Prevalence of and factors associated with poor sleep quality and short sleep in a working population in Singapore

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

Association between sleep quality and quality of life in Singapore

PURPOSE

Several studies have examined the relationship between sleep quality and health-related quality of life (HRQOL). However, there are a lack of generalizable data on this association in Singapore. Our study aimed to (1) investigate the association between sleep quality and HRQOL and (2) examine whether age moderated these associations.

METHODS

Data were obtained from nationwide Singapore Mental Health Study 2016 (n = 6126, response rate = 69.5%). Sleep quality was assessed using Pittsburgh Sleep Quality Index (PSQI) where higher scores indicate poorer sleep quality. HRQOL [Physical component summary (PCS) and Mental component summary (MCS)] were measured using 12-item Short-Form Survey. Associations were investigated using multivariable linear regression models, adjusted for sociodemographic, physical, and mental comorbidities. Interactions between age and sleep quality were examined by including interaction terms individually.

RESULTS

Poorer PSQI score was significantly associated with lower PCS (β = - 0.44) and lower MCS (β = - 0.73). Among PSQI components, lower PCS was significantly associated with subjective sleep quality (β = - 1.06), sleep duration (β = - 0.30), sleep disturbance (β = - 0.33), and daytime dysfunction (β = - 0.75). Lower MCS was significantly associated with subjective sleep quality (β = - 1.36), sleep latency (β = - 0.24), sleep disturbance (β = - 0.16), using of sleeping medication (β = - 1.12), and daytime dysfunction (β = - 2.08). Age moderated the association between PSQI score with PCS and MCS. Stronger association between PSQI score and MCS was observed in younger individuals (β = - 0.90) than older adults (β = - 0.45).

CONCLUSION

Our study found that poor sleep quality was associated with HRQOL of certain age groups more than others. Future studies may identify potential mediators to help people with chronic sleep problems.

Posttraumatic Stress Disorder Symptoms and Sleep Disturbances Among Asian Indians: A Systematic Review

Substantial comorbidity exists between posttraumatic stress disorder and sleep disturbances/disorders. Such comorbidities are understudied in minority groups, including Asian Indians residing in countries outside India. Thus, we synthesized the existing literature specific to this group of Asian Indians to determine (a) prevalence estimates of posttraumatic stress disorder (PTSD) and sleep disturbances/disorders; and (b) PTSD-sleep comorbidity estimates. For this systematic review, we searched four databases (PubMed, PsycInfo, PTSDpubs, Web of Science) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Of 3,796 screened articles, 9 articles (10 studies) met inclusion criteria. Study sample sizes ranged from 11 to 2,112 Asian Indians; studies were conducted in Singapore or Malaysia. No reviewed study examined PTSD. All studies examined sleep disturbances/disorders among Asian Indians; prevalence estimates were: 8.3% to 70.4% for short sleep duration, 2.0% to 22.9% for long sleep duration, 25.9% to 56.3% for poor sleep quality, 3.4% to 67.5% for insomnia diagnosis or probable insomnia, 7.7% for excessive daytime sleepiness, 3.8% to 54.6% for obstructive sleep apnea (OSA) diagnosis or high OSA risk, and 5.1% to 11.1% for sleep-disordered breathing. Specific to Asian Indians residing in countries outside India, this review advances PTSD-sleep literature by (a) suggesting substantial prevalence of sleep disturbances/disorders; (b) highlighting the need for culturally relevant sleep interventions; and (c) highlighting research gaps (e.g., no PTSD-focused research).

Prevalence of Sleep Disturbances in Latin American Populations and Its Association with Their Socioeconomic Status-A Systematic Review and a Meta-Analysis

BACKGROUND

The worldwide increase in the prevalence and incidence of sleep disturbances represents a major public health issue. Among multiple determinants affecting sleep health, an individual's socioeconomic status (SES) is the most ignored and underestimated throughout the literature. No systematic review on the relation between SES and sleep health has been previously conducted in Latin America.

METHODS

PRISMA guidelines were used.

RESULTS

Twenty articles were included in the final sample (all cross-sectional studies), and twelve among them were rated as fair or poor quality. Among these studies, 80.0% (n = 16) were performed in Brazil, 10.0% (n = 2) were performed in Peru, 5.0% (n = 1) were performed in Chile, and 5.0% (n = 1) were multicentric (11 countries). The combined total number of participants was N = 128.455, comprising 3.7% (n = 4693) children, 16.0% (n = 20,586) adolescents, and 80.3% (n = 103,176) adults. The results show the following: (1) The sleep outcomes analyzed were sleep duration, sleep quality/sleep disturbance, insomnia, excessive daytime sleepiness (EDS), obstructive sleep apnea (OSA)/sleep-disordered breathing (SDB) symptoms, and bruxism. (2) The most used determinants were income, education level, employment status/occupation, wealth/assets, and composite indices. (3) Higher SES was associated with shorter sleep duration. (4) Lower SES was associated with a decrease in sleep quality, less frequent snoring, more prevalent EDS, and sleep bruxism. (5) Lower education was associated with insomnia. (6) Higher education was associated with more sleep bruxism. (7) The pooled prevalence using a meta-analysis of the random effects model was 24.73% (95%CI, 19.98-30.19), with high heterogeneity (I2 = 100%). (8) The prevalence of sleep disturbances decreased with high education (OR, 0.83; 95%CI, [0.69-0.99]; I2 = 79%), while it increased with low income (OR, 1.26; 95%CI, [1.12-1.42]; I2 = 59%), unemployment (OR, 2.84; 95%CI, [2.14-3.76]; I2 = 0%), and being a housewife (OR, 1.72; 95%CI, [1.19-2.48]; I2 = 55%).

DISCUSSION

This meta-analysis shows that lower SES (education, income, and work) was associated with sleep disturbances in Latin America. Therefore, sleep disturbance management should be addressed with a multidimensional approach, and a significant investment in targeted public health programs to reduce sleep disparities and support research should be made by the government before the situation becomes uncontrollable.

Prevalence and associated factors of poor sleep quality among industrial workers in Addis Ababa, Ethiopia: findings from a cross-sectional study

OBJECTIVE

This study aimed to investigate the prevalence and associated factors of poor sleep quality among Bole Lemi Industrial Park workers in Addis Ababa, the capital of Ethiopia.

DESIGN

An institution-based cross-sectional study was conducted from July to August 2022. The data were collected using a validated and standardised Pittsburgh Sleep Quality Index Questionnaire. The collected data were entered into EpiData V.4.6 and analysed using Stata V.14. A multivariable binary logistic regression analysis was conducted to identify factors associated with poor sleep quality. The association was determined using an adjusted OR (AOR) with a 95% CI at a p value of <0.05.

SETTING

The study was conducted in Bole Lemi Industrial Park in Addis Ababa.

PARTICIPANTS

A total of 370 workers participated in this study.

OUTCOME MEASURES

The primary outcome of the study was the prevalence of poor sleep quality.

RESULTS

The total response rate was 87.7%. The majority (260, 70.3%) of the participants were female. The mean (±SD) age of the participants was 30.5 (±10.6) years. The prevalence of poor sleep quality during the last months was found to be 75.4% (n=279) (95% CI: 70.7% to 79.7%). Undiversified dietary intake (AOR: 1.63; 95% CI (1.09 to 2.78)), caffeine consumption more than 250 mg per day (AOR: 2.57; 95% CI (1.12 to 6.05)) and night shift work (AOR: 2.36; 95% CI (1.27 to 4.41)) were risk factors for poor sleep quality among sampled workers.

CONCLUSION

This study detected that poor sleep quality is highly prevalent among garment and textile industry workers, indicating that it is a significant public health issue that requires immediate attention. Therefore, modifications of the work environment involving night shift work and the improvement of workers' personal lifestyles involving dietary diversification and caffeine consumption are needed to minimise the burden of poor sleep quality among workers.

The prevalence and associated factors of sleep deprivation among healthy college students in China: a cross-sectional survey

Background

The prevalence of sleep deprivation among college students is increasing and has a few associated factors.

Methods

The present study analyzed 2,142 college students from 28 provinces in China. The Chinese version of the Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep duration. Binary logistic regression was conducted to explore the sleep deprivation related factors. Age and gender were controlled as covariates.

Results

Among the 2,142 college students (27.7% male, 72.3% female), 1,620 (75.6%) reported the average sleep duration was below 7 h per day for one month, 49.3% (1,055/2,142) slept 6∼7 h (contains 6 h), 21.0% (449/2,142) slept 5∼6 h (contains 5 h), and 5.4% (116/2,142) slept <5 h. Age increased the risk of sleep deprivation, the adjusted odds ratio = 1.05 (95% CI [1.01∼1.10]). The adjusted odds ratio (A-OR) for sleep deprivation was higher for students of more than 60 min nap duration per day (A-OR = 2.35, 95% CI [1.45∼3.80]), and age growth (A-OR = 1.05, 95% CI [1.01∼1.10]). In contrast, A-ORs were lower among sleeping inconsistency between work and rest days (A-OR = 0.61, 95% CI [0.49∼0.75]), accustomed to staying up late (A-OR = 0.45, 95% CI [0.36∼0.57]), staying up late to work or study (A-OR = 0.62, 95% CI [0.49∼0.78]), stress (A-OR = 0.75, 95% CI [0.58∼0.98]), and repeated thoughts in bed had (A-OR = 0.79, 95% CI [0.62∼0.99]).

Conclusions

Sleep deprivation is extremely common among healthy college students in China. It is necessary to perform methods maintaining enough sleep due to the current high incidence of sleep deprivation. Controlling the nap duration and getting enough sleep on rest days to replace missing hours of sleep on workdays might improve college students' sleep.

Establishing the root causes of unsafe behaviors among construction workers: an integrative interpretive structural modeling analysis

There has been a significant decline in worker productivity at construction sites globally owing to the increase in accidents and fatalities due to unsafe behavior among workers. Although many studies have explored the incidence of unsafe behaviors among construction workers, limited studies have attempted to evaluate the causal factors and to determine the root causes. An integrative interpretive structural modeling analysis of the interrelationships that exist between these causal factors established from relevant literature was conducted in this study to determine the root factors hence bridging this gap. Fifteen causal factors were identified through literature review, and the nature of interrelationships between them was determined using interpretive structural modeling (ISM) and a Cross-impact matrix multiplication applied to classification (MICMAC) analysis. Data was obtained from a purposively selected cohort of experts using semi-structured interviews. The emergent data was subsequently analyzed using the ISM and MICMAC analysis to ascertain the interrelationships between the causal factors. The results of the study showed that age, sleep quality, degree of interaction and workers' skillsets were the root causes of unsafe behavior among construction workers. Besides engendering the establishment of the root causes of unsafe behavior among construction workers, the results of this study will facilitate the prioritization of appropriate solutions for tackling the menace.

Socioeconomic status and sleep health: a narrative synthesis of 3 decades of empirical research

STUDY OBJECTIVES

This review aims to assess the association between socioeconomic status (SES) and sleep health in the general population and the mediating effects of lifestyle and mental and physical health in this relationship.

METHODS

Observational studies testing the independent association between objective or subjective SES indicators and behavioral/physiological or clinical sleep health variables in the general population were included. PubMed/MEDLINE was searched for reports published from January 1990 to December 2019. The direction of effect was used as the primary effect measure, testing the hypothesis that low SES is associated with poor sleep health outcomes. Results are presented in the form of direction effect plots and synthesized as binomial proportions.

RESULTS

Overall, 336 studies were identified. A high proportion of effects at the expected direction was noted for measures of sleep continuity (100% for sleep latency, 50-100% for awakenings, 66.7-100% for sleep efficiency), symptoms of disturbed sleep (75-94.1% for insomnia, 66.7-100% for sleep-disordered breathing, 60-100% for hypersomnia), and general sleep satisfaction (62.5-100%), while the effect on sleep duration was inconsistent and depended on the specific SES variable (92.3% for subjective SES, 31.7% for employment status). Lifestyle habits, chronic illnesses, and psychological factors were identified as key mediators of the SES-sleep relationship.

CONCLUSIONS

Unhealthy behaviors, increased stress levels, and limited access to health care in low-SES individuals may explain the SES-sleep health gradient. However, the cross-sectional design of most studies and the high heterogeneity in employed measures of SES and sleep limit the quality of evidence. Further research is warranted due to important implications for health issues and policy changes.

CITATION

Papadopoulos D, Etindele Sosso FA. Socioeconomic status and sleep health: a narrative synthesis of 3 decades of empirical research. J Clin Sleep Med. 2023;19(3):605-620.

Association of low back pain and sleep quality with presenteeism

BACKGROUND

Low back pain (LBP) and poor subjective sleep quality (SSQ) are major risk factors for presenteeism. However, no studies have investigated whether combined LBP and poor SSQ are associated with presenteeism.

AIMS

We aimed to examine whether a combination of LBP and poor SSQ is associated with presenteeism.

METHODS

This cross-sectional study included 936 workers (median age, 38 years; men, 89%), with evaluated presenteeism using the work limitations questionnaire. We divided them into 'no presenteeism' and 'presenteeism' categories. The presence of LBP was defined as a numerical rating scale (NRS) score of ≥1 in current pain intensity. SSQ was assessed using a single question regarding whether the participants typically got enough sleep. We categorized the participants into four groups: (i) LBP + poor SSQ, (ii) non-LBP + poor SSQ, (iii) LBP + good SSQ and (iv) non-LBP + good SSQ. Logistic regression analysis was used to investigate the association between presenteeism and the presence of LBP and poor SSQ, adjusting for age, sex, work type, education, marital status, smoking status, body mass index and weekly working hours.

RESULTS

The data from 533 participants were used for analysis (median age, 38 years; men, 90%, response rate, 66%). Combined LBP and poor SSQ were significantly associated with presenteeism (non-LBP + poor SSQ: adjusted odds ratio = 0.56, 95% CI 0.32-0.96; LBP + good SSQ: 0.33, 0.20-0.56; non-LBP + good SSQ: 0.29, 0.18-0.48).

CONCLUSIONS

Evaluating both LBP and SSQ may be beneficial in considering presenteeism.

Sleep quality of Singapore residents: findings from the 2016 Singapore mental health study

Given the importance of sleep for physical and mental wellbeing, it is crucial to understand the extent of insomnia among community dwellers. However, there is a paucity of population wide epidemiological studies to estimate the prevalence of poor sleep quality. This present study aimed to 1) characterize the sleep quality of a nationally representative sample (n = 6126) of Singapore residents using Pittsburg Sleep Quality Index (PSQI) and 2) identify the sociodemographic correlates of poor sleep in this population. A total of 27.6% of respondents reported poor sleep quality (PSQI score ≥5). Sociodemographic correlates of poor sleep quality in the Singapore population included, but were not limited to, females (AOR = 1.44, 95% CI = 1.17 to 1.77, p-value = 0.001), Malays (vs Chinese) (AOR = 1.53, 95% CI = 1.23 to 1.9, p-value < 0.001), Indians (vs Chinese) (AOR = 1.22, 95% CI = 1.02 to 1.47, p-value = 0.03), ex-smokers (vs non-smokers) (AOR = 1.43, 95% CI = 1.07 to 1.92, p-value = 0.02), persons with comorbid mental health conditions (vs no mental health conditions) (AOR = 14.11, 95% CI = 6.52 to 30.54, p-value < 0.01), and persons with physical multimorbidity (vs no physical conditions) (AOR = 1.63, 95% CI = 1.24 to 2.15, p-value < 0.001). The prevalence of poor sleep in Singapore is comparable to that of other countries in the Asian region. Targeted public health campaigns to psycho-educate vulnerable groups on the importance of good sleep hygiene may improve the overall wellbeing of residents in Singapore.

•

Sleep is important for physical and mental health, however data on population wide prevalence of insomnia is scarce.

•

Using the Pittsburgh Sleep Quality Index, we found that 27.6% of this nationally representative sample had poor sleep.

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Sociodemographic correlates of poor sleep quality in our population included females, Malays, Indians, and ex-smokers.

•

Persons with comorbid mental health conditions and physical multimorbidity were found to have poor sleep quality.

•

Targeted psychoeducation to the relevant subgroups could improve sleep quality of Singapore residents.

Food insecurity is associated with the sleep quality and quantity in adults: a systematic review and meta-analysis

OBJECTIVE

We evaluated associations between food insecurity (FI) and the quality and quantity of sleep in adults (≥18 years).

DESIGN

The current study represented a systematic review and meta-analysis of observational studies.

SETTING

Databases of PubMed, Scopus, Embase and Web of Science were searched from inception until 6 June 2022. Meta-analyses were conducted using random-effects models, and effect sizes were reported as OR and 95 % CI.

PARTICIPANTS

Data from ten eligible observational studies, including 83 764 participants, were included.

RESULTS

FI was associated with an increased risk of poor sleep quality (OR = 1·45; 95 % CI (1·24, 1·70), I2 = 95, P < 0·001, n 7). Besides, subgroup analysis showed increased risk of poor sleep quality corresponding to the severity of FI across mild (OR = 1·31; 95 % CI (1·16, 1·48), I2 = 0 %, P < 0·001, n 5), moderate (OR = 1·49; 95 % CI (1·32, 1·68), I2 = 0 %, P < 0·001, n 5) and severe (OR = 1·89; 95 % CI (1·63, 2·20), I2 = 0 %, P < 0·001, n 5) levels. Similarly, subgroup analysis by sleep problems showed that FI was associated with an increased the risk of trouble falling asleep (OR = 1·39; 95 % CI (1·05, 1·83), I2 = 91 %, P = 0·002, n 3) and trouble staying asleep (OR = 1·91; 95 % CI (1·37, 2·67), I2 = 89 %, P < 0·001, n 3). Moreover, FI was associated with the odds of shorter (OR = 1·14; 95 % CI (1·07, 1·21), I2 = 0 %, P < 0·001, n 4) and longer sleep duration (OR = 1·14; 95 % CI (1·03, 1·26), I2 = 0 %, P = 0·010, n 4).

CONCLUSIONS

Collective evidence supports that FI is associated with poor sleep quality and quantity in adults. Preventative and management strategies that address FI may provide health benefits beyond improving nutritional status per se.

Evaluation of sleep quality and duration using wearable sensors in shift laborers of construction industry: A public health perspective

Approximately 21% of the workers in developing and developed countries are shift laborers. The laborer's work shifts can affect personal life and sleep standards, adversely impacting laborers and their manage. This study assesses the impact of various shift plans (seven evenings/7 days, fixed-night or fixed-day, and backup shifts) on shift laborers, considering four shift schedules. Most laborers were on rotational shifts, whereas others were on a permanent day, permanent night, and standby shifts. In a cross-sectional study, 45 development laborers from the National Construction firm were enlisted. Bio-wearable sensors were provided to monitor sleep. Participants were approached and asked to complete a survey bundle comprising the Pittsburgh sleep quality index (PSQI) and Epworth sleepiness scale (ESS). Differences in sleep models were estimated using a Fitbit watch at various shift schedules. The average age of laborers who participated in the study was 37.5 years, and their average experience in the construction company was 6.5 years. The average total sleep time was 346 ± 46 min. The rotational shift laborers yielded the minimum total sleep time compared to the average PSQI and ESS scores of 7.66 ± 1.3 and 6.94 ± 3.4, respectively. Fifteen shift laborers (33.33%) were affected by a sleeping disorder in the present experimental investigation, and 30 participants had inadequate standards of sleep based on the PSQI scores. Poor sleep quality and duration among construction shift laborers decrease productivity at work. Additional studies are expected to assess sleep-related issues affecting construction shift laborers.

Sleep quality: An evolutionary concept analysis

AIM

To clarify the meaning of the concept sleep quality.

BACKGROUND

Sleep loss and sleep quality are global health concerns. Poor sleep quality has significant adverse health outcomes. A clarification of the term is necessary to inform patients and healthcare providers, promote consistent theoretical and operational definitions in research, and develop prevention and treatment strategies.

DESIGN

Concept analysis.

DATA SOURCES

Scientific literature from electronic databases (CINAHL, PsycINFO, PubMED, Web of Science, and JSTOR) and definitions from online dictionaries.

REVIEW METHODS

Rodgers' Evolutionary method was applied to guide the concept analysis to identify and determine the attributes, antecedents, and consequences.

RESULTS

Sleep quality is defined as an individual's self-satisfaction with all aspects of the sleep experience. Sleep quality has four attributes: sleep efficiency, sleep latency, sleep duration, and wake after sleep onset. Antecedents include physiological (e.g., age, circadian rhythm, body mass index, NREM, REM), psychological (e.g., stress, anxiety, depression), and environmental factors (e.g., room temperature, television/device use), and family/social commitments. Good sleep quality has positive effects such as feeling rested, normal reflexes, and positive relationships. Poor sleep quality consequences include fatigue, irritability, daytime dysfunction, slowed responses, and increased caffeine/alcohol intake.

CONCLUSIONS

Sleep quality is essential, and poor sleep quality contributes to disease and poor health outcomes. Given the extensive consequences of poor sleep quality, nurses and clinicians are vital in instructing the importance of good sleep.

Sleep Quality Is Associated with Vitamin B12 Status in Female Arab Students

Studies have explored how vitamin B12 status affects sleep among elders and children, but this remains to be investigated among young adults. We used the Pittsburgh Sleep Quality Index (PSQI) to assess the association between serum vitamin B12 and sleep among female college students in Saudi Arabia. In this cross-sectional study, we enrolled 355 participants (age (years), 20.7 ± 1.5; body mass index, 23.6 kg/m² ± 5.2) at King Saud University, Riyadh, Saudi Arabia. Fasting blood samples were analyzed regarding the serum vitamin B12 and blood lipids. Anthropometric, socio-demographic, clinical history, stress, physical activity, and dietary data were collected. We assessed the sleep statuses of the participants using the PSQI. Around 72% of the participants were “poor” sleepers (PSQI > 5). Subgroup analysis within the tertiles showed that participants with higher vitamin B12 in the second and third tertiles reported better scores for sleep quality (B ± SE = −12.7 ± 5.6, p = 0.03; B ± SE = −32.7 ± 16.4, p = 0.05, respectively) and also reported a lower use of sleep medication (B ± SE = −21.2 ± 9.9, p = 0.03, in the second tertile only), after adjusting for the waist–hip ratio and stress. However, sleep was not found to be directly associated with either serum vitamin B12 or dietary vitamin B12. In conclusion, the serum vitamin B12 results show that the participants with higher vitamin B12 in the second and third tertiles reported better scores on the sleep quality scale and a lower use of sleep medication. However, no such associations were observed with the overall PSQI. More studies with larger sample sizes are needed to establish a direct relationship between sleep and vitamin B12.

Effect of Pilates on Sleep Quality: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Objective: Pilates exercise is increasingly used to improve sleep quality, but relevant evidence remains unclear. We aimed to estimate the effect of Pilates on sleep quality.

Methods: Five databases were searched for articles published until 10 December 2019. Two investigators screened the articles and extracted data from each included study. A meta-analysis was performed to evaluate the effect of Pilates on sleep quality, assessed using the Pittsburgh Sleep Quality Index (PSQI).

Results: Six randomized controlled trials (RCTs) comprising 477 participants were included according to the inclusion and exclusion criteria in the study. All included studies reported the positive effects Pilates had on sleep quality. The Pilates group (PG) significantly lowered the PSQI total score (MD = −3.60, 95%CI: [−5.41, −1.78), P = 0.0001, I² = 97%) compared to the non-exercising control group (CG), whereas no significant improvement in use of sleep medication was observed (MD = −0.33, 95%CI: [−0.73, −0.06), P = 0.10, I² = 68%). However, in a subgroup analysis, we found that there was no significant reduction in the PSQI total score for healthy participants over 40 years old (MD = −3.73, 95%CI: [−7.89, 0.42], P = 0.08, I² = 98%) and for postmenopausal women (MD = −5.55, 95%CI: [−13.98, −2.89], P = 0.20, I² = 98%).

Conclusions: Overall, Pilates improved sleep quality but had no significant effect on the use of sleep medication. However, Pilates showed no significant impact on sleep quality for healthy individuals over 40 years old and for postmenopausal women. Well-designed and large-scale RCTs are needed in the future.