Telehealth cognitive behavioral therapy for insomnia in children with autism spectrum disorder: A pilot examining feasibility,satisfaction,and preliminary findings

Abstract

Insomnia is common in children with autism. Cognitive behavioral treatment for childhood insomnia may improve sleep and functioning in children with autism and their parents, but delivery involving multiple office visits limits accessibility. This single-arm pilot study tested telehealth delivery of eight-session cognitive behavioral treatment for childhood insomnia in 17 children (6–12 years) with autism spectrum disorder and insomnia and their parent(s). Treatment integrity was assessed each session (delivery, by therapist; receipt, participant understanding; and enactment, home practice). Treatment satisfaction was assessed after treatment. Children and parents wore actigraphs and completed electronic diaries for 2 weeks, children completed 5-min Holter Monitoring (assessed heart rate variability, physiological arousal indicator), and parents completed Aberrant Behavior Checklist before and after 1 month. Average integrity scores were high (98%, delivery; 93%, receipt; and 82%, enactment). Parents found cognitive behavioral treatment for childhood insomnia helpful, age-appropriate, and autism-friendly. Paired-samples t-tests (family-wise error controlled) indicated telehealth cognitive behavioral treatment for childhood insomnia improved child and parent sleep (objective and subjective) and functioning (child—decreased irritability, lethargy, stereotypy, hyperactivity; parent—decreased fatigue). At 1 month, inappropriate speech also decreased, but hyperactivity was no longer decreased. Other gains were maintained. Most children demonstrated reduced arousal following treatment. This pilot shows telehealth cognitive behavioral treatment for childhood insomnia is feasible and may improve child and parent sleep, child behavior and arousal, and parent fatigue. A randomized controlled trial of telehealth cognitive behavioral treatment for childhood insomnia for children with autism is needed.

Lay abstract

Insomnia is common in children with autism. Cognitive behavioral treatment for childhood insomnia (CBT-CI) may improve sleep and functioning in children with autism and their parents, but typical delivery involving multiple office visits can make it difficult for some children to get this treatment. This pilot study tested telehealth delivery of CBT-CI using computers, which allowed children and their parents to get the treatment at home. This pilot shows therapists that parents and children were able to use telehealth CBT-CI to improve child and parent sleep, child behavior and arousal, and parent fatigue. Parents found telehealth CBT-CI helpful, age-appropriate, and autism-friendly. Telehealth CBT-CI holds promise for treating insomnia in school-aged children with autism and deserves further testing.

Keywords

autism child cognitive behavioral therapy insomnia parent telehealth

Approximately 50%–80% of children with autism spectrum disorder (ASD) experience comorbid insomnia (Couturier et al., 2005; Krakowiak et al., 2008; Richdale & Schreck, 2009; Souders et al., 2009). Insomnia is associated with increased symptom severity in children with ASD (Goldman et al., 2011; Malow et al., 2006; Schreck et al., 2004) and worse sleep and increased stress in parents of children with ASD (Hodge et al., 2013; Hoffman et al., 2008). The etiology of comorbid insomnia in children with ASD is multifactorial due to various factors, including communication (Schreck et al., 2004), social skills (Malow et al., 2006), sensory difficulties (Goldman et al., 2009), mood (Hollway & Aman, 2011), cognitive and physiological hyperarousal (Mazurek & Petroski, 2015; Richdale, 1999), repetitive behaviors (Goldman et al., 2009), neurotransmitter and hormone regulation (Veatch et al., 2015), and genetics (Bourgeron, 2007). This prompts the need for insomnia treatments that target multiple symptoms.

Currently, the practice pathway developed by the Autism Treatment Network recommends behavioral sleep intervention as an initial approach to insomnia in children with ASD (Malow et al., 2012). These behavioral treatments (largely based on learning principles) improve sleep in very young children (Johnson et al., 2013; Malow et al., 2012, 2014) and lead to greater improvement relative to controls (Paine & Gradisar, 2011). However, a recent work (McCrae et al., 2020) suggests that a CBT-CI may hold greater promise than traditional behavioral-only techniques for treating insomnia and its consequences in school-aged children with ASD. Behavioral treatment alone does not address factors that significantly interfere with sleep but do not present until school age (i.e. school-related anxiety, worry due to bullying or other negative experiences, and increased academic and social demands). Thus, CBT-CI includes cognitive and other techniques that are not developmentally appropriate for younger children, but are important for older children. These techniques as well as the involvement of both child and parent as agents of change in CBT-CI are particularly important to introduce in school-aged children because they foster the development of self-management and self-initiation of sleep strategies, potentially maximizing long-term effects and possibly carrying over into adolescence and adulthood. To this effect, multiple studies have demonstrated the efficacy of CBT-CI in school-aged children with insomnia. For example, a systematic review and meta-analysis by Åslund et al. (2018) found that CBT-CI-based methods produced significant differences in total sleep time (TST) and sleep onset latency (SOL) in school-aged children and adolescents, consistent with what has been reported in adults following treatment with CBT-CI. A recent pilot study (McCrae et al., 2020) of in-person eight-session CBT-CI in school-aged youth with ASD showed that the intervention was feasible (15 out of 17 children with ASD completed all sessions), and preliminary results suggested promising efficacy. CBT-CI led to moderate to large improvements in child subjective sleep (SOL, total wake time (TWT), TST, and sleep efficiency (SE)), objective sleep (SOL, TWT, and SE), bedtime/wake time regulation, and daytime behavior. Importantly, immediate improvements in parent subjective sleep (SOL, TWT, and SE), objective sleep (SOL, TWT, TST, and SE), and fatigue were also observed.

While in-person behavioral and cognitive behavioral treatments have been shown to be effective in children with ASD, access to this type of supportive healthcare remains a challenge, particularly for those in rural and underserved areas and/or with limited resources. These challenges are greater for rural children with ASD, who have less access to autism specialists, are of older age at diagnosis, have decreased care access, and need disproportionately higher emergency room visits than children from urban or suburban areas (Carbone et al., 2010; Mandell et al., 2007; Zhang et al., 2017). In line with these statistics, in a recent pilot study (McCrae et al., 2020), 58% of interested families were unable to participate due to travel burden. Furthermore, when asked for feedback regarding how CBT-CI could be improved, >50% of parents suggested offering remote treatment. Thus, a goal of this study was to evaluate remote (i.e. telehealth) delivery of CBT-CI for improving insomnia and associated symptoms in children with ASD and their parents.

Remote delivery of the intervention has the potential to increase access and reduce costs for families in rural and underserved areas. Technology-based treatment delivery options have received growing interest (Boisvert et al., 2010; Vismara et al., 2012; Wainer & Ingersoll, 2015). Studies have examined the use of telehealth for treatment delivery in school-aged children for problems ranging from pediatric headaches to behavioral management and reported promising results (Law et al., 2015; Simacek et al., 2017; Witmans et al., 2008). Although none have investigated remote delivery of sleep interventions for children with ASD, some studies have focused on early intervention and diagnostic services. A recent qualitative study of remote delivery of early intervention services found that parents and providers unanimously perceived advantages of remote versus in-person delivery, including reduced costs, time, and travel; flexible and regular support from home; and enhanced skill-building (Ashburner et al., 2016). Other small-scale studies have found that telemedicine and videoconference delivery of applied behavior analysis and parent training for children with ASD reduce travel burden and are feasible, effective, and acceptable (Barretto et al., 2006; Heitzman-Powell et al., 2014; Ingersoll & Berger, 2015; Vismara et al., 2012, 2013). A recent feasibility study of a telehealth-delivered CBT for anxiety in youth with ASD found high acceptability and efficacy (i.e. improved child anxiety and parent competence; Hepburn et al., 2016).

Another goal of this study was to evaluate improvement in another potential insomnia mechanism—hyperarousal. The Hyperarousal Model of Insomnia (Bonnet & Arand, 2010) assumes increased levels of physiological arousal interfere with falling and/or staying asleep. This model is highly relevant to children with ASD because they are at high risk of arousal dysregulation (Klusek et al., 2015; Lydon et al., 2016; Taylor & Corbett, 2014). Hyperarousal and the corresponding low heart rate variability (HRV) are observed in children with ASD (Zamzow et al., 2016). Regarding arousal-related symptoms, children with ASD commonly demonstrate anxiety-related nighttime behaviors (Wiggs & Stores, 2004) and show greater sensitivity to the sleep environment than children with other developmental problems (Schreck & Mulick, 2000). Furthermore, in a large study (n = 1348) of children with ASD, anxiety and sensory over-responsivity were shown to be associated with sleep problems across age groups, providing preliminary evidence that hyperarousal underlies insomnia in children with ASD (Mazurek & Petroski, 2015). Given that CBT-CI specifically targets hyperarousal, its effectiveness in improving sleep and daytime functioning in this at-risk population may be related to its ability to decrease arousal. As such, assessment of arousal within the context of CBT-CI would inform our understanding of this potential mechanism of insomnia in children with ASD.

This pilot study used a single-arm design to test the feasibility of telehealth delivery of an adapted CBT-CI protocol in school-aged children with ASD and comorbid insomnia and their parent(s). Based on a single-arm pilot of traditional, in-person delivery of CBT-CI in this population (McCrae et al., 2020), we hypothesized that participants would complete at least seven sessions on average; that treatment integrity scores would be at least 90% for delivery (by therapist), receipt (participant understanding), and enactment (home practice); and that the majority (80+%) of parents would indicate that the treatment was helpful, age-appropriate, reasonable, and autism-friendly. We also hypothesized that telehealth delivery would not differ from in-person delivery (Ashburner et al., 2016) on treatment integrity scores and treatment satisfaction indicators. To help guide further development and testing of the treatment, parents were also asked to provide open-ended feedback on treatment length, need for booster sessions, and most/least helpful treatment components.

Although the single-arm design of this study does not allow for examination of traditional efficacy, we hypothesized improvements in both child and parent subjective sleep (i.e. parent-assisted reports of child sleep diaries and parent sleep diaries) and objective sleep (i.e. assessed through actigraphy) immediately and at 1 month after treatment. We also hypothesized immediate (post-treatment) and sustained (1 month after treatment) improvements in daytime functioning for both children (i.e. reduced problematic behaviors) and parents (i.e. reduced fatigue). Finally, we hypothesized that children would show reductions in physiological arousal (assessed through HRV) following treatment.

Methods

Participants

Children (n = 17, 6–12 years) who met full Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria for ASD (diagnosis prior to 2013, Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 2000); in 2013 or later, Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013)) and insomnia (2013) and who had full-scale IQ of 75 or above and their parent(s) (n = 17, 23–61 years) were recruited from a database maintained by the University of Missouri (MU) HealthCare Division of Information Technology at MU. The MU Institutional Review Board approved (IRB#2004675) this registered trial (NCT-02755051). Parents gave written informed consent. Children aged 7 years and older provided written assent.

Children were screened in stages: (1) brief telephone interview, (2) clinical interview including Pediatric Sleep Questionnaire (PSQ, Chervin et al., 2000) and Children’s Sleep Habits Questionnaire (Owens et al., 2000, #337), (3) Holter monitoring (portable ambulatory electrocardiography (ECG) recording; one night) if scored 8/22 on Sleep-Disordered Breathing subscale of the PSQ, and (4) electronic sleep diaries completed with parental assistance (2 weeks). Inclusion criteria were as follows: (1) children between the ages of 6 and 12 years with full-scale IQ ⩾75 (to ensure ability to participate in cognitive treatment components), (2) parent/guardian living in the same home, (3) parent/guardian(s) must be able to read and understand study materials, (4) previous diagnosis of ASD established through evaluation using Autism Diagnostic Observation Schedule (ADOS) and/or Autism Diagnostic Interview–Revised (ADI-R), (5) child or parent report of insomnia (3+ months), (6) parent report of daytime dysfunction (mood, cognitive, social, and school) due to insomnia during the clinical interview, and (7) baseline diaries that indicated >30 min of SOL, wake time after sleep onset (WASO), or >5 nights early awakening. Exclusion criteria were as follows: (1) other sleep disorder diagnoses (e.g. apnea), (2) suspected apnea based on single night ambulatory Holter monitoring (apnea–hypopnea index >0; SpaceLabs, Seattle, WA), (3) seizure or bipolar disorder, (4) other major psychopathologies except depression or anxiety (e.g. suicidal ideation/intent, psychosis), (5) antibiotics for HIV or TB, (6) chemotherapeutic drugs, (7) participation in other non-pharmacological treatments for sleep or mood, and (8) recently prescribed stimulants, sleep medications, and/or melatonin (children were eligible if stabilized on these medications for 3+ months). Participants were not required to have access to a computer in the family’s home. Participants who had issues with access to a computer or the Internet would have been provided iPads and access to Internet service as part of the study. However, none of our participants had to use this resource.

We conducted 84 telephone screens and disqualified 60 potential participants (35 due to lack of insomnia, 3 due to sleep apnea, 13 declined, 4 due to distance (travel required for assessments), 4 due to exclusionary diagnosis (1 disclosed seizure disorder and 3 did not disclose), and 1 due to lack of parental guardian). Two participants who qualified for baseline screening declined. We conducted 22 clinical interviews. No children required Holter monitoring for apnea screening. Twenty-two children completed baseline diaries (five were disqualified; see inclusion criteria for insomnia). One child withdrew after session 2 (reason unknown/lost to contact), another after session 5 (family illness), and a third after session 7 (family illness and relocation). Seventeen children completed baseline, 14 received all eight treatment sessions and completed post-treatment, and 12 completed the 1 month follow-up.

Children received usual medical care for ASD. Families received treatment, were allowed to park (during assessment visits) at no charge, and were compensated US$50 after baseline and post-treatment, US$75 after follow-up, and US$10/visit to cover travel (three assessment visits).

Measures

Clinical interview

A brief in-person clinical interview was administered by doctoral students in counseling psychology (M.M., S.T.) and school psychology (M.D., D.M.). This interview included basic demographic information (e.g. age, sex, family household information, whether parents/guardians were living in the same home (see Table 1)), PSQ (Chervin et al., 2000), and Children’s Sleep Habits Questionnaire (to assess for insomnia symptoms and other sleep disorders). As previously described, participants who scored 8+/22 on the Sleep-Disordered Breathing subscale of the PSQ underwent Holter monitoring to screen for sleep apnea.

Table 1.

Demographics and health characteristics of child and parent samples.

Variable	Child (N = 17)	Parent (N = 17)
Age, years, M (SD)	8.53 (1.70)	36.06 (9.20)
Female, n (%)	3 (18%)	17 (100%)
Marital status, n (%)
Married	n/a	12 (71%)
Single	n/a	2 (12%)
Divorced	n/a	3 (18%)
Separated	n/a	0 (0%)
Race, n (%)
White	13 (76%)	14 (82%)
Black	1 (6%)	1 (6%)
Asian American/Pacific Islander	1 (6%)	1 (6%)
Native American/Alaskan Native	0 (0%)	1 (6%)
Biracial	2 (12%)	0 (0%)
Ethnicity, n (%)
Hispanic	0 (0%)	0 (0%)
Non-Hispanic	17 (100%)	17 (100%)
Education, n (%)
Graduated high school	n/a	2 (12%)
Some college	n/a	2 (12%)
Graduated college	n/a	10 (59%)
Graduate or professional school	n/a	3 (18%)
Adults living in home, M (SD)	1.88 (0.60)
Children living in home, M (SD)	2.47 (1.23)
Family income, n (%)
Below US$19,999	4 (24%)
US$20,000–US$39,999	2 (12%)
US$40,000–US$59,999	3 (18%)
US$60,000–US$79,999	1 (6%)
US$80,000–US$99,999	3 (18%)
Above US$100,000	4 (24%)
BMI, M (SD)	17.12 (4.35)	37.29 (13.58)
Sleep disorders, n (%)
OSA	0 (0%)	6 (35%)
Central sleep apnea	0 (0%)	1 (6%)
Narcolepsy	0 (0%)	1 (6%)
Restless leg syndrome/periodic limb movements disorder	0 (0%)	1 (6%)
Insomnia	17 (100%)	6 (35%)
Pediatric daytime sleepiness, M (SD)	15.94 (5.72)	n/a
Sleep medication, n (%)
Melatonin	10 (59%)	3 (18%)
Other	4 (24%)	4 (24%)
Stimulant medication, n (%)
Amphetamine and dextroamphetamine	2 (12%)	n/a
Atomoxetine	1 (6%)	n/a
Dexmethylphenidate	3 (18%)	n/a
Lisdexamfetamine dimesylate	3 (18%)	n/a
Methylphenidate	6 (35%)	n/a
None	7 (41%)	n/a
Other Medication, n (%)
Guanfacine/Guanfacine hydrochloride	4 (24%)	n/a
Fluoxetine	1 (6%)	n/a
Omeprazole	1 (6%)	n/a
Allergy	4 (24%)	n/a
Risperidone	1 (6%)	n/a
Child health conditions,^† M (SD)
Developmental delays	5 (29%)	n/a
Asperger’s disorder	3 (18%)	n/a
Attention-deficit/hyperactivity disorder	14 (82%)	n/a
Oppositional defiant disorder	4 (24%)	n/a
Anxiety disorder	9 (53%)	n/a
Depression	4 (24%)	n/a
Other	2 (12%)	n/a
Pediatric symptom checklist, M (SD)	15.94 (4.90)	n/a
Parent health
Hypertension or high blood pressure	n/a	4 (24%)
Diabetes	n/a	3 (18%)
Heart condition	n/a	2 (12%)
Mental illness	n/a	9 (53%)
Anxiety	n/a	6 (35%)
Depression	n/a	8 (47%)
Other	n/a	1 (6%)

†

Number of health conditions child has been diagnosed with from the following list (asthma, juvenile arthritis, thyroid problems, metabolic syndrome, ulcerative colitis or Crohn’s Disease, Developmental Delays, Asperger’s Disorder, Attention Deficit-Hyperactivity Disorder [ADHD or ADD], Oppositional Defiant Disorder (ODD), Anxiety Disorder, Depression, Type 1 or Type 2 Diabetes).

SD: standard deviation; BMI: body mass index; OSA: obstructive sleep apnea; n/a: not applicable.

Sleep

Children (with parental assistance) and parents completed electronic daily diaries that provided the following variables: SOL—time from initial lights out until sleep onset; TWT—time awake from lights out until out of bed; TST—TWT minus time in bed; SE—ratio of TST to time spent in bed × 100%; fatigue rating—parents only (0, none; 100, most intense imaginable). Means were computed for 2-week baseline/post-treatment/follow-up periods. Child bedtime/wake time variability was derived using averaged individual standard deviations for each period. Children and parent(s) wore actigraphs (Actiwatch-2; Phillips Respironics, Bend, OR) on their nondominant wrist 24 h/day for 2 weeks at baseline/post-treatment/follow-up to monitor body movement. Data were analyzed using proprietary software in 30-s epochs to provide objective estimates of SOL, TWT, TST, and SE. Sleep scoring windows were determined using a combination of actigraphy and self-report data (Goldman et al., 2011). For instance, validated sleep scoring algorithms computed sleep intervals. If estimated bedtime and wake time were outside a 30-min window of diary-reported values, sleep intervals were determined according to a standardized hierarchical procedure requiring 10 consecutive epochs below (bedtime) or above (wake time) threshold criteria (i.e. 100 activity counts/30 s; light below 1 μw/cm²). Actigraphy has been validated against polysomnography in children aged 3–18 years (Iwasaki et al., 2010; Meltzer et al., 2012; Werner et al., 2008). All children in this pilot tolerated wearing the actigraphs on their wrists.

Diary completion was closely monitored, and study personnel contacted the parents after 1–2 days of diary incompletion to encourage completion of the missing data, resulting in a very low rate of missing data. Diary averages shown in Table 3 are based on 14 days at each assessment point with one exception—averages for a single family are based on only 9 days of data at post-treatment.

Aberrant Behavior Checklist

The Aberrant Behavior Checklist (ABC) is a 58-item parent report measure of daytime behaviors that is sensitive to treatment effects in children with ASD (Aman et al., 1985). Each item is rated on a scale from 0 (not at all a problem) to 3 (the problem is severe in degree). Items load onto one of five research-derived subscales: Irritability (15 items), Lethargy (16 items), Stereotypy (7 items), Hyperactivity (16 items), and Inappropriate Speech (4 items; that is, talking excessively, repetitive speech, talking to self loudly, and repeating words/phrases).

Heart rate variability

Using Holter Monitors, a 5-min ECG (with an additional 1-min informal acclimation) was recorded during rest at each assessment (conducted at home) using procedures sensitive to the sensory sensitivities associated with ASD. Our 6-min protocol provides sufficient data for analysis (Ferguson et al., 2017; Zamzow et al., 2016). Previous studies have collected ECG data for HRV analysis without significant issues from children with ASD with a wide range of intellectual functioning (Ferguson et al., 2017; Zamzow et al., 2016). Time domain analysis of short-term HRV was performed using Pathfinder (SpaceLabs) software. Time domain indices reflect beat-to-beat variability with respect to time (Shaffer & Ginsberg, 2017). The time domain outcome of interest was the root mean squared standard deviation of normal-to-normal heart beat intervals (RMSDNN), with higher values representing lower physiological arousal. Frequency domain spectral analysis was performed in HRV Interactive software. Frequency domain indices reflect underlying rhythms of mechanisms modulating HR (Shaffer & Ginsberg, 2017). The low frequency/high frequency (LF/HF) ratio, an index of autonomic nervous system regulation or the ratio of sympathetic to parasympathetic activity (Ellenbroek & Sengul, 2017; Shaffer & Ginsberg, 2017), was examined. Ratios >1 typically represent sympathetic predominance, whereas ratios <1 represent parasympathetic predominance (Shaffer & Ginsberg, 2017).

To teach participants and ease concerns about the Holter monitoring, a social story was shown and discussed with parents and children. This story included pictures displaying the Holter monitor, electrodes, and alcohol wipes. An additional picture showed where electrodes were placed. After 1 min of informal acclimation, children were told that they would wear the Holter monitor equipment for 5 min. Children often had questions about whether materials would shock or hurt them. So, a brief education described the purpose of the equipment being used to assess their “heart beat.” Assessors also confirmed this by showing children their initial heart rate data that informally appear on the screen and saying, “See, this is your heart beat.”

Treatment measures

Treatment credibility

Parents provided confidential ratings (1 = strongly disagree, 10 = strongly agree) of treatment reasonableness, opinion of therapist, improvement expectations, and willingness to recommend treatment (Borkovec & Nau, 1972) at the end of session 3. Questionnaire items were adapted for parent completion regarding their child’s sleep (e.g. The technique appears to be a reasonable, logical treatment for my (child’s) sleep problems.)

Treatment Satisfaction Questionnaire

Five questions developed by the study team assessed helpfulness (not helpful, minimally helpful, moderately helpful, and very helpful), age-appropriateness (yes/no), autism-friendliness of treatment (yes/no), and thoughts about treatment (i.e. most/least helpful techniques). Additional open-ended questions asked parents about general thoughts about treatment, length, suggestions for improvement, booster calls, and thoughts about treatment materials (workbooks).

Compliance logs

Daily goal charts (scored as % stickers/total possible) tailored to the child’s treatment recommendations were used to assess treatment enactment.

Procedures

Eligible children and their parent(s) received eight 50-min sessions of CBT-CI. Sessions were individually administered ~1 week apart by doctoral students in counseling psychology (M.M., S.T.) and school psychology (M.D., D.M.). Training, group supervision (1 h weekly), and individual supervision (as needed) were provided by licensed psychologists (C.M., M.M.). Each family received both parent and child workbooks containing detailed instructions. Sleep diaries and compliance logs were completed electronically. The weekly timeline was as follows: 1–2: baseline, 3–10: treatment, 11–12: post-treatment, and 25–26: follow-up. Session content was selected from seven modules (similar to the in-person version of the treatment previously reported) (McCrae et al., 2020), and the administration order was tailored to prioritize each child/family’s most pressing sleep concerns based on the clinical interview. All children received all seven modules. Session 8 reviewed skills, strategies, and maintenance plans. The seven modules included the following: (1) sleep hygiene and sleep prescription, (2) bedtime routine and parent management, (3) cue control and parent management, (4) co-sleeping/parents fading out of room, (5) circadian education, (6) cognitive therapy basics and relaxation, and (7) nighttime worries, anxieties, and nightmares. Details on module content are published elsewhere (McCrae et al., 2020). The therapist manual and child and parent workbooks were adapted by Drs McCrae and Mazurek from materials developed by Dr McCrae for research and clinic use with typically developing children. Example adaptations include visual depictions of key constructs, increased repetition and practice, attention to sensory problems, incorporation of special interests, and use of concrete language and metaphors. Additional details on those adaptations are published elsewhere (McCrae et al., 2020). The materials used in this study will be made available following demonstration of efficacy in a randomized clinical trial.

Treatment integrity procedures

Therapists and supervisors were blinded to outcome assessments and participant-completed treatment credibility questionnaires (Borkovec & Nau, 1972). Outcome assessors were blinded to treatment progress. Lichstein, Riedel, and Grieve’s model (Lichstein et al., 1994) was used to guide training and assess three components of treatment integrity: delivery, receipt, and enactment. Treatment satisfaction was assessed at the end of treatment. Training involved mock therapy with corrective feedback and audiotaped practice sessions with volunteers. To ensure the therapists delivered the treatment as intended, all sessions were videotaped through Zoom.us within a Secure4 high-performance computing and data storage environment that met or exceeded the Health Insurance Portability and Accountability Act (HIPAA) Security Rule requirements. Half (randomly selected) were scored by the therapists. A quarter were double-scored by a licensed psychologist (C.M.) to establish reliability. Therapist scoring of each other’s tapes (not their own) was used because they were highly qualified to evaluate session content. Also, viewing others’ tapes provided valuable booster training and enhanced consistency across therapists. Session parts were weighted by importance and scored 0, 0.5, or 1 for no, part, or full delivery, respectively. Scores were summed to provide an index of the degree of treatment delivery. A separate index of treatment purity was calculated using a similar weighted scoring procedure and subtracting points from 100 for the inclusion of alternative treatments. As a result, delivery scores for each session ranged from 0 (intervention not delivered as intended) to 100 (intervention fully delivered as intended). To ensure parents understood (i.e. receipt) the treatment, they completed a short quiz (10 questions) at the start of session 3. Quiz scores ranged from 0 (treatment not understood) to 10 (treatment fully understood). Participants also completed a treatment credibility questionnaire at the end of session 3. The therapists left the room prior to the completion of the credibility questionnaire, which was then completed by the parent, placed in a sealed envelope, and given to the project coordinator. To ensure home practice was completed (i.e. enactment), the workbooks contained written instructions and goal (sticker) charts tailored to the child’s treatment recommendations. Daily goal chart scores (% stickers/total possible) ranged from 0 (family did not complete home practice) to 100 (family were fully adherent to home practice). Treatment satisfaction was assessed at the end of treatment.

Telehealth procedures

Remote delivery of the treatment was conducted using Zoom.us, a computer-based teleconferencing service, vetted by MU HealthCare and the MU Division of Information Technology and approved for transmittal of HIPAA-governed data and protected health information (PHI). During the participants’ second (and last) baseline assessment visit (~1 day before treatment), they were provided with their treatment materials (game pieces for child activities, workbook with remote etiquette, and connection troubleshooting information) as opposed to receiving those materials as needed at each treatment session when treatment is conducted in-person. Participants used their own home computers and were assisted by study personnel in a trial run of Zoom connection. Connection problems were rare, but when they occurred, the therapist and participant attempted to resolve them. There was on-site IT support both during business hours and after hours. If problems persisted, the therapist and family either rescheduled or conducted a phone session.

Power analysis

This pilot study was powered to detect large effect sizes. An a priori power analysis was performed using G*Power 3.1 for t-tests (two dependent means), and a sample size of 12 was needed to detected large effects in the baseline to post-treatment comparisons and baseline to follow-up comparisons at alpha = 0.05 and power = 0.80.

Statistical analyses

Baseline/post-treatment and baseline/follow-up changes in outcomes were analyzed using paired-samples t-tests. All data for dyads who completed all outcome assessments were analyzed. Bonferroni correction was used to control for multiple comparisons. Clinical significance was also evaluated by examining the proportion of children still meeting study criteria for insomnia at post-treatment and follow-up or in the case of non-completers, at the point of study drop-out. Specifically, children continuing to report SOL or WASO >30 min >5 nights out of 14 at post-treatment and follow-up, respectively, were classified as meeting study insomnia criteria; otherwise, they were classified as no longer meeting criteria. Similarly, non-completers who continued to report SOL or WASO >30 min >5 nights out of 14 during the 2 weeks immediately prior to dropping out were classified as meeting study insomnia; otherwise, they were classified as no longer meeting criteria.

Results

See Table 1 for participant characteristics. The order of treatment module administration varied for seven participants; four shared the order 1, 2, 3, 5, 6, 7, 4; and two shared the order 1, 2, 6, 5, 7, 4, 3. Treatment integrity did not differ from a recent in-person pilot (McCrae et al., 2020) and was high across delivery (M = 97.85, SD = 5.67; t(24) =−0.57, p = 0.28, 100 = highest), receipt (M = 9.25, SD = 0.93; t(24) = 0.22, p = 0.83, 10 = highest), and enactment (M = 82.31, SD = 11.66; t(24) = 1.16, p = 0.26, 100 = highest). Similarly, satisfaction did not differ from a recent in-person pilot (McCrae et al., 2020) and was high: the treatment was rated as moderately helpful to very helpful (100%), appropriate to child’s age (100%), and autism-friendly (87.5%; χ² = 1.07, p = 0.30). The number of parents indicating a treatment technique was most helpful was as follows: cognitive therapy (n = 3); fears, anxiety, and nightmares (n = 4); bedtime/morning routines (n = 4); co-sleeping (n = 2); all other techniques (n = 1); and all techniques equally helpful and important (n = 2). The number of parents indicating a treatment technique was least helpful was as follows: relaxation (n = 2), morning routines (n = 2), sleep restriction (n = 2), co-sleeping (n = 2), all other techniques (n = 0), and no technique was least helpful (n = 6).

Parent responses on open-ended questions about the treatment were uniformly positive. Comments on general thoughts about treatment included “very comprehensive,” “very effective,” “learned a lot of great tips to help my son’s sleep,” “well organized,” and “liked having the workbook at home and being able to do the meeting online.” Most parents described the treatment length as good, with one parent describing the treatment as “long, but necessary.” Only two parents recommended changing the length, with one suggesting fewer sessions (“good length, but could have been a session or two shorter”) and the other suggesting more sessions (“I think we could have moved a little more gradually with the changes as we were implementing several new goals each week. Maybe 3–4 more sessions.”). Eight parents reported that booster calls would be an important addition to the treatment, while four parents reported that booster calls would not be important to them (“My treatment specialist was great at keeping in contact with me via e-mail, and I always felt I could contact her if I had questions”).

Descriptive and inferential statistical analyses for child and parent outcomes are shown in Table 2. Compared to baseline, there were post-treatment improvements in child objective (i.e. actigraphy) sleep (SOL, TWT, TST, and SE); subjective (child (with parental assistance) report) sleep (SOL, TWT, TST, SE, and bedtime/wake-time variability); and challenging behaviors (irritability, lethargy, stereotypy, and hyperactivity). At 1 month, inappropriate speech (i.e. excessive or repetitive speech and loud self-talk) also improved, while hyperactivity was no longer improved. All other post-treatment improvements were maintained. For parents, there were significant improvements from baseline to post-treatment in subjective (self-report) SOL, TWT, and SE; objective (i.e. actigraphy) SOL, TWT, TST, and SE; and fatigue. At follow-up, those improvements were maintained. In terms of clinical significance, 92% and 83% of children who completed all assessments no longer met study insomnia criteria at post-treatment and follow-up, respectively; and 80% of non-completers no longer met insomnia criteria at the point of drop-out.

Table 2.

Means and standard deviations for child and parent sleep and daytime functioning outcomes over time.

Measure	Baseline		Post-treatment		1-month follow-up		Base to post-treatment^a			Base to 1-month follow-up^a
Measure	M	SD	M	SD	M	SD	t	p	ES ^b	t	p	ES ^b
Child outcomes
Subjective sleep ^c —Electronic sleep diaries
Sleep latency (min)	54.63	27.36	21.10	13.51	21.94	10.50	4.18	0.01*	1.45	4.16	0.01*	1.47
Total wake time (min)	69.27	42.84	23.19	12.13	19.66	10.32	3.84	0.01*	1.36	4.50	0.001*	1.48
Total sleep time (min)	523.78	59.30	575.95	27.54	580.20	27.56	−4.23	0.001*	1.05	−4.12	0.01*	1.14
Sleep efficiency (%)^c	87.85	4.94	95.8	1.93	96.46	1.81	−5.78	0.00*	1.97	−6.47	0.00*	2.22
Objective sleep ^c —Actigraphy
Sleep latency (min)	34.58	18.73	17.05	9.23	18.13	8.95	4.88	0.001*	1.10	4.86	0.001*	1.04
Total wake time (min)	114.57	33.48	82.04	28.54	82.99	30.74	10.21	0.00*	0.97	6.65	0.00*	0.91
Total sleep time (min)	519.87	40.47	553.12	23.56	553.15	26.74	−4.58	0.00*	1.10	−3.59	0.00*	1.12
Sleep efficiency (%)^c	79.85	4.59	84.56	3.43	85.16	3.07	−4.32	0.001*	1.08	−4.72	0.001*	1.27
Bedtime/wake time regulation ^c,d
Bedtime variability (min)	51.19	29.98	21.36	17.54	19.23	16.25	3.42	0.01*	1.13	4.35	0.001*	1.23
Wake time variability (min)	47.54	18.49	17.71	11.28	12.56	9.23	5.43	0.00*	1.81	8.37	0.00*	2.27
Daytime functioning—Aberrant Behavior Checklist
Irritability	17.98	6.15	12.46	6.58	12.78	6.74	3.78	0.00*	0.86	3.78	0.00*	0.75
Lethargy	9.47	8.73	3.95	7.23	4.07	7.41	4.64	0.00*	0.64	4.22	0.00*	0.62
Stereotypy	2.98	1.54	1.21	1.23	1.45	0.87	5.05	0.00*	1.16	4.98	0.00*	1.14
Hyperactivity	19.87	10.53	15.68	9.15	13.53	9.87	4.55	0.001*	0.58	2.88	0.01	0.40
Inappropriate speech	5.37	2.87	3.45	1.98	3.24	2.04	3.10	0.01	0.72	3.85	0.00*	0.80
Parent outcomes
Subjective sleep ^c —electronic sleep diaries
Sleep latency (min)	22.51	10.56	13.04	5.89	10.13	7.2	3.11	0.01*	1.03	3.51	0.00*	1.27
Total wake time (min)	61.58	27.56	36.48	29.57	29.34	23.69	4.24	0.00*	0.82	4.74	.00*	1.17
Total sleep time (min)	453.87	85.26	479.72	73.58	478.72	71.43	−1.49	0.17	0.30	−1.25	0.24	0.29
Sleep efficiency (%)^c	84.86	5.54	93.30	2.58	92.72	2.32	−5.19	0.00*	1.82	−6.21	0.00*	1.72
Objective sleep ^c —actigraphy
Sleep latency (min)	29.45	7.87	19.22	7.72	20.47	5.87	3.56	0.00*	1.22	3.44	0.01*	1.20
Total wake time (min)	77.24	21.68	48.37	27.19	45.01	14.84	6.50	0.00*	1.09	6.33	0.00*	1.61
Total sleep time (min)	435.46	56.48	469.90	44.87	466.72	63.21	−5.21	0.00*	0.63	−5.31	0.00*	0.49
Sleep efficiency (%)^c	83.54	4.25	89.48	3.59	88.95	3.41	7.51	0.00*	1.40	−6.59	0.00*	1.31
Daytime functioning—electronic daily diaries
Fatigue	61.58	29.46	42.29	17.89	43.23	18.24	3.88	0.00*	0.74	3.79	0.00*	0.70

SD: standard deviation; ES: effect sizes.

Analyses include participants who completed all assessments (n = 12). ^b Guidelines for interpreting within-group effect sizes based on Hedges g_av 0.20 = small, 0.50 = moderate, and 0.80 = large; Lakens (2013). ^c All variables in minutes except sleep efficiency (ratio of time spent sleeping/time in bed × 100). ^d Estimated using a combination of actigraphy and self-report.

Significant after Bonferroni correction for multiple comparisons by domain for children (sleep diaries, p < 0.0125 (0.05/4); actigraphy, p < 0.0125 (0.05/4); bedtime/wake time regulation, p < 0.025 (0.05/2), daytime functioning, p < 0.01 (0.05/5)) and parents separately (sleep diaries and actigraphy, p < 0.0125 (0.05/4) and fatigue, p < 0.05).

Out of the 14 completers of the CBT-CI, the majority of participants (n = 10, 71%) completed the HRV assessments at baseline and post-treatment. The remaining four participants were not able to travel to the lab for the HRV recordings. Given the low number of participants, we were unable to perform any inferential statistical comparisons across time points. However, descriptive analysis revealed several important trends. As shown in Table 3, preliminary descriptive statistics revealed that 7 out of the 10 (70%) completers of the HRV assessments showed an increase in RMSDNN, indicating a reduction in physiological arousal. Furthermore, the majority of participants (n = 6, 60%) showed a reduction in the LF/HF ratio at post-treatment, suggesting reductions in sympathetic predominance.

Table 3.

Results of heart rate variability assessments.

Participant no.^a	Baseline	Post-Tx	∆Baseline/post-Tx	Baseline	Post-Tx	∆Baseline/post-Tx
Participant no.^a	RMSDNN			LF/HF Ratio
1	39	43	4	1.91	1.26	−0.65
2	55	60	5	0.41	0.5	0.09
3	15	27	12	2.25	0.95	−1.3
4	37	20	−17	2.09	1.34	−0.75
5	54	38	−16	0.72	1.62	0.9
6	39	48	9	2.06	0.81	−1.25
7	24	44	20	5.29	0.57	−4.72
8	49	30	−19	0.85	1.12	0.27
9	11	52	41	1.74	0.77	−0.97
10	11	36	25	1.46	1.96	0.5

Note: Positive values for RMSDNN baseline/post-treatment (Tx) change indicate reduction in physiological arousal (for RMSDNN). Negative values for LF/HF ratio baseline/post-Tx change indicate reduction in autonomic sympathetic nervous system predominance (relative to the parasympathetic nervous system), also suggesting overall reduction in high-level physiological arousal predominance. RMSDNN = root mean squared standard deviation of successive normal-to-normal heartbeats; LF/HF ratio = low frequency/high frequency ratio.

Based on completers of heart rate variability assessments only (n = 10).

Discussion

In this single-arm study, we provide preliminary evidence of the feasibility and potential efficacy of remotely delivered (i.e. telehealth) 8-week CBT-CI for school-aged children with ASD. Several findings support feasibility of the intervention. For instance, the majority of the participants completed at least seven sessions (80+%), as hypothesized. In addition, treatment integrity scores and treatment satisfaction indicators were found to be high and similar to those of in-person treatment delivery (McCrae et al., 2020), again consistent with our hypothesis. Feedback from the parents with regard to helpfulness, age-appropriateness, reasonableness, and autism-friendliness of the telehealth delivery of treatment was also found to be unanimously positive, as hypothesized. These results, although preliminary, indicate that telehealth-delivered CBT-CI is a feasible approach for improving subjective and objective sleep measures in children with ASD as well as their parents.

Consistent with our hypotheses regarding efficacy of telehealth CBT-CI, we observed immediate moderate-to-large improvements in child subjective sleep (SOL, TWT, TST, and SE), bedtime/wake time regulation, and daytime behavior. These improvements in sleep measures were sustained at 1-month follow-up as hypothesized. Results for subjective sleep are largely consistent with prior studies of in-person behavioral treatments for insomnia in children with ASD (Johnson et al., 2013; McCrae et al., 2020; Moss et al., 2014; Reed et al., 2009; Stuttard et al., 2015; Weiskop et al., 2005). Consistent with previously reported associations between sleep disturbances and problem behaviors in children with autism (Adams et al., 2014), challenging daytime behaviors, such as irritability, lethargy, stereotypy, and hyperactivity, were also found to improve following treatment. Importantly, the majority of children (75+%, including non-completers at the point of drop-out) no longer met study criteria for insomnia upon completion of the therapy.

Similar to a recent in-person pilot (McCrae et al., 2020), immediate improvements of moderate-to-large effect size were also noted in parent subjective sleep (SOL, TWT, and SE), followed by improvements in objective sleep (SOL, TWT, TST, and SE) and fatigue at follow-up. These findings demonstrate the benefit of remote CBT-CI not only for school-aged children with ASD but also for their parents.

As hypothesized, and consistent with results from a previously reported in-person study (McCrae et al., 2020), improvements of moderate-to-large effect size in child objective sleep (SOL, TWT, TST, SE) were also observed. Findings suggest that telehealth-delivered CBT-CI may offer greater benefit to objective sleep compared to other behavioral interventions for insomnia in children with ASD. For instance, parent-facilitated behavioral sleep training in young children with ASD was associated with small effect sizes related to actigraphy measures of TST (Johnson et al., 2013) and SOL (Johnson et al., 2013; Moon et al., 2010), while other studies observed no post-treatment change in actigraphic SE or TST (Reed et al., 2009). Similarly, telehealth-delivered CBT-CI appears to be more effective for improving actigraphic sleep (in terms of preliminary efficacy) than a parent sleep education program (Malow et al., 2014), which led to only small improvements in actigraphy SE and moderate improvement in SOL in younger children with ASD. It is possible that older school-aged children may be better able to implement the behavioral strategies taught in CBT-CI. It may also be possible that the additional cognitive component of therapy in CBT-CI and/or the greater variety of sleep-related challenges addressed by this treatment (e.g. anxiety and arousal) resulted in stronger effects as measured by objective sleep measures (actigraphy).

Importantly, this pilot study also revealed that recording HRV in children with autism is feasible since 71% of children with autism completed both assessments. Furthermore, preliminary results suggest that one of the mechanisms by which telehealth CBT-CI may facilitate sleep is by reducing physiological arousal and sympathetic system predominance. It is likely that the techniques taught in CBT-CI, which include relaxation and cognitive restructuring (techniques shown to reduce self-reported arousal), were responsible for the reduction in behavioral arousal (i.e. hyperactivity), as well as physiological measures of arousal. While a small sample precludes the formation of strong conclusions regarding arousal mechanisms underlying insomnia in ASD, these preliminary results are important, as they suggest HRV recordings are tolerable in behavioral treatment trials in this age group of ASD patients. These novel findings should be examined further in larger samples.

This study joins a growing area of research evaluating remote delivery of health service interventions in children with ASD, which have been rated favorably by parents. For instance, a recent study (Ashburner et al., 2016) qualitatively examined the experiences of parents and service providers who completed remotely delivered early health intervention services. Similar to the results of this study, parents of children with ASD had favorable comments regarding remotely delivered therapy and its flexibility, accessibility, and elimination of travel-related costs. The favorable parent ratings of treatment content, length, and study procedures in this study promote the use and potential large-scale disseminability of telehealth-delivered CBT-CI, which may be particularly relevant in rural and underserved locations.

To our knowledge, this is the first study to examine the use of remotely delivered CBT for school-aged children with insomnia and autism. This is especially relevant since childhood sleep disorders are not only known to persist well into adulthood and increase risk of other disorders (e.g. cardiovascular disorders), but also known to affect executive functioning, attention, irritability, and motivation (Anderson et al., 2009; Astill et al., 2012; Beebe, 2011; Biggs et al., 2011). Consequently, greater rates of depression are noted in children with sleep disorders as they enter adolescence (Liu et al., 2007). Based on improvements in sleep outcome measures for both children with autism and their parents, high levels of treatment integrity, evidence of decreased arousal, and uniformly positive feedback from parents with regard to the treatment, it appears that telehealth CBT-CI holds promise in addressing sleep problems in the autism population.

The strengths of this study include use of both subjective and objective sleep assessments. Participant engagement was high and likely supported by the use of tailored treatment and established treatment integrity techniques as well as e-mail accessibility to therapists. Feedback with regards to ease of use and effectiveness of treatment was obtained from parents, who are important stakeholders in directing autism-related research. Since parents and caregivers of children with autism are known to experience significantly greater levels of stress compared to their peers (Silva & Schalock, 2012), designing and developing interventions for autism and comorbidities should take into consideration the potential impact on parents and caregivers as well. Indeed, our use of telehealth delivery here was based on parent feedback from a prior in-person pilot (McCrae et al., 2020).

Parent feedback, combined with variation in families’ most pressing sleep needs (and resulting variation in the module administration order) in this pilot study, suggests future research on tailored intervention is important as no clear “one size fits all” set of treatment modules emerged. Furthermore, parent feedback indicates additional tailoring strategies such as varied treatment length (shorter or longer based on child/family’s treatment progress and desire) as well as inclusion of booster sessions are important considerations that may further promote participant engagement and treatment optimization. Future studies should also look at obtaining feedback from the children regarding ease of use and effectiveness of remotely delivered treatment to gain a more comprehensive understanding of its efficacy. More rigorous evaluation is needed to address study limitations, including the small sample size, lack of control group, lack of randomization, and short 1-month follow-up. Other limitations include generalizability and measurement of child behavior using parent report. Future research should use a randomized controlled design and an active control group; assess additional outcomes (e.g. burden/stress, cost, child objective daytime behavior), including other biomechanistic outcomes (e.g. melatonin secretion timing); and recruit a more diverse sample. These considerations will increase the significance and impact of this work and may uncover shared biopsychosocial mechanisms underlying the high comorbidity of insomnia in children with ASD and driving the impact of telehealth-delivered CBT-CI on both child and parent sleep and related outcomes, especially for patients and their families located in rural and underserved areas.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Funding was provided by a University of Missouri Research Board award (McCrae, PI; Mazurek, Co-PI).

ORCID iDs

Christina S McCrae

Micah O Mazurek

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