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Prevalence and risk factors of poor sleep quality among outpatients in cardiology at the Douala General Hospital.


Prévalence et facteurs associés à la mauvaise qualité du sommeil chez les patients en consultation externe de cardiologie à l’Hôpital Général de Douala.










Introduction : Le sommeil est un besoin essentiel de l’organisme auquel nous consacrons un tiers de notre vie. Les troubles du sommeil sont un problème émergeant de santé publique en Europe et en Amérique vu leurs impacts néfastes sur le profil cardiovasculaire de la population. Au Cameroun, cette prévalence des troubles du sommeil est peu documentée ainsi que son influence sur les facteurs de risque cardiovasculaire.

Méthodes : Nous avons mené une étude transversale et analytique chez 368 participants recrutés de façon consécutive dans l’Unité de Cardiologie de l’Hôpital Général de Douala de mars à juillet 2018. Les paramètres cliniques et paracliniques ont été évalués. La qualité du sommeil a été évaluée à l’aide de l’Index de Qualité du sommeil de Pittsburgh (PSQI) et un score PSQI > 5 correspond à une mauvaise qualité du sommeil.


 Résultats : La prévalence de la mauvaise qualité de sommeil était de 52,7%. La proportion du surpoids/obésité, de l’obésité abdominale, de l’hypertension artérielle (HTA), du syndrome métabolique, de la dyslipidémie, de l’hyperuricémie, et du diabète  était respectivement de : 81,5%, 81,0%, 77,4%, 47,3%, 48,9%, 40,5% et 13,9%. La mauvaise qualité du sommeil était associée à l’âge supérieur à 40 ans, au lieu de résidence urbain, à l’hypertension artérielle, au diabète, au syndrome métabolique et à  l’hypertriglycéridémie. En analyse multivariée, seuls le lieu de résidence urbain et l’hypertriglycéridémie étaient significativement associés à la mauvaise qualité du sommeil.

Conclusion : Cette étude montre que la mauvaise qualité du sommeil est fréquente au sein des patients vus en consultation externe de cardiologie et est significativement associé à l’hypertriglycéridémie et à la résidence en milieu urbaine.




Prévalence, qualité de sommeil, facteurs de risque cardiovasculaire, Hôpital Général de Douala.






Introduction: sleep is an essential body need to which we devote one-third of our lives. Sleep disorders are an emerging public health problem in Europe and America due to their adverse impact on the cardiovascular profile of the population. In Cameroon, this prevalence of sleep disorders is poorly documented and influences cardiovascular risk factors.

Methods: we conducted a cross-sectional descriptive and analytical study in 368 participants consecutively recruited in the Cardiology Unit of the Douala General Hospital from March to July 2018. Demographic, anthropometric and clinical and bioclinical parameters were assessed by usual methods. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and a PSQI score > 5 corresponds to poor sleep quality. Multivariate logistic regression was used to assess factors associated with poor sleep quality. Odd ratio (OR) was calculated and statistical significance was set for p<0.05.

Results: the prevalence of poor sleep quality was 52.7%. The proportion of overweight/obesity, abdominal obesity, hypertension, metabolic syndrome, dyslipidemia, hyperuricemia, and diabetes was respectively: 81.5%, 81.0%, and 77.4%, 47.3%, 48.9%, 40.5% and 13.9%. Poor sleep quality was associated with age over 40 years, urban residence, hypertension, diabetes, metabolic syndrome and hypertriglyceridemia. In a multivariate analysis, urban residence and hypertriglyceridemia remained the only ones significantly associated with poor sleep quality.

Conclusion: this study shows that poor sleep quality is common among cardiology outpatients and is significantly associated with hypertriglyceridemia and urban residence.




Prevalence, sleep quality, cardiovascular risk factors, Douala General Hospital.




1. Department of Internal Medicine, Douala General Hospital, Cameroon;

2. Faculty of Medicine and Pharmaceutical Sciences, University of Douala, Cameroon;

3. Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, Cameroon;

4. Intensive Care Unit, Douala General Hospital, Cameroon;

5. Faculty of Medicine and Biomedical Sciences, University of N'Gaoundéré, Cameroon;

6. Faculty of Health Sciences, University of Buea, Cameroon;

7. Physiology and Medicine of Physical Activities and Sports Unit, University of Douala, Cameroon. 


Adresse pour correspondance

KAMDEM Félicité

Department of Internal Medicine, Douala General Hospital

P.O BOX: 4856 Douala, Cameroon

Phone: +237 699 98 86 75

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.






Cardiovascular disease (CVD) is the leading cause of death worldwide [1]. The high incidence of CVD is mainly caused by the high rate of cardiovascular risk factors (CRFs) [2]. Non-compliance with dietary health measures (regular exercise, high-fiber, low-salt, low-fat diet, etc.) cannot fully explain the high prevalence of these cardiovascular diseases, as, beyond these various classical measures, emerging scientific evidence has shown that there is a real association between poor quality sleep and the high prevalence of CVD and CRFs [3,4].

According to the neurobiologist Michel Jouvet, sleep is defined as a natural and periodic reversible decrease in the perceptivity of the external environment with the conservation of reactivity and vegetative functions [5].  It is a vital physiological phenomenon of repair and restoration of the organism so disorders have a significantly negative impact on human health [5]. Reducing sleep as much as possible is considered harmless by many, especially in industrialized societies in relation to changing lifestyles, workloads and access to technology. However, in the last 50 years, this reduction coincides with the increased prevalence of hypertension, obesity, diabetes [6] .

Although the pathophysiological relationship is still poorly established, it is likely that sleep disorders lead to metabolic and neuro-hormonal abnormalities favoring the onset of cardiovascular diseases [7].  Thus, a study published   in  April   2018    in    the   Maghreb showed that poor sleep quality was associated with a greater probability of being overweight/obese [8]. Furthermore, a 2007 study in Italy concluded that sleep disorders are associated with significant comorbidities, such as hypertension and diabetes mellitus [9]. Similarly, work done in 2015 in China and 2017 in Cameroon showed that poor sleep quality was associated with a high prevalence of hypertension.

Sleep disorders are increasingly common, reaching prevalence of 10-15% in the global population [10]. The prevalence of sleep disorders and their adverse impacts on cardiovascular morbidity and mortality are known in America and Europe, making them an emerging public health problem. However, in Africa and particularly in Cameroon, few studies to date have assessed the prevalence of sleep disorders in association with cardiovascular risk factors. Hence the interest of this study whose aim is to determine the prevalence and factors associated with the quality of sleep in patients seen in cardiology consultations at the Douala General Hospital.






This is a cross-sectional descriptive and analytical study that took place between January and May 2018 at the Cardiology Unit of the Douala General Hospital (DGH). A total of 368 participants aged 21 years and above were consecutively recruited. Pregnant women, patients followed for sleep disorders and those with oedemato-ascitic syndrome were excluded from the study. The study was approved and ethical clearance obtained from the ethic and institutional committee of University of the Mountains and a research authorization from the DGH.

For each participant, we collected sociodemographic, anthropometric and personal history data, as well as current medical treatments. The parameters measured included blood pressure (BP) in mmHg, height in meters, weight in kilograms and waist circumference in centimeters. Height was measured to the nearest cm using a tape measure and weight was measured using a mechanical scale (Medisana PSD-150kgs). Body mass index (BMI) was determined by dividing weight in kilograms by the square of height in meters (kg/m²), Overweight was defined for a BMI ≥ 25 and < 30 Kg/m2 and obesity for a BMI ≥ 30 Kg/m2 [11]. Waist circumference was measured in centimeters at mid-distance between the iliac crest and the costal margin on the mid-axillary line, and abdominal obesity was defined by a waist circumference ≥ 94 cm in men and ≥ 80 cm in women [12]. Metabolic syndrome was defined according to the International Diabetes Federation (IDF) 2005 for patients who had in addition to abdominal obesity at least one of the following abnormalities: blood glucose ≥ 1g/l or treated diabetes, triglyceride level ≥ 1.50 g/l or specific treatment, HDL-cholesterol < 0.40 g/l in men and < 0.50 g/l in women or specific treatment, blood pressure elevation ≥ 130/85mmHg or the existence of treatment for hypertension [13] 

Dyslipidemia was defined as total cholesterol > 2g/L or HDL cholesterol < 0,40g/L for men and < 0,50g/L for women or LDL cholesterol > 1g/L and triglycerides > 1,5g/L [14]

Blood pressure was taken in participants sitting quietly, with their upper limbs free, without any pressing need, after an average of ten (10) minutes of rest and at least 30 minutes after the consumption of tobacco and stimulating drinks and meal. Measurements were taken three (03) times using an electronic blood pressure monitor (OMROM HEM-907) with a standard or obese adult size cuff as appropriate. Only the mean of the three (03) measurements was used for statistical analysis. Hypertension was defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg [15] and/or the use of antihypertensive drugs.

Biological parameters such as lipid profile and hyperuricemia were recorded in the participants' medical records if the results were less than three months old. If not, or if the participant did not have any results, a blood sample was taken after a minimum period of 12 hours of fasting. At the end of the daily recruitment, the samples collected were sent to the laboratory of the Douala General Hospital, then, using a ROTORIX 311 centrifuge, the samples were centrifuged at 3500 rpm for 10 minutes. The serum was collected with a pipette and stored in Cryo tubes in a freezer. Finally, the samples were analyzed using a COBAS C311, brand ROCHE. Capillary blood glucose was measured with an ACCU CHEK Active glucose meter on the outer edge of any finger after cleaning with water and diabetes mellitus was defined as fasting capillary blood glucose ≥ 1.26 g/L and/or the use of glucose-lowering drugs [16]. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire (28, 29). This questionnaire consists of 19 self-report questions coded into 4 items (0-3) and grouped into 7 components: subjective sleep quality; sleep latency; sleep duration; habitual sleep efficiency; sleep disturbance; use of sleep medication; and poor daytime form. Each component is given a score of 0-3 which is summed for an overall score between 0 and 21. A PSQI score >5 is considered representative of poor sleep quality, while a PSQI score ≤ 5 suggests good sleep quality. The PSQI has a sensitivity of 89.6% and a specificity of 86.5% (28).

Participation in the study was voluntary and an informed consent was signed by all participants prior to their inclusion in the study.  Confidentiality was ensured by the anonymity of the data sheets.





Data were treated and analyzed using SPSS 20 software. Quantitative data were presented as mean ± standard deviation while qualitative data were presented as counts and frequencies. Comparisons of quantitative and qualitative data between male and female participants were performed using Student t-test and Chi square test respectively. Multivariate logistic regression was used to determine factors associated to insomnia in the sample. Data included in the multivariate model where those with a p value < 0.1 in univariate analysis Differences were considered significant for p<0.05.







We recruited 368 participants with 59,2% of women.


Characteristics of participants


Table 1 shows demographic, anthropometric and bioclinical parameters of de study sample. Data are compared between male and female. Mean age was 54 ±13 years and was similar between male and female (p=0.971). Frequencies of cardiovascular risk factors in the sample were as follow: 81% for overweight/obesity, 81.5% for abdominal obesity, 77.4% for hypertension, 13.9% for diabetes, 48.9% for dyslipidemia and 47.3% for metabolic syndrome. Abdominal obesity and dyslipidemia were significantly higher in female (p<0.0001 and p=0.021 respectively) while diabetes was significantly higher in male (p=0.003). Differences were not significant for the other cardiovascular risk factors.


PSQI component scores and total score in all participants and compared by gender


Table 2 present PSQI component scores and total score compared between male and female participants. For each component, the frequent of participants with score 2 and 3 is presented as well as a PSQI score > 5. The mean score of sleep duration was significantly higher in male than female (p=0.039) while for the other components, the difference was not significant. Frequency of the participants with bad or very bad subjective sleep quality was 20.4% and was similar between male and female participants. Frequencies of participants with 2 or 3 score of sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, need for sleep medications and daytime dysfunction were similar between male and female participants (p>0.05). The mean of the total PSQI score was 6.24 ±2.97 and was similar between the two groups (p=0.297).  Frequency of participants with PSQI total score > 5 was 52.7% and was similar between male and female participants (p=0.172).


Univariate and multivariate logistic regression for prediction of insomnia in the sample


Table 3 shows frequency of poor sleep quality (PSQI score > 5) according to different variables of the study as well as univariate and multivariate coefficient for the determinants of insomnia. Only variables with p value <0.1 in univariate were used in the multivariate model. Poor sleep quality was positively associated with aging, hypertension, diabetes, metabolic syndrome and hypertriglyceridemia (all p<0.05). Poor sleep quality was not significantly associated with urban residency (p=0.07). After adjustment for all those variables, poor sleep quality was significantly associated with urban residency (p=0.048) and hypertriglyceridemia (p=0.045). Differences were not significant for other variables. 






Our study reveals that more than half of the participants (52.7%) had poor quality sleep. This prevalence is nevertheless lower than those recently reported in China by Lee et al. in 2012 and in Cameroon by Kamdem et al. in 2017, which were respectively 78% and 64.2% among adult subjects [17,18]. Berhanu et al. in Ethiopia in 2018 found a rate of 65.4% [19]. Our result is however much higher than the 15.9% rate found by Kai Lu et al. in 2015 in a rural population in China [20].  Overall, the high prevalence of poor sleep quality found in our series could be explained by the fact that our population, mostly urban (85.9% of the population), was subjected to the stress of urban areas: occupational stress, diversified television programs, noisy sleep environment are all factors likely to alter sleep quality. Similarly, overweight and obesity which represented 81% of our population, also favour the occurrence of certain sleep disorders [21].

Cardiovascular risk factors were high for abdominal circumference, overweight/obesity, hypertension, and metabolic syndrome. Our population had a 77.4% prevalence of hypertension and 47.3% for metabolic syndrome which can be explained by the fact that abdominal obesity was also high in our population. Our result is higher than that found by Aguilar et al. in 2015 in the United States which was 33%, and by Raposo et al. in Portugal in 2017 who found a rate of 36.5% [22,23]. This difference can be explained by the fact that the other studies have been carried out on general population while our study was hospital-based and probably included participants with a higher odd of having cardiometabolic risk factors including advanced age, overweight/obesity, metabolic syndrome, and very recently poor sleep quality [20,24]. Our result is also higher than that of Mesli et al. who found 69.6% and that of Brouri et al. who found 39.5% in Algeria in 2018 [24,25].

In our study, we obtained significant associations between advanced age, hypertension, diabetes, metabolic syndrome, hypertriglyceridemia and poor sleep quality. But after adjusting for confounding factors, only urban residence and hypertriglyceridemia remained significant. This result is similar to that of Wan Mahmood et al. in 2013 in an Irish diabetic population   showing   that,    subjects with PSQI > 5 had higher triglycerides [26]. Furthermore, Kaneita et al. in 2008 in Japan confirmed this correlation between poor sleep quality and hypertriglyceridemia [27]. Mechanisms such as decreased blood leptin concentration or increased blood ghrelin concentration due to sleep restriction may be involved in the biological mechanisms responsible for the associations between poor sleep quality and dyslipidemia [27].


Study limits


The main limit of the study is the population constituted of outpatients who are more likely to come for a previously documented health problem or for a cardiovascular complain. Another limitation is the use of the PSQI which is and indirect method to evaluate sleep quality.






 This study shows that poor sleep quality is common among patients seen in cardiology consultations at the Douala General Hospital. Given the high rate of hypertension, diabetes, metabolic syndrome and hypertriglyceridemia, and the significant correlations observed, this study allows us to conclude that poor sleep quality is a public health problem that deserves the attention of health care personnel.




Table 1

Characteristics of participants











Age, years

54 ±13

54 ±13

54 ±14


Rural residency (%)

52 (14.1)

31 (14.2)

21 (14.0)


Weight, kg

81.8 ±15.3

80.1 ±15.7

84.3 ±14.4


Height, m

167 ±8

164 ±7

173 ±6

< 0.0001

BMI, kg/m²

  29.2 ±5.3

29.9 ±5.7

28.2 ±4.6


Overweight/ obesity (%)

298 (81.0)

181 (83.0)

117 (78.0)


Waist circumference, cm

  96 ±12

95 ±12

98 ±11


Abdominal obesity (%)

300 (81.5)

197 (90.4)

103 (68.7)


Systolic BP, mmHg

138 ±20

136 ±20

142 ±20


Diastolic BP, mmHg

  86 ±14

84 ±12

87 ±15


Hypertension (%)

285 (77.4)

165 (75.7)

120 (80.0)


Blood glucose, g/L

0.96 ±0.27

0.94 ±0.23

0.99 ±0.33


Diabetes (%)

51 (13.9)

20 (9.2)

31 (20.7)


T-Chol (g/L)

2.04 ±0.48

2.08 ±0.49

1.98 ±0.47


T-Chol > 2g/L (%)

185 (50.3)

122 (56.0)

63 (42.0)


LDL-Chol (g/L)

1.27 ±0.45

1.28 ±0.46

1.24 ±0.44


LDL-Chol >1g/L (%)

251 (68.2)

151 (69.3)

100 (66.7)


HDL-Chol (g/L)

0.55 ±0.18

0.57 ±0.18

0.51 ±0.19


HDL-Chol* (%)

105 (28.5)

72 (33.0)

33 (22.0)


Triglycerides (g/L)

1.11 ±0.48

1.08 ±0.49

1.14 ±0.46


Triglycerides > 1.5g/L (%)

56 (15.2)

27 (12.4)

29 (19.3)


Dyslipidemia (%)

180 (48.9)

118 (54.1)

62 (41.3)


Metabolic syndrome (%)

174 (47.3)

104 (47.7)

70 (46.7)



BMI: body mass index; WC: waist circumference; BP: blood pressure; T-Chol: total cholesterol; HDL-Chol: HDL-Cholesterol;   LDL-Chol: LDL-Cholesterol;   *HDL-Chol ≤ 0.4g/L in male and ≤ 0.5g/L in female.



Table 2

PSQI component scores and total score in all participants and compared by gender. 










Subjective sleep quality, m±SD

1.07 ±0.74

1.04 ±0.78

1.11 ±0.68


Bad or very bad, %

75 (20.4)

46 (21.1)

29 (19.3)


Sleep latency, m±SD

1.03 ±0.99

1.06 ±1.01

0.99 ±0.96


Score ≥ 2, %

111 (30.2)

63 (28.9)

48 (32.0)


Sleep duration, m±SD

1.08 ±0.85

1.00 ±0.86

1.19 ±0.84


< 6h, % 

101 (27.4)

54 (24.8)

47 (31.3)


Habitual sleep efficiency, m±SD

0.68 ±0.95

0.64 ±0.92

0.75 ±1.00


< 75%, %

72 (19.6)

38 (17.4)

34 (22.7)


Sleep disturbance, m±SD

1.11 ±0.37

1.12 ±0.36

1.11 ±0.37


Score ≥ 2, %

40 (10.9)

25 (11.5)

15 (10.0)


Need for sleep medications, m±SD

0.10 ±0.44

0.11 ±0.48

0.08 ±0.38


≥ 1 times/week, %

10 (2.7)

7 (3.2)

3 (2.0)


Daytime dysfunction, m±SD

1.17 ±0.95

1.15 ±0.93

1.21 ±0.97


Score ≥ 2, %

126 (34.2)

69 (31.7)

57 (38.0)


PSQI total score, m±SD

6.24 ±2.97

6.11 ±3.04

6.44 ±2.89


PSQI score > 5, %

194 (52.7)

108 (49.5)

86 (57.3)


 PSQI: Pittsburg Sleep Quality Index



Table 3

Univariate and multivariate logistic regression for prediction of insomnia in the sample




PSQI > 5







Eff (%)

OR (95%CI) 


AOR (95%CI) 


Age, years

21 - 40

  24 (39.3)






41 - 60

109 (55.3)

1.91 (1.06- 3.43)


1.49 (0.74- 2.99)



>  60

  61 (55.5)

1.92 (1.02- 3.63)


1.38 (0.63- 3.02)




  86 (57.3)






108 (49.5)

0.73 (0.48- 1.11)





  22 (42.3)







172 (54.4)

1.63 (0.90- 2.94)


1.85 (1.08- 3.51)




  35 (42.2)







159 (55.8)

1.73 (1.06- 2.84)


0.77 (0.41- 1.43)




159 (50.2)







  35 (68.6)

2.17 (1.16- 4.09)


0.63 (0.31- 1.28)


Overweight/ obesity


  34 (48.6)






160 (53.7)

1.23 (0.73- 2.07)



Abdominal obesity


  31 (45.6)






163 (54.3)

1.42 (0.84- 2.41)



Metabolic syndrome


  89 (45.9)







105 (60.3)

1.80 (1.19- 2.72)


1.06 (0.61- 1.83)




189 (52.8)






    5 (50.0)

1.12 (0.32- 3.93)



T-Chol ≥ 2


  90 (49.2)






104 (56.2)

1.33 (0.88- 2.00)



LDL-Chol ≥ 1


61 (52.1)






133 (53.0)

1.03 (0.67- 1.61)





141 (53.6)






  53 (50.5)

0.88 (0.56- 1.39)



Triglycerides  ≥ 1.5


154 (49.4)







  40 (71.4)

2.56 (1.38- 4.77)


2.05 (1.01- 4.14)




  93 (49.5)






101 (56.1)

1.31 (0.87- 1.97)



PSQI: Pittsburg Sleep Quality Index





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