Sleep and Autism

People with autism generally experience more sleep problems than the neurotypical population (Richdale and Schreck, 2009, Morgan et al 2020), with insomnia, parasomnias and circadian rhythm disorders being amongst commonly reported problems. Autism is linked to social and communication impairments, which are influenced by autism genes, the autistic individual's unique environment and abnormalities in melatonin secretion (Richdale and Schreck 2009). These result in sleep disturbances, which in turn, have cascading effects on mental health, behaviour, family dynamics, and overall quality of life of the entire family.

For children and adolescents with autism and sleep disturbances, William-Buckley et al recommend in their guidelines that clinicians should address contributing medications or conditions, prioritise behavioural strategies as the first-line treatment, and consider low-dose melatonin if the former strategies are ineffective.

What's the value of Biometric Data?

Simply said, for an overloaded clinician, biometric data offers an additional lense to understand and improve psychological well-being interventions through remote monitoring and potential treatment of sleep-related problems.

PhiliaHealth leverages cutting-edge wearable technologies to measure key biometrics, helping clinicians and researchers make data-driven decisions for mental health interventions.

What Biometrics does PhiliaHealth include?

Let’s explore the critical metrics that PhiliaHealth tracks, their relevance to mental health, and how they are applied in clinical practice. For a real-world intervention, please see this case study with a somatic therapist and a psychologist using PhiliaHealth.

Currently Available

1. Sleep Duration

Sleep duration, the total time spent asleep during a sleep period, is one of the most well-researched and impactful metrics in understanding mental health. Consistently falling below recommended durations can have significant implications for both mental and physical well-being.

• Link to Mental Health: Sleep duration is a critical factor in mental health outcomes. Short sleep duration (SSD) is strongly associated with increased risk for disorders such as depression and anxiety. For instance, Zhang et al. (2024) demonstrated a significant correlation between SSD and the onset of depressive symptoms. Similarly, in children and adolescents with autism spectrum disorder (ASD), reduced sleep duration is linked to heightened emotional dysregulation and behavioural challenges, further exacerbating their condition.

• Clinical Application: PhiliaHealth tracks sleep duration trends to provide insights into the efficacy of mental health interventions. By comparing pre-and-post intervention changes, clinicians and caregivers can evaluate how treatments influence overall sleep patterns. This approach aligns with the American Academy of Neurology’s guidelines, which highlight total sleep time (TST) as a key metric in assessing treatment outcomes in populations with sleep disturbances, including those with ASD.

• Strengths: Sleep duration is supported by extensive population-level data, making it a highly reliable marker for tracking mental health. Additionally, its universal applicability across age groups and conditions ensures robust comparisons and targeted therapeutic adjustments.

• Recommendation: Monitor sleep duration trends over time to evaluate intervention outcomes effectively. This metric provides a clear, actionable measure to identify progress or setbacks in therapeutic efforts, ensuring personalised and adaptive care.

2. Sleep Efficiency

Sleep efficiency refers to the percentage of time spent asleep while in bed. This metric is a cornerstone of sleep quality assessment.

• Link to Mental Health:

Sleep efficiency is closely tied to mental health conditions. Research by Baglioni et al. (2017) highlights that lower sleep efficiency is often associated with panic disorder and anxiety. Furthermore, in children and adolescents with ASD, disrupted sleep efficiency exacerbates daytime behavioural disturbances, negatively affecting emotional regulation and cognitive functioning.

• Clinical Application:

Pre-and-post intervention changes in sleep efficiency are widely used to evaluate the effectiveness of treatments. For example, the American Academy of Neurology’s 2020 guidelines recommend tracking sleep efficiency as part of behavioural and pharmacological interventions in children with autism. Interventions like cognitive behavioural therapy and melatonin have shown improvements in sleep efficiency, providing measurable outcomes to assess intervention efficacy.

• Strengths:

Trends in sleep efficiency offer actionable insights into patient sleep quality and help tailor therapeutic approaches. Improved SE can directly correlate with better mood regulation and cognitive functioning, making it a valuable metric for both clinicians and caregivers.

• Recommendation: Regularly monitor sleep efficiency trends to identify improvements or disruptions in sleep patterns. Together with sleep duration, this metric can serve as an early indicator of intervention efficacy or the need for adjustment in treatment plans.

3. Heart Rate Variability (HRV)

Autonomic nervous system metrics have not yet found their way into treatment guidelines. However, in Arora et al's review, autistic individuals often exhibit reduced HRV, indicating lower parasympathetic activity and an imbalance favouring sympathetic arousal.

• Link to Mental Health: HRV, a measure of autonomic nervous system balance, is inversely linked to depression and anxiety. Lower HRV is often associated with heightened stress responses and poor mental health outcomes (Kim et al., 2018, Arora et al 2020).

• Clinical Application: HRV is analysed retrospectively to assess changes in vagal tone—an indicator of relaxation and emotional regulation. It is also used in biofeedback therapies for real-time stress management.

• Strengths: HRV-informed interventions, such as biofeedback or mindfulness, may enhance parasympathetic activity and improve overall well-being in autistic individuals.

• Recommendation: Combine nocturnal HRV trends e.g. average RMSSD value for the sleep episode with biofeedback techniques to enhance emotional regulation strategies. While measuring after waking up is regarded in the field as a good standardised method, we recommend nocturnal measurements as the burden of continual measurement on the client is reduced.

Upcoming

4. Initial Sleep Onset Latency

• Link to Mental Health: Sleep onset latency, or the time it takes to fall asleep, is a critical indicator of sleep health, though its implications are less explored in this dataset. However, studies like Altena et al. (2016) highlight its relevance to insomnia and mental health disturbances (Altena et al., 2016).

• Clinical Application: By tracking trends, clinicians can evaluate the impact of interventions on sleep initiation.

• Strengths: Provides a valuable indicator of sleep disruption or recovery and can help clinicians decide interventions that improve initiation or continuity of sleep.

• Recommendation: Use trends to detect gradual improvements or worsening in sleep patterns.

5. Correlate of Sympathetic Arousal

• Link to Mental Health: Greater improvements in sleep quality have been shown to improve mental health outcomes, as highlighted by Scott et al., 2021. Sympathetic arousal is closely tied to pre-sleep anxiety and acute stress.

• Clinical Application: Sympathetic arousal metrics help measure acute stress responses and assess autonomic regulation. They are particularly useful when combined with HRV data.

• Strengths: This metric provides an easier way to evaluate increased vagal balance and correlates directly with stress and anxiety levels.

• Recommendation: Track trends in nocturnal sympathetic arousal alongside nocturnal HRV to provide a comprehensive view of autonomic health. See this case study for more.

Why These Metrics Matter

Programs that support autistic adults in reducing arousal, promoting good sleep can be highly beneficial.

Professor Amanda Richdale et al (2023)

As many psychologists have told us, if a therapy has truly addressed the root cause of a client's stress, then the client should show one or more of the following symptoms i.e. they should be sleeping longer, less fragmented, more efficiently or one that enables more recovery.

PhiliaHealth’s biometric monitoring bridges the gap between subjective mental health experiences and objective physiological data.

By focusing on sleep and autonomic arousal, PhiliaHealth enables clinicians to:

• Measure the effectiveness of therapies with real-time and retrospective data, particularly where

• patient compliance with mood diaries is low

• patients' ability to communicate information needed for questionnaires is compromised due to communication or interoception impairments

• Identify underlying stressors or sleep disruptions that contribute to poor mental health outcomes.

• Personalise mental health interventions based on both questionnaire and physiological trends.

These metrics are not just numbers—they're meant to be another powerful toolset for improving lives and helping clients heal.

Whether used for anxiety management, stress reduction, or sleep optimisation, PhiliaHealth’s biometrics offer a holistic view of mental health interventions, ensuring better outcomes for patients and practitioners alike.

References

1. Richdale, A and Schreck, K, Sleep problems in autism spectrum disorders: Prevalence, nature, & possible biopsychosocial aetiologies, Sleep Medicine Reviews, 13(6), 2009, Pages 403-411, https://doi.org/10.1016/j.smrv.2009.02.003

2. Morgan, B. et al., Sleep in adults with Autism Spectrum Disorder: a systematic review and meta-analysis of subjective and objective studies, Sleep Medicine, 65, 2020, Pages 113-120, https://doi.org/10.1016/j.sleep.2019.07.019.

3. A. Williams Buckley, D. Hirtz, M. Oskoui, M. J. Armstrong, A. Batra, C. Bridgemohan, et al., Practice guideline: Treatment for insomnia and disrupted sleep behavior in children and adolescents with autism spectrum disorder: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology, Neurology 2020 Vol. 94 Issue 9 Pages 392-404, https://doi.org/10.1212/WNL.0000000000009033

4. Zhang, Y., et al. (2024). Short sleep duration and its link to mental health disorders. Sleep, https://doi.org/10.1016/j.sleep.2023.02.004.

5. Baglioni, C., et al. (2017). Sleep and mental health: A review of the links between sleep disturbances and anxiety disorders. Sleep Medicine Reviews, https://doi.org/10.1016/j.sleep.2016.10.003.

6. Kim, H., et al. (2018). Heart rate variability as an indicator of autonomic dysfunction in depression and anxiety. Psychoneuroendocrinology, https://doi.org/10.1016/j.psyneuen.2018.06.001.

7. Arora et al., Is autonomic function during resting-state atypical in Autism: A systematic review of evidence, Neuroscience & Biobehavioral Reviews, 125, 2021, Pages 417-441, https://doi.org/10.1016/j.neubiorev.2021.02.041.

8. Altena, E., et al. (2016). Sleep latency and its implications in mental health. Sleep, https://doi.org/10.1093/sleep/zsw003.

9. Scott, A., et al. (2021). Sympathetic arousal and mental health: The role of sleep quality. Sleep Medicine, https://doi.org/10.1016/j.sleep.2021.02.001.

10. Richdale, A., et al  (2023). Pathways to anxiety and depression in autistic adolescents and adults. Depression and Anxiety, 2023, Article 5575932. https://doi.org/10.1155/2023/5575932