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Maternal Perceived Work Schedule Flexibility Predicts Child Sleep Mediated by Bedtime Routines

  • Soomi Lee
  • Lauren Hale
  • Lawrence M. Berger
  • Orfeu M. Buxton
Original Paper

Abstract

Rigid work schedules are negatively associated with adults’ sleep. Less is known about whether parents’ work schedule flexibility influences their children’s sleep. We examined associations of mothers’ perceived work schedule flexibility with their children’s sleep over time and whether these associations were mediated by bedtime routine adherence. Two-waves of data were drawn from the Fragile Families and Child Wellbeing Study, a sample of socioeconomically disadvantaged households in large US cities (N = 1040). When the focal children were ages 5 and 9, mothers reported their work schedule flexibility and their child’s bedtime adherence, sleep duration, and difficulty getting to sleep. Cross-sectionally, higher levels of maternal perceived work schedule flexibility were associated with longer child sleep duration and a lower likelihood of having difficulty getting to sleep; these associations were mediated by greater child bedtime adherence. Longitudinally, increases in mothers’ perceived work schedule flexibility from child ages 5 to 9 predicted increases in child bedtime adherence at age 9, which, in turn, predicted increases in child sleep duration at age 9. Increases in perceived work schedule flexibility also predicted a decreased likelihood of children having difficulty getting to sleep, but this association was not mediated by changes in child bedtime adherence. These results suggest that mothers’ perceived work schedule flexibility may be a social determinant of child sleep health, largely through influencing bedtime adherence. Future interventions could consider how to improve bedtime practices in families with working mothers, including by increasing work schedule flexibility perceived by working parents to promote child sleep health.

Keywords

Bedtime routines Child sleep Sleep duration Difficulty getting to sleep Maternal work schedule 

Mothers who work outside of the home often also serve as primary caregivers for children (Bianchi 2009). Maternal work features are closely linked to child health and well-being (Lee et al. 2017). There is also accumulating evidence that mothers’ work schedules, rather than whether they work, have implications for their children’s sleep health. Long work hours and nonstandard work schedules for mothers are linked with shorter and poorer sleep for children (Kalil et al. 2014; Magee et al. 2012). While these findings have contributed to the understanding that mothers’ inflexible work schedules may degrade children’s sleep health, less attention has been focused on mothers’ perceptions about flexibility in their work, which may relate to important individual differences in the evaluation of work conditions (Hammer et al. 1997). For example, mothers who work the same schedule may differ in how they perceive the degree to which their work schedule allows flexibility to handle child care and family responsibilities (perceived work schedule flexibility), which may have implications for their children’s sleep beyond actual work schedules and work hours.

The work-home resources (W-HR) model (ten Brummelhuis and Bakker 2012) suggests that resources in the work domain can enhance resources and well-being in the family domain by increasing personal resources (e.g., energy, time). This model suggests that maternal work schedule flexibility—which has the potential to enhance mothers’ parenting resources—may be linked to their children’s sleep health. Prior research has examined emotional contagion or crossover of work stress to the well-being of family members (Bakker et al. 2009; Crouter et al. 1999; Demerouti et al. 2005; Pedersen 2014; Westman 2001; Westman and Hamilton 2004). Yet, there is no research linking mothers’ perceived work schedule flexibility to their children’s sleep.

Specifically, mothers’ perceived work schedule flexibility may positively influence child sleep to the extent that it allows mothers to be more involved in bedtime routines. According to the bioecological model of child development, optimal development emerges when children engage in consistent routines (Bronfenbrenner and Ceci 1994; Harris et al. 2005). Thus, consistent bedtime routines may be an important mechanism linking maternal work schedule flexibility and child sleep health. However, adhering to consistent bedtime routines may require that mothers are consistently available to put children to bed (or can access a substitute caregiver who can consistently do so). If working mothers have less flexibility in their work schedule, for example, working unpredictable hours, it may interfere with establishing consistent bedtime routines with their children (Presser 2003).

Previous research suggests potential links between maternal work schedule flexibility, bedtime routines, and child sleep. For example, Dush et al. (2013) examined mothers’ perceived work schedule inflexibility and lack of regular bedtime as elements of work- and home-related chaos, respectively, which independently predicted children’s poor general health. Maternal perceptions of an inflexible work schedule may be reflective of chaotic and unpredictable work that interferes with setting and practicing regular bedtime routines, which are critical for children’s sleep health. Other research consistently reports that having a regular bedtime routine is important for promoting healthy and self-regulated sleep behaviors for children (Buxton et al. 2015; Hale et al. 2011; Mindell et al. 2015). Higher levels of perceived work schedule flexibility may afford mothers more temporal resources for parenting (Lee et al. 2017; Roeters et al. 2010) that allow them to adhere to a regular bedtime for their children (i.e., bedtime adherence). Greater bedtime adherence may, in turn, predict better child sleep outcomes (Mindell et al. 2015).

Sleep duration is among the most frequently investigated child sleep measure in relation to child development and health outcomes. Comparing the average sleep duration in our sample to age-appropriate sleep duration standards (American Academy of Pediatrics 2016; Hirshkowitz et al. 2015; Paruthi et al. 2016) can provide practical implications regarding effect sizes. Difficulty getting to sleep is the second most common sleep problem among young children (after awakening during the night), and is closely related to bedtime practices (Hiscock et al. 2007). Focusing on both sleep duration and difficulty getting to sleep may provide insight into whether these two dimensions of child sleep health are influenced by maternal perceived work schedule flexibility and bedtime routines.

According to the bioecological model, it is important that children engage in predictable and regular routines and positive interactions with their environment over an extended period of time in order to develop healthy lifestyles and habits (Bronfenbrenner and Ceci 1994). There may be long-term influences of maternal perceived work schedule flexibility on child bedtime adherence and sleep over time. However, existing studies examining the link between maternal work and child sleep have used cross-sectional data (Kalil et al. 2014; Magee et al. 2012). There have been no longitudinal studies to investigate whether changes in mothers’ perceived work schedule flexibility (or other work conditions) are associated with changes in child sleep, and whether such associations are mediated by changes in child’s bedtime routines. Examining longitudinal change-change associations may rule out potential influence of unobserved variables that are confounded with maternal work schedule flexibility, child bedtime adherence, and child sleep, such as overall parenting style.

Parent work schedule flexibility is an important factor for disadvantaged families. Both the W-HR model (ten Brummelhuis and Bakker 2012) and bioecological model (Bronfenbrenner and Ceci 1994) highlight the importance of considering socioeconomic status of the household for child health. As parental resources are interconnected across domains of the family environment (ten Brummelhuis and Bakker 2012), mothers with low socioeconomic status may be particularly likely to lack of flexibility in their work schedule. Households with low socioeconomic status may also, on average, face more chaotic home environments than their higher socioeconomic peers, which poses risk for child health (Dush et al. 2013). As such, the associations of perceived work schedule flexibility with child sleep may be particularly salient for disadvantaged mothers who may have constrained parenting resources and temporal flexibility to manage work-family responsibilities. Moreover, mothers with low socioeconomic status may lack co-parenting resources, because a permanent-partner relationship is less common among these mothers (Lee et al. 2017; McLanahan 2004). With no father figure in the household to help them accommodate children’s sleep needs when faced with inflexible work conditions (Stewart 2014), children of disadvantaged mothers may experience less consistent bedtime routines than their more advantaged counterparts (Hale et al. 2009).

When examining “flexibility,” it is important to distinguish flexible work schedules arranged by employers and perceived flexibility by employees. (Lambert 2008; Lambert et al. 2012) have shown how conventional flexibility options (e.g., reducing work hours, and varying work timing) may not always work well for workers with low-level hourly jobs. Most practices to increase flexibility are designed for male-dominated managerial and professional jobs characterized by long and rigid work hours; also, implementing flexibility in hourly jobs may result in unintended negative consequences, such as inadequate income for living (Lambert et al. 2012). For low-income workers, the extent to which workers believe they have flexibility in their work schedule (i.e. perceived work schedule flexibility) may be a particularly important variable to inform whether they subjectively experience flexibility in their work schedule to balance work and family responsibilities (Hammer et al. 1997). Perceived work schedule flexibility may also relate to actual work schedules and work hours. Work schedules and work hours are stratified by workers’ socioeconomic status, such that nonstandard work schedules and hourly work are more common among mothers with low incomes and education (Enchautegui et al. 2015). Therefore, it seems necessary to examine the associations of mothers’ perceived work schedule flexibility with child sleep in disadvantaged households, after accounting for mothers’ work schedules and work hours.

The present study examined associations of mothers’ perceived work schedule flexibility with their children’s bedtime adherence and sleep characteristics in middle childhood (ages 5–9), a critical period when children start developing autonomy while still need primary caregivers’ involvement in bedtime practices. We used data from the Fragile Families and Child Wellbeing study that provides a sample of mothers and their children in households with low socioeconomic status, which can provide important policy implications to reduce later social disparities in sleep. To examine the unique influence of perceived work schedule flexibility, we controlled for work hours and work schedules, as well as sociodemographic and family characteristics. We first hypothesized that, cross-sectionally, higher levels of perceived work schedule flexibility would be associated with greater adherence to children’s bedtimes, which, in turn, would be associated with longer child sleep duration (H1) and a lower likelihood of child difficulty getting to sleep (H2). We further hypothesized that increases in mothers’ perceived work schedule flexibility over time (from child ages 5 to 9) would predict increases in bedtime adherence, which, in turn, would predict increases in child sleep duration (H3) and decreased child difficulty getting to sleep (H4).

Method

Participants

We analyzed data from the Fragile Families and Child Wellbeing Study (FFCWS; www.fragilefamilies.princeton.edu), a longitudinal study of children born between 1998 and 2000 in 20 U.S. cities. Using a stratified random sample of all U.S. cities with 200,000 or more people, investigators first sampled cities according to policy and labor market environments, then hospitals within cities, and finally births within hospitals (Reichman et al. 2001). The study includes an oversample of non-marital births, which resulted in a large proportion of racial/ethnic minority, low-income, and low-educated mothers.

Mothers (n = 4898) were initially interviewed in the hospital within 2 days of their child’s birth, and follow-up interviews were completed when the focal child was ages 1, 3, 5, and 9 (“Years”, hereafter). At each interview, the FFCWS collected information about mothers’ employment characteristics, including type and perceived flexibility of work schedules. Families were also asked to participate in an in-home interview at the Year 3, 5, and 9 interviews. Information about child sleep was collected in the Year 5 and 9 in-home interviews. We included in our analytic sample only children whose mothers completed the Year 5 and 9 in-home interviews and were currently employed at both years (defined by doing any regular work for pay in the past week). The FFCWS asked perceived work schedule flexibility questions only to those who were currently working, and focused on the extent of schedule flexibility on their primary job (the one in which they worked the most hours). Of 1515 mothers who met these criteria, 1513 mothers had worked at both time points and provided responses on perceived work schedule flexibility questions. We then retained mothers who did not have missing data for bedtime adherence (n = 1349), child difficulty getting to sleep (n = 1231), and child sleep duration (n = 1184) in either Year. After list-wise deletion for missing data on these main variables, our final analytic sample consisted of 1040 mothers.

Compared to those in the initial sample but not included in the current study (n = 3858; 4898–1040), our sample of mothers were older (t(4892) = 2.24, p< 0.05) and less likely to be Hispanic (χ2(3) = 30.04, p< 0.001). Compared to mothers who participated in the Year 5 survey (n = 4139) but were excluded from our analyses because they did not work at the time of the survey (n = 3099; 4139–1040), our sample of mothers were more likely to have some college or above education vs. high school or less; (χ2(1) = 72.14, p< 0.001) and more likely to have household income above 200% of the poverty line (χ2(4) = 145.83, p< 0.001). Thus, although the initial FFCWS sample included a large proportion of racial/ethnic minority and low-income and low-educated mothers, our final sample included mothers of relatively higher (but still lower than national average) socioeconomic status.

Measures

Mothers’ perceived work schedule flexibility

We used three items that assessed the degree to which mothers perceived flexibility in their work schedule: (1) “My shift and work schedule cause extra stress for me and my child” (reverse coded in our analyses), (2) “Where I work, it is difficult to deal with child care problems during working hours” (reverse coded), and (3) “In my work schedule I have enough flexibility to handle family needs.” This scale was previously used in several studies examining work schedule flexibility, including Dush et al. (2013) study that validated this scale in their robust measurement model of maternal work chaos. Responses were coded as 1(never) to 4 (always), we operationalized work schedule flexibility as the mean of the three items. Higher scores reflect greater work schedule flexibility (Range = 1–4). Cronbach’s α was 0.60 at Year 5 and 0.64 at Year 9.

Child bedtime adherence was measured by two items asking, “Does your child have a regular bedtime during the week?” and (if yes) “How many times last week Monday-Friday, your child adhered to that bedtime?” We combined responses on these two items to construct weekday frequency of bedtime adherence (Range = 0–5; higher scores indicate greater bedtime adherence). If a mother responded “no” to the first question, then her child’s bedtime adherence score was 0 (no bedtime adherence). If a mother responded “yes” to the first question, then she could get a score ranging from 1 (adhered once) to 5 (adhered every weekday).

Child difficulty getting to sleep was assessed by one item asking, “Does your child have trouble getting to sleep?” Responses were initially coded as 0 (=not true), 1(=somewhat true), and 3(=very true). We combined 1–2 responses to create a binary indicator of child’s difficulty getting to sleep (0=no difficulty, 1=has difficulty).

Child habitual sleep duration was assessed by one item asking, “How many hours of sleep a night does your child usually get?” Responses were coded as integer hours.

Covariates

Our analyses adjusted for a range of factors that may be associated with both maternal work schedule flexibility and child sleep. Sociodemographic characteristics included the mother’s age at the focal child’s birth (in years), education (some college or more education vs. high school or less = reference), race (Black, Hispanic, and Other race, vs. White= reference), foreign-born status vs. US born (reference), and household poverty indicator based on ratio of income to poverty threshold (1 = 49%, 2 = 50–99%, 3 = 100–199%, 4 = 200–299%, vs. 300% = reference). The household poverty indicator was constructed from household income values at each Year compared with the national poverty threshold incomes established by the US Census Bureau (http://www.census.gov/cps/data/povthresholds.html), which vary by household size and year. In addition, we controlled for married or living with child’s biological father vs. not (reference), not living with child most of the time vs. living with child most of the time (reference), child age (in moths), and whether child is male vs. female (reference). Mothers’ weekly work hours (categorical, 2034h, 3540h, and 41 or more hours, vs. 119h = reference) and specific types of work schedules (evening shifts: work 6–11pm, night shifts: work 11pm–7am, weekend shifts, and variable shifts, vs. standard daytime shifts = reference) were also controlled. All continuous variables were centered at sample means.

Data Analyses

We used multiple mediation analyses with bootstrapping using the SAS PROCESS macro (Hayes 2013). The bootstrapping method allows for the estimation of the indirect effect, based on the product (×) of the effect of a predictor (X) on a mediator (M) and the effect of the mediator (M) on an outcome (Y), not based on individual hypothesis testing, and even without the total effect of a predictor on an outcome (Hayes 2009; MacKinnon 2008). The bootstrapping method also produces a bias-corrected confidence interval for the indirect effect (Hayes 2013). For cross-sectional mediation models, we estimated pooled effects for both Year 5 and Year 9. For longitudinal mediation models, we used residualized gain scores (lagged dependent variable models) of a sleep outcome at Year 9, after controlling for its baseline level at Year 5; we used change scores for the predictor and mediator, also controlling for the baseline levels of the variables at Year 5. In all models, we set the number of bootstrap samples to 10,000.

Results

We first examined descriptive statistics of key variables (see Table 1). At Year 5, mothers reported a high level of flexibility in their work schedule, on average (M = 3.42, SD = 0.62, Range = 1–4). Moreover, from Year 5 to Year 9, mothers’ perceived work schedule flexibility tended to slightly increase. Child’s bedtime adherence was averaged at 4 times per week, Monday to Friday. The mean of child’s habitual sleep duration was 9.34 h per night and 28% of children had difficulty getting to sleep. Over the four years, there was an overall decrease in child’s bedtime adherence and sleep duration, which may suggest developmental changes in child sleep patterns. Child difficulty getting to sleep also decreased; only 8.6% of children had difficulty at Year 9 compared to 28% at age 5.
Table 1

Descriptive statistics of variables used in this study

 

Age 5

Age 9

Changes

(age 9–age 5)

Difference test

 

M or %

(SD)

M or %

(SD)

M or %

(SD)

t-test/x a

Main variables

 Mothers’ work schedule flexibility (1–4)

3.42

(0.62)

3.48

(0.61)

0.06

(0.76)

−2.28*

 Child bedtime adherence (0–5)

3.97

(1.57)

3.69

(1.59)

−0.28

(1.98)

4.00***

 Child sleep duration (in hours)

9.34

(1.20)

8.95

(1.07)

−0.39

(1.32)

7.74***

 Child having difficulty getting to sleep (%)

27.98

 

8.56

 

4% ↑, 23%↓

 

131.38***

Sociodemographic characteristics

 Mother’s age at birth (in years)

25.65

(5.93)

    

 Mothers’ education (%)

      

9.78**

 Some college or above

59.33

 

65.96

 

  

 High school or less

40.67

 

34.04

 

  

 Mothers’ race (%)

 Black, non-Hispanic

52.17

 

 

  

 Hispanic

22.15

 

 

  

 Other

2.41

 

 

  

 White

23.29

 

 

  

 Mothers’ foreign born status (%)

11.54

 

  

 

 Household income to poverty ratio (%)

      

21.36***

 <49%

10.29

 

7.17

    

 50–99%

17.60

 

13.18

    

 100–199%

27.02

 

34.30

    

 200–299%

18.17

 

17.93

    

 ≥300%

26.92

 

27.42

    

 Married or living with child’s biological father (%)

45.00

 

39.67

 

 

6.04*

 Living with child most of the time (%)

98.37

 

97.88

 

 

0.66

Child factors

 Child age (in months)

60.94

(2.26)

111.40

(3.73)

 

−373.49***

 Child sex: Boy (%)

49.90

 

 

  

Mothers’ work characteristics

 Mothers’ weekly work hours (%)

      

4.39

 Work hours < 20

6.06

 

7.03

 

  

 20 ≤ work hours < 35

19.15

 

19.25

 

  

 35 ≤ work hours < 40

54.38

 

56.69

 

  

 41 ≤ work hours

20.40

 

17.04

 

  

Mothers’ work shifts (%)

 Evening shiftsb

26.06

 

21.35

 

 

6.38

 Night shiftsb

10.96

 

9.42

 

 

1.34

 Weekend shifts

37.88

 

40.19

 

 

1.16

 Variable shifts

24.23

 

23.46

 

 

0.17*

Note. N = 1040. Each percentage represents whether mothers regularly worked that shift on their primary job; there are overlaps between these nonstandard shifts (e.g., evening work during weekends); At age 5, 47% of mothers worked standard/daytime shifts (not endorsed any of these nonstandard shifts); At age 9, 48% of mothers worked standard/daytime shifts

*p < 0.05, **p < 0.01, ***p < 0.001

aNight shifts: work during 11pm–7am

bEvening shifts: work during 6–11pm

In terms of sociodemographic characteristics, the mean maternal age at child birth was 25.65 years (SD = 5.93). At Year 5, 59% of these mothers had some college or above education and 41% had high school or less education. Approximately 52% of sample mothers were Black, non-Hispanic, 22% were Hispanic, 23% were White, non-Hispanic, and 3% were of another race/ethnicity (the remaining); about 12% were foreign born. Just over a quarter of the households were in poverty, having a family income less than 100% of the family’s poverty threshold (10% were below 49% of the poverty threshold and 18% between 50 and 99% of the threshold). Slightly less than half (45%) of sample mothers were married to or cohabiting with the focal child’s biological father, and nearly all (98%) were living with the child most of the time. About 12% of mothers lived with the child’s grandmother and about 5% lived with the child’s grandfather (results are not shown in Table 1). Mean child age was 61 months (SD = 2.26), and child sex was evenly distributed.

Turning to mothers’ work characteristics, 6% of mothers worked less than 20 h, 19% worked more than 20 h and less than 35 h, 54% worked more than 35 and less than 41 h, and 20% worked more than 41 h. Less than half the sample (47%) worked standard shifts, not endorsing any of the following nonstandard shifts: 26% worked evening shifts, 11% worked night shifts, 38% worked weekend shifts, and 24% worked variable shifts with different times each week. These mothers worked more than one nonstandard shift regularly on their primary job. Mothers’ work characteristics were related to their perceived work schedule flexibility. Those who worked 41 h or more per week, evening shifts, night shifts, weekend shifts, and variable shifts, all reported significantly lower perceived work schedule flexibility than their counterparts (Table 2).
Table 2

Correlations among predictor variables

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

1. Mother’s age at child birth

 

0.13

−0.16

0.00

0.06

0.15

−0.11

−0.11

−0.15

0.07

0.31

0.06

−0.07

0.01

0.02

−0.06

0.07

−0.08

0.01

−0.12

0.04

0.04

2. Mother’s education: Some college or above

0.20

 

0.02

−0.11

0.06

−0.08

−0.13

−0.17

−0.09

0.00

0.09

0.05

−0.13

0.01

0.02

0.03

0.06

−0.08

0.06

−0.11

0.03

0.03

3. Mothers’ race: Black, non-Hispanic

−0.16

0.04

 

−0.56

−0.16

−0.27

0.08

0.02

0.15

0.03

−0.27

0.01

0.00

0.04

−0.10

0.16

0.04

0.02

0.01

0.06

0.02

0.01

4. Mothers’ race: Hispanic

0.00

−0.10

−0.56

 

−0.08

0.37

0.01

0.05

0.01

0.03

0.08

−0.06

0.07

0.05

0.03

0.02

0.01

0.01

0.03

−0.07

0.01

0.01

5. Mothers’ race: Other

0.06

0.07

−0.16

−0.08

 

0.30

0.02

0.04

0.02

0.06

0.09

0.02

0.04

0.03

0.03

0.01

0.05

0.01

0.08

0.01

0.00

0.05

6. Mother foreign born

0.15

0.06

−0.27

0.37

0.30

 

0.03

0.03

0.04

0.03

0.17

0.05

0.07

0.01

0.05

−0.07

0.04

−0.08

0.04

0.01

0.03

−0.13

7. Household income to poverty: <49%

−0.15

−0.20

0.12

0.02

0.03

0.03

 

−0.11

−0.20

−0.13

−0.12

0.06

0.03

0.03

0.04

0.03

−0.07

0.03

0.04

0.05

0.09

−0.07

8. Household income to poverty: 50–99%

−0.14

−0.23

0.05

0.08

0.01

0.05

−0.16

 

−0.28

−0.18

−0.11

0.00

0.12

0.02

0.12

−0.07

0.05

0.06

0.07

0.13

0.10

−0.09

9. Household income to poverty: 100–199%

−0.10

0.03

0.11

0.05

−0.08

0.00

−0.21

−0.28

 

−0.34

−0.18

0.01

0.03

0.05

0.04

0.09

−0.06

0.07

0.02

0.02

0.00

0.03

10. Household income to poverty: 200–299%

0.04

0.09

0.02

0.01

0.03

0.01

−0.16

−0.22

−0.29

 

0.02

0.02

−0.09

0.02

0.02

0.05

0.01

−0.07

0.02

0.00

0.06

0.02

11. Married/living w/ child’s bio father

0.28

0.09

−0.26

0.05

0.07

0.15

−0.13

−0.16

−0.10

0.02

 

0.06

0.05

0.02

0.06

−0.09

0.02

0.01

0.02

0.04

0.02

0.01

12. Not living w/ child most of the time

0.05

0.01

0.02

0.05

0.02

0.05

0.02

0.00

0.01

0.04

−0.12

 

0.04

0.03

0.03

0.06

0.04

0.04

0.02

0.06

0.01

0.00

13. Child age

−0.10

−0.12

0.12

0.06

0.04

0.03

0.03

0.01

0.06

0.02

−0.15

0.00

 

0.02

0.02

0.03

0.06

0.01

0.02

0.03

0.03

0.05

14. Child sex: Boy

0.01

0.03

0.04

0.05

0.03

0.01

0.02

0.03

0.03

0.03

0.00

0.02

0.04

 

0.03

0.05

0.02

0.07

0.00

0.02

0.03

0.03

15. 20 ≤ Mothers’ work hours < 35

0.05

0.03

−0.08

0.02

0.04

0.01

0.21

0.05

−0.07

−0.08

0.01

0.00

0.06

0.01

 

−0.56

−0.22

0.07

0.05

0.08

0.12

0.01

16. 35 ≤ Mothers’ work hours < 41

0.06

0.05

0.08

0.00

0.02

0.00

−0.13

0.01

0.07

0.08

−0.10

0.01

0.06

0.01

−0.53

 

−0.52

−0.12

−0.09

−0.17

−0.19

0.01

17. 41 ≤ Mothers’ work hours

0.07

0.06

0.06

0.02

0.03

0.01

−0.08

0.06

0.02

0.02

0.02

0.01

0.01

0.01

−0.25

−0.55

 

0.06

0.07

0.11

0.06

0.00

18. Mothers’ evening shifts

0.04

−0.09

0.01

0.03

0.01

0.02

0.14

0.02

0.04

0.06

0.02

0.03

0.02

0.02

0.12

−0.20

0.12

 

0.27

0.39

0.32

−0.16

19. Mothers’ night shifts

0.00

−0.12

0.01

0.01

0.01

0.02

0.07

0.02

0.02

0.05

0.00

0.00

0.04

0.01

0.01

0.04

0.07

0.32

 

0.27

0.14

−0.11

20. Mothers’ weekend shifts

−0.13

−0.11

0.00

0.02

0.03

0.01

0.13

0.10

0.00

0.04

0.05

0.01

0.02

0.00

0.06

−0.12

0.09

0.38

0.27

 

0.37

−0.19

21. Mothers’ variable shifts

0.02

−0.08

0.04

0.02

0.02

0.01

0.11

0.06

0.03

−0.06

0.02

0.02

0.01

0.01

0.18

−0.21

0.05

0.39

0.23

0.36

 

−0.14

22. Mothers’ work schedule flexibility

0.04

0.03

0.06

0.00

0.01

0.04

−0.09

0.03

0.00

0.03

0.07

0.02

−0.07

0.02

0.02

0.02

−0.09

−0.10

−0.12

−0.13

−0.08

 

Note. N = 1040. Numbers below the diagonal are correlations at age 5 and those above the diagonal are correlations at age 9; Correlations in bold were significant at p < 0.05

Table 3 shows results of the cross-sectional mediation model examining the indirect effect of mothers’ perceived work schedule flexibility on child habitual sleep duration (in hours) through child’s bedtime adherence. The model revealed a significant association of mothers’ perceived work schedule flexibility with child bedtime adherence such that higher levels of perceived work schedule flexibility were associated with greater bedtime adherence within both Year 5 and Year 9. Moreover, greater bedtime adherence was associated with longer child sleep duration. For each additional day of bedtime adherence, children averaged 10 min/day of greater sleep duration (0.17 × 60 = 10.2). These results are adjusted for the full set of covariates, including maternal work shifts (evening, night, weekend, variable vs. standard daytime) and work hours. Before including bedtime adherence, there was a significant positive association of mothers’ perceived work schedule flexibility with child sleep duration (B = 0.09, SE = 0.04, p < 0.05). However, after including bedtime adherence, the direct association of mothers’ perceived work schedule flexibility with child sleep duration became non-significant. On the whole, the model revealed a significant indirect effect of mothers’ perceived work schedule flexibility on child sleep duration mediated by child bedtime adherence, supporting our first hypothesis.
Table 3

Results of the cross-sectional mediation model examining the indirect effect of mothers’ work schedule flexibility on child habitual sleep duration per night across waves (at age 5 and age 9), mediated by bedtime adherence

 

Consequent

Antecedent

M: Child bedtime adherence

Y: Child sleep duration

B

(SE)

B

(SE)

Intercept

0.24

(0.19)

9.90***

(0.13)

X: Mothers’ work schedule flexibility

0.21***

(0.06)

0.05

(0.04)

M: Child bedtime adherence

0.17***

(0.02)

Mothers’ age at child birth in years

0.00

(0.01)

0.00

(0)

Mothers’ education: Some college or above (vs. High school or less)

0.21**

(0.08)

0.04

(0.05)

Mothers’ race: Black, non-Hispanic (vs. White, non-Hispanic)

0.06

(0.1)

0.54***

(0.07)

Mothers’ race: Hispanic (vs. White, non-Hispanic)

0.22 †

(0.12)

0.27***

(0.08)

Mothers’ race: Other (vs. White, non-Hispanic)

0.03

(0.25)

0.40*

(0.17)

Mother foreign born (vs. Native born)

 

(0.13)

0.16 †

(0.09)

Household income to poverty ratio: <49% (vs. ≥300%)

0.02

(0.15)

0.29**

(0.11)

Household income to poverty ratio: 50–99% (vs. ≥300%)

0.13

(0.13)

0.24**

(0.09)

Household income to poverty ratio: 100–199% (vs. ≥300%)

0.19 †

(0.1)

0.18**

(0.07)

Household income to poverty ratio: 200–299% (vs. ≥300%)

0.23*

(0.11)

0.16*

(0.08)

Married or living with child’s biological father (vs. not)

0.14 †

(0.08)

0.02

(0.05)

Not living with child most of the time (vs. Living with child most of the time)

0.09

(0.26)

0.16

(0.18)

Child age in months

0.01

(0.01)

0.00

(0.01)

Child sex: Boy (vs. girl)

0.06

(0.07)

0.08

(0.05)

20 ≤ Mothers’ work hours < 35 (vs. <20 h)

0.22

(0.16)

0.27*

(0.11)

35 ≤ Mothers’ work hours < 41 (vs. < 20h)

0.13

(0.15)

0.40***

(0.1)

41 ≤ Mothers’ work hours (vs. <20 h)

0.22

(0.16)

0.30**

(0.11)

Mothers’ evening shifts (vs. standard, daytime shifts)

0.04

(0.09)

0.15*

(0.06)

Mothers’ night shifts (vs. standard, daytime shifts)

 

(0.12)

0.17*

(0.08)

Mothers’ weekend shifts (vs. standard, daytime shifts)

0.00

(0.08)

0.09

(0.06)

Mothers’ variable shifts (vs. standard, daytime shifts)

0.02

(0.09)

0.02

(0.06)

Model fit statistics

R2 = 0.0262

R2 = 0.1393

F(22,2041) = 2.50***

F(23,2040) = 14.35***

Indirect effect of X on Y

Effect = 0.04**, SE = 0.01, 95% CI = [0.0159 to 0.0574]

Note: N = 1040, 2080 observations across two waves; 2064 observations were used due to missing responses in variables; child age, mothers’ education, married and living arrangements, work hours, and types of work schedule were time-varying covariates

p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001

Mothers' work schedule flexibility --> Child bedtime adherence: B=0.21 (p=0.0002)

Mothers' work schedule flexibility --> Child sleep duration: B=0.05 (p=0.1741)

Child bedtime adherence --> Child sleep duration: B=0.17 (p=0.0000)

Indirect Effect = 0.04 (p=0.0005)

Table 4 shows results of the cross-sectional mediation model examining the indirect effect of mothers’ perceived work schedule flexibility on child difficulty getting to sleep through bedtime adherence. The associations of both mothers’ perceived work schedule flexibility and the covariates with child bedtime adherence were consistent with those found in the prior model (Table 3). In addition, greater bedtime adherence was associated with a lower likelihood of child difficulty getting to sleep. Each additional day of bedtime adherence was associated with 14% lower odds of a child having trouble getting to sleep (Exp(−0.15) = 0.86, 1–0.86 = 0.14). There was also a direct association of mothers’ perceived work schedule flexibility with child difficulty getting to sleep, such that higher perceived work schedule flexibility was associated with a lower likelihood of having difficulty getting to sleep. The indirect effect of mothers’ perceived work schedule flexibility on child difficulty getting to sleep, mediated by child bedtime adherence, was also statistically significant, supporting our second hypothesis.
Table 4

Results of the cross-sectional mediation model examining the indirect effect of mothers’ work schedule flexibility on child difficulty getting to sleep across waves (at age 5 and age 9), mediated by bedtime adherence

Consequent

Antecedent

M: Child bedtime adherence

Y: Child sleep difficulty

B

(SE)

B

(SE)

Intercept

0.24

(0.19)

0.59†

(0.3)

X: Mothers’ work schedule flexibility

0.21***

(0.06)

−0.46***

(0.09)

M: Child bedtime adherence

−0.15***

(0.03)

Mothers’ age at child birth in years

0.00

(0.01)

0.00

(0.01)

Mothers’ education: Some college or above (vs. High school or less)

0.21**

(0.08)

0.06

(0.14)

Mothers’ race: Black, non-Hispanic (vs. White, non-Hispanic)

0.06

(0.1)

0.53***

(0.16)

Mothers’ race: Hispanic (vs. White, non-Hispanic)

0.22†

(0.12)

0.03

(0.18)

Mothers’ race: Other (vs. White, non-Hispanic)

0.03

(0.25)

0.12

(0.41)

Mother foreign born (vs. Native born)

0.48***

(0.13)

0.16

(0.21)

Household income to poverty ratio: <49% (vs. ≥300%)

0.02

(0.15)

0.13

(0.26)

Household income to poverty ratio: 50–99% (vs. ≥300%)

0.13

(0.13)

0.08

(0.21)

Household income to poverty ratio: 100–199% (vs. ≥300%)

0.19†

(0.1)

0.45*

(0.18)

Household income to poverty ratio: 200–299% (vs. ≥300%)

0.23*

(0.11)

0.12

(0.18)

Married or living with child’s biological father (vs. not)

0.14†

(0.08)

0.14

(0.13)

Not living with child most of the time (vs. Living with child most of the time)

0.09

(0.26)

0.09

(0.46)

Child age in months

0.01

(0.01)

0.00

(0.02)

Child sex: Boy (vs. girl)

0.06

(0.07)

0.07

(0.12)

20 ≤ Mothers’ work hours < 35 (vs. <20 h)

0.22

(0.16)

0.23

(0.24)

35 ≤ Mothers’ work hours < 41 (vs. <20 h)

0.13

(0.15)

0.55*

(0.23)

41 ≤ Mothers’ work hours (vs. <20 h)

0.22

(0.16)

0.40

(0.25)

Mothers’ evening shifts (vs. standard, daytime shifts)

0.04

(0.09)

0.11

(0.16)

Mothers’ night shifts (vs. standard, daytime shifts)

0.25*

(0.12)

0.23

(0.2)

Mothers’ weekend shifts (vs. standard, daytime shifts)

0.00

(0.08)

0.24†

(0.14)

Mothers’ variable shifts (vs. standard, daytime shifts)

0.02

(0.09)

0.29†

(0.16)

Model fit statistics

R2 = 0.0262

2LL=1874.57

F(22,2041) = 2.50***

Model LL=90.86

Indirect effect of X on Y

Effect=−0.03**, SE=0.01, 95% CI=[−0.0598 to −0.0121]

Note: N = 1040, 2080 observations across two waves; 2064 observations were used due to missing responses in variables; child age, mothers’ education, married and living arrangements, work hours, and types of work schedule were time-varying covariates

p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001

Mothers' work schedule flexibility --> Child bedtime adherence: B=0.21 (p=0.0002)

Mothers' work schedule flexibility --> Child sleep difficulty: B= -0.46 (p=0.0000)

Child bedtime adherence --> Child sleep difficulty: B= -0.15 (p=0.0000)

Indirect Effect = -0.03(p=0.0059)

Table 5 shows results of the longitudinal mediation model examining the indirect effect of changes in mothers’ perceived work schedule flexibility on residualized gains in child habitual sleep duration (in hours) at Year 9 (after controlling for Year 5 sleep duration), and whether it was mediated by changes in bedtime adherence. Consistent with our prediction, an increase in mothers’ perceived work schedule flexibility (from Year 5 to Year 9) predicted an increase in bedtime adherence at Year 9 (after controlling for prior bedtime adherence). Furthermore, an increase in bedtime adherence predicted an increase in child sleep duration, by 8 min/day more than at Year 9 than Year 5 (0.13 × 60 = 7.8). There was no significant direct effect of a change in mothers’ perceived work schedule flexibility, nor its total effect (before adding changes in bedtime adherence), on a change in child sleep duration. However, consistent with our third hypothesis, the indirect effect of an increase in mothers’ perceived work schedule flexibility on increased child sleep duration, mediated by increased in bedtime adherence, was significant.
Table 5

Results of the longitudinal mediation model examining the indirect effect of changes in mothers’ work schedule flexibility on residualized Fgains in child habitual sleep duration per night at age 9, mediated by changes in bedtime adherence

 

Consequent

Antecedent

M: Changes in child bedtime adherence (age 9−age 5)

Y: Child sleep duration at age 9

B

(SE)

B

(SE)

Intercept

0.28

(0.26)

9.64***

(0.16)

X: Changes in mothers’ work schedule flexibility (age 9−age 5)

0.21*

(0.08)

−0.04

(0.05)

Mothers’ work schedule flexibility at age 5

0.11

(0.1)

0.03

(0.06)

M: Changes in child bedtime adherence (age 9−age 5)

0.13***

(0.02)

Child bedtime adherence at age 5

0.79***

(0.03)

0.16***

(0.03)

Child sleep duration at age 5

0.08†

(0.04)

0.21***

(0.03)

Mothers’ age at child birth in years

0.00

(0.01)

0.00

(0.01)

Mothers’ education: Some college or above (vs. High school or less)

0.14

(0.11)

0.13†

(0.08)

Mothers’ race: Black, non-Hispanic (vs. White, non-Hispanic)

0.32*

(0.13)

0.47***

(0.1)

Mothers’ race: Hispanic (vs. White, non-Hispanic)

0.25

(0.16)

0.37***

(0.22)

Mothers’ race: Other (vs. White, non-Hispanic)

0.09

(0.35)

0.72***

(0.11)

Mother foreign born (vs. Native born)

0.59**

(0.18)

0.37**

(0.14)

Household income to poverty ratio: <49% (vs. ≥300%)

0.08

(0.23)

0.19

 

Household income to poverty ratio: 50–99% (vs. ≥300%)

0.10

(0.18)

0.16

 

Household income to poverty ratio: 100–199% (vs. ≥300%)

0.36*

(0.14)

0.01

 

Household income to poverty ratio: 200–299% (vs. ≥300%)

0.39*

(0.16)

0.05

 

Married or living with child’s biological father (vs. not)

0.14

(0.11)

0.04

(0.21)

Not living with child most of the time (vs. Living with child most of the time)

0.08

(0.34)

0.17

(0.01)

Child age in months

0.00

(0.01)

0.01

(0.07)

Child sex: Boy (vs. girl)

0.06

(0.1)

0.06

(0.06)

20 ≤ Mothers’ work hours < 35 (vs. <20 h)

0.24

(0.22)

0.30*

(0.13)

35 ≤ Mothers’ work hours < 41 (vs. <20 h)

0.11

(0.2)

0.42***

(0.13)

41 ≤ Mothers’ work hours (vs. <20 h)

0.13

(0.22)

0.45**

(0.14)

Mothers’ evening shifts (vs. standard, daytime shifts)

0.02

(0.14)

0.03

(0.08)

Mothers’ night shifts (vs. standard, daytime shifts)

0.22

(0.18)

0.06

(0.11)

Mothers’ weekend shifts (vs. standard, daytime shifts)

0.06

(0.12)

0.04

(0.07)

Mothers’ variable shifts (vs. standard, daytime shifts)

0.02

(0.13)

0.00

(0.08)

Model fit statistics

R2 = 0.4101

R2 = 0.2181

 

F(25,1000) = 27.81***

F(26,999) = 10.71***

Indirect effect of X on Y

Effect=0.03*, SE=0.01, 95% CI=[0.0065 to 0.0528]

Note: N = 1040; 1026 observations were used due to missing responses in variables; child age, mothers’ education, household income to poverty, married and living arrangements, work hours, and types of work schedule were based on age 9 reports

p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001

Changes in mothers' work schedule flexibility --> Changes in child bedtime adherence: B=0.21 (p=0.0111)

Changes in mothers' work schedule flexibility --> Changes in child sleep duration: B= -0.04 (p=0.4401)

Changes in child bedtime adherence --> Changes in child sleep duration: B=0.13 (p=0.0000)

Indirect Effect = 0.03(p=0.0188)

Finally, Table 6 shows results of the longitudinal mediation model examining the indirect effect of mothers’ perceived work schedule flexibility on residualized gains in the likelihood of child difficulty getting to sleep, and whether it was mediated by changes in bedtime adherence. Again, an increase in mothers’ perceived work schedule flexibility predicted increased child bedtime adherence. Contrary to our expectation and prior literature, however, changes in bedtime adherence did not significantly predict changes in child difficulty getting to sleep; however, the direct effect of a change in mothers’ perceived work schedule flexibility on child difficulty getting to sleep was significant, such that an increase in work schedule flexibility from Year 5 to Year 9 predicted 44% decreased odds of the child having difficulty getting to sleep at Year 9 than at Year 5 (Exp(−0.58) = 0.56, 1–0.56 = 0.44). The indirect effect of a change in mothers’ perceived work schedule flexibility on a change in the likelihood of child difficulty getting to sleep, mediated by changes in bedtime adherence, was not significant. Thus, hypothesis 4 was not supported.
Table 6

Results of the longitudinal mediation model examining the indirect effect of changes in mothers’ work schedule flexibility on residualized gains in child difficulty getting to sleep at age 9, mediated by changes in bedtime adherence

 

Consequent

Antecedent

M: Changes in child bedtime adherence (age 9−age 5)

Y: Child sleep difficulty at age 9

B

(SE)

B

(SE)

Intercept

0.1

(0.27)

1.07***

(0.57)

X: Changes in mothers’ work schedule flexibility (age 9−age 5)

0.21*

(0.08)

−0.58**

(0.19)

Mothers’ work schedule flexibility at age 5

0.08

(0.1)

0.54*

(0.24)

M: Changes in child bedtime adherence (age 9−age 5)

−0.06

(0.08)

Child bedtime adherence at age 5

0.79***

(0.03)

0.12

(0.09)

Child difficulty getting to sleep at age 5

0.29**

(0.11)

1.34***

(0.25)

Mothers’ age at child birth in years

0.00

(0.01)

0.02

(0.02)

Mothers’ education: Some college or above (vs. High school or less)

0.12

(0.11)

0.05

(0.28)

Mothers’ race: Black, non-Hispanic (vs. White, non-Hispanic)

0.25†

(0.13)

0.76*

(0.32)

Mothers’ race: Hispanic (vs. White, non-Hispanic)

0.24

(0.16)

0.22

(0.36)

Mothers’ race: Other (vs. White, non-Hispanic)

0.06

(0.35)

0.67

(0.71)

Mother foreign born (vs. Native born)

0.58**

(0.18)

0.05

(0.43)

Household income to poverty ratio: <49% (vs. ≥300%)

0.15

(0.23)

0.07

(0.54)

Household income to poverty ratio: 50–99% (vs. ≥300%)

0.14

(0.18)

0.14

(0.44)

Household income to poverty ratio: 100–199% (vs. ≥300%)

0.40**

(0.14)

0.52

(0.37)

Household income to poverty ratio: 200–299% (vs. ≥300%)

0.41**

(0.16)

0.02

(0.38)

Married or living with child’s biological father (vs. not)

0.15

(0.11)

0.47

(0.29)

Not living with child most of the time (vs. Living with child most of the time)

0.10

(0.34)

0.23

(0.83)

Child age in months

0.00

(0.01)

0.02

(0.03)

Child sex: Boy (vs. girl)

0.06

(0.1)

0.20

(0.24)

20 ≤ Mothers’ work hours < 35 (vs. <20 h)

0.20

(0.22)

0.50

(0.4)

35 ≤ Mothers’ work hours < 41 (vs. <20 h)

0.07

(0.2)

1.46***

(0.39)

41 ≤ Mothers’ work hours (vs. <20 h)

0.07

(0.22)

1.24**

(0.46)

Mothers’ evening shifts (vs. standard, daytime shifts)

0.00

(0.14)

0.08

(0.34)

Mothers’ night shifts (vs. standard, daytime shifts)

0.21

(0.18)

0.36

(0.47)

Mothers’ weekend shifts (vs. standard, daytime shifts)

0.06

(0.12)

0.28

(0.3)

Mothers’ variable shifts (vs. standard, daytime shifts)

0.03

(0.13)

0.37

(0.33)

Model fit statistics

R2 = 0.4124

2LL=510.33

 

F(25,1000) = 28.07***

Model LL=85.42

Indirect effect of X on Y

Effect=−0.01, SE=0.02, 95% CI=[−0.0600 to 0.0165]

Note: N = 1040; 1026 observations were used due to missing responses in variables; child age, mothers’ education, married and living arrangements, work hours, and types of work schedule were based on age 9 reports; R2 is not available for models with binary outcome

p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001

Changes in mothers' work schedule flexibility --> Changes in child bedtime adherence: B=0.21 (p=0.0148)

Changes in mothers' work schedule flexibility --> Changes in child sleep difficulty: B= -0.58 (p=0.0026)

Changes in child bedtime adherence --> Changes in child sleep difficulty: B= -0.06 (p=0.4107)

Indirect Effect = -0.01(p=0.4674)

In sum, our hypotheses concerning cross-sectional and longitudinal mediating effects of child bedtime adherence on the links between mothers’ perceived work schedule flexibility and child sleep were fully supported in terms of child sleep duration (see Fig. 1), but not child difficulty getting to sleep (only a cross-sectional link was supported). These results did not change (all X → M and M → Y paths remained significant) in supplemental models in which we excluded mothers who worked less than 20 h per week at either year (results are not shown, but available from the corresponding author upon request). We also tested whether work hours interacted with perceived work schedule flexibility; there were no significant interactions predicting child bedtime adherence, child sleep duration, or child sleep difficulty.
Fig. 1

Indirect effects of maternal work schedule flexibility on child sleep duration mediated by child bedtime adherence over time. Note: Only statistically significant (p < 0.05) pathways from control predictors to mediator and outcome are depicted. Panel A shows cross-sectional associations across ages 5 & 9. Panel B shows longitudinal indirect effect of changes in mothers’ work schedule flexibility from age 5 to age 9 on residualized gains of child sleep duration at age 9 (adjusting for age 5 sleep duration) through changes in bedtime adherence from age 5 to age 9. *p < 0.05, ***p < 0.001

Discussion

This study examined associations of mothers’ perceived work schedule flexibility with children’s sleep and whether the associations were mediated by children’s bedtime adherence. By testing cross-sectional and longitudinal associations with two-waves of data collected during middle childhood (age 5 and 9), this study moved beyond examining whether maternal work itself is associated with child sleep, to investigate how maternal work may influence child sleep and which specific aspects of maternal work and child sleep are linked over time. Consistent with the Work-Home Resources (W-HR) model (ten Brummelhuis and Bakker 2012) that asserts interconnectedness of resources across work and family domains, we found that maternal perceived work schedule flexibility was positively associated with child sleep health over time. Moreover, the associations were mediated by child bedtime adherence, which may suggest that maternal work schedule flexibility enhanced mothers’ time and energy to be more involved in bedtime parenting, which may, in turn, promote child sleep health. These results are also in line with the bioecological model (Bronfenbrenner and Ceci 1994; Dush et al. 2013; Harris et al. 2005) and past research that highlights the importance of consistent bedtime routines for child sleep health (Buxton et al. 2015; Hale et al. 2011; Mindell et al. 2015). Specifically, for children’s sleep duration, both cross-sectional mediation and longitudinal mediation pathways were supported; for difficulty getting to sleep, only the cross-sectional mediation pathway was supported. This study identified a new potential social determinant of child sleep health―mothers’ perceived work schedule flexibility―that may enhance or hinder child’s adherence to a routine bedtime, and thereby, sleep duration over time. Our findings contribute to understanding the mediating role of bedtime adherence in the link between maternal work and child sleep health and provide implications for how to improve bedtime practices in families with working mothers, in order to promote child sleep health.

Mothers’ Perceived Work Schedule Flexibility is Positively Associated with Child Bedtime Adherence

Previous cross-sectional studies have found negative associations of mothers working long hours and nonstandard schedules with child sleep (Kalil et al. 2014; Magee et al. 2012). Guided by the W-HR model that suggests a link between parents’ work resources and family resources and well-being (ten Brummelhuis and Bakker 2012), we sought to understand whether mothers’ perceived work schedule flexibility was associated with bedtime routines, which are important for child sleep. We found that mothers’ perceived work schedule flexibility was positively related to child bedtime adherence. Mothers who perceived greater flexibility in their work schedule might have had more temporal resources that could be used in parenting and more involvement in bedtime practices (Davis et al. 2015; Kelly et al. 2011, 2014; Lee et al. 2017; Roeters et al. 2010). And, it may be particularly the case for our sample of mothers, many of whom were of low socioeconomic status and were not living with the child’s biological father (45%), grandmother (12%), or grandfather (5%). Importantly, increases in mothers’ perceived work schedule flexibility over time predicted increases in children’s bedtime adherence, a notable finding that has not been reported elsewhere. This longitudinal change-change association can rule out stable individual differences (e.g., maternal depression or parenting style) as unobserved variables that might otherwise explain the link between mothers’ perceived work schedule flexibility and child bedtime adherence. Furthermore, the positive association of perceived work schedule flexibility with child’s bedtime adherence was independent of actual work hours and specific types of work schedules, as well as sociodemographic characteristics, thus suggesting unique importance of perceived work schedule flexibility. Some studies have suggested unanticipated consequences of adopting flexible working practices, such as work intensification (Kelliher and Anderson 2009) and inadequate income among low-wage hourly workers (Lambert et al. 2012). Our findings, however, suggest that flexible work schedules—as perceived by the workers—may have the potential to benefit child sleep by improved bedtime practices, especially in socioeconomically disadvantaged families.

The Importance of Bedtime Adherence for Child Sleep Health

This study supports the notion that regular bedtime routines are critical for healthy and self-regulated sleep behaviors for children (Buxton et al. 2015; Hale et al. 2011; Mindell et al. 2015) by identifying the longitudinal association of bedtime adherence with child sleep duration. Children whose adherence to a parent-designated bedtime increased over time also experienced an increase in sleep duration, by 8 min per night between ages 5 and 9. That is, despite an overall trend of decreased sleep duration from age 5 to age 9, children who experienced improved bedtime adherence also experienced increased age-adjusted sleep duration. This within-person finding highlights the importance of bedtime adherence for improving child sleep quantity, independent of between-person level differences in sleep patterns and developmental trajectories in sleep found in prior research (Magee et al. 2014).

Overall, the cross-sectional and longitudinal links between bedtime adherence and child sleep duration found in this study resonate the bioecological model of child development, how having consistent routines is important for optimal development (Bronfenbrenner and Ceci 1994; Dush et al. 2013; Harris et al. 2005). Because we focused on adhering to a regular bedtime, however, we do not know whether adhering to a particular bedtime (early or late) is associated with maternal perceived work schedule flexibility and predicts child sleep health. Future research could further examine specific aspects of bedtime adherence, including specific parenting behaviors during bedtime (e.g., stories, bathing, and quiet time with parent) matter for children’s sleep. Given the challenges facing many low-income mothers who work in nonstandard and unpredictable hours (Lambert 2008; Lambert et al. 2012), it may be necessary to provide specific information and guidelines for them to promote their children’s bedtime routines.

It is also noteworthy that children in our sample, on average, did not meet the minimum age-appropriate amount of sleep necessary for optimal development and functioning. The recommended sleep duration for children 3 to 5 years of age is 10 to 13 h and that for children 6 to 12 years of age is 9–12 h per 24 h (Paruthi et al. 2016). Our sample of primarily disadvantaged children slept, on average, 9.34 h per night at age 5 and 8.95 h per night at age 9 (Table 1). Based on our finding that bedtime adherence is a significant mediator linking mothers’ work schedule flexibility and child sleep duration over time, future intervention research could consider how to improve bedtime practices in families with working mothers, particularly in disadvantaged households. Children of mothers with racial minority status (Black, Hispanic, or some other non-white race) had significantly shorter sleep duration at both ages 5 and 9 than those of white mothers (Table 3), and children from households with less family incomes had shorter sleep duration, in a dose-response manner (Table 3). These clearly indicate social disparities in sleep health (Jackson et al. 2013; Whinnery et al. 2014) during middle childhood.

In contrast to our results for sleep duration, we did not find evidence of a longitudinal indirect effect of mothers’ perceived work schedule flexibility on child difficulty getting to sleep, through bedtime adherence (although such a link was suggested by the cross-sectional results). This may reflect limited power to detect the link between bedtime adherence and difficulty getting to sleep over time, given general declines in both over the course of the study (see Table 1), or perhaps reflect the dichotomous and relatively insensitive nature of the sleep quality question. Further research is needed to understand whether improvement in bedtime routines (influenced by changes in mothers’ work conditions) are associated with less child sleep difficulty in different samples using different measures, and potentially objective measures.

Limitations and Future Directions

Some of this study’s limitations suggest directions for future research. First, we used mothers’ reports of their children’s bedtime adherence and sleep characteristics, which poses a potential risk for common-method bias (Podsakoff et al. 2003). Although mothers’ reports can be a suitable proxy for children’s actual sleep behaviors, and have been used in many prior studies (Kalil et al. 2014), such reports may overestimate children’s bedtime adherence due to mothers’ social desirability bias (Fisher 1993). There may be less concern about interpreting longitudinal change-change associations that can rule out time invariant maternal characteristics that may be associated with response bias. Future studies focusing on older children’s sleep could benefit from incorporating child-reported sleep behaviors and examine how they are linked with mothers’ work characteristics (McHale et al. 2015). Moreover, in this study we focused on the mediating pathway by child bedtime adherence, but there might be another pathway such as secure attachment or engaged/attentive parenting. For example, mothers with more flexible work schedules may be more available to build secure attachment with their children than those with less flexible work schedules (Barglow et al. 1987). Future research could explore different pathways linking maternal work schedule flexibility to child sleep health.

This study extends previous knowledge on the implications of maternal work for children by examining the effects of mothers’ perceived work schedule flexibility on children’s sleep and by testing the mediating role of bedtime adherence in a longitudinal sample of socioeconomically disadvantaged families. The results suggest that mother-perceived work schedule flexibility is associated with child sleep health, largely through its association with better bedtime practices. Early life bedtime practices can have long-term influences on individual sleep; researchers and practitioners could consider how to improve bedtime routines in families with disadvantaged working mothers. Future research could also continue to identify other modifiable factors linking parents’ work and child sleep and intervene in the process in order to promote sleep health at the population level. In short, perceived work schedule flexibility may be associated with improved sleep health, not only for employees, but also for their children.

Notes

Author Contributions

S.L.: initiated and performed the analyses and drafted the manuscript. L.H.: collaborated with the design and writing of the study. L.M.B.: collaborated with the design and writing of the study. O.M.B.: collaborated in the writing and editing the final manuscript. All authors contributed to the revision of the manuscript and approved the final submitted version.

Funding

This study was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) of the National Institutes of Health (R01HD36916, R01HD39135, R01HD40421 and R01HD073352). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Compliance with Ethical Standards

Conflict of Interest

The authors have indicated no financial conflicts of interest relevant to the current study. Outside of the current work, Orfeu M. Buxton received two subcontract grants to Penn State from Mobilesleeptechnologies (NSF/STTR #1622766, NIH/NIA SBIR R43AG056250).

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Aging StudiesUniversity of South FloridaTampaUSA
  2. 2.Program in Public Health; Department of Family, Population and Preventive MedicineStony Brook UniversityStony BrookUSA
  3. 3.Institute for Research on Poverty and School of Social WorkUniversity of Wisconsin-MadisonMadisonUSA
  4. 4.Department of Biobehavioral HealthPennsylvania State UniversityPennsylvaniaUSA
  5. 5.Division of Sleep MedicineHarvard Medical SchoolBostonUSA
  6. 6.Department of Social and Behavioral SciencesHarvard Chan School of Public HealthBostonUSA
  7. 7.Sleep Health Institute, Departments of Medicine and NeurologyBrigham and Women’s HospitalBostonUSA

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