Associations of Weekend Warrior and Other Leisure-time Physical Activity Patterns with the Risk of Insulin Resistance——Evidence from NHANES 2007-2018

doi:10.21203/rs.3.rs-5287438/v1

Download PDF

Research Article

Associations of Weekend Warrior and Other Leisure-time Physical Activity Patterns with the Risk of Insulin Resistance——Evidence from NHANES 2007-2018

https://doi.org/10.21203/rs.3.rs-5287438/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Insulin resistance (IR) is a critical precursor to various metabolic disorders, including type 2 diabetes and cardiovascular diseases. This study aims to explore the relationship between weekend warrior (WW) and other LTPA patterns with IR risk among American adults.

Methods: Data from 6 National Health and Nutrition Examination Survey (NHANES) cycles (2007-2008 to 2017-2018) were analyzed, with the final sample consisting of 10150 adults. Participants were assessed for IR using 6 indices: HOMA-IR, QUICKI, TyG index, TG/HDL-c, METS-IR, and TyG-BMI. LTPA patterns were determined using self-reported frequency and duration based on the global physical activity questionnaire and then categorized into inactive, insufficiently active, weekend warrior and regularly active. Analyses of variances and Rao-Scott adjusted chi-square tests were employed to compare the characteristics across LTPA patterns, and multivariate weighted logistic regression models were conducted to explore the associations of LTPA patterns and IR risk. Weighted restricted cubic splines were utilized to examine the dose-response associations of LTPA patterns and IR risk.

Results: The final sample included 10150 participants representative for approximately 170.4 million adults, with WW prevalence being 4.66%. Both WW and regularly active adults exhibited lower IR risk compared to inactive participants, and no significant differences in IR risk between weekend warriors and regularly active participants were observed. The relationship between LTPA pattern and IR risk was consistent across different subgroups, and 3 interaction effects were observed. Significant nonlinear relationships between LTPA and IR risk were only observed in TyG and TyG-BMI indices.

Conclusion: This study underscores the importance of WW and regular physical activity in mitigating IR risk, highlighting that even less frequent but intense physical activity can confer significant metabolic benefits.

weekend warrior

leisure-time physical activity

physical activity pattern

insulin resistance

Insulin resistance (IR), a pathophysiological condition where cells in the body become less responsive to insulin, often precedes and contributes to the development of type 2 diabetes mellitus and cardiovascular diseases, representing a significant public health concern globally [1–4]. Understanding modifiable lifestyle factors that influence IR is essential for developing effective prevention and management strategies, among which physical activity plays a crucial role [5–7].

Regular physical activity, widely recognized for its beneficial effects on glucose metabolism and insulin sensitivity, helps in maintaining a healthy body weight, improving muscle mass, and enhancing overall metabolic health [8–11]. Leisure time physical activity (LTPA) encompasses any planned, structured, and repetitive bodily movement performed during free time, aiming to improve or maintain physical fitness [12 13]. Various patterns of LTPA, such as frequency, duration, and intensity, contribute differently to health outcomes. For instance, the American Heart Association and 2018 Physical Activity Guidelines Advisory Committee recommend at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity aerobic activity per week, or an equivalent combination, for substantial health benefits [14 15]. However, not all individuals adhere to these guidelines uniformly due to the transformation of social rhythm and the increase of work pressure. Weekend warrior (WW) those engage in sporadic bouts of high-intensity activity became common and entered the public consciousness through a combination of societal trends, media influence, and evolving work-life dynamics, while others may distribute their physical activity more evenly throughout the week.

Emerging evidence suggests that both WW and regularly active LTPA patterns can be beneficial in reducing mortality and cardiovascular disease incidence, though the extent may differ [16 17]. Besides, our previous studies have revealed the benefits of WW and regularly active LTPA patterns on the visceral adiposity index and depression risk, with the identical profit presented [18 19]. Regular physical activity, characterized by consistent, moderate to vigorous exercise sessions spread throughout the week, is well-documented to improve insulin sensitivity and reduce IR risk. On the other hand, whether WW could reduce IR risk and even exhibit the same anti-IR effect like regularly activity has not been investigated to the best of our knowledge.

The National Health and Nutrition Examination Survey (NHANES) provides a valuable resource for examining these relationships. Therefore, this study leveraged data from 6 NHANES cycles (2007–2008 to 2017–2018) to investigate the association between LTPA patterns and IR among U.S. adults. By examining the relationships between LTPA patterns and multiple IR indices including the homeostatic model assessment of insulin resistance (HOMA-IR) and other 5 novel indices, we aim to provide a comprehensive understanding of its influence on metabolic health. We hypothesize that both WW and regular LTPA patterns are associated with reduced IR risk, with potentially varying degrees of impact. Understanding the nuances of these associations can inform public health recommendations and individual behavioral strategies, promoting physical activity in ways that accommodate diverse lifestyles and preferences. This research has the potential to refine guidelines and interventions aimed at reducing the burden of IR and associated metabolic disorders in the general population.

Data source and study population

NHANES, conducted biennially by the National Center for Health Statistics of the Centers for Disease Control and Prevention, is a continuous and population-based survey designed to assess the nutrition and health status of the U.S. population. NHANES encompasses demographic, dietary, examination, laboratory, and questionnaire data, offering comprehensive information on demographic characteristics, socioeconomic status, physiological measurements, biochemical indicators, and various health-related aspects through standardized questionnaires. The survey employs a complex, multistage probability sampling design and oversamples different subpopulations to ensure that participants in each 2-year cycle are representative of the national population. Additionally, participant compensation ensures the collection of reliable and high-quality data. Detailed information could be achieved at (https://www.cdc.gov/nchs/nhanes/index.htm).

In this study, we included 13,756 adults aged 20 years and older with complete information on 6 IR indices from 6 NHANES cycles (2007–2008 to 2017–2018). Details of the current study sampling and exclusion criteria are described in Fig. 1. We excluded adults without complete information on LTPA patterns (n = 3) and self-reported pregnant participants (n = 129). Additionally, adults without two 24-hour dietary recall information (n = 2,252), or those with a mean energy intake > 6000 or < 500 kcal per day (n = 66), were excluded. Participants with missing values for education (n = 9), family income (measured as the ratio of family income to poverty, PIR, n = 946), and marital status (n = 1) were also excluded. After further excluding participants without serum cotinine (n = 5), waist circumference (n = 188), and sedentary duration (n = 7), the final sample included 10,150 adults. Since all participants in NHANES provided written informed consent and only publicly available data were used in this analysis, no ethical approval was required. Meanwhile, the work has been reported in line with the STROCSS criteria [20].

Definitions of insulin resistance

Insulin resistance were assessed with HOMA-IR and 5 novel insulin resistance indices consisting of the the quantitative insulin sensitivity check index (QUICKI), triglyceride-glucose index (TyG index), triglyceride/high-density lipoprotein cholesterol (TG/HDL-c), metabolic score for IR (METS-IR), and TyG-body mass index (TyG-BMI) [21–26]. For subsequent logisitic regression models, IR was defined by QUICKI less than the first quartile or any of the other 5 IR indices higher than the third quartile. Fasting serum insulin (FINS) were measured with a two-site immunoenzymometric assay in Tosoh AIA-900 Chemistry Analyzer. Fasting plasma glucose (FPG), triglyceride (TG) and high-density lipoprotein cholesterol (HDL-c) were determined by an automated biochemical analysis instrument. Additional detailed information could be achieved at NHANES laboratory procedures (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overviewlab.aspx?BeginYear=2017). The formulas for the 6 indices were presented below. Since the cut-off value of 6 indices differ, we defined 25% of the participants as IR with the quartiles.

HOMA-IR = FINS (µU/ml) × FPG (mmol/L)/22.5 [21]

QUICKI = 1 / [ln FINS (µU/ml) + ln FPG (mg/dL)] [22]

TyG index = Ln [fasting TG (mg/dL) × FPG (mg/dL)/2] [23]

TG/HDL-c = fasting TG (mg/dL)/fasting HDL-c (mg/dL) [24]

METS-IR = Ln [(2 × FPG (mg/dL) + fasting TG (mg/ dL)] × BMI (kg/m²)

/Ln [HDL-c (mg/dL)] [25]

TyG-BMI = TyG index × BMI [26]

Ascertainment of leisure-time physical activity patterns

LTPA patterns were determined with self-reported frequency and duration of LTPA based on the Global Physical Activity Questionnaires (GPAQ) [27]. The GPAQ has been validated to collect daily moderate physical activity (MPA) and vigorous physical activity (VPA) in leisure time at least 10 continuous minutes, with sessions lasting at least 10 continuous minutes. MPA and VPA were calculated by multiplying the weekly frequency by the duration of each activity. According to American Physical Activity Guidelines, LTPA was calculated with the formula LTPA = MPA + 2 * VPA, as one minute of VPA was defined as equivalent to two minutes of MPA [15]. LTPA session was defined as the sum of the frequency of MPA and VPA per week. LTPA pattern was categorized into inactive (LTPA = 0), insufficiently active (0 < LTPA < 150 mins/wk), WW (LTPA ≥ 150 mins/wk in 1 to 2 sessions) and regularly active (LTPA ≥ 150 mins/wk in more than 2 sessions) [18]. Meanwhile, MPA percentage was calculated as the proportion of MPA in LTPA.

Assessment of covariates

Demographic variables include sex (male, female), age (years), race (non-Hispanic White, non-Hispanic Black, Mexican Americans, and other races), education level (less than high school degree, high school or GED equivalent, college or above), family income (PIR), marital status (married or living with partner; divorced, separated, or widowed; never married). Lifestyle factors include serum cotinine (ng/mL, to assess smoking status), alcohol intake (g/d), total energy intake (kcal/day), BMI (kg/m²), sedentary duration (mins/d) and MPA percentage (%). Comorbidities considered in the study include hypertension and central obesity. Hypertension was defined as average systolic pressure ≥ 130 mm Hg and/or diastolic pressure ≥ 90 mm Hg in 3 tests or self-reported hypertension [28–30]. Central obesity was defined as a waist circumference ≥ 102 cm for male adults and ≥ 88 cm for female adults [31].

Statistical analysis

According to analytic guidelines in NHANES, fasting subsample 2 year mobile examination center weight, clustering, and stratification were taken into account. To ensure the results represent the national non-institutionalized population, the fasting subsample two-year mobile examination center weight was divided by six, reflecting the combination of six consecutive cycles.

Statistical descriptions included continuous variables presented as weighted means (standard deviations) and categorical variables described as frequencies (weighted percentages). Analyses of variance and Rao-Scott adjusted chi-square tests were used to compare the characteristics between adults across different LTPA patterns. Univariate and multivariate weighted logistic regression models were employed to explore the relationship between LTPA patterns and IR risk in the general population. Model 1 was adjusted for demographic data, model 2 for demographic data and lifestyle factors, and model 3 additionally for comorbidities. Due to the exremely strong link between TyG-BMI and BMI, BMI was excluded in the models of TyG-BMI. Trend tests (p for trend) were performed by entering the LTPA pattern as a continuous variable and rerunning the corresponding regression models. Stratified analyses were conducted to investigate whether the association differed by demographic variables (gender, age, race, education level, and marital status), and interaction effects were tested using the likelihood test. Weighted restricted cubic spline analyses were utilized to examine the dose-response associations of LTPA patterns and IR risk.

Analyses of variance was conducted using R software (version 4.2.2), and all other statistical analyses were performed using Stata software (version 17.0, StataCorp LLC). All statistical tests were two-sided, with the significant threshold considered at α = 0.05.

Characteristics of participants according to the leisure-time physical activity patterns

The study population comprised 10150 participants representative of approximately 170.4 million adults (Mean [SD] age, 47.81 [16.66] years; 4859 [48.0%] male participants; 4598 [69.6%] non-Hispanic White participants; 419 [4.66%] WW; 3000 [32.98%] regularly active participants). The characteristics of participants according to the LTPA patterns were shown in Table 1. Compared with inactive participants, WW and regularly active adults were younger, more likely to be male, non-Hispanic White, never married, and less likely to comorbid with central obesity or hypertension. Moreover, WW and regularly active perticipants had higher education level, PIR, alcohol consumption, energy intake, QUICKI and lower serum cotinine, BMI, sedentary duration, HOMA-IR, TyG, TG/HDL-c, Mets-IR and TyG-BMI in comparison with inactive adults.

Table 1

Characteristics across different leisure-time physical activity patterns
Characteristics	Inactive (n = 5124)	Insufficiently active (n = 1607)	Weekend warrior (n = 419)	Regularly active (n = 3000)	Overall (N = 10150)	P value
Age (y), Mean (SD)	50.63 (16.60)	47.85 (16.09)	42.22 (16.29)	44.75 (16.36)	47.81 (16.66)	< 0.001
Sex, n (%)						< 0.001
Male	2355 (46.2%)	668 (43.3%)	296 (71.1%)	1540 (49.9%)	4859 (48.0%)
Female	2769 (53.8%)	939 (56.7%)	123 (28.9%)	1460 (50.1%)	5291 (52.0%)
Race, n (%)						< 0.001
Non-Hispanic White	2284 (67.2%)	740 (72.9%)	184 (69.5%)	1390 (71.1%)	4598 (69.6%)
Non-Hispanic Black	1007 (11.4%)	297 (9.0%)	79 (10.0%)	558 (9.8%)	1941 (10.4%)
Mexican Americans	833 (9.2%)	212 (6.4%)	59 (7.9%)	375 (6.6%)	1479 (7.8%)
Other races	1000 (12.2%)	358\ (11.7%)	97 (12.7%)	677 (12.5%)	2132 (12.2%)
Education level, n (%)						< 0.001
Less than high school degree	1560 (22.3%)	231 (9.3%)	74 (12.9%)	347 (6.8%)	2212 (14.5%)
High school or GED equivalent	1313 (27.7%)	344 (21.3%)	95 (20.9%)	529 (16.2%)	2281 (22.5%)
College or above	2251 (49.9%)	1032 (69.4%)	250 (66.3%)	2124 (77.0%)	5657 (63.0%)
PIR, Mean (SD)	2.62 (1.59)	3.26 (1.60)	2.97 (1.56)	3.42 (1.59)	3.01 (1.63)	< 0.001
Marital status, n (%)						< 0.001
Married or living with partner	3125 (63.7%)	1012 (67.0%)	257 (63.3%)	1829 (65.6%)	6223 (64.9%)
Divorced, separated, or widowed	1284 (21.5%)	335 (16.8%)	66 (15.6%)	507 (13.8%)	2192 (17.9%)
Never married	715 (14.8%)	268 (16.2%)	96 (21.1%)	664 (20.7%)	1735 (17.3%)
Cotinine (ng/mL), Mean (SD)	73.70 (142.70)	38.55 (103.34)	60.05 (134.15)	31.10 (92.03)	52.84 (122.53)	< 0.001
Alcohol intake (g/d), Mean (SD)	7.30 (21.27)	8.19 (18.16)	12.54 (23.73)	11.05 (21.52)	8.94 (21.04)	< 0.001
Energy intake (kcal/d), Mean (SD)	2066.89 (808.75)	2074.83 (723.30)	2342.06 (815.73)	2120.83 (775.56)	2098.91 (785.85)	< 0.001
BMI (kg/m²), Mean (SD)	30.19 (7.15)	29.10 (6.88)	28.17 (6.50)	27.76 (5.95)	29.11 (6.78)	< 0.001
Central obesity, n (%)						< 0.001
With	3362 (66.6%)	954 (59.8%)	184 (48.6%)	1436 (46.9%)	5936 (58.1%)
Without	1762 (33.4%)	653 (40.2%)	235 (51.4%)	1564 (53.1%)	4214 (41.9%)
Hypertension, n (%)						< 0.001
With	2790 (50.3%)	723 (38.4%)	153 (31.9%)	1177 (35.0%)	4843 (42.3%)
Without	2334 (49.7%)	884 (61.6%)	266 (68.1%)	1823 (65.0%)	5307 (57.7%)
Sedentary duration (mins/d), Mean (SD)	398.87 (531.08)	387.69 (271.57)	396.30 (496.94)	368.91 (214.83)	386.90 (407.63)	0.029
MPA percentage, Mean (SD)	0.00 (0.00)	0.89 (0.30)	0.48 (0.48)	0.52 (0.40)	0.35 (0.44)	< 0.001
HOMA-IR, Mean (SD)	4.32 (6.88)	3.61 (7.04)	3.49 (3.63)	2.83 (4.52)	3.66 (6.14)	< 0.001
QUICKI Mean (SD)	0.33 (0.04)	0.34 (0.04)	0.34 (0.04)	0.35 (0.04)	0.34 (0.04)	< 0.001
TyG, Mean (SD)	5.79 (0.67)	5.70 (0.66)	5.65 (0.60)	5.54 (0.64)	5.69 (0.66)	< 0.001
TG/HDL-c, Mean (SD)	3.05 (4.14)	2.73 (2.73)	2.59 (2.89)	2.34 (3.20)	2.74 (3.58)	< 0.001
Mets-IR, Mean (SD)	37.93 (12.10)	35.78 (11.43)	34.74 (11.21)	32.90 (10.37)	35.75 (11.61)	< 0.001
TyG-BMI, Mean (SD)	176.16 (51.14)	167.32 (48.47)	160.58 (46.65)	154.87 (42.28)	166.86 (48.61)	< 0.001

BMI, body mass index; HDL-c, high-density lipoprotein cholesterol; HOMA, homeostasis model assessment; IR, insulin resistance; MetS, metabolic syndrome; MPA, moderate physical activity; PIR, Ratio of family income to poverty; QUICKI, quantitative insulin sensitivity check index; TG, triglyceride; TyG, triglyceride glucose.

WW prevalence among different demographic populations

The trends in WW prevalence, stratified by sociodemographic factors across NHANES cycles, are illustrated in Fig. 2. When analyzed by age groups, WW prevalence decreased with age and remained stable across cycles. Additionally, WW prevalence was higher in male adults than in female adults, showing an increasing trend and a gradually widening difference between the sexes. By race, Mexican Americans experienced relatively large fluctuations in WW prevalence, with peaks in 2007–2008 and 2017–2018. Furthermore, WW prevalence was lower among adults with less than a high school degree compared to those with higher education levels.

Associations of leisure-time physical activity patterns with the risk of IR defined by 6 indices

Tables 2 and 3 demonstrated the associations of leisure-time physical activity patterns with the risk of IR defined by 6 indices. After adjustment for multiple covariates including demographic characteristics, lifestyle factors and comorbities, both WW and regularly active adults exhibited lower risks of IR in comparison with inactive adults. Notably, there were no significant differences in IR risk between WW and regularly active participants, suggesting that both types of physical activity offer similarly reduced risk of IR. Of particular note was that the consistent associations existed in all 6 IR indices and substantial models, underscoring the robustness of the findings.

Table 2

The associations of physical activity patterns with risk of IR using inactive group as reference
IR defined by indices	Inactive	Insufficiently active	Weekend warrior	Regularly active
IR defined as HOMA-IR > Q3
Model 1	Ref	0.84 (0.72,0.98)	0.72 (0.52,1.01)	0.49 (0.42,0.56)
Model 2	Ref	0.48 (0.36,0.64)	0.61 (0.40,0.93)	0.41 (0.33,0.52)
Model 3	Ref	0.52 (0.39,0.69)	0.63 (0.42,0.96)	0.44 (0.35,0.55)
IR defined as QUICKI < Q1
Model 1	Ref	0.84 (0.72,0.97)	0.71 (0.51,0.99)	0.48 (0.42,0.56)
Model 2	Ref	0.48 (0.36,0.64)	0.61 (0.39,0.93)	0.41 (0.33,0.52)
Model 3	Ref	0.52 (0.39,0.70)	0.63 (0.41,0.95)	0.44 (0.35,0.55)
IR defined as TyG > Q3
Model 1	Ref	0.89 (0.74,1.07)	0.59 (0.41,0.85)	0.61 (0.53,0.71)
Model 2	Ref	0.80 (0.58,1.09)	0.58 (0.38,0.88)	0.64 (0.52,0.79)
Model 3	Ref	0.85 (0.62,1.18)	0.60 (0.39,0.92)	0.68 (0.56,0.84)
IR defined as TG/HDL-c > Q3
Model 1	Ref	0.89 (0.763,1.08)	0.57 (0.40,0.80)	0.58 (0.49,0.67)
Model 2	Ref	0.74 (0.53,1.04)	0.53 (0.36,0.78)	0.58 (0.46,0.73)
Model 3	Ref	0.78 (0.56,1.10)	0.55 (0.37,0.81)	0.64 (0.48,0.76)
IR defined as Mets-IR > Q3
Model 1	Ref	0.83 (0.69,0.98)	0.53 (0.38,0.73)	0.47 (0.40,0.55)
Model 2	Ref	0.75 (0.48,1.18)	0.51 (0.31,0.84)	0.53 (0.40,0.72)
Model 3	Ref	0.78 (0.50,1.22)	0.52 (0.32,0.86)	0.55 (0.41,0.74)
IR defined as TyG-BMI > Q3
Model 1	Ref	0.82 (0.69,0.98)	0.56 (0.39,0.79)	0.48 (0.40,0.57)
Model 2	Ref	0.40 (0.29,0.56)	0.36 (0.24,0.54)	0.30 (0.23,0.39)
Model 3	Ref	0.62 (0.42,0.90)	0.44 (0.28,0.70)	0.44 (0.33,0.60)

Model 1 was adjusted for demographics characteristics (Age, sex, race, education level, family income and marital status).

Model 2 was additionally adjusted for lifestyle factors (Serum cotinine, alcohol intake, total energy intake, BMI, sedentary duration and MPA percentage) on the basis of Model 1.

Model 3 was further adjusted for comorbities (Central obesity and hypertension) on the basis of Model 2.

Table 3

The associations of physical activity patterns with risk of IR using regularly active group as reference
IR defined by indices	Regularly active	Inactive	Insufficiently active	Weekend warrior
IR defined as HOMA-IR > Q3
Model 1	Ref	2.06 (1.79,2.38)	1.74 (1.43,2.11)	1.49 (1.04,2.13)
Model 2	Ref	2.43 (1.94,3.04)	1.16 (0.93,1.45)	1.48 (1.02,2.14)
Model 3	Ref	2.29 (1.83,2.88)	1.19 (0.95,1.48)	1.15 (1.01,2.08)
IR defined as QUICKI < Q1
Model 1	Ref	3.53 (2.88,4.32)	1.30 (1.05,1.61)	1.50 (1.05,2.15)
Model 2	Ref	2.41 (1.92,2.04)	1.16 (0.93,1.45)	1.46 (1.01,2.11)
Model 3	Ref	2.28 (1.81,2.88)	1.19 (0.95,1.48)	1.43 (0.99,2.06)
IR defined as TyG > Q3
Model 1	Ref	1.63 (1.41,1.88)	1.46 (1.20,1.76)	0.95 (0.66,1.37)
Model 2	Ref	1.56 (1.27,1.91)	1.24 (0.99,1.56)	0.90 (0.61,1.33)
Model 3	Ref	1.47 (1.19,1.82)	1.26 (1.01,1.57)	0.88 (0.60,1.30)
IR defined as TG/HDL-c > Q3
Model 1	Ref	1.74 (1.48,2.03)	1.54 (1.27,1.87)	0.99 (0.69,1.40)
Model 2	Ref	1.73 (1.38,2.18)	1.28 (1.02,1.61)	0.92 (0.63,1.34)
Model 3	Ref	1.65 (1.31,2.09)	1.30 (1.03,1.63)	0.60 (0.62,1.32)
IR defined as Mets-IR > Q3
Model 1	Ref	2.14 (1.81,2.52)	1.77 (1.44.2.16)	1.13 (0.78,1.63)
Model 2	Ref	1.87 (1.39,2.52)	1.40 (0.99,2.00)	0.96 (0.60,1.54)
Model 3	Ref	1.83 (1.36,2.46)	1.42 (1.01,2.00)	0.96 (0.59,1.53)
IR defined as TyG-BMI > Q3
Model 1	Ref	2.10 (1.76,2.50)	1.73 (1.38,2.16)	1.17 (0.80,1.72)
Model 2	Ref	3.33 (2.58,4.29)	1.34 (1.05,1.71)	1.19 (0.80,1.75)
Model 3	Ref	2.26 (1.67,3.04)	1.39 (1.10,1.75)	1.00 (0.66,1.52)

Model 1 was adjusted for demographics characteristics (Age, sex, race, education level, family income and marital status).

Model 2 was additionally adjusted for lifestyle factors (Serum cotinine, alcohol intake, total energy intake, BMI, sedentary duration and MPA percentage) on the basis of Model 1.

Model 3 was further adjusted for comorbities (Central obesity and hypertension) on the basis of Model 2.

Demographic subgroup analysis in the associations of leisure-time physical activity patterns with the risk of IR

The associations between LTPA pattern and IR risk, as defined by six different indices, were examined across various sociodemographic subpopulations, with the findings detailed in Supplemental Tables 1–12. Generally, the relationship between LTPA pattern and IR risk was consistent across different subgroups. Compared to inactive adults, both WW and regularly active participants showed a lower risk of IR across multiple subgroups, with observable trends supporting this association. When comparing WW to regularly active participants, the anti-IR effects were similar across most subgroups and IR indices. Moreover, only three significant interaction effects were identified. For instance, the relationship between LTPA patterns and IR risk, as defined by the TG/HDL-c ratio, was more pronounced in males compared to females. Another interaction was observed where the association between LTPA patterns and IR, as measured by the Mets-IR index, was significant only among non-Hispanic White participants. Additionally, the relationship between LTPA patterns and IR risk, defined by the TyG-BMI index, was most significant among elderly participants.

Restricted cubic splines (RCS) analysis investigating nonlinear associations of LTPA with the risk of IR defined by 6 indices

To explore the nonlinear dose-response relationships between LTPA and the risk of IR defined by 6 indices in WW and regularly active adults, restricted cubic splines were employed with the results shown in Fig. 3. Besides, the similar relationship among regularly active adults was shown in Supplemental Fig. 1. Significant nonlinear relationships were observed in TyG and TyG-BMI indices, while no other associations were found in other 4 IR indices. To be more specific, the anti-IR effect of LTPA did not significantly increase with it when it was relatively high.

With the large cross-sectional study, we have found that both WW and regularly active adults exhibited lower risk of IR. Besides, we are the pioneer in revealing that WW had an undifferential anti-IR effect in comparison to regularly active group, with the associations being approved by 6 different IR indices and subgroup analyses. Furthermore, several interaction effect were observed, suggesting the potential existence of subpopulations those are more sensitive to WW in reducing IR risk. Our results suggest that public health strategies should emphasize promoting various forms of LTPA pattern to reduce IR risk and improve overall metabolic health.

Physical activity could be separated into occupational, leisure-time and commuting physical activity, and their benefits may differ [32–34]. Besides, a systematic overview including 17 studies have found that leisure time and occupational physical activity are opposed in their duration and intensity [12]. In other words, adults with light intensity occupational physical activity prefer to choose moderate to vigorous LTPA, vice versa. Meanwhile, multiple studies have found the reduced cardiovascular risk by LTPA higher than occupational physical activity, and in some studies it even did not exist in occupational physical activity [35 36]. A systematic-review and dose-response meta-analysis of 103 prospective cohort studies achieved similar results, in which an inverse dose-response relationship between LTPA and risk of cardiovascular disease, coronary heart disease, stroke, and atrial fibrillation while occupational physical activity showed no benefits in total or any type of cardiovascular disease [37]. As a result of this, we focus on LTPA in this study and further segment LTPA into different LTPA patterns based on duration and freqency.

WW emerged as a response to modern sedentary work environments and busy schedules, gaining popularity through media influence, cultural shifts, and scientific endorsement [38]. It offers a practical solution for those struggling to find time for regular exercise during the week, making it a viable option for maintaining health and fitness in the context of contemporary life. WW prevalence remained stable across 2007–2018 from 5.76% in 2007–2008 and 4.61% in 2017–2018, similar to previous studies [39 40]. Besides, WW prevalence in American adults is higher than that in South Korean, but lower than that in Chinese rural adults [41 42]. However, one significant commonality exist in all studies, which is WW prevalence being significantly higher in men than women.

Although WW increase the risk of shoulder and elbow injuries, the benefits of WW have been investigated by plenty of studies including arterial stiffness, mental disorders and cancer mortality apart from our previous and early mentioned studies [43–47]. Although no studies have evaluated the associations of WW and other LTPA pattern with IR risk, 2 studies have demonstrated the benefits of WW on the risk of metabolic syndrome in South Korean and Chinese rural adults [41 42]. Considered the strong links between IR and metabolic syndrome, we have a belief that their findings support our research results to some content. To further demonstrate the credibility of our results, we conducted stratified analyses and interaction effect, and the results revealed the consistency across demographic groups. Nevertheless, 3 interaction effects were observed in different subgroups and IR indices, which may be just a statistical correlation. Meanwhile, more prospective studies and randomized controlled trails are urgently warranted for further explorations about whether it was just a statistical relationship but not a actual connection or some subpopulations are indeed more susceptible to WW and reach a significant lower IR risk.

In addition, our results exhibited the nonlinear associations of LTPA and IR risked defined by TyG and TyG-BMI. From 150 mins/wk to 360 mins/wk (the median LTPA in WW and regularly active adults), adults had gradually decreased IR risk defined by TyG or TyG-BMI as LTPA increased. Besides, IR risk defined by TyG index decreased continuously as LTPA increased from 360 mins/wk to about 710 mins/wk, and no significant lowering risk was observed when LTPA exceeded 710 mins/wk. As for IR defined by TyG-BMI index, adults exceeding 360 mins/wk LTPA did not exhibit lower IR risk in comparison to the median. Meanwhile, the relationship was similar to that in regularly active adults as demonstrated in Supplemental Fig. 1. All these results indicated that the anti-IR effect of LTPA may have a threshold, which needs to be given priority attention. Understanding the threshold could help in establishing LTPA guidelines and reducing IR and related metabolic disorders.

Plenty of research have emphasized various potential molecular mechanism of LTPA against IR including insulin signaling pathway and chronic inflammation to enhance insulin secretion and skeletal muscle glucose transport, contributing to elevated insulin sensitivity and glucose tolerance [48–52]. Meanwhile, several components such as sestrins, fibronectin and growth differentiation factor 15 were also considered as mediators [53–55]. In our future studies, we would like to conduct a population-based study to explore the comprehensive impact of WW as well as dietary habits in Chinese adults and explore potential mediators based on the huge differences of LTPA pattern, dietary habits and IR prevalence between America and China.

A major strength of this study is the use of a large, nationally representative sample from NHANES, which enhances the generalizability of our findings. Additionally, the comprehensive assessment of IR using multiple indices provides a robust evaluation of metabolic health. The study also controlled for a wide range of potential confounders as well as various statistical analyses, including demographic variables, lifestyle factors, and comorbidities, further strengthening the validity of our results.

However, some limitations should be noted. The reliance on self-reported LTPA data may introduce recall bias and misclassification. Objective measures of physical activity, such as accelerometry, could provide more accurate assessments. Furthermore, the cross-sectional design of NHANES limits our ability to establish causality between LTPA patterns and IR. Longitudinal studies are needed to confirm these associations and to explore the long-term effects of different physical activity patterns on metabolic health.

This study underscores the importance of both WW and regularly active LTPA patterns in reducing IR risk, and the benefits were found to be indiscriminate. These findings support the promotion of flexible physical activity guidelines that accommodate different lifestyles and preferences. By highlighting the significant metabolic benefits of varied exercise regimens, public health initiatives can better address the growing challenge of IR and related metabolic disorders.

Acknowledgements

All authors thank the NHANES 2007-2018 participants for their invaluable contributions.

Funding

The study received no funding.

Availability of data and materials

The data were obtained from publicly available sources as previously stated, and the datasets would be shared based on reasonable request.

The authors declared that they had no competing interests.

Reaven GM. Role of insulin resistance in human disease (syndrome X): an expanded definition. Annu Rev Med 1993;44:121-31 doi: 10.1146/annurev.me.44.020193.001005.
Fan Y, Yan Z, Li T, et al. Primordial Drivers of Diabetes Heart Disease: Comprehensive Insights into Insulin Resistance. Diabetes Metab J 2024;48(1):19-36 doi: 10.4093/dmj.2023.0110 [published Online First: 20240103].
Hill MA, Yang Y, Zhang L, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism 2021;119:154766 doi: 10.1016/j.metabol.2021.154766 [published Online First: 20210322].
Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep 2018;20(2):12 doi: 10.1007/s11906-018-0812-z [published Online First: 20180226].
van der Velde J, Boone SC, Winters-van Eekelen E, et al. Timing of physical activity in relation to liver fat content and insulin resistance. Diabetologia 2023;66(3):461-71 doi: 10.1007/s00125-022-05813-3 [published Online First: 20221101].
Lakka TA, Lintu N, Vaisto J, et al. A 2 year physical activity and dietary intervention attenuates the increase in insulin resistance in a general population of children: the PANIC study. Diabetologia 2020;63(11):2270-81 doi: 10.1007/s00125-020-05250-0 [published Online First: 20200820].
Garcia-Hermoso A, Lopez-Gil JF, Izquierdo M, Ramirez-Velez R, Ezzatvar Y. Exercise and Insulin Resistance Markers in Children and Adolescents With Excess Weight: A Systematic Review and Network Meta-Analysis. JAMA Pediatr 2023;177(12):1276-84 doi: 10.1001/jamapediatrics.2023.4038.
Riddell MC, Gallen IW, Smart CE, et al. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes Endocrinol 2017;5(5):377-90 doi: 10.1016/S2213-8587(17)30014-1 [published Online First: 20170124].
Riddell MC, Peters AL. Exercise in adults with type 1 diabetes mellitus. Nat Rev Endocrinol 2023;19(2):98-111 doi: 10.1038/s41574-022-00756-6 [published Online First: 20221031].
Distefano G, Goodpaster BH. Effects of Exercise and Aging on Skeletal Muscle. Cold Spring Harb Perspect Med 2018;8(3) doi: 10.1101/cshperspect.a029785 [published Online First: 20180301].
Deng MG, Cui HT, Lan YB, Nie JQ, Liang YH, Chai C. Physical activity, sedentary behavior, and the risk of type 2 diabetes: A two-sample Mendelian Randomization analysis in the European population. Front Endocrinol (Lausanne) 2022;13:964132 doi: 10.3389/fendo.2022.964132 [published Online First: 20221103].
Janssen TI, Voelcker-Rehage C. Leisure-time physical activity, occupational physical activity and the physical activity paradox in healthcare workers: A systematic overview of the literature. Int J Nurs Stud 2023;141:104470 doi: 10.1016/j.ijnurstu.2023.104470 [published Online First: 20230218].
Tian Y, Jiang C, Wang M, et al. BMI, leisure-time physical activity, and physical fitness in adults in China: results from a series of national surveys, 2000-14. Lancet Diabetes Endocrinol 2016;4(6):487-97 doi: 10.1016/S2213-8587(16)00081-4 [published Online First: 20160428].
Virani SS, Newby LK, Arnold SV, et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation 2023;148(9):e9-e119 doi: 10.1161/CIR.0000000000001168 [published Online First: 20230720].
Piercy KL, Troiano RP, Ballard RM, et al. The Physical Activity Guidelines for Americans. JAMA 2018;320(19):2020-28 doi: 10.1001/jama.2018.14854.
Dos Santos M, Ferrari G, Lee DH, et al. Association of the "Weekend Warrior" and Other Leisure-time Physical Activity Patterns With All-Cause and Cause-Specific Mortality: A Nationwide Cohort Study. JAMA Intern Med 2022;182(8):840-48 doi: 10.1001/jamainternmed.2022.2488.
Khurshid S, Al-Alusi MA, Churchill TW, Guseh JS, Ellinor PT. Accelerometer-Derived "Weekend Warrior" Physical Activity and Incident Cardiovascular Disease. JAMA 2023;330(3):247-52 doi: 10.1001/jama.2023.10875.
Wang K, Xia F, Li Q, Luo X, Wu J. The Associations of Weekend Warrior Activity Patterns With the Visceral Adiposity Index in US Adults: Repeated Cross-sectional Study. JMIR Public Health Surveill 2023;9:e41973 doi: 10.2196/41973 [published Online First: 20230111].
Chen R, Wang K, Chen Q, et al. Weekend warrior physical activity pattern is associated with lower depression risk: Findings from NHANES 2007-2018. Gen Hosp Psychiatry 2023;84:165-71 doi: 10.1016/j.genhosppsych.2023.07.006 [published Online First: 20230728].
Mathew G, Agha R, Albrecht J, et al. STROCSS 2021: Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery. Int J Surg 2021;96:106165 doi: 10.1016/j.ijsu.2021.106165 [published Online First: 20211111].
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28(7):412-9 doi: 10.1007/BF00280883.
Katz A, Nambi SS, Mather K, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85(7):2402-10 doi: 10.1210/jcem.85.7.6661.
Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 2010;95(7):3347-51 doi: 10.1210/jc.2010-0288 [published Online First: 20100519].
McLaughlin T, Reaven G, Abbasi F, et al. Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease? Am J Cardiol 2005;96(3):399-404 doi: 10.1016/j.amjcard.2005.03.085.
Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol 2018;178(5):533-44 doi: 10.1530/EJE-17-0883 [published Online First: 20180313].
Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007-2010 Korean National Health and Nutrition Examination Survey. PLoS One 2019;14(3):e0212963 doi: 10.1371/journal.pone.0212963 [published Online First: 20190307].
Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health 2009;6(6):790-804 doi: 10.1123/jpah.6.6.790.
Wang K, Zhao Y, Nie J, Xu H, Yu C, Wang S. Higher HEI-2015 Score Is Associated with Reduced Risk of Depression: Result from NHANES 2005-2016. Nutrients 2021;13(2) doi: 10.3390/nu13020348 [published Online First: 20210125].
Wang K, Deng M, Wu J, et al. Associations of oxidative balance score with total abdominal fat mass and visceral adipose tissue mass percentages among young and middle-aged adults: findings from NHANES 2011-2018. Front Nutr 2023;10:1306428 doi: 10.3389/fnut.2023.1306428 [published Online First: 20231205].
Liu F, Nie J, Deng MG, et al. Dietary flavonoid intake is associated with a lower risk of diabetic nephropathy in US adults: data from NHANES 2007-2008, 2009-2010, and 2017-2018. Food Funct 2023;14(9):4183-90 doi: 10.1039/d3fo00242j [published Online First: 20230511].
Kang J, Smith S, Pavitt S, Wu J. Association between central obesity and tooth loss in the non-obese people: Results from the continuous National Health and Nutrition Examination Survey (NHANES) 1999-2012. J Clin Periodontol 2019;46(4):430-37 doi: 10.1111/jcpe.13091 [published Online First: 20190310].
Lee J, Kim HR, Jang TW, Lee DW, Lee YM, Kang MY. Occupational physical activity, not leisure-time physical activity, is associated with increased high-sensitivity C reactive protein levels. Occup Environ Med 2021;78(2):86-91 doi: 10.1136/oemed-2020-106753 [published Online First: 20200910].
Marruganti C, Baima G, Grandini S, et al. Leisure-time and occupational physical activity demonstrate divergent associations with periodontitis: A population-based study. J Clin Periodontol 2023;50(5):559-70 doi: 10.1111/jcpe.13766 [published Online First: 20230122].
Park S, Lee JH. Associations of occupational and leisure-time physical activity with self-rated health in Korea. Prev Med 2022;158:107022 doi: 10.1016/j.ypmed.2022.107022 [published Online First: 20220317].
Edimo Dikobo SJ, Lemieux I, Poirier P, Despres JP, Almeras N. Leisure-time physical activity is more strongly associated with cardiometabolic risk than occupational physical activity: Results from a workplace lifestyle modification program. Prog Cardiovasc Dis 2023;78:74-82 doi: 10.1016/j.pcad.2022.12.005 [published Online First: 20221222].
Neeland IJ, Lavie CJ. Leisure time versus occupational physical activity for cardiometabolic risk. Prog Cardiovasc Dis 2023;78:83-84 doi: 10.1016/j.pcad.2023.04.003 [published Online First: 20230411].
Kazemi A, Soltani S, Aune D, et al. Leisure-time and occupational physical activity and risk of cardiovascular disease incidence: a systematic-review and dose-response meta-analysis of prospective cohort studies. Int J Behav Nutr Phys Act 2024;21(1):45 doi: 10.1186/s12966-024-01593-8 [published Online First: 20240424].
O'Donovan G, Sarmiento OL, Hamer M. The Rise of the "Weekend Warrior". J Orthop Sports Phys Ther 2018;48(8):604-06 doi: 10.2519/jospt.2018.0611.
Liang JH, Huang S, Pu YQ, et al. Whether weekend warrior activity and other leisure-time physical activity pattern reduce the risk of depression symptom in the representative adults? A population-based analysis of NHANES 2007-2020. J Affect Disord 2023;340:329-39 doi: 10.1016/j.jad.2023.07.113 [published Online First: 20230804].
Dai W, Zhang D, Wei Z, et al. Whether weekend warriors (WWs) achieve equivalent benefits in lipid accumulation products (LAP) reduction as other leisure-time physical activity patterns? -Results from a population-based analysis of NHANES 2007-2018. BMC Public Health 2024;24(1):1550 doi: 10.1186/s12889-024-19070-z [published Online First: 20240609].
Jang YS, Joo HJ, Jung YH, Park EC, Jang SY. Association of the "Weekend Warrior" and Other Physical Activity Patterns with Metabolic Syndrome in the South Korean Population. Int J Environ Res Public Health 2022;19(20) doi: 10.3390/ijerph192013434 [published Online First: 20221018].
Xiao J, Chu M, Shen H, et al. Relationship of "weekend warrior" and regular physical activity patterns with metabolic syndrome and its associated diseases among Chinese rural adults. J Sports Sci 2018;36(17):1963-71 doi: 10.1080/02640414.2018.1428883 [published Online First: 20180118].
Vandercappellen EJ, Henry RMA, Savelberg H, et al. Association of the Amount and Pattern of Physical Activity With Arterial Stiffness: The Maastricht Study. J Am Heart Assoc 2020;9(20):e017502 doi: 10.1161/JAHA.120.017502 [published Online First: 20201015].
Hamer M, Biddle SJH, Stamatakis E. Weekend warrior physical activity pattern and common mental disorder: a population wide study of 108,011 British adults. Int J Behav Nutr Phys Act 2017;14(1):96 doi: 10.1186/s12966-017-0549-0 [published Online First: 20170714].
O'Donovan G, Lee IM, Hamer M, Stamatakis E. Association of "Weekend Warrior" and Other Leisure Time Physical Activity Patterns With Risks for All-Cause, Cardiovascular Disease, and Cancer Mortality. JAMA Intern Med 2017;177(3):335-42 doi: 10.1001/jamainternmed.2016.8014.
Hartnett DA, Milner JD, DeFroda SF. The Weekend Warrior: Common Shoulder and Elbow Injuries in the Recreational Athlete. Am J Med 2022;135(3):297-301 doi: 10.1016/j.amjmed.2021.08.015 [published Online First: 20210909].
Psoinos CM, Emhoff TA, Sweeney WB, Tseng JF, Santry HP. The dangers of being a "weekend warrior": a new call for injury prevention efforts. J Trauma Acute Care Surg 2012;73(2):469-73; discussion 73 doi: 10.1097/TA.0b013e318258437c.
Richter EA, Hargreaves M. Exercise, GLUT4, and skeletal muscle glucose uptake. Physiol Rev 2013;93(3):993-1017 doi: 10.1152/physrev.00038.2012.
Munoz VR, Gaspar RC, Kuga GK, et al. Exercise increases Rho-kinase activity and insulin signaling in skeletal muscle. J Cell Physiol 2018;233(6):4791-800 doi: 10.1002/jcp.26278 [published Online First: 20180115].
Li N, Shi H, Guo Q, et al. Aerobic Exercise Prevents Chronic Inflammation and Insulin Resistance in Skeletal Muscle of High-Fat Diet Mice. Nutrients 2022;14(18) doi: 10.3390/nu14183730 [published Online First: 20220910].
Zheng A, Arias EB, Wang H, et al. Exercise-Induced Improvement in Insulin-Stimulated Glucose Uptake by Rat Skeletal Muscle Is Absent in Male AS160-Knockout Rats, Partially Restored by Muscle Expression of Phosphomutated AS160, and Fully Restored by Muscle Expression of Wild-Type AS160. Diabetes 2022;71(2):219-32 doi: 10.2337/db21-0601.
Liu J, Aylor KW, Liu Z. Liraglutide and Exercise Synergistically Attenuate Vascular Inflammation and Enhance Metabolic Insulin Action in Early Diet-Induced Obesity. Diabetes 2023;72(7):918-31 doi: 10.2337/db22-0745.
Kim M, Sujkowski A, Namkoong S, et al. Sestrins are evolutionarily conserved mediators of exercise benefits. Nat Commun 2020;11(1):190 doi: 10.1038/s41467-019-13442-5 [published Online First: 20200113].
Kuramoto K, Liang H, Hong JH, He C. Exercise-activated hepatic autophagy via the FN1-alpha5beta1 integrin pathway drives metabolic benefits of exercise. Cell Metab 2023;35(4):620-32 e5 doi: 10.1016/j.cmet.2023.01.011 [published Online First: 20230221].
Zhang H, Mulya A, Nieuwoudt S, et al. GDF15 Mediates the Effect of Skeletal Muscle Contraction on Glucose-Stimulated Insulin Secretion. Diabetes 2023;72(8):1070-82 doi: 10.2337/db22-0019.

The supplementary files are not available with this version

The authors declare no competing interests.

Download PDF

Version 1

posted

You are reading this latest preprint version

Associations of Weekend Warrior and Other Leisure-time Physical Activity Patterns with the Risk of Insulin Resistance——Evidence from NHANES 2007-2018

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Data source and study population

Definitions of insulin resistance

Ascertainment of leisure-time physical activity patterns

Assessment of covariates

Statistical analysis

Results

Characteristics of participants according to the leisure-time physical activity patterns

WW prevalence among different demographic populations

Associations of leisure-time physical activity patterns with the risk of IR defined by 6 indices

Discussions

Conclusions

Declarations

References

Supplementary Files

Additional Declarations

Status:

Version 1