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Visual Impairment, Uncorrected Refractive Error, and Accelerometer-Defined Physical Activity in the United States

Visual Impairment, Uncorrected Refractive Error, and Accelerometer-Defined Physical Activity in... Abstract Objective To examine how accelerometer-measured physical activity is affected by visual impairment (VI) and uncorrected refractive error (URE). Design Cross-sectional study using data from the 2003-2004/2005-2006 National Health and Nutritional Examination Survey. Visual impairment was defined as better-eye postrefraction visual acuity worse than 20/40. Uncorrected refractive error was defined as better-eye presenting visual acuity of 20/50 or worse, improving to 20/40 or better with refraction. Adults older than 20 years with normal sight, URE, and VI were analyzed. The main outcome measures were steps per day and daily minutes of moderate or vigorous physical activity (MVPA). Results Five thousand seven hundred twenty-two participants (57.1%) had complete visual acuity and accelerometer data. Individuals with normal sight took an average of 9964 steps per day and engaged in an average of 23.5 minutes per day of MVPA, as compared with 9742 steps per day and 23.1 minutes per day of MVPA in individuals with URE (P >> .50 for both) and 5992 steps per day and 9.3 minutes/d of MVPA in individuals with VI (P < .01 for both). In multivariable models, individuals with VI took 26% fewer steps per day (P < .01; 95% CI, 18%-34%) and spent 48% less time in MVPA (P < .01; 95% CI, 37%-57%) than individuals with normal sight. The decrement in steps and MVPA associated with VI equaled or exceeded that associated with self-reported chronic obstructive pulmonary disease, diabetes mellitus, arthritis, stroke, or congestive heart failure. Conclusions Visual impairment, but not URE, impacts physical activity equal to or greater than other serious medical conditions. The substantial decrement in physical activity observed in nonrefractive vision loss highlights a need for better strategies to safely improve mobility and increase physical activity in this group. Physical activity is an important predictor for many health outcomes.1 Restrictions in physical activity have been associated with a decreased quality of life, higher morbidity, and higher mortality.2-10 Encouraging more physical activity may therefore provide important benefits.11 Vision loss has been shown to affect several aspects of mobility, including balance, falls, and movement through mobility courses.12-20 The perceived risks of mobility may also further limit physical activity among patients with low vision.21 Previous studies of mobility in individuals with decreased vision have largely relied on self-reports or proxy reports, which are subject to reporting biases, or on physical activity performed in a laboratory setting. However, the relationships of these measures to real-world physical activity patterns are unclear. Thus, there is a need to objectively characterize the relationship between decreased vision and real-world physical activity using technology such as accelerometers. To objectively evaluate real-world physical activity, the National Cancer Institute supported the use of accelerometers in the National Health and Nutrition Examination Surveys (NHANES) conducted during 2003-2004 and 2005-2006. Accelerometers have been validated as a measure of total energy expenditure and have been used as a preferred method for objective measurement of physical activity in several studies.22-30 Herein, we use NHANES visual acuity and accelerometer data to examine the relationship between decreased vision and objectively measured physical activity levels in adult Americans. Some causes of decreased vision, uncorrected refractive error (URE) in particular, are easily correctable with minimal cost. Attention was therefore given to how physical activity is affected by URE as compared with visual impairment (VI), defined as decreased vision not resulting from URE. Methods The NHANES 2003-2004/2005-2006 protocols were reviewed and approved by the National Center for Health Statistics research ethics review board. Informed consent was obtained from all participants. The research adhered to the tenets of the Declaration of Helsinki. Study population Data were obtained from the 2003-2004/2005-2006 rounds of NHANES, a cross-sectional study chosen to reflect a representative sample of the US civilian, noninstitutionalized population through a complex, multistage probability design.31 Survey participants were interviewed in their homes and invited to undergo a comprehensive health examination in a mobile examination center, including visual acuity testing and initiation of a 1-week physical activity measurement trial using an accelerometer. Survey participants provided basic demographic data such as age, sex, and ethnicity. Evaluation of visual acuity Visual acuity was measured for each eye as previously described.32 Presenting visual acuity for each eye was assessed using the ARK-760 (Nidek Co Ltd), an autorefractor containing built-in visual acuity charts. Participants were asked to wear their usual distance vision correction, if any. The 20/50 line was presented first. If the participant was unable to read the 20/50 line, the 20/200 line was presented. Participants who could not read the 20/200 line had their visual acuity categorized as worse than 20/200. Participants able to correctly read at least 4 of the 5 characters for the 20/50 line were allowed to move to the next line of smaller characters. This continued until the participant missed 2 or more characters per line for 2 lines in a row. Presenting visual acuity was recorded as the last line for which 4 or more characters were read correctly. Visual acuity was not tested in participants who reported during the home interview that they had no light perception. After presenting visual acuity was measured, corrective lenses were removed and the refraction of each eye was measured by the autorefractor. For eyes with presenting visual acuity worse than 20/25, corrected visual acuity was assessed using the measured refractive error correction. Visual acuity of the better-seeing eye was used to characterize visual impairment status. For participants with visual acuity data in only 1 eye, better-seeing eye visual acuity was taken as the acuity of the lone measured eye. When autorefraction results were missing from only 1 eye, we assumed that the visual acuity in that eye did not correct to 20/40 or better with refraction. Participants with missing presenting acuity in both eyes, or with visual acuity worse than 20/40 in both eyes with no autorefraction in either eye, were considered to have incomplete visual acuity data and were excluded from the analyses. Subjects whose presenting visual acuity was 20/40 or better were classified as having normal sight. Individuals in whom presenting visual acuity was worse than 20/40, but postrefraction visual acuity was 20/40 or better, were characterized as having URE. Subjects whose visual acuity was worse than 20/40 even after autorefraction, or who reported no light perception (10 of 10 020 participants), were classified as having VI. The degree of decreased visual acuity was further classified as moderate (worse than 20/40 but better than 20/200) or severe (20/200 or worse). Evaluation of physical activity Physical activity was measured by having participants wear an accelerometer (model 7164; Actigraph, LLC) over the right hip on an elastic belt for 7 days. Specific details of the accelerometer protocol have been previously described.33 Participants were instructed to wear the device while awake, but not during times of swimming or bathing. Participants then returned the device, at which point data were downloaded and the accelerometer was evaluated to ensure it still met the manufacturer's calibration specifications. The accelerometer measured and recorded the intensity of vertical acceleration produced by locomotion or other activity over 1-minute intervals. Raw data were recorded as counts per minute and were used to define the activity level for each minute as sedentary/light or moderate/vigorous. One-minute intervals with counts of 2020 or more were defined as having a moderate or vigorous level of physical activity (MVPA).25 Additionally, the device measured the steps taken over each 1-minute interval. Data on intensity of activity were made publically available for both the 2003-2004 and 2005-2006 rounds of NHANES, but step data were made available only for the 2005-2006 round. Data analyses Participants with invalid accelerometer data, defined using the SAS code available at http://riskfactor.cancer.gov/tools/nhanes_pam/, were excluded.25,34 Excluded individuals included those with insufficient wear time and individuals whose accelerometer was found to be out of calibration when returned. Wear time for each day was defined after excluding all intervals of at least 60 consecutive minutes of zero activity intensity counts, with allowance for 1 to 2 minutes of counts between 0 and 100.25 When an individual's wear time was less than 10 hours per day for at least 4 days of the week, that individual's accelerometer data were considered invalid. Differences between groups defined by visual status were calculated using χ2 analyses and univariate negative binomial regression models. Differences in physical activity were then compared across visual acuity status using multivariable negative binomial regression models. Negative binomial models were chosen because steps and minutes of MVPA were in the form of count data that failed tests of normality and displayed evidence of overdispersion.35 Regression coefficients from negative binomial models represent rate ratios, which reflect the relative rate of events (ie, steps or minutes of MVPA) for each variable in the model compared with its control. Covariates included in multivariable models included age, sex, race, obesity (body mass index ≥30 [calculated as weight in kilograms divided by height in meters squared]), and education.25,36 The following systemic diseases were also included as covariates: arthritis, congestive heart failure, chronic obstructive pulmonary disease/asthma, diabetes mellitus, and stroke. The presence of these systemic diseases was based on the participant's responses to the question “Has a doctor or other health professional ever told you that you had (specific disorder)?” If patients noted that they “didn't know,” they were categorized as not having that disorder. Analyses were restricted to individuals 20 years or older because self-reported comorbidity data were fully available only for this age range. Summary measures of physical activity were generated in SAS version 9.2 (SAS Institute Inc) and subsequently analyzed using Stata version 11 (StataCorp).37 All analyses used the examination weights provided with NHANES data sets to adjust for the complex NHANES design; 4-year and 2-year sample weights were used for analysis of MVPA and steps data, respectively.31 Results A total of 20 470 individuals participated in NHANES during the 2003-2004 and 2005-2006 periods. Among these participants, 10 020 (48.9%) were 20 years or older. Within this age group, 5722 participants (57.1%) had complete visual acuity and accelerometer data. In 2005 and 2006, when steps data were collected, there were 10 348 participants, of whom 4979 (48.1%) were 20 years or older. Within this age group, 2852 participants (57.3%) had complete visual acuity and accelerometer data. Subjects who had complete data were older, differed in their racial/ethnic distribution, were less often obese, and more often had at least some college education when compared with individuals with incomplete visual acuity and/or accelerometer data (P ≤ .01 for all) (Table 1). Subjects with complete data were also significantly less likely to report a history of stroke but significantly more likely to report a history of arthritis (P = .01 for both). There were no significant differences in physical activity between those with complete and incomplete visual acuity data (Table 1). Age, race/ethnicity, education, and the frequency of arthritis, congestive heart failure, diabetes, and stroke all varied significantly by vision status (P ≤ .01 for all) (Table 2). Compared with subjects with normal sight, subjects with VI were older, had less college education, and were more likely to report a history of arthritis, congestive heart failure, diabetes, and stroke (P < .05 for all). Subjects with URE, on the other hand, had less college education, were older, were more likely to be Mexican American or non-Hispanic black, and were more likely to report a history of diabetes or stroke as compared with subjects with normal sight (P < .05 for all). After adjusting for age, the comorbid conditions evaluated herein did not differ in frequency across vision status except for diabetes being more common in the URE group than the group with normal sight (P = .04; P >> .09 for all other pairwise comparisons). Valid days and wear time on valid days In the 2003-2004/2005-2006 NHANES data, there was no meaningful difference in the average number of valid days of accelerometer data across those with normal sight (6.0 days), URE (5.9 days), and VI (5.9 days). The mean wear time of the accelerometer per valid day was 14.3 hours among participants with normal sight, as compared with 14.2 hours in subjects with URE (P = .49) and 14.3 hours in subjects with VI (P = .79). Physical activity No differences in physical activity measures were found between individuals with moderate or severe vision loss due to URE (P >> .10) (Figure 1). Individuals with moderately and severely decreased vision from URE were therefore analyzed together in all subsequent analyses. Individuals with different degrees of VI all had physical activity measures that were significantly lower than subjects with normal sight or URE (P < .01 for all), without demonstrating a clear dose response (Figure 1). Hence, in further analyses, individuals with moderate and severe VI were grouped together. The average number of steps taken per day in individuals with normal sight was 9964 as compared with 9742 steps per day in individuals with URE (P = .57) and 5993 steps per day in individuals with VI (P < .01) (Figure 2). On average, individuals with normal sight accumulated approximately 24 daily minutes of MVPA, as compared with 23 daily minutes for individuals with URE (P = .77) and 9 daily minutes for individuals with VI (P < .01) (Figure 2). Individuals with VI took significantly fewer steps and engaged in fewer daily minutes of MVPA when compared with individuals with URE (P < .01 for all). In multivariable models, participants with VI took 26% fewer steps per day (P < .01; 95% CI, 18%-34%) and engaged in 48% (P < .01; 95% CI, 37%-57%) less time in MVPA when compared with participants with normal sight (Table 3). Multivariable models also showed that physical activity outcomes were significantly worse in those with VI as compared with those with URE (19% fewer steps per day; P < .01; 95% CI, 5%-31%; 38% less time in MVPA; P < .01; 95% CI, 23%-50%). There were no significant differences in physical activity measures between those with URE and normal sight (Table 3). Comment Adult Americans with VI, but not those with URE, engage in less physical activity. The impact of VI on walking and physical activity was found to be equal to or greater than all systemic conditions examined, including obesity, chronic obstructive pulmonary disease, arthritis, stroke, and congestive heart failure. To our knowledge, these findings represent the first report of an association between VI and objectively measured physical activity and demonstrate that the cause of vision loss (URE vs nonrefractive causes) is highly relevant to physical activity measures. Decreased physical activity has been associated with numerous adverse health and quality of life outcomes,2-10 suggesting possible ties between VI and systemic well-being.38 Previous work has shown that VI is associated with slower walking, errors when walking through mobility courses, and worse self-reported mobility.15,19,20 Our work extends these previous findings, demonstrating that individuals with VI also walk less and are less physically active. It also confirms previous studies showing that older age, female sex, and higher body mass index are associated with lower physical activity levels, while Mexican American ethnicity is associated with greater physical activity.25,28,36,39,40 Several plausible pathways link VI to lower physical activity.21 Individuals with VI may be less confident walking or may choose a less active lifestyle because of greater difficulty with social interactions.41 Visual impairment can also affect balance, leading to more frequent falls or engendering greater fear of falling.42 Physical activity is also generally greater away from home,43 and VI may affect physical activity by making individuals more homebound, particularly when poor vision affects driving ability.44,45 Previous work did not consistently distinguish between nonrefractive and refractive vision loss, though the current work strongly suggests that this distinction is important when relating mobility outcomes to VI. There are several possible reasons why URE, even when associated with presenting acuities worse than 20/40, was not observed to impact measures of physical activity. In uncorrected myopia, objects become clear as they come closer, which might be sufficient to allow mobility. Alternately, the URE group may be enriched for individuals who are able to function in the presence of their refractive error, minimizing the apparent impact of URE. A final possibility is that decreased visual acuity caused by organic disease fundamentally affects walking and physical activity levels more than decreased acuity from URE. The NHANES does not identify the cause of VI, making study of physical activity restriction with specific eye diseases an important area for future studies. The health consequences of poor physical activity have been widely stated.46,47 However, the extent to which these health consequences are experienced by individuals with VI remains unclear. Visual impairment typically presents later in life48 and may not persist long enough to cause systemic diseases that occur as a result of years of decreased physical activity.49 Indeed, after age adjustment, we did not find that the presence of VI was associated with an increased prevalence of several systemic diseases, though the data available did not allow for subanalyses within individuals with long-term VI. Therefore, restriction of walking and physical activity resulting from VI may not profoundly affect the prevalence of systemic illness but instead have other impacts, such as worsening of existing disease, a greater reliance on others for tasks requiring mobility, greater social isolation, decreased strength and fitness,50 and lower quality of life.51,52 The impact of VI on physical activity suggests that better systems are necessary to encourage walking and physical activity through low-vision rehabilitation. Currently, it is estimated that only 29% of low-vision rehabilitation entities/hospitals offer orientation and mobility training.53 Only 20% of low-vision rehabilitation entities have mobility specialists53 and they often do not stress physical activity as a priority. Our current results suggest that better systems are required to increase physical activity in individuals who are visually impaired, assuming greater physical activity does not translate into more falls or injuries. Our study has several limitations. In the visual acuity examination portion of the study, there was a sizeable nonparticipation rate (12.8%), which may be due to insufficient time to participate, inability to cooperate with the visual acuity examination protocol, or equipment malfunction. Additionally, the refractive correction worn or not worn during visual acuity testing may not have matched the worn correction over the week of accelerometer testing. Considerable accelerometer data (39.2%) were also missing owing to a combination of nonparticipation and questions about data quality for some subjects. Some differences were noted between subjects who completed both the visual acuity and physical activity testing and subjects who failed to complete one or both of these measures, raising the possibility of bias introduced through selective nonparticipation. However, the rates of VI were similar in subjects with and without complete data, as were the rates of most of the diseases studied. This argues against selective nonparticipation among sicker subjects. Indeed, younger subjects were more likely to not complete accelerometer testing, suggesting that participation may have been less common among working individuals, which would unlikely bias findings in a positive direction. While accelerometers are objective measures of physical activity, the values provided by them should not necessarily be taken as absolute. Specifically, the use of accelerometers to assess physical activity does not capture moments of upper extremity exercise nor swimming-related activities. In addition, because our analysis did not account for the decline in exercise capacity with age and used a single cut point value for evaluating moderate and vigorous activity, we may have underestimated the activity levels for older adults.25 Moreover, previous work has noted that the accelerometers used in NHANES tend to overestimate the measurement of steps.54 In summary, VI, but not URE associated with the decreased presenting visual acuity, is associated with lower levels of objectively measured physical activity, with an effect size comparable with or greater than serious medical conditions such as congestive heart failure, diabetes, or stroke. These findings highlight the potential adverse impact of VI on fitness, health, and quality of life. Individuals with VI are an important group to target with regard to increasing physical activity, and increasing physical activity levels without compromising safety (ie, falls) should be a focus of low-vision rehabilitation and mobility training. Back to top Article Information Correspondence: Pradeep Y. Ramulu, MD, MHS, PhD, Johns Hopkins Hospital, 600 N Wolfe St, Maumenee B110, Baltimore, MD 21287 (pramulu1@jhmi.edu). Submitted for Publication: July 16, 2011; final revision received September 28, 2011; accepted October 1, 2011. Author Contributions: Dr Ramulu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Financial Disclosure: None reported. Funding/Support: The NHANES is sponsored by the National Center for Health Statistics, Centers for Disease Control and Prevention. Additional funding for the NHANES Vision Component was provided by the National Eye Institute, National Institutes of Health (Intramural Research Program grant Z01EY000402) and funding for the accelerometry data was provided by the National Cancer Institute. The work was also sponsored by National Eye Institute grant EY018595 and a Research to Prevent Blindness Robert & Helen Schaub Special Scholar Award. Role of the Sponsor: The National Center for Health Statistics was involved in the design and conduct of the study and in data collection. However, the findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institutes of Health, the National Cancer Institute, or the National Eye Institute. References 1. Barker WH, Mullooly JP. Stroke in a defined elderly population, 1967-1985: a less lethal and disabling but no less common disease. Stroke. 1997;28(2):284-2909040676PubMedGoogle ScholarCrossref 2. Bijnen FC, Caspersen CJ, Feskens EJ, Saris WH, Mosterd WL, Kromhout D. Physical activity and 10-year mortality from cardiovascular diseases and all causes: the Zutphen Elderly Study. Arch Intern Med. 1998;158(14):1499-15059679790PubMedGoogle ScholarCrossref 3. Cummings SR, Kelsey JL, Nevitt MC, O’Dowd KJ. Epidemiology of osteoporosis and osteoporotic fractures. Epidemiol Rev. 1985;7:178-2083902494PubMedGoogle Scholar 4. Grand A, Grosclaude P, Bocquet H, Pous J, Albarede JL. Disability, psychosocial factors and mortality among the elderly in a rural French population. J Clin Epidemiol. 1990;43(8):773-7822143526PubMedGoogle ScholarCrossref 5. Kaplan GA, Seeman TE, Cohen RD, Knudsen LP, Guralnik J. Mortality among the elderly in the Alameda County Study: behavioral and demographic risk factors. Am J Public Health. 1987;77(3):307-3123812836PubMedGoogle ScholarCrossref 6. Leon AS, Connett J, Jacobs DR Jr, Rauramaa R. Leisure-time physical activity levels and risk of coronary heart disease and death: the Multiple Risk Factor Intervention Trial. JAMA. 1987;258(17):2388-23953669210PubMedGoogle ScholarCrossref 7. Lynch BM, Cerin E, Owen N, Hawkes AL, Aitken JF. Prospective relationships of physical activity with quality of life among colorectal cancer survivors. J Clin Oncol. 2008;26(27):4480-448718802160PubMedGoogle ScholarCrossref 8. Paffenbarger RS Jr, Hyde RT, Wing AL, Hsieh CC. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med. 1986;314(10):605-6133945246PubMedGoogle ScholarCrossref 9. Rakowski W, Mor V. The association of physical activity with mortality among older adults in the Longitudinal Study of Aging (1984-1988). J Gerontol. 1992;47(4):M122-M1291624695PubMedGoogle Scholar 10. Simonsick EM, Lafferty ME, Phillips CL, et al. Risk due to inactivity in physically capable older adults. Am J Public Health. 1993;83(10):1443-14508214236PubMedGoogle ScholarCrossref 11. Blair SN, Church TS. The fitness, obesity, and health equation: is physical activity the common denominator? JAMA. 2004;292(10):1232-123415353537PubMedGoogle ScholarCrossref 12. Popescu ML, Boisjoly H, Schmaltz H, et al. Age-related eye disease and mobility limitations in older adults. Invest Ophthalmol Vis Sci. 2011;52(10):7168-717421862652PubMedGoogle ScholarCrossref 13. Cahill MT, Banks AD, Stinnett SS, Toth CA. Vision-related quality of life in patients with bilateral severe age-related macular degeneration. Ophthalmology. 2005;112(1):152-15815629836PubMedGoogle ScholarCrossref 14. Wood JM, Lacherez PF, Black AA, Cole MH, Boon MY, Kerr GK. Postural stability and gait among older adults with age-related maculopathy. Invest Ophthalmol Vis Sci. 2009;50(1):482-48718791170PubMedGoogle ScholarCrossref 15. Wood JM, Lacherez P, Black AA, Cole MH, Boon MY, Kerr GK. Risk of falls, injurious falls, and other injuries resulting from visual impairment among older adults with age-related macular degeneration. Invest Ophthalmol Vis Sci. 2011;52(8):5088-509221474773PubMedGoogle ScholarCrossref 16. Hassan SE, Lovie-Kitchin JE, Woods RL. Vision and mobility performance of subjects with age-related macular degeneration. Optom Vis Sci. 2002;79(11):697-70712462538PubMedGoogle ScholarCrossref 17. Lee AG, Coleman AL. Research agenda-setting program for geriatric ophthalmology. J Am Geriatr Soc. 2004;52(3):453-45814962164PubMedGoogle ScholarCrossref 18. Radvay X, Duhoux S, Koenig-Supiot F, Vital-Durand F. Balance training and visual rehabilitation of age-related macular degeneration patients. J Vestib Res. 2007;17(4):183-19318525144PubMedGoogle Scholar 19. Redfern MS, Yardley L, Bronstein AM. Visual influences on balance. J Anxiety Disord. 2001;15(1-2):81-9411388359PubMedGoogle ScholarCrossref 20. Boutin T, Kergoat MJ, Latour J, Massoud F, Kergoat H. Vision in the global evaluation of older individuals hospitalized following a fall [published online May 27, 2011]. J Am Med Dir Assoc. 2011;21621474PubMedGoogle Scholar 21. Rudman DL, Durdle M. Living with fear: the lived experience of community mobility among older adults with low vision. J Aging Phys Act. 2009;17(1):106-12219299842PubMedGoogle Scholar 22. Heil DP. Predicting activity energy expenditure using the Actical activity monitor. Res Q Exerc Sport. 2006;77(1):64-8016646354PubMedGoogle ScholarCrossref 23. Houwen S, Hartman E, Visscher C. Physical activity and motor skills in children with and without visual impairments. Med Sci Sports Exerc. 2009;41(1):103-10919092701PubMedGoogle ScholarCrossref 24. Janney CA, Richardson CR, Holleman RG, et al. Gender, mental health service use and objectively measured physical activity: data from the National Health and Nutrition Examination Survey (NHANES 2003-2004). Ment Health Phys Act. 2008;1(1):9-1619946571PubMedGoogle ScholarCrossref 25. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181-18818091006PubMedGoogle Scholar 26. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc. 2005;37(11):(suppl) S582-S58816294121PubMedGoogle ScholarCrossref 27. Cooper AR, Page AS, Wheeler BW, et al. Mapping the walk to school using accelerometry combined with a global positioning system. Am J Prev Med. 2010;38(2):178-18320117574PubMedGoogle ScholarCrossref 28. Davis MG, Fox KR. Physical activity patterns assessed by accelerometry in older people. Eur J Appl Physiol. 2007;100(5):581-58917063361PubMedGoogle ScholarCrossref 29. Denkinger MD, Franke S, Rapp K, et al; ActiFE Ulm Study Group. Accelerometer-based physical activity in a large observational cohort: study protocol and design of the activity and function of the elderly in Ulm (ActiFE Ulm) study. BMC Geriatr. 2010;10:5020663209PubMedGoogle ScholarCrossref 30. Tudor-Locke C. Steps to better cardiovascular health: how many steps does it take to achieve good health and how confident are we in this number? Curr Cardiovasc Risk Rep. 2010;4(4):271-27620672110PubMedGoogle ScholarCrossref 31. NHANES web tutorials. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nchs/tutorials/nhanes/index.htm. Accessed February 21, 2011 32. CDC/National Center for Health Statistics. National Health and Nutrition Examination Survey: Vision Procedure Manual. Atlanta: GA Centers for Disease Control and Prevention; 2005 33. CDC/National Center for Health Statistics. NHANES: Anthropometry and Physical Activity Monitor Procedures Manual. Atlanta: GA Center for Disease Prevention and Control; 2005 34. Risk factor monitoring and methods: SAS programs for analyzing NHANES 2003-2004 accelerometer data. National Cancer Institute Web site. http://riskfactor.cancer.gov/tools/nhanes_pam/. Accessed February 10, 2011 35. Oliver M, Schluter PJ, Schofield G. A new approach for the analysis of accelerometer data measured on preschool children. J Phys Act Health. 2011;8(2):296-30421415457PubMedGoogle Scholar 36. Yeom HA, Fleury J, Keller C. Risk factors for mobility limitation in community-dwelling older adults: a social ecological perspective. Geriatr Nurs. 2008;29(2):133-14018394514PubMedGoogle ScholarCrossref 37. StataCorp. Stata 11. College Station, TX: Stata Corp; 2010 38. McCarty CA, Nanjan MB, Taylor HR. Vision impairment predicts 5 year mortality. Br J Ophthalmol. 2001;85(3):322-32611222339PubMedGoogle ScholarCrossref 39. Tudor-Locke C, Brashear MM, Johnson WD, Katzmarzyk PT. Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women. Int J Behav Nutr Phys Act. 2010;7:6020682057PubMedGoogle ScholarCrossref 40. Harris TJ, Owen CG, Victor CR, Adams R, Cook DG. What factors are associated with physical activity in older people, assessed objectively by accelerometry? Br J Sports Med. 2009;43(6):442-45018487253PubMedGoogle ScholarCrossref 41. Girdler S, Packer TL, Boldy D. The impact of age-related vision loss. OTJR: Occupation, Participation and Health. 2008;28(3):110-120Google ScholarCrossref 42. Ray CT, Horvat M, Croce R, Mason RC, Wolf SL. The impact of vision loss on postural stability and balance strategies in individuals with profound vision loss. Gait Posture. 2008;28(1):58-6118023185PubMedGoogle ScholarCrossref 43. Ramulu PY, Chan ES, Loyd TL, Ferrucci L, Friedman DS. Comparison of Home and Out-Of-Home Physical Activity Using Accelerometers and Cellular Network Based Tracking Devices. Baltimore, MD: Johns Hopkins University; 2011 44. Lotfipour S, Patel BH, Grotsky TA, et al. Comparison of the visual function index to the Snellen Visual Acuity Test in predicting older adult self-restricted driving. Traffic Inj Prev. 2010;11(5):503-50720872306PubMedGoogle ScholarCrossref 45. West CG, Gildengorin G, Haegerstrom-Portnoy G, Lott LA, Schneck ME, Brabyn JA. Vision and driving self-restriction in older adults. J Am Geriatr Soc. 2003;51(10):1348-135514511153PubMedGoogle ScholarCrossref 46. Carlson SA, Fulton JE, Schoenborn CA, Loustalot F. Trend and prevalence estimates based on the 2008 Physical Activity Guidelines for Americans. Am J Prev Med. 2010;39(4):305-31320837280PubMedGoogle ScholarCrossref 47. Kruger J, Carlson SA, Buchner D. How active are older Americans? Prev Chronic Dis. 2007;4(3):A5317572957PubMedGoogle Scholar 48. Vitale S, Cotch MF, Sperduto RD. Prevalence of visual impairment in the United States. JAMA. 2006;295(18):2158-216316684986PubMedGoogle ScholarCrossref 49. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801-80916534088PubMedGoogle ScholarCrossref 50. Park H, Park S, Shephard RJ, Aoyagi Y. Yearlong physical activity and sarcopenia in older adults: the Nakanojo Study. Eur J Appl Physiol. 2010;109(5):953-96120336310PubMedGoogle ScholarCrossref 51. Yasunaga A, Togo F, Watanabe E, Park H, Shephard RJ, Aoyagi Y. Yearlong physical activity and health-related quality of life in older Japanese adults: the Nakanojo Study. J Aging Phys Act. 2006;14(3):288-30117090806PubMedGoogle Scholar 52. Motl RW, McAuley E. Pathways between physical activity and quality of life in adults with multiple sclerosis. Health Psychol. 2009;28(6):682-68919916636PubMedGoogle ScholarCrossref 53. Owsley C, McGwin G Jr, Lee PP, Wasserman N, Searcey K. Characteristics of low-vision rehabilitation services in the United States. Arch Ophthalmol. 2009;127(5):681-68919433720PubMedGoogle ScholarCrossref 54. Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps per day in US adults. Med Sci Sports Exerc. 2009;41(7):1384-139119516163PubMedGoogle ScholarCrossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Ophthalmology American Medical Association

Visual Impairment, Uncorrected Refractive Error, and Accelerometer-Defined Physical Activity in the United States

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American Medical Association
Copyright
Copyright © 2012 American Medical Association. All Rights Reserved.
ISSN
0003-9950
eISSN
1538-3687
DOI
10.1001/archopthalmol.2011.1773
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Abstract

Abstract Objective To examine how accelerometer-measured physical activity is affected by visual impairment (VI) and uncorrected refractive error (URE). Design Cross-sectional study using data from the 2003-2004/2005-2006 National Health and Nutritional Examination Survey. Visual impairment was defined as better-eye postrefraction visual acuity worse than 20/40. Uncorrected refractive error was defined as better-eye presenting visual acuity of 20/50 or worse, improving to 20/40 or better with refraction. Adults older than 20 years with normal sight, URE, and VI were analyzed. The main outcome measures were steps per day and daily minutes of moderate or vigorous physical activity (MVPA). Results Five thousand seven hundred twenty-two participants (57.1%) had complete visual acuity and accelerometer data. Individuals with normal sight took an average of 9964 steps per day and engaged in an average of 23.5 minutes per day of MVPA, as compared with 9742 steps per day and 23.1 minutes per day of MVPA in individuals with URE (P >> .50 for both) and 5992 steps per day and 9.3 minutes/d of MVPA in individuals with VI (P < .01 for both). In multivariable models, individuals with VI took 26% fewer steps per day (P < .01; 95% CI, 18%-34%) and spent 48% less time in MVPA (P < .01; 95% CI, 37%-57%) than individuals with normal sight. The decrement in steps and MVPA associated with VI equaled or exceeded that associated with self-reported chronic obstructive pulmonary disease, diabetes mellitus, arthritis, stroke, or congestive heart failure. Conclusions Visual impairment, but not URE, impacts physical activity equal to or greater than other serious medical conditions. The substantial decrement in physical activity observed in nonrefractive vision loss highlights a need for better strategies to safely improve mobility and increase physical activity in this group. Physical activity is an important predictor for many health outcomes.1 Restrictions in physical activity have been associated with a decreased quality of life, higher morbidity, and higher mortality.2-10 Encouraging more physical activity may therefore provide important benefits.11 Vision loss has been shown to affect several aspects of mobility, including balance, falls, and movement through mobility courses.12-20 The perceived risks of mobility may also further limit physical activity among patients with low vision.21 Previous studies of mobility in individuals with decreased vision have largely relied on self-reports or proxy reports, which are subject to reporting biases, or on physical activity performed in a laboratory setting. However, the relationships of these measures to real-world physical activity patterns are unclear. Thus, there is a need to objectively characterize the relationship between decreased vision and real-world physical activity using technology such as accelerometers. To objectively evaluate real-world physical activity, the National Cancer Institute supported the use of accelerometers in the National Health and Nutrition Examination Surveys (NHANES) conducted during 2003-2004 and 2005-2006. Accelerometers have been validated as a measure of total energy expenditure and have been used as a preferred method for objective measurement of physical activity in several studies.22-30 Herein, we use NHANES visual acuity and accelerometer data to examine the relationship between decreased vision and objectively measured physical activity levels in adult Americans. Some causes of decreased vision, uncorrected refractive error (URE) in particular, are easily correctable with minimal cost. Attention was therefore given to how physical activity is affected by URE as compared with visual impairment (VI), defined as decreased vision not resulting from URE. Methods The NHANES 2003-2004/2005-2006 protocols were reviewed and approved by the National Center for Health Statistics research ethics review board. Informed consent was obtained from all participants. The research adhered to the tenets of the Declaration of Helsinki. Study population Data were obtained from the 2003-2004/2005-2006 rounds of NHANES, a cross-sectional study chosen to reflect a representative sample of the US civilian, noninstitutionalized population through a complex, multistage probability design.31 Survey participants were interviewed in their homes and invited to undergo a comprehensive health examination in a mobile examination center, including visual acuity testing and initiation of a 1-week physical activity measurement trial using an accelerometer. Survey participants provided basic demographic data such as age, sex, and ethnicity. Evaluation of visual acuity Visual acuity was measured for each eye as previously described.32 Presenting visual acuity for each eye was assessed using the ARK-760 (Nidek Co Ltd), an autorefractor containing built-in visual acuity charts. Participants were asked to wear their usual distance vision correction, if any. The 20/50 line was presented first. If the participant was unable to read the 20/50 line, the 20/200 line was presented. Participants who could not read the 20/200 line had their visual acuity categorized as worse than 20/200. Participants able to correctly read at least 4 of the 5 characters for the 20/50 line were allowed to move to the next line of smaller characters. This continued until the participant missed 2 or more characters per line for 2 lines in a row. Presenting visual acuity was recorded as the last line for which 4 or more characters were read correctly. Visual acuity was not tested in participants who reported during the home interview that they had no light perception. After presenting visual acuity was measured, corrective lenses were removed and the refraction of each eye was measured by the autorefractor. For eyes with presenting visual acuity worse than 20/25, corrected visual acuity was assessed using the measured refractive error correction. Visual acuity of the better-seeing eye was used to characterize visual impairment status. For participants with visual acuity data in only 1 eye, better-seeing eye visual acuity was taken as the acuity of the lone measured eye. When autorefraction results were missing from only 1 eye, we assumed that the visual acuity in that eye did not correct to 20/40 or better with refraction. Participants with missing presenting acuity in both eyes, or with visual acuity worse than 20/40 in both eyes with no autorefraction in either eye, were considered to have incomplete visual acuity data and were excluded from the analyses. Subjects whose presenting visual acuity was 20/40 or better were classified as having normal sight. Individuals in whom presenting visual acuity was worse than 20/40, but postrefraction visual acuity was 20/40 or better, were characterized as having URE. Subjects whose visual acuity was worse than 20/40 even after autorefraction, or who reported no light perception (10 of 10 020 participants), were classified as having VI. The degree of decreased visual acuity was further classified as moderate (worse than 20/40 but better than 20/200) or severe (20/200 or worse). Evaluation of physical activity Physical activity was measured by having participants wear an accelerometer (model 7164; Actigraph, LLC) over the right hip on an elastic belt for 7 days. Specific details of the accelerometer protocol have been previously described.33 Participants were instructed to wear the device while awake, but not during times of swimming or bathing. Participants then returned the device, at which point data were downloaded and the accelerometer was evaluated to ensure it still met the manufacturer's calibration specifications. The accelerometer measured and recorded the intensity of vertical acceleration produced by locomotion or other activity over 1-minute intervals. Raw data were recorded as counts per minute and were used to define the activity level for each minute as sedentary/light or moderate/vigorous. One-minute intervals with counts of 2020 or more were defined as having a moderate or vigorous level of physical activity (MVPA).25 Additionally, the device measured the steps taken over each 1-minute interval. Data on intensity of activity were made publically available for both the 2003-2004 and 2005-2006 rounds of NHANES, but step data were made available only for the 2005-2006 round. Data analyses Participants with invalid accelerometer data, defined using the SAS code available at http://riskfactor.cancer.gov/tools/nhanes_pam/, were excluded.25,34 Excluded individuals included those with insufficient wear time and individuals whose accelerometer was found to be out of calibration when returned. Wear time for each day was defined after excluding all intervals of at least 60 consecutive minutes of zero activity intensity counts, with allowance for 1 to 2 minutes of counts between 0 and 100.25 When an individual's wear time was less than 10 hours per day for at least 4 days of the week, that individual's accelerometer data were considered invalid. Differences between groups defined by visual status were calculated using χ2 analyses and univariate negative binomial regression models. Differences in physical activity were then compared across visual acuity status using multivariable negative binomial regression models. Negative binomial models were chosen because steps and minutes of MVPA were in the form of count data that failed tests of normality and displayed evidence of overdispersion.35 Regression coefficients from negative binomial models represent rate ratios, which reflect the relative rate of events (ie, steps or minutes of MVPA) for each variable in the model compared with its control. Covariates included in multivariable models included age, sex, race, obesity (body mass index ≥30 [calculated as weight in kilograms divided by height in meters squared]), and education.25,36 The following systemic diseases were also included as covariates: arthritis, congestive heart failure, chronic obstructive pulmonary disease/asthma, diabetes mellitus, and stroke. The presence of these systemic diseases was based on the participant's responses to the question “Has a doctor or other health professional ever told you that you had (specific disorder)?” If patients noted that they “didn't know,” they were categorized as not having that disorder. Analyses were restricted to individuals 20 years or older because self-reported comorbidity data were fully available only for this age range. Summary measures of physical activity were generated in SAS version 9.2 (SAS Institute Inc) and subsequently analyzed using Stata version 11 (StataCorp).37 All analyses used the examination weights provided with NHANES data sets to adjust for the complex NHANES design; 4-year and 2-year sample weights were used for analysis of MVPA and steps data, respectively.31 Results A total of 20 470 individuals participated in NHANES during the 2003-2004 and 2005-2006 periods. Among these participants, 10 020 (48.9%) were 20 years or older. Within this age group, 5722 participants (57.1%) had complete visual acuity and accelerometer data. In 2005 and 2006, when steps data were collected, there were 10 348 participants, of whom 4979 (48.1%) were 20 years or older. Within this age group, 2852 participants (57.3%) had complete visual acuity and accelerometer data. Subjects who had complete data were older, differed in their racial/ethnic distribution, were less often obese, and more often had at least some college education when compared with individuals with incomplete visual acuity and/or accelerometer data (P ≤ .01 for all) (Table 1). Subjects with complete data were also significantly less likely to report a history of stroke but significantly more likely to report a history of arthritis (P = .01 for both). There were no significant differences in physical activity between those with complete and incomplete visual acuity data (Table 1). Age, race/ethnicity, education, and the frequency of arthritis, congestive heart failure, diabetes, and stroke all varied significantly by vision status (P ≤ .01 for all) (Table 2). Compared with subjects with normal sight, subjects with VI were older, had less college education, and were more likely to report a history of arthritis, congestive heart failure, diabetes, and stroke (P < .05 for all). Subjects with URE, on the other hand, had less college education, were older, were more likely to be Mexican American or non-Hispanic black, and were more likely to report a history of diabetes or stroke as compared with subjects with normal sight (P < .05 for all). After adjusting for age, the comorbid conditions evaluated herein did not differ in frequency across vision status except for diabetes being more common in the URE group than the group with normal sight (P = .04; P >> .09 for all other pairwise comparisons). Valid days and wear time on valid days In the 2003-2004/2005-2006 NHANES data, there was no meaningful difference in the average number of valid days of accelerometer data across those with normal sight (6.0 days), URE (5.9 days), and VI (5.9 days). The mean wear time of the accelerometer per valid day was 14.3 hours among participants with normal sight, as compared with 14.2 hours in subjects with URE (P = .49) and 14.3 hours in subjects with VI (P = .79). Physical activity No differences in physical activity measures were found between individuals with moderate or severe vision loss due to URE (P >> .10) (Figure 1). Individuals with moderately and severely decreased vision from URE were therefore analyzed together in all subsequent analyses. Individuals with different degrees of VI all had physical activity measures that were significantly lower than subjects with normal sight or URE (P < .01 for all), without demonstrating a clear dose response (Figure 1). Hence, in further analyses, individuals with moderate and severe VI were grouped together. The average number of steps taken per day in individuals with normal sight was 9964 as compared with 9742 steps per day in individuals with URE (P = .57) and 5993 steps per day in individuals with VI (P < .01) (Figure 2). On average, individuals with normal sight accumulated approximately 24 daily minutes of MVPA, as compared with 23 daily minutes for individuals with URE (P = .77) and 9 daily minutes for individuals with VI (P < .01) (Figure 2). Individuals with VI took significantly fewer steps and engaged in fewer daily minutes of MVPA when compared with individuals with URE (P < .01 for all). In multivariable models, participants with VI took 26% fewer steps per day (P < .01; 95% CI, 18%-34%) and engaged in 48% (P < .01; 95% CI, 37%-57%) less time in MVPA when compared with participants with normal sight (Table 3). Multivariable models also showed that physical activity outcomes were significantly worse in those with VI as compared with those with URE (19% fewer steps per day; P < .01; 95% CI, 5%-31%; 38% less time in MVPA; P < .01; 95% CI, 23%-50%). There were no significant differences in physical activity measures between those with URE and normal sight (Table 3). Comment Adult Americans with VI, but not those with URE, engage in less physical activity. The impact of VI on walking and physical activity was found to be equal to or greater than all systemic conditions examined, including obesity, chronic obstructive pulmonary disease, arthritis, stroke, and congestive heart failure. To our knowledge, these findings represent the first report of an association between VI and objectively measured physical activity and demonstrate that the cause of vision loss (URE vs nonrefractive causes) is highly relevant to physical activity measures. Decreased physical activity has been associated with numerous adverse health and quality of life outcomes,2-10 suggesting possible ties between VI and systemic well-being.38 Previous work has shown that VI is associated with slower walking, errors when walking through mobility courses, and worse self-reported mobility.15,19,20 Our work extends these previous findings, demonstrating that individuals with VI also walk less and are less physically active. It also confirms previous studies showing that older age, female sex, and higher body mass index are associated with lower physical activity levels, while Mexican American ethnicity is associated with greater physical activity.25,28,36,39,40 Several plausible pathways link VI to lower physical activity.21 Individuals with VI may be less confident walking or may choose a less active lifestyle because of greater difficulty with social interactions.41 Visual impairment can also affect balance, leading to more frequent falls or engendering greater fear of falling.42 Physical activity is also generally greater away from home,43 and VI may affect physical activity by making individuals more homebound, particularly when poor vision affects driving ability.44,45 Previous work did not consistently distinguish between nonrefractive and refractive vision loss, though the current work strongly suggests that this distinction is important when relating mobility outcomes to VI. There are several possible reasons why URE, even when associated with presenting acuities worse than 20/40, was not observed to impact measures of physical activity. In uncorrected myopia, objects become clear as they come closer, which might be sufficient to allow mobility. Alternately, the URE group may be enriched for individuals who are able to function in the presence of their refractive error, minimizing the apparent impact of URE. A final possibility is that decreased visual acuity caused by organic disease fundamentally affects walking and physical activity levels more than decreased acuity from URE. The NHANES does not identify the cause of VI, making study of physical activity restriction with specific eye diseases an important area for future studies. The health consequences of poor physical activity have been widely stated.46,47 However, the extent to which these health consequences are experienced by individuals with VI remains unclear. Visual impairment typically presents later in life48 and may not persist long enough to cause systemic diseases that occur as a result of years of decreased physical activity.49 Indeed, after age adjustment, we did not find that the presence of VI was associated with an increased prevalence of several systemic diseases, though the data available did not allow for subanalyses within individuals with long-term VI. Therefore, restriction of walking and physical activity resulting from VI may not profoundly affect the prevalence of systemic illness but instead have other impacts, such as worsening of existing disease, a greater reliance on others for tasks requiring mobility, greater social isolation, decreased strength and fitness,50 and lower quality of life.51,52 The impact of VI on physical activity suggests that better systems are necessary to encourage walking and physical activity through low-vision rehabilitation. Currently, it is estimated that only 29% of low-vision rehabilitation entities/hospitals offer orientation and mobility training.53 Only 20% of low-vision rehabilitation entities have mobility specialists53 and they often do not stress physical activity as a priority. Our current results suggest that better systems are required to increase physical activity in individuals who are visually impaired, assuming greater physical activity does not translate into more falls or injuries. Our study has several limitations. In the visual acuity examination portion of the study, there was a sizeable nonparticipation rate (12.8%), which may be due to insufficient time to participate, inability to cooperate with the visual acuity examination protocol, or equipment malfunction. Additionally, the refractive correction worn or not worn during visual acuity testing may not have matched the worn correction over the week of accelerometer testing. Considerable accelerometer data (39.2%) were also missing owing to a combination of nonparticipation and questions about data quality for some subjects. Some differences were noted between subjects who completed both the visual acuity and physical activity testing and subjects who failed to complete one or both of these measures, raising the possibility of bias introduced through selective nonparticipation. However, the rates of VI were similar in subjects with and without complete data, as were the rates of most of the diseases studied. This argues against selective nonparticipation among sicker subjects. Indeed, younger subjects were more likely to not complete accelerometer testing, suggesting that participation may have been less common among working individuals, which would unlikely bias findings in a positive direction. While accelerometers are objective measures of physical activity, the values provided by them should not necessarily be taken as absolute. Specifically, the use of accelerometers to assess physical activity does not capture moments of upper extremity exercise nor swimming-related activities. In addition, because our analysis did not account for the decline in exercise capacity with age and used a single cut point value for evaluating moderate and vigorous activity, we may have underestimated the activity levels for older adults.25 Moreover, previous work has noted that the accelerometers used in NHANES tend to overestimate the measurement of steps.54 In summary, VI, but not URE associated with the decreased presenting visual acuity, is associated with lower levels of objectively measured physical activity, with an effect size comparable with or greater than serious medical conditions such as congestive heart failure, diabetes, or stroke. These findings highlight the potential adverse impact of VI on fitness, health, and quality of life. Individuals with VI are an important group to target with regard to increasing physical activity, and increasing physical activity levels without compromising safety (ie, falls) should be a focus of low-vision rehabilitation and mobility training. Back to top Article Information Correspondence: Pradeep Y. Ramulu, MD, MHS, PhD, Johns Hopkins Hospital, 600 N Wolfe St, Maumenee B110, Baltimore, MD 21287 (pramulu1@jhmi.edu). Submitted for Publication: July 16, 2011; final revision received September 28, 2011; accepted October 1, 2011. Author Contributions: Dr Ramulu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Financial Disclosure: None reported. Funding/Support: The NHANES is sponsored by the National Center for Health Statistics, Centers for Disease Control and Prevention. Additional funding for the NHANES Vision Component was provided by the National Eye Institute, National Institutes of Health (Intramural Research Program grant Z01EY000402) and funding for the accelerometry data was provided by the National Cancer Institute. The work was also sponsored by National Eye Institute grant EY018595 and a Research to Prevent Blindness Robert & Helen Schaub Special Scholar Award. Role of the Sponsor: The National Center for Health Statistics was involved in the design and conduct of the study and in data collection. However, the findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institutes of Health, the National Cancer Institute, or the National Eye Institute. References 1. Barker WH, Mullooly JP. Stroke in a defined elderly population, 1967-1985: a less lethal and disabling but no less common disease. Stroke. 1997;28(2):284-2909040676PubMedGoogle ScholarCrossref 2. Bijnen FC, Caspersen CJ, Feskens EJ, Saris WH, Mosterd WL, Kromhout D. Physical activity and 10-year mortality from cardiovascular diseases and all causes: the Zutphen Elderly Study. Arch Intern Med. 1998;158(14):1499-15059679790PubMedGoogle ScholarCrossref 3. Cummings SR, Kelsey JL, Nevitt MC, O’Dowd KJ. Epidemiology of osteoporosis and osteoporotic fractures. Epidemiol Rev. 1985;7:178-2083902494PubMedGoogle Scholar 4. Grand A, Grosclaude P, Bocquet H, Pous J, Albarede JL. Disability, psychosocial factors and mortality among the elderly in a rural French population. J Clin Epidemiol. 1990;43(8):773-7822143526PubMedGoogle ScholarCrossref 5. Kaplan GA, Seeman TE, Cohen RD, Knudsen LP, Guralnik J. Mortality among the elderly in the Alameda County Study: behavioral and demographic risk factors. Am J Public Health. 1987;77(3):307-3123812836PubMedGoogle ScholarCrossref 6. Leon AS, Connett J, Jacobs DR Jr, Rauramaa R. Leisure-time physical activity levels and risk of coronary heart disease and death: the Multiple Risk Factor Intervention Trial. JAMA. 1987;258(17):2388-23953669210PubMedGoogle ScholarCrossref 7. Lynch BM, Cerin E, Owen N, Hawkes AL, Aitken JF. Prospective relationships of physical activity with quality of life among colorectal cancer survivors. J Clin Oncol. 2008;26(27):4480-448718802160PubMedGoogle ScholarCrossref 8. Paffenbarger RS Jr, Hyde RT, Wing AL, Hsieh CC. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med. 1986;314(10):605-6133945246PubMedGoogle ScholarCrossref 9. Rakowski W, Mor V. The association of physical activity with mortality among older adults in the Longitudinal Study of Aging (1984-1988). J Gerontol. 1992;47(4):M122-M1291624695PubMedGoogle Scholar 10. Simonsick EM, Lafferty ME, Phillips CL, et al. Risk due to inactivity in physically capable older adults. Am J Public Health. 1993;83(10):1443-14508214236PubMedGoogle ScholarCrossref 11. Blair SN, Church TS. The fitness, obesity, and health equation: is physical activity the common denominator? JAMA. 2004;292(10):1232-123415353537PubMedGoogle ScholarCrossref 12. Popescu ML, Boisjoly H, Schmaltz H, et al. Age-related eye disease and mobility limitations in older adults. Invest Ophthalmol Vis Sci. 2011;52(10):7168-717421862652PubMedGoogle ScholarCrossref 13. Cahill MT, Banks AD, Stinnett SS, Toth CA. Vision-related quality of life in patients with bilateral severe age-related macular degeneration. Ophthalmology. 2005;112(1):152-15815629836PubMedGoogle ScholarCrossref 14. Wood JM, Lacherez PF, Black AA, Cole MH, Boon MY, Kerr GK. Postural stability and gait among older adults with age-related maculopathy. Invest Ophthalmol Vis Sci. 2009;50(1):482-48718791170PubMedGoogle ScholarCrossref 15. Wood JM, Lacherez P, Black AA, Cole MH, Boon MY, Kerr GK. Risk of falls, injurious falls, and other injuries resulting from visual impairment among older adults with age-related macular degeneration. Invest Ophthalmol Vis Sci. 2011;52(8):5088-509221474773PubMedGoogle ScholarCrossref 16. Hassan SE, Lovie-Kitchin JE, Woods RL. Vision and mobility performance of subjects with age-related macular degeneration. Optom Vis Sci. 2002;79(11):697-70712462538PubMedGoogle ScholarCrossref 17. Lee AG, Coleman AL. Research agenda-setting program for geriatric ophthalmology. J Am Geriatr Soc. 2004;52(3):453-45814962164PubMedGoogle ScholarCrossref 18. Radvay X, Duhoux S, Koenig-Supiot F, Vital-Durand F. Balance training and visual rehabilitation of age-related macular degeneration patients. J Vestib Res. 2007;17(4):183-19318525144PubMedGoogle Scholar 19. Redfern MS, Yardley L, Bronstein AM. Visual influences on balance. J Anxiety Disord. 2001;15(1-2):81-9411388359PubMedGoogle ScholarCrossref 20. Boutin T, Kergoat MJ, Latour J, Massoud F, Kergoat H. Vision in the global evaluation of older individuals hospitalized following a fall [published online May 27, 2011]. J Am Med Dir Assoc. 2011;21621474PubMedGoogle Scholar 21. Rudman DL, Durdle M. Living with fear: the lived experience of community mobility among older adults with low vision. J Aging Phys Act. 2009;17(1):106-12219299842PubMedGoogle Scholar 22. Heil DP. Predicting activity energy expenditure using the Actical activity monitor. Res Q Exerc Sport. 2006;77(1):64-8016646354PubMedGoogle ScholarCrossref 23. Houwen S, Hartman E, Visscher C. Physical activity and motor skills in children with and without visual impairments. Med Sci Sports Exerc. 2009;41(1):103-10919092701PubMedGoogle ScholarCrossref 24. Janney CA, Richardson CR, Holleman RG, et al. Gender, mental health service use and objectively measured physical activity: data from the National Health and Nutrition Examination Survey (NHANES 2003-2004). Ment Health Phys Act. 2008;1(1):9-1619946571PubMedGoogle ScholarCrossref 25. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181-18818091006PubMedGoogle Scholar 26. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc. 2005;37(11):(suppl) S582-S58816294121PubMedGoogle ScholarCrossref 27. Cooper AR, Page AS, Wheeler BW, et al. Mapping the walk to school using accelerometry combined with a global positioning system. Am J Prev Med. 2010;38(2):178-18320117574PubMedGoogle ScholarCrossref 28. Davis MG, Fox KR. Physical activity patterns assessed by accelerometry in older people. Eur J Appl Physiol. 2007;100(5):581-58917063361PubMedGoogle ScholarCrossref 29. Denkinger MD, Franke S, Rapp K, et al; ActiFE Ulm Study Group. Accelerometer-based physical activity in a large observational cohort: study protocol and design of the activity and function of the elderly in Ulm (ActiFE Ulm) study. BMC Geriatr. 2010;10:5020663209PubMedGoogle ScholarCrossref 30. Tudor-Locke C. Steps to better cardiovascular health: how many steps does it take to achieve good health and how confident are we in this number? Curr Cardiovasc Risk Rep. 2010;4(4):271-27620672110PubMedGoogle ScholarCrossref 31. NHANES web tutorials. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nchs/tutorials/nhanes/index.htm. Accessed February 21, 2011 32. CDC/National Center for Health Statistics. National Health and Nutrition Examination Survey: Vision Procedure Manual. Atlanta: GA Centers for Disease Control and Prevention; 2005 33. CDC/National Center for Health Statistics. NHANES: Anthropometry and Physical Activity Monitor Procedures Manual. Atlanta: GA Center for Disease Prevention and Control; 2005 34. Risk factor monitoring and methods: SAS programs for analyzing NHANES 2003-2004 accelerometer data. National Cancer Institute Web site. http://riskfactor.cancer.gov/tools/nhanes_pam/. Accessed February 10, 2011 35. Oliver M, Schluter PJ, Schofield G. A new approach for the analysis of accelerometer data measured on preschool children. J Phys Act Health. 2011;8(2):296-30421415457PubMedGoogle Scholar 36. Yeom HA, Fleury J, Keller C. Risk factors for mobility limitation in community-dwelling older adults: a social ecological perspective. Geriatr Nurs. 2008;29(2):133-14018394514PubMedGoogle ScholarCrossref 37. StataCorp. Stata 11. College Station, TX: Stata Corp; 2010 38. McCarty CA, Nanjan MB, Taylor HR. Vision impairment predicts 5 year mortality. Br J Ophthalmol. 2001;85(3):322-32611222339PubMedGoogle ScholarCrossref 39. Tudor-Locke C, Brashear MM, Johnson WD, Katzmarzyk PT. Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women. Int J Behav Nutr Phys Act. 2010;7:6020682057PubMedGoogle ScholarCrossref 40. Harris TJ, Owen CG, Victor CR, Adams R, Cook DG. What factors are associated with physical activity in older people, assessed objectively by accelerometry? Br J Sports Med. 2009;43(6):442-45018487253PubMedGoogle ScholarCrossref 41. Girdler S, Packer TL, Boldy D. The impact of age-related vision loss. OTJR: Occupation, Participation and Health. 2008;28(3):110-120Google ScholarCrossref 42. Ray CT, Horvat M, Croce R, Mason RC, Wolf SL. The impact of vision loss on postural stability and balance strategies in individuals with profound vision loss. Gait Posture. 2008;28(1):58-6118023185PubMedGoogle ScholarCrossref 43. Ramulu PY, Chan ES, Loyd TL, Ferrucci L, Friedman DS. Comparison of Home and Out-Of-Home Physical Activity Using Accelerometers and Cellular Network Based Tracking Devices. Baltimore, MD: Johns Hopkins University; 2011 44. Lotfipour S, Patel BH, Grotsky TA, et al. Comparison of the visual function index to the Snellen Visual Acuity Test in predicting older adult self-restricted driving. Traffic Inj Prev. 2010;11(5):503-50720872306PubMedGoogle ScholarCrossref 45. West CG, Gildengorin G, Haegerstrom-Portnoy G, Lott LA, Schneck ME, Brabyn JA. Vision and driving self-restriction in older adults. J Am Geriatr Soc. 2003;51(10):1348-135514511153PubMedGoogle ScholarCrossref 46. Carlson SA, Fulton JE, Schoenborn CA, Loustalot F. Trend and prevalence estimates based on the 2008 Physical Activity Guidelines for Americans. Am J Prev Med. 2010;39(4):305-31320837280PubMedGoogle ScholarCrossref 47. Kruger J, Carlson SA, Buchner D. How active are older Americans? Prev Chronic Dis. 2007;4(3):A5317572957PubMedGoogle Scholar 48. Vitale S, Cotch MF, Sperduto RD. Prevalence of visual impairment in the United States. JAMA. 2006;295(18):2158-216316684986PubMedGoogle ScholarCrossref 49. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801-80916534088PubMedGoogle ScholarCrossref 50. Park H, Park S, Shephard RJ, Aoyagi Y. Yearlong physical activity and sarcopenia in older adults: the Nakanojo Study. Eur J Appl Physiol. 2010;109(5):953-96120336310PubMedGoogle ScholarCrossref 51. Yasunaga A, Togo F, Watanabe E, Park H, Shephard RJ, Aoyagi Y. Yearlong physical activity and health-related quality of life in older Japanese adults: the Nakanojo Study. J Aging Phys Act. 2006;14(3):288-30117090806PubMedGoogle Scholar 52. Motl RW, McAuley E. Pathways between physical activity and quality of life in adults with multiple sclerosis. Health Psychol. 2009;28(6):682-68919916636PubMedGoogle ScholarCrossref 53. Owsley C, McGwin G Jr, Lee PP, Wasserman N, Searcey K. Characteristics of low-vision rehabilitation services in the United States. Arch Ophthalmol. 2009;127(5):681-68919433720PubMedGoogle ScholarCrossref 54. Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps per day in US adults. Med Sci Sports Exerc. 2009;41(7):1384-139119516163PubMedGoogle ScholarCrossref

Journal

Archives of OphthalmologyAmerican Medical Association

Published: Mar 12, 2012

Keywords: physical activity,refractive errors,visual impairment,accelerometers,cerebrovascular accident,ischemic stroke,congestive heart failure,arthritis,visual acuity,mobility,diabetes mellitus,chronic obstructive airway disease,vision,blindness

References