Poor sleep quality, cardiometabolic risk factors and physical activity, among paramedical staff in Douala,Cameroon

doi:10.21203/rs.3.rs-4700970/v1

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Poor sleep quality, cardiometabolic risk factors and physical activity, among paramedical staff in Douala,Cameroon

https://doi.org/10.21203/rs.3.rs-4700970/v1

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Background: Paramedical personnel used to night shift predisposing them to poor sleep (PS) and its health consequences. Their work requests an important physical demand associated to physical activity (PA).

Objectives: This study aimed at determining prevalence of PS, its association with cardiometabolic risk factors (CFRs), and PA among paramedical staff.

Methods: A cross-sectional study was conducted among nurses and medical-health technicians (MTH) in reference hospitals of Douala, Cameroon. PS and PA were assessed with standard questionnaires. CFRs included overweight, obesity, waist circumference (WC), Waist-to-height ratio (WhtR), Waist-to-hip ratios (WhR), hyperglycemia and sedentary behaviors.

Results: A total of 331 participants were enrolled, including 53.4% nurses. The overall prevalence of PS was 68.3% (95%CI:63.01-73.01). PS was higher in women (73.5%,95%CI:67.3-78.8, p=0.04) and nurses (58.8%; 95%CI: 52.3-65.1, p=0.01). Among participants with PS, more than half more affected were overweight and obese (39.9% and 34% respectively) with abnormal WC (55.8%), abnormal WhR (78.2%), and sedentary behaviors (65.5%). However, 28.4% had abnormal WhtR, 40.6% had hyperglycemia, and 47.7% had low moderate level of PA. Among nurses with poor sleep, ponderal status appeared more normal among those with moderate PA (64.7%, p=0.009). Nurses with low and high levels were more overweight and obese (48.5%, p=0.009; 15.2%, p=0.009 respectively). Still among paramedical staff with poor sleep, it was noted that nurses with low levels of activity had more abnormal WhR (50.9%, p=0.0008), while WhR was more abnormal among nurses with moderate (51.9%, p=0.0008) and high (29.6%, p=0.0008) levels of PA. MTH were less in risk to suffer from PS quality (OR: 0.48.95%CI: 0.29-0.80; p= 0.004) but in risk of short sleep duration (OR= 2.4; 95%CI: 1.45-3.8; p=0.0006). Paramedical personnel with work seniority more than 5 years were at risk of PS (OR= 1.67, 95%CI :1.04-2.70, p=0.03). Eventhose aged under 30 years were protected against sleep duration (OR=0.50, 95%CI: 0.31-0.94; p=0.03). No association was found between PA level and PS and cardiometabolic risk cluster factors.

Conclusion: PS is a reality with alarming prevalence among paramedical staff, with high value in females and nurses. No associations were found with CRFs and PA.

Poor sleep

cardiometabolic risk factors

physical activity

paramedical staff

Douala-Cameroon

Paramedical staff constitute the largest part of the healthcare workforce worldwide [1]. They are responsible for coordinating and delivering healthcare (nurses) and medical laboratory examinations (medical health technicians). Paramedical personnel play a key role in patient care and comfort [2, 3]. In many countries, to provide patient care and conduct biomedical examinations 24 hours a day, 7 days a week, paramedical staff participate in a rotating schedule, requiring shifts to rotate through prescribed intervals between day, evening, night and early morning making way for night shift predisposing them to poor sleep (PS) [4].

Unfortunately, this night shift is the cause of sudden deviations from the normal sleep rhythm, often disrupting their internal biological clock and giving way to poor quality of sleep which has harmful repercussions on their daily functioning. Night shift working practices disrupt cortisol and melatonin cycles which directly impact sleep quality [5, 6]. PS is a reality among healthcare workers on night shifts, with worldwide, 61.0% of nursing staffs prevalence of PS [7].

Adequate sleep is a fundamental physiological process that determines human health and performance. This condition is necessary for physiological mechanisms essential to life, such as the energy restoration, neural plasticity, and secretion of the growth hormone [8]. Also, sleep regulation impacts brain function and behavior [9]. Thus, PS is a detrimental condition that may lead to mood disorders, behavioral problems, and other mental health problems [10]. The performance of paramedical personnel tasks requires intensive attention in order to limit errors in patient care or biomedical diagnosis [11]. In this context, PS in paramedical personnel leading to cognitive impairment, mental fatigue, decline in attention, and efficiency in their workplace [12].

Besides, PS constitutes an important predicting factor that affects the occurrence of cardiometabolic risk factors (CRFs) and diseases [13–15] and some cancers [11]. PS and its corollaries are increasingly becoming a major and growing public health problem affecting millions of people in many professional sectors around the world [15, 16].

Physical activity (PA) is an important non pharmacological intervention and preventive therapy for PS [17, 18]. PA is beneficial for mental and physical health, including good sleep improvement [19] and reducing cardiometabolic health markers [20]. Good sleep and good level of PA are both important health behaviors in the general population [21]. PS and low PA levels are related to a wide range of cardiometabolic diseases in many occupational sectors.

However, it should be noted that paramedical profession requires an important physical demand where the characteristics of the work and the associated levels of PA can vary and make some to observe sedentary behaviors [22]. For example, in the course of a shift, nurses carry out physically demanding tasks, such as administering care to highly dependent patients whose mobility is reduced in the case of nurses, or reading slides under a microscope in the case of medical-sanitary technicians, which can lead to sedentary lifestyles or a variety of level of PA [23, 24]. Despites the rise of PS among paramedical staff, several studies have shown that paramedical staff present a variability of level of PA according to their function and work service [22, 25]. Therefore, is there a relationship between PA, PS CRFs among paramedical personnels? The aim of the present study was to determine the prevalence of PS and its pattern association with CRFs and PA among paramedical staff.

Study design

Participants

A cross-sectional prospective and analytical study was conducted among paramedical personnel preciously Nurses and Medical-health technicians (MTH) in reference hospitals of the town of Douala, the capital town of the Littoral region, Cameroon. The study was approved by the Institutional Ethics Committee of the University of Douala (reference: 3995CEI-Uo/09/2023/M) and was conducted in conformity with the recommendations of the declaration of Helsinki revised in 1989. Participants provided an informed consent before their enrolment in the study. They were informed about the anonymous analysis of their medical information’s, and assured that personal information would not be disclosed.

Measurements

Sociodemographic and professional characteristics

A structured questionnaire was used to assess sociodemographic (gender, age, and marital status) and professional characteristics (occupation, work seniority).

Sample size

The minimal sample size was determined using the Lorentz formula with a prevalence of 75.5% reported previously [26] among Ethiopian nurses and a minimal sample required was 285 participants. Participants a work seniority more than one year with weekly night shift were included. Those a work seniority less than one year without night shift were excluded; even those suffering from diabetes, hypertension or any other cardiometabolic disease.

Anthropometric parameters

Height was measured using a rod graduated to the nearest centimeter,

Weight was measured using a bioelectric impedancemeter balance Terraillon Wellness Coach (USA).
The body mass index (BMI) was calculated to assess ponderal status of each participant according to the Quetelet formula as the weight (Kg) divided by the square of the height (m²). The participants were classified according to their BMI as follows: normal weight (BMI: 18,5-2530 kg/m²).), overweight (BMI≥25–29.930 kg/m²).) and obese (BMI>30 kg/m²).
Waist circumference (WC) was measured with an inelastic tape between the lower edge of the costal arch and the iliac bone’s upper crest in a standing position with an accuracy of 0.5 cm [27] and hip circumference (HC) was measured at the level of the greater trochanter [27].
Ratios waist-to-height (WHtR) and waist-to-hip (WhR) were calculated as WC divided by the height (WhtR) and by hip circumference (WhR);
Sedentary lifestyle was considered for all patients spending more than two hours sitting or in front of a screen or remaining seated.

Blood pressure, heart rate and glycaemia

Systolic blood pressure, diastolic blood pressure, and heart rate were taken using an electronic blood pressure monitor Omron (Kyoto, Japan) placed on the participants left arm in a sitting position. The first measurement was taken after a ten minutes rest in a sitting position and was followed by another measurement after five minutes intervals, the average of the two measurements was used to assess the presence or absence of high blood pressure. Fasting blood glucose was measured in each fasting participant (12-14 hours of fasting). Blood glucose was measured between 8 and 10 am invasively using a lancet inserted into a lancet pen, which pricked the finger, which had been cleaned with water and dried with a handkerchief. The first drop of blood had been cleaned, the second was placed on an electronic (Gluco-one) strip inserted into a blood glucose meter, which displayed the blood glucose level after a few seconds.

Poor sleep

The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality [28]. This questionnaire assesses the quality of sleep of a participant for a month. PSQI consists of 19 items on seven components of sleep quality: sleep onset latency, sleep duration, efﬁciency, quality, disturbances, medication, and daytime dysfunction. Each component of PSQI is scored between 0–3, and the sum of the seven components yield a global score of sleep quality with a total score ranging from 0- 21 points; a high score (PSQI score > 5) is an indication of poor quality of sleep.

Level of physical activity and sedentary behaviors

The International Physical Activity Questionnaire (IPAQ) short form was administered to assess the level of PA of participants. This questionnaire has good validity and reliability in quantifying PA[29]. This tool assesses PA in three domains (at work, travel to and from places and recreational activity). The first six items of this tool require the participants to report frequency and duration involvement in low, moderate and vigorous PA during the previous week. The seventh item quantifies the total sedentary time during the weekdays.

PA was expressed as Metabolic Equivalent of Task (MET)-min (where the metabolic equivalent of 1 MET is defined as the amount of oxygen consumed when sitting at rest and is 3.5 mL O₂ per kg body weight/min) per week and calculated by multiplying the MET assigned to it (vigorous – 8 MET, moderate – 4 MET, and walking – 3.3 MET) by the number of days it was performed during the previous 7 days [29, 30]. According to the IPAQ guidelines, the total PA of the participants was divided into 3 categories: high, moderate, and low. sedentary behavios was assessed by determining the number of hours spent sitting or in front of a screen for more than than 2 hours.

Cardiometabolic risk factors

The CRFs assessed in this study were follows:

Ponderal status, including overweight (BMI: 25-29.9 Kg.m^-2) and obesity (BMI: 30Kg.m^-2);
The waist circumference (WC) assessed central obesity. Abnormal WC was defined by a waist circumference ≥ 94 centimeters in men and ≥ 80 centimeters in women [27];

High fasting blood glucose level ≥ 100 g/dL[31];
Waist-to-height ratio (WhtR), which is commonly used to assess cardiometabolic risk in both children and adolescents and adults. Therefore, values >0.5 were considered at risk [27];
Abnormal WhR values were ≥ 0.85 centimeters in women and ≥ 90 centimeters in men risk [27].

Statistical analyses

The data were entered in the software Microsoft Excel 2016 and analyzed with StatView 5.0 (SAS Institute, Inc., Chicago, USA) software. The Kolmogorov–Smirnov test was performed to verify the normality of data. Qualitative variables were presented as percentages (%) and quantitative variables as mean ± standard deviation (SD). In the descriptive stage of the analysis, mean and SD of quantitative variables were calculated, as well as the absolute. Relative frequencies of categorical variables and lower and upper limits of the 95% confidence interval for a proportion confidence intervals of PS were determined [32]. The Pearson-s Chi-squared test was used to compare unpaired variables and the student’s t-test on unpaired series was performed to compare quantitative variables according to the gender. Then, associations between poor sleep, sociodemographic parameters, CRFs and PA was quantified through computing crude and adjusted odd ratios at 95% of confidence interval (95%CI). The statistical significance threshold was set for any value at P<0.05.

Sociodemographic professional, and cardiometabolic profiles of the participants were summarized in Table 1. A total of 331 participants were included in this study. More than half (53.4%) of the participants were nurses. The sample consisted of approximately three quarters women (74.9%). Women were also more numerous in the nursing profession (58.5%; p = 0.009) than in medical-health technicians. In addition, participants aged more than 30 years old and those with long professional seniority accounted for 71.9% and 65% of the participants, respectively. Near of half participants were singles (47.7%), more men (50.6%)

Table 1

Sociodemographic and cardiometabolic profile according to gender and profession
Parameters	Categories	Overall n(%)	Males n(%)	Females n(%)	P-value	Nurses n(%)	MHT n(%)	P-value
Profession	Nurses	180(53.4)	145(58.5)	103(41.5)	0.009
	MHT	151(45.6)	35(41.2)	48(57.8)
Ages (years)	≤ 30	93(20.1)	25(26.9)	68(73.1)	0.63	38(40.9)	55(59.1)	0.002
Ages (years)	> 30	238(71.9)	58(24.4)	180(75.6)		142(59.7)	96(40.3)
Seniority (years)	≤ 5	216(65.0)	56(25.9)	160(74.1)	0.62	117(54.2)	99(45.8)	0.94
Seniority (years)	> 5	115(35.0)	27(23.5)	88(76.5)		63(54.8)	52(45.2)
Marital Status	Single	150(47.7)	42(50.6)	108(43.5)	0.02	69(38.3)	81(53.6)	0.01
	Married	122(36.9)	21(25.3)	101(40.7)		78(43.3)	44(29.2)
	Divorced	59(17.8)	20(24.1)	39(15.7)		33(18.4)	26(17.2)
		150(47.7)	42(50.6)	108(43.5)	0.02	69(38.3)	81(53.6)	0.01
Ponderal status	Normal	92(27.8)	41(49.4)	51(20.6)		45(25.0)	47(31.1)
	Overweight	122(36.9)	35(42.2)	87(35.1)	< 0.0001	69(38.3)	53(35.1)	0.46
	Obese	117(35.3)	7(8.4)	110(44.4)		66(36.7)	51(33.8)
WC	Normal	147(44.4)	74(89.2)	73(29.4)	< 0.0001	73(40.6)	74(49.0)	0.12
	Abnormal	184(55.6)	9(10.8)	175(70.6)		107(59.4)	77(51.0)
WhtR	Normal	235(71.0)	76(91.6)	159(64.1)	< 0.0001	125(69.4)	110(72.8)	0.48
	Abnormal	96(29.0)	7(8.4)	89(35.9)		55(30.6)	41(27.2)
WhR	Normal	70(21.1)	56(67.5)	14(5.6)	< 0.0001	37(20.6)	33(21.9)	0.72
	Abnormal	261(78.9)	27(32.5)	234(94.4)		143(79.4)	118(78.1)
Glycaemia	Normal	200(60.4)	46(55.4)	154(62.1)	0.24	109(60.6)	91(60.3)	0.93
Glycaemia	High	131(39.6)	37(44.6)	94(37.9)		71(39.4)	60(39.7)
Sleep duration	< 7hrs	224(67.7)	50(60.2)	174(70.2)	0.09	136(75.6)	88(58.3)	0.0008
Sleep duration	≥ 7hrs	107(32.3)	33(39.8)	74(29.8)		44(24.4)	63(41.7)
Level of PA	Low	159(48.0)	38(45.8)	121(48.8)		79(43.9)	80(53.0)
	Moderate	136(41.1)	36(43.4)	100(40.3)	0.87	78(43.3)	58(38.4)	0.20
	High	36(10.9)	9(10.8)	27(10.9)		23(12.8)	13(8.6)
Sedentarity	Yes	220(66.5)	56(25.5)	164(74.5)	0.82	116(64.4)	104(68.9)	0.39
Sedentarity	No	111(33.5)	27(24.3)	84(75.7)		64(35.6)	47(31.1)
MHT: Medical-health technicians; WC: Waist circumference; WhtR: waist to heigh ratio, WhR: waist to hip ratio, PA: Physical activity

With respect to anthropometric CRFs more than half of paramedical personnel were overweight and obese (36.9%, 8.4% respectively) had abnormal WC (55.6%); WhR ratio (78.9%) and a sleep duration of less than 7 hours (67.7%). As for WhtR, 29.0% had an abnormal ratio. Almost half of the participants had hyperglycemia (44.6%). For the level of PA few had a high (10.9%) and the majority had low and moderate (48%, 41.1% respectively). According to the gender, women were more obese (44.4%; p < 0.0001), with abnormal WhtR and WhR (35.9%, p < 0.0001; 94.4%, p < 0.0001respectively) than men. For the profession, MTHaged less than or equal to 30 were more represented (59.1%%; p = 0.002) and had more short sleep duration (58.3%; p = 0.0008).

Subsequently in Table 2, women were aged (p = 0.008) and had significantly higher weight, body mass index, waist circumference, hip circumference and WhtR (p < 0.01) than men. On the other hand, high systolic and diastolic blood pressure, fasting blood glucose and metabolic equivalent task were noticed in men (p < 0.01) and heart rate in female (p < 0.0001).

Table 2

Anthropometric and physiological parameters according to the gender
Parameters	Categories	Total (Mean ± SD)	Males (Mean ± SD)	Females (Mean ± SD)	P-value
Age and seniority	Age (years)	37 ± 9	34 ± 7	37 ± 9	0.008
Age and seniority	Seniority	6 ± 6	5 ± 6	6 ± 5.5	0.12
Anthropometric	Weight (Kg)	79 ± 15.3	77.2 ± 12.8	79.7 ± 16.2	0.2
parameters	Height (m)	1.66 ± 0.8	1.74 ± 0.73	1.63 ± 0.63	< 0.0001
	BMI (kg.m-2)	28.7 ± 5.9	25.4 ± 4.6	29.8 ± 5.9	< 0.0001
	WC (cm)	0.9 ± 0.1	0.89 ± 0.1	0.95 ± 0.13	0.0006
	HC (cm)	99 ± 13	91 ± 9	102 ± 13	< 0.0001
	WhtR	0.56 ± 0.80	0.51 ± 0.7	0.58 ± 0.08	< 0.0001
	WhR	0.94 ± 0.06	0.98 ± 0.57	0.92 ± 0.55	< 0.0001
	SBP (mmHg)	123 ± 15	128 ± 14	122 ± 16	0.0047
Blood pressure	DBP (mmHg)	77 ± 11	80 ± 11	76 ± 11	0.0086
	HR (bpm)	75 ± 11	71 ± 11	76 ± 10	< 0.0001
Metabolic	FBG	1.06 ± 0.16	1.08 ± 0.15	1.04 ± 0.16	0.11
	MetS (Kcal/min)	1174.7 ± 1475	1341.1 ± 1641.4	1119.1 ± 1414.1	0.23
PSQI	PSQI	7.3 ± 3.2	7.4 ± 3.5	7.2 ± 3.1	0.6
	Subjectivity	1.3 ± 0.8	1.3 ± 0.9	1.3 ± 0.8	0.80
	Latency	1.5 ± 0.8	1.5 ± 0.9	1.5 ± 0.8	0.8
Sleep	Duration	1.8 ± 1	1.6 ± 1.1	1.8 ± 0.9	0.11
components	Efficiency	0.6 ± 0.9	0.6 ± 0.9	0.6 ± 0.9	0.71
	Disturbance	1.2 ± 0.5	1.2 ± 0.5	1.2 ± 0.5	0.5
	Medication	0.2 ± 0.5	0.3 ± 0.6	0.1 ± 0.4	0.01
BMI: Body mass index, WC: waist circumference, HR: Heart rate; Waist circumference; WhtR: waist to heigh ratio, WhR: waist to hip ratio, SBP: Systolic blood pressure; DBP: Diastolic blood pressure, FBG: Fasting blood glucose.

The distribution of PS by gender and professional occupation illustrated in Fig. 1 established that the overall prevalence of PS was 68.3% (95%CI:63.01–73.01). Furthermore, PS rate was significantly higher in women (73.5%,95%CI:67.3–78.8, p = 0.04) and nurses (58.8%; 95%CI: 52.3–65.1, p = 0.01) compared to MHT (41.2%, 95%CI:34.9–47.7).

Among participants with PS, more than half aged over 30 years old (69%) more affected, were overweight and obese (39.9% and 34% respectively) with abnormal waist circumference (55.8%), abnormal WhR (78.2%), and sedentary (65.5%). However, 28.4% had abnormal WhtR, more than a third had hyperglycemia (40.6%). Almost near 47.7% had low level of PA 40.1% moderate and 12.2% high level of PA (Table 3).

Table 3

Cardiometabolic risk factors according to the gender in participants with poor sleep
Parameters	Categories	Total n(%)	Nurses (%)	MTH (%)	P-value	Males n(%)	Females n(%)	P-value
	≤ 30	70(31)	31(44.3)	102(65.4)	0.002	18(25.7)	52(74.3)	0.84
Range of age	> 30	156(69)	39(55.7)	54(34.6)		42(27)	114(73)
	Normal	52(26.4)	34(25.6)	26(28.0)	0.88	29(48.3)	31(18.7)	< 0.0001
Ponderal status	Overweight	78(39.6)	51(38.3)	33(35.4)		26(43.4)	77(46.4)
	Obese	67(34.0)	48(36.1)	34(36.6)		5(8.3)	58(34.9)
WC	Normal	87(44.2)	56(42.1)	44(47.3)	0.43	52(86.7)	48(28.9)	< 0.0001
	Abnormal	110(55.8)	77(57.9)	49(52.7)		8(13.3)	1184(71.1)
WhtR	Normal	141(71.6)	93(69.9.)	66(71.0)	0.86	40(66.7)	8(4.8)	< 0.0001
	Abnormal	56(28.4)	40(30.1)	27(29.0)		20(33.3)	158(95.2)
WhR	Normal	43(21.8)	27(20.3)	21(22.6)	0.68	36(83.7)	7(16.3)	< 0.0001
	Abnormal	154(78.2)	106(79.7)	72(77.4)		18(11.7)	136(88.3)
Glycaemia	Normal	117(59.4)	78(58.6)	55(59.1)	0.94	33(55.0)	100(60.2)	0.47
	High	80(40.6)	55(41.4)	38(40.9)		27(45.0)	66(39.8)
Sedentarity	Yes	129(65.5)	81(60.9)	67(72.0)	0.08	20(27.1)	58(34.9)	0.82
	No	68(34.5)	52(39.1)	26(28.0)		40(27.9)	108(65.1)
Level of PA	Low	94(47.7)	59(44.4)	48(51.6)	0.52	29(48.4)	80(48.2)	0.19
	Moderate	79(40.1)	58(43.6)	35(37.6)		26(43.3)	67(40.4)
	High	24(12.2)	16(12.0)	10(10.8)		7(8.3)	7(11.4)
MHT: Medical-health technicians, WC: waist circumference, Waist circumference; WhtR: waist to heigh ratio, WhR: waist to hip ratio, PA: physical activity

CRFs according to PA and the professions are summarized in Table 4, nurses with low and high levels of PA were more overweight and obeses (47.4%, 14.8%, p = 0.02 respectively). On the other hand, those with a moderate level of PA (37.8%, p = 0.02) were less so. A significant association was established between WhR and PA levels, where nurses with a low level had a higher abnormal WhR (50.3%, p = 0.0005). This ratio was normal and high in nurses with moderate (54.1%, p = 0.0005) and high (27.0%, p = 0.0005) levels of PA. Subsequently, among nurses with poor sleep, ponderal status appeared more normal among those with moderate PA (64.7%, p = 0.009). Nurses with low and high levels were more overweight and obese (48.5%, p = 0.009; 15.2%, p = 0.009 respectively). Still among paramedical staff with poor sleep, it was noted that nurses with low levels of activity had more abnormal WhR (50.9%, p = 0.0008), while WhR was more abnormal among nurses with moderate (51.9%, p = 0.0008) and high (29.6%, p = 0.0008) levels of PA.(Table.4)

Table 4

Cardiometabolic risk factors according to level physical activity
	Parameters	Categories	Nurses				Medical-health technicians
	Parameters	Categories	Low n(%)	Moderate n(%)	High n(%)	P	Low n(%)	Moderate n(%)	High n(%)	P
Overall participants	Ponderal status	Normal	15(33.3)	27(60)	3(6.7)	0.02	25(53.2)	20(42.6)	2(4.3)	0.40
	Ponderal status	Overweight & Obese	64(47.4)	51(37.8)	20(14.8)		55(51.4)	38(35.5)	11(10.3)
	WC	Normal	31(42.5)	35(47.9)	7(9.6)	0.44	39(52.7)	31(41.9)	4(5.4)	0.33
	WC	Abnormal	48(44.9)	43(40.1)	16(15)		41(53.2)	27(35.1)	9(11.7)
	WhtR	Normal	51(40.8)	58(46.4)	16(12.8)	0.41	63(57.3)	38(34.5)	9(8.2)	0.21
	WhtR	Abnormal	28(50.9)	20(36.4)	7(12.7)		17(41.4)	20(48.8)	4(9.8)
	WhR	Normal	7(18.9)	20(54.1)	10(27.0)	0.0005	20(60.6)	12(36.4)	1(3.0)	0.36
	WhR	Abnormal	72(50.3)	58(40.6)	13(9.1)		60(50.8)	46(40)	12(10.2)
	Glycaemia	Normal	49(45.0)	48(44.0)	12(11.0)	0.67	54(59.3)	29(31.9)	8(8.8)	0.11
	Glycaemia	Abnormal	30(42.3)	30(42.3)	11(15.4)		26(43.3)	29(48.4)	5(8.3)
			Low n(%)	Moderate n(%)	High n(%)	P	Low n(%)	Moderate n(%)	High n(%)	P
Participants With poor sleep	Ponderal Status	Normal	11(32.4)	22(64.7)	1(2.9)	0.009	14(53.8)	10(38.5)	2(7.7)	0.83
	Ponderal Status	Overweight & Obese	48(48.5)	36(36.3)	15(15.2)		34(50.7)	25(37.3)	8(11.9)
	WC	Normal	22(39.3)	30(53.6)	4(7.1)	0.09	23(52.3)	18(40.9)	3(6.8)	0.48
	WC	Abnormal	37(48.1)	28(36.3)	12(15.6)		25(51.0)	17(34.7)	7(14.3)
	WhtR	Normal	36(38.7)	45(48.4)	12(12.9)	0.13	36(54.5)	23(34.8)	7(10.6)	0.65
	WhtR	Abnormal	23(57.5)	13(32.5)	4(10)		12(44.4)	12(44.4)	3(11.2)
	WhR	Normal	5(18.5)	14(51.9)	8(29.6)	0.0008	13(61.9)	8(38.1)	0(0)	0.17
	WhR	Abnormal	54(50.9)	44(41.6)	8(7.5)		35(48.6)	27(37.5)	10(13.9)
	Glycaemia	Normal	35(44.9)	35(44.9)	8(10.4)	0.75	31(56.4)	18(32.7)	6(10.9)	0.48
	Glycaemia	Abnormal	24(43.6)	23(41.8)	8(14.5)		17(44.7)	17(44.7)	4(10.6)
WC: waist circumference, Waist circumference; WhtR: waist to heigh ratio, WhR: waist to hip ratio

Multivariate analysis established that MTH were less in risk to suffer from PS quality (OR: 0.48.95%CI: 0.29–0.80; p = 0.004) but in risk of short sleep duration (OR = 2.4; 95%CI: 1.45–3.8; p = 0.0006). Paramedical personnel with work seniority more than 5 years were at risk of PS (OR = 1.67, 95%CI :1.04–2.70, p = 0.03). Even those aged under 30 years were protected against sleep duration (OR = 0.50, 95%CI: 0.31–0.94; p = 0.03). No association was found between PA level and PS and cardiometabolic risk cluster factors.

The present study was design to determine prevalence of poor sleep, PA and their association with cardiometabolic risk among paramedical personnel. Based on the PSQI the overall prevalence of PS notice was 68.3% (95%CI:63.01–73.01). This prevalence is higher than that reported by Barcelos et al.[33] in a cross-sectional study in Brazil among many health professionals (55.7%), and Zurutuza et al. [34] in a recent study conducted in Mexico among clinical care personnel (doctors and nurses) and non-clinical personnel in intermittent contact with patients.

Furthermore, in our study PS was significantly observed in women which is consistent with previous studies [35, 36]. Hormonal difference may be the main justification [37]. It is also known that the brain's orexin/hypocretin system is modulated by gonadal hormones, and is thought to play an important role in the regulation of wakefulness and sleep hormonal modulation of the orexin/hypocretin system is more important in females [38].

PS was more in nursing (58.8%; 95%CI: 52.3–65.1, P = 0.01). This prevalence is less than reported in many studies in Africa. In Nigeria Kolo et al.[39]reported a prevalence of 77.1%, in Ethiopia PS range 70.6–75.5% % [26, 40]. However, our prevalence is similar to the worldwide prevalence of PS among nurse of 61.0% [7] and less than the 70.4% noticed in tertiary healthcare professionals in Jeddah, KSA by Alghamdi et al.[41]. This prevalence variability of PS would be due to differences in terms of human resources, adequate facilities and health system compared to the present study.

CRFs were found among participants with PS with significant difference in females. This result is similar to that noticed in a recent study in Northern Finland where associations between cardiometabolic health markers (including WC, high glucose level, higher body mass index) and irregular wake‑up time [42] which is a component of quality of sleep. Clinical experimental studies highlighted that PS in healthy humans affects levels of ghrelin and leptin, two primary hormones involved in energy balance that regulate appetite and body weigh ghrelin levels rise primarily in response to acute sleep deprivation, while leptin levels fall [43].

Some researchers have shown that ghrelin levels increase at night [44, 45] and PS decreases nocturnal ghrelin levels [44], but leads to increased diurnal levels in humans, resulting in food intake [46]. Leptin is secreted mainly by adipocytes and signals to the hypothalamus the extent of fat reserves in the body; its decrease indicates a lack of calories, while an increase favors energy expenditure. Unlike ghrelin, bad leptin decreases the amplitude and levels of leptin during the day [46]. On the metabolic view, PS can lead to elevated levels of cortisol, which is associated with an increase of abdominal fat accumulation and a higher risk of metabolic disorders [14]. This abnormal gain of weight can highlight cardiometabolic risk such as factors overweight and obesity, WC, WhtR, WhR [14].

High fasting blood glucose has been noticed among participants with PS (Table 3). Inadequate sleep has been associated with impaired glucose metabolism, including decreased insulin sensitivity and glucose tolerance. PS disrupt hormonal regulation of glucose, leading to elevated blood glycaemia and an increased risk of developing insulin resistance and type 2 diabetes [47]. PS constitutes a stress and is associated with elevated plasma cortisol secretion as well as markers of sympathetic activation [46] and circulating catecholamines [48] in people with PS [49]. These hormones are known for their hyperglycemic potential. Furthermore, PS increases inflammation and metabolic disorders [50, 51].

In the current study, based on multivariate analysis, MTH were less in risk to suffer from PS quality (Table 5). This result may be linked to the fact that nurses’ night shift is more stressful than that of medical-health technicians. Nurses are confronted with medical emergencies likely to alter their sleep, which is not necessarily the case in MTH who conducted biomedical analysis in laboratories and are less confronted with health emergencies. With regard to the risk of sleep duration in MTH (Table 5). This result would be related to mental exhaustion, to which MTH were more exposed than nurses, despite the fact that mental fatigue was not assessed in this study. Nevertheless, it is important to note that mental exhaustion can have different causes, and that sleep duration [52]. Also, the job of MTH requires more mental effort and concentration in interpreting the results of biomedical analyses, and less physical effort, predisposing them to sedentary lifestyles, despite the complex relationship, sedentary behavior contributes to shortened sleep duration [53–55].

Table 5

Associated factors with poor sleep and short sleep duration
		Poor sleep		Sleep duration < 7hrs
Parameters	Categories	OR (95%CI)	P-Value	OR(95%CI)	P-Value
Profession	Nurses	1		1
Profession	MHT	0.48(0.29–0.80)	0.0041	2.4 (1.45–3.8)	0.0006
Gender	F	1		1
Gender	M	2.3(0.95–5.47)	0.06	1.4 (0.81–2.4)	0.22
Seniority (years)	≤ 5	1		1
Seniority (years)	> 5	1.67(1.04–2.70)	0.03	0.71(0.44–1.14)	0.14
Range age (years)	> 30	1		1
Range age (years)	≤ 30	1.7(0.92–3.16)	0.91	0.50(0.31–0.94)	0.03
WC	Normal	1		1
WC	Abnormal	1.1(0.48–2.30)	0.90	1.1(0.6–1.9)	0.92
WhtR	Normal	1		1
WhtR	Abnormal	1(0.45–2.27)	0.64	0.83(0.41–1.67)	0.62
WhR	Normal	1		1
WhR	Abnormal	1.54(0.65–3.67)	0.32	1.67(0.91–3.15)	0.11
Ponderal status	Normal	1		1
Ponderal status	Overweight & obese	1.21(0.72–2.1)	0.63	0.75(0.4–1.3)	0.23
Level of PA	High	1		1
	Low	0.85(0.40-2)	0.71	1.2(0.54–2.6)	0.71
	Moderate	0.90 (0.4–2.12)	0.80	1.1 (0.5–2.5)	0.86
Glycemia	Normal	1		1
Glycemia	Abnormal	1.2(0.8-2)	0.40	1.2(0.8-2)	0.43
MHT: Medical-health technicians, F: Female, M: Males WC: waist circumference, HR: Heart rate; Waist circumference; WhtR: waist to heigh ratio, WhR: waist to hip ratio, OR: Odd ratio, CI: confidence intervals, PA: physical activity.

Paramedical personnel with work seniority more than 5 years were at risk of poor sleep. This result is not in an accordance to that of Deng et al. [56] who found no significant link between job seniority and sleep quality. Also; no significant associations were found between PA and PS (Table 5). According to some authors the relationship between PA and sleep is complex and bidirectional and PS may also contribute to reduced PA [57, 58] making a vicious circle that maintains reduced quality of life. A study of Uchida et al. [59] revealed possible pathways for PA effects on sleep involving endocrine, autonomic nervous system, metabolism, circadian rhythm, and somatic functions. On the whole studies confirm beneficial effects of PA on sleep quality but, as recent studies demonstrated, this relationship is more complex than a linear association. Thus, there are some controversies concerning the role of type of PA, its regimen, and intensity. There is an evidence that intense/vigorous PA (compared to moderate PA) does not have any positive impact on sleep quality[60]. The relationship between PS and PA depends to the level of PA and the context of it practice. It’s well documented that when the type of PA is considered high precisely occupational PA (compared to leisure-time PA) alter the quality of sleep [61, 62].

The present study revealed that PS is a reality in full expansion with alarming prevalence among paramedical staff with high value in females and nurses. The study also outlined the existence of CRFs among paramedical staff with PS. No protective effect of PA in preventing PS was found. This outcome has many implications for patients care and provides a framework for establishing preventive interventions to reduce cardiometabolic health markers among paramedical staff in a way to preserve the well-being of this important healthcare sector.

PS: Poor sleep

CRFs: Cardiometabolic risk factors

PA: Physical activity

BMI: Body mass index

WC: Waist circumference

HC: Hip circumference

WHtR: Waist-to-height ratio

WhR: hip circumference

PSQI: Pittsburgh Sleep Quality Index

MTH: medical-health technicians

The study was approved by the Institutional Ethics Committee of the University of Douala (reference: 3995CEI-Uo/09/2023/M) and was conducted in conformity with the recommendations of the declaration of Helsinki revised in 1989. Participants provided an informed consent before their enrolment in the study. They were informed about the anonymous analysis of their medical information’s, and gave signed consent forms prior to their participation.

Acknowledgments

Authors thank hospitals administrations staff for the support. We also extend our acknowledgments to all paramedical personnels who accepted to participate in this study.

Author contributions

JMN, ECBL, PBAN conceptualized the study and wrote the manuscript. EGNN, NCB and AULT collected datas. LPKF and WRG conducted and revised the statistical analyses. A, WMM, AACN, JG, LJOM, BB and SHM interpreted the results and contributed substantially to the content of the manuscript. All authors reviewed the manuscript and approved the final version.

Funding

The authors reported that there is no funding associated with the work featured in this article

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing of interest.

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Poor sleep quality, cardiometabolic risk factors and physical activity, among paramedical staff in Douala,Cameroon

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