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Exploratory Analyses of Sleep Intraindividual Variability and Fatigue in Parents of Children on the Autism Spectrum

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Title: Exploratory Analyses of Sleep Intraindividual Variability and Fatigue in Parents of Children on the Autism Spectrum
Language: English
Authors: Braden Hayse (ORCID 0000-0002-1033-1562), Melanie A. Stearns (ORCID 0000-0002-7699-2996), Micah O. Mazurek (ORCID 0000-0001-7715-6538), Ashley F. Curtis, Neetu Nair, Wai Sze Chan, Melissa Munoz, Kevin D. McGovney, David Q. Beversdorf, Mojgan Golzy, Kristin A. Sohl (ORCID 0000-0003-0588-8742), Zarah H. Ner, Beth Ellen Davis, Nicole Takahashi, Christina S. McCrae
Source: Autism: The International Journal of Research and Practice. 2025 29(4):958-974.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 17
Publication Date: 2025
Sponsoring Agency: US Department of Defense (DOD)
Contract Number: W81XWH2010399
Document Type: Journal Articles
Reports - Research
Descriptors: Fatigue (Biology), Sleep, Individual Characteristics, Parents, Children, Parent Influence, Autism Spectrum Disorders, Psychological Patterns, Affective Behavior, Child Behavior
Geographic Terms: Missouri
Assessment and Survey Identifiers: Autism Diagnostic Observation Schedule
DOI: 10.1177/13623613241292691
ISSN: 1362-3613
1461-7005
Abstract: Fatigue is associated with numerous harmful physical and mental health outcomes. Despite the established relationship between sleep and fatigue, research examining sleep variability within a person (i.e. intraindividual variability; IIV) and fatigue is limited. In addition, the associations between child and parent sleep regarding parent fatigue have not been explicitly explored, which could be relevant for parents of autistic children with increased sleep disturbance likelihood. The current study used two weeks of objective sleep (actigraphy) and subjective fatigue data from 81 parents and their children to explore associations among child sleep IIV, parent sleep IIV, and parent average daily fatigue, including evaluating evidence for mediation. Sleep IIV was estimated using a validated Bayesian model. Linear regression analyses indicated that greater parent total sleep time IIV predicted significantly higher fatigue levels. Child sleep IIV was unrelated to parent sleep IIV and fatigue, unsupportive of hypothesized mediation. Similarly, post hoc analyses examining child sleep averages, parent total sleep time IIV, and average parent fatigue were insignificant. Findings cautiously support the uniqueness of total sleep time IIV within parental sleep's relationship with fatigue, independent of child sleep. Objective sleep IIV should continue to be examined in addition to average levels.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1466089
Database: ERIC
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  Value: <anid>AN0184233761;f9d01apr.25;2025Apr08.02:17;v2.2.500</anid> <title id="AN0184233761-1">Exploratory analyses of sleep intraindividual variability and fatigue in parents of children on the autism spectrum </title> <p>Fatigue is associated with numerous harmful physical and mental health outcomes. Despite the established relationship between sleep and fatigue, research examining sleep variability within a person (i.e. intraindividual variability; IIV) and fatigue is limited. In addition, the associations between child and parent sleep regarding parent fatigue have not been explicitly explored, which could be relevant for parents of autistic children with increased sleep disturbance likelihood. The current study used two weeks of objective sleep (actigraphy) and subjective fatigue data from 81 parents and their children to explore associations among child sleep IIV, parent sleep IIV, and parent average daily fatigue, including evaluating evidence for mediation. Sleep IIV was estimated using a validated Bayesian model. Linear regression analyses indicated that greater parent total sleep time IIV predicted significantly higher fatigue levels. Child sleep IIV was unrelated to parent sleep IIV and fatigue, unsupportive of hypothesized mediation. Similarly, post hoc analyses examining child sleep averages, parent total sleep time IIV, and average parent fatigue were insignificant. Findings cautiously support the uniqueness of total sleep time IIV within parental sleep's relationship with fatigue, independent of child sleep. Objective sleep IIV should continue to be examined in addition to average levels. Fatigue is associated with numerous harmful physical and mental health outcomes. Despite research indicating a relationship between fatigue and sleep, there has been a limited focus on how the variability of a person's sleep may be associated with fatigue. In addition, previous studies have not explicitly explored relationships among child sleep, parent sleep, and parent fatigue. Increasing knowledge about this area of research could be particularly relevant for families with autistic children with an increased likelihood of sleep disturbances. The current study used two weeks of objective sleep (actigraphy) data and subjective ratings of parent fatigue from 81 parents and their autistic children to examine associations among child and parent within-person sleep variability regarding average parent fatigue levels. Evidence was assessed for the role of parent sleep variability in hypothesized connections between child sleep variability and parent fatigue. We found that only greater variability in parents' total sleep time was associated with higher levels of parents' average daily fatigue rating over the two weeks. Child sleep variability was not significantly associated with parent sleep variability or average daily fatigue. In addition, average levels of child sleep were unrelated to parent total sleep time variability and fatigue. Although cautious interpretation is required, findings support the idea that variability in total sleep time may be a unique aspect of parental sleep's association with fatigue, independent of child sleep. In addition, sleep variability could be important to consider when examining sleep in addition to average levels of parameters like total sleep time.</p> <p>Keywords: actigraphy; autism spectrum disorders (ASD); fatigue; intraindividual variability; sleep</p> <p>Fatigue can be defined as a relatively common sense of overwhelming, lasting mental and physical exhaustion and lack of energy that is not easily alleviated by rest ([<reflink idref="bib90" id="ref1">90</reflink>]; [<reflink idref="bib96" id="ref2">96</reflink>]; [<reflink idref="bib102" id="ref3">102</reflink>]). Despite its prevalence and various adverse mental and physical outcomes ([<reflink idref="bib42" id="ref4">42</reflink>]; [<reflink idref="bib45" id="ref5">45</reflink>]; [<reflink idref="bib49" id="ref6">49</reflink>]; [<reflink idref="bib122" id="ref7">122</reflink>]), it remains difficult to assess and treat adequately given its subjectivity ([<reflink idref="bib33" id="ref8">33</reflink>]; [<reflink idref="bib34" id="ref9">34</reflink>]; [<reflink idref="bib59" id="ref10">59</reflink>]). In addition, there is evidence that children might be at risk for some of the harmful influences of higher fatigue levels in their parents, such as increased parental stress, ineffective coping, and irritability in parent-child interactions ([<reflink idref="bib29" id="ref11">29</reflink>]; [<reflink idref="bib101" id="ref12">101</reflink>]). Understanding the relationship between sleep and fatigue may be particularly important in certain populations. For instance, parental fatigue may be an especially important factor for families of children diagnosed with autism spectrum disorder (ASD; [<reflink idref="bib58" id="ref13">58</reflink>]). There is a higher prevalence of sleep disturbances in both parents and autistic children ([<reflink idref="bib80" id="ref14">80</reflink>]; [<reflink idref="bib108" id="ref15">108</reflink>]), and resulting parental fatigue may have negative downstream effects on a family's ability to participate in and benefit from early intervention ([<reflink idref="bib17" id="ref16">17</reflink>]; [<reflink idref="bib31" id="ref17">31</reflink>]; [<reflink idref="bib58" id="ref18">58</reflink>]). The purpose of the current study is to determine the associations between children's sleep variability, parents' sleep variability, and mean parental fatigue, including potential mediation within relationships in a sample of families with children on the autism spectrum.</p> <hd id="AN0184233761-2">Fatigue and sleep</hd> <p>Sleep disturbances are a predisposing factor for experiencing fatigue in several neurological diseases ([<reflink idref="bib93" id="ref19">93</reflink>]). Furthermore, [<reflink idref="bib4" id="ref20">4</reflink>] found that poor sleepers from community samples of older adults and college students reported significantly more fatigue than good sleepers. However, the association between sleep and fatigue has been established using mainly subjective measures of sleep, such as questionnaires and sleep diaries. Objective methods of sleep measurement, such as wrist actigraphy and polysomnography (PSG), can be described as better capturing true amounts of sleep and wakefulness, though not necessarily more valuable than subjective methods ([<reflink idref="bib2" id="ref21">2</reflink>]; [<reflink idref="bib53" id="ref22">53</reflink>]). Both subjective and objective sleep assessment techniques can determine standard sleep parameters ([<reflink idref="bib53" id="ref23">53</reflink>]). However, discrepancies and a lack of correspondence between objective and subjective sleep parameters have long been reported ([<reflink idref="bib10" id="ref24">10</reflink>]; [<reflink idref="bib57" id="ref25">57</reflink>]; [<reflink idref="bib100" id="ref26">100</reflink>]; [<reflink idref="bib113" id="ref27">113</reflink>]). This discordance is particularly present in individuals with sleep disturbances ([<reflink idref="bib91" id="ref28">91</reflink>]).</p> <p>The relationship between fatigue and sleep appears to be an area where discrepancies between objective and subjective sleep assessment necessitate further study based on findings showing an association between subjective sleep variables and fatigue but not for objective parameters ([<reflink idref="bib43" id="ref29">43</reflink>]; [<reflink idref="bib99" id="ref30">99</reflink>]; [<reflink idref="bib119" id="ref31">119</reflink>]). These inconsistent findings by assessment type appear to provide evidence that questions whether objective sleep is truly related to fatigue, with several potential explanations. For example, subjective measures of sleep may be more closely related to fatigue, given that fatigue is also a subjective experience ([<reflink idref="bib89" id="ref32">89</reflink>]). Similarly, an individual's perception of sleep could be influenced by feelings of fatigue after waking or throughout the day ([<reflink idref="bib43" id="ref33">43</reflink>]). However, the possibility also exists that other, less frequently analyzed aspects of objective sleep could be relevant determinants of fatigue.</p> <hd id="AN0184233761-3">Intraindividual variability</hd> <p>Associations between individuals' common objective sleep parameters and other variables are typically examined using mean values ([<reflink idref="bib86" id="ref34">86</reflink>]). Less frequently analyzed is the variability of sleep parameters within a person (intraindividual variability; IIV), which quantifies the daily variation around the individual's mean value across multiple days ([<reflink idref="bib15" id="ref35">15</reflink>]). IIV may provide an alternative explanation for some of the discrepancies observed between subjective and objective measures of sleep and their associations. Sleep IIV may play an independent role in poor sleep and insomnia ([<reflink idref="bib15" id="ref36">15</reflink>]; [<reflink idref="bib22" id="ref37">22</reflink>]; [<reflink idref="bib111" id="ref38">111</reflink>]), as sleep consistency is often a primary strategy to improve sleep disturbances ([<reflink idref="bib24" id="ref39">24</reflink>]; [<reflink idref="bib36" id="ref40">36</reflink>]; [<reflink idref="bib87" id="ref41">87</reflink>]). The prevailing thought is that inconsistent sleep patterns contribute to and may help sustain sleep difficulties ([<reflink idref="bib65" id="ref42">65</reflink>]; [<reflink idref="bib114" id="ref43">114</reflink>]), as good-sleeping adults tend to have less variable sleep ([<reflink idref="bib77" id="ref44">77</reflink>]; [<reflink idref="bib94" id="ref45">94</reflink>]). IIV in sleep parameters may also have a separate impact on correlates of sleep difficulties, like various mental and physical health outcomes ([<reflink idref="bib15" id="ref46">15</reflink>]). Evidence for connections between total sleep time IIV and variables such as pain, depression, neurological problems, subjective well-being, and reported sleep quality suggest that sleep IIV may be a unique facet of sleep disturbance, especially since average TST has shown weaker or inconsistent relationships ([<reflink idref="bib40" id="ref47">40</reflink>]; [<reflink idref="bib65" id="ref48">65</reflink>]; [<reflink idref="bib105" id="ref49">105</reflink>]). However, only two studies have investigated whether sleep IIV was related to fatigue, with one showing higher total sleep time IIV associated with more fatigue in adolescents ([<reflink idref="bib44" id="ref50">44</reflink>]). In another study, [<reflink idref="bib61" id="ref51">61</reflink>] reported that bedtime variability was not correlated with fatigue scores in undergraduate students. Notably, both studies used subjective sleep diary IIV rather than objective sleep measurements.</p> <hd id="AN0184233761-4">Sleep and autism spectrum disorder</hd> <p>Autism spectrum disorder is a neurodevelopmental disorder characterized by deficits in social communication, restricted interests, and repetitive behaviors ([<reflink idref="bib7" id="ref52">7</reflink>]). Insomnia, which involves persistent problems initiating or maintaining sleep, is more prevalent in children with ASD (50%-80%) than in their typically developing (TD) peers (~25%; [<reflink idref="bib37" id="ref53">37</reflink>]; [<reflink idref="bib83" id="ref54">83</reflink>]; [<reflink idref="bib98" id="ref55">98</reflink>]; [<reflink idref="bib108" id="ref56">108</reflink>]). Furthermore, there is evidence that sleep problems are associated with some symptoms that commonly co-occur with ASD, such as anxiety and sensory sensitivity ([<reflink idref="bib32" id="ref57">32</reflink>]; [<reflink idref="bib51" id="ref58">51</reflink>]; [<reflink idref="bib52" id="ref59">52</reflink>]; [<reflink idref="bib72" id="ref60">72</reflink>]). Sleep difficulties are also more frequent among parents of autistic children as compared to parents of TD children, such as poorer sleep quality, earlier waking, and a worse sleep schedule across child age ranges ([<reflink idref="bib6" id="ref61">6</reflink>]; [<reflink idref="bib9" id="ref62">9</reflink>]; [<reflink idref="bib21" id="ref63">21</reflink>]; [<reflink idref="bib60" id="ref64">60</reflink>]; [<reflink idref="bib84" id="ref65">84</reflink>]; [<reflink idref="bib125" id="ref66">125</reflink>]). In addition, less sleep overall and more daytime sleepiness in parents of autistic children may arise from increased child nighttime awakenings ([<reflink idref="bib66" id="ref67">66</reflink>]). Parent sleep quality may be further negatively impacted by higher rates of anxiety, depressive symptoms, and stress associated with greater child sleep and behavior problems ([<reflink idref="bib26" id="ref68">26</reflink>]).</p> <p>Most studies examining sleep associations within families interpret findings based on the presumed impact of child sleep on parent sleep. For example, [<reflink idref="bib48" id="ref69">48</reflink>] found that every additional child in a household was associated with parents reporting less sleep and more daytime sleepiness over time. Expectedly, parents of younger children indicated the worst subjective sleep. Several other investigations suggest a direction of child sleep to parent sleep, such as more child sleep problems predicting increased daytime sleepiness in mothers ([<reflink idref="bib18" id="ref70">18</reflink>]) and worse parental sleep quality ([<reflink idref="bib115" id="ref71">115</reflink>]). Longer sleep and better sleep efficiency than typical in children additionally predict the same in mothers ([<reflink idref="bib63" id="ref72">63</reflink>]). However, the potential exists that parent sleep may also impact child sleep, either unilaterally or reciprocally, through mechanisms like habit modeling, co-sleeping, family routine, or genetics ([<reflink idref="bib39" id="ref73">39</reflink>]; [<reflink idref="bib95" id="ref74">95</reflink>]). Moreover, sleep IIV may be specifically associated with less positive parenting practices ([<reflink idref="bib79" id="ref75">79</reflink>]) and increased irritability, hyperactivity, and anxiety in children on the spectrum ([<reflink idref="bib11" id="ref76">11</reflink>]), which may directly and indirectly relate to fatigue levels in parents. Given limited prior research and established negative outcomes of poor parent sleep and fatigue ([<reflink idref="bib16" id="ref77">16</reflink>]; [<reflink idref="bib17" id="ref78">17</reflink>]; [<reflink idref="bib45" id="ref79">45</reflink>]; [<reflink idref="bib79" id="ref80">79</reflink>]), it is worth pursuing a better understanding of how objective sleep IIV in children on the autism spectrum and their parents relate to parent fatigue.</p> <p>Currently, the authors are unaware of any studies that have explicitly analyzed the relationship between child and parent objective sleep IIV. Although other child sleep variables (e.g. child total sleep averages) may be associated with parent IIV or parent fatigue, hypotheses focused on the role of child IIV given parents' role in child sleep factors that could impact child sleep regularity through habit modeling, routine, and co-sleeping ([<reflink idref="bib39" id="ref81">39</reflink>]). Further rationale included the relative lack of attention to IIV in the literature and previously reported insignificant associations between actigraphy-measured child and parent sleep averages ([<reflink idref="bib64" id="ref82">64</reflink>]). Evidence also suggests that increased variability in objective child sleep parameters is associated with poorer child sleep quality and more negative mood ([<reflink idref="bib14" id="ref83">14</reflink>]), potentially affecting parent sleep and fatigue. In addition, the relationships between parent sleep IIV and poorer daytime functioning ([<reflink idref="bib15" id="ref84">15</reflink>]) and less positive parenting ([<reflink idref="bib79" id="ref85">79</reflink>]) may influence perceived fatigue levels.</p> <hd id="AN0184233761-5">Specific aims and hypotheses</hd> <p>The overarching aim of this study was to examine the associations between IIV of child sleep variables (actigraphy-measured total sleep time (TST; that is, amount of time spent sleeping), sleep onset latency (SOL; that is, time taken to fall asleep for the first time), sleep efficiency (SE; that is, percentage of sleep of total time spent in bed), and total wake time (TWT; that is, wake time counting initial sleep onset latency); combination-measured bedtime and waketime), IIV of parent sleep variables (actigraphy-measured TST, SOL, SE, and TWT; combination-measured bedtime and waketime), and mean self-reported parent fatigue, including potentially mediated relationships. While most sleep variables were solely measured by actigraphy, participant bedtimes and waketimes were determined by additional reference to sleep diaries (i.e. combination-measured). The general hypothesis was that greater IIV of child sleep variables would be associated with higher mean levels of self-reported parent fatigue. In addition, significant relationships between IIV of child sleep variables and mean self-reported parent fatigue would be mediated by IIV of parent sleep variables given the pathway from child sleep to parent sleep and/or functioning most often suggested ([<reflink idref="bib46" id="ref86">46</reflink>]; [<reflink idref="bib55" id="ref87">55</reflink>]).</p> <hd id="AN0184233761-6">Method</hd> <p></p> <hd id="AN0184233761-7">Participants</hd> <p>This study involved two weeks of baseline data from two separate pilot studies and a randomized controlled trial conducted at the University of Missouri. Eighty-one parents (ages 25–57, 72% female) and their children (ages 6–12, 72% male) participated in the studies. All three studies examined the impact of cognitive behavioral therapy as a treatment for insomnia in school-aged children with ASD ([<reflink idref="bib75" id="ref88">75</reflink>]; [<reflink idref="bib75" id="ref89">75</reflink>]; [<reflink idref="bib76" id="ref90">76</reflink>]). All children met full criteria for a diagnosis of ASD (<emph>Diagnostic and Statistical Manual of Mental Disorders</emph>, 5th ed.; <emph>DSM</emph>–5; [<reflink idref="bib7" id="ref91">7</reflink>]). Participants were recruited through the Thompson Center for Autism and Neurodevelopment at the University of Missouri. Inclusion criteria were as follows: (<reflink idref="bib1" id="ref92">1</reflink>) children between the ages of 6 and 12 years with full-scale IQ > 75, (<reflink idref="bib2" id="ref93">2</reflink>) parent/guardian living in the same home, (<reflink idref="bib3" id="ref94">3</reflink>) parent/guardians(s) able to read and understand study materials, (<reflink idref="bib4" id="ref95">4</reflink>) previous diagnosis of ASD established using the Autism Diagnostic Observation Schedule ([<reflink idref="bib67" id="ref96">67</reflink>]; [<reflink idref="bib68" id="ref97">68</reflink>]), (<reflink idref="bib5" id="ref98">5</reflink>) child or parent report of child sleep meeting criteria for insomnia disorder (persistent, that is, 3+ months) based on the <emph>DSM</emph>–5 ([<reflink idref="bib7" id="ref99">7</reflink>]), and (<reflink idref="bib6" id="ref100">6</reflink>) parent report of child daytime dysfunction (mood, cognitive, social, or school) due to insomnia during a clinical interview. Additional child-related exclusion criteria included (<reflink idref="bib1" id="ref101">1</reflink>) any untreated or uncontrolled medical comorbidities, including other sleep disorders (e.g. apnea, epilepsy, psychotic disorders, suicidal ideation/intent, or frequent parasomnias) and (<reflink idref="bib2" id="ref102">2</reflink>) any medication changes during the past 3 months. IRB approval was obtained for the studies at the site (University of Missouri). There was no community involvement in the reported study.</p> <hd id="AN0184233761-8">Procedures</hd> <p>Sleep assessment procedures were identical across the studies from which participants were drawn. Parents and their children completed daily morning sleep diaries (with parental assistance for the children) and concurrently wore an Actiwatch-2 (Actiwatch-2, Philips Respironics) for 24 h a day over 14 days. Where appropriate, the "s" subscript is used to distinguish the subjective (s) bedtime and waketime sleep variables from the combination of subjective and objective bedtimes and waketimes used in statistical analyses. The "o" subscript is used to distinguish the objective (o) measures of TST, SOL, SE, and TWT.</p> <hd id="AN0184233761-9">Measures</hd> <p></p> <hd id="AN0184233761-10">Subjective Sleep</hd> <p>Each morning, parents and their children (with parental assistance) completed electronic daily sleep diaries in which they reported, among other variables not used in the current study, time entering bed with the intention to fall asleep (i.e. bedtime<subs>s</subs>) and time out of bed following the final morning awakening (i.e. waketime<subs>s</subs>).</p> <hd id="AN0184233761-11">Actigraphic Sleep</hd> <p>Actiwatch data was collected via the Actiwatch-2 (Philips Respironics), which was worn continuously on the non-dominant wrist and sampled physical activity (activity counts) and ambient light (lux) every 30 seconds. Actiwatch data was downloaded onto actigraphy analysis software (Philips Actiware v.6.0.8), which was configured to set one rest interval per day under medium sensitivity. To accurately capture the sleep variables used in this study (e.g. SOL), sleep scoring intervals were set from the time participants first entered bed to the time they got out of bed. Hence, we used a procedure to identify time-in-bed and time-out-of-bed to set sleep scoring intervals to capture the period intended for sleep. Studies have shown that accurate time-in-bed/time-out-of-bed estimations can be obtained by combining visual inspection with self-reports ([<reflink idref="bib20" id="ref103">20</reflink>]). For this reason, time-in-bed/time-out-of-bed self-reports (i.e. via simultaneously collected sleep diaries) were used in conjunction with visual inspection to identify likely regions containing time-in-bed/time-out-of-bed.</p> <p>Actigraphic visual inspection identified likely time-in-bed and time-out-of-bed regions with a following hierarchical procedure ([<reflink idref="bib78" id="ref104">78</reflink>]): 1) referencing sleep diaries to identify self-reported time-in-bed/time-out-of-bed and 2) examining the actogram for sudden decreases (time-in-bed) or increases (time-out-of-bed) in ambient light and/or physical activity around self-reported time-in-bed/time-out-of-bed. After regions with likely times-in-bed/times-out-of-bed were identified, exact times-in-bed/times-out-of-bed were estimated using a standardized protocol ([<reflink idref="bib78" id="ref105">78</reflink>]) requiring 10 consecutive epochs below (time-in-bed) or above (time-out-of-bed) threshold criteria (i.e. 100 activity counts/epoch, white light of 1uW/cm^2). After time-in-bed (combination bedtime) and time-out-of-bed (combination waketime) were set within Actiware, validated sleep interval detection algorithms scored epochs during these rest intervals as wake or sleep using default settings which require 10 consecutive immobile minutes to trigger sleep onset and the converse to terminate sleep. The following objective sleep outcomes were then computed in Actiware software: TST<subs>o</subs> (duration of the rest interval minus the duration of wakeful epochs), SOL<subs>o</subs> (calculated as the elapsed time between the start of the rest interval and the end of 10 consecutive immobile epochs), SE<subs>o</subs> (percentage of total sleep time divided by the total time from bedtime to waketime), and TWT<subs>o</subs> (wake time during the rest interval counting initial SOL).</p> <p>Actigraphy has been validated as a consistent, clinically acceptable objective measure of sleep in adults, both healthy and those with most sleep-related disorders ([<reflink idref="bib3" id="ref106">3</reflink>]; [<reflink idref="bib28" id="ref107">28</reflink>]; [<reflink idref="bib71" id="ref108">71</reflink>]; [<reflink idref="bib107" id="ref109">107</reflink>]), as well as in children ages 3–18 ([<reflink idref="bib56" id="ref110">56</reflink>]; [<reflink idref="bib81" id="ref111">81</reflink>]; [<reflink idref="bib120" id="ref112">120</reflink>]). Actigraphy is also widely used as a feasible assessment method for sleep in children on the autism spectrum, although data loss is not uncommon due to device intolerance and malfunction ([<reflink idref="bib41" id="ref113">41</reflink>]; [<reflink idref="bib86" id="ref114">86</reflink>]). Notably, parents did not report any difficulty tolerating the Actiwatch-2 on their own wrists. The parents of only four children in the studies reported their children had difficulty tolerating the Actiwatch-2 on their wrists and the children were given a t-shirt to wear at night as part of a validated alternative placement method of the Actiwatch-2 ([<reflink idref="bib1" id="ref115">1</reflink>]) with additional troubleshooting provided if needed.</p> <hd id="AN0184233761-12">Fatigue</hd> <p>Parental Fatigue ratings were collected daily from the sleep diaries over the 14 days. A visual analogue scale was used, ranging from 0 (none) to 100 (most intense imaginable). This type of fatigue assessment is commonly used in the literature ([<reflink idref="bib4" id="ref116">4</reflink>]), and can be advantageous over single timepoint evaluations because it captures the fluctuating nature and rhythm of fatigue symptoms ([<reflink idref="bib93" id="ref117">93</reflink>]).</p> <hd id="AN0184233761-13">Statistical analyses</hd> <p>Potential covariates and confounding variables in the relationships between child sleep IIV, parent sleep IIV, and parent fatigue were examined, such as parent and child age and sex, as well as race, sleep medication use, and co-occurring depression, anxiety, or sleep disorder in the parent ([<reflink idref="bib15" id="ref118">15</reflink>]). Variables were determined to be added by calculating the correlation of each numerical variable with the outcome, parent average daily fatigue, and examining preexisting group differences in factorial variables using ANOVA. Variables with significant correlations with or group differences in parent average daily fatigue were to be added to subsequent models as independent variables.</p> <p>The most common method of calculating IIV is sleep parameter individual standard deviation ([<reflink idref="bib13" id="ref119">13</reflink>]; [<reflink idref="bib15" id="ref120">15</reflink>]). However, individual standard deviation can be prone to bias from uncontrolled outside factors (e.g. seasonal daylight variation) and poor reliability (i.e. measurement error). Bias is especially likely if calculated over fewer nights with less individual sleep parameter variability ([<reflink idref="bib38" id="ref121">38</reflink>]; [<reflink idref="bib121" id="ref122">121</reflink>]). Fortunately, [<reflink idref="bib121" id="ref123">121</reflink>] proposed a method to calculate Bayesian estimations for sleep IIV using a multilevel model approach that has been demonstrated to be more reliable and consistently higher-powered than individual standard deviation, and has been previously used successfully ([<reflink idref="bib14" id="ref124">14</reflink>]; [<reflink idref="bib117" id="ref125">117</reflink>]).</p> <p>Thus, analyses were conducted using the [<reflink idref="bib121" id="ref126">121</reflink>] method in R v3.3.0 ([<reflink idref="bib30" id="ref127">30</reflink>]) and Stan ([<reflink idref="bib23" id="ref128">23</reflink>]) v2.10.0 via the R packages <emph>varian</emph> v0.3.0, RStan v2.10.1, and <emph>mediation</emph> v4.5.0 ([<reflink idref="bib112" id="ref129">112</reflink>]). Due to the hierarchical structure of the data and repeated measures within participants, the <emph>varian</emph> package used multilevel regression to calculate Bayesian estimates of sleep parameter variability around each participant's mean. The derived Bayesian IIV estimates of TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, TWT<subs>o</subs>, bedtime, and waketime were then used as predictors in multiple linear regressions to test connections between child sleep parameter IIV on parent sleep parameter IIV. These initial exploratory analyses follow the first step advised to establish potential mediation by showing significant association between a hypothesized predictor and mediator ([<reflink idref="bib69" id="ref130">69</reflink>]). Following recommendations for calculating IIV ([<reflink idref="bib15" id="ref131">15</reflink>]), corresponding sleep parameter intraindividual means were also included as independent variables in the initial regressions due to the strong general correlation between averages and standard deviations ([<reflink idref="bib88" id="ref132">88</reflink>]).</p> <p>Subsequently, the derived Bayesian IIV estimates of parent sleep were then used as predictors in multiple linear regressions, along with their corresponding sleep parameter intraindividual means, to test connections between parent sleep IIV and parent average daily fatigue. These analyses achieved the second step recommended to demonstrate mediation via a statistically significant relationship between a hypothesized mediator and outcome ([<reflink idref="bib69" id="ref133">69</reflink>]). Any pair of child and parent sleep IIV parameters with associations suggesting a mediated relationship planned to be examined with causal mediation analyses of indirect effects using Quasi-Bayesian estimation through Markov Chain Monte Carlo simulation within the <emph>mediation</emph> and <emph>varian</emph> R packages ([<reflink idref="bib112" id="ref134">112</reflink>]).</p> <p>As recommended for exploratory studies with the purpose of future hypothesis generation by [<reflink idref="bib110" id="ref135">110</reflink>]), multiple statistical comparisons were not accounted for, and alpha significance remained at <emph>p</emph> < 0.05. If one of the most common correction methods were used, Bonferroni ([<reflink idref="bib35" id="ref136">35</reflink>]; [<reflink idref="bib109" id="ref137">109</reflink>]), the <emph>p</emph>-value required for statistical significance would be reduced to <emph>p</emph> < 0.008. While the multiple statistical comparisons correction would reduce the number of Type 1 errors (i.e. reporting a significant effect when none exists), it would also increase the number of Type 2 errors (i.e. failing to report a significant effect when one exists). In that regard, [<reflink idref="bib110" id="ref138">110</reflink>] argue that it is better to tolerate potential Type 2 errors without correction so as to not prematurely abandon possibly useful observations.</p> <p>Power analyses were conducted in G*Power version 3.1.9.7 for Windows 10. Power analyses were examined for fixed model multiple regressions with single regression coefficients. For a two-tailed <emph>t</emph>-test with alpha significance level at <emph>p</emph> = 0.05, a sample size of 81, and six IIV predictors (TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, TWT<subs>o</subs>, bedtime, and waketime), results showed that analyses would be powered at 0.12, 0.70, and 0.98 for Cohen's f2 of 0.02, 0.15, and 0.35 respectively for each planned set of initial exploratory analyses on one outcome (parent average daily fatigue and IIV of TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, TWT<subs>o</subs>, bedtime, and waketime). Cohen's f2 of 0.02, 0.15, and 0.35 are generally considered small, medium, and large effects ([<reflink idref="bib27" id="ref139">27</reflink>]), respectively, so proposed analysis sets would be powered at >0.80 for medium-to-large effects and greater (Cohen's f2 > 0.19), but not smaller. However, it should be noted that the [<reflink idref="bib121" id="ref140">121</reflink>] method maximizes power.</p> <hd id="AN0184233761-14">Results</hd> <p></p> <hd id="AN0184233761-15">Demographics, potential covariates, and descriptive statistics</hd> <p>As shown in Table 1, most parents in the sample were female and most children were male. None of the covariates tested were significantly associated with average daily fatigue, although parent age and a past diagnosis of depression or anxiety were trending toward significant relationships with fatigue (<emph>p</emph> = 0.07). Thus, no variables were added to the multiple linear regression models as independent variables in keeping with the planned analytic approach. Per the recommendation of [<reflink idref="bib15" id="ref141">15</reflink>]), the mean-centered Bayesian estimated intraindividual mean values of the respective model sleep variable were added as covariates. Table 2 displays for the sample means and standard deviations of the Bayesian estimated sleep parameter IIV for parents (<emph>N</emph> = 81) and children (<emph>N</emph> = 72), and Table 3 the sample means and standard deviations of analyzed sleep parameters. Nine children had insufficient actigraphy data to estimate IIV (i.e. less than 5 days) due to data loss by equipment malfunction or unreported noncompliance (e.g. taking the Actiwatch-2 off after time-in-bed).</p> <p>Table 1. Participants' demographic information with potential covariate correlations with parent self-report fatigue and group difference p -values in parent self-report fatigue.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variable</th><th align="left" rowspan="2"><italic>M</italic> (<italic>SD</italic>)</th><th align="left" rowspan="2">Range</th><th align="left" colspan="2">Correlation with Fatigue</th></tr><tr><th /><th align="left"><italic>p</italic>-value</th><th align="left">Correlation</th></tr></thead><tbody><tr><td>Age</td><td>37.6 (6.85)</td><td>25-57</td><td>0.07</td><td>-0.21</td></tr><tr><td>*Child Age</td><td>8.81 (2.03)</td><td>6-12</td><td>0.12</td><td>-0.18</td></tr><tr><td>Number of Children</td><td>2.48 (1.37)</td><td>1-8</td><td>0.22</td><td>-0.14</td></tr><tr><th /><th align="left" rowspan="2">n (%)</th><th /><th align="left" colspan="2">Group Difference in Fatigue</th></tr><tr><th /><th /><th align="left"><italic>p</italic>-value</th><th /></tr><tr><td>Gender (Female)</td><td>58 (72)</td><td /><td>0.92</td><td /></tr><tr><td>*Child Gender (Male)</td><td>52 (72)</td><td /><td>0.77</td><td /></tr><tr><td>Race (White)</td><td>64 (79)</td><td /><td>0.89</td><td /></tr><tr><td>Marital Status (Married)</td><td>52 (64)</td><td /><td>0.81</td><td /></tr><tr><td>Sleep Apnea dx</td><td>8 (10)</td><td /><td>0.57</td><td /></tr><tr><td>Other Sleep Disorder dx</td><td>6 (7)</td><td /><td>0.77</td><td /></tr><tr><td>Use of Sleep Medication</td><td>32 (40)</td><td /><td>0.86</td><td /></tr><tr><td>Past Depression or Anxiety dx</td><td>45 (56)</td><td /><td>0.07</td><td /></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph> Variables are for parents unless specified as child. <emph>M</emph> = Mean, <emph>SD</emph> = Standard deviation, dx = Diagnosis. <emph>N</emph> = 81, *<emph>N</emph> = 72.</p> <p>Table 2. Characteristics of Bayesian sleep parameter variability estimates and parent average daily fatigue.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variable (Parent)*</th><th align="left"><italic>M</italic> (<italic>SD</italic>)</th><th align="left">Range</th></tr></thead><tbody><tr><td>Bedtime IIV (hh:mm)</td><td>1:09 (0:38)</td><td>0:20-4:46</td></tr><tr><td>Waketime IIV (hh:mm)</td><td>1:12 (0:35)</td><td>0:24-3:19</td></tr><tr><td>TST IIV (minutes)</td><td>75.92 (26.94)</td><td>19.76-167</td></tr><tr><td>SOL IIV (minutes)</td><td>26.18 (18.59)</td><td>5.44-121.4</td></tr><tr><td>SE IIV (percentage)</td><td>8.01 (4.36)</td><td>1.79-25.66</td></tr><tr><td>TWT IIV (minutes)</td><td>40.59 (19.29)</td><td>9.76-130.72</td></tr><tr><td>Average Daily Fatigue (0–100)</td><td>50.83 (17.69)</td><td>2.31-83</td></tr><tr><th align="left">Variable (Child) <sup>+</sup></th><th align="left"><italic>M</italic> (<italic>SD</italic>)</th><th align="left">Range</th></tr><tr><td>Bedtime IIV (hh:mm)</td><td>1:07 (0:38)</td><td>0:15-3:47</td></tr><tr><td>Waketime IIV (hh:mm)</td><td>1.00 (0:21)</td><td>0:22-1:50</td></tr><tr><td>TST IIV (minutes)</td><td>79.99 (29.81)</td><td>33.68-170.16</td></tr><tr><td>SOL IIV (minutes)</td><td>37.44 (37.47)</td><td>7.4-171.84</td></tr><tr><td>SE IIV (percentage)</td><td>8.88 (7.06)</td><td>2.17-33.23</td></tr><tr><td>TWT IIV (minutes)</td><td>56.38 (42.47)</td><td>14.19-187.92</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note.</emph> IIV = Intraindividual Variability, TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, <emph>M</emph> = Mean, <emph>SD</emph> = Standard deviation. *<emph>N</emph> = 81. <sups>+</sups><emph>N</emph> = 72.</p> <p>Table 3. Characteristics of sleep parameter averages for parents and children.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variable (Parent)*</th><th align="left"><italic>M</italic> (<italic>SD</italic>)</th><th align="left">Range</th></tr></thead><tbody><tr><td>Bedtime (hh:mm)</td><td>11:03 p.m. (01:17)</td><td>08:33 p.m.-04:27 a.m.</td></tr><tr><td>Waketime (hh:mm)</td><td>07:19 a.m. (01:19)</td><td>04:56 a.m.-01:05 p.m.</td></tr><tr><td>TST (minutes)</td><td>398.31 (49.32)</td><td>226.75-481.21</td></tr><tr><td>SOL (minutes)</td><td>27.70 (19.1)</td><td>5.25-124.19</td></tr><tr><td>SE (percentage)</td><td>81.32 (6.45)</td><td>62.08-92.24</td></tr><tr><td>TWT (minutes)</td><td>91.50 (34.36)</td><td>35.35-205.25</td></tr><tr><th align="left">Variable (Child)<sup>+</sup></th><th align="left"><italic>M</italic> (<italic>SD</italic>)</th><th align="left">Range</th></tr><tr><td>Bedtime (hh:mm)</td><td>09:50 p.m. (01:22)</td><td>07:52 p.m.-04:49 a.m.</td></tr><tr><td>Waketime (hh:mm)</td><td>07:26 a.m. (01:15)</td><td>05:09 a.m.-12:21 p.m.</td></tr><tr><td>TST (minutes)</td><td>453.92 (54.53)</td><td>213.7-538.17</td></tr><tr><td>SOL (minutes)</td><td>35.07 (29.2)</td><td>4.71-178.21</td></tr><tr><td>SE (percentage)</td><td>79.07 (8.03)</td><td>41.25-89.92</td></tr><tr><td>TWT (minutes)</td><td>121.75 (47.63)</td><td>45.8-293</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note.</emph> TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, <emph>M</emph> = Mean, <emph>SD</emph> = Standard deviation. *<emph>N</emph> = 81. <sups>+</sups><emph>N</emph> = 72.</p> <p>Sample means of both parents and children suggest possible overall poor sleep quality as defined by an SE of <85% and SOL of >30 min ([<reflink idref="bib108" id="ref142">108</reflink>]). Although parent average SOL is just within the recommended range, the mean parent SE of 81% is lower than the cut-off. In addition, the average TST for the sample is less than the recommended amount of sleep for a healthy adult (7 hours; [<reflink idref="bib118" id="ref143">118</reflink>]). As expected, based on study inclusion criteria, children's SOL was above the cut-off for poor sleep quality and TST was well below the recommended minimum of 9 hours for school-aged children ([<reflink idref="bib92" id="ref144">92</reflink>]).</p> <hd id="AN0184233761-16">Multiple linear regression analyses</hd> <p>See Table 4 for results of regression estimates and confidence intervals for the initial thirty-six multiple linear regressions performed with IIV of child bedtime, waketime, TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, and TWT<subs>o</subs> as the predictors and IIV of parent bedtime, waketime, TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, and TWT<subs>o</subs> as the outcomes. Contrary to hypothesized relationships between child sleep IIV and parent sleep IIV, no significant associations were observed between any estimated IIV of a child sleep parameter and estimated IIV of a parent sleep parameter, even without correcting for multiple comparisons (<emph>p</emph> > 0.072 for all). Proposed mediation analyses, therefore, were not conducted since initial relationships between a child sleep IIV predictor and parent sleep IIV mediator could not be established ([<reflink idref="bib69" id="ref145">69</reflink>]). However, the second set of proposed analyses relevant to hypothesized relationships between parent sleep IIV and average daily fatigue were still completed.</p> <p>Table 4. Estimates and 95% confidence intervals from linear regression analyses between all child sleep Bayesian intraindividual variability estimates as predictors (X) and parent sleep Bayesian intraindividual variability estimates as outcomes (Y).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Predictor</th><th align="left" colspan="6">Outcome</th></tr><tr><th align="left">Parent BTIIV</th><th align="left">Parent WT IIV</th><th align="left">Parent TST IIV</th><th align="left">Parent SOL IIV</th><th align="left">Parent SEIIV</th><th align="left">Parent TWT IIV</th></tr></thead><tbody><tr><td>Child BT IIV</td><td>0.09[-0.21-0.40]</td><td>0.22[-0.05-0.49]</td><td>5.22[-6.85-17.29]</td><td>-0.71[-7.97-6.55]</td><td>-0.47[-2.26-1.32]</td><td>-1.2[-8.95-6.54]</td></tr><tr><td>Child WT IIV</td><td>0.19[-0.29-0.67]</td><td>0.39[-0.04-0.82]</td><td>12.81[-5.96-31.58]</td><td>4.39[-6.83-15.62]</td><td>2.32[-0.46-5.10]</td><td>10.67[-0.99-22.32]</td></tr><tr><td>Child TST IIV</td><td>0[-0.01-0.00]</td><td>0[-0.01-0.01]</td><td>0.13[-0.10-0.36]</td><td>0.04[-0.10-0.17]</td><td>0[-0.03-0.04]</td><td>0.05[-0.09-0.20]</td></tr><tr><td>Child SOL IIV</td><td>0[-0.00-0.01]</td><td>0[-0.00-0.01]</td><td>0.07[-0.12-0.26]</td><td>0.04[-0.07-0.15]</td><td>0[-0.02-0.03]</td><td>0.01[-0.11-0.13]</td></tr><tr><td>Child SE IIV</td><td>0.01[-0.03-0.04]</td><td>-0.01[-0.04-0.02]</td><td>0.13[-1.26-1.53]</td><td>0.14[-0.65-0.93]</td><td>-0.03[-0.23-0.17]</td><td>-0.07[-0.90-0.76]</td></tr><tr><td>Child TWT IIV</td><td>0[-0.00-0.01]</td><td>0[-0.01-0.00]</td><td>0.08[-0.16-0.31]</td><td>0.03[-0.10-0.16]</td><td>0[-0.03-0.04]</td><td>0[-0.14-0.14]</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>Note.</emph> IIV = Intraindividual Variability, BT = Bedtime, WT = Waketime, TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time. Values in square brackets indicate the 95% confidence interval for each regression estimate. Covariates: Bayesian estimated intraindividual mean values of the respective child sleep variables. Bedtime and Waketime were measured by a combination of objective actigraphy and subjective visual inspection. <emph>N</emph> = 72.</p> <p>Table 5 shows estimates, confidence intervals, and <emph>p</emph>-values of the six multiple linear regressions performed with IIV of parent bedtime, waketime, TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, and TWT<subs>o</subs> as the predictors and parent average daily fatigue as the outcome. Results indicated that greater parent Bayesian estimated TST<subs>o</subs> IIV was significantly associated with higher average daily fatigue (β = 0.17, 95% CI (0.02, 0.32), <emph>F</emph>(<reflink idref="bib3" id="ref146">3</reflink>, 78) = 5.39, <emph>p</emph> = 0.023, <emph>R</emph><sups>2</sups><emph>Adjusted</emph> =.051), as seen in Figure 1. No other significant associations were found between estimated parent sleep parameter IIV and average daily fatigue.</p> <p>Table 5. Multiple linear regression analyses with parent Bayesian sleep parameter intraindividual variability estimates as predictors (X) and parent daily average fatigue as the outcome (Y).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Model Variable</th><th align="left">Estimate</th><th align="left">CI</th><th align="left"><italic>p</italic>-value</th></tr></thead><tbody><tr><td>Parent Bedtime IIV</td><td>4.62</td><td>-1.82 to 11.05</td><td>0.157</td></tr><tr><td>Parent Waketime IIV</td><td>5.29</td><td>-2.29 to 12.87</td><td>0.169</td></tr><tr><td>Parent TST IIV</td><td>0.17</td><td>0.02 to 0.32</td><td><bold>0.023</bold></td></tr><tr><td>Parent SOL IIV</td><td>0.20</td><td>-0.06 to 0.46</td><td>0.138</td></tr><tr><td>Parent SE IIV</td><td>0.59</td><td>-0.51 to 1.69</td><td>0.289</td></tr><tr><td>Parent TWT IIV</td><td>0.16</td><td>-0.09 to 0.41</td><td>0.206</td></tr></tbody></table> </ephtml> </p> <p>5 <emph>Note.</emph> IIV = Intraindividual Variability, TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, CI = Confidence Interval. Covariates: Bayesian estimated intraindividual mean values of the respective sleep variables. Bedtime and Waketime were measured by a combination of objective actigraphy and subjective visual inspection. <emph>N</emph> = 81. Bold text marks a significant <emph>p</emph>-value at <0.05.</p> <p>Graph: Figure 1. The association between parent average daily fatigue (0–100) and mean-centered Bayesian estimated total sleep time intraindividual variability (minutes). Note. TST = Total sleep time, IIV = Intraindividual variability. N = 81.</p> <hd id="AN0184233761-17">Post hoc analyses</hd> <p>Despite insufficient evidence to examine proposed mediation effects, post hoc analyses were completed to explore possible relationships between child sleep IIV and average parent fatigue unmediated by parent sleep IIV. Table 6 shows estimates, confidence intervals, and <emph>p</emph>-values of six multiple linear regressions performed with IIV of child bedtime, waketime, TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, and TWT<subs>o</subs> as the predictors and parent average daily fatigue as the outcome. Echoing initial exploratory analysis results, no significant relationships were observed between any child sleep variable IIV and parent fatigue. Regressions of parent TST<subs>o</subs> IIV on average levels and variability of other parent sleep variables were also conducted due to its exclusive significance. Results from six additional linear regression models with outcomes of other parent sleep variables often used to indicate sleep quality showed that higher parent TST<subs>o</subs> IIV was significantly associated with lower average TST<subs>o</subs> and SE<subs>o</subs>, higher bedtime and waketime IIV, and higher average SOL<subs>o</subs> and TWT<subs>o</subs> (Table 7). These findings are consistent with previous research showing TST IIV's association with poor sleep and more sleep complaints ([<reflink idref="bib85" id="ref147">85</reflink>]). Additional post hoc analyses outside the original study scope explored whether intraindividual average levels of child bedtime, waketime, TST<subs>o</subs>, SOL<subs>o</subs>, SE<subs>o</subs>, or TWT<subs>o</subs>, rather than IIV estimates, could be related to either parent objective TST IIV as the sole significant step two predictor or parent fatigue as the outcome. No significant results were observed when the child sleep variable intraindividual averages were regressed on parent objective TST IIV (Table 8) and parent average fatigue (Table 9), providing no evidence for mediation effects involving child sleep parameter averages. Although unexpected, the lack of significant associations between child and parent actigraphic variables is not unprecedented ([<reflink idref="bib64" id="ref148">64</reflink>]) and should continue to be explored.</p> <p>Table 6. Multiple linear regression analyses with child Bayesian sleep parameter intraindividual variability estimates as predictors (X) and parent daily average fatigue as the outcome (Y).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Model Variable</th><th align="left">Estimate</th><th align="left">CI</th><th align="left"><italic>p</italic>-value</th></tr></thead><tbody><tr><td><bold>Child Bedtime IIV</bold></td><td>3.01</td><td>-4.91 to 10.92</td><td>0.451</td></tr><tr><td><bold>Child Waketime IIV</bold></td><td>4.17</td><td>-8.16 to 16.50</td><td>0.502</td></tr><tr><td><bold>Child TST IIV</bold></td><td>0.02</td><td>-0.13 to 0.17</td><td>0.782</td></tr><tr><td><bold>Child SOL IIV</bold></td><td>0</td><td>-0.12 to 0.13</td><td>0.968</td></tr><tr><td><bold>Child</bold> SE <bold>IIV</bold></td><td>0.63</td><td>-0.26 to 1.51</td><td>0.162</td></tr><tr><td><bold>Child TWT IIV</bold></td><td>0.09</td><td>-0.06 to 0.24</td><td>0.229</td></tr></tbody></table> </ephtml> </p> <p>6 <emph>Note.</emph> IIV = Intraindividual Variability, TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, CI = Confidence Interval. Covariates: Bayesian estimated intraindividual mean values of the respective sleep variables. Bedtime and Waketime were measured by a combination of objective actigraphy and subjective visual inspection. <emph>N</emph> = 72.</p> <p>Table 7. Multiple linear regression analyses with parent Bayesian sleep parameter intraindividual variability total sleep time estimate as predictor (X) and parent Bayesian sleep parameter variabilities and averages as the outcomes (Y).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Model Variable</th><th align="left">Estimate</th><th align="left">CI</th><th align="left"><italic>p</italic>-value</th></tr></thead><tbody><tr><td><bold>Bedtime IIV</bold></td><td>0.01</td><td>0.01 to 0.02</td><td><bold><0.001</bold></td></tr><tr><td><bold>Waketime IIV</bold></td><td>0.01</td><td>0.01 to 0.02</td><td><bold><0.001</bold></td></tr><tr><td><bold>TST Average</bold></td><td>-0.34</td><td>-0.64 to -0.04</td><td><bold>0.028</bold></td></tr><tr><td><bold>SOL Average</bold></td><td>0.11</td><td>0.03 to 0.19</td><td><bold>0.005</bold></td></tr><tr><td><bold>SE Average</bold></td><td>-0.06</td><td>-0.10 to -0.03</td><td><bold><0.001</bold></td></tr><tr><td><bold>TWT Average</bold></td><td>0.40</td><td>0.19 to 0.61</td><td><bold><0.001</bold></td></tr></tbody></table> </ephtml> </p> <p>7 <emph>Note.</emph> IIV = Intraindividual Variability, TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, CI = Confidence Interval. Covariate: Bayesian estimated intraindividual TST average value, except when TST average was the outcome. Bedtime and Waketime were measured by a combination of objective actigraphy and subjective visual inspection. <emph>N</emph> = 81. Bold text marks a significant <emph>p</emph>-value at <0.05.</p> <p>Table 8. Multiple linear regression analyses child Bayesian sleep parameter intraindividual average estimates as predictors (X) and parent Bayesian total sleep time intraindividual variability estimates as the outcome (Y).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Model Variable</th><th align="left">Estimate</th><th align="left">CI</th><th align="left"><italic>p</italic>-value</th></tr></thead><tbody><tr><td><bold>Child Bedtime Average</bold></td><td>6.58</td><td>-0.60 to 13.77</td><td>0.072</td></tr><tr><td><bold>Child Waketime Average</bold></td><td>4.57</td><td>-1.44 to 10.57</td><td>0.134</td></tr><tr><td><bold>Child TST Average</bold></td><td>-0.09</td><td>-0.28 to 0.11</td><td>0.379</td></tr><tr><td><bold>Child SOL Average</bold></td><td>-0.00</td><td>-0.58 to 0.57</td><td>0.991</td></tr><tr><td><bold>Child SE Average</bold></td><td>-0.36</td><td>-1.15 to 0.43</td><td>0.364</td></tr><tr><td><bold>Child TWT Average</bold></td><td>0.05</td><td>-0.08 to 0.18</td><td>0.453</td></tr></tbody></table> </ephtml> </p> <p>8 <emph>Note.</emph> TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, CI = Confidence Interval. Bedtime and Waketime were measured by a combination of objective actigraphy and subjective visual inspection. <emph>N</emph> = 72.</p> <p>Table 9. Multiple linear regression analyses with child Bayesian sleep parameter intraindividual average estimates as predictors (X) and parent daily average fatigue as the outcome (Y).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Model Variable</th><th align="left">Estimate</th><th align="left">CI</th><th align="left"><italic>p</italic>-value</th></tr></thead><tbody><tr><td><bold>Child Bedtime Average</bold></td><td>-0.34</td><td>-5.04 to 4.37</td><td>0.886</td></tr><tr><td><bold>Child Waketime Average</bold></td><td>-0.04</td><td>-3.95 to 3.87</td><td>0.984</td></tr><tr><td><bold>Child TST Average</bold></td><td>0.02</td><td>-0.10 to 0.15</td><td>0.718</td></tr><tr><td><bold>Child SOL Average</bold></td><td>-0.02</td><td>-0.39 to 0.35</td><td>0.927</td></tr><tr><td><bold>Child SE Average</bold></td><td>0.01</td><td>-0.50 to 0.52</td><td>0.965</td></tr><tr><td><bold>Child TWT Average</bold></td><td>-0.01</td><td>-0.09 to 0.08</td><td>0.835</td></tr></tbody></table> </ephtml> </p> <p>9 <emph>Note.</emph> TST = Total Sleep Time, SOL = Sleep Onset Latency, SE = Sleep Efficiency, TWT = Total Wake Time, CI = Confidence Interval. Bedtime and Waketime were measured by a combination of objective actigraphy and subjective visual inspection. <emph>N</emph> = 72.</p> <hd id="AN0184233761-18">Discussion</hd> <p></p> <hd id="AN0184233761-19">Principal findings</hd> <p>The purpose of this study was to examine the associations between IIV of child sleep variables, IIV of parent sleep variables, and mean self-reported parent fatigue a sample of families with children on the autism spectrum, including potential mediation effects. It was specifically hypothesized that greater IIV of objective child sleep variables would be associated with higher mean levels of self-reported parent fatigue and that significant relationships between IIV of child sleep variables and mean self-reported parent fatigue would be mediated by IIV of objective parent sleep variables. Hypotheses were generally unsupported related to predicted effects, as insignificant exploratory analyses offered no evidence for further investigation into mediation. Only objective parent TST IIV was significantly related to parent mean fatigue. To the author's knowledge, this is the first study to show that greater objective TST IIV is associated with higher average levels of fatigue. However, none of the remaining parent predictors (IIV of parent combination bedtime, combination waketime, objective SOL, objective SE, and objective TWT) were significantly associated with parent average fatigue. Notably, limitations in sample size may not have allowed for the detection of mediation effects, which should continue to be explored.</p> <hd id="AN0184233761-20">Interpretation</hd> <p>Although significant findings were limited, an important contribution of the current study stems from the use of sleep IIV as a potential explanation for previously reported inconsistencies related to associations between objective sleep and fatigue. This result provides possible support for the use of IIV in future research on fatigue, particularly objective TST IIV. The potential association of objective TST IIV and fatigue in parents supports past findings suggesting that TST variability may be a unique aspect of sleep problems and related symptoms ([<reflink idref="bib40" id="ref149">40</reflink>]), especially considering no other significant associations were found. Among sleep variables, variability in TST is associated with the highest increase in risk for a range of mental health and medical conditions, such as depression, stress, pain, breathing issues, and gastrointestinal problems ([<reflink idref="bib15" id="ref150">15</reflink>]; [<reflink idref="bib105" id="ref151">105</reflink>]). This finding is also consistent with a recent review by the National Sleep Foundation ([<reflink idref="bib106" id="ref152">106</reflink>]) that highlights the importance of sleep regularity in duration and timing, pointing to evidence of TST variability's association with less average sleep, increased daytime sleepiness, and lower sleep quality. Of note, the current result would not have survived multiple-test correction. However, post hoc analyses showing that higher parent TST variability is correlated with poorer sleep as indicated by other parent variables (see Table 7) may help bolster the claim of its significance, in line with some previous research ([<reflink idref="bib8" id="ref153">8</reflink>]).</p> <p>Although prior research on sleep IIV and fatigue is limited, subjective TST IIV has been shown to correlate with fatigue in adolescents ([<reflink idref="bib44" id="ref154">44</reflink>]) and to predict overall well-being ([<reflink idref="bib65" id="ref155">65</reflink>]). One potential explanation for the specific influence of TST variability is its connection to subjective sleep quality. For example, the relationship between higher subjective TST IIV and poorer subjective well-being found by [<reflink idref="bib65" id="ref156">65</reflink>] was partially mediated by subjective sleep quality. In addition, a study by [<reflink idref="bib103" id="ref157">103</reflink>] found that individuals with multiple sclerosis who had poor sleep quality and high objective TST variability scored highest on measures of fatigue. These findings could suggest a relationship between TST variability and sleep quality regarding fatigue that could be found in both objective and subjective measures of sleep duration. In fact, the combination of TST variability and subjective sleep quality might be a valuable focus of future research and treatment ([<reflink idref="bib103" id="ref158">103</reflink>]).</p> <p>As suggested previously ([<reflink idref="bib54" id="ref159">54</reflink>]), TST variability may encourage a desynchrony between the body's two processes that regulate sleep: 1) the homeostatic/physiological sleep drive (Process S) and 2) the endogenous circadian rhythm (Process C; [<reflink idref="bib19" id="ref160">19</reflink>]). Any initial mismatch between Process S and Process C could lead to maintained periods of variable sleep duration due to an incongruent homeostatic sleep drive and circadian rhythm, including both sleep deprivation and longer than average sleep. Mechanistically, independent spans of short and long TST could potentially influence fatigue through associations with perceived worse sleep quality, negative mood, poorer cognitive performance, elevated blood pressure, increased body-mass index, lower energy expenditure, chronic stress system activation, and social jetlag ([<reflink idref="bib13" id="ref161">13</reflink>]; [<reflink idref="bib25" id="ref162">25</reflink>]; [<reflink idref="bib47" id="ref163">47</reflink>]; [<reflink idref="bib123" id="ref164">123</reflink>]; [<reflink idref="bib124" id="ref165">124</reflink>]). Switching between periods of short, typical, and longer recovery sleep (i.e. IIV) may be particularly relevant to fatigue, which average TST alone does not fully capture.</p> <p>The current study highlights the potential importance of further exploring objective sleep parameter variability in addition to average levels, particularly TST. While previous research on sleep parameter variability has reported many associations, the majority of IIV findings are regarding TST, bedtime, and waketime ([<reflink idref="bib14" id="ref166">14</reflink>]). Along with the present results, the lack of significant findings of both objective and subjective SOL, SE, and TWT variability may suggest smaller general correlations with commonly studied variables. However, one potential reason may be a broader range of IIV mathematically allotted to TST, bedtime, and waketime via individual standard deviation compared to variables such as SOL and TWT. Although the current study chose to focus on measurement error reduction using Bayesian estimation of individual standard deviation, the most common measurement of IIV ([<reflink idref="bib15" id="ref167">15</reflink>]; [<reflink idref="bib121" id="ref168">121</reflink>]), future studies could consider IIV measurements of each variable that better handle between-variable comparisons, such as the unitless coefficient of variation (i.e. ratio of the standard deviation to the mean as a percentage; [<reflink idref="bib62" id="ref169">62</reflink>]).</p> <p>Another proposed explanation for the higher frequency of TST, bedtime, and waketime findings suggests that IIV of other variables (e.g. SOL, TWT) is less susceptible to direct environmental and behavioral impacts resulting in less variation overall ([<reflink idref="bib14" id="ref170">14</reflink>]). However, the current study's nonsignificant results for bedtime and waketime variability are also not entirely surprising, as [<reflink idref="bib61" id="ref171">61</reflink>] reported that subjective bedtime IIV was not significantly correlated with fatigue in undergraduate students. Despite TST IIV appearing to be most relevant for average daily fatigue in this sample, the IIV of other sleep variables should continue to be explored using both subjective and objective measurement with alternative analytic methods.</p> <p>In addition, the current study did not find evidence to support significant relationships or subsequent mediation effects among child objective sleep IIV, parent objective TST IIV, and average parent daily fatigue despite an established relationship between sleep in autistic children and their parents ([<reflink idref="bib80" id="ref172">80</reflink>]). However, it is possible that significant relationships exist between IIV of the same objective sleep parameters in autistic children and their parents in other samples or for different areas of parent functioning, which future research could examine. Another hypothesized explanation for the lack of mediation was the focus on child sleep IIV rather than other aspects of child sleep or behavior that could impact parent sleep variability or fatigue. Since the data were available, post hoc analyses were run using average levels of the same child sleep parameters (bedtime, waketime, objective TST, objective SOL, objective SE, and objective TWT). Although no significant results were found to suggest evidence for direct or indirect relationships among average child sleep variables, parent objective TST IIV, and parent average fatigue (see Table 8 and Table 9), future studies could continue to explore other aspects of child sleep and behavior, such as sleep quality, challenging behaviors, or autism symptom severity. In addition, the connection between parent objective TST IIV and fatigue in families with children on the spectrum should be compared to families with TD children. Currently, there are no studies known to the authors comparing sleep variability in parents of autistic children to parents of TD children.</p> <p>The primary finding of this study has several clinical implications. First, increased awareness of parent sleep, independent of child sleep, may be important for overall parent health and well-being related to negative outcomes associated with fatigue, such as anxiety, depression, stress, and impaired cognitive ability ([<reflink idref="bib49" id="ref173">49</reflink>]; [<reflink idref="bib122" id="ref174">122</reflink>]). Specifically, research focus on the consistency of objective TST could help further determine its potential connections with sleep quality and fatigue, and ultimately, whether sleep duration variability could be targeted in the treatment of fatigue. These results add to the growing literature suggesting the importance of sleep consistency for many aspects of health ([<reflink idref="bib25" id="ref175">25</reflink>]). Furthermore, findings from the current and past studies ([<reflink idref="bib12" id="ref176">12</reflink>]) may indicate that sleep consistency is more impactful than sleep duration in certain contexts, which could be explored. Second, future research could examine the relationships among TST IIV, parenting, and parent-child interactions. Results may highlight the importance of sleep duration consistency in parents for the well-being of children, potentially mediated by factors like parental fatigue given evidence for its associations with negative parenting ([<reflink idref="bib29" id="ref177">29</reflink>]; [<reflink idref="bib101" id="ref178">101</reflink>]). Finally, a focus on parent TST IIV for the health of both parents and children may be particularly relevant for families with children on the autism spectrum.</p> <p>Although their children's sleep likely impacts all parents, parents of children with higher levels of sleep disturbance, more likely in autistic children, typically report worse sleep than parents of children who are good sleepers ([<reflink idref="bib50" id="ref179">50</reflink>]; [<reflink idref="bib64" id="ref180">64</reflink>]). One study included a finding of greater bedtime variability in parents of children with sleep problems ([<reflink idref="bib116" id="ref181">116</reflink>]). Some basic sleep parameter averages and IIV from our sample are in line with previously reported levels of sleep variables in children with sleep disturbances and their parents, while some are not. For example, parents in our sample had less TST, worse SE, and more SE IIV than [<reflink idref="bib82" id="ref182">82</reflink>] reported for healthy-sleeping adults. Compared to children without sleep disturbances and their parents ([<reflink idref="bib116" id="ref183">116</reflink>]), children in our sample had more variability in bedtimes and waketimes and higher levels of SOL. Our sample's parents also had increased SOL but higher SE and comparative TST. However, our results are relatively consistent with another recent study examining objective sleep in autistic children and adolescents and their parents ([<reflink idref="bib64" id="ref184">64</reflink>]), though our parents and children had higher levels of SOL. Overall, the wide variance of sleep parameter collection methods (e.g. objective versus subjective) and values reported from between studies makes comparison relatively difficult, with further examination particularly warranted into how objective sleep in different populations may be related to sleep quality.</p> <hd id="AN0184233761-21">Study strengths</hd> <p>This study was the first to explore associations between objective sleep IIV and fatigue in parents of children with ASD to the author's knowledge. Exploring these variables in this population is important, given the level of sleep problems experienced by both children with ASD and their parents ([<reflink idref="bib37" id="ref185">37</reflink>]; [<reflink idref="bib84" id="ref186">84</reflink>]). The primary finding increases understanding regarding the relationship of consistency in TST related to parent fatigue, which may have implications for interventions with family-wide benefits. In addition, this study provides an impetus to continue exploring associations among child sleep variability, parent sleep variability, and other aspects of parent well-being with possible indirect impacts on child well-being.</p> <p>The statistical methods performed are another strength, which allowed observations to be used efficiently while accounting for measurement error ([<reflink idref="bib121" id="ref187">121</reflink>]). Furthermore, the current sample size is larger than most previous studies examining actigraphic sleep in families of children with autism ([<reflink idref="bib5" id="ref188">5</reflink>]; [<reflink idref="bib70" id="ref189">70</reflink>]). Finally, the 14 days of data collected for this study is at least double that of most prior studies ([<reflink idref="bib6" id="ref190">6</reflink>]; [<reflink idref="bib97" id="ref191">97</reflink>]).</p> <hd id="AN0184233761-22">Study limitations and future directions</hd> <p>The current findings should be interpreted in light of limitations. First, it is important to note that interpretations of the results should be viewed with caution due to the exploratory nature of the analyses, the limited sample size, and large number of tests performed without multiple test corrections. Therefore, future research should attempt to replicate this finding. In addition, the strain on power from the aforementioned limitations may have restricted the current study's ability to detect meaningful effects to establish mediation evidence, including insufficient child actigraphy data that reduced the sample size of initial exploratory analyses. Second, some evidence suggests that actigraphy may underestimate SOL ([<reflink idref="bib104" id="ref192">104</reflink>]), which is particularly salient given that trouble falling asleep is one of the most common sleep problems reported in children on the spectrum ([<reflink idref="bib73" id="ref193">73</reflink>]). Therefore, the use of actigraphy may be unreliable for the lack of findings regarding SOL. Third, unaccounted-for aspects of variability, such as seasonal changes and weekend versus weekday differences, may have influenced results and could be explored in later investigations. Fourth, participants are from a convenience sample of three separate studies addressing sleep difficulties in children on the autism spectrum. As such, results may not generalize to the broader population of families with autistic children. Finally, study variables were collected simultaneously, thus not allowing for causal implications.</p> <p>Future exploration should focus on the comparison of sleep, including sleep variability, in parents of autism children and parents of TD children, as current research is limited. In addition, research could increase knowledge about the role of sleep variability (parent and child) in parental fatigue development by implementing experimental or longitudinal design, increasing sample size, and using both subjective and objective sleep measures to better understand the different sleep dimensions ([<reflink idref="bib2" id="ref194">2</reflink>]), including child sleep quality. Subsequent studies could also examine whether other variables play a role in the significant association found between parent objective TST IIV and average daily fatigue, such as co-occurring child diagnoses, child IQ, and parent stress and social support ([<reflink idref="bib45" id="ref195">45</reflink>]). In addition, potential bidirectional effects could be inspected between sleep IIV in autistic children and their parents with significant results compared to non-autistic child and parent dyads, as it remains to be seen whether this study's results are specific to autism.</p> <p>Treatment for parents with sleep problems should continue to be explored, particularly those with autistic children. Reducing IIV in sleep variables (e.g. TST, bedtime, waketime) is a core component of cognitive-behavioral therapy for insomnia (CBT-I), which is the first recommendation for improvement of significant sleep problems ([<reflink idref="bib87" id="ref196">87</reflink>]). The potential benefit of additional management strategies for stress, anxiety, and depression symptoms could also be examined in relation to sleep IIV. As has been previously reported ([<reflink idref="bib74" id="ref197">74</reflink>]), reducing child sleep difficulties through tailored sleep therapy for children on the spectrum may also positively impact parent sleep and fatigue despite the current insignificant child and parent sleep IIV associations.</p> <hd id="AN0184233761-23">Conclusion</hd> <p>Few studies have looked at the relationship between sleep IIV and fatigue, despite its potential as an explanation for inconsistent findings regarding objective sleep measurement and fatigue. The current study found that greater objective TST IIV was associated with higher average daily fatigue levels in parents of children on the autism spectrum, although child sleep IIV was unrelated to parent objective TST IIV. Results indicate that objective TST variability may be linked to parental fatigue with potential future treatment implications, like emphasis on parent sleep consistency. Subsequent research could examine causal links, the role of other child sleep variables or behavior, and indirect influences of TST IIV through fatigue.</p> <p>Dr Shawn Christ, Dr Chelsea B. Deroche, Dr Stephanie Takamatsu, Dr Deija McLean, Dr Mattina Davenport, Dr Pradeep Sahota, Dillon McCann, Julie E. Muckerman, MPH, Samantha Hunter, MS, University of Missouri—Department of Psychiatry, Department of Psychological Sciences, and Department of Child Health, Thompson Center for Autism and Neurodevelopment, Targeting Insomnia in School Aged Children with Autism Spectrum Disorder Clinical Trial (RECHArge; NCT04545606).</p> <ref id="AN0184233761-24"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref92" type="bt">1</bibl> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the United States Department of Defense USAMRAA Autism Research Program (W81XWH2010399).</bibtext> </blist> <blist> <bibl id="bib2" idref="ref21" type="bt">2</bibl> <bibtext> Braden Hayse</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-1033-1562 Melanie A Stearns</bibtext> </blist> <blist> <bibl id="bib3" idref="ref94" type="bt"></bibl> <bibtext>Graph</bibtext> </blist> <blist> <bibl id="bib4" idref="ref20" type="bt"></bibl> <bibtext>https://orcid.org/0000-0002-7699-2996 Micah O Mazurek</bibtext> </blist> <blist> <bibl id="bib5" idref="ref98" type="bt"></bibl> <bibtext>Graph</bibtext> </blist> <blist> <bibl id="bib6" idref="ref61" type="bt"></bibl> <bibtext>https://orcid.org/0000-0001-7715-6538 Kristin A Sohl</bibtext> </blist> <blist> <bibl id="bib7" idref="ref52" type="bt"></bibl> <bibtext>Graph https://orcid.org/0000-0003-0588-8742</bibtext> </blist> <blist> <bibtext> Neetu Nair is now affiliated to University of Kentucky, USA.</bibtext> </blist> </ref> <ref id="AN0184233761-25"> <title> References </title> <blist> <bibtext> Adkins K. 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  Data: Exploratory Analyses of Sleep Intraindividual Variability and Fatigue in Parents of Children on the Autism Spectrum
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  Data: <searchLink fieldCode="AR" term="%22Braden+Hayse%22">Braden Hayse</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1033-1562">0000-0002-1033-1562</externalLink>)<br /><searchLink fieldCode="AR" term="%22Melanie+A%2E+Stearns%22">Melanie A. Stearns</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7699-2996">0000-0002-7699-2996</externalLink>)<br /><searchLink fieldCode="AR" term="%22Micah+O%2E+Mazurek%22">Micah O. Mazurek</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7715-6538">0000-0001-7715-6538</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ashley+F%2E+Curtis%22">Ashley F. Curtis</searchLink><br /><searchLink fieldCode="AR" term="%22Neetu+Nair%22">Neetu Nair</searchLink><br /><searchLink fieldCode="AR" term="%22Wai+Sze+Chan%22">Wai Sze Chan</searchLink><br /><searchLink fieldCode="AR" term="%22Melissa+Munoz%22">Melissa Munoz</searchLink><br /><searchLink fieldCode="AR" term="%22Kevin+D%2E+McGovney%22">Kevin D. McGovney</searchLink><br /><searchLink fieldCode="AR" term="%22David+Q%2E+Beversdorf%22">David Q. Beversdorf</searchLink><br /><searchLink fieldCode="AR" term="%22Mojgan+Golzy%22">Mojgan Golzy</searchLink><br /><searchLink fieldCode="AR" term="%22Kristin+A%2E+Sohl%22">Kristin A. Sohl</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0588-8742">0000-0003-0588-8742</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zarah+H%2E+Ner%22">Zarah H. Ner</searchLink><br /><searchLink fieldCode="AR" term="%22Beth+Ellen+Davis%22">Beth Ellen Davis</searchLink><br /><searchLink fieldCode="AR" term="%22Nicole+Takahashi%22">Nicole Takahashi</searchLink><br /><searchLink fieldCode="AR" term="%22Christina+S%2E+McCrae%22">Christina S. McCrae</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Autism%3A+The+International+Journal+of+Research+and+Practice%22"><i>Autism: The International Journal of Research and Practice</i></searchLink>. 2025 29(4):958-974.
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  Data: 10.1177/13623613241292691
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  Data: 1362-3613<br />1461-7005
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  Label: Abstract
  Group: Ab
  Data: Fatigue is associated with numerous harmful physical and mental health outcomes. Despite the established relationship between sleep and fatigue, research examining sleep variability within a person (i.e. intraindividual variability; IIV) and fatigue is limited. In addition, the associations between child and parent sleep regarding parent fatigue have not been explicitly explored, which could be relevant for parents of autistic children with increased sleep disturbance likelihood. The current study used two weeks of objective sleep (actigraphy) and subjective fatigue data from 81 parents and their children to explore associations among child sleep IIV, parent sleep IIV, and parent average daily fatigue, including evaluating evidence for mediation. Sleep IIV was estimated using a validated Bayesian model. Linear regression analyses indicated that greater parent total sleep time IIV predicted significantly higher fatigue levels. Child sleep IIV was unrelated to parent sleep IIV and fatigue, unsupportive of hypothesized mediation. Similarly, post hoc analyses examining child sleep averages, parent total sleep time IIV, and average parent fatigue were insignificant. Findings cautiously support the uniqueness of total sleep time IIV within parental sleep's relationship with fatigue, independent of child sleep. Objective sleep IIV should continue to be examined in addition to average levels.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1466089
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1466089
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613241292691
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 958
    Subjects:
      – SubjectFull: Fatigue (Biology)
        Type: general
      – SubjectFull: Sleep
        Type: general
      – SubjectFull: Individual Characteristics
        Type: general
      – SubjectFull: Parents
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: Parent Influence
        Type: general
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Affective Behavior
        Type: general
      – SubjectFull: Child Behavior
        Type: general
      – SubjectFull: Missouri
        Type: general
      – SubjectFull: Autism Diagnostic Observation Schedule
        Type: general
    Titles:
      – TitleFull: Exploratory Analyses of Sleep Intraindividual Variability and Fatigue in Parents of Children on the Autism Spectrum
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Braden Hayse
      – PersonEntity:
          Name:
            NameFull: Melanie A. Stearns
      – PersonEntity:
          Name:
            NameFull: Micah O. Mazurek
      – PersonEntity:
          Name:
            NameFull: Ashley F. Curtis
      – PersonEntity:
          Name:
            NameFull: Neetu Nair
      – PersonEntity:
          Name:
            NameFull: Wai Sze Chan
      – PersonEntity:
          Name:
            NameFull: Melissa Munoz
      – PersonEntity:
          Name:
            NameFull: Kevin D. McGovney
      – PersonEntity:
          Name:
            NameFull: David Q. Beversdorf
      – PersonEntity:
          Name:
            NameFull: Mojgan Golzy
      – PersonEntity:
          Name:
            NameFull: Kristin A. Sohl
      – PersonEntity:
          Name:
            NameFull: Zarah H. Ner
      – PersonEntity:
          Name:
            NameFull: Beth Ellen Davis
      – PersonEntity:
          Name:
            NameFull: Nicole Takahashi
      – PersonEntity:
          Name:
            NameFull: Christina S. McCrae
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Autism: The International Journal of Research and Practice
              Type: main
ResultId 1