999) are associated with fatigue. A recent study of 1276 parents of young children (aged 0–5 years) found that a range of demographic, parent, child and contextual factors significantly accounted for 49% of the variance in fatigue scores. The strongest predictors of fatigue in the model were inadequate social support, poorer diet, poorer sleep quality and ineffective coping styles, including self-blame and behaviour disengagement (Cooklin, et al., 2010). It remains unclear whether these factors are also relevant to parents of children with an ASD. This information would provide important information about potential targets for intervention to decrease parental fatigue.

Finally, the relationship between fatigue and parental self-efficacy (PSE) was of particular interest in the current study. PSE refers to parents’ beliefs about their ability to parent successfully (Jones and Prinz, 2005). In the general parenting literature, it is well established that PSE is associated with parents’ use of positive parenting strategies, coping and persistence in demanding parenting situations (Coleman and Karraker, 1998; Jones and Prinz, 2005). In addition, PSE is associated with a wide range of child socio-emotional, behavioural and academic outcomes (Coleman and Karraker, 1998; Jones and Prinz, 2005). In families of children with developmental disabilities, research has shown that PSE is predictive of parenting stress (Frey, et al., 1989; Friedrich, et al., 1985; Krauss, 1993). More recently, in a small study of mothers and fathers of children with an ASD, Hastings and Brown (2002) found that PSE mediated the relationship between child behaviour problems and maternal depression/anxiety, and moderated the effect of child behaviour problems on paternal anxiety. Given the growing body of research into the importance of the relationship between PSE and a range of parent and child outcomes, there is heightened interest in understanding how PSE develops and what parent/family factors are associated with it.

According to Bandura’s (1989) theory, self-efficacy beliefs are developed and shaped by success and failure experiences, perceived difficulty of tasks, interpersonal interactions with others, and aversive physiological and psychological arousal (e.g. anxiety). For parents of children with an ASD, parental stress and depression have been associated with PSE (Kuhn and Carter, 2006). Fatigue is another physiological and psychological experience that has the potential to have a negative impact on PSE. When parents of children with an ASD are physically and emotionally exhausted, they may find it harder to access their personal resources to manage caregiving demands and parenting challenges. This, in turn, might undermine perceptions of their competence and satisfaction in the parenting role. There are no known studies on the relationship between fatigue and PSE, and therefore this was a focus of the current study.

The aims of the current study were fourfold. First, to report on the extent to which mothers of children with an ASD experience fatigue, and to compare this to available data for mothers ofTD children. Second, to examine relationships between fatigue and other aspects of wellbeing, including depression, stress and anxiety. Third, to identify a range of demographic (socioeconomic status, number of children in the family), child (child behaviour problems) andparent (age, quality of diet, exercise and social support) factors associated with parental fatigue. The final aim was to identify whether fatigue was a significant predictor of PSE when taking into account other factors that may be associated with PSE.

Methods

Participants and procedure

Ethical approval for the study was obtained from the Swinburne University Human Research Ethics Committee. Parents of children with ASDs aged between 2 and 5 years were recruited through parent support groups and a range of disability and autism specific services across Australia. Of the 123 support groups and organizations contacted, 46 agreed to advertise the study, 3 actively declined and 74 did not respond. The organizations assisted in advertising the project by posting the recruitment flyer online, via email lists or in newsletters. A project information sheet provided an overview of the study and explained that participation was anonymous and voluntary. Informed consent was implied upon completion of the questionnaire. Participants were invited to complete the questionnaire online or were sent a hard copy. Parents with more than one child with an ASD were asked to select one child as their ‘focus child’.

Measures

The Fatigue Assessment Scale (Adapted FAS; Michielsen, et al., 2003) is an adapted version for use with parents. It consists of five statements to which participants respond regarding how they ‘usually feel’ on a 5-point scale, with higher scores representing higher levels of fatigue. Discriminant validity between fatigue using this scale and the Depression subscale of the DASS has been established in a large community sample of mothers in the postnatal period (Giallo, et al., 2011). Internal consistency for the current sample was 0.89.

The Depression Anxiety Stress Scale 21 (DASS; Lovibond and Lovibond, 1995) assesses the negative emotional states of depression, anxiety and tension or stress over the past week. The items are rated on a 4-point scale. Cronbach’s alpha for the Depression, Anxiety and Stress subscales for the current sample were .91, .81 and .89, respectively.

The Parenting Sense of Competence Scale (PSOC; Johnston and Mash, 1989) is a 16-item measure that assess parenting self-efficacy. The Satisfaction subscale assesses enjoyment, motivation and satisfaction in the parenting role and the Efficacy subscale assesses perceived knowledge, confidence and competence in parenting. Cronbach’s alpha for the current sample was 0.75 for the Satisfaction scale and 0.76 for the Efficacy scale.

The Developmental Behaviour Checklist P24 (DBC-P; Einfeld and Tonge, 1995) short form consists of 24 items that assess behavioural and emotional problems in children with developmental and intellectual disabilities. Items are rated on a 3-point scale, with a summed score of 46 or more suggesting the presence of psychopathology (Taffe, et al., 2007). The scale is reported to be an accurate and reliable measure in research contexts (Taffe, et al., 2007). Cronbach’s alpha for the current sample was .85.

The Pittsburgh Sleep Quality Index (PSQI; (Buysse, et al., 1989) comprises 19 self-report items designed to assess seven components of sleep quality: subjective sleep quality; sleep latency; sleep duration; habitual sleep efficiency; sleep disturbances; use of sleeping medication; and daytime dysfunction. Cronbach’s alpha for the current sample was .82.

The Parent Social Support Index (Telleen, 1985) measures parents’ support needs and satisfaction with social support. Three additional questions pertaining to parents’ support needs relating to fatigue, who they receive support from, and their satisfaction with support for fatigue were added. Internal consistency for the Support Needs and Satisfaction with Support subscales for the current sample was 0.78 and 0.83, respectively.

The Health Behaviour scale was included for the purpose of the study and asks respondents to rate the quality of their diet and their overall level of physical activity on a 5-point scale ranging from 0 (poor) to 4 (excellent).

The Socio-economic Indexes for Areas Index of Relative Socioeconomic Disadvantage (Trewin, 2003) scale is based on 2001 population census data and was used to identify families’ socioeconomic status based on the postal area in which they live. This index is based on variables, such as low income, low educational attainment and high unemployment. Higher scores reflect an area of relatively better economic status. The mean value for the geographical areas in Victoria is 1020.00.

Results

Sample characteristics

A total of 59 parents completed the survey. Of these, seven were excluded because the child was the specified age range or because of incomplete data. Given the small number of fathers who participated in the study (n = 2) these were also excluded. The final sample consisted of 50 mothers, and their demographic characteristics are presented in Table 1. Approximately 95% of the sample completed the survey online. The majority of mothers were partnered, Australian born, and were not in paid employment. Approximately one quarter of the sample had more than one child with an ASD. The majority of the focus children were male, had a diagnosis of autism, and the mean time since diagnosis was approximately 12 months.

Table 1.

Sample charactristics (N = 50).

Variable
Maternal characteristics
Maternal age (M, SD, range)	35.28 (4.66, 21–46 years)
Family type (n, %)
Couple	44 (88.0%)
Single-parent family	6 (12.0%)
Country of birth (n, %)
Australia	46 (92.0%)
Other	3 (6.0%)
Not reported	1 (2.0%)
Language spoken (n, %)
English only	49 (98.0%)
Not reported	1 (2.0%)
Employment status (n, %)
Employed full or part-time	18 (36.0%)
Not in paid employment	32 (64.0%)
Highest level of education completed (n, %)
High school, TAFE, trade, certificate, diploma	28 (56.0%)
Tertiary (degree, post-graduate degree)	22 (44.0%)
SEIFA^a index of Relative Socioeconomic Disadvantage (M, SD)	1009.58 (59.69)
Number of children in the family (M, SD)	2.32 (0.94)
Number of children with ASD
One child	38 (76.0%)
More than one child	12 (24.0%)
Focus child characteristics
Age of focus child (in years) (M, SD, range)	4.20 (1.26, 2–6 years)
Child gender (n, %)
Male	44 (88.0%)
Female	6 (12.0%)
Diagnosis
Autism	41 (82.0%)
Asperger’s Syndrome	5 (10.0%)
Pervasive Developmental Disorder NOS	4 (8.0%)
Time since diagnosis (in years) (M, SD)	0.93 (1.27)

Socio-economic indexes for areas.

Note: TAFE = Technical and Further Education.

Data screening

Missing data were approximately less than 10% across all items on the dependent measures. Five cases had more than 20% missing data, and were excluded from the study. Comparisons between the excluded cases and the final sample on the demographic characteristics in Table 1 were non-significant (p > .05) except for age of the focus child (p = .029) and household status (p = .012). There were significantly more single mothers with missing data than mothers from two-parent families, and the age of the focus children in the excluded group was significantly higher than in the final sample. This was not surprising given that several cases were excluded for having a child outside the age range of interest in the study. Missing data on the remaining cases were less than 1%. All missing data were imputed using the expectation-maximization algorithm via the missing values analysis option available in SPSS 16.0 (Schafer and Graham, 2002). The KS Lilliefors’ tests of normality indicated that distribution on the Pittsburgh Sleep Quality Index had some skewness (p < .001) and graphical normality plots indicated some skewness on the Depression and Anxiety subscales of the DASS. Data on all remaining variables were normally distributed. No data transformation procedures were conducted. The descriptive statistics for the study measures are presented in Table 2.

Table 2.

Descriptive statistics for study variables (N = 50).

	Range	M (SD)
Fatigue	7–25	15.94 (5.07)
Depression	0–36	11.20 (9.87)
Anxiety	0–30	7.28 (7.33)
Stress	0–42	18.08 (10.14)
Parenting efficacy	14–38	27.54 (5.59)
Parenting satisfaction	16–51	32.68 (7.64)
Sleep quality	2–17	8.5 (3.88)
Social support – need	12–36	25.46 (6.00)
Social support – satisfaction	8–48	31.18 (8.59)
Difficult Behaviour Checklist	9–38	21.02 (8.05)
Quality of diet (n, %)
Poor	5	10.0%
Fair	13	26.0%
Good	15	30.0%
Very good	16	32.0%
Excellent	1	2.0%
Quality of exercise (n, %)
Poor	15	30.0%
Fair	16	32.0%
Good	15	30.0%
Very good	4	8.0%
Excellent	–	–

Maternal fatigue

Overall, mothers in the ASD group reported moderate levels of fatigue on the FAS. These scores were compared with available mean scores from a large community sample of 1122 mothers of young children (0–5 years) in Australia (Ward and Giallo, 2008) using a one-sample t-test. Mothers of children with an ASD reported significantly higher fatigue (mean = 15.94, SD = 5.07, range = 7–25) than mothers in the community sample (mean = 14.21, SD = 4.40, range = 5–25), t(49)=2.41, p= .02, Cohen’s d = 0.39, 95% confidence interval = 0.11–0.67.

Relationship between maternal fatigue and wellbeing

Given that fatigue is a symptom of depression, it was necessary to establish discriminant validity between fatigue and the wellbeing constructs using the FAS and DASS-21. Previous research with a community sample of parents of TD children (Giallo et al., 2011) and preliminary analyses with the current study using confirmatory factor analysis revealed that fatigue is a separate and distinct construct from stress, anxiety and depression (results available upon request from authors). Correlation analyses revealed that fatigue was moderately associated with depression (r(N = 50) = 0.57, p < .001) anxiety (r(N = 50) = 0.44, p < .001) and stress (r(N = 50) = 0.52, p < .001).

Demographic, child and parent factors associated with maternal fatigue

Before conducting the multivariate regression analysis, bivariate analyses were conducted to examine the relationships between fatigue and potential factors associated with fatigue (see Table 3). Pearson’s r correlations were computed for all continuous variables, and point-biserial correlations for educational attainment and maternal employment as these were dichotomized (see notes for Table 3). Higher maternal fatigue scores were associated with lower educational attainment (high school, trade certificate or diploma), more problematic child behaviour, poorer quality of diet, exercise and sleep, and high perceived need for social support. The relationship between fatigue, maternal age, maternal employment and number of children with ASD approached significance.

Table 3.

Correlations between parental fatigue and potential predictor variables.

	Pearson’s r	p
Demographic factors
Maternal age	−.24	.087
Family type^a	.13	.379
Maternal highest educational attainment^b	−.46	.001
Maternal employment ^c	.25	.082
Socio-economic status^d	.15	.299
Total number of children	.16	.257
Number of children with an ASD^e	.28	.051
Age of focus child with ASD	−.17	.246
Gender of focus child with ASD^f	−.17	.246
Child characteristics
Time since diagnosis	.04	.778
Developmental Behaviour Checklist	.35	.015
Maternal characteristics
Quality of diet	−.29	.039
Quality of exercise	−.44	.002
Sleep quality	.62	< .001
Social support – need	.52	< .001
Social support – satisfaction	−.13	.368

Notes. ^aFamily type 0 = two parent family, 1 = one parent family. ^bMaternal highest educational attainment 1-High school, TAFE or trade certificate, 2 = Tertiary Education. ^cMaternal employment 1 = Full-time/Part-time, 2 = Not in paid employment. ^dSEIFA Index of Social Disadvantage. ^eNumber of children with ASD 1 = One child, 2 = More than one child. ^f Gender of focus child 1 = male, 2 = female.

Next, the variables significantly associated with maternal fatigue were entered into a regression model (see Table 4). The model accounted for 63% of the variance in maternal fatigue, R² = .67, adj R² = .63, F(6,48) = 14.49, p < .001. The factors that provided a significant amount of unique predictive variability in the final model were quality of maternal sleep, perceived need for social support, and quality of physical activity. Maternal education, quality of diet, and child behaviour problems were not significant predictors of fatigue in the model.

Table 4.

Results of the regression analyses predicting maternal fatigue

	B	SE B	β	t	p
Maternal highest educational attainment ^a	−.2.07	1.10	−.20	−1.87	.067
Difficult child behaviour	−0.06	0.07	−.09	−0.84	.404
Quality of diet	−0.67	0.60	−.14	−1.11	.274
Quality of exercise	−1.61	0.63	−.30	−2.55	.014
Sleep quality	0.61	0.15	.47	4.16	< .001
Social support – need	0.25	0.09	.29	2.84	.007

Note: ^aMaternal highest educational attainment 1 = High school, TAFE or trade certificate, 2 = Tertiary Education.

B = unstandardised beta coefficient, SE B = standardised error of beta, β = standardised beta coefficient.

Relationships between fatigue and parental self-efficacy

Bivariate correlations between the PSE subscales (parenting efficacy and parenting satisfaction) and potential predictor variables were conducted (see Table 5). Results revealed that fatigue, depression, stress, sleep quality and need for social support were significantly associated with parenting efficacy, whereas fatigue, depression, anxiety, stress, sleep quality and the child with ASD’s difficult behaviour were significantly associated with parenting satisfaction.

Table 5.

Correlations between parenting variables and potential predictor variables

	Parenting efficacy		Parenting satisfaction
	Pearson’s r	p	Pearson’s r	p
Demographic factors
Maternal age	−.14	.325	.12	.427
Family type^a	.14	.325	.032	.826
Maternal highest educational attainment^b	.05	.759	.14	.318
Maternal employment ^c	−.05	.744	−.10	.475
Socio-economic status^d	−.10	.482	−.19	.200
Total number of children	.16	.263	−.13	.350
Number of children with an ASD^e	−.07	.759	−.19	.194
Age of focus child with ASD	.05	.759	.04	.779
Gender of focus child with ASD^f	.10	.501	.24	.100
Child characteristics
Time since diagnosis	.10	.507	.13	.365
Difficult behaviour	−.13	.378	−.33	.023
Maternal characteristics
Fatigue	−.44	.001	−.38	.007
Depression	−.35	.013	−.47	.001
Anxiety	−.17	.228	−.42	.003
Stress	−.39	.005	−.39	.005
Sleep quality	−.38	.006	−.30	.038
Social support – need	−.30	.033	−.23	.110
Social support – satisfaction	.02	.900	.20	.179

Next, the variables significantly associated with parenting efficacy and satisfaction were entered into two separate regression models (see Table 6). The first model accounted for 15% of the variance in parenting efficacy, R² = .24, adj R² = .15, F(5, 49) = 2.75, p = .03. Despite significant zero-order correlations, none of the variables provided a significant amount of unique predictive variability in the model. The second model accounted for 19% of the variance in parenting satisfaction, R² = .29, adj R² = .19, F(6, 48) = 2.92, p = .018. Similarly, despite significant zero-order correlations, none of the variables provided a significant amount of unique predictive variability in the model.

Table 6.

Results of the regression analyses predicting parenting efficacy and satisfaction.

	B	SE B	β	t	p
Parenting efficacy
Fatigue	−.29	.21	−.27	−1.41	.167
Depression	.02	.12	.03	.13	.900
Stress	−.10	.12	−.19	−.87	.388
Sleep quality	−.18	.26	−.12	−.68	.502
Social support – need	−.03	.16	−.03	−.17	.864
Parenting satisfaction
Fatigue	−.31	.27	−.21	−1.18	.246
Depression	−.19	.18	−.25	−1.04	.304
Stress	−.02	.15	−.03	−.12	.905
Anxiety	−.21	.21	−.20	−.98	.331
Sleep quality	.31	.37	.16	.84	.406
Child’s difficult behaviour	−.13	.15	−.14	−.86	.393

Note: B = unstandardised beta coefficient, SE B = standardised error of beta, β = standardised beta coefficient.

Discussion

Mothers in the current study reported moderate levels of fatigue and significantly higher levels than reported by parents of TD children. This was somewhat expected given the additional caregiving demands, parenting challenges and pressures of managing family life when raising a child with an ASD. Furthermore, mothers who reported high levels of fatigue reported higher depression, anxiety and stress. A range of demographic, child, and parent factors were associated with maternal fatigue, including lower educational attainment (high school or trade certificate), more problematic child behaviour, poorer quality diet, exercise and sleep and high perceived need for social support. This combination of factors accounted for 63% of the variance in maternal fatigue. The strongest predictors of fatigue were quality of maternal sleep, perceived need for social support, and quality of physical activity. Finally, mothers who reported high fatigue were less efficacious and less satisfied in their parenting role; however, these relationships were not significant after accounting for other contextual factors associated with PSE. These findings will be discussed in more detail below.

It was not surprising that poor sleep quality was associated with higher levels of fatigue given the plethora of research indicating that inadequate sleep and poor sleep quality is associated with a range of physical health and wellbeing difficulties (Alvarez and Ayas, 2004). Parents of children with ASDs are at particular risk of sleep disruption and poor sleep quality owing to the high rate of sleep problems for children with ASD (Hoffman, et al., 2006; Polimeni, et al., 2005; Richdale and Prior, 1995). This was somewhat reflected in parents’ open ended responses for the cause of sleep disruption in the current study. Of the 27 parents who responded to this item, 19 reported that their child waking was the primary reason for sleep disruption. For the remaining parents, stress, anxiety and not being able to wind down were stated reasons for sleep disruption. These findings highlight that in addition to interventions targeting child sleep problems, parents may benefit from information and support about how they can minimize and/or cope with the effects of sleep disruption on their daily functioning. Addressing other factors that affect sleep quality, such as stress and worry, would also be important.

Another potential target for intervention is social support. The current study revealed that mothers indicating a high need for social support also reported high levels of fatigue. Parents with limited practical assistance to share the daily demands of caregiving and family life are likely to be at greater risk of fatigue than parents with more support. For parents with limited support, there might also be fewer opportunities to engage in self-care behaviours (e.g. engaging in relaxing and pleasant activities, healthy eating and exercise) that are likely to alleviate or protect them from fatigue. For instance, the current study found that mothers who reported that they did not engage in adequate exercise reported higher levels of fatigue. This highlights the importance of parents having opportunities to engage in self-care behaviours, such as getting adequate exercise, and that access to adequate social support to create these opportunities may be required. It is well established that social support is protective of optimal parent wellbeing (Hogan, et al., 2002) and, therefore, is an important component of any intervention to address fatigue and wellbeing of parents of children with an ASD.

Although it is not possible to infer causality, fatigue has the potential to have an impact on parent wellbeing and parenting. The current study found that fatigue was significantly related to mother’s level of depression, anxiety and stress. The relationship between fatigue and these wellbeing variables is likely to be bidirectional. That is, physical and mental fatigue might make it harder for parents to access their personal coping resources to manage stressors, putting them at risk of developing wellbeing difficulties, such as depression. Research on mothers in the postnatal period provides some support for this contention by showing that fatigue is a risk factor for the development of new onset depression (Runquist, 2007). It is also plausible that symptoms of depression, anxiety, stress and worry (body tension, increased heart rate and rumination) can be mentally taxing and may contribute to, or exacerbate fatigue. Further work is needed to understand how fatigue may contribute to, exacerbate or maintain depression and other aspects of wellbeing as, such as burn-out in parents of children with an ASD.

The relationship between fatigue and parental self-efficacy was also of interest in this study. Findings revealed that mothers who reported high levels of fatigue were less efficacious and less satisfied in their parenting role. Fatigue may serve to undermine parents’ perceptions of their parenting competence in several ways. First, if a parent lacks the physical energy to engage in, or meet personal expectations of aspects of parenting (e.g. providing educational activities or responding consistently to behaviour) this may lead to self-doubt and lowered confidence in perceived ability to parent effectively. Second, given that fatigue has the potential to affect cognitive processes, such as clarity of thought and problem-solving, fatigued parents may have difficulty thinking through how to deal with some of the parenting challenges they face, such as responding to a specific child behaviour problem. This, in turn may lower parents’ confidence in their ability to meet the needs of their children.

It is important to note that the significant relationships between fatigue and parenting efficacy and satisfaction were no longer significant when considering other contextual variables, such as sleep quality and child behaviour problems. This further highlights that there are complex relationships between fatigue, wellbeing, family context variables and parenting that require further investigation. Research is needed to understand what factors may mediate or moderate relationships between fatigue and parenting, and what role fatigue may have as a mediator in relationships between contextual variables, such as child sleep or behaviour problems and parenting behaviour.

Limitations and future directions

Before considering the significance of the current study, there are several limitations to note. First, although adequate for the analyses, the sample was small and consisted of mothers who volunteered to participate. Moreover, some sample demographics, such as mothers with higher education level, from two-parent families and from English-speaking backgrounds, were over-represented in the study. Therefore, the results may not be representative of all parents of children with an ASD. Furthermore, data from two fathers who participated were not included owing to insufficient numbers. It is likely that mothers’ and fathers’ experiences of fatigue and factors associated with fatigue will differ, and this is an area for further study.

Second, the final response rate for the study could not be determined because the survey was available online. Although online surveys are convenient and easily accessible, it is not possible to determine how many potential participants hear about the study and opt to take part or not. It is possible that mothers from a more advantaged social economic position who are technologically competent participated in the study.

Third, although this study provided an important first look at the relationships between fatigue, wellbeing and parenting, it is likely that the relationships are complex and bidirectional. With a larger sample size, sophisticated modelling procedures (structural equation modelling) could be used to test more complex models of the interrelationships and pathways among the variables. For instance, sleep quality and social support may mediate the relationship between fatigue and parental self-efficacy.

Finally, as with all modelling procedures, alternative models with other variables not explored in this study may be tested. For instance, other variables, such as parenting stress, social support and the quality of the couple relationship, may have an important role in understanding the relationships between fatigue and parental self-efficacy. Fatigue also potentially has an impact on other parenting variables not explored in this study, such as parent engagement in play, learning and other early intervention activities, parenting warmth and hostility, and consistency in managing child behaviour.

Implications and conclusions

This study has conceptualized fatigue as a health and wellbeing outcome for parents of children with an ASD, and provides a platform for further research to understand how fatigue may influence other wellbeing and parenting outcomes. Importantly, this study highlights that parental fatigue is an important construct for inclusion when considering theoretical frameworks pertaining to parent adaptation to disability or chronic illness in the family. Such frameworks include the Double ABCX model of family stress and adaptation (McCubbin, et al., 1996) or the transactional model of Hastings and Brown (2002) of the relationship between difficult behaviour in children with ASD, parental stress and maladaptive coping.

From a clinical perspective, for professionals working with families of children with an ASD, an assessment of fatigue in addition to other wellbeing difficulties, such as stress, is important. This might be particularly beneficial for parents who find it difficult to talk about mental health difficulties, such as depression or anxiety. Fatigue can be less stigmatizing to talk about given that exhaustion is a common experience for parents of children with and without disabilities. An assessment of the presence and severity of the physical, cognitive and emotional symptoms of fatigue, as well as the perceived impact on daily functioning, mood, relationships, parenting and other aspects of caregiving is important. Helping parents understand factors that contribute to their fatigue is needed to inform how they can be best supported to manage fatigue and promote their overall wellbeing.

The current study provides an empirical basis for specific target areas for the prevention and management of fatigue in parents of children with an ASD. Parents are likely to benefit from psycho-education about fatigue and its potential effects on wellbeing, parenting and caregiving. In addition, parents may benefit from strategies to minimize and/or cope with the effects of sleep disruption, increase health and self-care behaviours, and strengthen opportunities for social support. Future work in this area will involve the development and evaluation of information resources and intervention approaches to assist parents of children with an ASD to manage fatigue. The longer-term benefits for parents in terms of strengthening their overall health, wellbeing and parenting will also be investigated.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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