Is the number of daily walking steps in sedentary workers affected by age,gender,body mass index,education,and overall energy expenditure?

Abstract

BACKGROUND:

Healthy adults should take 10,000 steps per day to gain the resulting health benefits. Knowledge regarding the individual characteristics associated with daily walking steps would enhance resource allocation to those most likely to benefit from the 10,000-steps-per-day campaign.

OBJECTIVE:

To determine the extent to which age, gender, body mass index (BMI), education, and energy expenditure influence daily walking steps in white-collar workers and to assess the correlation of daily walking steps among pedometer, wristband activity tracker, and smartphone application.

METHODS:

A cross-sectional study was conducted on 49 sedentary workers. Daily walking steps were simultaneously assessed by three activity trackers in free-living conditions for 7 consecutive days. Associations between daily walking steps and individual factors were examined using linear regression. Correlation tests were conducted to assess the association among the three devices.

RESULTS:

Multiple regression analyses showed that BMI was associated with daily walking steps. A moderate to good correlation in daily walking steps was found between the wristband activity tracker and pedometer, as well as between the smartphone application and pedometer.

CONCLUSIONS:

BMI influenced daily walking steps in white-collar workers. Daily walking steps assessed by the wristband activity tracker and smartphone application differed from those assessed by the pedometer.

Keywords

Physical activity white-collar worker pedometer activity tracker

1 Introduction

A sedentary lifestyle is common in contemporary society. However, the health hazards of physical inactivity are well documented [1, 2]. An insufficient level of daily physical activity increases the risk of non-communicable diseases, such as coronary heart diseases, type 2 diabetes, stroke, breast and colon cancers, and musculoskeletal complaints, as well as shortens life expectancy [1 , 4]. As a result, the promotion of regular physical activity has been prioritized as part of a comprehensive strategy to reduce non-communicable diseases worldwide [1].

Increased daily walking steps is one common method used by public health initiatives to promote greater physical activity among the public [5]. A previous study has shown that a walking program of 10,000 steps per day for 15 weeks can improves cardiovascular performance and personal growth as well as positively influences on several variables that are indicators of health, fitness, and psychological well-being [6]. However, this is not easily achieved in working adults. Schmidt et al. [7] reported median steps per day to be 8,761 among adults aged between 26–36 years. Lee et al. [8] showed that the average number of daily steps per day was 8,661 among working adults in Hong Kong. Sitthipornvorakul et al. [4] reported even lower daily walking steps in white-collar workers with 8,296 steps for males and 7,649 steps for females.

To enhance the effectiveness of the 10,000-steps-per-day campaign, knowledge about participant characteristics associated with daily walking steps is required. Identification of persons most likely to have a low level of daily walking steps mean the enhancement of resource allocation to those most in need and most likely to benefit from the campaign. Without such knowledge, a large number of people would receive attention, which is likely to compromise the effectiveness of campaigning. Individual factors, such as age [9 –11], gender [11], and body mass index (BMI) [9, 11] have been shown to influence the number of daily walking steps in adults aged 65 years or more.

One of the most widely-accepted, valid, and reliable measurement devices for assessing daily walking steps is the pedometer [12, 13]. However, the wearing position of the pedometer may pose difficulties for many people. To date, there is an increasing number of different activity trackers available in the consumer market. One such device is the wristband activity tracker, which is a slim wristband worn similar to a watch. Wristband activity trackers are small, noninvasive, and easy to use. The devices have been found to be useful for increasing activity among young adults [14]. Another activity tracker comes in the form of a smartphone application. The advantage of a smartphone application is its being embedded within an individual’s lifestyle, and thus it could be used for population-based physical activity monitoring and intervention. Several previous studies have examined different activity tracker accuracy, and the results have indicated that these devices are reliable and valid [15 –19]. However, previous studies examined tracker accuracy by conducting research in the laboratory or within a short period of follow up in free-living conditions. It has been recommended that accurate monitoring frames used to quantify pedometer-assessed ambulatory activity in free-living conditions require at least 5–6 days, with the inclusion of weekend days [20]. To date, no study has investigated the correlation of daily walking steps measured by different activity trackers in free-living conditions for 7 consecutive days.

Thus, the primary aim of this study was to investigate the extent to which age, gender, body mass index, education, and overall energy expenditure per week are associated with daily walking steps measured in free-living conditions for 7 consecutive days in those with sedentary jobs, theoretically prone to the adverse effects of decreased daily physical activity on health. The secondary aim was to examine the correlation of daily walking steps measured using three activity trackers: pedometer, wristband activity tracker, and smartphone application.

2 Methods

2.1 Participants and procedures

A cross-sectional study was conducted on a convenience sampling of healthy sedentary workers aged 20–55 years. Sedentary workers were defined as those working in an office environment with their main tasks involving use of a computer, participation in meetings, presentations, reading, and phoning [21]. Potential participants were screened into the study using a self-administered questionnaire and were excluded if they had reported musculoskeletal symptoms in the spine in the previous 3 months with pain intensity greater than 30 mm on a 100 mm visual analog scale, reported pregnancy, had a history of trauma or accidents in the spinal region, had a history of spinal, intra-abdominal and femoral surgery in the previous 12 months, or had been diagnosed with congenital anomaly of the spine, rheumatoid arthritis, infection of the spine and discs, ankylosing spondylitis, spondylolisthesis, spondylosis, tumor, systemic lupus erythematosus, or osteoporosis. Potential participants who did not use a smartphone were also excluded from the study. Written informed consent was obtained from all participants. The study was approved by the Institutional Human Ethics Committee.

Data collection consisted of the completion of a self-administered questionnaire to gather demographic information and the global physical activity questionnaire (GPAQ) to assess a person’s overall energy expenditure per week [22]. Daily walking steps were measured using 3 devices: a pedometer (Yamax^® Digiwalker CW-700), a wristband activity tracker (Fitbit^® Flex), and a smartphone application (Accupedo^® pedometer application). Each participant was asked to install the smartphone application on their personal smartphone and was given the pedometer and a wristband activity tracker with the instruction to carry all devices for 7 consecutive days, from getting up in the morning until going back to bed at night, in order to record daily steps during these days. Participants were allowed to remove all devices only while immersing the body in water. Participants received a short message via mobile phone in the morning every day to remind them to wear the devices. After 7 consecutive days, the data on daily walking steps of each participant were extracted from the memory of each device by the researcher. The average steps per day for the three devices were recorded and calculated for each participant, who had more than four daily measurements [20, 23].

2.2 Instruments

Pedometer: The pedometer used in the present study was the Yamax^® Digiwalker CW-700 (Yamax, Tokyo, Japan), which has been found to be accurate and reliable for counting steps [24]. The pedometer can measure steps taken, distance traveled, and calories burned. The pedometer, which comes with a dual screen display and goes up to 6 digits (i.e. 999,999 steps), has a 3-year battery life and an internal memory to store data for up to 7 days. Participants were instructed to carry the pedometer on the right side of the belt, in the midline of the thigh.

Wristband activity tracker: The wristband activity tracker used in the present study was the Fitbit^® Flex (Fitbit Inc., San Francisco, CA, USA), which has been found to be accurate and reliable for tracking step counts [15, 25]. The device is a wrist-worn tri-axial accelerometer that can measure steps taken, distance traveled, calories burned, and sleep quality. The device has a 5- to 10-day battery life and an internal memory to store data for up to 30 days. The features of the device are that it synchs automatically and wirelessly to a tablet, computer, and smartphone using Bluetooth 4.0 wireless technology. The device was bought from a retailer and not through the manufacturer. Participants were instructed to wear the device on the non-dominant arm.

Smartphone application: The smartphone application used in this study was the Accupedo^® Pedometer (Corusen LLC, TX, USA), which is an off-the-shelf smartphone application that can measure steps taken, distance traveled, and calories burned. The application uses the phone’s built-in accelerometer in its algorithm and is designed to work regardless of whether the phone is placed in an individual’s pocket, waist belt, or bag. Participants were encouraged to use their smartphone as they normally would to promote ecological validity. Previous studies reported that pedometer applications on the smartphone are valid and reliable for counting steps [25 –27].

Before data collection, the repeatability of data from the pedometer, wristband activity tracker, and smartphone application was assessed in 10 office workers. Each subject was tested on two 7-day occasions separated by an interim of 7 days between measurements.

2.3 Statistical analyses

For the reliability study, the intraclass correlation coefficient (ICC) was calculated for the average daily steps measured by a pedometer, wristband activity tracker, and smartphone application.

Descriptive statistics were calculated for demographic data, overall energy expenditure per week and daily walking steps. Linear least-square regression was performed on daily walking steps on age, gender, BMI, education level, and overall energy expenditure per week using the following general model: $\begin{matrix} Y = b 0 + b_{1} age + b_{2} gender + b_{3} BMI + b_{4} \\ education level + b_{5} overall energy \\ expenditure per week \end{matrix}$ Where, Y = daily walking steps assessed by pedometer, wristband activity tracker, and smartphone application; b0 = intercept; b1–b5 = regression coefficients. All variables were treated as continuous variables, but gender and education level were treated as dummy variables (reference category; gender: 1 = females and 2 = male, and education level: 1 = lower bachelor degree, 2 = bachelor degree and 3 = higher bachelor degree). Multicollinearity between independent variables, normality of the standardized residuals, and equality of variances were assessed to check if the data met the assumptions underlying linear regression models.

The Pearson product-moment correlation coefficient test was applied to assess the association between the daily walking steps assessed by pedometer, wristband activity tracker and smartphone application. The correlation values were interpreted as follows: above 0.75 was good to excellent, 0.50–0.75 was moderate to good, 0.25–0.50 was fair, and below 0.25 was no relationship [28]. The Bland-Altman plots were created to assess the level of agreement between the devices. All statistical analyses were performed using SPSS^® statistical software, version 23.0 (SPSS Inc, Chicago, IL, USA). Statistical significance was set at the 5% level.

3 Results

The reliability results demonstrated fair to excellent reliability for the smartphone application, pedometer, and wristband activity tracker with an ICC (3,7) score of 0.43, 0.77 and 0.78, respectively.

A total of 49 workers participated in the study (Table 1). The sample population comprised mainly middle-aged females with Bachelor’s degrees. Their average BMI was at the upper limit of normal ranges for Asians [29]. A majority of participants (73.5%) were classified as inactive to moderately active, according to self-reported overall energy expenditure per week.

Table 1
Characteristics of participating sedentary workers (n = 49)

Characteristics N (%) Mean (SD)

Age 31.9 (6.2)

Gender

Male 13 (26.5)

Female 36 (73.5)

Body mass index (kg/m²) 22.9 (4.7)

Education

Lower than Bachelor’s degree 5 (10.2)

Bachelor’s degree 29 (59.2)

Higher than Bachelor’s degree 15 (30.6)

Overall energy expenditure per week (MET-minutes) 1706.9 (1674.3)

Inactive 17 (34.7)

Moderate active 19 (38.8)

Highly active 13 (26.5)

Daily walking steps measured by

Smartphone application (steps/day) 5,351 (2,722)

Pedometer (steps/day) 6,814 (2,253)

Wristband activity tracker (steps/day) 7,399 (2,352)

Characteristics	N (%)	Mean (SD)
Age		31.9 (6.2)
Gender
Male	13 (26.5)
Female	36 (73.5)
Body mass index (kg/m²)		22.9 (4.7)
Education
Lower than Bachelor’s degree	5 (10.2)
Bachelor’s degree	29 (59.2)
Higher than Bachelor’s degree	15 (30.6)
Overall energy expenditure per week (MET-minutes)		1706.9 (1674.3)
Inactive	17 (34.7)
Moderate active	19 (38.8)
Highly active	13 (26.5)
Daily walking steps measured by
Smartphone application (steps/day)		5,351 (2,722)
Pedometer (steps/day)		6,814 (2,253)
Wristband activity tracker (steps/day)		7,399 (2,352)

3.1 Effects of age, gender, body mass index, education, and energy expenditure on daily walking steps

Multiple regression analyses showed that BMI was associated with daily walking steps assessed by both pedometer (B = –192.50, 95% CI = –328.84 ––56.16, P = 0.01) and wristband activity tracker (B = –147.46, 95% CI = –279.58 ––15.35, P = 0.03) (Table 2). On average, workers with low BMI had a greater number of daily walking steps than their counterparts with high BMI (Fig. 1).

Table 2
Multiple regression model of factors associated with daily walking steps for each tracking device in office workers (n = 49)

Variables B 95% CI P-value

Lower Upper

Pedometer

Intercept 12372.68 6390.95 18354.41 0.00

Gender –485.59 –1964.61 993.42 0.51

Age 48.61 –55.11 152.34 0.35

BMI –192.50 –328.84 –56.16 0.01*

Education level –903.72 –1942.95 135.51 0.09

Energy expenditure 0.09 –0.31 0.50 0.65

Wristband activity tracker

Intercept 10355.73 4559.60 16151.85 0.00

Gender –185.43 –1618.55 1247.70 0.80

Age 79.49 –21.02 179.99 0.12

BMI –147.46 –279.58 –15.35 0.03*

Education level –958.99 –1965.97 48.00 0.06

Energy expenditure 0.08 –0.32 0.47 0.70

Smartphone application

Intercept 9819.53 1957.33 17681.72 0.02

Gender –1420.36 –3364.33 523.60 0.15

Age 6.20 –130.13 142.54 0.93

BMI –83.22 –262.42 95.99 0.35

Education level –282.04 –1647.98 1083.90 0.68

Energy expenditure 0.19 –0.35 0.72 0.48

Variables	B	95% CI	P-value
Pedometer
Intercept	12372.68	6390.95	18354.41	0.00
Gender	–485.59	–1964.61	993.42	0.51
Age	48.61	–55.11	152.34	0.35
BMI	–192.50	–328.84	–56.16	0.01*
Education level	–903.72	–1942.95	135.51	0.09
Energy expenditure	0.09	–0.31	0.50	0.65
Wristband activity tracker
Intercept	10355.73	4559.60	16151.85	0.00
Gender	–185.43	–1618.55	1247.70	0.80
Age	79.49	–21.02	179.99	0.12
BMI	–147.46	–279.58	–15.35	0.03*
Education level	–958.99	–1965.97	48.00	0.06
Energy expenditure	0.08	–0.32	0.47	0.70
Smartphone application
Intercept	9819.53	1957.33	17681.72	0.02
Gender	–1420.36	–3364.33	523.60	0.15
Age	6.20	–130.13	142.54	0.93
BMI	–83.22	–262.42	95.99	0.35
Education level	–282.04	–1647.98	1083.90	0.68
Energy expenditure	0.19	–0.35	0.72	0.48

BMI, body mass index. *Significant at < 0.05.

Fig. 1

Daily walking steps by body mass index in sedentary workers (n = 49). A: daily walking steps assessed by pedometer and B: daily walking steps assessed by wristband activity tracker.

Multicollinearity was considered not to be critical according to the tolerance index (>0.20) and the variance inflation factor (<5) [30]. Visual inspection of the histograms of standardized residuals and Levene’s test of equality of variances also indicated that the assumptions associated with linear regression were not significantly violated.

3.2 Correlations of daily walking steps among three activity trackers

There were moderate to good correlations in the daily walking steps assessed by the wristband activity tracker and pedometer (r = 0.70, P < 0.001) as well as the smartphone application and pedometer (r = 0.61, P < 0.001). The daily walking steps assessed by the wristband activity tracker (7,399 steps per day) were significantly higher than the pedometer (6,814 steps per day) (P = 0.02). The daily walking steps assessed by the smartphone application (5,351 steps per day) were significantly less than the pedometer (6,814 steps per day) (P < 0.001). The Bland-Altman plots revealed no systematic difference in daily walking steps between the pedometer and either the wristband activity tracker (Fig. 2) or smartphone application (Fig. 3).

Fig. 2

Bland-Altman plot for pedometer and wristband activity tracker measured daily walking steps (n = 49).

Fig. 3

Bland-Altman plot for pedometer and smartphone application measured daily walking steps (n = 49).

4 Discussion

The results showed that BMI, but not age, gender, education level, and self-reported overall energy expenditure per week, was inversely associated with daily walking steps. We also found that the wristband activity tracker was more accurate than the smartphone application in measuring daily walking steps when using the pedometer as a criterion for comparison.

The daily walking steps of the sample population, of which a majority were middle-aged females, was approximately 6,814 steps. The average daily walking steps was lower in our study than reported in previous studies [7 , 31]. A reason for this discrepancy could be the different populations under study. In the previous studies, participants’ occupation was not controlled, while in the present study the participants were white-collar workers. Physical activity engagement differs across occupational categories. Blue-collar workers showed significantly higher occupational physical activity and were thus involved in more moderate- and high-intensity activity types, while white-collar workers spent most of their time at work sitting and performing light occupational activities [32]. In the present study, about one third of participants were classified as inactive, according to self-reported physical activity during a typical week using the GPAQ.

The results showed that sedentary workers with low BMI had on average a higher number of daily walking steps than those with high BMI. This finding is in line with previous studies on elderly people [9, 11]. The present study is one of the first of its kind to identify the factors associated with daily walking steps among young sedentary workers. We observed a decrease of 192.5 steps per day with every 1 kg/m² increase of BMI. However, the cross-sectional design of this study only allows the association between exposure and outcome to be examined. It is difficult to establish the causal relationship between an increase in BMI and a decrease in daily walking steps. Therefore, a prospective study design is required to validate the findings of this study. Corporate wellness is an essential component for workforce sustainability. Walking with its low cost, easy access, and low impact on musculoskeletal structures can be effectively used as a way to improve health and fitness. Several health promotion campaigns may be implemented in workplaces, e.g. reshaping scheduled break and encouraging active breaks, providing in-house training about the health benefits of increased daily walking steps and ways to achieve it, or initiating the 10,000-steps-per-day campaign in a workplace. The awareness that BMI is a strong influencer on daily walking steps can be used to specifically target health promotion in those with high BMI, which would mean the enhancement of resource allocation to those most in need and most likely to benefit from the campaign. For research purposes, the results suggest that the effect of BMI on daily walking steps should be taken into consideration by researchers when planning research in the field of physical activity. No control for participants’ BMI could result in misleading results and, consequently, the internal validity of the study.

Age, gender, education level, and self-reported overall energy expenditure per week did not influence daily walking steps in our sample of sedentary workers. Previous studies have found age, gender, education, and time spent sitting (hours per day) to influence daily walking steps in older people [9, 33]. The reason for the discrepancy between the present and previous studies could be related to different populations under study. Young people certainly have different lifestyles to older people, and lifestyle may influence their physical activity level. This issue warrants further investigation.

The accurate quantification of daily walking steps is not only necessary for the advancement of knowledge regarding associations between physical activity level and various health problems, but it is also important for an individual to be accurately informed about their daily physical activity level in order to achieve health benefits. The results showed that daily walking steps assessed by the wristband activity tracker and smartphone application moderately correlated to that assessed by the pedometer. However, the wristband activity tracker was more accurate than the smartphone application in measuring daily walking steps, when using the pedometer as a criterion for comparison. The mean absolute difference in daily walking steps between wristband activity trackers and pedometer was relatively small (585 steps), while the mean absolute difference in daily walking steps between the smartphone application and pedometer was relatively large (1,463 steps). Differences in daily walking steps between the wristband activity tracker and pedometer may be partly explained by the repetitive movements of the arms during free-living condition. Participants in the present study were office workers who spend most of their working hours performing activities with repetitive movements of the arms and hands. A previous study showed that activity trackers worn close to the body exhibited better validity than wrist-worn activity trackers [15]. Thus, the wristband activity tracker may capture higher daily walking steps than the pedometer in free-living condition.

We found that the wristband activity tracker led to overestimating and the smartphone application led to underestimating daily walking steps. This finding is in line with recent studies [15, 34]. Kooiman et al. [15] reported an overestimation of daily walking steps assessed by the Fitbit^® Flex compared to the gold standard (i.e. the ActivPAL^® accelerometer) during one-day free-living condition assessment. Orr et al. [34] found that the smartphone application generally under-reported daily walking steps compared to the pedometer during a 3-day free-living condition assessment.

Based on the findings of the study, both the wristband activity tracker and smartphone application seem to be suitable for practical use, although the wristband activity tracker provided more accurate measurement of daily walking steps than the smartphone application. Both devices have potential as devices to motivate people to increase their daily walking steps because of their additional features and attractive user-friendly graphic displays. However, users of both the wristband activity tracker and smartphone application should be reminded that the wristband activity tracker usually leads to an overestimation of daily walking steps, and the smartphone application usually leads to an underestimation of daily walking steps.

The major strength of this study is the relative homogeneity of the population and the monitoring frames of daily walking steps measurement. The homogenous participants, in terms of working characteristics, were selected for the present study because different occupations are exposed to different working conditions and physical activity levels. The present study assessed daily walking steps in free-living condition for 7 consecutive days. It has been recommended that accurate monitoring frames for ambulatory activity in free-living conditions should be around 5–6 days [20]. However, there are a number of methodological limitations that should be taken into consideration when interpreting the results of the present study. First, the pedometer was used as a criterion measure for daily walking steps in free-living condition. The pedometer is generally not sensitive to non-ambulatory activities and over-counts inclined surfaces and stair steps. Despite the limitations of a pedometer, the use of this device is a relatively simple way to monitor the performance-based physical activity status of healthy people. Second, the sample size was moderate for this type of study. Based on the sample size (n = 49), a power analysis revealed that the study had sufficient power (80%) to detect a large effect (effect size = 0.78–0.83). Future studies with a larger sample size are required to confirm the present study findings. Lastly, no physical verification method was implemented to check if participants used the three activity trackers as instructed, although they were reminded to carry all three through a short message via mobile phone during the 7-day period of daily walking steps measurement.

5 Conclusions

The results showed that the BMI influenced daily walking steps during the seven-day free-living condition assessment in healthy white-collar workers, while age, gender, education, and overall energy expenditure per week did not. Daily walking steps assessed by the wristband activity tracker and smartphone application moderately correlated to that assessed by the pedometer. The wristband activity tracker was more accurate than the smartphone application in measuring daily walking steps when using the pedometer as the criterion. Thus, using the pedometer and wristband activity tracker to evaluate daily walking steps should be used with caution in studies with a large dispersion in BMI.

Conflict of interest

None to report.

Footnotes

Acknowledgments

This research is supported by the National Research University Project, Office of Higher Education Commission (WCU-58-003-HR) and the Rachadapisek Sompote Fund for Postdoctoral Fellowship, Chulalongkorn University.

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