Next Article in Journal
Women Are More Infected and Seek Care Faster but Are Less Severely Ill: Gender Gaps in COVID-19 Morbidity and Mortality during Two Years of a Pandemic in Israel
Next Article in Special Issue
Foot Reflexology: Recent Research Trends and Prospects
Previous Article in Journal
Parental Educational Intervention to Facilitate Informed Consent for Pediatric Procedural Sedation in the Emergency Department: A Parallel-Group Randomized Controlled Trial
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Healthcare
Volume 10
Issue 12
10.3390/healthcare10122354
healthcare-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Sedentary Time and Falls among Middle-Aged Women in Japan

by
Etsuko Ozaki
1,*,
Daisuke Matsui
1,
Nagato Kuriyama
1,2,
Satomi Tomida
1,3,
Yukiko Nukaya
1,4 and
Teruhide Koyama
1
1
Department of Epidemiology for Community Health and Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
2
Division of Social Health Medicine, Shizuoka Graduate University of Public Health, Shizuoka 420-0881, Japan
3
Division of Endocrine and Breast Surgery, Department of Surgery, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
4
Department of Dental Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
*
Author to whom correspondence should be addressed.
Healthcare 2022, 10(12), 2354; https://doi.org/10.3390/healthcare10122354
Submission received: 21 October 2022 / Revised: 16 November 2022 / Accepted: 21 November 2022 / Published: 23 November 2022
(This article belongs to the Special Issue Preventive Care in Healthcare)
Browse Figure
Versions Notes

Abstract

:
There are many reports on the risk of falls in older adults but none regarding the risk among middle-aged people. We aimed to determine fall risk factors among middle-aged women. The participants comprised 1421 women aged 40 to 64 years; anthropometric and other measurements were obtained, and lifestyle factors were examined using a self-administered questionnaire. The participants were categorized into two groups (No-fall and Fall/Almost-fall) based on their questionnaire responses. The No-fall and Fall/Almost-fall groups comprised 1114 and 307 participants, respectively. Body mass index, abdominal circumference measurements, and prevalence of dyslipidemia were significantly higher in the Fall/Almost-fall group. Additionally, those in the Fall/Almost-fall group had a shorter two-step test, experienced difficulty performing the 40 cm single-leg sit-to-stand test, and had higher 25-question Geriatric Locomotive Function Scale (GLFS-25) scores than those in the No-fall group. The results of the adjusted logistic regression analysis indicated that physical activity, higher GLFS-25 scores, and sedentary time of more than seven hours were all risk factors for falling or almost falling. Longer sedentary time is a new risk factor for falls among middle-aged women. It is necessary for people to be concerned with their sedentary behavior, such as by reducing or interrupting continuous sedentary time.

1. Introduction

According to the World Health Organization’s “World Health Statistics 2021,” people from Japan have a long average life expectancy worldwide at 81.5 years for men (second-longest globally) and 86.9 for women (longest globally). Additionally, healthy life expectancy, which is defined as having no limitations in daily life and no health problems, is 72.6 years for men and 75.5 years for women in Japan [1]. According to the Government of Japan’s Annual Report on the Ageing Society 2018, the difference in average life expectancy and healthy life expectancy is 8.84 years for men and 12.34 for women, with both men and women living inconvenient or bedridden lives for approximately 10 years. The main factors necessitating nursing care among older adults are frailty (13.8%) and fractures/falls (12.5%) [2]. According to the Health and Longevity Network, more than 90% of proximal femur, distal radius, and proximal humerus fractures are caused by falls, and preventing falls can reduce the number of older adults requiring nursing care [3].
The Japanese Orthopaedic Association advocates for the term “locomotive syndrome” to describe the decline in motor functions due to age-related muscle weakness, joint and spinal diseases, and osteoporosis, resulting in either having an increased risk of or becoming bedridden or requiring nursing care [4]. To close the gap between healthy life expectancy and average life expectancy, prevention of this locomotive syndrome is recommended [5]. Three tests are used to diagnose locomotive syndrome [6], namely, the 25-question Geriatric Locomotive Function Scale (GLFS-25) [7,8], which consists of 25 questions and requires participants to select the option (on a five-point scale) that best describes their physical and living conditions during the past month; the two-step test, which measures the distance of two steps; and the stand-up test, which requires one to stand up using only one leg from a seated height of 40, 30, 20, and 10 cm. There is a significant association between locomotive syndrome assessment and falls, with a reported 3.5-fold risk of falling when nonlocomotive syndrome is determined to be locomotive syndrome [9].
Falls are a recurring event. Older adults who had fallen had a fear of falling again [10,11], which led to reduced activity and was associated with reduced activities of daily living [12]. Actually, Rubenstein and Josephson reported that 50% of the older adults who had fallen in the past fell again [13]. For this reason, prevention of falls in old age is important. In order to prevent falls in old age, risk factors for falls in middle-aged and older adults need to be identified and prevented. However, there are few reports examining factors related to falls in middle-aged people. Since it has been reported that women are more likely than men to fall [14], the study aim was to determine the risk factors for falls among adult women between the ages of 40 and 65 years in Japan.

2. Methods

2.1. Study Sample

Of a total of 2154 women who participated in the Japan Multi-Institutional Collaborative Cohort Study from Kyoto, Japan, between April 2016 and December 2017 [15,16,17], 17 women under 40 years old and 686 women aged 65 years and over were excluded from participating in this study; thus, 1451 women aged 40 to 64 years remained. Following the exclusion of those with incomplete measurements (lower-limb muscle strength, n = 3; grip strength, n = 1; 40 cm single-leg sit-to-stand test, n = 8; two-step test, n = 2) and questionnaires (GLFS-25, n = 9; physical activity, n = 7), 1421 participants were finally included in the study. The participants were divided into two groups (No-fall, Fall/Almost-fall) based on their answers to the question of whether they had fallen in the last month (Figure 1).
Before participating in this study, all participants provided written informed consent. Ethical approval was obtained from the Institutional Ethics Committee at Kyoto Prefectural University of Medicine (ERB-E-36), and this study was conducted in accordance with the principles of the World Medical Association Declaration of Helsinki.

2.2. Self-Administered Questionnaire

A self-administered questionnaire was used to collect data on the following: having fallen or almost fallen during the last month; having a history of diabetes mellitus (DM), hypertension (HT), or dyslipidemia (DL); using medications such as prescription stabilizers or sleeping pills; and the activities of daily living and GLFS-25 scores. The GLFS-25 consists of 25 items, including pain, activities of daily activity living, social functions, and mental health status during the last month. Participants who consumed alcohol or smoked cigarettes daily were categorized into the alcohol consumption and smoking groups, respectively.
Based on daily physical activity, the participants were classified into one of eight categories: none, <1 h/day, 1 to <3 h/day, 3 to <5 h/day, 5 to <7 h/day, 7 to <9 h/day, 9 to <11 h/day, and ≥11 h/day for heavy physical work, walking time, and standing time. Leisure time exercise was measured using the intensity, frequency per week, and duration of exercise. Physical activity was assessed in terms of metabolic equivalents and calculated based on heavy physical work, walking time, and leisure-time exercise [18]. Sedentary time not included as part of daily physical activity was divided into four categories: none to <5 h/day, 5 to <7 h/day, 7 to <9 h/day, and ≥9 h/day.

2.3. Measurements

Height and abdominal circumference were measured to the nearest 0.1 cm and weight to the nearest 0.1 kg. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2). Lower-limb muscle strength was measured using the Digital muscle strength system TP-776 (Toyo Physical Co. Ltd., Fukuoka, Japan); grip strength using the Smedley Hand Dynamometer (Matsumiya Medical Precision Machinery Co., Tokyo, Japan); and the two-step test using the maximum distance of two-steps-to-height ratio. The single-leg sit-to-stand test from a seat height of 40 cm consisted of using only one leg to stand up from a seated height of 40 cm with arms crossed in front of the chest to avoid recoil.

2.4. Medical Conditions

Participants being treated for HT, with a systolic blood pressure of ≥140 mmHg, or with diastolic blood pressure of ≥90 mmHg were considered to have hypertension. Blood pressure was measured with an HEM-759P Fuzzy Digital Automatic Blood Pressure Monitor (Omron Co., Kyoto, Japan) on the left arm in a sitting position after resting. Blood samples were collected from all participants. Triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and glycated hemoglobin A1c (HbA1c) levels were all measured by enzymatic methods. Those being treated for DL and those with TG levels ≥ 150 mg/dL, HDL-C levels < 40 mg/dL, and LDL-C levels ≥ 140 mg/dL were considered to have DL. Participants who had developed and were being treated for DM, as well as those with HbA1c levels ≥ 6.5%, were considered to have DM.

2.5. Statistical Analyses

Continuous variables with a normal distribution were expressed as mean ± standard deviation. For categorical variables, the data were presented as values and percentages (%). All data were analyzed using SPSS Statistics 25.0 (IBM-SPSS Japan Inc., Tokyo, Japan) and a p < 0.05 was considered statistically significant. The differences in categorical variables between groups were analyzed using Pearson’s chi-square tests and the correlations between age and the various measurements were determined using Spearman’s correlation coefficient test.
A simple and multiple logistic regression analysis were used to determine the risk of falling. Three models were computed as follows: Model 1 was adjusted for age; Model 2 was adjusted for the covariates in Model 1 plus smoking, alcohol consumption, use of prescription stabilizers or sleeping pills, DM, HT, and DL; and Model 3 was adjusted for the covariates in Model 2 plus BMI, abdominal circumference, GLFS-25 score, lower-limb muscle strength, grip strength, physical activity, the 40 cm single-leg sit-to-stand test, and sedentary time. For sedentary time, the shortest group (none to <5 h/day) was considered to be the reference.

3. Results

The participants’ anthropometric, biochemical, and clinical characteristics are shown in Table 1. The No-fall group comprised 1114 participants aged 52.2 ± 7.0 years, and the Fall/Almost-fall group comprised 307 participants aged 52.5 ± 6.9 years. There were no differences in the incidence of DM, HT, or use of mood stabilizers and sleeping pills between the two groups. Compared to the No-fall group, BMI and abdominal circumference measurements and the prevalence of DL were significantly higher in the Fall/Almost-fall group (p < 0.05). Additionally, those in the Fall/Almost-fall group scored higher on the GLFS-25 than those in the No-fall group (p < 0.01), had a shorter two-step test, and experienced difficulty performing the single-leg sit-to-stand test (p < 0.01).
The correlations between age and the various measurements are shown in Table 2. Abdominal circumference, physical activity, and GLFS-25 score were positively associated with age (p < 0.01), although the two-step test and grip strength were negatively associated with age (p < 0.01). GLFS-25 score was positively associated with BMI and abdominal circumference (p < 0.01) and negatively associated with the two-step test (p < 0.01), lower limb muscle strength (p < 0.01), and grip strength (p < 0.05). Lower-limb muscle strength was positively associated with the two-step test and grip strength (p < 0.01).
The logistic regression analyses for the No-fall and Fall/Almost-fall groups are shown in Table 3. The unadjusted logistic regression analysis indicated that higher BMI (OR: 1.054, 95% CI: 1.016–1.093), abdominal circumference measurements (OR: 1.024, 95% CI: 1.010–1.037), and higher GLFS-25 scores (OR: 1.072, 95% CI: 1.049–1.095) were risk factors for falling in the Fall/Almost-fall group, and that the two-step test (OR: 0.211, 95% CI: 0.084–0.525), higher grip strength (OR: 0.968, 95% CI: 0.942–0.989), and the ability to perform the 40 cm single-leg sit-to-stand test (OR: 0.676, 95% CI: 0.519–0.882) were protective factors in the Fall/Almost-fall group. Similar results were obtained after adjusting for age, use of prescription stabilizers or sleeping pills, presence of DM, HT, DL, alcohol consumption, and smoking. However, when we added predictors such as BMI, abdominal circumference, GLFS-25 scores, lower-limb muscle strength, grip strength, the 40 cm single-leg sit-to-stand test, physical activity, and sedentary time to our analysis, we found that physical activity (OR: 1.018, 95% CI: 1.036–1.084), higher GLFS-25 scores (OR: 1.060, 95% CI: 1.036–1.084), and a sedentary time of 7–9 h (OR: 1.701, 95% CI: 1.117–2.592) and ≥9 h (OR: 1.680, 95% CI: 1.099–2.567) were all risk factors for falls.

4. Discussion

BMI, abdominal circumference measurements, and the presence of DL were significantly higher in the Fall/Almost-fall group than in the No-fall group. Ren et al. previously reported an association between obesity and falls in adults over 45 years of age [19], which is similar to the findings of this study, which showed a higher prevalence of obesity among the participants in the Fall/Almost-fall group. In particular, a greater abdominal circumference and the presence of dyslipidemia were frequently noted, suggesting that falls among the middle-aged are associated with visceral fat obesity. Additionally, many of the participants had high GLFS-25 scores, a short two-step test, and were unable to perform the 40 cm single-leg sit-to-stand test, suggesting that body pain and gait disturbances may make them more prone to falling. Similar to the results of this study, lower back and knee pain are reported to be associated with multiple falls [20,21]. It has been reported that older adults and women with pain have a slow walking speed [22,23]. In addition, pain not only slows walking speed but also makes walking unstable and leads to falls [24]. These finding suggest that reduction of physical pain and improvement of walking disorders are necessary to prevent falls in middle-aged and older adults.
Although there was no difference in lower-limb strength between the two groups, grip strength was significantly higher among participants in the No-fall group. Luisa et al. indicated that, while lower extremity muscle strength is associated with stumbling, high muscle strength could not prevent falls due to stumbling, and lower extremity muscle strength and balance can be associated or not [25]. Grip strength is associated with motor skills, lower limb strength, and dynamic balance [26,27]. In the older adults, decreased balance ability is a risk factor for falls, and grip strength is associated with physical function, including balance. In middle-aged people, the grip strength was lower in the Fall/Almost-fall group than in the No-fall group, suggesting that, as in the elderly, poor balance may be a risk for falls [28]. Since there is a negative correlation between age and grip strength, it is suggested that preventing the decline of grip strength and maintaining strength by not avoiding activities that require grip strength in daily life from a young age will be effective in preventing falls.
The presence of DM and medication use have also been reported to be associated with an increased risk of falls in older adults [29,30]. However, among the middle-aged women of this study, there was no difference in the presence of DM and medication use between the two groups. Furthermore, the results of Model 1 and Model 2 did not change when adjusted for age, diabetes, hypertension, dyslipidemia, stabilizers, and sleeping pills, indicating that illness and medication status do not affect falls in middle-aged women. However, when all factors, such as motor function, muscle strength, and physical activity, were adjusted for, factors related to obesity and muscle strength were no longer associated with falls. Contrarily, the amount of physical activity and a sedentary time of seven hours or more, which were not previously associated with GLFS-25 scores, became risk factors for falls.
In this study, pain remained one of the factors that increased the likelihood of falling, as GLFS-25 scores including the pain level were high. The fact that physical activity was a risk factor for falls may be explained by the positive correlation between age and physical activity. Older age and health consciousness influence the amount of physical activity one performs [31], and established exercise habits are more common in older-aged persons [32]. Therefore, after adjusting for all these factors, it is possible that physical activity levels pose a risk for falling. However, since no correlation was found between the amount of physical activity and grip strength, which is related to balance, it is believed that there is no relationship between a high physical activity level and good balance; therefore, performance of physical activity is not the only way to prevent falls. When all the factors were adjusted for, sedentary time became a risk factor for falls. Globally, Japan is reported to be one of the countries with the longest total sedentary time, at approximately seven hours per day [33]. In addition to falling, prolonged sedentary times increase mortality risk [34,35,36] and cognitive decline [37]. Sedentary time is a risk factor for obesity [38], whereby longer sedentary times are associated with higher visceral fat [39]. In this study, visceral obesity was considered a risk factor for falling, and with longer sedentary times, the risk of falling increases. It would be necessary to encourage people to stand instead of sitting on buses, trains, etc., when commuting to work, and to frequently interrupt their sitting time to walk or stand during breaks.
This study has several limitations. First, because it is a cross-sectional study, causality is not known. Second, the type of leisure-time exercise is not known. However, there are also no significant differences between the two groups with regard to the amount of physical activity or sedentary time. Furthermore, no difference was found in the amount of physical activity in leisure time (No-fall: 1.90 ± 2.6, Fall/Almost-fall: 1.99 ± 3.5, p = 0.598), and there is no association between leisure-time activity and falls. Therefore, it is unlikely that the type of leisure-time exercise has an effect on falls. Finally, we did not know the participants’ history of falls. In older adults, a history of falls is one of the strongest risk factors [40], and it has been reported that about half of older adults that have fallen have experienced multiple falls [13]. In this study, only six (1.9%) of the participants experienced multiple falls in one month. A history of falls may not be as strong a risk factor in middle-aged as it is in older adults, but we were unable to investigate this in the present study.

5. Conclusions

In this study, longer sedentary times were a new risk factor for falls among middle-aged women, particularly sedentary time of more than seven hours per day. To the best of our knowledge, this is the first time that sedentary time has been reported to be a risk factor for falling. For a healthy life expectancy, it is important to establish a lifestyle whereby sedentary time is reduced and standing or active time is increased. In the future, it is necessary to conduct longitudinal studies on the causal relationship between prolonged sedentary time and falls.

Author Contributions

E.O. and T.K., contributed to the study concept and its design. E.O., D.M., N.K., S.T., Y.N., and T.K. contributed to data acquisition. E.O. contributed to analyses. E.O. and T.K. contributed to data interpretation. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by a Grant-in-Aid for Scientific Research on Priority Areas of Cancer (Grant No. 17015018) and Innovative Areas (Grant No. 221S0001) and by JSPS KAKENHI (Grant No. 17K09121, No. 16H06277 [CoBiA]) from the Japanese Ministry of Education, Culture, Sports, Science, and Technology.

Institutional Review Board Statement

Ethical approval was obtained from the Institutional Ethics Committee at Kyoto Prefectural University of Medicine (ERB-E-36), and this study was conducted in accordance with the principles of the World Medical Association Declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Acknowledgments

The authors thank Isao Watanabe for their work for acquisition of data, and Yoshiyuki Watanabe and Ritei Uehara, former principal investigators of the J-MICC study in the Kyoto area.

Conflicts of Interest

Etsuko Ozaki, Daisuke Matsui, Nagato Kuriyama, Satimi Tomida, Yukiko Nukaya, and Teruhide Koyama declare that they have no conflict of interest.

References

  1. World Health Organization. World Health Statistics 2021: Monitoring Health for the SDGs, Sustainable Development Goals. Published 2021. Available online: https://apps.who.int/iris/bitstream/handle/10665/342703/9789240027053-eng.pdf (accessed on 30 November 2021).
  2. The Government of Japan. The Annual Report on the Aging Society: 2018 (Summary). Published 2019. Available online: https://www8.cao.go.jp/kourei/whitepaper/w-2019/zenbun/pdf/1s2s_02_01.pdf (accessed on 30 November 2021).
  3. The Japan Foundation for Aging and Health. Operation of the “Kenko-Choju Net”. Published 2020. Available online: https://www.tyojyu.or.jp/net/topics/tokushu/koreisha-undoki-kenko/tento-kossetsuyobo-torikumi.html (accessed on 30 November 2021).
  4. Nakamura, K. A “super-aged” society and the “locomotive syndrome”. J. Orthop. Sci. 2008, 13, 1–2. [Google Scholar] [CrossRef] [Green Version]
  5. The Japan Foundation for Aging and Health. Operation of the “Kenko-Choju Net”. Published 2016. Available online: https://www.tyojyu.or.jp/net/byouki/locomotive-syndrome/about.html (accessed on 24 December 2021).
  6. Japan Locomo Challenge Promotion Conference. Locomotive Syndrome Pamphlet 2015. Published 2015. Available online: https://locomo-joa.jp/check/test/ (accessed on 30 November 2021).
  7. Seichi, A.; Hoshino, Y.; Doi, T.; Akai, M.; Tobimatsu, Y.; Iwaya, T. Development of a screening tool for risk of locomotive syndrome in the elderly: The 25-question Geriatric Locomotive Function Scale. J. Orthop. Sci. 2012, 17, 163–172. [Google Scholar] [CrossRef]
  8. Seichi, A.; Kimura, A.; Konno, S.; Yabuki, S.J. Epidemiologic survey of locomotive syndrome in Japan. Orthop. Sci. 2016, 21, 222–225. [Google Scholar] [CrossRef]
  9. Matsumoto, H.; Nakano, N.; Matsuura, A.; Akita, T.; Hagino, H. Relationship between severity of locomotive syndrome and the incidence of falling, prevalence of low bone mass, and sarcopenia. Phys. Ther. Jpn. 2016, 43, 38–46. [Google Scholar] [CrossRef]
  10. Tinetti, M.E.; Powell, L. Fear of falling and low self-efficacy: A cause of dependence in elderly persons. J. Gerontol. 1993, 48, 35–38. [Google Scholar] [CrossRef]
  11. Tinetti, M.E.; Speechley, M.; Ginter, S.F. Risk factors for falls among elderly persons living in the community. N. Engl. J. Med. 1988, 319, 1701–1707. [Google Scholar] [CrossRef]
  12. Murakami, Y.; Shiba, Y.; Watanabe, S.; Obuchi, S.; Inaba, Y. Factors of Fear of Falling among the Community Dwelling Elderly. Rigakuryoho Kagaku 2008, 23, 413–418. [Google Scholar] [CrossRef] [Green Version]
  13. Rubenstein, L.Z.; Josephson, K.R. The epidemiology of falls and syncope. Clin. Geriatr. Med. 2002, 18, 141–158. [Google Scholar] [CrossRef] [PubMed]
  14. Government of Japan. Results of a Survey on Attitudes toward Housing and Living Environments for the Elderly (2010). Published 2011. Available online: https://www8.cao.go.jp/kourei/ishiki/h22/sougou/zentai/ (accessed on 30 November 2021).
  15. Hamajima, N.; The J-MICC Study Group. The Japan Multi-institutional Collaborative Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer. Asian Pac. J. Cancer Prev. 2007, 8, 317–323. [Google Scholar] [PubMed]
  16. Takeuchi, K.; Naito, M.; Kawai, S.; Tsukamoto, M.; Kadomatsu, Y.; Kubo, Y.; Okada, R.; Nagayoshi, M.; Tamura, T.; Hishida, A.; et al. Study profile of the Japan Multi-institutional Collaborative Cohort (J-MICC) Study. J. Epidemiol. 2021, 31, 660–668. [Google Scholar] [CrossRef] [PubMed]
  17. Haraguchi, N.; Koyama, T.; Kuriyama, N.; Ozaki, E.; Matsui, D.; Watanabe, I.; Uehara, R.; Watanabe, Y. Assessment of anthropometric indices other than BMI to evaluate arterial stiffness. Hypertens. Res. 2019, 42, 1599–1605. [Google Scholar] [CrossRef] [PubMed]
  18. Hara, M.; Hachiya, T.; Sutoh, Y.; Matsuo, K.; Nishida, Y.; Shimanoe, C.; Tanaka, K.; Shimizu, A.; Ohnaka, K.; Kawaguchi, T.; et al. Genomewide Association Study of Leisure-Time Exercise Behavior in Japanese Adults. Med. Sci. Sport. Exerc. 2018, 50, 2433–2441. [Google Scholar] [CrossRef] [PubMed]
  19. Ren, J.; Waclawczyk, A.; Hartfield, D.; Yu, S.; Kuang, X.; Zhang, H.; Alamgir, H. Analysis of fall injuries by body mass index. South Med. J. 2014, 107, 294–300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Muraki, S.; Akune, T.; Oka, H.; En-yo, Y.; Yoshida, M.; Nakamura, K.; Kawaguchi, H.; Yoshimura, N. Prevalence of falls and the association with knee osteoarthritis and lumbar spondylosis as well as knee and lower back pain in Japanese men and women. Arthritis Care Res. 2011, 63, 1425–1431. [Google Scholar] [CrossRef] [PubMed]
  21. Kitayuguchi, J.; Kamada, M.; Okada, S.; Kamioka, H.; Mutoh, Y. Association between musculoskeletal pain and trips or falls in rural Japanese community-dwelling older adults: Across sectional study. Geriatr. Gerontol. Int. 2015, 15, 54–64. [Google Scholar] [CrossRef]
  22. Taylor, J.L.; Parker, L.J.; Szanton, S.L.; Thorpe, R.J., Jr. The association of pain, race and slow gait speed in older adults. Geriatr. Nurs. 2018, 39, 580–583. [Google Scholar] [CrossRef]
  23. Ayis, S.; Ebrahim, S.; Williams, S.; Jüni, P.; Dieppe, P. Determinants of reduced walking speed in people with musculoskeletal pain. J. Rheumatol. 2007, 34, 1905–1912. [Google Scholar]
  24. Guimaraes, R.M.; Isaacs, B. Characteristics of the gait in old people who fall. Int. Rehabil. Med. 1980, 2, 177–180. [Google Scholar] [CrossRef]
  25. Pizzigalli, L.; Filippini, A.; Ahmaidi, S.; Jullien, H.; Alberto, R. Prevention of falling risk in elderly people: The relevance of muscular strength and symmetry of lower limbs in postural stability. J. Strength Cond. Res. 2011, 25, 567–574. [Google Scholar] [CrossRef]
  26. Alonso, A.C.; Ribeiro, S.M.; Luna, N.M.S.; Peterson, M.D.; Bocalini, D.S.; Serra, M.M.; Brech, G.C.; Greve, J.M.D.; Garcez-Leme, L.E. Association between handgrip strength, balance, and knee flexion/extension strength in older adults. PLoS ONE 2018, 13, e0198185. [Google Scholar] [CrossRef] [Green Version]
  27. Hiraoka, A.; Tamura, R.; Oka, M.; Izumoto, H.; Ueki, H.; Tsuruta, M.; Yoshino, T.; Ono, A.; Aibiki, T.; Okudaira, T.; et al. Prediction of risk of falls based on handgrip strength in chronic liver disease patients living independently. Hepatol. Res. 2019, 49, 823–829. [Google Scholar] [CrossRef] [PubMed]
  28. Ikeda, N.; Murata, S.; Otao, H.; Murata, J.; Horie, J.; Mizota, K. The relationship between grip Strength and physical function in elderly community dwelling women. Rigakuryoho Kagaku 2011, 26, 255–258. [Google Scholar] [CrossRef] [Green Version]
  29. Hayakawa, T.; Hashimoto, S.; Kanda, H.; Hirano, N.; Kurihara, Y.; Kawashima, T.; Fukushima, T. Risk factors of falls in inpatients and their practical use in identifying high-risk persons at admission: Fukusima medical university hospital cohort study. BMJ Open. 2014, 4, e005385. [Google Scholar] [CrossRef] [Green Version]
  30. Mets, M.A.J.; Volkerts, E.R.; Olivier, B.; Verster, J.C. Effect of hypnotic drugs on body balance and standing steadiness. Sleep Med. Rev. 2010, 14, 259–267. [Google Scholar] [CrossRef] [PubMed]
  31. Trujillo, K.M.; Brougham, R.R.; Walsh, D.A. Age differences in reasons for exercising. Curr. Psychol. 2004, 22, 348–367. [Google Scholar] [CrossRef]
  32. The National Health and Nutrition Survey in Japan, 2019. Published 2020. Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/eiyou/r1-houkoku_00002.html (accessed on 30 November 2021).
  33. Bauman, A.; Ainsworth, B.E.; Sallis, J.F.; Hagströmer, M.; Cora, C.; Bull, F.; Pratt, M.; Venugopal, K.; Chau, J.; Sjöström, M.; et al. The descriptive epidemiology of sitting. A 20-country comparison using the International Physical Activity Questionnaire (IPAQ). Am. J. Prev. Med. 2011, 41, 228–235. [Google Scholar] [CrossRef] [PubMed]
  34. Patel, A.V.; Maliniak, M.L.; Rees-Punia, E.; Matthews, C.E.; Gapstur, S.M. Prolonged leisure time spent sitting in relation to cause-specific mortality in a large US cohort. Am. J. Epidemiol. 2018, 187, 2151–2158. [Google Scholar] [CrossRef] [Green Version]
  35. Koyama, T.; Ozaki, E.; Kuriyama, N.; Tomida, S.; Yoshida, T.; Uehara, R.; Tanaka, K.; Hara, M.; Hishida, A.; Okada, R.; et al. Effect of underlying cardiometabolic diseases on the association between sedentary time and all-cause mortality in a large Japanese population: A cohort analysis based on the J-MICC study. J. Am. Heart Assoc. 2021, 10, e018293. [Google Scholar] [CrossRef]
  36. Koyama, T.; Kuriyama, N.; Ozaki, E.; Tomida, S.; Uehara, R.; Nishida, Y.; Shimanoe, C.; Hishida, A.; Tamura, T.; Tsukamoto, M.; et al. Sedentary time is associated with cardiometabolic diseases in a large Japanese population: A cross-sectional study. J. Atheroscler. Thromb. 2020, 27, 1097–1107. [Google Scholar] [CrossRef] [Green Version]
  37. Siddarth, P.; Burggren, A.C.; Eyre, H.A.; Small, G.W.; Merrill, D.A. Sedentary behavior associated with reduced medial temporal lobe thickness in middle-aged and older adults. PLoS ONE 2018, 13, e0195549. [Google Scholar] [CrossRef]
  38. Campbell, S.D.I.; Brosnan, B.J.; Chu, A.K.Y.; Skeaff, C.M.; Rehrer, N.J.; Perry, T.L.; Peddie, M.C. Sedentary behavior and body weight and composition in adults: A systematic review and meta-analysis of prospective studies. Sport Med. 2018, 48, 585–595. [Google Scholar] [CrossRef] [PubMed]
  39. Ando, S.; Koyama, T.; Kuriyama, N.; Ozaki, E.; Uehara, R. The association of daily physical activity behaviors with visceral fat. Obes. Res. Clin. Pract. 2020, 14, 531–535. [Google Scholar] [CrossRef]
  40. Tinetti, M.E.; Kumar, C. The patient who falls: “It’s always a trade-off”. JAMA 2010, 303, 258–266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Healthcare 10 02354 g001 550
Figure 1. Flowchart detailing the enrollment process for the study.
Figure 1. Flowchart detailing the enrollment process for the study.
Healthcare 10 02354 g001
Table
Table 1. Anthropometric, biochemical, and clinical characteristics of the study participants, women aged 40 to 64.
Table 1. Anthropometric, biochemical, and clinical characteristics of the study participants, women aged 40 to 64.
AllNo FallFall/Almost-Fallp-Value
n (%)14211114 (78.4)307 (21.6)
Age (years)52.3 (±7.0)52.2 (±7.0)52.5 (±6.9)0.560
BMI (kg/m2)21.4 (±3.3)21.3 (±3.2)21.9 (±3.2)0.008
AC (cm)77.9 (±9.1)77.5 (±8.8)79.5 (±9.7)0.001
SBP (mmHg)122.0 (±18.1)121.9 (±17.9)122.3 (±18.8)0.750
DBP (mmHg)76.1 (±11.2)75.9 (±11.0)76.8 (±11.8)0.214
HbA1c (%)5.49 (±0.36)5.48 (±0.34)5.52 (±0.43)0.063
HDL-C (mg/dL)75.5 (±16.2)76.2 (±16.2)72.9 (±16.0)0.001
LDL-C (mg/dL)126.4 (±32.3)125.5 (±32.0)129.6 (±33.4)0.047
TG (mg/dL)83.6 (±52.8)82.9 (±53.5)86.4 (±49.9)0.298
Physical activity (METs)14.2 (±9.9)14.1 (±9.6)14.6 (±11.0)0.487
GLFS-25 (point)5.10 (±5.81)4.53 (±5.22)7.17 (±7.22)0.000
Two-step test (m)1.48 (±0.14)1.48 (±0.14)1.45 (±0.14)0.001
Lower limb muscle strength (kg)18.8 (±6.5)18.9 (±6.5)18.6 (±6.2)0.410
Grip strength (kg)31.2 (±5.3)31.4 (±5.3)30.5 (±5.5)0.004
Current drinker (n, %)763 (53.7)594 (53.3)169 (55.0)0.591
Current smoker (n, %)70 (4.9)54 (4.8)16 (5.2)0.794
DM patient (n, %)27 (1.9)18 (1.6)9 (2.9)0.135
HT patient (n, %)327 (23.0)246 (22.1)81 (26.4)0.113
DL patient (n, %)574 (40.4)435 (39.0)139 (45.3)0.049
Prescription stabilizers or sleeping pills use (n, %)71 (5.0)50 (4.5)21 (6.8)0.094
40 cm single-leg sit-to-stand test successful (n, %)989 (69.6)796 (71.5)193 (62.9)0.004
Sedentary times
None to <5 h/day (n, %)369 (26.0)300 (26.9)69 (22.5)0.148
5 to <7 h/day (n, %)337 (23.7)271 (24.3)66 (21.5)
7 to <9 h/day (n, %)278 (19.6)209 (18.8)69 (22.5)
≥9 h/day (n, %)437 (30.7)334 (30.0)103 (33.6)
Continuous variables are expressed as the mean (±SD). Prevalence was reported as a value (n) and percentage (%). Boldface indicates statistical significance (p < 0.05). DBP, diastolic blood pressure; DM, diabetes mellitus; GLFS-25, 25-question Geriatric Locomotive Function Scale; HbA1c, glycated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; DL, dyslipidemia; HT, hypertension; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglycerides; AC, abdominal circumference; METs, metabolic equivalents.
Table
Table 2. Correlations between age and the various measurements using Spearman’s correlation coefficient.
Table 2. Correlations between age and the various measurements using Spearman’s correlation coefficient.
AgeBMIAbdominal
Circumference		Physical
Activity		GLFS-25Two-Step TestLower-Limb Muscle StrengthGrip
Strength
Age10.0320.119 **0.125 **0.111 **−0.074 **0.027−0.223 **
BMI 10.839 **−0.0130.079 **−0.114 **0.263 **0.064 *
Abdominal circumference 1−0.0250.116 **−0.159 **0.234 **0.058 *
Physical activity 1−0.0270.0410.066 *−0.010
GLFS-25 1−0.151 **−0.074 **−0.135 **
Two-step test 10.221 **0.226 **
Lower-limb muscle strength 10.281 **
Grip strength 1
GLFS-25 = 25-Geriatric Locomotive Function Scale. Spearman’s correlation coefficient was used to measure these values. Boldface indicates statistical significance (* p < 0.05, ** p < 0.01).
Table
Table 3. Crude and adjusted odds ratio for the risk of fall.
Table 3. Crude and adjusted odds ratio for the risk of fall.
CrudeModel 1 aModel 2 bModel 3 c
OR95% CIp-Value OR95% CIp-Value OR95% CIp-Value OR95% CIp-Value
BMI1.0541.016–1.0930.0051.0541.016–1.0930.0051.0451.003–1.0880.0360.9850.905–1.0720.729
Abdominal circumference1.0241.010–1.0370.0011.0231.010–1.0370.0011.0211.006–1.0360.0071.0210.989–1.0530.201
Two-step test0.2110.084–0.5250.0010.2130.085–0.5330.0010.2360.093–0.5970.0020.5690.204–1.5850.281
Lower-limb muscle strength0.9920.972–1.0120.4100.9920.972–1.0110.4010.9910.972–1.0110.3870.9980.976–1.0200.837
Grip strength0.9680.942–0.9890.0040.9650.941–0.9890.0050.9660.942–0.9910.0070.9740.948–1.0010.056
Physical activity1.0040.992–1.0170.4861.0040.992–1.0170.5221.0050.992–1.0180.4621.0181.036–1.0840.020
GLFS-251.0721.049–1.095<0.0011.0721.049–1.095<0.0011.0691.046–1.092<0.0011.0601.036–1.084<0.001
40 cm single-leg sit-to-stand test0.6760.519–0.8820.0040.6770.517–0.8880.0050.7000.533–0.9210.0110.8940.663–1.2050.461
Sedentary times
  None to <5 h/dayRef Ref Ref Ref
  5 to <7 h/day1.0590.728–1.5410.7651.0520.722–1.5310.7931.0730.735–1.5670.7151.2250.819–1.8330.323
  7 to <9h/day1.4350.984–2.0940.0611.4300.980–2.0860.0641.4110.964–2.0630.0761.7011.117–2.5920.013
  9 h/day1.3410.952–1.8880.0931.3130.953–1.8910.0921.3320.943–1.8820.1031.6801.099–2.5670.016
a Adjusted for age. b Adjusted for the covariates in Model 1 plus smoking, alcohol consumption, use of prescription stabilizers or sleeping pills, DM, HT, and DL. c Adjusted for the covariates in Model 2 plus BMI, abdominal circumference, the GLFS-25 score, lower limb muscle strength, grip strength, physical activity, the 40 cm single-leg sit-to-stand test, and sedentary time. GLFS-25, 25-Geriatric Locomotive Function Scale; DM, diabetes mellitus; HT, hypertension; DL, dyslipidemia.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ozaki, E.; Matsui, D.; Kuriyama, N.; Tomida, S.; Nukaya, Y.; Koyama, T. Association between Sedentary Time and Falls among Middle-Aged Women in Japan. Healthcare 2022, 10, 2354. https://doi.org/10.3390/healthcare10122354

AMA Style

Ozaki E, Matsui D, Kuriyama N, Tomida S, Nukaya Y, Koyama T. Association between Sedentary Time and Falls among Middle-Aged Women in Japan. Healthcare. 2022; 10(12):2354. https://doi.org/10.3390/healthcare10122354

Chicago/Turabian Style

Ozaki, Etsuko, Daisuke Matsui, Nagato Kuriyama, Satomi Tomida, Yukiko Nukaya, and Teruhide Koyama. 2022. "Association between Sedentary Time and Falls among Middle-Aged Women in Japan" Healthcare 10, no. 12: 2354. https://doi.org/10.3390/healthcare10122354

APA Style

Ozaki, E., Matsui, D., Kuriyama, N., Tomida, S., Nukaya, Y., & Koyama, T. (2022). Association between Sedentary Time and Falls among Middle-Aged Women in Japan. Healthcare, 10(12), 2354. https://doi.org/10.3390/healthcare10122354

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop