Effect of Person-Environment-Occupation-Performance (PEOP) Model–Based Sleep Intervention on Sleep Quality Among Parents of Children With Special Needs

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Chronobiol Med. 2026;8(2):71-76

Publication date (electronic) : 2026 June 30

doi : https://doi.org/10.33069/cim.2026.0003

Vinitha Lakshmi Ravichandran, Hannah Joy Herbert

Saveetha College of Occupational Therapy, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Corresponding author: Vinitha Lakshmi Ravichandran, MOT, Saveetha College of Occupational Therapy, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha nagar, Thandalam, Chennai-602105, India. Tel: 91-44-26492396, E-mail: vinithalakshmi171@gmail.com

Received 2026 January 7; Revised 2026 February 15; Accepted 2026 February 26.

Abstract

INTRODUCTION

Sleep is a naturally recurring, reversible behavioral state characterized by decreased consciousness, perennial dissociation, and relative immobility [1,2]. It is a biological necessity and is essential for the health and well-being of children, adolescents, and adults. Optimal sleep is important for cognitive functioning, mood, mental health, and cardiovascular, cerebrovascular, and metabolic health [3-5]. Since it occupies approximately one-third of a person’s life, sleep is considered one of the vital activities [6]. Sleep has historically been undervalued, with its significance in health often being disregarded.

Sleep quality is defined as one’s satisfaction with the sleep experience, integrating aspects of sleep initiation, sleep maintenance, sleep quantity, and refreshment upon awakening [7]. Sleep quality has four attributes: sleep efficiency, sleep latency, sleep duration, and wake after sleep onset [8].

Sleep efficiency is defined as the proportion of time a person spends asleep relative to the total time spent in bed, and it typically declines with advancing age [8]. The National Sleep Foundation considers a sleep efficiency of 85% or higher to be optimal for good health. A sleep efficiency of 85% or higher is indicative of good sleep quality [9]. In contrast, a sleep efficiency of less than 74% is indicative of poor sleep quality among most age groups, except for young adults (18–25 years), for whom a value of less than 64% denotes poor sleep quality.

Sleep latency, also known as sleep onset latency, is the time it takes to transition from the state of wakefulness to sleep and can vary from person to person [8]. It was found in all age groups that a sleep latency of 16–30 minutes is considered good sleep quality, and a sleep latency of 60 minutes or more indicates poor sleep quality. The American Academy of Sleep Medicine and the Sleep Research Society jointly issued a consensus statement recommending that adults obtain at least 7 hours of sleep per night, teenagers aged 13–18 years receive 8–10 hours, and children aged 6–12 years get 9–12 hours of sleep on a regular basis to promote optimal health and well-being. Sleep duration is the total time a person spends asleep, excluding periods of wakefulness during the night or across a 24-hour period [10].

Humans miss out on sleep or experience disrupted sleep for many different reasons, and research has explored factors associated with short and disturbed sleep in various groups (e.g., shift workers, short sleepers, those experiencing insomnia) [11]. Sleep among parents, particularly parents caring for children and adolescents with special needs, is an understudied research area [11]. Studies reported by Gergov et al. [12] state that parents caring for children with special needs frequently experience disrupted and poor-quality sleep compared to parents of typically developing children. Empirical studies have consistently demonstrated that caregivers report worse global sleep quality, with higher rates of disturbances and associated health concerns [13-15]. Parents of children with special needs may undergo chronic sleep disruption due to complex factors across personal, environmental, occupational, and performance domains. While sleep is inherently adaptable, persistent deprivation and irregular schedules can negatively impact health, functioning, and caregiving capacity [15]. Recent findings further emphasize the severity of this issue: Priya et al. [16] observed that among 66 parental caregivers, 93% reported poor sleep quality, underscoring the ongoing relevance of this problem in contemporary contexts.

Gregory et al. [11] introduced the concept of “sleep privilege,” noting that many individuals, particularly caregivers, cannot achieve ideal sleep conditions. In conventional routine occupational therapy practice, sleep management is typically addressed through specific, targeted intervention strategies rather than through a single unified theoretical framework. Commonly employed approaches include the use of assistive devices or environmental modifications, activity-based interventions, cognitive behavioral therapy for insomnia, and lifestyle-oriented programs. These strategies are often implemented independently, focusing on particular aspects of sleep disturbance such as habits, cognition, environment, or daily engagement [17].

Occupational therapists generally emphasize the work–leisure–rest cycle, also known as occupational balance. Here, rest and sleep are activities related to obtaining restorative rest and sleep to support healthy, active engagement in other occupations [17]. The Person-Environment-Occupation-Performance (PEOP) model is a client-centered model organized to improve the everyday performance of necessary and valued occupations of individuals, organizations, and populations and their meaningful participation in the world around them [18].

In the PEOP model, occupational therapy intervention is viewed as a process of using a broad range of purposeful client-centered strategies that engage the individuals or groups to develop or use resources that enable successful performance of the necessary and meaningful occupations [16]. The PEOP model provides a structured framework to address sleep issues by considering individual, occupational, and environmental factors [17]. This model uses a combination of various types of interventions rather than just one intervention.

Intervention strategies informed by the PEOP model are designed to address salient personal, environmental, and occupational factors that hinder or promote healthy sleep among parents of children with special needs. Because the model emphasizes the dynamic interaction between person, environment, occupation, and performance, it provides a flexible framework that can be tailored to accommodate individual choices, preferences, and caregiving demands [19-22]. Such strategies may or may not require direct engagement in sleep-related activities, nor do they necessarily involve physiological changes. For example, when modifying the sleep environment to make it more supportive and manageable, the parent’s role may primarily involve collaborating with the therapist to identify meaningful goals and strategies that reduce barriers and enhance participation in restorative sleep routines. Occupation-based models have already been applied to sleep intervention in insomnia [23], supporting the rationale for extending this approach to parents of children with special needs, who often experience chronic sleep disruption due to caregiving responsibilities. By emphasizing individualized and flexible strategies, the PEOP model provides a theoretically sound basis for addressing sleep quality in this underserved population.

Despite the magnitude of this issue, a significant research gap exists: parental sleep is almost exclusively treated as a secondary outcome rather than a primary clinical priority [24]. Furthermore, existing guidelines designed for general populations often may not fully account for the unique, interrupted nature of disability caregiving. This study addresses these deficiencies by shifting the focus toward a PEOP framework and evaluating the effectiveness of a PEOP-based intervention in improving sleep quality among parents of children with special needs.

METHODS

This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Participants

In this quasi-experimental study, parents of children with special needs were initially screened for eligibility. A formal a priori sample size calculation was not performed. Parents of children aged 6–12 years who had poor sleep quality, defined by a Pittsburgh Sleep Quality Index (PSQI) total score >5, and who were willing to participate and able to understand the intervention were included in the study. Parents with a diagnosis of psychotic spectrum disorders, those taking sleep medications, and those with primary sleep disorders were excluded. Based on these selection criteria, 42 parents met the eligibility requirements. Of these, 32 participants were recruited using a convenience sampling technique based on their geographical accessibility. Two participants withdrew due to personal reasons, resulting in a final sample of 30 participants. The final sample was nonrandomly allocated into the experimental group (n=15) and the control group (n=15) (Figure 1).

Figure 1.

Flow diagram of the study. PSQI, Pittsburgh Sleep Quality Index; PEOP, Person-Environment-Occupation-Performance.

Intervention

Participants in the experimental group received a PEOP model–based sleep intervention designed to address person-related factors, environmental influences, and occupational routines affecting sleep. The intervention was developed in accordance with a previous study that evaluated the effect of an occupation-based model among persons with insomnia [23]. The intervention carried out in the experimental group is described in Table 1. The control group received conventional occupational therapy routinely provided to caregivers at the study site. This program included three structured components: 1) general caregiver education (balancing caregiving demands with self-care); 2) stress management advice (relaxation techniques, coping strategies); and 3) brief sleep hygiene education (regular sleep–wake schedules, limiting caffeine, reducing screen exposure before bedtime). Each session followed a consistent format, beginning with brief didactic instruction, followed by discussion and practical guidance. The control group attended three sessions per week for 12 weeks, with each session lasting 45 minutes, ensuring that frequency, duration, and overall contact time were equivalent to the experimental group.

Table 1.

Details on the actual intervention content consisting of 36 sessions

RESULTS

A total of 30 participants were enrolled in the study, with 15 participants in the experimental group and 15 participants in the control group. The mean age of participants in the experimental group was 34.80 years, while in the control group it was 35.33 years. Baseline characteristics, such as parent age, child age, baseline PSQI scores, sex distribution among parents and children, and children’s diagnoses, were collected prior to intervention. There were no statistically significant differences between groups at baseline in terms of demographic variables and baseline PSQI scores (all p> 0.05), indicating group comparability (Tables 2 and 3).

Table 2.

Baseline comparability of continuous variables between the control and experimental groups

Table 3.

Baseline comparability of categorical variables between the control and experimental groups

In the control group, the mean PSQI score significantly decreased after the intervention (p=0.001), with a Hedges’ g of 1.005 (95% CI: 0.378–1.610) (Table 4). The experimental group also demonstrated a significant reduction in mean PSQI score (p<0.001), with a larger effect size (Hedges’ g=2.748; 95% CI: 1.618–3.859) (Table 5). Post-test PSQI scores were compared between the control and experimental groups using an independent t-test. A statistically significant difference was observed between the groups (p<0.001), with a large effect size (Hedges’ g=1.863; 95% CI, 1.003–2.700) (Table 6), indicating a substantial intervention effect.

Table 4.

Comparison of PSQI scores between pre- and post-tests in the control group (paired t-test)

Table 5.

Comparison of PSQI scores between pre- and post-tests in the experimental group (paired t-test)

Table 6.

Comparison of post-test level PSQI scores between control and experimental group (independent t-test)

DISCUSSION

This study investigated the effect of a PEOP model–based sleep intervention on parents of children with special needs. At baseline, both groups demonstrated poor sleep quality, consistent with previous reports that caregivers of children with developmental disabilities frequently experience disrupted sleep [12,13,16].

The control group, which received conventional occupational therapy including sleep hygiene, relaxation techniques, and caregiver education, showed statistically significant improvement in sleep quality (p=0.001). This aligns with prior studies demonstrating that sleep hygiene interventions improved sleep quality in the insomniac patients [26].

The experimental group, which received the PEOP model–based intervention, also showed significant improvement (p<0.001). Unlike conventional therapy, this approach incorporated daytime activity engagement, environmental modifications, and lifestyle restructuring in addition to sleep hygiene. Literature supports that multi-component interventions addressing behavioral, environmental, and psychosocial factors are more effective than single-component strategies, which explains the stronger within-group gains observed here [23].

When comparing groups, the experimental group demonstrated significantly greater improvement than the control group (p<0.001). This finding is consistent with previous research showing that occupation-centered interventions incorporating contextual and role-based modifications produced superior results [23]. The present findings extend this evidence by showing that addressing sleep as a meaningful occupation within the PEOP framework yields clinically meaningful benefits for caregivers.

The quasi-experimental design and non-random allocation may introduce potential selection bias. The absence of randomization also limits causal inference. The study was conducted only among two child developmental centers, which may limit generalizability to other populations or geographic locations, and it is also limited by a small sample size, which may also affect the generalizability of the intervention. Additionally, sleep outcomes were measured using a self-reported instrument, which may introduce response bias. Future studies could incorporate objective sleep measures such as actigraphy and utilize randomized controlled designs to strengthen evidence.

In conclusion, the present study provides evidence that a PEOP model–based sleep intervention significantly improves sleep quality among parents of children with special needs. Addressing sleep as an occupation within the broader person–environment context appears to be an effective and clinically meaningful approach. These findings support the integration of occupation-centered sleep interventions into rehabilitation and caregiver support programs. Such interventions are cost-effective and scalable and warrant consideration in special-needs family support policies.

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