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Poor sleep quality in newly diagnosed patients with opioid use disorder: Prevalence and associated factors
*Corresponding author: Pawan Nivrutti Gadgile, Department of Psychiatry, Seth Gordhandas Sunderdas Medical College and KEM Hospital, Mumbai, Maharashtra, India. pawangadgile177@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Gadgile PN, Kadam KS, Morparia SH. Poor sleep quality in newly diagnosed patients with opioid use disorder: Prevalence and associated factors. Arch Biol Psychiatry. 2025;3:70-6. doi: 10.25259/ABP_26_2025
Abstract
Objectives:
The objectives of the study are to assess the quality of sleep and its associated factors in newly diagnosed patients with opioid use disorder (OUD), particularly during the early stages of treatment.
Material and Methods:
This cross-sectional observational study was conducted at the Deaddiction Outpatient Department of KEM Hospital. Eighty participants with newly diagnosed OUD were recruited using comprehensive enumeration. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), and socio-demographic and opioid use data were collected through a semi-structured pro forma.
Results:
The mean global PSQI score was 10.16, with 80% of participants scoring above 5, indicating poor sleep quality. Statistical analysis revealed no significant associations between PSQI scores and variables such as age, gender, marital status, occupation, opioid use patterns, or family structure.
Conclusion:
Poor sleep quality is highly prevalent among patients with OUD, regardless of demographic or substance use characteristics. These findings underscore the importance of incorporating sleep-focused interventions into comprehensive substance use treatment plans.
Keywords
Opioid use disorder
Opioid use patterns
Pittsburgh sleep quality index
Sleep quality
Socio-demographic factors
Treatment implications
INTRODUCTION
Substance use disorder (SUD) represents a prevalent psychiatric issue frequently observed in everyday clinical practice.[1] The National Mental Health Survey report (2016) indicates that the prevalence of any SUD stands at 22.4%, with opioids accounting for 2.1%, which translates to approximately 2.26 million individuals.[2] The predominant opioid used is heroin, accounting for 1.14%, followed by pharmaceutical opioids at 0.96% and opium at 0.52%.[3] Opioids have frequently been employed for the alleviation of pain, for recreational enjoyment, as a means to navigate adverse emotional conditions, and to facilitate sleep, particularly among those not actively seeking treatment.[4] Insufficient sleep duration and quality are frequently documented health issues among individuals utilizing opioids. Sleep functioning seems to be a prevalent issue among individuals dependent on opioids. Opioid consumption is significantly linked to disruptions in circadian rhythms and sleep patterns.[5] The misuse of both illicit and prescription medications has significant detrimental impacts on sleep, effects that likely endure or escalate with prolonged use and deteriorate further during withdrawal.[6] Furthermore, contemporary findings indicate that issues related to sleep and levels of alertness are linked to both the initiation and persistence of opioid consumption and serve as a contributing factor to the likelihood of relapse.[5] Sleep disturbances are prevalent and frequently intense among individuals with opioid use disorder (OUD), particularly during the withdrawal phase, as well as among those undergoing opioid maintenance therapies.[7] Sleep disturbances encompass a decrease in total sleep duration, interruptions in sleep continuity, and diminished sleep quality, which frequently correlate with unfavorable outcomes in SUD treatment. Sleep disturbances exhibit a bidirectional relationship with various factors that contribute to adverse treatment outcomes, such as diminished positive affect, heightened negative affect, persistent pain, and cravings for substances.[8] The Pittsburgh Sleep Quality Index (PSQI) is a standardized questionnaire that assesses sleep quality and disturbances over a 1-month period. It evaluates seven key components – such as sleep duration, latency, and efficiency – and generates a global score ranging from 0 to 21. A score of 5 or below indicates good sleep quality, while scores above 5 reflect poor sleep, with higher values associated with increased risk of daytime dysfunction, fatigue, and long-term health issues such as mood disorders, cardiovascular problems, and impaired cognitive performance. The PSQI helps identify individuals who may benefit from clinical intervention or sleep-focused therapies.[9]
Early identification of sleep disturbances in patients with OUD is critical, as it allows clinicians to integrate sleep-focused strategies into addiction management from the outset. Recognizing poor sleep at baseline not only informs individualized treatment planning but also serves as a predictor of long-term outcomes, including treatment retention, relapse risk, and functional recovery. By operationalizing tools such as the PSQI in routine clinical practice, addiction services can proactively address sleep dysfunction as a core component of care, thereby enhancing the overall effectiveness of therapeutic interventions. This underscores the necessity of the present study, which highlights the prevalence of poor sleep quality in newly diagnosed OUD patients and provides a rationale for embedding systematic sleep assessment within deaddiction programs. This research aimed to investigate the quality of sleep in individuals with OUD and the various factors that contribute to diminished sleep quality. The primary objective of this study was to assess the prevalence of poor sleep quality in newly diagnosed patients with OUD using the PSQI. Secondary objectives included evaluating the relationship between sleep quality and socio-demographic variables such as age, gender, marital status, occupation, and family structure, as well as opioid use patterns, including duration and quantity of use. Given the inclusion of detailed gender-based comparisons in the results, gender analysis was also considered a secondary objective. However, it is important to note that only six female participants were enrolled, rendering subgroup analyses by gender statistically underpowered. As such, the broad confidence intervals and non-significant findings should be interpreted with caution, and future studies with more balanced gender representation are recommended to validate these observations.
MATERIAL AND METHODS
Study design
This cross-sectional observational study was conducted over 4 months at the Deaddiction outpatient department (OPD) of KEM Hospital, focusing on newly diagnosed patients with OUD. The source of data comprises patients attending the OPD during the designated data collection window of 2 months. A sample size of 80 participants has been calculated using the formula n = Z2 P (1–P)/d2, where Z represents the value for a 95% confidence interval (1.96), P is the estimated prevalence of sleep disturbances among individuals with OUD (30%), and d is the margin of error (0.1).[10] The sampling technique employed was complete enumeration, ensuring that all eligible patients presenting during the data collection period and meeting inclusion and exclusion criteria were recruited for the study.
A semi-structured pro forma was employed to collect comprehensive socio-demographic information of study participants, including age, sex, marital status, education, occupation, and income. Furthermore, it was capturing detailed data on opioid use, such as the type of opioid consumed, route of administration, frequency, duration, and the amount of substance used. To assess sleep quality, the PSQI was utilized. This validated instrument evaluates sleep patterns in adults by examining seven domains: Subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction over the past month. The PSQI comprises 28 items structured across six factors, accounting for 62.6% of total variance. Construct validity has been established by significant score differences between insomniac and non-insomniac groups (t = –13.89, P = 0.000). A global score of 5 or greater indicates poor sleep quality, warranting further discussion of sleep habits with a healthcare provider.
Criteria for selection of cases
Inclusion criteria
The inclusion criteria for this study encompass both male and female patients attending the Deaddiction OPD who have been clinically diagnosed with OUD. Participants should fall within the age range of 18–55 years and must express a willingness to participate by providing informed consent. Only individuals who meet all three conditions will be recruited for the study, ensuring ethical participation and relevance to the study objectives.
Exclusion criteria
The exclusion criteria for this study are clearly defined to maintain the integrity and specificity of the research findings. Patients who use substances other than opioids and nicotine were excluded, as individuals presenting with comorbid physical or psychiatric illnesses that could confound the study results. Those diagnosed with organic disorders, such as delirium or dementia, are also not eligible. Furthermore, any participant who was unable to communicate effectively with the researcher or complete the required questionnaires due to language barriers or severe learning disabilities was excluded from the study.
Selection of subjects
Subjects for this study were selected based on predefined inclusion and exclusion criteria to ensure appropriate enrolment, scientific rigor, and ethical compliance.
Screening tools
All patients presenting to the Deaddiction OPD with a clinical diagnosis of OUD during the data collection period were screened for eligibility based on predefined inclusion and exclusion criteria. Those meeting the criteria were invited to participate in the study. After obtaining informed consent, participants underwent a structured clinical interview using a semi-structured pro forma to document socio-demographic details and opioid use history. Sleep quality was assessed using the PSQI. No standardized screening tools were used to assess psychiatric or physical comorbidities; patients with known comorbid conditions were excluded based on clinical history and presentation. All interviews were conducted in a confidential setting within the OPD to ensure privacy and minimize response bias.
Ethical clearance and consent
Ethical clearance for the study was obtained from the Institutional Ethics Committee under reference number IEC (III)/OUT/280/2023 before its initiation. Based on the defined inclusion and exclusion criteria, a total of 80 patients diagnosed with OUD were selected for participation. Each individual was thoroughly briefed about the study’s objectives, procedures, and potential applications. Written informed consent was obtained from all participants to ensure voluntary and informed involvement. For each subject, a semi-structured pro forma was completed to systematically document relevant clinical and demographic data.
Sample size
The sample size was fixed at 80 patients.
Duration of protocol therapy
The treatment period was fixed at 120 days.
Statistical analysis
Assessment of results was carried out as per protocol using GraphPad Prism 8.0.2 and MS Excel. Results were statistically evaluated by applying the Student unpaired t-test. The result was expressed as the mean ± standard error of the mean (SEM).
RESULTS
Age and gender
A cross-sectional observational study was conducted, and the subjects were categorized based on age and gender. The age and gender of the patients were analyzed using an unpaired t-test with Welch’s correction to account for potential unequal variances between the groups. The mean average age of male patients (Group A) was 30.72 years, while the mean average age of female patients (Group B) was 26.50 years. The difference between the means (B-A) was calculated to be −4.216 with a SEM of 3.42. The resulting P value was 0.28. As this P-value is more than the commonly accepted significance level of 0.05, we accept the null hypothesis of no difference. This suggests that the observed difference in mean ages between male and female patients is not statistically significant. Figure 1a provides a visual representation of the distribution of patients according to age and gender.
Opioids use
A cross-sectional observational study was conducted, and the subjects were categorized based on the duration of opioid use in years and the number of packets consumed. The use of opioids and the number of years were analyzed using an unpaired t-test with Welch’s correction to account for potential unequal variances between the groups. The mean average of the number of opioids used (Group A) was 11.05 packets, while the mean average number of years (Group B) was 6.40 years. The difference between the means (B-A) was calculated to be −4.64 with a SEM of 0.70. The resulting P-value was 0.14. As this P-value is more than the commonly accepted significance level of 0.05, we accept the null hypothesis of no difference. This suggests that the observed difference in opioid use between patients and the number of years is not statistically significant. Figure 1b depicts the mean result of the duration of opioid use (in years) and the number of packets consumed.
- (a) Distribution of patients according to age and gender, (b) Mean result of duration of opioid use (in years) and number of packets consumed.
PSQI
Based on the findings presented in Table 1, the study population exhibited a mean subjective sleep quality score of 1.62, while the average sleep latency was reported as 2.00. Sleep duration had a mean score of 1.25, and habitual sleep efficiency was noted at 1.41. Participants reported an average sleep disturbance score of 1.65, and those using sleep medication had a mean value of 1.06. Furthermore, daytime dysfunction among the participants yielded a mean score of 1.25. The overall mean global PSQI score for the study population was found to be 10.16. Notably, among the 80 individuals assessed, approximately 80% (64 participants) recorded a PSQI score greater than 5, which is indicative of poor sleep quality within the group.
| PSQI Component (n = 80) | Mean ± SD | Correlation parameter | PSQI scores | |
|---|---|---|---|---|
| R-value | P-value | |||
| Subjective sleep quality | 1.62 ± 0.86 | Age of the patient | r = −0.18 | P = 0.12 |
| Sleep latency | 2.00 ± 0.95 | Number of packets consumed | r = −0.02 | P = 0.85 |
| Sleep duration | 1.25 ± 0.94 | Duration of opioid use | r = −0.034 | P = 0.77 |
| Habitual sleep efficiency | 1.41 ± 0.84 | |||
| Sleep disturbances | 1.65 ± 0.83 | |||
| Use of sleep medications | 1.06 ± 0.79 | |||
| Daytime dysfunction | 1.25 ± 0.91 | |||
| Global PSQI | 10.16 ± 4.29 | |||
P> 0.05 is significant. PSQI: Pittsburgh Sleep Quality Index, SD: Standard deviation.
Table 1 also shows that the correlation analysis between PSQI scores and various factors, such as age of patients, number of opioid packets used, and duration of opioid use, revealed no statistically significant associations. As the P-value in each case exceeded 0.05, the study concluded that there is no meaningful relationship between these variables and sleep quality as measured by PSQI. This suggested that, within the sample population, sleep disturbances and overall sleep quality may not be influenced by age or patterns of opioid usage.
Based on the data presented in Table 2, the study found no statistically significant difference in mean PSQI scores between male and female participants. Similarly, marital status – whether married, unmarried, or divorced – did not yield any notable variation in sleep quality scores. The analysis further indicated no positive correlation between mean PSQI scores and the occupational status of the individuals. Furthermore, the type of family structure in which participants lived showed no statistically significant impact on their sleep quality, as reflected by their PSQI scores. These findings suggested that demographic and social factors such as gender, marital status, occupation, and family type may not independently influence sleep quality in the study population.
| Parameter (n = 80) | PSQI score Mean ± SD |
F/t value, P-value |
|---|---|---|
| Gender | ||
| Male (n = 74) | 10.19 ± 4.35 | 0.19, 0.85 |
| Female (n = 6) | 9.83 ± 3.86 | |
| Marital status | ||
| Married (n = 42) | 9.93 ± 4.50 | 1.17, 0.32 |
| Unmarried (n = 34) | 10.76 ± 3.96 | |
| Divorced (n = 4) | 7.50 ± 4.79 | |
| Occupation status | ||
| Self-employed (n = 23) | 9.70 ± 4.76 | 0.84, 0.44 |
| Unemployed (n = 41) | 10.76 ± 4.22 | |
| Service (n = 16) | 9.31 ± 3.79 | |
| Type of family | ||
| Nuclear (n = 51) | 10.75 ± 4.43 | 1.63, 0.10 |
| Joint (n = 29) | 9.14 ± 3.91 | |
P > 0.05 is significant, PSQI: Pittsburgh Sleep Quality Index, SD: Standard deviation.
DISCUSSION
The present study was conducted in the outpatient department of a drug deaddiction center, utilizing the PSQI alongside a semi-structured pro forma to evaluate sleep patterns in individuals newly diagnosed with OUD. Following informed consent, structured interviews were completed with 80 participants over a 4-month period, and the data were subjected to rigorous statistical analysis. The mean age of the sample was 30.4 years (standard deviation [SD] = 8.09), consistent with demographic trends reported in similar studies. The cohort exhibited a pronounced male predominance, with 74 males and only 6 females, reflecting gender distributions commonly observed in substance use research. Educational attainment averaged 8.32 years (SD = 3.57), and the majority of participants reported a monthly income of ₹11,166. Regarding religious affiliation, 58% identified as Hindu, while the remaining participants followed other faiths. Employment status revealed that 51.2% were unemployed, 28% were self-employed, and the remainder were engaged in service-based occupations. Marital status distribution included 52.5% married, 40% unmarried, and 5% divorced. Family structure analysis showed that 63% resided in nuclear families, with the rest living in joint family settings. On average, participants reported consuming 11.05 opioid packets (SD = 4.05) over a mean duration of 6.4 years (SD = 4.79). Sleep quality assessments using the PSQI revealed elevated scores across multiple domains: Subjective sleep quality (mean = 1.62), sleep latency (2.0), sleep duration (1.25), habitual sleep efficiency (1.41), sleep disturbances (1.65), and daytime dysfunction (1.25). The mean global PSQI score was 10.16, significantly exceeding the clinical threshold of 5. Notably, 64 out of 80 participants (80%) recorded scores indicative of poor sleep quality. Correlation analyses demonstrated no statistically significant relationships between PSQI scores and variables such as age, quantity of opioid use, or duration of use. Furthermore, no significant associations were found between sleep quality and demographic factors, including gender, marital status, employment status, or family structure. These findings suggest that poor sleep quality is a widespread concern among individuals with OUD, independent of socio-demographic or substance use characteristics.
Sleep and the cycle of opioid use: Precipitation and perpetuation
Sleep disturbances are increasingly recognized as both a precipitating and perpetuating factor in SUDs. Theoretical models such as the self-medication hypothesis posit that individuals may initiate opioid use to alleviate insomnia, anxiety, or emotional distress conditions, often exacerbated by poor sleep. Over time, opioid-induced alterations in sleep architecture, including reduced slow-wave and REM sleep, contribute to a vicious cycle of dependence, withdrawal-related insomnia, and relapse risk. For instance, Tripathi et al. emphasized that sleep disruption not only results from opioid use but also predicts poorer treatment outcomes, including increased cravings and relapse rates.[7] Bolshakova et al. highlighted that sleep impairment persists even during maintenance therapy, suggesting long-lasting neurobiological changes in sleep regulation.[6]
Implications for clinical assessment and treatment planning
The findings of this study underscore the need to incorporate sleep assessments into routine evaluations of patients with OUD. Given that sleep quality was uniformly poor across the sample regardless of age, gender, or opioid use history, clinicians should treat sleep dysfunction as a core symptom of OUD rather than a secondary concern. Tools like the PSQI can be easily integrated into intake assessments to identify patients at risk for sleep-related complications. Furthermore, addressing sleep disturbances may enhance the efficacy of addiction treatment. Interventions such as Cognitive Behavioral Therapy for Insomnia have shown promise in improving sleep outcomes in substance-using populations. Pharmacological options, including trazodone or low-dose doxepin, may be considered cautiously, especially in patients with severe sleep disruption, although risks of sedation and misuse must be carefully managed. Emerging evidence also supports the role of mindfulness-based therapies, sleep hygiene education, and chronotherapy (targeting circadian rhythm alignment) in improving sleep and reducing substance use behaviors. These approaches align with the biopsychosocial model, which emphasizes the interplay of biological vulnerability, psychological distress, and social context in perpetuating addiction.
The pervasive sleep disturbances identified in this study have direct consequences for treatment outcomes in OUD. Poor sleep compromises retention in therapy by reducing motivation and adherence to structured schedules, while simultaneously heightening relapse risk through intensified cravings and negative affect. Daytime dysfunction resulting from inadequate sleep further impairs cognitive performance, occupational functioning, and social reintegration, thereby perpetuating psychosocial stressors that destabilize recovery. Routine use of standardized screening tools such as the PSQI could be operationalized in addiction clinics to systematically identify patients at risk for sleep-related complications. Incorporating PSQI assessments at intake and follow-up visits would enable clinicians to tailor interventions ranging from cognitive behavioral therapy for insomnia to mindfulness-based approaches, thus embedding sleep management as a core component of comprehensive OUD treatment frameworks.
Limitations
This study provides important insights into the prevalence and characteristics of sleep disturbances among individuals newly diagnosed with OUD; however, several methodological limitations must be acknowledged. The cross-sectional observational design inherently restricts causal inference, offering only a temporal snapshot of sleep quality without accounting for longitudinal changes associated with treatment progression, withdrawal phases, or recovery trajectories. The study was conducted exclusively at a single tertiary care center, namely the Deaddiction OPD of KEM Hospital in Mumbai, which may limit the external validity of the findings. Patients attending this facility may differ systematically from those in rural, community-based, or private healthcare settings, thereby constraining the generalizability of results. The sample size of 80 participants, although derived using standard epidemiological calculations, remains relatively modest and insufficiently powered for robust subgroup analyses. This limitation is particularly evident in gender-based comparisons, as only six female participants were included, resulting in statistical imprecision and wide confidence intervals that preclude definitive conclusions regarding gender-specific sleep patterns. The use of complete enumeration within a fixed 2-month data collection window may have introduced selection bias, potentially excluding individuals who sought treatment outside this timeframe or declined participation due to stigma, logistical barriers, or symptom severity. Further limitations arise from the reliance on self-reported data through the PSQI, which is vulnerable to recall bias and social desirability effects. The absence of standardized diagnostic tools for psychiatric and physical comorbidities limits the ability to control for confounding variables such as depression, anxiety, chronic pain, or other sleep-disrupting conditions. Although nicotine use was permitted, its influence on sleep architecture was not analyzed, and the use of sleep medications – while documented – was not statistically adjusted for, potentially confounding PSQI outcomes. In addition, the study did not account for socioeconomic stressors, trauma history, or environmental factors, all of which may exert significant influence on sleep quality and substance use behaviors. Taken together, these limitations underscore the need for cautious interpretation of the findings and highlight the importance of future research employing longitudinal, multi-center designs with larger, demographically balanced samples and comprehensive psychiatric assessments. Such studies would enhance the understanding of sleep disturbances in OUD and inform the development of targeted, evidence-based interventions.
CONCLUSION
The study highlights the pervasive nature of sleep disturbances among individuals with OUD, as evidenced by elevated global PSQI scores in the majority of participants. Despite thorough statistical analysis, no significant associations were found between sleep quality and socio-demographic or opioid usage variables. These findings suggested that poor sleep quality is a consistent clinical concern in OUD patients, independent of age, gender, occupation, marital status, or substance use patterns. This underscores the need for integrating sleep assessment and management into the therapeutic framework for addiction treatment, potentially improving patient well-being and long-term recovery outcomes.
Acknowledgment:
The authors are highly thankful to the Department of Psychiatry, KEM Hospital, for carrying out this study.
Ethical approval:
The research/study was approved by the Institutional Review Board at KEM Hospital, number IEC(III)/OUT/280/2023, dated 23rd March 2023.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patients have given their consent for their clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that they have used Copilot solely for language refinement and to improve the clarity of writing. No AI assistance was employed in the generation of scientific content, data analysis or interpretation.
Financial support and sponsorship: Nil.
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