Daily step counts and intensity both associated with obesity in Chinese older women

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Daily step counts and intensity both associated with obesity in Chinese older women

https://doi.org/10.21203/rs.3.rs-3084911/v1

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OBJECTIVES: To investigate the associations of objectively measured daily step counts and intensity with overweightness/obesity in older women.

METHODS: Totally, 1 085 community-dwelling older women were included. Overweightness/obesity indicators were measured by body composition analyzer. Daily step counts, peak 1-minute cadence (peak 1 cadence), and peak 30-minute cadence (peak 30 cadence) were measured via tri-axial accelerometer. Multiple linear regression analyses were used to assess the association of total daily step counts, peak cadence and their joint effects with different overweightness/obesity indicators. Receiver operating characteristic curve (ROC) analysis was used to determine the optimal daily steps, peak 1 cadence, peak 30 cadence cut-off values to distinguish overweightness/obesity.

RESULTS:After adjusting for confounders, a significant association was found between daily step counts, cadence or intensity and the overweightness/obesity. Each 1 000 step increase in daily step counts was associated with a 0.2% reduction in body fat ratio (BFR). Each 1 steps/min increase in peak 1 cadence and peak 30 cadence was associated with a decrease in BFR 0.09% and 0.07%, respectively. The joint effect showed that the high step and intensity (HSHI) group had the best effect on reducing obesity. ROC curve analysis showed that the optimal cut-off points of daily step count, peak 1 cadence and peak 30 cadence for predicting overweightness/obesity were 9 135 steps, 126.9 steps/min and 89.0 steps/min, respectively.

CONCLUSION: Increasing daily step count and intensity in walking activities is an effective way to lose weight although daily step count is not associated with BMI. It is recommended that older women walk more than 9 135 steps a day. Different steps and intensity combination can be considered according to the actual condition of older women, and increase daily step count or step intensity appropriately.

Health sciences/Health care/Disease prevention/Lifestyle modification

Health sciences/Health care/Weight management

Obesity is one of the major public health problems and its leads to increased prevalence of cardiovascular disease as well as all-cause mortality^[1]. According to The Global Burden of Disease group, 603.7 million adults have become obese in recent years^[2]. The prevalence of overweightness/obesity among adults worldwide has reached 39%^[3] which higher in women than men and increasing3 with age^[4]. WHO states that overweightness/obesity can be prevented through lifestyle changes^[3]. Hence, identifying factors that can improve overweightness/obesity in older women is important to reduce the burden on society and improve public health wellness.

Several studies have found that physical activity (PA) can be used to reduce the risk of obesity^{[5, 6]}. Replacing sedentary behaviour (SB) with low intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) can reduce body mass index (BMI) and BFR^{[7, 8]}. However, LPA and MVPA are based on metabolic equivalents that are not easily understood and applied for older people. Walking is the most common and easily performed physical activity^[9]. Daily step counts are simple and easy to measure and can provide an objective assessment of total PA^[10]. Therefore, making recommendations derived from walking measures are more acceptable to older people ^{[11, 12]}. Evidences have shown that increasing daily steps reduces BMI ^{[13, 14]}, while some others revealed nonsignificant association^[15]. More studies are needed to explore the association between step counts and overweightness/obesity outcomes^[16]. Step intensity may influence the relationship between steps and BMI, and it may be one of the reasons for this contradictory result.

Advances in wearable technology have made it possible to evaluate walking patterns in greater detail. Step intensity has entered the public eye as an indicator of walking activity. When step intensity is expressed as steps per unit of time (e.g., steps per minute), it is often represented by step rate or cadence, where step rate is usually assessed under controlled conditions^[17], and peak cadence captures the highest intensity execution of naturally occurring dynamic activity^[18], reflecting free living conditions. Previous evidence suggests that step rate is lower in overweight and obese people compared to the general population^[19], that increasing step rate reduces all-cause mortality and cardiovascular disease risk^[20], and that walking at 130step/min may have health benefits^[21]. However, there are some limitations to the currently available studies on the association between walking and health outcome indicators: i. Existing studies have focused more on step counts in younger populations and less on step intensity. ii. Most studies have obtained step rate under controlled laboratory conditions^[21–24]. Such as treadmill walking, which cannot represent the walking activities in the natural state of daily life. iii. Studies evaluating walking activity in the natural walking state have mostly used self-reported forms^[25–27], which is subjective to recall and social desirability bias compared to objective methods. iv. There are fewer studies on peak cadence and limited evidence remain inconsistent on the association between peak 30 cadence and overweightness/obesity^{[28, 29]}. Compared with the rate obtained under laboratory conditions, the cadence can better reflect the walking intensity in daily life. We need to further explore the association between objectively measured cadence and overweightness/obesity ^[18].

Therefore, this study aims to investigate the association of different step counts and step intensities on overweightness/obesity in older women in the community by accelerometer-measured, in order to further provide scientific and effective recommendations for promoting public health and reducing overweightness/obesity rates in older women.

STUDY POPULATION

Cross-sectional data were taken from the Physical Activity and Health in Older Women Study (PAHIOWS) baseline survey (March to June 2021)^[30]. PAHIOWS is an ongoing community-based cohort study aimed at exploring the association between PA, SB and health status in older women, details have been reported in previous articles^[31–33].

This study was approved by the Ethics Committee of the School of Nursing and Rehabilitation, Shandong University, China (2020-R-067), and all participants provided written informed consent.

STEPPING COUNTS INTENSITY

Daily step counts and step intensity were recorded using a triaxial accelerometer (ActiGraph wGT3X-BT, ActiGraph LLC, Pensacola, FL, USA) for 7 consecutive days. Daily steps (steps/day) are calculated by dividing the number of valid steps for compliance by the number of valid days for compliance. Step intensity, or cadence, was estimated using 2 metrics. Peak 1 cadence indicates total number of uncensored steps accumulated during the highest single minute in a whole day. Peak 30 cadence average of the uncensored steps/min accumulated during the highest, but not necessarily consecutive, 30 minutes of the whole day. All participants were asked to wear the accelerometer on the left side of their waist, which was secured via a belt, and to remove the device only before going to bed or when engaging in water activities such as swimming and bathing. Two follow-up telephone calls were made to remind the subjects to wear the accelerometer and to check on the quality of wear during the wear period. Sampling frequency of raw data was setting as 30 Hz. Raw data were transformed into counts of movement in 60 s epoch lengths using ActiLife software version 6.13.4. Non-wear time was defined as ≥ 90 consecutive minutes of no activity and were removed from the analysis. The data during which the accelerometer was worn for ≥ 4 valid wear days (at least 10 h of wearing time per day)were included in the analysis. The accelerometer was collected by the researcher in the 8th day.

ADIPOSITY INDICATORS

Body composition analyzer Tanita MC-180 ( Bailida Co., Tokyo, Japan ) was used for testing body mass index (BMI), body fat ratio (BFR), visceral fat mass (VFM), subcutaneous fat mass (SFM) and trunk fat mass (TFM). Using bioelectrical resistance analysis, eight-point contact electrode and multi-loop method to comprehensively analyze multiple body composition indexes of human body can effectively evaluate the overweight and obesity of older women^{[34, 35]}. During the test, the subject is required to remove any heavy objects from her clothing, stand upright and barefoot on the foot electrodes. Wait for the weight reading to stabilize, hold the electrode handle for 20 seconds, and leave the test bench when the screen indicates the end of the test. People with pacemakers, metals, steel nails, and steel plates in the body cannot perform this test.

MEASUREMENTS OF OTHER VARIABLES

The questionnaire for older women in the community was designed in-house after consultation with relevant experts. It consisted of two sections. i. Basic socio-demographic information: age, monthly income, alcohol consumption and chronic disease status. ii. Health-related questionnaires: evaluation scales for cognition and nutritional status. Mini-mental state examination ( MMSE ) was used to assess the cognitive status of the subjects. Nutritional status was measured using the Mini Nutritional Assessment Short Form ( MNA-SF ).

STATISTICAL ANALYSIS

Data were summarized using EXCEL and analysed using STATA 17.0 and Graphpad prism. Normally distributed data in measure were expressed as mean ± standard deviation. Count data were expressed as frequency and percentage. Chi-square test and Kruskal-Wallis H test were used to calculate differences between groups.

Multivariate linear regression was used to analyze the correlation between different indicators. Total daily step counts, peak 1 cadence and peak 30 cadence were used as independent variables, and obesity indicators (BMI, BFR, VFM, SFM, TFM) were used as dependent variables.

Two models were used to adjust for confounders. Model 1 adjusted for age, income, alcohol consumption, disease, MNA score, MMSE score, and accelerometer wear time. Model 2 additionally adjust for peak 1 cadence to daily step counts^[36], and adjust for daily step counts to peak 1 cadence and peak 30 cadence^[37]. Variance inflation factors (VIF) were calculated for all variables to detect the presence of covariance. In the fully adjusted model, the VIF for each covariate was below 3, which was considered acceptable. Two-sided P values < 0.05 were considered statistically significant.

We further explored the joint associations of step counts and intensity on overweightness/obesity using multiple linear regression. Daily step count and peak 1 cadence were both classified into two level by median. The whole sample were then classified into low steps and intensity (LSLI), high steps and low intensity (HSLI), low steps and high intensity (LSHI), and high steps and intensity (HSHI) group by combining different levels. Based on the results of the joint effect, we plotted a clustered heatmaps for visual analysis. The darker the colour the more association it had on the overweightness/obesity index.

ROC curves were used to determine the predictive power and cut-off points for daily steps, peak 1 cadence, peak 30 cadence and overweightness/obesity. The larger the area under the ROC curve, the greater the discriminatory ability. In the medical field, diagnostic accuracy is poor when the area under the ROC curve AUC is between 0.5 and 0.6, sufficient between 0.6 and 0.7, and good above 0.9^[38].

The final analysis of the study included data from 1 085 older women. The characteristics of the study population are shown in Table 1. Among these, there were between-group differences in each of the overweightness/obesity indicators, daily step counts, peak 1 cadence, peak 30 cadence, disease, and wear time. No significant difference was observed between group in other variables.

Associations between total daily steps, peak 1 cadence, peak 30 cadence and indicators of overweightness/obesity are show in Tables 2 and 3. Model 1 found a significant linear association between total daily steps, cadence and all overweightness/obesity indicator variables. After adjusting for peak 1 cadence and other confounders, the association between total daily steps and BMI disappeared. Total daily steps, step intensity and other overweightness/obesity indicators remained significantly negatively associated. Every 1 000 steps increase in daily steps was associated with a decrease of 0.2% ( 95% CI : -0.35,0.06 ) in BFR, 0.05 kg ( 95% CI : -0.09,0.00 ) in VFM, 0.16 kg ( 95% CI : -0.29, -0.04 ) in SFM, and 0.12 kg ( 95% CI : -0.23, -0.01 ) in TFM.

We further explored the joint association of step count, step intensity with overweightness/obesity, as shown in Fig. 1. Compared with the LSLI, all three groups were significantly negatively associated with each overweightness/obesity indicator, and the absolute values of the regression coefficients showed an increasing trend with daily step counts and step intensity.

In this study, the ROC curve was performed according to the BFR classification. We further obtained the optimal cut-off value for daily step counts, peak 1 cadence, and peak 30 cadence in distinguishing overweightness/obesity The optimal cut-off points of daily steps, peak 1 cadence and peak 30 cadence for predicting overweightness/obesity were 9 135 steps, 126.9 steps/min and 89.0 steps/min, respectively, as shown in Fig. 2.

Our study explored the association between daily step count, step intensity and overweightness/obesity indicators in older women in the community. To the best of our knowledge, this is the first study to report on the joint effect of step count and step intensity on overweightness/obesity and the recommended amount of step intensity in older Chinese community women. Overall, this cross-sectional study showed that in a fully adjusted model, total daily steps, peak 1 cadence and peak 30 cadence were all significantly and negatively associated with indicators of overweightness/obesity and the effect was most pronounced in the HSHI. Notably, daily step count is not associated with BMI. Older community women were more likely to be overweight/obese when daily step count, peak 1 cadence, peak 30 cadence was less than 9 135, 126.9steps/min, 89.0steps/min respectively. This suggests that increasing the count or intensity of steps in walking activities is an effective method for weight reduction.

One of the significant findings emerging from this study is that daily steps were an independent factor in reducing the risk of overweightness/obesity. Each 1 000 step increase in daily steps was associated with a 0.2%, 0.05kg, 0.16kg and 0.12kg reduction in BFR, VFM, SFM and TFM respectively. There has been numerous evidence that daily step counts reduce the risk of overweightness/obesity. Bruce Wayne Bailey et al.^[39] who found that daily step counts in young adult women were associated with BFR, but this relationship diminished with increasing steps. An intervention study found a reduction in weight in subjects by increasing participants' steps by 1 000 steps per week (up to 10 000 steps)^[40]. Other studies have found that people with high step counts have lower BMI^[41] and that increasing step count reduces BMI ^{[13, 14]}. Nevertheless, these studies did not take into account the effect of step intensity. We found that the linear association between total daily steps and BMI disappeared after additional control for peak 1 cadence. It’s consistent with the findings of Gerson Ferrari et al. ^[15] who found that daily step count was not associated with BMI in women. However, in addition to BMI, we included more disaggregated indicators of overweightness/obesity. Such as BFR, and their negative linear association with daily step count persisted after adjusting for peak 1 cadence and other confounders. It seems possible that these results are due to BMI can be influenced by muscle mass, organs, bone structure and fat mass, but cannot be used to measure body tissue composition. It cannot be used as an absolute measure of being overweight/obese. What’s more, the ageing process can lead to nutritional, morphological and physiological changes, such as a decrease in height, weight, muscle mass and fat mass (especially in women). And most older people will lose more muscle tissue than fat ^{[42, 43]}. So BMI can underestimate the level of obesity in older women to some extent. Overall, although we did not find an independent association between daily steps and BMI, in the fully adjusted model, daily steps were significantly negatively associated with other indicators of overweightness/obesity. These outcomes still suggests that daily step counts have an effect on indicators of overweightness/obesity and are an independent risk factor for reducing it. In order to provide further walking advice to older community women to improve their overweightness/obesity, ROC analysis was conducted. As BMI is not an appropriate measure of obesity levels in older women ^{[42, 43]}, predictions were made based on BFR classification criteria only in this study. Our results concluded that higher steps was associated with a reduction in overweightness/obesity. The optimal cut-offs for predicting overweightness/obesity of steps per day were 9 135 (AUC = 0.59). Ferrero-Hernández, P et al. conducted a ROC analysis according to waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR) classifications in women aged 51–65 years. They came up with optimal cut-off values of 9 127 steps (AUC = 0.586) and 10 465 steps (AUC = 0.546) per day to distinguish overweightness/obesity^[44]. It’s close to our results and supports our study.

After controlling for confounding factors such as total daily steps, we found that peak 1 cadence and peak 30 cadence were significantly negatively associated with each of the overweightness/obesity indicators. Each 1 step/minute increase in peak 1 cadence was significantly associated with a 0.05kg/m², 0.09%, 0.04kg, 0.09kg, and 0.08kg decrease in BMI, VFM, SFM, and TFM, respectively. Each 1 step/minute increase in peak 30 cadence was associated with a 0.05kg/m², 0.07%, 0.03kg, 0.08kg and 0.06kg decrease in BMI, BFR, VFM, SFM and TFM, respectively. This suggests that step intensity is an independent factor in reducing the risk of overweightness/obesity. Peak cadence, as the best natural effort in a free-living environment, have often been applied in recent years to explore the association with risk of various diseases ^[36]. For example, a cross-sectional study concluded that daily step count and peak cadence appear to offset the risk of sedentary time and cardiometabolic health in older adults ^[45]. In addition, studies have also found the association between peak 30 cadence and overweightness/obesity indicators^[29]. Their results suggest that peak 30 cadence is inversely related to body weight, waist circumference, and BMI, consistent with our results. However, John M Schuna Jr et al.^[28] found that the association between BMI and peak 30 cadence was reduced and the difference was not statistically significant after controlling for daily steps. This may be due to the heterogeneity of analytical method used. The former used Spearman's correlation analysis, which performed a biased correlation analysis by controlling for individual variables. Whereas we used a multivariate linear regression model for data analysis by controlling for multiple confounding factors, controlling for more covariates. No studies have yet explored the association between peak 1 cadence and indicators of overweightness/obesity. Our results shed new light on this. In short, step intensity was associated with reduced risk of overweightness/obesity. Based on these results and the ROC analysis, we found that the peak 1 cadence and peak 30 cadence cut-off values for differentiating overweightness/obesity were 126.9steps/min (AUC = 0.61), 89.0steps/min (AUC = 0.59) respectively. These recommendations reported in this study should be considered as a simple first attempt. Our results provide a new reference for public recommendations and health programs to improve overweightness/obesity in older community women.

Walking activity indicators include both the step counts and intensity. In daily walking activities, there are cases where people walk a lot but slowly or walk less but fast, so we further investigated the joint effect of step count and step intensity on overweightness/obesity in older women.Our results show that the association with overweightness/obesity is stronger in the HSHI compared to the LSLI. It indicating that the HSHI was more effective than the LSHI in reducing overweightness/obesity than the HSLI. To our knowledge, no studies have reported the combined effect of step count and walking intensity on indicators of overweightness/obesity in older community women. But previous cohort studies have found a joint associations between step counts and intensity with the risk of hospitalization and all-cause mortality. The group having high step counts and intensity are significantly associated with lower risk of hospitalisation and all-cause mortality in older adults ^[46]. And there is also evidence for the joint association between daily steps, step intensity and diabetes risk^[47]. It suggests that at a certain accumulation of daily step count, the greater the proportion of high-intensity steps, the lower the risk of diabetes. We report for the first time the joint effect of different combinations of step count and intensity on overweightness/obesity. Our results can be further translated into three specific messages that can be easily conveyed to the public: i. Under the condition of a certain intensity, the more walking, the better. ii. The higher the walking intensity, the better, given a certain steps counts and the ability to do so. iii. To improve overweightness/obesity, the most effective way is to increase the daily step count and walking intensity at the same time.

There are several limitations to this study. Firstly, it is a cross-sectional study and the findings do not reflect a causal association between overweightness/obesity and daily step counts and intensity, and future in-depth studies on vertical design are needed. Secondly, the study population was from one city in China, therefore it is not fully representative of older Chinese people. Thirdly, the participants were from a coastal city and may have overlooked the measure of water-related activities such as swimming on overweightness/obesity. What's more, it is difficult to obtain an area under the ROC curve of 0.9 or more as the results are often confounded by various factors in studies on physical fitness and health promotion. Hence, in condition of this study, our daily step count and cadence cut points should only be used as recommended thresholds rather than assessment criteria in a clinical setting. Finally, participants were based on a voluntary basis and those who were willing to participate in this study may have been healthier.

Both daily step count and step intensity were independently associated with overweightness/obesity although daily step count is not associated with BMI. Compared to the LSLI, increasing step count or intensity were beneficial in preventing overweightness/obesity, with the best benefit in the HSHI. This suggests that when it comes to free-living walking activities, the more you walk the better, given a certain level of intensity; if you are able, it is important to focus on both steps counts and intensity. When prescribing exercise to older women for weight loss, it is recommended that they achieve a daily step count of 9 135 or more for walking, and according to the older 's own situation to consider personalized walking activities to improve the count of steps or walking intensity.

ACKNOWLEDGEMENTS

We are gratefull for all the professors and students who conducted the research and all participants for their cooperation and the various resident community organisations.

AUTHOR CONTRIBUTIONS

All authors have read and approved of the submission of this manuscript. Conceptualization, L.D., Q.H., S.C., X.Z.; Data curation, S.C., X.Z.; Formal analysis, Q.W., S.Y.; Funding acquisition, X.Z., S.C.; Investigation, L.D., X.R., T.L.; Methodology, S.C., Q.H., Y.P.; Project administration, Q.H., Y.P.; Supervision, X.Z., Writing—original draft, Q.W., L.D.; Writing—review and editing, Q.W., Q.H., Y.P., S.C., X.Z.; All authors contributed to the acquisition of data.

COMPETING INTERESTS

There are no conflicts of interest directly relevant for any of the authors of the article.

DATA AVAIL ABILITY STATEMENT

The data presented in this study are available upon reasonable request from the corresponding author.

Supplementary information The online version contains supplementary methods available.

Correspondence and requests for materials should be addressed to Kristin Suorsa.

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Tables 1 to 3 are available in the Supplementary Files section.

There is NO conflict of interest to disclose

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Daily step counts and intensity both associated with obesity in Chinese older women

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Abstract

Figures

INTRODUCTION

METHODS

STUDY POPULATION

STEPPING COUNTS INTENSITY

ADIPOSITY INDICATORS

MEASUREMENTS OF OTHER VARIABLES

STATISTICAL ANALYSIS

RESULTS

DISCUSSION

CONCLUSION

Declarations

References

Tables

Additional Declarations

Supplementary Files

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