Relationship Between Objectively Measured Physical Activity, Cardiovascular Disease Biomarkers, and Hearing Sensitivity Using Data From the National Health and Nutrition Examination Survey 2003–2006 Purpose Limited research has examined the interrelationships among cardiometabolic parameters, physical activity, and hearing function, which was this study's purpose. Method Data from the National Health and Nutrition Examination Survey (NHANES) 2003–2006 were used in the path analyses. Physical activity and hearing function were both objectively measured. Various ... Research Article
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Research Article  |   June 13, 2017
Relationship Between Objectively Measured Physical Activity, Cardiovascular Disease Biomarkers, and Hearing Sensitivity Using Data From the National Health and Nutrition Examination Survey 2003–2006
 
Author Affiliations & Notes
  • Paul D. Loprinzi
    Department of Health, Exercise Science, and Recreation Management, The University of Mississippi, Oxford
  • Chelsea Joyner
    Department of Health, Exercise Science, and Recreation Management, The University of Mississippi, Oxford
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Paul D. Loprinzi: pdloprin@olemiss.edu
  • Editor: Sumitrajit Dhar
    Editor: Sumitrajit Dhar×
  • Associate Editor: Owen Murnane
    Associate Editor: Owen Murnane×
Article Information
Hearing & Speech Perception / Research Issues, Methods & Evidence-Based Practice / Research Articles
Research Article   |   June 13, 2017
Relationship Between Objectively Measured Physical Activity, Cardiovascular Disease Biomarkers, and Hearing Sensitivity Using Data From the National Health and Nutrition Examination Survey 2003–2006
American Journal of Audiology, June 2017, Vol. 26, 163-169. doi:10.1044/2017_AJA-16-0057
History: Received June 10, 2016 , Revised November 8, 2016 , Accepted January 28, 2017
 
American Journal of Audiology, June 2017, Vol. 26, 163-169. doi:10.1044/2017_AJA-16-0057
History: Received June 10, 2016; Revised November 8, 2016; Accepted January 28, 2017

Purpose Limited research has examined the interrelationships among cardiometabolic parameters, physical activity, and hearing function, which was this study's purpose.

Method Data from the National Health and Nutrition Examination Survey (NHANES) 2003–2006 were used in the path analyses. Physical activity and hearing function were both objectively measured. Various cardiometabolic parameters were assessed from a blood sample. Adults 30–85 years (N = 1,070) constituted the analytic sample.

Results Physical activity was negatively associated with triglycerides (β = −0.11, p < .05) and insulin (β = −0.27, p < .05); triglycerides (β = 0.01, p < .05), and insulin (β = 0.05, p < .05) were positively associated with high-frequency pure-tone average (HPTA). The direct path from physical activity to HPTA was nonsignificant (β = 0.01, p = .99).

Conclusion Physical activity was associated with select cardiovascular disease risk factors. Several cardiovascular disease risk factors were associated with hearing function.

In addition to the commonly reported causes of hearing loss (i.e., repeated exposure to hazardous levels of noise, viral-based diseases, ototoxic medications, neurochemical changes, and anoxia; Johnsson & Hawkins, 1972), emerging evidence indicates that various metabolic risk factors are associated with hearing dysfunction. Studies specifically show that adiposity (Hwang, Wu, Hsu, Liu, & Yang, 2009), immune function (Azuma et al., 2010; Suzuki & Kitahara, 1992), insulin (Bamanie & Al-Noury, 2011; Gopinath et al., 2010), triglycerides (Evans et al., 2006), and high-density lipoprotein (HDL) cholesterol (Suzuki, Kaneko, & Murai, 2000) may influence hearing function.
Physical activity may play a role in preserving hearing function because emerging empirical evidence demonstrates that regular participation in physical activity is negatively associated with adiposity (Ekelund et al., 2011), markers of immune function such as C-reactive protein (CRP; Plaisance & Grandjean, 2006), insulin levels (Camhi, Sisson, Johnson, Katzmarzyk, & Tudor-Locke, 2011; Colberg et al., 2010), triglycerides (Camhi et al., 2011), and positively associated with HDL cholesterol (Camhi et al., 2011). It is unfortunate that few studies have examined the association between physical activity and hearing function and thus there is little evidence to conclude whether physical activity is indeed associated with hearing function. Shedding some light on this area, Loprinzi et al. (2011)  recently examined the direct influence of physical activity on hearing function and found no relationship. This is in contrast to more recent findings by Haas, Bishop, Gao, Griswold, and Schweinfurth (2016), who demonstrated a small direct association between physical activity and hearing function among African Americans. Further, given that physical activity is associated with numerous health outcomes that, in turn, are associated with hearing function, it is possible that the relationship between physical activity and hearing function is not only direct, but also indirectly influenced through mediating variables such as HDL cholesterol.
We wanted to investigate the indirect influence of physical activity on hearing function, so the purpose of the present study was to examine whether various health outcomes associated with both physical activity and hearing function mediates this relationship among adults using a nationally representative sample.
Method
Design and Participants
Data from the National Health and Nutrition Examination Survey (NHANES) 2003–2006 were used in the analyses. NHANES uses a representative sample of noninstitutionalized U.S. civilians, selected by a complex, multistage probability design. In brief, participants were interviewed in their homes and subsequently examined in mobile examination centers (MEC) across 15 U.S. geographic locations. The study was approved by the National Center for Health Statistics Ethics Review Board, with informed consent obtained from all participants prior to data collection.
In the sample, 2,964 participants (1,236 men, 1,383 women) provided complete audiometry data. Among these, 1,880 (933 men, 947 women) provided complete accelerometry data and, among these, 1,070 (533 men, 537 women) were 30+ years of age. The final sample of the present study included 1,070 NHANES participants 30 years and older after the exclusion of participants who had insufficient accelerometry data; missing audiometry data from nontested participants; audiometry data with nonresponse; audiometry data that was not obtainable; individuals who had a cold, sinus, or earache in the 24 hours prior to audiometry testing; exposed to loud noise or listened to music with headphones in the 24 hours prior to audiometry testing; and those with impacted cerumen. Adults 30 years and older were chosen because this is the age when hearing function may start to progressively decline (Gordon-Salant, 2005; Morrell, Gordon-Salant, Pearson, Brant, & Fozard, 1996). It is notable that results were not appreciably altered (i.e., similar results were obtained) when including adults aged under 30 years. Of the 1,070 participants, 490 (253 men, 237 women) attended a morning session in the MEC where fasting triglycerides and insulin levels were obtained. The remaining 580 (280 men, 300 women) participants did not attend the morning fasting session and had their blood taken for assessment of HDL cholesterol and CRP. All participants had their height and weight measured in the MEC for assessment of body mass index (BMI).
Measurement of Physical Activity
At the MEC, participants who were not prevented by impairments of walking or wearing an accelerometer wore an ActiGraph 7164 accelerometer (ActiGraph, Pensacola, FL). Participants were asked to wear the accelerometer on the right hip for 7 days following their examination.
The accelerometer output is digitized using an analog-to-digital converter. Once digitized, the signal passes through a digital filter that detects accelerations ranging from 0.05 to 2.00 g in magnitude with frequency responses ranging from 0.25 Hz to 2.5 Hz to filter motion outside normal human movement. The filtered signal is then rectified and summed over a predetermined epoch period. After the activity count is sorted into an epoch, it is stored in the internal memory and the integrator is reset to zero. Detailed information on the ActiGraph accelerometer can be found elsewhere (Chen & Bassett, 2005). The Freedson MVPA (moderate to vigorous physical activity) cut points were applied to define MVPA (Freedson, Melanson, & Sirard, 1998). Accelerometry data were reduced to mean duration (min) of MVPA bouts accumulated over 1-min intervals. For the analyses described here and as is recommended, only those participants with at least 4 days of 10 or more hours per day of monitoring data were included in the analyses (Troiano et al., 2008).
Measurement of Hearing Ability
In the MEC, audiometry was conducted in a dedicated, sound-isolating room by a trained examiner on participants age 20–69 years in the NHANES 2003–2004 cycle, and participants age 12–19 years and 70–85 years in the NHANES 2005–2006 cycle using a modified Hughson Westlake procedure (Shargorodsky, Curhan, Curhan, & Eavey, 2010), a standardized method of measuring pure-tone detection thresholds, as described elsewhere (Walker, Cleveland, Davis, & Seales, 2013). Prior to and after audiometry testing, the audiometer was calibrated according to manufacturer specifications. Hearing threshold testing was conducted on participants at seven frequencies (500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz) across an intensity range of −10 to 120 dB.
Consistent with previous hearing studies of NHANES data, low-frequency pure-tone average (LPTA) was obtained in the worse ear by calculating the average of air conduction pure-tone thresholds at 500, 1000, and 2000 Hz and high-frequency pure-tone average (HPTA) was obtained by the average of air conduction pure-tone thresholds at 3000, 4000, 6000, and 8000 Hz, with higher LPTA and HPTA dB values indicating increasing degrees of impairment (Agrawal, Platz, & Niparko, 2008; Loprinzi, 2013, 2015, 2016; Loprinzi, Cardinal, & Gilham, 2012; Loprinzi, Gilham, & Cardinal, 2014; Niskar et al., 1998, 2001; Shargorodsky et al., 2010). Results were computed separately for LPTA and HPTA because previous research demonstrates that health behaviors may differentially influence hearing at different frequencies (Mizoue, Miyamoto, & Shimizu, 2003).
Other Measurements
Information about age, sex, race-ethnicity, education, marital status, and smoking status were obtained from a questionnaire during a household interview. Trained household interviewers administered the questionnaire with interview data recorded using a Blaise format computer-assisted personal interview system. During examination at the MEC, BMI was calculated from measured weight and height (weight in kilograms divided by the square of height in meters). Prior to audiometry testing in the MEC, information pertaining to whether the participant had a cold, sinus or other infection, listened to loud music within 24 hours preceding audiometry testing, or had an impacted cerumen was obtained from a pre-exam audiometric questionnaire.
Data Analysis
Data from NHANES 2003–2004 and 2005–2006 were combined, and, to account for oversampling and nonresponse, all analyses included the use of appropriate sample weights. Descriptive data were analyzed using Stata (Version 10.0; Stata Corp., College Station, TX). Based on the covariance matrix and using maximum likelihood estimation as implemented in Mplus (Version 5.0; Muthén & Muthén, Los Angeles, CA), various linear regression models were tested to examine the direct (i.e., physical activity → hearing function) and indirect (i.e., physical activity → metabolic risk factor → hearing function) influence of physical activity on hearing function. In all models, LPTA or HPTA served as the terminal variable. In the direct and indirect models, age, sex, race-ethnicity, and smoking status were controlled for, as these variables are associated with both physical activity and hearing function (Kiely, Gopinath, Mitchell, Luszcz, & Anstey, 2012). Other covariates (e.g., hypertension and diabetes) were considered, but their inclusion did not alter the observed associations. Indirect models (e.g., physical activity → triglycerides → LPTA) were tested separately for each metabolic risk factor. Model fit was based on generally accepted thresholds for the chi-square index, comparative fit index (CFI), root-mean-square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR). These fit indices evaluate the extent to which the observed data fit the hypothetical models. In brief, CFI indicates the fit of the target model to the fit of the hypothesized model by comparing the observed model to a null model; values approximating 1.0 indicate acceptable fit. The RMSEA and SRMR evaluate discrepancies between observed and predicted covariances, with values approximating zero indicating acceptable fit.
Results
Table 1 shows the study variable characteristics of the NHANES analytical sample. The mean age was 52 years, there was similar distribution for both sexes, the mean BMI was 28 kg/m2, and the majority of participants were White (81.5%). Participants, on average, engaged in 23 min/day of MVPA.
Table 1. Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.
Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.×
Variable Point estimate (SE)
N 1,070
Age, mean year 52.2 (0.5)
% male 45.1
BMI, mean kg/m2 27.9 (0.2)
Ethnicity
 % Mexican American 6.7
 % Other Hispanic 3.5
 % Non-Hispanic White 81.5
 % Non-Hispanic Black 8.2
Education
 % < High school 12.6
 % High school 21.7
 % > High school 65.6
Smoking status
 % Never smoked 49.5
 % Former smoker 29.2
 % Currently smoke 21.2
Marital status
 % Married 83.7
 % Separated 2.2
 % Never married 7.3
 % Living with partner 6.8
Total cholesterol, mean mg/dL 205.1 (1.7)
Triglycerides, mean mg/dL 145.0 (9.2)
Insulin, mean pmol/L 65.6 (3.9)
CRP, mean mg/dL 0.34 (0.01)
Low-frequency pure-tone average, mean 17.4 (0.5)
High-frequency pure-tone average, mean 32.4 (1.0)
MVPA, mean min/day 22.9 (1.0)
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.×
Table 1. Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.
Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.×
Variable Point estimate (SE)
N 1,070
Age, mean year 52.2 (0.5)
% male 45.1
BMI, mean kg/m2 27.9 (0.2)
Ethnicity
 % Mexican American 6.7
 % Other Hispanic 3.5
 % Non-Hispanic White 81.5
 % Non-Hispanic Black 8.2
Education
 % < High school 12.6
 % High school 21.7
 % > High school 65.6
Smoking status
 % Never smoked 49.5
 % Former smoker 29.2
 % Currently smoke 21.2
Marital status
 % Married 83.7
 % Separated 2.2
 % Never married 7.3
 % Living with partner 6.8
Total cholesterol, mean mg/dL 205.1 (1.7)
Triglycerides, mean mg/dL 145.0 (9.2)
Insulin, mean pmol/L 65.6 (3.9)
CRP, mean mg/dL 0.34 (0.01)
Low-frequency pure-tone average, mean 17.4 (0.5)
High-frequency pure-tone average, mean 32.4 (1.0)
MVPA, mean min/day 22.9 (1.0)
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.×
×
The model with LPTA serving as the terminal variable is presented in Figure 1. The model with HPTA as the terminal variable is shown in Figure 2. For ease of presentation, all direct and indirect models with LPTA as the terminal variable are presented in Figure 1. In a similar manner, all direct and indirect models for HPTA are shown together in Figure 2. All models demonstrated an acceptable fit to the data (Table 2).
Figure 1.

Standardized parameter estimates for a proposed theoretical model for low-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

 Standardized parameter estimates for a proposed theoretical model for low-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.
Figure 1.

Standardized parameter estimates for a proposed theoretical model for low-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

×
Figure 2.

Standardized parameter estimates for proposed theoretical model for high-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

 Standardized parameter estimates for proposed theoretical model for high-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.
Figure 2.

Standardized parameter estimates for proposed theoretical model for high-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

×
Table 2. Fit indices for each indirect and direct model.
Fit indices for each indirect and direct model.×
Model χ2 CFI RMSEA SRMR
Low-frequency pure tone
 Direct
  Physical activity → hearing 298.65 1.00 < .01 < .01
 Indirect
  HDL cholesterol 460.82 .99 .03 < .01
  Triglycerides 188.40 .97 .09 .01
  Insulin 197.71 .96 .13 < .01
  CRP 341.69 .99 .05 < .01
  BMI 322.06 .99 .03 < .01
High-frequency pure tone
 Direct
  Physical activity → hearing 658.21 1.00 < .01 < .01
 Indirect
  HDL cholesterol 810.27 1.00 < .01 < .01
  Triglycerides 303.37 .99 .08 < .01
  Insulin 314.06 .99 .04 < .01
  CRP 691.98 1.00 < .01 < .01
  BMI 675.02 1.00 < .01 < .01
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.×
Table 2. Fit indices for each indirect and direct model.
Fit indices for each indirect and direct model.×
Model χ2 CFI RMSEA SRMR
Low-frequency pure tone
 Direct
  Physical activity → hearing 298.65 1.00 < .01 < .01
 Indirect
  HDL cholesterol 460.82 .99 .03 < .01
  Triglycerides 188.40 .97 .09 .01
  Insulin 197.71 .96 .13 < .01
  CRP 341.69 .99 .05 < .01
  BMI 322.06 .99 .03 < .01
High-frequency pure tone
 Direct
  Physical activity → hearing 658.21 1.00 < .01 < .01
 Indirect
  HDL cholesterol 810.27 1.00 < .01 < .01
  Triglycerides 303.37 .99 .08 < .01
  Insulin 314.06 .99 .04 < .01
  CRP 691.98 1.00 < .01 < .01
  BMI 675.02 1.00 < .01 < .01
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.×
×
For the LPTA model, physical activity was negatively associated with triglycerides (β = −.11, p = .03) and insulin (β = −.27, p = .02). None of these health outcomes were significantly associated with LPTA. The direct path from physical activity to LPTA was not significant (β = −.03, p = 0.06). It is notable that HDL cholesterol was inversely associated with LPTA, whereas BMI was positively associated with LTPA (ps < .05).
For the HPTA, triglycerides (β = .01, p = .04) and insulin (β = .05, p = .04) were positively associated with HPTA. It is notable that HDL-cholesterol (HDL-C) was inversely associated with LPTA. The direct path from physical activity to HPTA was nonsignificant (β = .01, p = .99).
Discussion
Some research indicates that regular participation in physical activity does not directly influence hearing function. This result is not surprising given that regular participation in physical activity is indirectly associated with chronic diseases (e.g., cardiovascular disease) through favorable levels of various intermediate health outcomes (e.g., reductions in triglycerides). Similar to this cardiovascular disease example, the present findings suggest that regular engagement in physical activity is associated with favorable levels in intermediate outcomes, such as triglycerides and insulin levels; that is, in the present study, physical activity was associated with triglycerides and insulin; these parameters were associated with hearing. We did not observe a direct association between physical activity and hearing.
Our findings for triglycerides and insulin are in support of previous work demonstrating that elevated triglycerides are associated with worse hearing function (Evans et al., 2006). In support of a relationship between insulin and hearing function, Bamanie and Al-Noury (2011)  showed that among 196 individuals (age range: 29–69 years) with (n = 109) and without (n = 87) type 2 diabetes mellitus, patients with type 2 diabetes mellitus were at a higher risk of hearing loss (particularly in the low- and mid-hearing frequencies), specifically with hyperglycemic control by insulin demonstrating to be a risk factor for hearing loss. The pathology underlying the association between hyperinsulinemia and hearing loss is not clear (Bamanie & Al-Noury, 2011), but a plausible explanation is that elevated insulin levels may result in microvascular disease of the cochlea (Kraft, 1998). It is notable that previous work demonstrates that physical activity is favorably associated with both triglyceride and insulin levels (Loprinzi, Lee, & Cardinal, 2013, 2015).
Although this study did not observe a significant association between physical activity on HDL-C and BMI, previous work has demonstrated this. In the ATTICA study, Skoumas et al (2003)  showed that, among 578 men and 584 women who were classified as physically active, they had higher HDL-C when compared to sedentary individuals. Likewise, in a study by Crichton and Alkerwi (2015), after adjusting for socio-demographic, diet, and smoking factors, physical activity time was associated with higher HDL-C. Further, research demonstrates that replacing time spent in sedentary behavior with MVPA has favorable associations with BMI (Chastin, Palarea-Albaladejo, Dontje, & Skelton, 2015). The significant association between physical activity between HDL-C and BMI, coupled with our observation that HDL-C and BMI were associated with hearing function, suggests that HDL-C and BMI may also play a role in the relationship between physical activity and hearing.
A major strength of this study was the use of objectively measured physical activity, metabolic risk factors, and hearing sensitivity data in a nationally representative sample. A limitation of this study includes the cross-sectional nature of the study design. As a consequence, we were not able to examine changes in physical activity and intermediate health outcomes to determine whether any causal relationships among physical activity, metabolic risk factors, and hearing function exist. Another limitation is the potential of residual confounding. Other studies have demonstrated that certain diets, such as intakes of fats (saturated and monounsaturated fats and cholesterol), and certain food groups, such as butter and margarine, are associated with prevalence of poor hearing function (Gopinath, Flood, Teber, McMahon, & Mitchell, 2011). On the contrary, Spankovich and Le Prell (2014)  suggested that current findings support the association between a healthier diet and better hearing at higher frequencies. In the future, any work regarding the effects of physical activity on hearing should consider the role of dietary behavior.
Acknowledgment
No funding was used to conduct this study and no conflicts of interest are declared.
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Figure 1.

Standardized parameter estimates for a proposed theoretical model for low-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

 Standardized parameter estimates for a proposed theoretical model for low-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.
Figure 1.

Standardized parameter estimates for a proposed theoretical model for low-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

×
Figure 2.

Standardized parameter estimates for proposed theoretical model for high-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

 Standardized parameter estimates for proposed theoretical model for high-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.
Figure 2.

Standardized parameter estimates for proposed theoretical model for high-frequency pure-tone average. HDL = high-density lipoprotein; MVPA = moderate to vigorous physical activity; CRP = C-reactive protein; BMI = body mass index. *p < .05.

×
Table 1. Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.
Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.×
Variable Point estimate (SE)
N 1,070
Age, mean year 52.2 (0.5)
% male 45.1
BMI, mean kg/m2 27.9 (0.2)
Ethnicity
 % Mexican American 6.7
 % Other Hispanic 3.5
 % Non-Hispanic White 81.5
 % Non-Hispanic Black 8.2
Education
 % < High school 12.6
 % High school 21.7
 % > High school 65.6
Smoking status
 % Never smoked 49.5
 % Former smoker 29.2
 % Currently smoke 21.2
Marital status
 % Married 83.7
 % Separated 2.2
 % Never married 7.3
 % Living with partner 6.8
Total cholesterol, mean mg/dL 205.1 (1.7)
Triglycerides, mean mg/dL 145.0 (9.2)
Insulin, mean pmol/L 65.6 (3.9)
CRP, mean mg/dL 0.34 (0.01)
Low-frequency pure-tone average, mean 17.4 (0.5)
High-frequency pure-tone average, mean 32.4 (1.0)
MVPA, mean min/day 22.9 (1.0)
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.×
Table 1. Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.
Weighted means and percentages (standard error) for selected characteristics of the analytical sample, National Health and Nutrition Survey, 2003–2006.×
Variable Point estimate (SE)
N 1,070
Age, mean year 52.2 (0.5)
% male 45.1
BMI, mean kg/m2 27.9 (0.2)
Ethnicity
 % Mexican American 6.7
 % Other Hispanic 3.5
 % Non-Hispanic White 81.5
 % Non-Hispanic Black 8.2
Education
 % < High school 12.6
 % High school 21.7
 % > High school 65.6
Smoking status
 % Never smoked 49.5
 % Former smoker 29.2
 % Currently smoke 21.2
Marital status
 % Married 83.7
 % Separated 2.2
 % Never married 7.3
 % Living with partner 6.8
Total cholesterol, mean mg/dL 205.1 (1.7)
Triglycerides, mean mg/dL 145.0 (9.2)
Insulin, mean pmol/L 65.6 (3.9)
CRP, mean mg/dL 0.34 (0.01)
Low-frequency pure-tone average, mean 17.4 (0.5)
High-frequency pure-tone average, mean 32.4 (1.0)
MVPA, mean min/day 22.9 (1.0)
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.
Note. BMI = body mass index; CRP = C-reactive protein; MVPA = moderate to vigorous physical activity.×
×
Table 2. Fit indices for each indirect and direct model.
Fit indices for each indirect and direct model.×
Model χ2 CFI RMSEA SRMR
Low-frequency pure tone
 Direct
  Physical activity → hearing 298.65 1.00 < .01 < .01
 Indirect
  HDL cholesterol 460.82 .99 .03 < .01
  Triglycerides 188.40 .97 .09 .01
  Insulin 197.71 .96 .13 < .01
  CRP 341.69 .99 .05 < .01
  BMI 322.06 .99 .03 < .01
High-frequency pure tone
 Direct
  Physical activity → hearing 658.21 1.00 < .01 < .01
 Indirect
  HDL cholesterol 810.27 1.00 < .01 < .01
  Triglycerides 303.37 .99 .08 < .01
  Insulin 314.06 .99 .04 < .01
  CRP 691.98 1.00 < .01 < .01
  BMI 675.02 1.00 < .01 < .01
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.×
Table 2. Fit indices for each indirect and direct model.
Fit indices for each indirect and direct model.×
Model χ2 CFI RMSEA SRMR
Low-frequency pure tone
 Direct
  Physical activity → hearing 298.65 1.00 < .01 < .01
 Indirect
  HDL cholesterol 460.82 .99 .03 < .01
  Triglycerides 188.40 .97 .09 .01
  Insulin 197.71 .96 .13 < .01
  CRP 341.69 .99 .05 < .01
  BMI 322.06 .99 .03 < .01
High-frequency pure tone
 Direct
  Physical activity → hearing 658.21 1.00 < .01 < .01
 Indirect
  HDL cholesterol 810.27 1.00 < .01 < .01
  Triglycerides 303.37 .99 .08 < .01
  Insulin 314.06 .99 .04 < .01
  CRP 691.98 1.00 < .01 < .01
  BMI 675.02 1.00 < .01 < .01
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.
Note. CFI = comparative fit index; RMSEA = root-mean-square of approximation; SRMR = standardized root-mean-square residual; HDL = high-density lipoprotein; CRP = C-reactive protein; BMI = body mass index.×
×