The Association Between Non-exercise Activity Thermogenesis and Diabetes Status in U.S. Adults: NHANES 2013

Abstract

Non-exercise activity thermogenesis (NEAT), the energy expended through daily unstructured movement, may influence type 2 diabetes (T2D) risk but is rarely emphasized in diabetes care. Few studies have examined NEAT–diabetes associations using nationally representative data. We conducted a cross-sectional analysis of 4436 U.S. adults aged ≥18 years from NHANES 2013-2014 with ≥4 days of valid wrist accelerometer wear. Daily movement was quantified using Monitor-Independent Movement Summary (MIMS) units as a proxy for NEAT and categorized as low, moderate, or high. Multivariable logistic regression estimated adjusted odds ratios (ORs) for T2D across NEAT levels, controlling for key demographic and socioeconomic factors. Stratified models examined racial and ethnic differences. Low NEAT was associated with nearly threefold higher odds of diabetes compared with high NEAT (OR = 2.88). Moderate NEAT also showed elevated odds (OR = 1.61). Associations were significant among Hispanic, non-Hispanic White, and non-Hispanic Asian adults and marginal among non-Hispanic Black adults. Lower NEAT levels are strongly associated with higher odds of diabetes, highlighting NEAT as an accessible behavior that may support equitable diabetes prevention and management.

Keywords

non-exercise activity thermogenesis type 2 diabetes accelerometry lifestyle medicine health disparities racial and ethnic groups

“Lower levels of non-exercise activity thermogenesis were strongly associated with higher odds of diabetes among U.S. adults, with notable variation across racial and ethnic groups.”

Introduction

Type 2 diabetes (T2D) affects more than 38 million Americans and accounts for over 90% of all diabetes cases, generating more than 400 billion dollars in annual economic burden.^1,2 Disparities in T2D prevalence persist across racial and ethnic groups, with Hispanic, non-Hispanic Black, and non-Hispanic Asian populations experiencing disproportionately higher rates compared to non-Hispanic White adults.^3,4 Although genetic and environmental factors contribute to diabetes risk, modifiable lifestyle behaviors, including diet, physical activity, sleep, stress, and social connectedness, play central roles in the development and progression of T2D.^5,6

The American College of Lifestyle Medicine (ACLM) identifies regular physical activity as one of the six foundational pillars of lifestyle medicine.⁵ However, structured exercise remains challenging for many individuals due to time constraints, socioeconomic barriers, cultural norms, physical limitations, and limited access to safe environments.⁴ These barriers often disproportionately affect populations already experiencing elevated T2D burden, underscoring the need for movement-based strategies that are more accessible and adaptable to daily life.

Non-exercise activity thermogenesis (NEAT), the energy expended through unstructured daily movement such as standing, light walking, fidgeting, or household tasks, may offer such an alternative.^7,8 NEAT contributes substantially to total daily energy expenditure and has been shown to improve glucose regulation, insulin sensitivity, and post-meal blood sugar levels.^8,9 NEAT consists of simple, brief, low-intensity behaviors that can be integrated throughout the day, making it highly relevant for lifestyle medicine practice and populations with barriers to traditional exercise.

Despite growing evidence of NEAT’s metabolic benefits, it remains underrepresented in diabetes prevention and clinical counseling.¹⁰ In addition, few studies have evaluated NEAT using objective, raw accelerometry data within nationally representative samples.^11,12 Even fewer have explored how NEAT–diabetes associations differ across racial and ethnic groups; however, prior research suggests that NEAT patterns vary widely due to cultural expectations, occupational demands, environmental factors, and structural inequities.^4,13 Understanding these differences is essential for developing culturally appropriate and equitable physical activity interventions within lifestyle medicine.

Monitor-Independent Movement Summary (MIMS) units provide a validated method of capturing total movement from raw triaxial accelerometry without requiring activity classification.^11,12 Because NEAT encompasses unstructured and variable movements that cannot be easily categorized using traditional activity counts or metabolic equivalents, MIMS offers a more accurate and inclusive approach for quantifying daily movement patterns relevant to NEAT.

To address these gaps, this study examined the association between MIMS-derived NEAT levels and diabetes status in a nationally representative sample of U.S. adults and assessed whether these associations differ across racial and ethnic groups. Given NEAT’s alignment with the physical activity pillar of lifestyle medicine and its ability to reduce barriers to structured exercise, these findings may support culturally adaptable strategies that enhance diabetes prevention and management while also helping to reduce health disparities.

Methods

Study Design and Sample

Design and Sample We conducted a cross-sectional study using data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a nationally representative survey of the noninstitutionalized U.S. population.¹⁴ Eligible participants were adults aged 18 years or older who had complete data on diabetes status and at least 4 days of accelerometer data with 10 or more hours of wake-wear time per day. The final analytic sample included 4436 adults drawn from this community-based national survey (Figure 1).

Figure 1.

Flow diagram of participant selection for the NHANES 2013-2014 analytic sample. Adults aged ≥18 years with diabetes status data and accelerometer wear-time data (≥4 days with ≥10 hours/day) were included, resulting in a final sample of 4,436 participants

This study used publicly available NHANES data files, which are de-identified by the National Center for Health Statistics (NCHS) to ensure participant confidentiality.¹⁵ All potentially identifiable information was removed before public release, following federal data protection standards. This study was determined to be exempt by the Loma Linda University Institutional Review Board. We used the STROBE cross-sectional checklist when writing this report.¹⁶

Neat Measurement

NHANES participants wore wrist-worn ActiGraph GT3X + accelerometers continuously for one week to assess their physical activity patterns.¹¹ We employed Monitor-Independent Movement Summary (MIMS) units to measure physical activity because they are well-suited for capturing NEAT.¹² Traditional physical activity measurement methods, such as Metabolic Equivalent of Task (MET) values, are inadequate for quantifying NEAT because they require identifying specific activity types and assigning standardized energy costs. NHANES accelerometer data do not classify activity type, which makes MET-based approaches incompatible with this dataset. Additionally, NEAT includes numerous small, unstructured movements that vary across individuals and throughout the day, many of which are not captured by MET or step-based methods.^4,7

In contrast, MIMS units measure movement continuously without requiring activity classification. MIMS processes raw triaxial accelerometer data to generate a device-independent summary of total movement.^16,17 This approach captures all daily movement, including subtle behaviors such as fidgeting or shifting posture, which are often missed by step counts or moderate-to-vigorous activity measures.⁷

MIMS algorithms avoid activity count cut points that tend to filter out light-intensity movement.¹² As a result, MIMS units more accurately reflect daily movement patterns that align with NEAT. Because these data capture total movement during wake-wear time and do not separate structured exercise from unstructured exercise, our measure reflects overall daily movement. In population-based samples, structured exercise tends to make up a small portion of total movement, with most activity coming from light-intensity, everyday movements that reflect NEAT.

Following established procedures,¹⁸ we calculated each participant’s average MIMS units per minute by summing all triaxial movement recorded during wake-wear time and averaging across valid days. Participants were categorized into three NEAT groups: low (≤12.0), moderate (12.01-17.36), and high (≥17.37), representing distinct patterns of daily movement. Low NEAT indicates prolonged sedentary behavior with minimal movement. Moderate NEAT reflects mixed activity patterns with intermittent walking or household tasks. High NEAT reflects frequent low-intensity activity throughout the day, such as stair use, occupational movement, and active household tasks.

Diabetes Classification

Diabetes status was derived from the NHANES item DIQ010, which asks, “Has a doctor or other health professional ever told you that you have diabetes?” Participants responding “yes” were classified as having diabetes, while those reporting gestational diabetes were excluded. Although NHANES does not distinguish between diabetes types, T2D accounts for most diagnosed cases in U.S. adults.¹⁷ Therefore, diabetes classifications in this study reflect population-level patterns consistent with T2D.

Covariates

Covariates included age, sex, race and ethnicity, education level, socioeconomic status (SES) using the poverty-income ratio, and body mass index (BMI). Chi-square tests were used to examine group differences by diabetes status for categorical variables. Multivariable binary logistic regression models estimated the odds of diabetes across NEAT categories, adjusting for all covariates.

These covariates were selected because they are established determinants of diabetes risk and correlate with physical activity patterns. SES and education affect access to healthy environments and health-promoting resources. BMI reflects metabolic risk and overall health status. Race and ethnicity represent important social determinants that shape diabetes risk through structural, cultural, and environmental mechanisms.^6,13

Statistical Analysis

Race and ethnicity categories included Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and Other or Multiracial. Stratified analyses were conducted to explore whether associations between NEAT and diabetes differed across these groups.

All statistical analyses were performed using IBM SPSS Statistics version 29 (IBM Corp., Armonk, NY). Significance was set at P < .05. Sensitivity analyses additionally adjusted for comorbidity burden, including self-reported history of heart disease, stroke, cancer, emphysema, liver disease, and kidney disease, categorized as none, one condition, or two or more. These adjustments did not materially alter effect sizes or significance.

Results

The final analytic sample included 4436 adults aged 18 years or older, with a mean age of 46.8 years. The overall prevalence of diagnosed diabetes was 12.8% (n = 566). Participants with diabetes were more likely to identify as Hispanic or non-Hispanic Black, have lower educational attainment, and fall into lower-income categories. Among participants without diabetes, more than half were women (52.9%), 42.9% identified as non-Hispanic White, 30.6% had a college degree or higher, and 33.9% had a high income (defined as at least 400% of the federal poverty level) (Table 1). These patterns are consistent with previously documented demographic disparities in diabetes prevalence.^3,13

Table 1.

Sociodemographic and Health Characteristics of Study Sample^a (n = 4436), NHANES, 2013-2014.

Covariate	No Diabetes (n = 3870)	Diabetes^b (n = 566)	P-value^c
Race/ethnicity%
Hispanic	22.2	25.9	<.001
Non-Hispanic White	42.9	36.1
Non-Hispanic Black	19.8	25.8
Non-Hispanic Asian	11.9	10.2
Other/multiracial^d	3.2	2.1
Sex%
Male	47.1	46.9	< .001
Female	52.9	53.1
Age group, yrs%
18-29	22.2	1.1	< .001
30-39	17.3	5.4
40-49	18.7	13.4
50-59	18.2	26.1
60-69	13.1	30.4
≥70	10.5	23.6
Education%
< high school	14.5	22.3	< .001
High school grad/GED	22.9	25.9
Some college	32.0	37.3
College grad or above	30.6	14.5
SES^e%
Low	24.8	27.3	< .001
Medium	41.3	45.3
High	33.9	27.4
BMI^f%
Underweight	2.1	0.4	< .001
Normal	31.8	13.5
Overweight	31.9	27.9
Obese	34.2	58.2
NEAT^g%
Low	15.2	37.0	< .001
Moderate	57.9	51.8
High	26.9	11.2

Abbreviations: GED, General Educational Development; NHANES, National Health and Nutrition Examination Survey; SES, socioeconomic status.

^aThe analytic sample (n = 4436) met the following conditions: (1) they were adults aged 18 years or older; (2) they participated in the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a nationally representative survey of the noninstitutionalized US population; (3) they had complete data on diabetes status; and (4) they had at least 4 days of accelerometer data with 10 or more hours of wake-wear time per day.

^bDefined as a “yes” response to the question, “Has a doctor or other health professional ever told you that you have diabetes?” (NHANES item DIQ010). Participants reporting gestational diabetes were excluded. Responses were coded as 1 for diabetes and 0 for no diabetes. Although NHANES does not differentiate by diabetes type, most diagnosed cases in the US are type 2 diabetes.

^cP values calculated using χ² tests.

^dIncludes participants identifying as American Indian/Alaska Native, multiracial, or other races not separately listed.

^eIncome-to-poverty ratio. Low: <1.30 (<130% of the Federal Poverty Level); Medium: 1.30-3.99 (130-399%); High: ≥4.00 (≥400%).

^fBMI <18.5 kg/m², underweight; BMI 18.5 to <25.0 kg/m², normal; BMI 25.0 to <30.0 kg/m², overweight; BMI ≥30.0 kg/m², obese.

^gBased on average daily Monitor-Independent Movement Summary (MIMS) units per minute of ≥4 days of wake-wear time: Low (≤12.0), Moderate (12.01-17.36), High (≥17.37).

Health-related characteristics also differed significantly by diabetes status. Compared with participants without diabetes, those with diabetes were more likely to have obesity (58.2% vs 34.2%) and to fall into the lowest NEAT category (37.0% vs 15.2%) (P < .001 for both comparisons) (Table 1).

Multivariable logistic regression models indicated a strong association between NEAT levels and diabetes status. Compared with participants in the high NEAT group, those in the low NEAT group had significantly higher odds of diabetes (odds ratio [OR], 2.88; 95% CI, 2.03-4.08). Participants in the moderate NEAT group also had higher odds of diabetes (OR, 1.61; 95% CI, 1.18-2.20). A significant linear trend across NEAT categories was observed (P = .005) (Table 2).

Table 2.

Adjusted Logistic Regression of NEAT Level Versus Diabetes^a, NHANES, 2013-2014, n = 4436.

Characteristic^b	Odds ratio (95% CI)	P-value^c
Neat, %		.005
Low	2.88 (2.03, 4.08)	<.001
Moderate	1.61 (1.18, 2.20)	.003
High	(Ref)
Age group, yrs		<.001
18-29	0.05 (0.02, 0.11)	<.001
30-39	0.19 (0.12, 0.30)	<.001
40-49	0.55 (0.40, 0.77)	<.001
50-59	(Ref)
60-69	1.53 (1.15, 2.02)	.003
70+	1.26 (0.94, 1.70)	.13
Sex
Male	1.03 (0.84, 1.30)	.80
Female	(Ref)
Education		.01
<High school	2.70 (1.91, 3.80)	<.001
High school grad/GED	1.82 (1.30, 2.55)	<.001
Some college	1.70 (1.23, 2.32)	.001
College grad or above	(Ref)
SES^d		.50
Low	1.21 (0.89, 1.64)	.24
Medium	1.15 (0.87, 1.53)	.33
High	(Ref)
BMI (kg/m²)^e		.03
Underweight	0.41 (0.09, 1.74)	.003
Normal	(Ref)
Overweight	1.60 (1.18, 2.19)	<.001
Obese	3.15 (2.40, 4.20)	<.001

Abbreviations: NEAT, non-exercise activity thermogenesis; NHANES, National Health and Nutrition Examination Survey; SES, socioeconomic status; BMI, body mass index.

^aDiabetes = 1 (n = 566), no diabetes = 0 (n = 3870).

^bReference categories: NEAT Level: High; Age group: 50-59; Sex: female; Education: College Grad or more; SES:

High; BMI: Normal (18.5 to <25.0 kg/m²).

^cP-values obtained using logistic regression models. Values in bold represent P for trend.

^dIncome-to-poverty ratio. Low: <1.30 (<130% of the Federal Poverty Level); Medium: 1.30-3.99 (130-399%); High: ≥4.00 (≥400%).

^eBMI <18.5 kg/m², underweight; 18.5 to <25.0 kg/m², normal; 25.0 to <30.0 kg/m², overweight; ≥30.0 kg/m², obese.

Other covariates also demonstrated significant associations. Older age was associated with higher diabetes odds, particularly among adults aged 60 to 69 years (OR, 1.53; 95% CI, 1.15-2.02; P = .003). Lower educational attainment was associated with elevated diabetes risk, with the strongest effect observed among adults with less than a high school education (OR, 2.70; 95% CI, 1.91-3.80; P < .001). Obesity was the strongest individual predictor, with adults classified as obese demonstrating more than three times the odds of diabetes compared with those with normal BMI (OR, 3.15; 95% CI, 2.40-4.20; P < .001). The lowest odds of diabetes occurred among adults aged 18 to 29 years (OR, 0.05; 95% CI, 0.02-0.11; P < .001) compared with adults aged 50 to 59 years (Table 2). These demographic and clinical patterns align with established determinants of diabetes risk.^3,13

Sensitivity analyses adjusting for comorbidity burden, including heart disease, stroke, cancer, emphysema, liver disease, and kidney disease, did not meaningfully alter effect sizes or statistical significance.

Stratified logistic regression analyses by race and ethnicity revealed differences in the magnitude of NEAT–diabetes associations (Table 3; Figure 2).

Table 3.

Adjusted Odds Ratios of Diagnosed Diabetes^a by NEAT Level Stratified by Race/Ethnicity, NHANES, 2013-2014, n = 4436.

Race/Ethnicity	NEAT^b Category	Odds Ratio^c (95% CI)	p-value^d
Hispanic only			.004
	Low	3.50 (1.63, 7.50)	.001
	Moderate	2.12 (1.21-3.70)	.008
	High	Ref
Non-Hispanic White			<.001
	Low	3.13 (1.76, 5.60)	<.001
	Moderate	2.00 (1.17, 3.40)	.01
	High	Ref
Non-Hispanic Black			.005
	Low	2.14 (0.99, 4.62)	.05
	Moderate	0.99 (0.48, 2.10)	.99
	High	Ref
Non-Hispanic Asian			.04
	Low	3.35 (1.07, 10.52)	.04
	Moderate	1.13 (0.43, 2.96)	.81
	High	Ref
Other/Multiracial^e			.07
	Low	3.63 (0.30, 44.55)	.31
	Moderate	0.40 (0.04, 3.80)	.43
	High	Ref

Abbreviations: NEAT, non-exercise activity thermogenesis; NHANES, National Health and Nutrition Examination Survey.

^aDiabetes = 1, no diabetes = 0.

^bBased on average daily Monitor-Independent Movement Summary (MIMS) units per minute of ≥4 days of wake-wear time: Low (≤12.0), Moderate (12.01-17.36), High (≥17.37).

^cOdds ratios were obtained from logistic regression models adjusted for: Age (1-6); Gender (1-2); Education (1-4); SES (1-3); BMI (1-4). The reference category for all is high NEAT.

^dValues in bold represent P for trend.

^eIncludes participants identifying as American Indian/Alaska Native, multiracial, or other races not separately listed.

Figure 2.

Adjusted odds ratios for type 2 diabetes by NEAT level and race/ethnicity among U.S. adults in NHANES 2013-2014. Odds ratios compare low and moderate NEAT levels with the reference group (high NEAT), adjusted for age, sex, education, socioeconomic status, and BMI. Error bars represent 95% confidence intervals.

Among Hispanic adults, those in the low NEAT group had 3.5 times the odds of diabetes compared with those in the high NEAT group (95% CI, 1.63-7.50; P = .001). A similar pattern was observed among non-Hispanic White adults, with significantly higher odds in both the low (OR, 3.13; 95% CI, 1.76-5.60; P < .001) and moderate NEAT groups (OR, 2.00; 95% CI, 1.17-3.40; P = .01). Among non-Hispanic Asian adults, low NEAT was associated with higher odds of diabetes (OR, 3.35; 95% CI, 1.07-10.52; P = .04), although the moderate NEAT category was not statistically significant (OR, 1.13; 95% CI, 0.43-2.96; P = .81). For non-Hispanic Black adults, the association for low NEAT approached significance (OR, 2.14; 95% CI, 0.99-4.62; P = .05), while the moderate NEAT category was not significant. These findings reflect known racial and ethnic patterns in diabetes risk and physical activity behaviors.^6,13

Discussion

Summary of Findings

In this nationally representative sample of U.S. adults, lower levels of non-exercise activity thermogenesis (NEAT) were strongly associated with higher odds of diagnosed diabetes, independent of demographic, socioeconomic, and lifestyle factors. Adults in the low NEAT category had nearly three times the odds of diabetes compared with those in the high NEAT group, and those with moderate NEAT also demonstrated elevated risk. Stratified analyses showed that these associations differed across racial and ethnic groups, with significant associations among Hispanic, non-Hispanic White, and non-Hispanic Asian adults, and marginal associations among non-Hispanic Black adults. These variations are consistent with prior evidence of racial and ethnic differences in diabetes burden and physical activity patterns.^6,13

Comparison With Prior Research

These findings align with existing evidence that light-intensity, routine movement contributes to glycemic regulation, insulin sensitivity, and overall cardiometabolic health.^4,7 Prior studies demonstrate that interrupting sitting with brief walking or standing improves post-meal glucose and insulin responses, supporting the idea that small, frequent movements complement structured physical activity.⁹ The present study extends this literature by using MIMS-derived accelerometry, which captures a broad spectrum of daily movement typical of NEAT.^16,17 The racial and ethnic differences observed align with known disparities in diabetes risk and environmental, occupational, and cultural factors that shape movement patterns.^6,13

Implications for Lifestyle Medicine

This study highlights NEAT as an accessible, clinically relevant target within the physical activity pillar of lifestyle medicine.^5,10 NEAT may be especially valuable for individuals facing barriers to structured exercise, including limited time, transportation constraints, physical limitations, or lack of access to safe environments.⁴ Lifestyle medicine practitioners can promote NEAT by helping patients identify opportunities for movement within daily routines, setting achievable goals, encouraging brief post-meal walks, and using strategies like building the new habit onto an existing routine and setting up their environment to make it easier to follow through. Using feedback from wearable devices can highlight progress and help patients stay accountable.⁶

Health Equity and Public Health Implications

NEAT-based approaches may support health equity because they do not require equipment, designated facilities, or substantial time investment. This simplicity makes NEAT particularly feasible for individuals in lower-income communities or those with caregiving responsibilities. The strong associations observed among Hispanic and non-Hispanic Asian adults suggest that increasing NEAT may have particular relevance for groups experiencing elevated diabetes risk and distinct cultural or occupational movement patterns.^3,6,13 Public health strategies that incorporate NEAT into community health worker programs, culturally tailored diabetes prevention efforts, and employer wellness initiatives may help reduce structural barriers to movement and broaden the reach of lifestyle interventions.

Strengths and Limitations

Key strengths of this study include the use of objective, raw accelerometry data; a large, diverse, nationally representative sample; and stratified analyses by race and ethnicity. Limitations include the cross-sectional design and the reliance on self-reported diabetes diagnosis. Further, our accelerometry-based measure captures total movement during wake-wear time and does not distinguish between structured and unstructured activity. However, because structured exercise represents a small fraction of total daily movement for most adults,^7-9 our MIMS-derived measure primarily reflects the low-intensity, unstructured behaviors that characterize NEAT. Accelerometry also cannot identify specific behaviors contributing to NEAT, limiting interpretation of which activities offer the greatest metabolic benefit.^11,12

Future Directions

Future research should examine NEAT and diabetes outcomes prospectively to clarify temporal relationships. Studies should also evaluate which NEAT behaviors or contexts, such as occupational tasks or household activities, offer the greatest metabolic benefits.

It is important to note that the accelerometry data used in this study captures total daily movement and does not allow us to separate structured exercise from non-exercise activity. As a result, our NEAT measure reflects overall daily movement patterns, which are predominantly unstructured, light-intensity activity, but may include some structured exercise. Future research using activity classification methods could more distinctly separate structured exercise from NEAT to examine their independent metabolic effects.

Randomized and culturally tailored NEAT interventions may help identify effective strategies for diverse populations. Digital health tools, including wearable monitors and mobile prompts, represent promising methods for supporting NEAT engagement and should be further explored in primary care, lifestyle medicine settings, and public health programs.⁶

Conclusion

Lower levels of non-exercise activity thermogenesis were strongly associated with higher odds of diabetes among U.S. adults, with notable variation across racial and ethnic groups. These findings support evidence that routine, low-intensity movement contributes meaningfully to metabolic health^4,7 and highlight NEAT as an accessible, culturally adaptable behavior aligned with the physical activity pillar of lifestyle medicine.⁵ Integrating NEAT into routine clinical practice may enhance diabetes prevention, support equitable movement strategies, and help address persistent disparities in metabolic health.^6,13 Public health and clinical efforts that prioritize NEAT may strengthen the reach and effectiveness of lifestyle interventions across diverse populations.

Footnotes

Acknowledgments

The authors would like to thank the National Center for Health Statistics (NCHS) for providing publicly available NHANES data used in this study. No copyrighted materials, proprietary instruments, or third-party tools were used.

ORCID iDs

Krystal D Eskildsen

Ernesto Medina

W Lawrence Beeson

Ethical Considerations

This study used publicly available, de-identified NHANES data. Ethical approval was waived by the Loma Linda University Institutional Review Board, which determined the study to be exempt (IRB# 5250095).

Author Contributions

Krystal D Eskildsen: Conceptualization; methodology; formal analysis; data curation; writing (original draft, review, and editing); visualization. Hildemar Dos Santos: Methodology; writing (review and editing); supervision. Ernesto Medina: Methodology; writing (review and editing); supervision. Dixon Anjejo: Writing (review and editing); validation. W Lawrence Beeson: Conceptualization; methodology; writing (review and editing); supervision; project administration.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets analyzed during the current study are publicly available in the National Health and Nutrition Examination Survey (NHANES) repository:

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