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Abstract

Background:

Although a significant lack of physical activity has been linked to an increase in obesity among Emirati women, the factors associated with accelerometer-measured sitting and physical activity times in Emirati women remain unclear.

Objectives:

To explore the association of accelerometer-measured sitting and physical activity times with sociodemographic, anthropometric, and sleep quality factors among Emirati working women.

Design:

A cross-sectional study.

Methods:

A convenience sample of 163 healthy working Emirati women aged 18–45 years was used. Sedentary and physical activity times were measured using the Fibion accelerometers worn on the thighs for 4–7 days. General demographic information, anthropometric measurements, and self-reported sleep quality (Pittsburgh sleep quality index score) were collected. Only participants who had valid data (i.e. wear time of ⩾600 min (10 h) per day for a minimum of 4–7 days) were evaluated. All values were normalized to a 16-h day to mitigate differences in wear time among the participants.

Results:

Overall, 110 Emirati women were included. The mean sitting time per 16-h day was 11.6 ± 1.1 h; mean moderate activity time per day, 40.88 ± 17.99 min; and mean vigorous activity time per day, 2.41 ± 1.21 min. Longer sitting time was associated with high body fat, secondary education, and divorce. Sitting time was reduced in those with good sleep quality. Moderate-to-vigorous physical activity time was increased in women with postgraduate education and was decreased in women with a longer work experience and with comorbidities. The total activity time increased with increasing age and good sleep quality, whereas it decreased with increasing body fat, presence of at least one comorbidity, secondary education, and divorce.

Conclusion:

Certain demographic, anthropometric, and sleep quality factors were associated with accelerometer-measured sitting and physical activity times among Emirati working women. Future longitudinal studies should consider these factors when investigating predictors of physical activity levels in this population.

Keywords

accelerometer Emirati physical activity sedentary behavior sleep quality women

Introduction

The World Health Organization (WHO) 2020 guidelines on physical activity (PA) and sedentary behavior stipulate that adults of both sexes should indulge in 150–300 min of moderate-intensity PA, 75–150 min of vigorous-intensity PA, or an equivalent combination of moderate- and vigorous-intensity PA per week to improve overall health.¹ However, recent global estimates indicate that nearly 28% of adults² and 81% of adolescents³ are not meeting the previous WHO recommendations for PA.

Physical inactivity is a crucial risk factor for poor health and several chronic diseases such as cardiovascular diseases,^4
–6 diabetes mellitus,⁷ obesity,⁷ and some cancers.^5,7 Globally, 22% of patients with ischemic cardiac disease and approximately 10%–16% of patients with diabetes mellitus and breast, colon, or rectal cancer have a sedentary lifestyle.⁸ This creates an urgent demand to prioritize PA across all age groups to promote healthy lives and well-being.¹ However, detailed national PA guidelines for children, adults, older adults, pregnant women, and patients with chronic diseases are lacking in the United Arab Emirates (UAE). The WHO recommends that countries should establish national guidelines, set PA targets, and implement appropriate national policies to help support populations to achieve their recommended PA levels and maintain their health.⁹

PA levels are influenced by sociodemographic factors such as age, sex, health status, self-efficacy, socioeconomic status, ethnic origin, and the environment in which the activity is performed (at home, at work, or during transport). In general, women of all ages are less physically active than their men counterparts.¹⁰ Reduced activity in women is a major contributing factor to the large proportion of the observed activity inequality across 111 countries.¹¹ Moreover, a study on Saudi women working at desks identified them as a high-risk group for sedentary behavior.¹²

Emirati women had lower PA levels¹³ and poorer sleep quality¹³ than Emirati men during the coronavirus disease 2019 (COVID-19) pandemic.^13,14 Sleep quality can indirectly predict PA levels (high, moderate, and low intensity) over time because of its positive impact on emotion regulation and vice versa.¹⁵ Moderate activity is more effective than vigorous activity in improving sleep quality in both young and older adults.¹⁶ PA may have protective effects on sleep among overweight and obese women.¹⁷

The frequency of PA is also significantly affected by a country’s income. For example, occupational, household, and transport activities increase the level of PA or account for the most common type of PA in low- and middle-income countries, whereas leisure activities contribute more to the total PA time in high-income countries.¹⁸ Inadequate PA is prevalent in 19% (16%–21% (95% confidence interval)) of women in low-income countries, 30.1% (26%–40%) in middle-income countries, and 42% (39%–44%) in high-income countries; the higher prevalence in high-income countries is primarily owing to their lack of adherence to the WHO 2010 guidelines for PA.²

The UAE has experienced remarkable socioeconomic and healthcare changes and has developed into a high-technology, oil-rich nation in the past few decades. These changes have greatly influenced PA levels and food consumption behaviors (i.e. less fruit and vegetables; frequent snacking; and more meat, poultry, sugar-sweetened beverages, and fat), contributing to an increase in chronic diseases (e.g. cardiovascular disease, cancer, diabetes, hypertension, and obesity), especially among female adolescents.^6,19,20 A significantly low level of self-reported PA among the UAE population has been widely documented^19,21,22 and this has been linked to an increased prevalence of obesity among Emirati women.²³

Most office working adults spend long periods (77% of office hours)⁴ of their days with uninterrupted sitting bouts longer than current recommendations. This might be a larger contributor to the overall sitting time than sitting during leisure time.^4,15 Therefore, the workplace has recently been identified as a key setting to focus on to assess and enhance the level of activity of working adults.¹⁵ Self-reported questionnaires,²¹ pedometers,²⁴ and waist-worn accelerometers¹⁹ have been used in previous studies investigating PA levels in the UAE population. However, self-reported questionnaires might be available only in certain languages and could be limited by memory, comprehension, perception, and social desirability^18,25 and incorrect estimates of sedentary behavior or PA due to cognitive/recall bias.²⁶ Furthermore, although pedometers could increase step count²⁴ among adult females in the UAE, they could only measure step count and not other types/intensities of activities.

Dalibalta et al. used a waist-worn accelerometer (wGT3X-BT ActiGraph) to measure the PA levels of university students in the UAE.¹⁹ However, such waist/hip-worn devices may have relatively low accuracy in identifying sedentary behavior.^27,28 Furthermore, there may be more discomfort while sleeping or embarrassment with device visibility²⁹ compared with wrist- or thigh-worn devices. Meanwhile, thigh-worn accelerometers are valid tools for assessing changes in posture and PAs.^30
–33 Therefore, a precise assessment of sedentary behavior (sitting time) and PA levels using thigh-worn accelerometers in Emirati women may be more appropriate. Despite the documented association between PA and obesity among Emirati women, the factors associated with accelerometer-measured sitting and PA times in Emirati women have not been clarified to date.

This study had two objectives: (1) to assess the prevalence of sedentary behavior (sitting time) and PA levels among working Emirati women, and (2) to investigate the association of sociodemographic, anthropometric, and sleep quality factors with accelerometer-measured sitting and PA times among working Emirati women.

Method

Study design, setting, and participants

This observational cross-sectional study was conducted in Sharjah and Dubai in the UAE. The STROBE Guidelines for observational studies were followed to ensure transparent reporting. In total, 163 healthy Emirati working women aged 18–45 years who worked a desk job in government offices for a minimum of 5 days a week were included. Participants were excluded if they met any of the following criteria: (1) pregnant or within 6 months postpartum; (2) with musculoskeletal, rheumatic, cardiorespiratory, or metabolic/systemic diseases; recent surgery; or had comorbidities affecting PA; and (3) use of assistive devices or walking aids for ambulation.

Emirati women were recruited between September 2020 and June 2021 through advertisements posted on social networking websites, office notice boards, and flyers and by contacting the administrators of their offices and/or word-of-mouth. To confirm eligibility for participation, each participant was required to complete an Arabic version of the Nordic Musculoskeletal Questionnaire to evaluate any history of musculoskeletal pain in the previous 12 months.³⁴

Sociodemographic data collection and anthropometric measurements

A physiotherapist (R.A.) visited the participants at their workplaces on 2 separate days (days 1 and 8). On day 1, an interviewer-administered questionnaire was used to obtain general demographic information (age, education, marital status, nature of employment). A portable digital scale set with an adjustable height rod (stadiometer) was used to measure weight and height. Waist circumference was measured using a non-elastic measuring tape at the midpoint between the lower margin of the ribcage and the upper margin of the iliac crest in the horizontal plane tape to the nearest 1 mm at the end of normal expiration.³⁵

Bioelectrical impedance analysis (TANITA DC 430 SMA, Tokyo, Japan) was performed at around 8:00 in the office to assess body composition (mass, body fat percentage, visceral fat rating, muscle mass, and bone mass). The participants were not asked to fast or refrain from exercise before the bioelectrical impedance analysis, because previous studies have reported only small and clinically insignificant changes in fat mass/percentage estimates despite violating the recommended assumptions (with water ingestion, exercise, or food intake).^36
–38 All anthropometric measurements were performed thrice, and the average values were recorded. The waist-to-height ratio (WHR) was calculated as the ratio between waist circumference (cm) and height (cm). The fat mass index (FMI) was calculated as the total body fat weight (kg) divided by height in meters squared.²⁶ The visceral fat area was estimated by multiplying the visceral fat rating, based on the TANITA device manufacturer guidelines,²⁷ by 10.

Sleep quality assessment

Sleep quality during the past month was self-reported using the Arabic version of the Pittsburgh Sleep Quality Index (PSQI) questionnaire (Arabic version).^28
–30 Briefly, the PSQI comprises nine items assessing quality, latency, duration, efficiency, disturbance, medication, and dysfunction related to sleep. The total scores range from 0 to 21, with scores >5 indicating poor sleep quality and scores ⩽5 indicating good sleep quality.³¹ This questionnaire has been validated to have good internal consistency, test–retest reliability, and construct validity for those with and without sleep problems.^30,32,33

Sedentary behavior and PA-level assessment

Objectively assessed sedentary and PA time data were collected from each participant for 1 week. The Fibion accelerometer (Fibion Inc., Jyväskylä, Finland) was given on day 1 and returned on day 8. In addition to a demonstration of device placement, instructions for wearing the device and a website link of the device manufacturer (https://fibion.com/) were provided to all participants. The participants wore the Fibion accelerometer on their right anterior thigh throughout the day, except during water-based activities. The Fibion device (weighing 20 g) was positioned anteriorly on the right mid-thigh.³⁴ The device was affixed to a non-allergic tape or thigh strap provided by Fibion. Sedentary and PA times were recorded for up to 7 days.³⁵ In case the participants removed the device during the night or water-based activities, reminders to use the device were given through text messages sent to their mobile phones every morning. The Fibion device can measure triaxial raw acceleration and associated PA levels²¹ and is valid and reliable for recording sedentary time and PA in daily living.^20,36

Fibion data processing

PA data were processed according to previously reported methods.^21,22 Fibion data, along with the age, sex, weight, and height, were uploaded to the Fibion website (www.fibion.com/upload). Detailed reports on the durations of sitting and upright positions (standing, slow walking, brisk walking, light activity, moderate activity, and vigorous activity) were retrieved from the Fibion website. Only the data of participants who had a wear time of ⩾600 min (10 h) per day for a minimum of 4 of 7 days (including at least 1 weekend day) were analyzed.^9,37

Although the participants were asked to wear the Fibion device during the day and night, the device might record the reclining posture as sitting time and perceive turning motions on the bed as an activity other than sleeping.^38,39 Previous studies investigating PA with thigh- and/or wrist-worn accelerometers determined waking hours from 7:00 to 23:00.^39,40 Working hours in UAE government offices usually fall between 8:00 and 16:00. Owing to cultural and social norms and barriers to PA for Emirati women reported in previous studies,^6,41 our participants were not expected to perform outdoor exercises (e.g. walking) or gym workouts at night (before sunrise). Therefore, we excluded standard nighttime data (23:00–7:00) to reduce the confounding effects of sleep time on real sedentary time. Recent studies on accelerometer-measured PA in the UAE have also excluded nighttime data from 23:00 to 7:00.^19,42 A customized data fixer tool from Fibion Inc. was used to remove the nighttime data. To mitigate variations in Fibion wear time between participants, the time spent in each activity is normalized to 16 h of waking time per day^43,44 using the following formula: normalized sitting/activity time = (observed sitting/activity time)/(observed wear time) × 16 h, where 16 h is the assumed maximum wake time per day.

Age, body mass index (BMI), WHR, body fat (%), FMI, marital status (single, married, divorced, or widow), education level (secondary school, undergraduate or postgraduate), work experience (years of work experience and number of working hours per day), presence of one or more comorbidities (neck or back pain, hypertension, diabetes, polycystic ovary syndrome, dyslipidemia, or any other illness), number of children, number of housemaids, and salary range (<10,000, 10,000–20,000, >20,000 AED per month) were included as independent (predictor) variables in the analysis. Sitting, moderate-intensity activity, vigorous-intensity activity, moderate-to-vigorous PA, and total activity times were normalized to 16 h of wake time and included as dependent variables in the statistical analysis.⁴¹ Moderate-to-vigorous PA was determined as the sum of moderate and vigorous PA. The total activity time was the sum of light, moderate, and vigorous activity times.

Statistical analysis

Demographic and anthropometric data were reported as descriptive statistics. The normality of data distribution was evaluated using the Kolmogorov–Smirnov test, and normally and non-normally distributed continuous data (e.g. number of children) were reported as the means and standard deviations (SD) and as the medians and interquartile range, respectively. Forward stepwise selection regression analyses were used, taking the first category as a reference, to determine the association between the independent variables (age, marital status, education, number of children, child assistance, number of years of work, hours of work, salary, comorbidities, BMI, WHR, body fat, estimated visceral fat area, FMI, and sleep quality (total PSQI score)) and the dependent variables normalized to 16 h (sitting time, moderate activity time, vigorous activity time, moderate-to-vigorous physical activity (MVPA) time, and total activity time). All statistical analyses were performed using Stata-13.1 (Stata Corporation, Texas, USA). P < 0.05 was considered significant.

Patient and public involvement

None.

Results

After excluding 53 Emirati women with inadequate or invalid accelerometer data, 110 Emirati women (mean accelerometer wear time: 13.18 ± 1.43 h) were included in the analysis. Except for seven women who were working partly from home, the remaining participants worked at their offices for 7 h daily, 5 days per week. Table 1 summarizes the sociodemographic and anthropometric characteristics and PA variables of the participants. Most participants (74.55%; 82/110) had a university degree, 20.91% (23/110) had secondary education, and 4.55% (5/110) were postgraduates. More than half of the participants (57.27%) were married (63/110), 34.55% (38/110) were single, 6.36% (7/110) were divorced, and 1.82% (2/110) were widowed. Most participants had assistance from one maid (82.73%; 91/110), and 8.18% (9/110) had two maids, while 9.09% (10/110) did not have maids. Of the 110 participants, 50% (55/110) had no comorbidities, while 25.00% had one comorbidity (27/110) or two or more comorbidities (28/110). Two participants worked 8 h/day, while the remaining 108 participants worked 7 h/day. Based on the PSQI scores, more than half (57.26%; 67/110) had good sleep quality, and 42.74% (50/110) had poor sleep quality.

Table 1.

Sociodemographic, anthropometric, and sleep quality and physical activity characteristics of the participants (n = 110).

Variables	Value
Age (years)	34.65 ± 6.28
BMI (kg/m²)	29.4 ± 5.6
WHR	0.55 ± 0.07
Body fat (%)	37.23 ± 6.43
FMI (kg/m²)	29.13 ± 5.76
Marital status
Single	38 (34.55%)
Married	63 (57.27%)
Divorced	7 (6.36%)
Widow	2 (1.82%)
Number of children	2^a (0.3^b)
Educational level
University	82 (74.55%)
Secondary	23 (20.91%)
Postgraduate	5 (4.55%)
Number of housemaids
0	10 (9.09%)
1	91 (82.73%)
2	9 (8.18%)
No. of years of working	7.78 (4.81)
Length of workday (h)
7	108 (98.18%)
8	2 (1.82%)
Salary (AED/month)
<10,000	1 (0.91%)
10,000–20,000	68 (61.82%)
>20,000	41 (37.27%)
Comorbidities
No comorbidity	55 (50.00%)
1	27 (24.55%)
⩾2	28 (25.45%)
Sleep quality–Global PSQI score^a
Poor (>5 PSQI score)	50 (42.74%)
Good (⩽5 PSQI score)	67 (57.26%)
Dependent variables Sedentary and physical activity measures (min per 16-h day)
Sitting time	701.28 ± 67.45
Moderate activity time	40.88 ± 17.99
Vigorous activity time	2.41 ± 1.21
MVPA	44.36 ± 21.78
Total activity time	255.48 ± 67.21

Data are presented as n (%) or the mean ± SD. BMI: body mass index; FMI: fat mass index; MVPA: moderate to vigorous physical activity; PSQI: Pittsburgh Sleep Quality Index; WHR: waist–height ratio.

Median.

Interquartile range.

Prevalence of PA among Emirati women

The mean sitting time was 701.28 ± 67.5 min; moderate activity time, 40.88 ± 17.99 min; vigorous activity time, 2.41 ± 1.21 min; and MVPA, 44.36 ± 21.78 min per a 16-h day. The mean total activity time was 255.48 ± 67.21 min per a 16-h day (Table 1).

Sitting time

As shown in Table 2, sitting time was positively associated with body fat (P < 0.001), with an increase of 11.74 min for every 1% increase in body fat. Sitting time was longer by 58.03 min in the participants who had secondary education than in those who had a university degree. Sitting time was also longer by 58.45 min in divorced participants than in single participants. The fat area and sleep quality were negatively associated with sitting time. Every unit increase in fat area was associated with a 1.71-min decrease in sitting time. Sitting time was shorter by 39.76 min in participants who had good sleep quality than in those who had poor sleep quality.

Table 2.

Variables associated with sitting time.

Variable	β coefficient	95% CI	P-value	Adjusted R²
Constant	383.51	(239.00, 528.00)	<0.001	28.52%
Body fat (%)	11.74	(21.24, 94.82)	<0.001
Secondary education	58.03	(21.24, 94.82)	0.002
Estimated visceral fat area	–1.71	(–2.84, –0.58)	0.004
Good sleep quality (PSQI score ⩽5)	–39.76	(–66.57, –12.95)	0.004
Divorcee	58.45	(7.78, 109.12)	0.024

CI: confidence interval; PSQI: Pittsburgh Sleep Quality Index.

F (5, 72) = 6.96, mean variance inflation factor = 2.81.

Moderate activity time

BMI and marital status were positively associated with moderate activity time (Table 3). As BMI increased by 1 kg/m², the moderate PA time increased by 2.78 min. Similarly, moderate activity time was longer by 6.80 min in those who were married than in those who were single. The estimated visceral fat area was negatively associated with moderate PA duration; moderate PA duration reduced by 0.61 min/day per 1 unit increase in visceral fat area.

Table 3.

Variables associated with moderate activity time.

Variable	β coefficient	95% CI	P-value	Adjusted R²
Constant	–3.64	(–4.66, –1.02)	<0.001	12.37%
Estimated visceral fat area	–0.61	(–0.99, –0.23)	0.002
BMI	2.78	(0.75, 4.82)	0.008
Married	6.80	(0.33, 13.94)	0.041

CI: confidence interval; BMI: body mass index.

F (3, 74) = 4.62, mean variance inflation factor = 6.65.

Vigorous activity time

The duration of vigorous PA was negatively associated with the presence of comorbidities (one, two, or more) and good sleep quality (Table 4). Vigorous PA time was shorter by 1.46 min in the participants who had two or more comorbidities than in those who did not have any comorbidities. Similarly, vigorous PA time was shorter by 1.82 min in the participants who had one comorbidity than in those who did not have any comorbidities. Moreover, vigorous PA time was shorter by 1.15 min in the participants with good sleep quality than in those with poor sleep quality. Vigorous activity time increased by 0.09 min per 1-year increase in work experience.

Table 4.

Variables associated with vigorous activity time.

Variable	β coefficient	95% CI	P-value	Adjusted R²
Constant	2.45	(1.33, 3.57)	<0.001	13.27%
⩾2 comorbidities	–1.46	(–2.69, –0.24)	0.019
1 comorbidity	–1.82	(–3.16, –0.49)	0.008
Good sleep quality	–1.15	(–2.16, –0.13)	0.026
Length of work experience (years)	0.090	(0.002, 0.19)	0.036

CI: confidence interval.

F (4, 71) = 5.30, mean variance inflation factor = 1.03.

Moderate to vigorous physical activity

The level of education was positively associated with MVPA (Table 5). MVPA time was longer by approximately 23 min in the participants with a postgraduate level of education than in those with a university level (undergraduate) qualification. Meanwhile, the MVPA decreased by 0.56 min per 1-year increase in work experience. MVPA time was shorter by approximately 6 min in the participants who had two or more comorbidities than in those who had no comorbidities.

Table 5.

Variables associated with MVPA.

Variable	β coefficient	95% CI	P-value	Adjusted R²
Constant	48.93	(40.62, 57.24)	<0.001	4.48%
Postgraduate	23.29	(3.14, 43.44)	0.02
Length of work experience (years)	–0.56	(–1.43, –0.31)	0.021
⩾2 comorbidities	–5.66	(–15.15, –3.83)	0.024

CI: confidence interval; MVPA: moderate to vigorous physical activity.

F (3, 105) = 2.85, and mean-variance inflation factor = 1.03.

Total activity time

Total activity time was negatively associated with secondary education, body fat, marital status (divorce), the presence of one comorbidity, and the presence of two or more comorbidities. In contrast, this variable was positively associated with good sleep quality, and age (Table 6). Total activity time was shorter by 56.48 in the participants with secondary education than in those with a university education. Total activity time was longer by 28.25 min in the participants with good self-reported sleep quality than in those with poor self-reported sleep quality. Every 1% increase in body fat was associated with a 4.22-min decrease in total activity time. Total activity time was shorter by 88.10 min in divorced participants than in single participants. Every 1-year increase in age was associated with a 3.19-min increase in total activity time. Total activity time was shorter by 45.85 min in the participants with one comorbidity and by 29.32 min in those with two or more comorbidities than in those without any comorbidities.

Table 6.

Variables associated with total activity time.

Variable	Coefficient	95% CI	P-value	Adjusted R²
Constant	318.84	(221.98, 415.70)	<0.001	31.63%
Secondary education	–56.48	(–92.15, –20.81)	0.002
Good sleep quality	28.25	(1.70, 54.80)	0.037
Body fat (%)	–4.22	(–6.49, –1.95)	<0.001
Divorcee	–88.10	(–138.90, –37.30)	0.001
Age	3.19	(0.86, 5.51)	0.008
1 comorbidity	–45.85	(–80.48, –11.21)	0.01
⩾2 comorbidities	–29.32	(–61.08, –2.43)	0.04

CI: confidence interval.

F (7, 69) = 5.11, mean variance inflation factor = 1.51.

Discussion

In this observational study, Emirati women sat for an average of 11.6 ± 1.1 h per 16-h days. Importantly, sociodemographic, anthropometric, sleep quality, accelerometer-measured sedentary time are associated with PA levels among Emirati women employed in desk-based office jobs.

Sitting time is an indicator of sedentary behavior. In this study, the sedentary behavior level of Emirati women was higher than the suggested maximum of 7 h/day.⁴⁵ The all-cause mortality risk appears to increase if adults sit for >7 h per day. For instance, the risk increases by 5% for every 1-h increment in daily sitting time, after considering the effects of PA.⁴⁵ Emirati women in our study had a mean moderate activity time of 40.88 ± 17.99 min and 2.41 ± 1.21 min of vigorous activity per 16-h day. However, the WHO PA guidelines for adults recommend at least 150–300 min of moderate-intensity aerobic PA, at least 75–150 min of vigorous-intensity aerobic PA, or an equivalent combination of MVPA during the week for essential health benefits.¹ Although the Emirati women who participated in the study might have met the minimum requirements of MVPA per week, their vigorous activity time was negligible. In contrast, previous studies^10
–13 have reported inadequate PA in women, possibly because they mainly relied on self-reported measures or smartphone data. Compared with the Fibion (thigh-worn) accelerometers used in our study, smartphone applications⁴⁶ and self-reported outcome measures⁴⁷ might underestimate sedentary behavior and PA levels.

Several factors contribute to a reduction in PA among the UAE population; these include the increased availability of housemaids, inactive occupations, use of cars for transport, labor-saving devices, television, video games, occasional participation in sports, sedentary leisure time, sunny weather, and restrictive traditional attire.^6,22 Despite having walkable cities, the country’s hot desert climate has not supported PA engagement for most time of the year. Moreover, its dusty nature increases the prevalence of respiratory conditions. Cultural and societal barriers may have also had a negative effect on PA engagement among the women included in this study.^6,19 Particularly, women in Islamic countries often need to be accompanied by men in their families while going outdoors, which may reduce their participation in PA.⁴⁸ Furthermore, married women are required to take care of their family and household needs, which could affect PA engagement.⁴⁸ Moreover, a qualitative study reported that some Emirati women believe that PA is not a social activity accompanied by relatives or friends, which could be a barrier to their participation in PA.⁶ Given that this study included only some of these variables, future studies are warranted to study the association of all these factors with sedentary behavior and PA levels in Emirati women.

In addition, sitting time was longer by 58 min and total activity time was shorter by 56 min in the Emirati female participants with secondary education than in those with university education. Meanwhile, MVPA was longer by 29 min in our participants with a postgraduate qualification than in those with university education. A previous study showed that participants with lower education levels had significantly lower PA levels (during normal and leisure times) than those with higher schooling or a university degree.^13,43 Those with better education might have better awareness of the importance of PA in maintaining health benefits.

Divorced participants had 58 min more sitting time and 88 min less total activity time than single women; however, married women had 6.8 min more moderate activity than single women. Our findings are consistent with previous evidence that married women had higher probabilities of practicing PA for >150 min/week than divorced ones.⁴⁴ Divorced Emirati women may lack the psychological and social support needed for their integration into society.^49,50 They may face psychological distress⁵¹ and refrain from participating in social activities.⁵² In addition to economic hardship,⁵³ they experience stigmatization and loss of social support^50,54,55 that might affect their engagement in PA. Therefore, marital status must be considered when investigating the influencing factors of PA levels in women.

We also found that participants who had good sleep quality reported 39 min less sitting time and 28 min more total activity time than those with poor sleep quality. Our findings are in agreement with those reported by Kishida and Elavsky that middle-aged women with a high BMI and low PA levels have poor sleep quality.¹⁷ In addition, an increase in PA among these women was associated with an increase in total sleep time.¹⁷ Sleep quality can indirectly predict PA levels (high, moderate, and low intensity) over time through its positive effects on emotion regulation.⁵⁶ In this study, vigorous PA time was 1.15 min shorter in the Emirati women with good sleep quality than in those with poor sleep quality. A recent systematic review of 14 studies found that moderate-intensity PA was more effective than vigorous-intensity PA in improving sleep quality.¹⁶ Vigorous PA performed close to bedtime may adversely affect sleep, perhaps due to inadequate cardiovascular recovery, leading to an elevated heart rate and reduced parasympathetic activity.⁵⁷ However, few studies have evaluated the effects of vigorous PA on sleep quality.¹⁶ Future research should explore the timing of PA at different intensities and its association with sleep quality among Emirati women.

Women with one comorbidity had 45 min less total activity time and 1.15 min less vigorous activity than those without any comorbidities. The participants who had two or more comorbidities had 12.48 less MVPA and 23.32 min less total activity time than those who did not have any comorbidities. This is in agreement with earlier reports that step count decreased with increasing age, greater disability, poorer general health, a high BMI, and diabetes.⁵⁸ In contrast, an increase in body fat percentage reduced the total activity time by 4.22 min and increased the sitting time by 11.74 min. An increase in fat mass percentage is associated with a decrease in levels of physical fitness.⁵⁹ The total activity time increased by 3.19 min per 1-year increase in age. Although PA levels decrease with increasing age,⁶⁰ the participants in this study were relatively young women aged 18–45 years. With respect to length of work experience, the MVPA time was reduced by 1.12 min and the vigorous activity time increased by 0.09 min for every additional year of work experience. However, further studies are required to confirm these findings.

Although the estimated visceral fat area was negatively associated with sitting and moderate PA times, body fat percentage was positively associated with sitting time in this study. However, the association between sedentary time and visceral fat showed equivocal findings in the previous studies.^61
–63 The differences in these associations might be partly due to differences in the measurement method (e.g. the device used for measuring or estimating visceral fat) or the type of outcome measures used (e.g. self-reported sedentary or PA measures, visceral fat rating, or area). Further magnetic resonance imaging validation of the estimated visceral fat area is recommended.

Methodological considerations and limitations of the study

Although a standard exclusion of 8 h (23:00–7:00) was performed, participant log diaries to remove nighttime data from Fibion output is another option. We anticipated less PA between 23:00 and 7:00, and the differences in PA were likely to be smaller if the actual bedtime was removed. There might be a selection bias in the study owing to convenience sampling because some participants who volunteered for the study sought weight reduction. In addition, reactivity bias or the Hawthorne effect may have occurred if some of our participants changed their sedentary behavior and PA levels because they were being monitored with accelerometers. However, their sitting times were high, and their PA levels were low.

Among the 163 women enrolled in the study, only 110 participants had valid PA data (i.e. wear time of ⩾600 min per day for at least 4 days) for analysis. Adherence to wearing the Fibion device was a challenge because the participants were asked to wear the device for 7 days. Considering at least 10 participants per predictor, we included a convenience sample of 163 healthy Emirati working women aged 18–45 years, accounting for approximately 11 participants per predictor variable. However, among the 163 women initially enrolled in the study, only 110 women with valid accelerometer data were included in the final analysis. However, we performed a post hoc power analysis using G*Power 3.1.9.7. Considering the R² value of 0.28 for the final model with significant variables associated with sitting time, we calculated an f² value of 0.39. With an effect size (f²) of 0.39, an α value of 0.05, a sample size of 110, and 14 independent variables, a power of 0.99 was obtained. Therefore, the 110 participants included in the final analysis can be considered adequate for the current study.

One possible factor that might affect PA is smoking;⁶⁴ however, smoking was not addressed in this study because of cultural barriers. It is considered inappropriate for Emirati women to smoke. Some limitations of using accelerometers include limited knowledge regarding the functions of a sensor, choosing the appropriate device, standards of device wear and field use, and curating and analyzing the accelerometer output.⁶⁵ Monitoring PA with an accelerometer requires at least 4–7 days; thus, the amount of data gained can be large.⁶⁵ The steps involved in cleaning, curating, analyzing, and interpreting accelerometer data are important. Using accelerometers during nighttime would confound sedentary time and sleep duration⁴² because device-measured sedentary time includes the sum of time spent sitting and reclining/lying. Furthermore, thigh-worn accelerometers will not detect upper extremity activities, isometric exercise, resistance exercise, and swimming.⁶⁶ To monitor activities that could not be recorded with accelerometers, future studies can measure activities and strength training performed with the upper extremities using self-reported diaries and appropriate questionnaires and compare the differences between the methods.

Although we collected the data during the COVID-19 pandemic, all participants worked during the data collection period. Data were collected during the lockdown, and this might have affected PA levels to a certain extent. However, none of the participants reported having COVID-19 before or during the study. Further longitudinal studies are warranted to verify whether the factors identified in this study predict sedentary behavior and PA levels in Emirati women.

Strengths of the study

To our best knowledge, the current study is the first to objectively quantify PA levels and sedentary behavior in working Emirati women. As a cross-sectional study, no causal relationship could be derived between the independent and dependent variables; however, the findings of the current study would aid in designing (1) longitudinal cohort studies investigating predictors of sedentary behavior and PA levels and (2) clinical trials to reduce sedentary behavior and promote PA levels to achieve essential health benefits in Emirati women employed in desk-based jobs.

Conclusion

Emirati working women had long sitting time, acceptable moderate activity time, and low vigorous activity time. Age, educational status, marital status, presence of comorbidities, length of work in years, anthropometric characteristics, and sleep quality had variable associations with PA, depending on the type of outcome variable chosen.

Footnotes

Acknowledgements

The authors thank all the Emirati women who participated in this study. Special thanks go to the MAPI trust for granting permission to use the Arabic version of the PSQI.

Declarations

ORCID iD

Ashokan Arumugam

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Association of sociodemographic,anthropometric,and sleep quality factors with accelerometer-measured sitting and physical activity times among Emirati working women during the COVID-19 pandemic: A cross-sectional study

Abstract

Background:

Objectives:

Design:

Methods:

Results:

Conclusion:

Keywords

Introduction

Method

Study design, setting, and participants

Sociodemographic data collection and anthropometric measurements

Sleep quality assessment

Sedentary behavior and PA-level assessment

Fibion data processing

Statistical analysis

Patient and public involvement

Results

Prevalence of PA among Emirati women

Sitting time

Moderate activity time

Vigorous activity time

Moderate to vigorous physical activity

Total activity time

Discussion

Methodological considerations and limitations of the study

Strengths of the study

Conclusion

Footnotes

Acknowledgements

Declarations

ORCID iD

References