Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults

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Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults Eun Sil Koh b,c, Soong-Nang Jang d, Nam-Jong Paik a, Ki Woong Kim e,f, Jae-Young Lim a,* a Department of Rehabilitation Medicine, Seoul National Univerisity College of Medicine, Seoul National University Bundang Hospital, 300, Gumi-dong, Bundang-gu, 463-707 Seongnam-si, Gyeonggi-do, Republic of Korea b Department of Rehabilitation Medicine, National Medical Center, 245, Eulji-ro, Jung-gu, 100-799 Seoul, Republic of Korea c Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea d Red Cross College of Nursing, Chung-Ang University, 221, Heukseok-Dong, Dongjak-Gu, 156-756 Seoul, Republic of Korea e Department of Neuropsychiatry, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 300, Gumi-dong, Bundang-gu, 463-707 Seongnam-si, Gyeonggi-do, Republic of Korea f Department of Brain and Cognitive Science, Seoul National Univesity College of Natural Sciences, Bldg. 501 Shilim, Gwanak, 151-746 Seoul, Republic of Korea

A R T I C L E I N F O

A B S T R A C T

Article history: Received 14 June 2013 Received in revised form 11 May 2014 Accepted 12 May 2014 Available online xxx

Purpose: To investigate age and gender patterns in associations between lifestyle factors and physical performance in community-dwelling older Korean adults. Design and methods: A cross-sectional study was conducted in a population-based sample of an urban area. Randomly sampled older Korean adults (n = 664; mean age, 74.6 years) participated. Data on current physical activity level and doing exercise, social participation and hobbies, smoking status, drinking status, sleep quality, and physical performance were obtained. Binary logistic regression analyses were used to identify the age and gender patterns in associations between various lifestyle factors and physical performance. Results: In younger (age <85 years) men, significant predictors of poor physical performance by logistic regression analysis after adjusting for covariates were current physical activity time, doing exercise, and engagement in social activities. In younger women, current physical activity time and sleep quality were related to poor physical performance. In older (age 85 years) men, family gatherings were a significant factor. In older women, no lifestyle factor assessed showed a significant relationship with poor physical performance. Conclusion: Interventions implemented to modify lifestyle factors need to focus on age and gender subgroups in the elderly population. Lifestyle modification should be emphasised as a targeted treatment program for Korean adults aged <85 years. ß 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords: Lifestyle Physical performance Gender Older adults

1. Introduction Basic physical functioning, such as walking and moving, deteriorates in old age. Thus, maintaining autonomy and independence while growing older are key goals for healthy and successful aging. The physical functioning of elders is related to such factors as age, gender, cognitive function, physical and psychological characteristics, diseases, and living environment (Antonelli-Incalzi et al., 2007; Auyeung et al., 2008; Buchman et al., 2007; Dam, von Mu¨hlen, & Barrett-Connor, 2009; Fiser et al., 2010; Ishizaki et al., 2011; Ko, Park, Lim, Kim, & Paik, 2009; Kuh et al., 2005; Martin et al., 2011;

* Corresponding author. Tel.: +82 31 787 7732; fax: +82 31 787 4056. E-mail address: [email protected] (J.-Y. Lim).

McDaniel, Renner, Sloane, & Kraus, 2011; Penninx et al., 1998; Takata et al., 2008; Tiedemann, Sherrington, & Lord, 2007; Valentine, Misic, Rosengren, Woods, & Evans, 2009; Woo, Leung, & Lau, 2009). ‘‘Lifestyle’’ is a generic term that describes the way a person lives; lifestyle includes diet, physical activity, social activity, leisure activity, smoking, sleep pattern, and habitual exercise. Among the many factors influencing physical functioning, lifestyle is one that is modifiable (Martin, Syddall, Dennison, Cooper, & Sayer, 2009). Thus, lifestyle modification has been considered an important aspect of treatment programs in geriatric medicine (Fuente Mde, Cruces, Hernandez, & Ortega, 2011; Rejeski et al., 2005; Yokochi, Watanabe, Ida, Yoshida, & Sato, 2012). However, our understanding of how lifestyle factors actually influence physical performance and the degree to which they are important for independent living among the older population is incomplete.

http://dx.doi.org/10.1016/j.archger.2014.05.002 0167-4943/ß 2014 Elsevier Ireland Ltd. All rights reserved.

Please cite this article in press as: Koh, E.S., et al., Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.002

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Previous studies have mostly investigated the effects of lifestyle on mortality or on medical conditions such as metabolic syndrome, cancer, and cardiovascular disease (Gu, Li, & Zhang, 2010; Khare et al., 2012; Sasazuki et al., 2012; Silveira, Horta, Gigante, & Azevedo Junior, 2010). However, results from studies examining associations between lifestyle factors and mortality or diseases do not seem to be sufficient to design an effective lifestyle intervention program targeting improvement in functional status and maintenance of independent living in elderly individuals. Moreover, no study on lifestyle factors in the ‘‘oldest old’’ population (85 years) has been reported, although this group may suffer to a greater degree and account for a higher proportion of healthcare spending. Furthermore, these individuals have usually been explicitly excluded from public health intervention studies, which may have resulted in even greater deterioration in their health and functioning (Wetle, 2008). Understanding the relationship between lifestyle factors and physical performance is important in designing a treatment program focusing on habitual health behaviors for community-dwelling older adults. Thus, we focused here on determining lifestyle factors that can influence physical performance as a requirement for active aging. It is well known that physical function declines progressively with age and that there are gender differences in the pattern of decline. In this regard, previous reports have shown that physical function decreases progressively with age only in the female elderly population, especially those older than 80 years. In contrast, men younger than 85 years of age typically show preserved physical function (Lim et al., 2007). These studies suggest that interventions to improve the health status of elderly people should consider gender difference (Eun, Song, & Gu, 2008; Prus & Gee, 2003). Thus, it is important to identify modifiable risk factors that influence physical function by gender and age and to develop targeted interventions to reduce or prevent functional decline with age. Changing lifestyles is a slow process, so healthcare professionals must provide a comprehensive prescription to improve adherence to healthy behaviors for older people at high risk for chronic conditions. The aim of this study was to investigate age and gender patterns in associations between lifestyle factors and physical performance in community-dwelling elderly Korean adults. 2. Subjects and methods 2.1. Study population Data were obtained from the baseline survey of the Korean Longitudinal Study on Health and Aging (KLoSHA) (Park et al., 2007) This survey focused on the general health and functional status of Korean adults aged 65 or older and on risk factors for common geriatric disorders. Seongnam City is one of large satellite cities of metropolitan Seoul and has nearly one million inhabitants. Data on the population composition, housing patterns, and income distribution are close to the national average (‘‘Korean Statistical Information Service’’). The KLoSHA subjects were drawn from two different cohorts: a random sample of those aged 65 or older and a sample of volunteers aged 85 or older. The total population of Seongnam was 931,019 in 2005, and 61,730 (6.6%) of these were aged 65 years. A simple random sample (n = 1118) was drawn from a roster of 61,730 persons aged 65 years residing in Seongnam on August 1, 2005. Subjects were invited to participate in the study by letter and telephone. The mean age of the sample was 72.2  6.6 (range, 64–99) years, and 405 (36.2%) of the subjects were males. Of the 1118 subjects, 714 agreed to participate and completed the present study (response rate = 63.9%). The mean age of the subjects was 71.9  5.7 years, and 42.2% were males. Those aged 85 years represented only a small proportion of the sample and

showed a poor response rate; thus, all residents aged 85 years in Seongnam (n = 3166) were also invited to participate by letter and telephone. As a result, 278 additional subjects agreed to participate. Accordingly, in total, 992 individuals completed the baseline survey conducted by three trained nurses and three physicians at Seoul National University Bundang Hospital from September 2005 to September 2006. From the 992 participants, we recruited individuals who were able to walk independently, with or without an aid, who had no acute exacerbation of any chronic diseases or terminal illnesses in the second evaluation, which included a test of isokinetic strength and the short physical performance battery (SPPB). In total, 690 persons participated in the second evaluation. Among these, 26 persons were excluded because of missing data for the SPPB. Finally, 664 subjects (335 men, 329 women) with a mean age of 74.64  7.93 years were included in the analysis (Fig. 1). We divided the subjects into two groups according to age (younger group, aged <85 years, and older group, aged 85 years). All subjects were fully informed regarding study participation. Written informed consent was obtained from all subjects or their legal guardians. The Seoul National University, Bundang Hospital, Institutional Review Board approved the study protocol. 2.2. Demographic and lifestyle factors Age, gender, and lifestyle factors were assessed by three research nurses using standardised questionnaires and interviews. The physical activity questions were based on the Baltimore Longitudinal Study of Aging (BLSA) activity questionnaire and the Canadian Study of Health and Aging (CSHA) community questionnaire. Social participation and hobby questions were based on the BLSA activity questionnaire (Park et al., 2007). Physical activity: Current physical activity levels were estimated by summing times spent walking, gardening, woodworking, lifting, or shoveling over a typical 24-h period (Baek et al., 2010). Doing exercise was defined as any type of exercise performed voluntarily and regularly, including sports activities. Participants were asked to indicate (yes/no) whether they exercised regularly (more than once per week), and its intensity (sweating during exercise), duration (hours/session), frequency (times per week), and type (golf, ball games, hiking, dancing, swimming, martial arts, jogging, walking, and other exercises). Social participation: Social participation was defined as engagement in activities including social and religious activities, family gatherings, and volunteer activity more than once per week (yes/ no). Hobbies: Hobbies were defined as activities performed mostly for pleasure at least once per week (yes/no). Current alcohol drinking status: The questions on alcohol drinking status were based on the Alcohol Use Disorders Identification Test (AUDIT) (Kim, Oh, Park, Lee, & Kim, 1999; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). Alcohol drinking status was categorised as non-, ex-, and current. Smoking status: The questions on smoking were based on validated instruments from the American Thoracic SocietyDivision of Lung Disease (ATS-DLD) questionnaire (Ferris, 1978). Cigarette pack-years, a measure of smoking intensity and duration, were derived by multiplying the number of packs of cigarettes (assuming 20 per pack) smoked per day (intensity) by the number of years the person had smoked (duration) (Strand, Mishra, Kuh, Guralnik, & Patel, 2011). Thus, 1 cigarette pack-year is equal to smoking one pack of cigarettes per day for 1 year (or one-half pack per day for 2 years). We next categorised the smokers into light, moderate, and heavy smokers by 3-pack-year categories (according to the 33rd and 66th percentiles of pack-years). Finally, the following four groups were used in the analysis: non-smoker, light smoker, moderate smoker, and heavy smoker.

Please cite this article in press as: Koh, E.S., et al., Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.002

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Fig. 1. Flowchart of the study.

Sleep quality: The Pittsburgh Sleep Quality Index (PSQI) is a selfreported questionnaire that measures sleep quality during the previous month (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). The PSQI consists of 18 questions pertaining to sleep quality, sleep-onset latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction. Additionally, there are five questions rated by the subject’s bed partner or roommate, but these are not used for scoring. Each question is scored 0–3. The total score on the PSQI is 0–21, based on the seven components of sleep and daytime function. Higher mean scores indicate poorer sleep quality. 2.3. Covariates The Korean version of the Geriatric Depression Scale (GDS) and the Hasegawa Dementia Scale (HDS) were used to assess depression and cognitive functioning, respectively. The GDS is one of the most widely used instruments for screening elderly persons for depression (Bae & Cho, 2004; Montorio & Izal, 1996). This scale is a 30-item easy-to-administer inventory, and it has been used widely to evaluate elderly people residing in both communities and institutions, including hospitals and nursing homes. A higher score indicates a greater number of symptoms. The HDS is a simple memory scale that serves as a useful screening tool for dementia in the elderly Korean population (Kim, Lee, Ahn, Jhoo, & Kil, 2002). The highest total score on the HDS is 30, and higher scores indicate better cognitive functioning. The comorbidity index was determined by summing self-reported illnesses, including hypertension, heart disease, central nervous system disease, diabetes, cancer, arthritis, fracture, liver cirrhosis, and respiratory disease (Huh et al., 2011). Body mass index (BMI, kg/ m2) was used to assess obesity (Jensen & Friedmann, 2002; Lang, Llewellyn, Alexander, & Melzer, 2008). Subjective health status (SHS) was assessed using the following question: ‘‘Generally, would you say your health is: excellent (coded as 1), very good (2), good (3), fair (4), poor (5)?’’ (Han, Lee, Iwaya, Kataoka, & Kohzuki, 2004).

2.4. Muscle strength Isokinetic knee extensor muscle strength (tan-angular velocity of 608/s) was measured using an isokinetic device (Biodex Medical Systems, Inc., Shirley, NY) at the Department of Rehabilitation Medicine, SNUBH. Subjects were asked to perform two sets of five repetitions, with a 30-s rest between sets, exerting maximum pressure on the isokinetic device through the entire range of movement. The concentric peak torque values (Nm) obtained from five torque–angle curves for each set were used to evaluate extensor muscle strength of the knees by averaging these values, and these were subsequently normalised with respect to body weight (Nm/kg). We used normalised peak torque per kilogram body weight (Nm/kg) of the right knee extensor muscles as an indicator of muscle strength. 2.5. Dependent variables Physical performance: The SPPB is a valid measure of lowerextremity mobility that is predictive of mortality and institutionalisation in aged adults (Guralnik et al., 1994; Volpato et al., 2008). The SPPB consists of three tests: (1) three standing balance trials (tandem, semi-tandem, and side-by-side stands), (2) five continuous chair stands, and (3) a 4-m walk. Based on normative data, the performance times of these tasks were graded on a scale from 0 to 4. The sum of the three subscores yielded the total SPPB score, which ranged from 0 (worst) to 12 (best function). Elderly persons with SPPB scores 9 have a significantly higher risk of subsequent disability than do those with SPPB scores 10 (Guralnik et al., 2000). Thus, the SPPB score was converted to a binary variable by coding a score 9 as poor physical performance and >9 as good physical performance. 2.6. Statistical analysis Using a binary logistic regression model, we assessed associations between lifestyle factors and physical performance (good/

Please cite this article in press as: Koh, E.S., et al., Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.002

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poor). Results are presented as odds ratio (OR) with 95% confidence intervals (CIs). We also evaluated possible multiple collinearities between covariates by correlation analysis and collinearity statistical tests (tolerance and variance inflation factor tests), as suggested for logistic regression. Results are presented separately for older men and women and younger men and women. All statistical analyses were performed using the SPSS software (ver. 18.0). p values <0.05 were considered to indicate statistical significance.

3. Results Characteristics of the study participants are summarised in Table 1. The subjects were aged 65–96 years (mean  standard deviation (SD), 74.64  7.93 years). In total, 243 subjects (36.6%) were younger men, and 272 (41.0%) were younger women (younger than 85 years of age); 92 subjects (13.9%) were older men, and 57 (8.6%) were older women (older than 85 years of age). In total, 27.2%, 53.3%, 69.6%, and 91.2% of the younger men, younger women, older men, and older women, respectively, had poor physical performance. 3.1. Lifestyle factors by gender and age The current amount of time spent in physical activity per day was 1.32  1.77 h in younger men and 0.70  1.10 h in younger women; older men and women spent much less time in physical activity (0.11  0.68 h in men and 0.05  0.20 h in women). A higher proportion of men carried out exercise (71.6% of younger men, 71.7% of older men) compared with women (45.3% of younger women, 29.1% of older women). Younger women were more likely to

participate in religious activities than were younger men (52.5% of younger women, 35.7% of younger men). The proportions participating in social activities and family gatherings were not significantly different among the four groups. In total, 7.2% of younger men and 5.1% of younger women engaged in volunteer activities, versus none of the older men and women. Older men were more likely to have a hobby than were younger men (51.3%). Current drinkers included 51.7% of the younger men and 32.6% of the older men, compared with 7.0% of the younger women and 8.9% of the older women. Regarding smoking, 92.6% of younger women and 78.9% of older women were non-smokers, compared with 24.2% of younger men and 33.7% of older men. A higher proportion of men than women were heavy smokers (28.8%, 0%, 23.9%, and 3.5% of younger men, younger women, older men, and older women, respectively). The PSQI score in younger men, 5.79  3.04, indicated better sleep quality compared with younger and older women (7.36  3.57 and 7.35  3.56, respectively). 3.2. Physical measures and health status by age and gender Muscle strength in younger men was 1.27  0.37 Nm/kg, which was greater than that in older men, younger women, and older women (0.88  0.30, 0.87  0.31, and 0.62  0.25, respectively). Muscle strength was greater in younger women than in older women and was greater in older men than in older women. BMIs in younger men and women (24.25  3.24 and 24.68  3.12 kg/m2, respectively) were higher than those in older men and women (23.02  3.01 and 22.35  3.98 kg/m2, respectively). Younger men had lower scores on the GDS than did younger women and older women. Comorbidity index scores did not differ significantly among the groups. Younger women had poorer SHS (2.82  0.90) than did the other groups (2.30  1.15 in older women, 2.37  0.93 in younger

Table 1 Characteristics of the sample by age and gender. Men

Women

p value

<85 years

85 years

<85 years

85 years

243 (36.6)

92 (13.9)

272 (41.0)

57 (8.6)

66 (27.2)

64 (69.6)

145 (53.3)

52 (91.2)

<0.001

1.32  1.77 71.6

0.11  0.68 71.7

0.70  1.10 45.3

0.05  0.20 29.1

<0.001 <0.001

35.7 57.5 42.9 7.2 34.0

34.8 48.4 42.2 0 51.3

52.5 53.0 40.8 5.1 37.3

50.0 50.9 38.6 0 32.7

<0.001 0.445 0.924 0.014 0.045 <0.001

25.6 22.7 51.7

45.7 21.7 32.6

86.3 6.6 7.0

83.9 7.1 8.9

24.2 22.5 24.6 28.8 5.79  3.04

33.7 17.4 25.0 23.9 6.75  3.32

92.6 5.5 1.8 0.0 7.36  3.57

78.9 14.0 3.5 3.5 7.35  3.56

<0.001

Physical measures Muscle strength (Nm/kg)  SD BMI (kg/m2)  SD

1.27  0.37 24.25  3.24

0.87  0.31 23.02  3.01

0.88  0.30 24.68  3.12

0.62  0.25 22.35  3.98

<0.001 <0.001

Health status (mean  SD) GDS (point/30)  SD Comorbidity index  SD SHS  SD HDS (point/30)  SD

9.02  6.76 0.28  0.55 2.37  0.925 23.90  3.70

10.14  6.70 0.26  0.59 2.08  1.019 19.79  4.18

12.26  7.23 0.38  0.66 2.82  0.904 23.78  4.12

12.98  7.09 0.50  0.67 2.30  1.149 16.40  5.92

<0.001 0.170 <0.001 <0.001

Subject number (%) Physical performance (%) Poor Lifestyle factors Current physical activity time (h) Doing exercise (%) Social participation Religious activity (%) Social activity (%) Family gathering (%) Volunteer (%) Hobbies (%) Alcohol drinking (%) Non Ex Current Smoking status (%) Non Light Moderate Heavy PSQI  SD

<0.001

p value by one-way ANOVA or Chi-square test.

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Table 2 ORs (95% CI) for poor physical performance according to lifestyle factors by age group and gender. Men

Women

<85 years OR (95% CI) Current physical activity time Doing exercise Social meeting Family gathering PSQI GDS Muscle strength SHS

0.776 (0.616–0.977) 0.368 (0.176–0.769) 0.306 (0.149–0.629)

0.089 (0.029–0.273) 1.695 (1.105–2.600)

85 years p value

<85 years

OR (95% CI)

p value

0.031 0.008 0.001 4.039 (1.377–11.850)

0.011

1.121 (1.030–1.220)

0.008

<0.001 0.016

men, and 2.08  1.02 in older men). HDS scores were higher in younger men and women (23.90  3.70 and 23.78  4.12, respectively) than in older men and women (19.79  4.18 and 16.40  5.92, respectively), and HDS scores in older men were higher than those in all other groups. 3.3. Factors associated with poor physical performance by gender and age (Table 2) No significant collinearity was identified for any of the covariates in statistical tests of collinearity. In the younger men, significant predictors of poor physical performance in the logistic regression analysis after adjusting for covariates were current physical activity time (p = 0.031), doing exercise (p = 0.008), social activities (p = 0.001), SHS (p = 0.016), and muscle strength (p < 0.001). In the younger women, current physical activity time (p = 0.021), PSQI score (p = 0.013), and muscle strength (p < 0.001) were related to poor physical performance. In the older men, participation in family gatherings (p = 0.011) and GDS (p = 0.008) were significant factors. In the older women, no lifestyle factor examined was significantly related to poor physical performance; indeed, SHS was the only factor associated with poor physical performance (p = 0.034). No model was rejected for goodness of fit due to p values greater than 0.05 according to the Hosmer–Lemeshow test. 4. Discussion We found lifestyle factors to be associated with physical performance among elderly people aged <85 years. However, in those aged 85 years, no lifestyle factors were significantly associated with physical functioning except family gatherings in men. In younger men, after adjusting for covariates, current physical activity time, doing exercise, and social activities were lifestyle factors, significantly and positively associated with physical performance. Current physical activity time and PSQI score were significantly associated with physical performance in younger women. Men younger than 85 years of age maintained relatively good physical performance compared with women in the same age group. These results were consistent with our previous study (Lim et al., 2007), in which physical function decreased progressively with age in younger women but was maintained to 85 years of age in men. These differences in physical performance between elderly men and women are well known as the ‘‘gender paradox.’’ Thus, men are more likely to die than women, but women have persistently higher levels of morbidity than men (Arber & Cooper, 1999; Verbrugge, 1995; Wray & Blaum, 2001). In the younger group, we found that current physical activity time was

OR (95% CI)

85 years p value

0.672 (0.479–0.942)

0.021

1.123 (1.025–1.230)

0.013

0.028 (0.007–0.110)

<0.001

OR (95% CI)

p value

2.583 (1.074–6.213)

0.034

significantly associated with physical performance in men and women, but doing exercise was only significant in men. Physical activity is defined as body movement produced by the contraction of skeletal muscles, resulting in energy expenditure. Exercise is physical activity that is planned, structured, repetitive bodily movement to improve or maintain physical fitness (Caspersen, Powell, & Christenson, 1985). Thus, exercise is one type of physical activity, but not all physical activity is exercise. Although an individual may be physically active throughout the day, walking often and doing work around the home, this person may not be doing any exercise. Previous studies suggest that regular exercise confers greater benefit for physical capacity than does physical activity performed throughout the day in older adults (Brach, Simonsick, Kritchevsky, Yaffe, & Newman, 2004). However, the present study revealed that the association of physical performance with current physical activity and doing exercise differed between men and women. Considering the decline in physical function in women compared with men in this age group, increased or continued current activity may be more important than doing exercise to preserve their physical performance. Women are disproportionately more likely to be afraid of falling than men and are more likely than men to avoid activity (Myers et al., 1996). Thus, interventions should focus on increasing levels of physical activity time in older women. Regular participation in social activities was a significant factor for good physical performance in younger men. Interestingly, the rate of participation in social activities was not significantly different among the four groups. Because social activities were closely linked to physical performance, maintaining social relationships could be important in improving physical performance in younger men (<85 years). Social activities are potentially modifiable factors associated with physical function in older persons (Park & Lee, 2007), and a causal association between social engagement and disability has been shown (James, Boyle, Buchman, & Bennett, 2011; Leon, Glass, & Berkman, 2003). Social ties had a stronger protective effect for high-functioning men aged 70– 79 years (Unger, McAvay, Bruce, Berkman, & Seeman, 1999). Voluntarily chosen social activities may be a better indicator of a sense of value and belonging in an older adult’s post-retirement life. Thus, this may be especially important for older men, who frequently have smaller social networks (Antonucci & Akiyama, 1987). However, these results should be interpreted with caution because of the cross-sectional nature of this study. For example, poor physical performance could result in limited social activities; that is, the significant association between participation in social activities and good physical performance may be reciprocal in nature. Participation in family gatherings was associated with poor physical performance among older men, but it is difficult to argue

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that participation in family gatherings is a high-risk factor. A causal relationship between family gatherings and poor physical performance seems unlikely. Perhaps, the families of older men whose physical condition is poor visit the men frequently to care for them. Thus, family gatherings may be an early indicator of difficulty performing activities of daily living, which leads family members to come often to help. We did not investigate under which circumstances family gatherings occurred (festive occasions vs. meetings to provide health assistance). This is another potential limitation of the study. PSQI scores were significant predictors of physical performance in younger women. A previous study showed that poorer sleep was associated with worse physical functioning in elderly women 65 years of age (Goldman et al., 2007), and sleep complaints in elderly people predict physical and mental health-related quality of life (Reid et al., 2006). Sleep disturbances have been shown to be more common in women (Middelkoop, Smilde-van den Doel, Neven, Kamphuisen, & Springer, 1996), and a short nap and moderate exercise were effective in improving sleep quality in elderly people (Tanaka et al., 2002). Regular moderate-intensity exercise training has also been shown to improve self-rated sleep quality in older adults with moderate sleep complaints (King, Oman, Brassington, Bliwise, & Haskell, 1997). In elderly women <85 years of age, various interventions to improve quality of sleep should be provided. Muscle strength was a significant predictor of physical performance in both younger men and women. It is well known that muscle strength decreases with age (Lim et al., 2007). In the present study, muscle strength was significantly associated with physical performance in both men and women <85 years of age, but not in the older groups. This result may be due to relatively preserved muscle strength in those younger than 85 years. GDS was significantly associated with poor physical performance in older men. Depression is common among elderly individuals and is often underdiagnosed and untreated (Bergdahl et al., 2005); furthermore, depressive symptoms predict subsequent physical decline in older adults (Penninx et al., 1998). It was difficult to identify causal relationships between physical performance and lifestyle factors in this study due to the crosssectional design, which is not suitable for revealing causal relationships between health outcomes and risks over the course of a life. Thus, we should be cautious in interpreting the results. Although lifestyle factors such as smoking, alcohol consumption, exercise habits, and social participation influence the functional status of the older population, declining physical functioning may also threaten independence and self-management of health behaviors. Lifestyle and physical functioning may thus show reciprocal causation, with each affecting the other. This link suggests that interventions targeted to improve one variable could result in improvement in the other. Intervention on both sides could be possible and effective to improve health. For example, in younger men, increasing social and physical activities can improve physical performance, and maintaining good physical performance, in turn, results in participation in more social and physical activities until later in life. In contrast to younger elderly subjects, family gatherings and depressive symptoms appeared to be major issues for the older men in maintaining physical performance, and self-rated health was associated with physical performance among the oldest women. This result may be explained by the fact that older men and women already have poor physical performance, which may be the result of their previous lifestyles. In older women, it is difficult to detect any significant association of most independent variables with physical performance because of the high percentage of subjects with poor physical performance (91.2%). Further research on older women with good physical performance will be

necessary to determine modifiable lifestyle factors that can improve physical functioning in this group. Older adults typically have poor physical performance, and variance in lifestyle factors is typically low. This low variance may explain why no lifestyle factor assessed in this study was significantly associated with physical performance; that is, lifestyle modification for this group is likely less effective than it is in the younger group. Careful consideration of the role of lifestyle modification is needed if we aim to improve poor physical performance in those >85 years of age. Additionally, the methods we used to measure lifestyle mostly reflected the current state; thus, greater efforts should be made to develop a tool to evaluate both current and past behavior. Given that current lifestyle factors were associated with physical performance in younger subjects, men and women in this age group should be encouraged to engage in healthier lifestyles to foster better physical performance in later life. We cannot exclude the possibility that the results of this study were biased due to selective survival. That is, individuals who survived until 85 years of age and older were more likely to have better health and physical performance than those who did not. Survival effects probably resulted in an underestimation of the association between lifestyle factors and physical performance compared with the younger group. This limitation of the present study is due to its cross-sectional design, so caution should be exercised in interpreting the results in the older group. In addition, sample size of older women group was relatively small compared to other groups, even though that’s just enough to achieve statistical significance. Another limitation of this study is that objective tools to assess the activity such as pedometer, were not included in our cohort study although we used the valid tools to evaluate physical activity. 5. Conclusions Our findings suggest that interventions implemented to modify lifestyle factors need to focus on each age and gender subgroup in the elderly population. Lifestyle modification should be emphasised as a targeted treatment program in younger men and women. In younger men, participation in social activities, doing exercise, and an increase in physical activity time are significant lifestyle factors that are positively related to physical performance. Younger women should be encouraged to increase their physical activity time for good physical performance. The results suggest that physical activity time is a common key factor related to physical performance among the younger population. Lifestyles in older adults need to be approached from a different point of view. In the older adults, no lifestyle factor was significantly associated with physical performance, except family gatherings in men. These older individuals had already developed poor physical performance, perhaps as a result of their previous lifestyles. Further longitudinal studies are needed to assess causal relationships between various lifestyle factors and physical performance according to age and gender. 6. Funding This study was supported by a grant from the Korean Health Technology R&D project, Ministry for Health, Welfare and Family Affairs (No. A092077) and the Bio & Medical Technology Development Program of the National Research Fund (NRF) funded by the Korean Governement (No. 2011-0030135). Conflict of interest statement None.

Please cite this article in press as: Koh, E.S., et al., Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.002

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Please cite this article in press as: Koh, E.S., et al., Age and gender patterns in associations between lifestyle factors and physical performance in older Korean adults. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.002