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Research Quarterly for Exercise and Sport Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/urqe20
Association Between Accelerometer-Assessed Physical Activity and Objectively Measured Hearing Sensitivity Among U.S. Adults With Diabetes a
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Paul D. Loprinzi , Ben Gilham & Bradley J. Cardinal a
Bellarmine University
b
Hearing, Speech and Deafness Center
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Oregon State University Published online: 20 Aug 2014.
To cite this article: Paul D. Loprinzi, Ben Gilham & Bradley J. Cardinal (2014) Association Between Accelerometer-Assessed Physical Activity and Objectively Measured Hearing Sensitivity Among U.S. Adults With Diabetes, Research Quarterly for Exercise and Sport, 85:3, 390-397, DOI: 10.1080/02701367.2014.930404 To link to this article: http://dx.doi.org/10.1080/02701367.2014.930404
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Research Quarterly for Exercise and Sport, 85, 390–397, 2014 Copyright q SHAPE America ISSN 0270-1367 print/ISSN 2168-3824 online DOI: 10.1080/02701367.2014.930404
Association Between Accelerometer-Assessed Physical Activity and Objectively Measured Hearing Sensitivity Among U.S. Adults With Diabetes Paul D. Loprinzi Bellarmine University
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Ben Gilham Hearing, Speech and Deafness Center
Bradley J. Cardinal Oregon State University
Purpose: The purpose of this study was to examine the association between objectively measured physical activity and hearing sensitivity among a nationally representative sample of U.S. adults with diabetes. Method: Data from the 2003 –2006 National Health and Nutrition Examination Survey were used. One hundred eighty-four U.S. adults with diabetes wore an ActiGraph 7164 accelerometer and had their hearing function objectively assessed. A negative binomial logistic regression was used to examine the association between moderate-to-vigorous physical activity (MVPA) and hearing sensitivity. Results were adjusted for age, gender, race/ethnicity, education, body mass index, comorbidity index, marital status, cotinine, homocysteine, high-density lipoprotein cholesterol, glycohemoglobin (HbA1c), C-reactive protein, microalbuminuria, noise exposure, and vision impairment. Results: Compared to those with hearing within normal limits, results showed that participants with mild hearing loss and moderate or greater hearing loss, respectively, engaged in 93% fewer minutes of MVPA (incident rate ratio ¼ 0.07; 95% CI [0.01, 0.60]) and 94% fewer minutes of MVPA (incident rate ratio ¼ 0.06; 95% CI [0.01, 0.54]). Conclusion: Adults with diabetes who have greater hearing impairment are less physically active. Future research is needed to determine the direction of causality. Keywords: accelerometry, epidemiology, exercise, National Health and Nutrition Examination Survey (NHANES)
Diabetes has been implicated in the development of sensory impairment, particularly vision loss (Pugliese et al., 2012). Individuals with diabetes, compared with those without diabetes, are also more likely to have hearing loss (Bainbridge, Hoffman, & Cowie, 2008; Cullen & Cinnamond, 1993; Dalton, Cruickshanks, Klein, Klein, & Submitted February 20, 2013; accepted January 18, 2014. Paul D. Loprinzi is now at The University of Mississippi. Correspondence should be addressed to Paul D. Loprinzi, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, 215 Turner Center, PO Box 1848, University, MS 38677. E-mail: [email protected]
Wiley, 1998; Frisina, Mapes, Kim, Frisina, & Frisina, 2006; Lisowska, Namyslowski, Morawski, & Strojek, 2001). Although the definitive mechanisms explaining the association between diabetes and hearing loss have not been determined, suggested pathogenesis includes cochlear microangiopathy, elevated blood glucose in the cerebrospinal fluid, auditory neuropathy, and diabetic encephalopathy (Austin et al., 2009). We hypothesize that physical activity is positively associated with hearing sensitivity among those with diabetes. The primary reason for this is twofold. First, it is well established that regular engagement in physical activity
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PHYSICAL ACTIVITY, HEARING, AND DIABETES
can help lower blood glucose levels among those with diabetes (Sigal, Kenny, Wasserman, Castaneda-Sceppa, & White, 2006), which may attenuate the potential hyperglycemia-induced hearing loss among adults with diabetes. A meta-analysis by Boule, Haddad, Kenny, Wells, and Sigal (2001) showed that among adults with type 2 diabetes, glycohemoglobin (HbA1c) levels were significantly lower in those who engaged in an exercise intervention (7.65%), compared with those in a control group (8.31%). Notably, physical activity seems to have less of an effect on glycemic control among those with type 1 diabetes (Kennedy et al., 2013). Second, there is emerging evidence that enhanced cardiorespiratory fitness, a consequence of regular physical activity participation, is associated with enhanced hearing sensitivity in the broader population (Loprinzi, Cardinal, & Gilham, 2012). Given that physical activity increases blood flow to the brain (Querido & Sheel, 2007), it is possible that it may also increase blood flow to the cochlea, which is consistent with the pathogenic mechanisms proposed by Austin et al. (2009); however, future research on this topic is warranted. Additionally, emerging research shows that individuals engaging in greater amounts of physical activity are less likely to have tinnitus, or a ringing, roaring, or buzzing noise that is heard in the ears in the absence of external stimuli (Loprinzi, Lee, Gilham, & Cardinal, 2013). To our knowledge, there are no studies testing the hypothesis that physical activity is associated with hearing sensitivity among adults with diabetes. As a result, the purpose of this study was to examine the association between accelerometer-determined physical activity and hearing sensitivity in a nationally representative sample of adults with evidence of diabetes.
METHODS 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. Briefly, participants were interviewed in their homes and were subsequently examined in mobile examination centers (MECs) across numerous 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. The final sample of the present study included 184 NHANES participants after the exclusion of participants who did not have evidence of diabetes, who had insufficient accelerometry data, missing audiometry data, or a cold, sinus, or earache in the 24 hr prior to audiometry
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testing, those with impacted cerumen, and those with missing data on the covariates used in the analytic model.
Assessment of Diabetes Status Administered in the home, participants were asked several questions related to diabetes. First, they were asked whether they had ever been told by a doctor or health professional that they had or have diabetes or sugar diabetes; unfortunately, it was not possible to determine if they had type 1 or type 2 diabetes as this was not asked by the NHANES staff. Second, they were asked whether they were taking insulin. Third, they were asked whether they were taking diabetic pills to lower blood sugar. In the present study, participants who answered yes to any of these three questions were considered to have evidence of diabetes. A subsample of the NHANES participants was examined in a morning fasting session. Fasting glucose was measured from a blood sample, and participants with a fasting glucose level of 126 mg/dL or higher were considered to have diabetes (“Diagnosis and classification of diabetes mellitus,” 2010). Lastly, participants with an HbA1c level of 6.5% or greater were considered to have diabetes (“Executive summary,” 2012).
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 (Shalimar, FL). Participants were asked to wear the accelerometer on the right hip for 7 days following their examination. An accelerometer objectively measures the frequency, intensity, and duration of physical activity by generating an activity count that is proportional to the measured acceleration. Activity counts are summed over a predetermined epoch period, with 1-min epochs used in the present study. A weighted average of four accelerometer-derived intensity-related count cut points (i.e., $ 2,020 counts/min) was used to classify moderate-to-vigorous physical activity (MVPA; Troiano et al., 2008). Accelerometry data were reduced to MVPA bouts accumulated over 10-min bout intervals (consistent with current physical activity guidelines (U.S. Department of Health and Human Services, 2008). A 10-min activity bout was defined as 10 or more consecutive minutes above the relevant cut point for physical activity intensity, with allowance for interruptions of 1 min or 2 min below the cut point. A bout was terminated by 3 min below the threshold. Only those participants with at least 4 days of 10 or more hr per day of monitoring data were included in the analyses to ensure habitual patterns of physical activity were represented (Troiano et al., 2008). To determine when the monitor was worn, nonwear was defined by a period of a minimum of 60 consecutive minutes of 0 activity counts,
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with the allowance of 1 min to 2 min of activity counts between 0 and 100 (Troiano et al., 2008).
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Measurement of Hearing Ability At the MEC, audiometry was conducted in a dedicated, sound-isolating room by a trained examiner on participants aged 20 to 69 years old in the NHANES 2003 –2004 cycle and participants aged 12 to 19 years old and 70 to 85 years old in the NHANES 2005 –2006 cycle using a modified Hughson Westlake procedure, a standardized method of measuring pure-tone detection thresholds. Prior to and after audiometry testing, the audiometer was calibrated according to manufacturer specifications. Hearing threshold testing was conducted on both ears of participants at seven frequencies (500 Hz, 1,000 Hz, 2,000 Hz, 3,000 Hz, 4,000 Hz, 6,000 Hz, and 8,000 Hz) across an intensity range of 2 10 dB to 120 dB. Low-frequency pure-tone average (LPTA) was obtained by calculating the average of air conduction pure-tone thresholds at 500 Hz, 1,000 Hz, and 2,000 Hz, and high-frequency pure-tone average (HPTA) was obtained by the average of air conduction pure-tone thresholds at 3,000 Hz, 4,000 Hz, 6,000 Hz, and 8,000 Hz (Agrawal, Platz, & Niparko, 2008; Niskar et al., 1998, 2001; Shargorodsky, Curhan, Curhan, & Eavey, 2010). Consistent with previous hearing loss studies using NHANES data (Agrawal et al., 2008; Loprinzi et al., 2012; Niskar et al., 1998, 2001; Shargorodsky et al., 2010), measures of hearing loss were categorized according to the hearing sensitivity in the worse ear and were defined as hearing within normal limits (LPTA and HPTA , 25 dB), mild hearing loss (LPTA or HPTA 25 – 39 dB), and moderate-to-severe hearing loss (LPTA or HPTA $ 40 dB). Among the 184 participants, 35, 40, and 109, respectively, had hearing within normal limits, mild hearing loss, and moderate-to-severe hearing loss. Other Measurements Various demographic, behavioral, and biological covariates were included in the analytic models as previous studies have shown these variables to be associated with physical activity and/or hearing sensitivity. Information about age, gender, race/ethnicity, marital status, and education was obtained from a questionnaire. Noise exposure was selfreported as exposure to loud noise or having listened to music with headphones in the 24 hr prior to audiometric testing. Additionally, self-reported vision impairment was included as a covariate. Participants were asked whether their vision limits their ability to do activities such as work or other daily or community-based activities. Possible responses included none of the time, a little of the time, some of the time, most of the time, or all of the time. In the analytic model, this variable was recoded as none of the time and a little of the time or more, as few participants reported that
their vision impairs their activities some of the time (n ¼ 14), most of the time (n ¼ 4), or all of the time (n ¼ 2). This self-reported vision variable was only asked among participants aged 50 years and older. As a result, when including this covariate in the analytic model, the total sample size reduced from 184 to 160. The sample sizes for hearing within normal limits, mild hearing loss, and moderate or greater hearing loss went from 35, 40, and 109, respectively, to 16, 37, and 107. Therefore, to prevent this reduction in sample size in the analytic model, particularly among those with hearing within normal limits, participants with missing data on the vision impairment variable (i.e., those younger than 50 years old; n ¼ 24) were recoded. Those with a missing value were recoded as having their vision limit their ability to do activities none of the time. This recoding is reasonable as it is likely that participants younger than 50 years old have fairly normal vision. Analyses were also rerun with those with missing vision data recoded as a little of the time, and results were similar to when they were coded as none of the time; therefore, in the final analytic model, the 24 participants with missing vision data were recoded as none of the time. Body mass index was calculated from measured weight and height (weight in kilograms divided by the square of height in meters). A comorbidity index variable (Charlson, Pompei, Ales, & MacKenzie, 1987; Quan et al., 2011) was created, which included participants having 0 or 1 þ of the following self-reported chronic diseases/events: arthritis, coronary heart disease, and stroke. Hypertension, which was also included in the comorbidity calculation, was defined as having a systolic blood pressure $ 140 mmHg, diastolic blood pressure $ 90 mmHg, or self-reporting taking hypertensive-lowering medication. As a marker of active smoking status or as an index of environmental exposure to tobacco (i.e., passive smoking), serum cotinine was measured. Serum cotinine was measured by an isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. High-density lipoprotein (HDL) cholesterol was directly measured from a serum sample. Homocysteine, a marker of endothelial function, was measured from a blood sample using the Abbott Homocysteine assay, a fully automated fluorescence polarization immunoassay. Highsensitivity C-reactive protein (a biomarker of inflammation) concentration was quantified using latex-enhanced nephelometry. HbA1c was measured using the Primus instrument, which is a fully automated HbA1c analyzer using highperformance liquid chromatography. Urinary creatinine was measured using the Jaffe rate reaction, and urinary albumin was measured using solid-phase fluorescent immunoassay. Microalbuminuria was assessed as it can be considered a proxy for cochlear vasculopathy, because renal and ontological pathological mechanisms may be shared (Abbasi, Ramadan, Hoffman, & Abassi, 2007). Microalbuminuria was calculated as albumin concentration (mg/
PHYSICAL ACTIVITY, HEARING, AND DIABETES
dL) divided by creatinine concentration (g/L). Typically, a cutoff ratio of $ 30 mg/g is used to determine microalbuminuria; however, the following lower genderspecific cut points were used as they have recently demonstrated better detection of microalbuminuria (Erman et al., 2011): $ 21 mg/g for men and $ 24 mg/g for women.
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Data Analysis All statistical analyses were performed using procedures from sample survey data using STATA (Version 12.0, College Station, TX) to account for the complex survey design used in NHANES. To account for oversampling, nonresponse, and noncoverage and to provide nationally representative estimates, all analyses included the use of appropriate survey sample weights, clustering, and primary sampling units. Given that audiometry examination was administered to half of the sample of participants in the MEC for the 2003 – 2004 NHANES cycle, audiometry sample weights were used among participants in the 2003 – 2004 cycle; MEC weights were used among those in the 2005 –2006 NHANES cycle. Means and standard errors were calculated for continuous variables, and proportions were calculated for categorical variables. In the situation where an analysis resulted in a stratum with a single cluster, the variance contribution from a stratum with a single cluster was centered at the overall cluster mean. To examine the association between accelerometerassessed MVPA and hearing impairment, a negative binomial regression was employed, as MVPA time (outcome variable expressed in integral minutes) was in the form of count data, was positively skewed, and failed tests of normality. Incident rate ratios (IRR) from the multivariate negative binomial model represent the relative rate of events for hearing impairment while holding the other variables in the model constant. Regression coefficients from the multivariate negative binomial regression model can be interpreted as follows: For a one-unit change in the independent variable, the difference in the logs of expected counts of the dependent variable is expected to change by the respective regression coefficient, while holding the other independent variables in the model constant (Institute for Digital Research and Education, 2013). The three-level categorical hearing impairment variable (hearing within normal limits, mild hearing loss, and moderate or greater hearing loss) served as the independent variable, with hearing within normal limits serving as the referent group. Covariates included age, gender, race/ ethnicity, education, body mass index, comorbidity index, marital status, cotinine, homocysteine, HDL cholesterol, HbA 1c , C-reactive protein, microalbuminuria, noise exposure, and vision impairment. All covariates were entered into the model at the same time as there was no evidence of multicollinearity; the variance inflation factor was less than 2 for all covariates and the tolerance statistic
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was . 0.7 for all covariates. Additionally, a reduced analytic model (i.e., only including covariates that substantively contributed to the model [i.e., p , .20 in the model]) was computed to ensure overadjustment did not occur. Statistical significance was established as an alpha nominal value p , .05.
RESULTS Characteristics of the analyzed sample are shown in Table 1. With regard to the main focus of the study (Table 2) and compared with those with hearing within normal limits (referent group), the negative binomial regression results showed that participants with mild hearing loss and moderate or greater hearing loss, respectively, engaged in 93% fewer minutes of MVPA (IRR ¼ 0.07, 95% CI [0.01, 0.60]; coef. ¼ 2 2.65, 95% CI [2 4.8, 2 0.5]) and 94% fewer minutes of MVPA (IRR ¼ 0.06, 95% CI [0.01, 0.54]; coef. ¼ 2.78, 95% CI [2 4.9, 2 0.6]). Results were adjusted for age, gender, race/ethnicity, education, body mass index, comorbidity index, marital status, cotinine, homocysteine, HDL cholesterol, HbA1c, C-reactive protein, microalbuminuria, noise exposure, and vision impairment. As a whole, the model was statistically significant, F(17, 10) ¼ 12.60, p ¼ .0001. A minimally adjusted model was subsequently computed that adjusted only for covariates that contributed (i.e., p , .20) to the model. After adjusting for, race/ethnicity, gender, marital status, cotinine, HbA1c, C-reactive protein, and noise exposure, participants with mild hearing loss and moderate or greater hearing loss, respectively, engaged in 93% fewer minutes of MVPA (IRR ¼ 0.07, 95% CI [0.01, 0.51]; coef. ¼ 2 2.7, 95% CI [2 4.7, 2 0.6]) and 95% fewer minutes of MVPA (IRR ¼ 0.05, 95% CI [0.01, 0.21]; coef. ¼ 2 2.9, 95% CI [2 4.2, 2 1.5]). As a whole, the model was statistically significant, F(9, 18) ¼ 12.85, p , .0001. DISCUSSION The purpose of this study was to test our hypothesis that physical activity is associated with hearing sensitivity among U.S. adults with diabetes. Our findings showed that U.S. adults with diabetes who had greater hearing impairment were less active, which underscores the need to promote physical activity among this population. Given the limited studies examining the association between physical activity and hearing sensitivity among adults with diabetes, it is difficult to compare our findings to others. However, the present findings are similar to those of Loprinzi et al. (2012), who showed that among those in the general population, participants with higher cardiorespiratory fitness, an indicator of physical activity, have better hearing function. Similarly, Loprinzi, Lee, and colleagues
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P. D. LOPRINZI ET AL. TABLE 1 Characteristics of the Analytic Sample (Mean/Proportion [95% CI]), NHANES 2003– 2006 Mean/Proportion (95% CI)
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Variable N Age (years) BMI (kg/m2) % Male Race/Ethnicity % Non-Hispanic White % Other Race Marital Status % Married or Living with Partner % Other Education % High School or Less % Some College or More Comorbidity Index % 0 Comorbidities % 1 Comorbidities % 2 þ Comorbidities Cotinine (ng/mL) Homocysteine (umol/L) HDL Cholesterol (mg/dL) HbA1c (%) C-Reactive Protein (mg/dL) Creatinine (g/L) Albumin (mg/L) Albumin-to-Creatinine Ratio (mg/g) % Microalbuminuria % Exposed to Loud Noise Within Last 24 Hr % Vision Affecting Activities of Daily Living Moderate-to-Vigorous Physical Activity (min/day)
Entire Sample
Hearing Within Normal Limits
Mild Hearing Loss
Moderate þ Hearing Loss
184 58.5 [55.7, 61.4] 30.0 [28.7, 31.3] 58.0 [47.5, 68.5]
35 44.9 [40.2, 49.7] 28.9 [26.8, 31.0] 39.7 [19.7, 59.8]
40 59.1 [55.1, 63.2] 28.9 [26.5, 31.4] 65.6 [49.6, 81.6]
109 65.6 [63.2, 68.0] 31.1 [29.4, 32.6] 64.6 [55.7, 73.5]
69.7 [58.5, 80.9] 30.2 [19.0, 41.4]
64.6 [44.4, 84.8] 35.3 [15.1, 55.5]
57.1 [37.0, 77.2] 42.8 [22.7, 62.9]
77.6 [67.0, 88.1] 22.3 [11.8, 32.9]
69.6 [60.3, 78.8] 30.3 [21.1, 39.6]
82.9 [67.0, 98.7] 17.0 [1.2, 32.9]
58.8 [32.1, 85.5] 41.1 [14.4, 67.8]
66.8 [55.6-, 78.1] 33.1 [21.8, 44.3]
54.2 [46.2, 62.1] 45.7 [37.8, 53.7]
44.8 [23.6, 65.9] 55.1 [34.0, 76.3]
40.0 [19.4, 60.6] 59.9 [39.3, 80.5]
65.0 [50.5, 79.5] 34.9 [20.4, 49.4]
16.5 [9.7, 23.2] 32.9 [21.3, 44.5] 50.4 [38.8, 62.1] 63.1 [34.3, 91.8] 9.8 [9.2, 10.5] 49.9 [47.3, 52.6] 7.03 [6.7, 7.3] 0.42 [0.33, 0.51] 1.1 [0.9, 1.4] 86.3 [26.0, 146.7] 88.6 [20.3, 156.9] 36.1 [24.9, 47.2] 6.3 [0.1, 12.6] 13.6 [6.8–20.5] 4.8 [3.2, 6.4]
41.3 [19.9, 62.6] 38.1 [14.4, 61.8] 20.5 [4.8, 36.1] 65.2 [11.5, 118.9] 7.5 [6.5, 8.4] 51.7 [45.2, 58.1] 7.7 [7.0, 8.3] 0.43 [0.24, 0.62] 1.1 [0.7, 1.4] 12.1 [4.9, 19.3] 11.7 [4.5, 18.8] 9.6 [0.0, 19.5] 14.6 [2.6, 26.7] 0.4 [0.0, 1.3] 7.9 [2.5, 13.3]
10.8 [0.0, 24.5] 45.6 [25.8, 65.3] 43.5 [24.5, 62.5] 65.7 [5.5, 125.9] 10.8 [9.5, 12.2] 51.8 [43.5, 60.2] 6.7 [6.2, 7.2] 0.31 [0.18, 0.42] 1.1 [0.9, 1.3] 90.9 [15.6, 166.2] 91.8 [15.3, 168.4] 39.5 [17.5, 61.4] 1.6 [0.0, 5.0] 2.3 [0.0, 5.4] 4.9 [0.1, 9.6]
5.6 [0.2, 11.0] 25.0 [11.3, 38.7] 69.2 [54.8, 83.6] 60.8 [15.4, 106.3] 10.7 [9.8, 11.6] 48.3 [44.9, 51.6] 6.8 [6.5, 7.1] 0.46 [0.34, 0.58] 1.3 [0.9, 1.5] 124.0 [16.7, 231.3] 128.3 [4.8, 251.8] 48.8 [32.3, 65.3] 3.9 [0.0, 10.1] 25.3 [11.6, 39.1] 3.2 [0.7, 5.6]
Note. CI ¼ confidence interval; BMI ¼ body mass index; HDL ¼ high-density lipoprotein; HbA1c ¼ glycohemoglobin.
(2013) showed that physical activity is inversely associated with tinnitus, or a ringing, roaring, or buzzing sound heard in the ears in the absence of external stimuli. Importantly, given the cross-sectional nature of the present study, it is not possible to determine the direction of influence. We acknowledge that those with hearing impairment were less active because of their condition. For example, individuals with sensory impairments may be more likely to have balance deficits (Baloh, Ying, & Jacobson, 2003), which in turn, may influence their physical activity levels. It is also possible that regular participation in physical activity may help to preserve hearing function as a result of physical activity-induced changes in parameters influencing hearing sensitivity. Future studies, particularly prospective and experimental designs, are needed to determine the direction of causality between physical activity and hearing function. These future studies will help to determine whether decreased physical activity contributes to impaired hearing, whether impaired hearing decreases physical activity, or whether causality is bidirectional. The following narrative will discuss possible mechanisms through which physical activity may have a
preserving effect for hearing, which may help inform the development of future studies needed in this area. There are several possible underlying mechanisms that may help to explain the assertion that physical activity may have a preserving effect on hearing function. The proposed mechanisms explaining the diabetes – hearing link include: cochlear microangiopathy, elevated blood glucose in the cerebrospinal fluid, auditory neuropathy, and diabetic encephalopathy (Austin et al., 2009). It is plausible to suggest that physical activity may have a preserving effect on hearing among adults with diabetes through favorable changes in these proposed mechanisms. With regard to cochlear microangiopathy, a microvasculature disease of the small vessels of the cochlea, physical activity may help to prevent such microvasculature disease through a variety of mechanisms. For example, physical activity is associated with improved endothelial function (Loprinzi & Cardinal, 2012), reduced systemic inflammation (Loprinzi, Cardinal, et al., 2013), decreased resistance to blood flow (Gielen, Schuler, & Adams, 2010), and attenuated blood pressure (Kokkinos, Giannelou, Manolis, & Pittaras, 2009), all of which may, in theory, influence cochlear microangiopathy.
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TABLE 2 Negative Binomial Regression Results Examining the Association Between Hearing Impairment and Physical Activity
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Incident Rate Ratio (95% CI) Fully Adjusted Hearing Mild Hearing Loss Versus Hearing Within Normal Limits Moderate or Greater Hearing Loss Versus Hearing Within Normal Limits Covariates Age, 1 year older BMI, kg/m2 Non-Hispanic White Versus Other Female Versus Male Some College or More Versus High School or Less Not Married Versus Married 1 þ Comorbidity Versus 0 Comorbidities Cotinine, 1 ng/mL Higher C-reactive Protein, 1 mg/dL Higher HbA1c, 1 % Higher Homocysteine, 1 umol/L Higher HDL Cholesterol, 1 mg/dL Higher Microalbumineria Versus None Noise Exposure Versus None Vision Limitation Versus None
p
Incident Rate Ratio (95% CI) Minimally Adjusted
p
.01 ,.01
0.07 [0.01, 0.60] 0.06 [0.01, 0.54]
.02 .01
0.07 [0.01, 0.51] 0.05 [0.01, 0.21]
0.99 [0.93, 1.04] 0.93 [0.83, 1.05] 0.29 [0.09, 0.87] 0.24 [0.04, 1.33] 0.92 [0.44, 1.90] 0.16 [0.04, 0.57] 0.95 [0.23, 3.89] 0.99 [0.98, 0.99] 0.07 [0.02, 0.30] 0.51 [0.34, 0.78] 1.03 [0.83, 1.27] 1.00 [0.94, 1.05] 0.69 [0.12, 3.74] 0.23 [0.03, 1.56] 1.99 [0.53, 7.41]
.75 .24 .03 .10 .81 ,.01 .94 ,.01 ,.01 ,.01 .76 .99 .65 .12 .29
N/A N/A 0.33 [0.14, 0.78] 0.24 [0.06, 0.95] N/A 0.13 [0.03, 0.46] N/A 0.99 [0.98, 0.99] 0.04 [0.01, 0.10] 0.54 [0.40, 0.71] N/A N/A N/A 0.18 [0.03, 1.06] N/A
.01 .04 ,.01 ,.01 ,.01 ,.01
.06
Note. Fully adjusted model included all covariates. Minimally adjusted model included only covariates from the fully adjusted model that had a p , .20. CI ¼ confidence interval; BMI ¼ body mass index; HbA1c ¼ glycohemoglobin; HDL ¼ high-density lipoprotein; N/A ¼ not applicable.
Although not directly examining the association between physical activity and blood glucose levels in the cerebrospinal fluid, there is considerable empirical support for the exercise-lowering effects of blood glucose in the circulatory system (Sigal et al., 2006). With regard to auditory neuropathy, there is evidence to support a neuroprotective effect of exercise on the central nervous system (Zigmond, Cameron, Hoffer, & Smeyne, 2012). Whether this exercise effect also occurs in the central auditory system is still yet to be fully determined. However, there is some evidence to suggest an exerciseinduced protective effect on the central auditory system. For example, Ismail and colleagues (1973) demonstrated that an 8-week aerobic training program enhanced the participants’ auditory ability to recover from noiseinduced auditory fatigue. As a result, exercise-induced neurotrophic factor expression may help to attenuate noise- and/or age-induced auditory neuropathy. Lastly, physical activity may be associated with reduced diabetic encephalopathy, which is an increased cognitive decline among those with diabetes, through a reduced risk for cognitive decline from physical activity (Yaffe, Barnes, Nevitt, Lui, & Covinsky, 2001). This is likely through physical activity-induced increases in neurotrophic factors, such as glial cells and brain-derived neurotrophic factor (Zigmond et al., 2012). In summary, our findings suggest an association between MVPA and hearing sensitivity among U.S. adults with
evidence of diabetes. Major strengths of this study include using a population-based sample of adults with diabetes, examining this novel relationship among this population, and using objective measures of both hearing sensitivity and physical activity. A limitation to the study is our inability to infer cause and effect. Due to the crosssectional design, it is likely that those with more severe hearing impairments were less active because of their condition. Although this assertion cannot be discounted, there are plausible biological mechanisms to suggest that physical activity may have auditory protective effects. Future studies, particularly prospective and experimental designs, are needed in this relatively new area of inquiry. Another limitation was the relatively small number of participants who had hearing within normal limits. Additionally, due to the relatively small sample size, we were not able to test for moderation effects. Future studies are encouraged to employ a larger sample size and examine whether, for example, type of diabetes (i.e., type 1 or type 2), age, gender, and race/ethnicity moderate the association between physical activity and hearing sensitivity among adults with diabetes. Lastly, and although similar to other estimates (Troiano et al., 2008), participants with diabetes in the present sample engaged in very little MVPA, with only a 5-min MVPA difference occurring between those who had hearing within normal limits compared with those who had moderate or greater hearing loss.
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Regardless of direction of influence, what we know at this point is that U.S. adults with diabetes who have greater hearing impairment are less active than their counterparts with better hearing function. As a result, these findings suggest that efforts are particularly needed to increase the activity levels of adults with diabetes who have hearing impairment to reduce further health complications associated with an inactive lifestyle.
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WHAT DOES THIS ARTICLE ADD? Recent research has demonstrated that diabetes increases the risk for hearing impairment. Additionally, considerable research implicates physical activity as an effective strategy to help prevent and treat diabetes. Therefore, it is plausible to suggest an association between physical activity and hearing function among those with diabetes. However, to our knowledge, no study has specifically examined this association in this population. Our findings suggest that adults with diabetes who are physically active have better hearing function than their counterparts who are not as physically active. This suggests that physical activity may attenuate the progression of diabetes-induced hearing impairment, which is another potential reason for people to remain active for life. If we can prevent, delay, or otherwise improve this through increased MVPA participation, it is of practical use to audiologists too. It also brings audiologists into the fight against physical inactivity, which is consistent with the global Exercise is Medicinew initiative (Blair, Sallis, Hutber, & Archer, 2012). Physical activity promotion is needed among adults with diabetes, and in particular, adults with diabetes who have greater hearing impairment.
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Boule, N. G., Haddad, E., Kenny, G. P., Wells, G. A., & Sigal, R. J. (2001). Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: A meta-analysis of controlled clinical trials. Journal of American Medical Association, 286, 1218–1227. Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40, 373–383. Cullen, J. R., & Cinnamond, M. J. (1993). Hearing loss in diabetics. Journal of Laryngology & Otology, 107, 179– 182. Dalton, D. S., Cruickshanks, K. J., Klein, R., Klein, B. E., & Wiley, T. L. (1998). Association of NIDDM and hearing loss. Diabetes Care, 21, 1540– 1544. Diagnosis and classification of diabetes mellitus. (2010). Diabetes Care, 33(Suppl. 1), S62–S69. Erman, A., Rahamimov, R., Mashraki, T., Levy-Drummer, R. S., Winkler, J., David, I., . . . Chagnac, A. (2011). The urine albumin-to-creatinine ratio: Assessment of its performance in the renal transplant recipient population. Clinical Journal of the American Society of Nephrology, 6, 892–897. Executive summary: Standards of medical care in diabetes—2012. (2012). Diabetes Care, 35(Suppl. 1), S4 –S10. Frisina, S. T., Mapes, F., Kim, S., Frisina, D. R., & Frisina, R. D. (2006). Characterization of hearing loss in aged type II diabetics. Hearing Research, 211, 103–113. Gielen, S., Schuler, G., & Adams, V. (2010). Cardiovascular effects of exercise training: Molecular mechanisms. Circulation, 122, 1221– 1238. Institute for Digital Research and Education. (2013). Stata annotated output: Negative binomial regression. Retrieved from http://www.ats. ucla.edu/stat/stata/output/stata_nbreg_output.htm Ismail, A., Corrigan, D. L., MacLeod, D. F., Anderson, V. L., Kasten, R. N., & Elliot, P. W. (1973). Biophysiological and audiological variables in adults. Archives of Otolaryngology, 97, 447–451. Kennedy, A., Nirantharakumar, K., Chimen, M., Pang, T. T., Hemming, K., Andrews, R. C., & Narendran, P. (2013). Does exercise improve glycaemic control in type 1 diabetes? A systematic review and meta analysis. PLos One, 8, e58861. Kokkinos, P. F., Giannelou, A., Manolis, A., & Pittaras, A. (2009). Physical activity in the prevention and management of high blood pressure. Hellenic Journal of Cardiology, 50, 52 –59. Lisowska, G., Namyslowski, G., Morawski, K., & Strojek, K. (2001). Early identification of hearing impairment in patients with type 1 diabetes mellitus. Otology & Neurotology, 22, 316 –320. Loprinzi, P. D., & Cardinal, B. J. (2012). Interrelationships among physical activity, depression, homocysteine, and metabolic syndrome with special considerations by sex. Preventive Medicine, 54, 388 –392. Loprinzi, P., Cardinal, B., Crespo, C., Brodowicz, G., Andersen, R., Sullivan, E., & Smit, E. (2013). Objectively measured physical activity and C-reactive protein: National Health and Nutrition Examination Survey 2003–2004. Scandinavian Journal of Medicine & Science in Sports, 23, 164 –170. doi:10.1111/j.1600-0838.2011.01356.x Loprinzi, P. D., Cardinal, B. J., & Gilham, B. (2012). Association between cardiorespiratory fitness and hearing sensitivity. American Journal of Audiology, 21, 33–40. Loprinzi, P. D., Lee, H., Gilham, B., & Cardinal, B. (2013). Association between accelerometer-assessed physical activity and tinnitus, NHANES 2005– 2006. Research Quarterly for Exercise and Sport, 84, 177– 185. doi:10.1080/02701367.2013.784840 Niskar, A. S., Kieszak, S. M., Holmes, A., Esteban, E., Rubin, C., & Brody, D. J. (1998). Prevalence of hearing loss among children 6 to 19 years of age: The Third National Health and Nutrition Examination Survey. Journal of the American Medical Association, 279, 1071– 1075. Niskar, A. S., Kieszak, S. M., Holmes, A. E., Esteban, E., Rubin, C., & Brody, D. J. (2001). Estimated prevalence of noise-induced hearing threshold shifts among children 6 to 19 years of age: The Third National
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Health and Nutrition Examination Survey, 1988–1994, United States. Pediatrics, 108, 40 –43. Pugliese, G., Solini, A., Zoppini, G., Fondelli, C., Zerbini, G., Vedovato, M., . . . Penno, G. (2012). High prevalence of advanced retinopathy in patients with type 2 diabetes from the Renal Insufficiency And Cardiovascular Events (RIACE) Italian Multicenter Study. Diabetes Research and Clinical Practice, 98, 329 –337. Quan, H., Li, B., Couris, C. M., Fushimi, K., Graham, P., Hider, P., . . . Sundararajan, V. (2011). Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. American Journal of Epidemiology, 173, 676 –682. Querido, J. S., & Sheel, A. W. (2007). Regulation of cerebral blood flow during exercise. Sports Medicine, 37, 765–782. Shargorodsky, J., Curhan, S. G., Curhan, G. C., & Eavey, R. (2010). Change in prevalence of hearing loss in US adolescents. Journal of the American Medical Association, 304, 772–778.
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Sigal, R. J., Kenny, G. P., Wasserman, D. H., Castaneda-Sceppa, C., & White, R. D. (2006). Physical activity/exercise and type 2 diabetes: A consensus statement from the American Diabetes Association. Diabetes Care, 29, 1433–1438. Troiano, R. P., Berrigan, D., Dodd, K. W., Masse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40, 181–188. U.S. Department of Health and Human Services. (2008). Physical activity guidelines for Americans. Retrieved from http://www.health.gov/ paguidelines Yaffe, K., Barnes, D., Nevitt, M., Lui, L. Y., & Covinsky, K. (2001). A prospective study of physical activity and cognitive decline in elderly women: Women who walk. Archives of Internal Medicine, 161, 1703–1708. Zigmond, M. J., Cameron, J. L., Hoffer, B. J., & Smeyne, R. J. (2012). Neurorestoration by physical exercise: Moving forward. Parkinsonism & Related Disorders, 18(Suppl. 1), S147–S150.
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Association Between Accelerometer-Assessed Physical Activity and Objectively Measured Hearing Sensitivity Among U.S. Adults With Diabetes a
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Paul D. Loprinzi , Ben Gilham & Bradley J. Cardinal a
Bellarmine University
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Hearing, Speech and Deafness Center
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Oregon State University Published online: 20 Aug 2014.
To cite this article: Paul D. Loprinzi, Ben Gilham & Bradley J. Cardinal (2014) Association Between Accelerometer-Assessed Physical Activity and Objectively Measured Hearing Sensitivity Among U.S. Adults With Diabetes, Research Quarterly for Exercise and Sport, 85:3, 390-397, DOI: 10.1080/02701367.2014.930404 To link to this article: http://dx.doi.org/10.1080/02701367.2014.930404
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Research Quarterly for Exercise and Sport, 85, 390–397, 2014 Copyright q SHAPE America ISSN 0270-1367 print/ISSN 2168-3824 online DOI: 10.1080/02701367.2014.930404
Association Between Accelerometer-Assessed Physical Activity and Objectively Measured Hearing Sensitivity Among U.S. Adults With Diabetes Paul D. Loprinzi Bellarmine University
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Ben Gilham Hearing, Speech and Deafness Center
Bradley J. Cardinal Oregon State University
Purpose: The purpose of this study was to examine the association between objectively measured physical activity and hearing sensitivity among a nationally representative sample of U.S. adults with diabetes. Method: Data from the 2003 –2006 National Health and Nutrition Examination Survey were used. One hundred eighty-four U.S. adults with diabetes wore an ActiGraph 7164 accelerometer and had their hearing function objectively assessed. A negative binomial logistic regression was used to examine the association between moderate-to-vigorous physical activity (MVPA) and hearing sensitivity. Results were adjusted for age, gender, race/ethnicity, education, body mass index, comorbidity index, marital status, cotinine, homocysteine, high-density lipoprotein cholesterol, glycohemoglobin (HbA1c), C-reactive protein, microalbuminuria, noise exposure, and vision impairment. Results: Compared to those with hearing within normal limits, results showed that participants with mild hearing loss and moderate or greater hearing loss, respectively, engaged in 93% fewer minutes of MVPA (incident rate ratio ¼ 0.07; 95% CI [0.01, 0.60]) and 94% fewer minutes of MVPA (incident rate ratio ¼ 0.06; 95% CI [0.01, 0.54]). Conclusion: Adults with diabetes who have greater hearing impairment are less physically active. Future research is needed to determine the direction of causality. Keywords: accelerometry, epidemiology, exercise, National Health and Nutrition Examination Survey (NHANES)
Diabetes has been implicated in the development of sensory impairment, particularly vision loss (Pugliese et al., 2012). Individuals with diabetes, compared with those without diabetes, are also more likely to have hearing loss (Bainbridge, Hoffman, & Cowie, 2008; Cullen & Cinnamond, 1993; Dalton, Cruickshanks, Klein, Klein, & Submitted February 20, 2013; accepted January 18, 2014. Paul D. Loprinzi is now at The University of Mississippi. Correspondence should be addressed to Paul D. Loprinzi, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, 215 Turner Center, PO Box 1848, University, MS 38677. E-mail: [email protected]
Wiley, 1998; Frisina, Mapes, Kim, Frisina, & Frisina, 2006; Lisowska, Namyslowski, Morawski, & Strojek, 2001). Although the definitive mechanisms explaining the association between diabetes and hearing loss have not been determined, suggested pathogenesis includes cochlear microangiopathy, elevated blood glucose in the cerebrospinal fluid, auditory neuropathy, and diabetic encephalopathy (Austin et al., 2009). We hypothesize that physical activity is positively associated with hearing sensitivity among those with diabetes. The primary reason for this is twofold. First, it is well established that regular engagement in physical activity
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can help lower blood glucose levels among those with diabetes (Sigal, Kenny, Wasserman, Castaneda-Sceppa, & White, 2006), which may attenuate the potential hyperglycemia-induced hearing loss among adults with diabetes. A meta-analysis by Boule, Haddad, Kenny, Wells, and Sigal (2001) showed that among adults with type 2 diabetes, glycohemoglobin (HbA1c) levels were significantly lower in those who engaged in an exercise intervention (7.65%), compared with those in a control group (8.31%). Notably, physical activity seems to have less of an effect on glycemic control among those with type 1 diabetes (Kennedy et al., 2013). Second, there is emerging evidence that enhanced cardiorespiratory fitness, a consequence of regular physical activity participation, is associated with enhanced hearing sensitivity in the broader population (Loprinzi, Cardinal, & Gilham, 2012). Given that physical activity increases blood flow to the brain (Querido & Sheel, 2007), it is possible that it may also increase blood flow to the cochlea, which is consistent with the pathogenic mechanisms proposed by Austin et al. (2009); however, future research on this topic is warranted. Additionally, emerging research shows that individuals engaging in greater amounts of physical activity are less likely to have tinnitus, or a ringing, roaring, or buzzing noise that is heard in the ears in the absence of external stimuli (Loprinzi, Lee, Gilham, & Cardinal, 2013). To our knowledge, there are no studies testing the hypothesis that physical activity is associated with hearing sensitivity among adults with diabetes. As a result, the purpose of this study was to examine the association between accelerometer-determined physical activity and hearing sensitivity in a nationally representative sample of adults with evidence of diabetes.
METHODS 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. Briefly, participants were interviewed in their homes and were subsequently examined in mobile examination centers (MECs) across numerous 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. The final sample of the present study included 184 NHANES participants after the exclusion of participants who did not have evidence of diabetes, who had insufficient accelerometry data, missing audiometry data, or a cold, sinus, or earache in the 24 hr prior to audiometry
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testing, those with impacted cerumen, and those with missing data on the covariates used in the analytic model.
Assessment of Diabetes Status Administered in the home, participants were asked several questions related to diabetes. First, they were asked whether they had ever been told by a doctor or health professional that they had or have diabetes or sugar diabetes; unfortunately, it was not possible to determine if they had type 1 or type 2 diabetes as this was not asked by the NHANES staff. Second, they were asked whether they were taking insulin. Third, they were asked whether they were taking diabetic pills to lower blood sugar. In the present study, participants who answered yes to any of these three questions were considered to have evidence of diabetes. A subsample of the NHANES participants was examined in a morning fasting session. Fasting glucose was measured from a blood sample, and participants with a fasting glucose level of 126 mg/dL or higher were considered to have diabetes (“Diagnosis and classification of diabetes mellitus,” 2010). Lastly, participants with an HbA1c level of 6.5% or greater were considered to have diabetes (“Executive summary,” 2012).
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 (Shalimar, FL). Participants were asked to wear the accelerometer on the right hip for 7 days following their examination. An accelerometer objectively measures the frequency, intensity, and duration of physical activity by generating an activity count that is proportional to the measured acceleration. Activity counts are summed over a predetermined epoch period, with 1-min epochs used in the present study. A weighted average of four accelerometer-derived intensity-related count cut points (i.e., $ 2,020 counts/min) was used to classify moderate-to-vigorous physical activity (MVPA; Troiano et al., 2008). Accelerometry data were reduced to MVPA bouts accumulated over 10-min bout intervals (consistent with current physical activity guidelines (U.S. Department of Health and Human Services, 2008). A 10-min activity bout was defined as 10 or more consecutive minutes above the relevant cut point for physical activity intensity, with allowance for interruptions of 1 min or 2 min below the cut point. A bout was terminated by 3 min below the threshold. Only those participants with at least 4 days of 10 or more hr per day of monitoring data were included in the analyses to ensure habitual patterns of physical activity were represented (Troiano et al., 2008). To determine when the monitor was worn, nonwear was defined by a period of a minimum of 60 consecutive minutes of 0 activity counts,
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with the allowance of 1 min to 2 min of activity counts between 0 and 100 (Troiano et al., 2008).
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Measurement of Hearing Ability At the MEC, audiometry was conducted in a dedicated, sound-isolating room by a trained examiner on participants aged 20 to 69 years old in the NHANES 2003 –2004 cycle and participants aged 12 to 19 years old and 70 to 85 years old in the NHANES 2005 –2006 cycle using a modified Hughson Westlake procedure, a standardized method of measuring pure-tone detection thresholds. Prior to and after audiometry testing, the audiometer was calibrated according to manufacturer specifications. Hearing threshold testing was conducted on both ears of participants at seven frequencies (500 Hz, 1,000 Hz, 2,000 Hz, 3,000 Hz, 4,000 Hz, 6,000 Hz, and 8,000 Hz) across an intensity range of 2 10 dB to 120 dB. Low-frequency pure-tone average (LPTA) was obtained by calculating the average of air conduction pure-tone thresholds at 500 Hz, 1,000 Hz, and 2,000 Hz, and high-frequency pure-tone average (HPTA) was obtained by the average of air conduction pure-tone thresholds at 3,000 Hz, 4,000 Hz, 6,000 Hz, and 8,000 Hz (Agrawal, Platz, & Niparko, 2008; Niskar et al., 1998, 2001; Shargorodsky, Curhan, Curhan, & Eavey, 2010). Consistent with previous hearing loss studies using NHANES data (Agrawal et al., 2008; Loprinzi et al., 2012; Niskar et al., 1998, 2001; Shargorodsky et al., 2010), measures of hearing loss were categorized according to the hearing sensitivity in the worse ear and were defined as hearing within normal limits (LPTA and HPTA , 25 dB), mild hearing loss (LPTA or HPTA 25 – 39 dB), and moderate-to-severe hearing loss (LPTA or HPTA $ 40 dB). Among the 184 participants, 35, 40, and 109, respectively, had hearing within normal limits, mild hearing loss, and moderate-to-severe hearing loss. Other Measurements Various demographic, behavioral, and biological covariates were included in the analytic models as previous studies have shown these variables to be associated with physical activity and/or hearing sensitivity. Information about age, gender, race/ethnicity, marital status, and education was obtained from a questionnaire. Noise exposure was selfreported as exposure to loud noise or having listened to music with headphones in the 24 hr prior to audiometric testing. Additionally, self-reported vision impairment was included as a covariate. Participants were asked whether their vision limits their ability to do activities such as work or other daily or community-based activities. Possible responses included none of the time, a little of the time, some of the time, most of the time, or all of the time. In the analytic model, this variable was recoded as none of the time and a little of the time or more, as few participants reported that
their vision impairs their activities some of the time (n ¼ 14), most of the time (n ¼ 4), or all of the time (n ¼ 2). This self-reported vision variable was only asked among participants aged 50 years and older. As a result, when including this covariate in the analytic model, the total sample size reduced from 184 to 160. The sample sizes for hearing within normal limits, mild hearing loss, and moderate or greater hearing loss went from 35, 40, and 109, respectively, to 16, 37, and 107. Therefore, to prevent this reduction in sample size in the analytic model, particularly among those with hearing within normal limits, participants with missing data on the vision impairment variable (i.e., those younger than 50 years old; n ¼ 24) were recoded. Those with a missing value were recoded as having their vision limit their ability to do activities none of the time. This recoding is reasonable as it is likely that participants younger than 50 years old have fairly normal vision. Analyses were also rerun with those with missing vision data recoded as a little of the time, and results were similar to when they were coded as none of the time; therefore, in the final analytic model, the 24 participants with missing vision data were recoded as none of the time. Body mass index was calculated from measured weight and height (weight in kilograms divided by the square of height in meters). A comorbidity index variable (Charlson, Pompei, Ales, & MacKenzie, 1987; Quan et al., 2011) was created, which included participants having 0 or 1 þ of the following self-reported chronic diseases/events: arthritis, coronary heart disease, and stroke. Hypertension, which was also included in the comorbidity calculation, was defined as having a systolic blood pressure $ 140 mmHg, diastolic blood pressure $ 90 mmHg, or self-reporting taking hypertensive-lowering medication. As a marker of active smoking status or as an index of environmental exposure to tobacco (i.e., passive smoking), serum cotinine was measured. Serum cotinine was measured by an isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. High-density lipoprotein (HDL) cholesterol was directly measured from a serum sample. Homocysteine, a marker of endothelial function, was measured from a blood sample using the Abbott Homocysteine assay, a fully automated fluorescence polarization immunoassay. Highsensitivity C-reactive protein (a biomarker of inflammation) concentration was quantified using latex-enhanced nephelometry. HbA1c was measured using the Primus instrument, which is a fully automated HbA1c analyzer using highperformance liquid chromatography. Urinary creatinine was measured using the Jaffe rate reaction, and urinary albumin was measured using solid-phase fluorescent immunoassay. Microalbuminuria was assessed as it can be considered a proxy for cochlear vasculopathy, because renal and ontological pathological mechanisms may be shared (Abbasi, Ramadan, Hoffman, & Abassi, 2007). Microalbuminuria was calculated as albumin concentration (mg/
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dL) divided by creatinine concentration (g/L). Typically, a cutoff ratio of $ 30 mg/g is used to determine microalbuminuria; however, the following lower genderspecific cut points were used as they have recently demonstrated better detection of microalbuminuria (Erman et al., 2011): $ 21 mg/g for men and $ 24 mg/g for women.
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Data Analysis All statistical analyses were performed using procedures from sample survey data using STATA (Version 12.0, College Station, TX) to account for the complex survey design used in NHANES. To account for oversampling, nonresponse, and noncoverage and to provide nationally representative estimates, all analyses included the use of appropriate survey sample weights, clustering, and primary sampling units. Given that audiometry examination was administered to half of the sample of participants in the MEC for the 2003 – 2004 NHANES cycle, audiometry sample weights were used among participants in the 2003 – 2004 cycle; MEC weights were used among those in the 2005 –2006 NHANES cycle. Means and standard errors were calculated for continuous variables, and proportions were calculated for categorical variables. In the situation where an analysis resulted in a stratum with a single cluster, the variance contribution from a stratum with a single cluster was centered at the overall cluster mean. To examine the association between accelerometerassessed MVPA and hearing impairment, a negative binomial regression was employed, as MVPA time (outcome variable expressed in integral minutes) was in the form of count data, was positively skewed, and failed tests of normality. Incident rate ratios (IRR) from the multivariate negative binomial model represent the relative rate of events for hearing impairment while holding the other variables in the model constant. Regression coefficients from the multivariate negative binomial regression model can be interpreted as follows: For a one-unit change in the independent variable, the difference in the logs of expected counts of the dependent variable is expected to change by the respective regression coefficient, while holding the other independent variables in the model constant (Institute for Digital Research and Education, 2013). The three-level categorical hearing impairment variable (hearing within normal limits, mild hearing loss, and moderate or greater hearing loss) served as the independent variable, with hearing within normal limits serving as the referent group. Covariates included age, gender, race/ ethnicity, education, body mass index, comorbidity index, marital status, cotinine, homocysteine, HDL cholesterol, HbA 1c , C-reactive protein, microalbuminuria, noise exposure, and vision impairment. All covariates were entered into the model at the same time as there was no evidence of multicollinearity; the variance inflation factor was less than 2 for all covariates and the tolerance statistic
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was . 0.7 for all covariates. Additionally, a reduced analytic model (i.e., only including covariates that substantively contributed to the model [i.e., p , .20 in the model]) was computed to ensure overadjustment did not occur. Statistical significance was established as an alpha nominal value p , .05.
RESULTS Characteristics of the analyzed sample are shown in Table 1. With regard to the main focus of the study (Table 2) and compared with those with hearing within normal limits (referent group), the negative binomial regression results showed that participants with mild hearing loss and moderate or greater hearing loss, respectively, engaged in 93% fewer minutes of MVPA (IRR ¼ 0.07, 95% CI [0.01, 0.60]; coef. ¼ 2 2.65, 95% CI [2 4.8, 2 0.5]) and 94% fewer minutes of MVPA (IRR ¼ 0.06, 95% CI [0.01, 0.54]; coef. ¼ 2.78, 95% CI [2 4.9, 2 0.6]). Results were adjusted for age, gender, race/ethnicity, education, body mass index, comorbidity index, marital status, cotinine, homocysteine, HDL cholesterol, HbA1c, C-reactive protein, microalbuminuria, noise exposure, and vision impairment. As a whole, the model was statistically significant, F(17, 10) ¼ 12.60, p ¼ .0001. A minimally adjusted model was subsequently computed that adjusted only for covariates that contributed (i.e., p , .20) to the model. After adjusting for, race/ethnicity, gender, marital status, cotinine, HbA1c, C-reactive protein, and noise exposure, participants with mild hearing loss and moderate or greater hearing loss, respectively, engaged in 93% fewer minutes of MVPA (IRR ¼ 0.07, 95% CI [0.01, 0.51]; coef. ¼ 2 2.7, 95% CI [2 4.7, 2 0.6]) and 95% fewer minutes of MVPA (IRR ¼ 0.05, 95% CI [0.01, 0.21]; coef. ¼ 2 2.9, 95% CI [2 4.2, 2 1.5]). As a whole, the model was statistically significant, F(9, 18) ¼ 12.85, p , .0001. DISCUSSION The purpose of this study was to test our hypothesis that physical activity is associated with hearing sensitivity among U.S. adults with diabetes. Our findings showed that U.S. adults with diabetes who had greater hearing impairment were less active, which underscores the need to promote physical activity among this population. Given the limited studies examining the association between physical activity and hearing sensitivity among adults with diabetes, it is difficult to compare our findings to others. However, the present findings are similar to those of Loprinzi et al. (2012), who showed that among those in the general population, participants with higher cardiorespiratory fitness, an indicator of physical activity, have better hearing function. Similarly, Loprinzi, Lee, and colleagues
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P. D. LOPRINZI ET AL. TABLE 1 Characteristics of the Analytic Sample (Mean/Proportion [95% CI]), NHANES 2003– 2006 Mean/Proportion (95% CI)
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Variable N Age (years) BMI (kg/m2) % Male Race/Ethnicity % Non-Hispanic White % Other Race Marital Status % Married or Living with Partner % Other Education % High School or Less % Some College or More Comorbidity Index % 0 Comorbidities % 1 Comorbidities % 2 þ Comorbidities Cotinine (ng/mL) Homocysteine (umol/L) HDL Cholesterol (mg/dL) HbA1c (%) C-Reactive Protein (mg/dL) Creatinine (g/L) Albumin (mg/L) Albumin-to-Creatinine Ratio (mg/g) % Microalbuminuria % Exposed to Loud Noise Within Last 24 Hr % Vision Affecting Activities of Daily Living Moderate-to-Vigorous Physical Activity (min/day)
Entire Sample
Hearing Within Normal Limits
Mild Hearing Loss
Moderate þ Hearing Loss
184 58.5 [55.7, 61.4] 30.0 [28.7, 31.3] 58.0 [47.5, 68.5]
35 44.9 [40.2, 49.7] 28.9 [26.8, 31.0] 39.7 [19.7, 59.8]
40 59.1 [55.1, 63.2] 28.9 [26.5, 31.4] 65.6 [49.6, 81.6]
109 65.6 [63.2, 68.0] 31.1 [29.4, 32.6] 64.6 [55.7, 73.5]
69.7 [58.5, 80.9] 30.2 [19.0, 41.4]
64.6 [44.4, 84.8] 35.3 [15.1, 55.5]
57.1 [37.0, 77.2] 42.8 [22.7, 62.9]
77.6 [67.0, 88.1] 22.3 [11.8, 32.9]
69.6 [60.3, 78.8] 30.3 [21.1, 39.6]
82.9 [67.0, 98.7] 17.0 [1.2, 32.9]
58.8 [32.1, 85.5] 41.1 [14.4, 67.8]
66.8 [55.6-, 78.1] 33.1 [21.8, 44.3]
54.2 [46.2, 62.1] 45.7 [37.8, 53.7]
44.8 [23.6, 65.9] 55.1 [34.0, 76.3]
40.0 [19.4, 60.6] 59.9 [39.3, 80.5]
65.0 [50.5, 79.5] 34.9 [20.4, 49.4]
16.5 [9.7, 23.2] 32.9 [21.3, 44.5] 50.4 [38.8, 62.1] 63.1 [34.3, 91.8] 9.8 [9.2, 10.5] 49.9 [47.3, 52.6] 7.03 [6.7, 7.3] 0.42 [0.33, 0.51] 1.1 [0.9, 1.4] 86.3 [26.0, 146.7] 88.6 [20.3, 156.9] 36.1 [24.9, 47.2] 6.3 [0.1, 12.6] 13.6 [6.8–20.5] 4.8 [3.2, 6.4]
41.3 [19.9, 62.6] 38.1 [14.4, 61.8] 20.5 [4.8, 36.1] 65.2 [11.5, 118.9] 7.5 [6.5, 8.4] 51.7 [45.2, 58.1] 7.7 [7.0, 8.3] 0.43 [0.24, 0.62] 1.1 [0.7, 1.4] 12.1 [4.9, 19.3] 11.7 [4.5, 18.8] 9.6 [0.0, 19.5] 14.6 [2.6, 26.7] 0.4 [0.0, 1.3] 7.9 [2.5, 13.3]
10.8 [0.0, 24.5] 45.6 [25.8, 65.3] 43.5 [24.5, 62.5] 65.7 [5.5, 125.9] 10.8 [9.5, 12.2] 51.8 [43.5, 60.2] 6.7 [6.2, 7.2] 0.31 [0.18, 0.42] 1.1 [0.9, 1.3] 90.9 [15.6, 166.2] 91.8 [15.3, 168.4] 39.5 [17.5, 61.4] 1.6 [0.0, 5.0] 2.3 [0.0, 5.4] 4.9 [0.1, 9.6]
5.6 [0.2, 11.0] 25.0 [11.3, 38.7] 69.2 [54.8, 83.6] 60.8 [15.4, 106.3] 10.7 [9.8, 11.6] 48.3 [44.9, 51.6] 6.8 [6.5, 7.1] 0.46 [0.34, 0.58] 1.3 [0.9, 1.5] 124.0 [16.7, 231.3] 128.3 [4.8, 251.8] 48.8 [32.3, 65.3] 3.9 [0.0, 10.1] 25.3 [11.6, 39.1] 3.2 [0.7, 5.6]
Note. CI ¼ confidence interval; BMI ¼ body mass index; HDL ¼ high-density lipoprotein; HbA1c ¼ glycohemoglobin.
(2013) showed that physical activity is inversely associated with tinnitus, or a ringing, roaring, or buzzing sound heard in the ears in the absence of external stimuli. Importantly, given the cross-sectional nature of the present study, it is not possible to determine the direction of influence. We acknowledge that those with hearing impairment were less active because of their condition. For example, individuals with sensory impairments may be more likely to have balance deficits (Baloh, Ying, & Jacobson, 2003), which in turn, may influence their physical activity levels. It is also possible that regular participation in physical activity may help to preserve hearing function as a result of physical activity-induced changes in parameters influencing hearing sensitivity. Future studies, particularly prospective and experimental designs, are needed to determine the direction of causality between physical activity and hearing function. These future studies will help to determine whether decreased physical activity contributes to impaired hearing, whether impaired hearing decreases physical activity, or whether causality is bidirectional. The following narrative will discuss possible mechanisms through which physical activity may have a
preserving effect for hearing, which may help inform the development of future studies needed in this area. There are several possible underlying mechanisms that may help to explain the assertion that physical activity may have a preserving effect on hearing function. The proposed mechanisms explaining the diabetes – hearing link include: cochlear microangiopathy, elevated blood glucose in the cerebrospinal fluid, auditory neuropathy, and diabetic encephalopathy (Austin et al., 2009). It is plausible to suggest that physical activity may have a preserving effect on hearing among adults with diabetes through favorable changes in these proposed mechanisms. With regard to cochlear microangiopathy, a microvasculature disease of the small vessels of the cochlea, physical activity may help to prevent such microvasculature disease through a variety of mechanisms. For example, physical activity is associated with improved endothelial function (Loprinzi & Cardinal, 2012), reduced systemic inflammation (Loprinzi, Cardinal, et al., 2013), decreased resistance to blood flow (Gielen, Schuler, & Adams, 2010), and attenuated blood pressure (Kokkinos, Giannelou, Manolis, & Pittaras, 2009), all of which may, in theory, influence cochlear microangiopathy.
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TABLE 2 Negative Binomial Regression Results Examining the Association Between Hearing Impairment and Physical Activity
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Incident Rate Ratio (95% CI) Fully Adjusted Hearing Mild Hearing Loss Versus Hearing Within Normal Limits Moderate or Greater Hearing Loss Versus Hearing Within Normal Limits Covariates Age, 1 year older BMI, kg/m2 Non-Hispanic White Versus Other Female Versus Male Some College or More Versus High School or Less Not Married Versus Married 1 þ Comorbidity Versus 0 Comorbidities Cotinine, 1 ng/mL Higher C-reactive Protein, 1 mg/dL Higher HbA1c, 1 % Higher Homocysteine, 1 umol/L Higher HDL Cholesterol, 1 mg/dL Higher Microalbumineria Versus None Noise Exposure Versus None Vision Limitation Versus None
p
Incident Rate Ratio (95% CI) Minimally Adjusted
p
.01 ,.01
0.07 [0.01, 0.60] 0.06 [0.01, 0.54]
.02 .01
0.07 [0.01, 0.51] 0.05 [0.01, 0.21]
0.99 [0.93, 1.04] 0.93 [0.83, 1.05] 0.29 [0.09, 0.87] 0.24 [0.04, 1.33] 0.92 [0.44, 1.90] 0.16 [0.04, 0.57] 0.95 [0.23, 3.89] 0.99 [0.98, 0.99] 0.07 [0.02, 0.30] 0.51 [0.34, 0.78] 1.03 [0.83, 1.27] 1.00 [0.94, 1.05] 0.69 [0.12, 3.74] 0.23 [0.03, 1.56] 1.99 [0.53, 7.41]
.75 .24 .03 .10 .81 ,.01 .94 ,.01 ,.01 ,.01 .76 .99 .65 .12 .29
N/A N/A 0.33 [0.14, 0.78] 0.24 [0.06, 0.95] N/A 0.13 [0.03, 0.46] N/A 0.99 [0.98, 0.99] 0.04 [0.01, 0.10] 0.54 [0.40, 0.71] N/A N/A N/A 0.18 [0.03, 1.06] N/A
.01 .04 ,.01 ,.01 ,.01 ,.01
.06
Note. Fully adjusted model included all covariates. Minimally adjusted model included only covariates from the fully adjusted model that had a p , .20. CI ¼ confidence interval; BMI ¼ body mass index; HbA1c ¼ glycohemoglobin; HDL ¼ high-density lipoprotein; N/A ¼ not applicable.
Although not directly examining the association between physical activity and blood glucose levels in the cerebrospinal fluid, there is considerable empirical support for the exercise-lowering effects of blood glucose in the circulatory system (Sigal et al., 2006). With regard to auditory neuropathy, there is evidence to support a neuroprotective effect of exercise on the central nervous system (Zigmond, Cameron, Hoffer, & Smeyne, 2012). Whether this exercise effect also occurs in the central auditory system is still yet to be fully determined. However, there is some evidence to suggest an exerciseinduced protective effect on the central auditory system. For example, Ismail and colleagues (1973) demonstrated that an 8-week aerobic training program enhanced the participants’ auditory ability to recover from noiseinduced auditory fatigue. As a result, exercise-induced neurotrophic factor expression may help to attenuate noise- and/or age-induced auditory neuropathy. Lastly, physical activity may be associated with reduced diabetic encephalopathy, which is an increased cognitive decline among those with diabetes, through a reduced risk for cognitive decline from physical activity (Yaffe, Barnes, Nevitt, Lui, & Covinsky, 2001). This is likely through physical activity-induced increases in neurotrophic factors, such as glial cells and brain-derived neurotrophic factor (Zigmond et al., 2012). In summary, our findings suggest an association between MVPA and hearing sensitivity among U.S. adults with
evidence of diabetes. Major strengths of this study include using a population-based sample of adults with diabetes, examining this novel relationship among this population, and using objective measures of both hearing sensitivity and physical activity. A limitation to the study is our inability to infer cause and effect. Due to the crosssectional design, it is likely that those with more severe hearing impairments were less active because of their condition. Although this assertion cannot be discounted, there are plausible biological mechanisms to suggest that physical activity may have auditory protective effects. Future studies, particularly prospective and experimental designs, are needed in this relatively new area of inquiry. Another limitation was the relatively small number of participants who had hearing within normal limits. Additionally, due to the relatively small sample size, we were not able to test for moderation effects. Future studies are encouraged to employ a larger sample size and examine whether, for example, type of diabetes (i.e., type 1 or type 2), age, gender, and race/ethnicity moderate the association between physical activity and hearing sensitivity among adults with diabetes. Lastly, and although similar to other estimates (Troiano et al., 2008), participants with diabetes in the present sample engaged in very little MVPA, with only a 5-min MVPA difference occurring between those who had hearing within normal limits compared with those who had moderate or greater hearing loss.
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Regardless of direction of influence, what we know at this point is that U.S. adults with diabetes who have greater hearing impairment are less active than their counterparts with better hearing function. As a result, these findings suggest that efforts are particularly needed to increase the activity levels of adults with diabetes who have hearing impairment to reduce further health complications associated with an inactive lifestyle.
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WHAT DOES THIS ARTICLE ADD? Recent research has demonstrated that diabetes increases the risk for hearing impairment. Additionally, considerable research implicates physical activity as an effective strategy to help prevent and treat diabetes. Therefore, it is plausible to suggest an association between physical activity and hearing function among those with diabetes. However, to our knowledge, no study has specifically examined this association in this population. Our findings suggest that adults with diabetes who are physically active have better hearing function than their counterparts who are not as physically active. This suggests that physical activity may attenuate the progression of diabetes-induced hearing impairment, which is another potential reason for people to remain active for life. If we can prevent, delay, or otherwise improve this through increased MVPA participation, it is of practical use to audiologists too. It also brings audiologists into the fight against physical inactivity, which is consistent with the global Exercise is Medicinew initiative (Blair, Sallis, Hutber, & Archer, 2012). Physical activity promotion is needed among adults with diabetes, and in particular, adults with diabetes who have greater hearing impairment.
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