Background: Sleep deprivation impacts physical and emotional health, particularly among incarcerated individuals. Ethiopian prisoners often experience poor sleep quality, which impacts their health and rehabilitation. However, more research is needed, especially in Jimma zone.

Objectives: The aim of this study is to assess the magnitude of poor sleep quality and associated factors among incarcerated individuals in Jimma Correctional Facility, Jimma, Oromia, and Southwest Ethiopia in 2024.

Methods: A cross-sectional research design was employed from April 1 to June 30. The simple random sampling method was used to select 300 study participants. Data were collected by trained data collectors using pretested and structured questionnaires. Standardized tools, such as the Pittsburgh Sleep Quality Index, PHQ-9, GAD-7, PSS-10, Sleep Hygiene Index, and Oslo-3 Item Scales, were used to collect quantitative data. The data were entered into Epi data version 4.6 and then exported to SPSS version 26 for analysis. Bivariate and multivariate logistic regression analysis was applied to assess associations between dependent and independent variables. The odds ratio with a 95% confidence interval was used to determine the strength of associations between dependent and independent variables. P-values less than 0.05 were considered statistically significant. Results were summarized and presented through text, tables, and figures.

Result: Response rate for this study was 100%. Among the 300 participants, the prevalence of poor sleep quality was 72% (95%CI: 67.3-77.0) and it was found to be statistically associated with religion (AOR = 17.39, 95% CI (4.36, 69.31)), drinking tea and coffee close to bedtime (AOR = 2.39, 95% CI (1.140, 5.016)), depression (AOR = 4.195, 95% CI (1.956, 9.00)), and anxiety (AOR = 2.39, 95% CI (1.151, 5.002)).

Conclusion and Recommendation: Poor sleep quality among inmates at Jimma correctional facility was associated with factors like sex, religion, crime type, and khat use. This highlights the need for routine screening and management for sleep quality, as inmate health is negatively impacted by inmate care and sleep deprivation.

• Poor Sleep Quality

• Sleep quality

• Prisoners

• Jimma

Keno BF (2026) Poor Sleep Quality and Its Associated Factors among Incarcerated Individuals at Jimma Correctional Facility, Oromia, Ethiopia, 2024. Ann Nurs Pract 13(1): 1140.

AOR: Adjusted Odds Ratio; BSc: Bachelor of Science; CI: Confidence Interval; CMD: Common Mental Disorder; COR: Crude Odds Ratio; IRB: Institutional Review Board; LMICs: Low- and Middle-Income Countries; M.Sc: Master of Science; PHQ: Patient Health Questionnaire; PSS: Perceived Stress Scale; PSQI: Pittsburgh Sleep Quality Index; GAD: Generalized Anxiety Disorder; SHI: Sleep Hygiene Index; SPSS: Statistical Package for Social Sciences; UK: United Kingdom; US: United States; WHO: World Health Organization.

All humans require sleep in order to survive. The human body uses sleep for vital physiological functions that have a dramatic impact on a variety of biological processes. Sleep deprivation can impair normal physiological performance since sleep is essential for the healthy operation of the neurological, cardiovascular, metabolic, and immune systems. Human health and quality of life depend on sleep, which has an impact on cultural, bio-psycho-social, and bio-functional aspects of life as well as general well-being [1-4].

A crucial component of quality of life and a significant clinical dimension is sleep quality [5]. Sleep quality measures the quality of sleep, focusing on restful, restorative sleep. It is a crucial predictor of physical and mental health, wellness, and overall vitality [6]. Poor sleep quality is considered a main feature of insomnia and a global phenomenon that affects all age groups throughout the world and can lead to changes in body functions, mental illness, and other health problems [7].

Prison conditions pose significant health risks, affecting family and communication, and increasing the risk of insomnia and poor sleep quality among prisoners, making them a vulnerable population. Due to institutionalization, boredom, noise, crowding, anxiety, lack of autonomy, substance abuse, light, temperature, and discomfort, a significant number of prisoners may experience poor sleep hygiene as a result of their prison surroundings [2-9].

Different studies across the country identified poor sleep quality as more common in prisoners. However, many of them are only based on quantitative studies using Pittsburgh Sleep Quality Index (PSQI), a recommended measure for sleep quality and insomnia symptoms [10]. Therefore, this study uses a mixed design to determine the magnitude of poor sleep quality and associated factor among incarcerated people in Jimma town correctional institution, 2024.

Poor sleep quality is a public health concern with several associated negative health-related outcomes [11]. Poor sleep quality negatively impacts brain functions, health, and mental abilities, leading to performance issues, fatigue, and psychiatric disorders. It also increases risk of aggressive behavior, suicidal tendencies, depression, and vascular complications, as well as Sleep quality impairment impacts judgment, psychomotor skills, memory, decision making, concentration, attention, and may contribute to new physical health problems and exacerbate existing ones [2-13].

More than 10.77 million people are held in penal institutions throughout the world, either as pre-trial detainees/remand prisoners or having been convicted and sentenced [14]. Prisoners face higher sleep issues due to institutionalization, boredom, noise, crowding, anxiety, and substance abuse, with a 30%-36% higher prevalence than non-prisoned people globally, especially in low- and middle-income countries [15,16].

Globally due to institutionalization, boredom, noise, crowding, anxiety, and substance abuse in prison among Incarcerated people there is high prevalence of poor sleep quality. In England and Wales 11 to 81% of prisoners experienced poor sleep quality, and the prevalence varied between 52 to 60% in the United States, 88.2% in UK, 44% in Swiss, 10% and 48% in China, 6% to 78% Taiwan and Hong Kong, 62% in Estonia, 87.5% in Switzerland, 61.2% in Italy, 41.2% in Norway, 73% in South Africa, 37.7% in Nigeria and 53.9% in Ethiopia [3-20].

While insufficient sleep exacerbates pre-existing medical conditions, gives rise to new health issues, and impairs a prisoner’s capacity to make a positive contribution, to the best of our’ knowledge, little research has been conducted in Ethiopia regarding the quality of sleep that prisoners receive [4]. The management of sleep issues in prison environments is complicated by the paucity of research and data, and medical professionals address these complaints in a range of highly varied ways. They typically undervalue the issue and accept it as normal [21]. This study assessed the magnitude of poor sleep quality and its associated factors among incarcerated people in Jimma Correctional Facility, southwest Ethiopia. The findings provide baseline evidence to guide correctional administrations in implementing educational and corrective measures, support mental health professionals in addressing inmate needs, and serve as a foundation for future research on sleep quality in similar settings.

Study design, area and period

The A cross sectional study design was employed. was conducted from June 1, 2024, to August 30, 2024, at Jimma Town Correctional Institution. Jimma Town is the largest city in the south-western part of Oromia Region, at a distance of 355.2 km from Addis Ababa, the capital city of Ethiopia. The town has 21 kebeles, which are organized under three weredas. The total population of Jimma Town projected to date is 144,232. The town has one hospital, one health center, and one MCH clinic as public facilities [22].

At the moment, Jimma Town has one correctional facility housing 1460 inmates. Of these, 1256 were male inmates, making up the majority, and 204 were female [23]. The prison commenced its operations subsequent to the Italian occupying forces’ departure in 1943. The Oromia regional state, the Southern Nations and Nationalities and Peoples Region (SNNPR), and the Gambella region are the regions that the prison serves [23]. The prisons average room size was 141 m2, with the smallest room measuring 30 m2 (323 ft2) and the largest one at 195 m2 (2098 ft2) (1517 square feet) [24]

Study Populations

The source population for this study comprised all incarcerated individuals at Jimma Town Correctional Facility, while the study population included those selected individuals who met the inclusion criteria. The inclusion criteria consisted of all incarcerated individuals available at the time of data collection. Exclusion criteria included incarcerated individuals awaiting trial, those with previously diagnosed psychiatric disorders (such as personality disorder, substance use disorder, or other mental disorders), pregnant women, individuals with medical conditions such as epilepsy, hypertension, cardiovascular disease, or diabetes who were on medication, and those with intellectual disabilities or cognitive impairments, including speech or hearing problems.

Sample Size Determination and Sampling Procedure

Sample size determination: The sample size for this study was estimated using the single population proportion formula. The assumptions taken into consideration during calculation of sample size include: Taking 77.1 % as prevalence of poor sleep quality from study done in Mettu town correctional facility [5]; Zα⁄2 value of 1.96 at a 95% confidence interval, 5%, margin of error and a non response rate of 10%.

Where,

n = (Zα/2)2 P (1-p)/ d²

n = (1.96) (1.96) (0.771) (1- 0.771)/0.05²

= (3.84) (0.177)/0.0025 = 271.194 ≈272, by adding 10% non- response rate

n =272 + (272*10/100) = 299.119 ≈ 300 so, the final sample size was 300

Sampling procedure

After obtaining the total number of incarcerated people under the Jimma Town Correctional Center administration, a sample of the study population is allocated by proportional allocation to the sample size for each individual based on the gender of the incarcerated people. Study participants were selected by simple random sampling. A sampling frame was developed based on selection criteria.

Data Collection Tool and Procedure

Data collection tools: Firstly, the data collection instrument was adapted after reviewing relevant literature [2-27]. The questionnaire has both open and closed-ended items. The English version of the questionnaire was translated into the local languages, Amharic and Afaan Oromo, and then independently translated back into English to ensure consistency. The questionnaire consisted of nine parts which consist of Socio-demographic related factors, Criminal and work-related factors, Personal and behavior-related factors, Pittsburgh Sleep Quality Index (PSQI), Perceived stress-related factors, Anxiety related factors, Depression-related factors, Patient Health Questionnaire (PHQ-9), which is used to assess depression, and Oslo Social Support Scale (OSSS-3).

The PSQI was used to determine the presence or absence of poor sleep quality. It distinguishes between “poor” and “good” sleep by assessing seven domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, and sleep disturbances, use of sleep medication, and daytime dysfunction over the past month. The respondent rates each of these seven areas of sleep.

Data collection procedure: Data were collected through face-to-face interviews. Data were collected by four B.Sc. nurse data collectors and one B.Sc. nurse supervisor. The questionnaires were initially prepared in English, then translated into the local languages, Afaan Oromo and Amharic, and finally retranslated back into English by experts to ensure consistency.

To prepare for the qualitative part of the study, interview guides and consent forms were prepared in advance. Trained and experienced research assistants conducted audio-taped interviews, while taking notes to ensure comprehensive data collection. The interviews lasted approximately 30-40 minutes each on average and were conducted in a quiet, separate room with permission from correctional facility administrators to ensure privacy and comfort. Participants’ consent was obtained and each session was audio-recorded.

The dependent variable for this study was poor sleep quality, while independent variables included sociodemographic factors (age, sex, marital status, religion, educational status, average monthly income, and social support), psychological and behavioral factors (anxiety, depression, perceived stress, lifetime substance use such as alcohol and cigarettes, and current use of caffeinated drinks for non-medical purposes), as well as crime-related and work-related factors (type of crime, duration of sentence, day work shift, and night work shift). Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), with a score greater than 5 indicating poor sleep quality and a score of 5 or less indicating good sleep quality. Depression was assessed with the PHQ-9, where a score of ≥5 indicated depression, and anxiety was measured using the GAD-7 scale, with a score of ≥8 indicating generalized anxiety disorder. Social support was classified as poor (score 3–8), moderate (score 9–11), or strong (score 12–14). Perceived stress was measured using the Perceived Stress Scale (PSS), with scores of 0–13 indicating low stress, 14–26 moderate stress, and 27–40 high stress; moderate and high stress were combined to indicate the presence of perceived stress. Lifetime substance use referred to the use of substances such as tobacco, khat, and alcohol for non-medical purposes prior to imprisonment, while current use of caffeinated drinks was defined as consumption for non-medical purposes within the previous three months.

Data processing and analysis

After data collection, the data were rechecked for completeness, entered into Epi-data version 4.6, and then exported to SPSS version 26.0 for analysis. Appropriate coding and recoding were done at each step for the variables as necessary. Descriptive statistics like frequencies, percentages was used. A binary logistic regression analysis was done to sort variables that are candidates for multiple logistic regression and have a value less than or equal to 0.25.

A multivariate logistic regression analysis was conducted to identify factors associated with poor sleep quality. A P-value less than 0.05 was used to declare that the observed association is statistically significant. The magnitude of the association between independent variables and the outcome variable were measured by the Odds Ratio (OR) with a 95% Confidence Interval (CI). A collinearity diagnostic test was undertaken to show that there was no multicollinearity, as the VIF is less than 10 and the Tolerance Test (TT) is greater than 10%. The normality of the data was checked. The model goodness of-fit test was checked using the Hosmer and Lemeshow test. Accordingly, the Hosmer and Lemeshow test statistics indicated that the logistic regression model was a good fit for factors associated with sleep quality (P-value = 0.678), and the results of the study were presented in the form of tables, figures, and text.

Sleep quality was assessed by using the Pittsburgh Sleep Quality Index (PSQI), which comprises of seven components of sleep (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, daytime dysfunction, and use of sleep medications) over the past month using 19 items with three possible responses ranging from 0 to 3 with higher scores indicating worse sleep (indicating the most negative response on the Likert scale). A total global PSQI score ranges from 0 to 21.A cutoff point of “5” or higher indicates a “poor” sleeper, and less than 5 indicates a “good” sleeper [28,29].

Depression is assessed by the Patient Health Questionnaire (PHQ-9), which consists of 9 items. Each item uses a Likert scale response ranging from 0 to 3, with 3 indicating the most negative response. A cutoff point of “5” or higher indicates the presence of depression [23]. General Anxiety Disorder is assessed by the GAD-7 tool, which consists of seven items. Each item uses a Likert scale response ranging from 0 to 3, with 3 indicating the most negative response. A cutoff point of “8” or higher indicates the presence of anxiety [23].

Individual scores on the PSS can range from 0 to 40, with higher scores indicating higher perceived stress. Scores from 0 to 13 are considered low stress, while scores from 14 to 26 are classified as moderate stress. Scores from 27 to 40 indicate high perceived stress, but the categories for moderate and high stress are merged and combined, as both indicate the presence of perceived stress. Those with total summation scores from 14 to 26 and 27 to 40 were merged and recoded as “0,” as both indicate the presence of perceived stress. Participants’ scores from 0 to 13 were recoded as “1,” indicating there was no presence of perceived stress [30].

The Oslo 3-item social support scale was used to assess social support. Participants scored a 3-8 on the OSSS-3 scale, indicating poor social support. In contrast, participants who scored a 9-11 on the OSSS-3 suggested moderate social support. Additionally, participants who scored a 12-14 on the OSSS-3 indicated strong social support [26].

Data Quality Assurance

Before start of data collection, training was given to data collectors for one day on technique of data collection, purpose of data collection, and content of the questionnaires and on how to approach the respondents and how to deal with difficulties that may arise during data collection. Pretest was done in Agaro town prison facility by taking 5 % of the total sample size before two weeks of the actual data collection to assess instrument simplicity, flow and consistency and to improve the validity and reliability of the instrument.

The validity and reliability were improved by using enough questions to assess competence, ensuring participants are familiar with the assessment user interface, and ensuring that methods and measurement techniques are high quality and targeted to measure exactly the objectives. The reliability of the questionnaire was checked using Cronbach’s Alpha, which yielded a value of 0.813 for sleep quality, showing a good level of internal consistency. Also, modification was made concerning the arrangement of questions according to the specific objective it addresses. The data were checked for completeness and accuracy each day.

Socio-demographic factors among incarcerated individuals at Jimma Correctional Facility

Three hundred questionnaires were distributed and 300 incarcerated individuals were interviewed, resulting in a response rate of 100%. Among all study participants, more than one-third 186 (62.0%) were in the age category of 20–39. Just over half of the respondents 167 (55.7%) were male. In terms of marital status, the majority of them 131(43.7%) were unmarried. More than half of the respondents 159(53.0%) were urban residents. Most of them had primary education 140(46.7%). The majority 214(71.3%) of respondents were Muslim. Just under one third of the respondents 88(29.3%) were farmers (Table 1).

Table 1: Socio-demographic factors among incarcerated people at Jimma Correctional Facility, 2024(n=300)

Others* Government employee, NGO organization worker

Crime type and detention related characteristics of incarcerated people at Jimma Correctional Facility, 2024(N=300)

Among the total study participants (n = 300), just less than one third 91 (30.3%) were involved in violent crimes. Just under half 143 (47.7%) of the participants received a prison sentence of 1-5 years (Table 2).

Table 2: Crime and detention-related characteristics of incarcerated people at Jimma Correctional Facility, 2024(N=300)

Violent crimes*: murder, robbery, kidnapping, assault, and rape;

Non-violent crime**: theft and fraud.

Others***: drug crime, traffic violation, traffic accidents, corruption, gambling activity, not following principles, not respecting appointments, and tax evasion.

Life time Substance use behaviors among incarcerated people at Jimma Correctional facility

Among the total study participants (N=300), the vast majority of respondents 243(81.0%) were non-alcohol users. Additionally, with regards to cigarette use, most of them (219, 73.0%) had never smoked. Furthermore, over half of the respondents 176(58.7%) reported never chewing khat. Just over half of the respondents 170 (56.7%) admitted to drinking tea and coffee close to bedtime (Table 3).

Table 3: Substance use behaviors among incarcerated people at Jimma Correctional facility, 2024(N=300)

Prevalence of poor sleep quality among incarcerated people at Jimma Correctional facility

Among the total study participants (n=300), 216 (72.0%) of them scored PSQI ≥5. Based on this, 72% of respondents had poor sleep quality among incarcerated people at Jimma Correctional Facility, with a 95% CI (67.3, 77.0). Meanwhile, 84 (28.0%) had good sleep quality, with a 95% CI (23.0, 32.7) (Figure 1) (Table 4).

Figure 1: Sampling procedure and technique of magnitude of poor sleep quality and associated factors among incarcerated people in Jimma correctional facility, Jimma, Oromia, south-west Ethiopia, 2024.

Table 4: Frequency distributions Perceived stress among incarcerated people at Jimma Correctional facility, 2024(n=300)

Perceived stress among incarcerated people at Jimma Correctional facility

Among the total study participants (n=300), 240 (80.0%) of respondents scored below half of the maximum possible score (<13). This indicates that the majority of the respondents (80.0%, 95% CI [75.3-84.7]) did not experience stress at the Jimma Correctional Facility, but (20.0%, 95% CI [15.3, 24.7]) experienced stress (Figure 2).

Figure 2: Prevalence of poor sleep quality among inmates in Jimma correctional facility, 2024.

Anxiety among incarcerated people at Jimma Correctional facility

Among the total study participants (n=300), 158(52.7%) of them scored a score of ≥ 8 on the GAD-7 scale. Based on this, of respondents (52.7%, 95% CI (47.7, 58.3) had no general anxiety disorder among incarcerated people at Jimma Correctional Facility, but (47.3%, 95% CI [41.7, 52.3]) had general anxiety disorder (Figure 3). Prevalence of depression among incarcerated individuals at Jimma Correctional facility (Table 5).

Figure 3: Perceived stress among incarcerated people at Jimma Correctional facility, 2024(n=300)

Table 5: Distribution of GAD Scores among incarcerated individuals at Jimma Correctional facility, 2024 (n=300)

Among the total study participants (n=300), 88 (29.3%) scored a score of ≥ 5 on PHQ, indicating depression. This means that 29.3% of respondents at Jimma Correctional Facility had depression, with a 95% CI (24.0, 34.7). On the other hand, 212 (70.7%) did not have depression, with a 95% CI (65.3, 76.0) (Figure 4) (Table 6).

Figure 4: Anxiety related questionnaires among incarcerated people at Jimma Correctional facility, 2024(n=300)

Table 6: Distribution of PHQ-9 scores among incarcerated individuals at Jimma Correctional Facility

Social Support among the Participants

Among the total study participants (n=300),219(73.0%) scored a 3-8 on the OSSS-3 scale, indicating poor social support. In contrast, 66(22.0%) scored a 9-11 on the OSSS-3, suggesting moderate social support. Additionally, 15(5.0%) scored a 12-14 on the OSSS-3, indicating strong social support (Table 7 and 8).

Table 7: Oslo Social Support Scale (OSSS-3)

Table 8: A multivariable logistic regression analysis of the various risk factors for reported poor sleep quality among incarcerated people at Jimma Correctional Facility, 2024(n=300)

Key: 1: Reference Category; *: statically highly significant

In bivariate logistic regression, Age, Sex, Religion, Type of crime, khat use, drinking tea and coffee near bedtime,Depression, Anxiety, and Perceived Stress were factors significantly associated with poor sleep quality among incarcerated people at Jimma Correctional Facility at p-value <0.25. In multivariable logistic regression, eight factors were significantly associated with the outcome variable at p-value <0.05, as described below with their respective AOR and p-value.

Adjusted for other variables, the religion of incarcerated individuals was found to be a significant factor in their sleep quality. Specifically, those who were Muslim were 17.3 times more likely to be poor sleepers compared to those who followed the orthodox religion, with an (AOR =17.39, 95% CI (4.36, 69.31)), Similarly, incarcerated individuals who were Protestant were found to be 6.4 times more likely to be poor sleepers compared to those who followed the orthodox religion, with an (AOR =6.42, 95% CI (2.009, 20.53)).

Drinking tea and coffee close to bedtime was found to significantly affect the sleep quality of incarcerated individuals. Specifically, those who did not consume tea and coffee before going to bed were 2.3 times more likely to have poor sleep compared to those who did not drink these beverages before bedtime (AOR = 2.39, 95% CI (1.140,5.016)).

Incarcerated individuals with depression were found to be 4.2 times more likely to experience poor sleep compared to those without depression (AOR = 4.195, 95% CI 1.96, 9.0)). Similarly, incarcerated individuals with anxiety were found to be 2.4 times more likely to have poor sleep compared to those without anxiety with an (AOR = 2.39, 95% CI (1.15,5.0)) (Table 9).

This study showed that three-fourths 216 (72.0%) of respondents had poor quality sleep at the Jimma correctional facility, which suggests a significant proportion of inmates are affected by sleep disturbances that can stem from various factors unique to the local prison environment. The management of sleep disorders in inmates is complicated due to the lack of research on sleep quality in prison environments. This study aimed to evaluate the extent of poor sleep quality among incarcerated individuals and the variables associated with it, as well as inmates’ feelings and experiences about sleep quality in the correctional facility, in order to fill the gap in existing data. Factors such as sex, religion, type of crime, khat use, drinking tea and coffee near bedtime, depression, and anxiety were found to have a significant association with poor sleep quality.

The findings of this study revealed that 72% of the participants with a 95% CI (67.3, 77.0) experienced poor sleep quality. This aligns with a study conducted in the United States in 2015, which found that 72% of incarcerated women met the criteria for “poor sleepers” according to the PSQI [4]. Additionally, a study conducted in Mettu Town Prison, Oromia, South West Ethiopia in 2019 showed that 77.1% of participants had poor sleep quality [26].

The current finding is also consistent with a study conducted in South Africa, which was 73%. These findings are also consistent with a systematic review on the prevalence of insomnia and poor sleep quality among the population of Ethiopia, which found varying rates of poor sleep quality ranging from 42.8% to 88.2% [13].

The current finding is higher than several other studies conducted in different countries. For instance, a study in Tartu, Estonia, discovered poor sleep quality in 62% of elderly prisoners. Another study in the US revealed poor sleep quality in 62.7% of men and 81.2% of women. In Italy, the rate was 61.2%, while in a Swiss jail it was 44%. In Norway, it was 41.2%, and in Nigeria, it was 37.7%. In Diredawa, eastern Ethiopia, a study found that 53.9% of prisoners had poor sleep quality [21].

The current finding is higher than the systematic review and meta-analysis conducted on the Prevalence of Poor Sleep Quality in the Ethiopian Population in 2019, which was 53% [2]. The variation across countries due to factors like prison conditions, mental health services within prison, and cultural attitudes. Factors such as overcrowding, noise levels, and food quality also impact sleep.

The current finding is lower than a study conducted in the UK on poor sleep quality among prisoners, which reported a rate of 88.2% [31]. Additionally, it is lower than a study conducted in Switzerland, which reported a rate of 87.5%. This variation could be due to differences in socio economic status and the availability of recreational areas within the correctional facility. Overall, the level of care may also differ from our specific context.

Respondents who did not consume tea and coffee before going to bed were 2.3 times more likely to have poor sleep compared to those who did not drink these beverages before bedtime. This finding is in line with a study done in Turkey, which showed that average daily coffee consumption (p<0.05) was associated with poor sleep [1].

The recent findings revealed that incarcerated individuals with anxiety were found to be 2.4 times more likely to have poor sleep than their counterparts, and incarcerated individuals with depression were found to be 4.2 times more likely to experience poor sleep than their counterparts. This aligns with a study conducted in an Italian jail in 2023, which found that increased anxiety and depression were significantly associated with reduced sleep amount and increased sleep latency among prisoners [32]. The current finding showed that lifetime substance use had no association with poor sleep. This finding is inconsistent with the studies conducted in Mettu [23] and Dire Dawa [21].

Strength and limitation of the study

This study’s strengths include the use of a mixed methods approach, combining qualitative and quantitative data to provide a comprehensive understanding of sleep quality issues. This approach enhanced credibility, reliability, and flexibility, allowing for cross-validation and effective exploration of complex research questions that require both statistical analysis and insight into human behavior. However, the study also had limitations. Analyzing and interpreting data from both qualitative and quantitative sources posed challenges, and some findings were conflicting regarding factors and coping strategies. Additionally, the study was limited to a single correctional facility, restricting the ability to generalize results across diverse facilities with different security levels and incarceration types.

Poor sleep quality was found to be highly prevalent among incarcerated individuals at Jimma Correctional Facility, with factors such as sex, religion, type of crime, khat use, consumption of tea and coffee near bedtime, depression, and anxiety significantly associated with poor sleep. Incarceration negatively impacts both physical and mental health, and existing coping mechanisms are insufficient to address these challenges. To address the problem, it is recommended that Jimma Town Correctional Facility improve living conditions and reduce overcrowding, while psychiatry and mental health professionals implement mental health programs and provide sleep hygiene education. Policymakers should consider legislative reforms and support rehabilitation programs, and community organizations can offer partnerships for counselling, rehabilitation, and awareness campaigns. Additionally, researchers are encouraged to conduct longitudinal and interventional studies across diverse correctional settings to evaluate interventions and understand contextual factors affecting inmates’ mental health and sleep quality.

Before data collection, ethical approval and clearance to conduct the study was be obtained from the Institutional Review Board (IRB) of Jimma University; Institute of Health. Prior to data collection, informed consent was obtained from all participants, ensuring voluntary participation and confidentiality of their information. This was delivered to the prison’s administrative office. Prisoners who were chosen at random were therefore verbally informed that they may decide whether or not to participate in the study and that they would not be. Privacy and confidentiality in the data collection process were maintained through two main measures: Anonymizing personal information and securely storing data to prevent unauthorized access. Furthermore, informed consent was obtained from study participants, guaranteeing that they fully comprehend how their information will be used and protected.

Beka Furi Keno was responsible for the conceptualization, proposal writing, database searches, supervision of data collection, analysis and interpretation of results, drafting the initial article, and serving as the corresponding author.

We extend our sincere gratitude to Jimma University for funding this study and to the Jimma Zone Health Office for their cooperation and support throughout the research process. Additionally, we would like to express our deepest appreciation to the study participants for providing the necessary information.

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