Next Article in Journal
Content Analysis of Assessment Tools Used in Post-Stroke Rehabilitation: A Scoping Review with Linkage to the International Classification of Functioning
Previous Article in Journal
Consumption of Ultra-Processed Foods and Metabolic Parameters in Type 2 Diabetes Mellitus: A Cross-Sectional Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 22
Issue 8
10.3390/ijerph22081276
ijerph-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

Risk Factors for Locomotive Syndrome in Brazilian Older Adults: A Nested Case–Control Study

by
Julia de Carvalho Galiano
1,2,*,
Patricia de Castro Rodrigues
1,
Fania Cristina dos Santos
1 and
Virginia Fernandes Moça Trevisani
2
1
Discipline of Geriatrics and Gerontology, Federal University of São Paulo (UNIFESP), São Paulo 04025-002, SP, Brazil
2
Department of Evidence-Based Medicine, Brazilian Cochrane Centre, Federal University of São Paulo (UNIFESP), São Paulo 04024-002, SP, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2025, 22(8), 1276; https://doi.org/10.3390/ijerph22081276
Submission received: 20 July 2024 / Revised: 7 November 2024 / Accepted: 12 November 2024 / Published: 15 August 2025
Review Reports Versions Notes

Abstract

This study aimed to describe the risk factors for locomotive syndrome (LS) in Brazilian oldest old individuals. Study subjects were older adults aged ≥80 years who were cognitively healthy, independent for activities of daily living and had been followed since 2011 by a Brazilian cohort study entitled the Longevos Project. A nested case–control study evaluating demographic and clinical characteristics was conducted. Physical tests including the 5 times sit-to-stand, hand-grip strength, 4-m gait speed and two-step test were performed. The World Health Organization Quality of Life Questionnaire short form (WHOQOL-BREF) and Numerical Rating Scale plus Verbal Rating Scale were applied to assess quality of life (QOL) and chronic pain (CP), respectively. LS was diagnosed using the Brazilian version of the 25-question Geriatric Locomotive Function Scale (GLFS-25-p), and sarcopenia by the SARC-F screening tool. The study sample included 52 participants, had a mean age of 89.3 years (±3.9 years) and was predominantly female (76.9%). Of this sample, 24 were diagnosed with LS and 28 were not. The prevalence of osteoporosis (20%), osteoarthritis (22%), depression (13%) and use of a walking device (14%) was higher in the LS group. Participants with LS had worse performance in physical tests. A multivariate logistic regression analysis identified the main risk factors for LS as osteoporosis (OR 10.80, 95%CI 1.08–108.48) and presence of moderate-to-severe chronic musculoskeletal pain (OR 8.92, 95%CI 1.25–63.89).

1. Introduction

Living a long life is a triumph. In Brazil, official 2022 data estimate there were more than 32 million people aged 60 years or older, and over 2 million beyond the eighth decade of life [1]. The World Health Organization (WHO) defines healthy aging as the process of development and maintenance of functional capacity that allows well-being in old age. In a dynamic process, the capabilities of older individuals are facilitated or impaired by the environment. The set of attributes of the physical, mental and sensory aspects of older adults sustain intrinsic capacityand are inherent to the individual and their particular characteristics during the aging process. Physical attributes support locomotion and allow the basic human need of moving around and enjoying an independent life. Mobility is associated with the perception of quality of life (QOL) and well-being in old age [2,3,4].
The increase in the aging population is a worldwide phenomenon, where each society has created strategies to deal with this shift [5,6]. In Japan, more than 30% of the population is aged 60 years or older. Since the year 2000, the government of Japan has been developing policies to address the locomotion issue in this population. These public measures have come in response to the high demand for long-term care among older adults with orthopedic problems. In 2007, the Japanese Orthopaedic Association (JOA) defined locomotive syndrome (LS) as the decline in locomotion due to impairment of structures such as muscles and nerves, joints, the spine and intervertebral disks [7,8,9]. LS carries a high risk of walking disability. The main conditions associated with the syndrome are osteoporosis and fragility fractures, osteoarthritis, spondylosis, sarcopenia and neural disorders [10,11,12,13].
Questionnaires and physical function tests are proposed by the JOA for recognizing patients with LS [14]. In 2016, the diagnostic tool called the 25-question Geriatric Locomotive Function Scale (GLFS-25-p) was translated into Brazilian Portuguese [10,15]. In Brazil, the estimated prevalence of LS among octogenarians is 55% [16]. According to social security data, osteoarthritis is the fourth-leading cause of retirement. Osteoporosis affects 20 million people in Brazil and is associated with around 121,700 hip fractures a year. Vertebral fractures were observed in 29% of individuals aged >65 years from São Paulo city [17,18,19,20,21]. All of these pathologies can lead to LS and dependence on care. When an older individual loses functional independence, they are more susceptible to becoming confined to the home or institutionalized. There are an estimated 45,000 older residents in long-term care facilities in Brazil. Several reasons prompt the institutionalization of this population, one of which is functional dependency [8,11,21,22].
Scientifically, it is important to raise awareness of LS in Brazil to facilitate health policies and help prevent functional decline in aging people or the need for long-term care. This study aimed to identify the risk factors associated with LS in Brazilian elderly people.

2. Materials and Methods

A nested case–control study of outpatients was carried out at the Federal University of São Paulo, a public health service in São Paulo city, Brazil. Participants had previously taken part in a follow-up cohort study entitled the Longevos Project that started in 2011 [23]. The present investigation was conducted from April to November 2021. The study design was viable because it was quick and practical, as it was carried out in a center with a large flow of elderly people, especially the oldest old.
The LS cases were diagnosed using the GLFS 25-p, adopting a cut-off score of ≥19 points [16]. Control subjects were selected from the group scoring <19 points on the scale from the same site and were matched for characteristics, such as age, and drawn from the Longevos Project population. The Brazilian GLFS-25 cut-off score of 19 was defined by comparing it with other functional mobility tests using the mosaic plot described in the Arbex et al. study. Subsequent ROC analysis for the GLFS-25p score 19 obtained a sensitivity of 0.86, a specificity of 0.67, and an area under the ROC curve of 0.83 [15].
The local Research Ethics Committee approved the study. Inclusion criteria comprised being an older adult (aged ≥ 80 years) of either gender, being independent for activities of daily living and for walking (irrespective of the use of an assistive device such as a walker or cane). Exclusion criteria comprised hospitalization in the last year (up to one year before data collection) or diagnosis with dementia. Individuals with uncontrolled diseases or undergoing parenteral chemotherapy or radiotherapy were also excluded.
The data were collected using a semi-structured protocol. The information collected included sociodemographic data (age, gender), personal history, medications in use, occurrence of previous fragility fracture, falls in last year, and use of assistive walking devices. Data on anthropometry, data on physical activity (≥150 min/week) and pain assessment were collected. Scores on the GLFS 25-p and QOL scale (World Health Organization Quality of Life Questionnaire short form—WHOQOL-BREF) were also collected [24]. Physical performance was assessed using the 4-m gait speed [25], 5 times sit-to-stand test [26], two-step test (TSS) [8], and hand-grip strength test [27], and by sarcopenia screening with the Brazilian version of the SARC-F tool [28]. Relevant information was also taken from medical records.

2.1. Measurements

2.1.1. Definition of Locomotive Syndrome

The Brazilian validated version of the GLFS 25-p consists of 25 questions divided into domains that comprise questions relating to pain, activities of daily living, social performance and mental health status. Each question is graded on 5-point scale from 0 to 4 points. The sum of total scores ranges from 0 to 100. The cut-off score for identifying LS cases in Brazil is 19 points, as established by Arbex et al. [14,16].

2.1.2. Quality of Life and Chronic Pain

QOL assessment was performed using the WHOQOL-BREF. The short form questionnaire covers four domains of QOL: physical health (7 items), psychological health (6 items), social relationships (3 items) and environmental health (8 items). The scale also covers 2 other items on overall QOL and general health items, giving a total of 24 facets and 26 questions. All of these items are rated on a 5-point scale, with higher scores indicating better QOL. Final scores on each domain are calculated by a syntax that compares linearly against a scale of 0 to 100 [24].
Chronic pain (CP) (for a period of at least 3 months) was classified based on location and intensity. Intensity was rated by the Numerical Rating Scale (NRS) and Verbal Rating Scale (VRS) [29]. For the NRS, patients were asked to rate their pain between 0 and 10, with 0 representing “no pain” and 10 indicating “the worst pain imaginable”. For the VRS, patients rated intensity as 0 for “no pain”, 1 for “mild pain”, 2 for “moderate pain” and 3 for “severe pain”. The Portuguese version of the Douleur Neuropathique 4 (DN4) questionnaire was applied to identify cases of neuropathic pain. The cut-off score of 4 points suggests neuropathic pain [30]. Cases of musculoskeletal pain were analyzed by body segment: upper limb (arms, shoulders and neck); lower limb (hips, knees and legs); and spine (thoracic or lumbar). Pain of non-musculoskeletal origin was not analyzed. Intensity of pain was divided into two categories: no/mild pain and moderate-to-severe pain on the VRS, while pain on the NRS was assessed as a continuous variable.

2.1.3. Physical Performance and Sarcopenia Screening

The 4 m-gait Speed test: participants were asked to walk at their normal pace along a linear path of 4 m in a flat area twice. The fastest time taken out of the 2 tries was selected. A cut-off speed of 0.8 m/s was defined, where values below this cut-off denote poor performance [25].
The 5 times sit-to-stand test: keeping both arms folded across their chest, participants were asked to stand and sit 5 times while being timed with a stopwatch. For participants aged ≥80 years, the cut-off time was 14.8 s. If the individual was unable to get up from the chair 5 times, no score was assigned [26].
The two-step test (TSS): The participant was instructed to take two strides forward to the maximum extent possible without losing balance. Use of their usual walking assistive device was allowed. The maximum length of the 2 strides (cm) was divided by the individual’s height (cm). No cut-off point has been defined for Brazilian older adults, but values < 1.1 for Japanese adults indicate impairment of lower limb mobility [8].
The hand-grip strength (HG) test: a Jamar® hydraulic dynamometer (Sammons Preston, Bolingbrook, IL, USA) was used, together with the application protocol defined by the American Society of Hand Therapy. The dominant hand was tested with the participant sitting in the upright position, and the highest value out of 3 tries was used [27].
The SARC-F test: participants answered a 5 question tool that measures the ability to carry weight, walk unaided, transfer from chair or bed, climb stairs, and number of falls. Each item was scored from 0 to 2, with 0 for no difficulty, 1 for some difficulty and 2 for failure/unable. A score ≥6 is suggestive of sarcopenia [28].

2.2. Statistical Analysis

Data were expressed as absolute and relative frequencies and means of summary measures. Associations were analyzed using the Chi-square test or, in cases of small samples, Fisher’s Exact test. Adjusted standardized residuals were used to identify local differences, where absolute values >1.96 indicated evidence of associations between categories. Comparisons of means was performed using Student’s t-test, followed by verification using the Kolmogorov–Smirnov test or non-parametric Mann–Whitney test.
The effects of demographic and clinical characteristics on the occurrence of LS were assessed by fitting univariate and multivariate logistic regressions. Due to the number of predictive variables compared to the sample size, variables whose associations with the dependent variable were significant at 10% on the univariate analysis were selected for entry into the initial multivariate model. Subsequently, variables not significant at 5% were excluded stepwise in order of significance (backward method). In addition, the goodness-of-fit of the final model was evaluated using the Hosmer–Lemeshow test. A significance level of 5% was adopted for all statistical tests. Statistical analyses were carried out using the SPSS 20.0 (IBM SPSS Statistics Base, Armonk, NY, USA) and STATA 17 (Stata Statistical Software: Release 17, College Station, TX, USA) statistical software packages.

3. Results

Of the 52 participants, 24 were diagnosed with LS and 28 were not. The overall sample had a mean age of 89.3 (SD ± 3.9) years and was predominantly female (76.9%). LS prevalence was higher in women than men (91.7% vs. 8.3%). Demographic and clinical data are shown in Table 1. The rates of osteoarthritis, osteoporosis, depression and need for a walking device were higher in the LS group. No association with the other characteristics was found.
Subjects without LS attained better results on the 4-m gait speed, 5 times sit-to-stand and TSS tests (Table 2). Adopting the Japanese cut-off, performance on the TSS was not associated with LS. Sarcopenia and CP rates were lower in the group without LS.
Regarding CP, patients reported CP affecting the neck, shoulders, lumbar spine, hips, knees, legs and abdomen. A positive association was found between LS and CP intensity, as classified by the NRS and VRS. There was one case of neuropathic pain in the thoracic region and one case of chronic headache. WHOQOL-BREF—physical domain scores were worse in the LS group, while no difference was evident for the other domains (Table 3).
Logistic regression results are presented in Table 4 and Table 5. Osteoporosis (p = 0.043), WHOQOL—physical health (p = 0.003), moderate or severe CP (p = 0.029) and TSS (p = 0.016) performance remained significant in the final model. Thus, the risk of LS in patients with osteoporosis was 10.8 times greater than in patients without this condition. Also, the risk of LS in patients with moderate or severe CP was 8.9 times greater than in subjects with mild pain or no pain. In addition, for every 1 point increase on WHOQOL-BREF—physical health, the odds of LS were lower (10% (1.00–0.90) × 100%). Similarly, for every 1 unit increase on the TSS, the odds of LS were lower (99.9% (1.000–0.0007) × 100%). According to the Hosmer-Lemeshow test, the model exhibited a good fit (p = 0.141).

4. Discussion

The present study comprised oldest old participants without cognitive dysfunction who were independent for activities of daily living. Results showed higher LS prevalence in women than men (p = 0.019). Similarly, data from a Japanese population found that, among individuals aged 80 years or older, 76% of women had LS versus 62% of men. Furthermore, people from urban regions had an 80% higher risk of LS, which was even greater among women [31,32]. Motor function declines with age, and recognizing LS is important because this condition results in reduced mobility, greater dependence and higher demand for long-term care [7]. The Pan American Health Organization predicts that long-term care demands may triple in the next three decades [2]. Meanwhile, the number of older Brazilians needing long-term care (at home or in an institution) has increased, yet support provided by public health services remains limited [22,33].
Three tools for identifying LS were proposed by the JOA, but some diagnostic cut-off limits adopted in Japan differ from those used in Brazil [9,10,16]. The GLFS-25 assesses symptoms and locomotor function and adopts 16 points as the cut-off for the Japanese population. The TSS identifies lower limb function in a horizontal trajectory, while the stand-up test assesses muscle power and locomotor function for a vertical task. On the JOA screening test, the ability to stand with a double-leg or single-leg stance from stools of different heights is evaluated [8]. Tasks involving sitting and standing rely on strength of the lower limbs and aerobic capacity of the subject and, for the 5 times sit-to-stand test, meta-analysis studies have validated the cut-off according to age [25,26,34]. In a Japanese study, more than 96% of the participants aged over 80 years were unable to perform the JOA stand-up test, although individuals aged ≥90 years were not included [35]. Compared with the present study sample, 86% of participants were able to perform the 5 times sit-to-stand test, even those with LS. Arbex et al., based on the results of the LOCOMOV Project, found that performance levels on the 4-m gait speed and the 5 times sit-to-stand tests were strongly correlated with each other and with the TSS [16,36]. The TSS cut-off in older Brazilians might also differ because of differences in body composition and functional capacity between Japanese and Brazilian older adults [37].
An article from the University of Tokyo reported that fragility fractures and falls rank fourth among the conditions that most cause disability and lead to the need for support for activities of daily living [31]. By contrast, in the current study, the presence of falls and personal history of fractures were not significant for LS, but osteoporosis was found to be a risk factor for the syndrome (OR, 10.80; 95%CI, 1.08–108.48). According to the epidemiologic study on LS by Yoshimura et al., the prevalence of osteoporosis in participants aged over 80 years was 50% [31]. New studies are needed to establish the causality between osteoporosis and LS.
LS occurs due to impairment to structures of the locomotor organs. In the absence of significant deleterious effects, the affected individual is often unaware of decline in their locomotion. These degenerative processes can accompany the individual for years, progressively worsening their locomotor ability by causing pain, deformities and loss of muscle strength [7]. In the current sample, the presence of CP and its location were associated with LS. Greater musculoskeletal pain intensities were also associated with LS (OR 8.92, 95%CI 1.25–63.89). Iizuka et al. demonstrated that musculoskeletal pain located in the lower lumbar region (OR 2.60, 95%CI 1.29–5.24), knees (OR 2.97, 95%CI 1.41–6.28) and shoulders (OR 2.16, 95%CI 1.00–4.66) was also significant for LS [38].
Pain prevalence increases with age and reduces QOL [39]. Appropriate interventions can be proposed for treatment and rehabilitation, considering the hopes, expectations and feelings of each individual patient as the main outcome [40,41]. There is also a strong association between QOL and social participation [42]. In 2020, the COVID-19 pandemic worsened social life of the older population, while lockdowns during the pandemic negatively impacted activities of daily living. A review study showed that older individuals experienced social isolation and reduced QOL [43]. Another recent publication showed that the development of LS may have increased 2.4 fold in people aged 75 years or older during the pandemic period, especially among sedentary individuals [44]. The results of the present study underscore the importance of QOL measurement, since older participants with higher physical health scores on the WHOQOL-BREF proved to be less affected by LS (OR, 0.90; 95%CI, 0.84–0.96). The present study highlights the importance of other factors in addition to physical tests. The additional factors evaluated in this study, quality of life and CP, demonstrated a greater association with LS. An Indian study demonstrated the relationship between advancing age, reduced quality of life and a comparison between individuals with and without CP. In elderly people with pain, the perception of quality of life was worse. New controlled studies are needed to clarify whether the treatment of CP can provide improvements in locomotion and quality of life [39].
The current study’s results contribute to LS research in that the population investigated comprised oldest old individuals. Knowing the risk factors for LS is important for preventing functional decline and delaying the need for care. The fact that older adults all over the world were affected during the pandemic might constitute a limitation of the present study and a cause of bias. Other limitations include the small sample size and observational study design. Future strategies to tackle LS are needed, particularly in Brazil.

5. Conclusions

The present study demonstrated that osteoporosis and moderate-to-severe chronic pain were risk factors for LS in Brazilian older adults.

Author Contributions

All authors were involved in data collection, results evaluation and article writing for the study. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the following Brazilian agency under the Ministry of Education:“Coordenação de Aperfeiçoamento do Pessoal de Nível Superior (CAPES—process number: 88887.638137/2021-00)”. However, this entity had no involvement in the study design, data collection, analysis and interpretation, writing of the report, or in the decision to submit the paper for publication.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Federal University of São Paulo (Approval Number: 42336720.1.0000.5505, 15 September 2021).

Informed Consent Statement

Written informed consent was obtained from all participants involved in the study.

Data Availability Statement

Data presented in this study are available on request from the corresponding author. Data are not publicly available due to privacy.

Acknowledgments

We would like to thank all the participants from the Geriatrics and Gerontology outpatient service at the Federal University of São Paulo.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. World Health Organization. World Report on Ageing and Health. 2015. Available online: https://iris.who.int/bitstream/handle/10665/186468/WHO_FWC_ALC_15.01_por.pdf?sequence=6 (accessed on 20 July 2024).
  2. World Health Organization. Integrated Care for Older People (ICOPE): Guidance for Person-Centred Assessment and Pathways in Primary Care. 2019. Available online: https://www.who.int/publications/i/item/WHO-FWC-ALC-19.1 (accessed on 20 July 2022).
  3. Lin, D.; Cui, J. Transport and Mobility Needs for an Ageing Society from a Policy Perspective: Review and Implications. Int. J. Environ. Res. Public Health 2021, 18, 11802. [Google Scholar] [CrossRef] [PubMed]
  4. Aguiar, B.; Macário, R. The need for an Elderly centred mobility policy. Transp. Res. Procedia 2017, 25, 4355–4369. [Google Scholar] [CrossRef]
  5. Rantanen, T. Promoting mobility in older people. J. Prev. Med. Public Health 2013, 46 (Suppl. S1), S50–S54. [Google Scholar] [CrossRef] [PubMed]
  6. Mänty, M.; Heinonen, A.; Leinonen, R.; Törmäkangas, T.; Sakari-Rantala, R.; Hirvensalo, M.; von Bonsdorff, M.B.; Rantanen, T. Construct and predictive validity of a self-reported measure of preclinical mobility limitation. Arch. Phys. Med. Rehabil. 2007, 88, 1108–1113. [Google Scholar] [CrossRef] [PubMed]
  7. Nakamura, K. A “super-aged” society and the “locomotive syndrome”. J. Orthop. Sci. 2008, 13, 1–2. [Google Scholar] [CrossRef] [PubMed]
  8. Ishibashi, H. Locomotive syndrome in Japan. Osteoporos. Sarcopenia 2018, 4, 86–94. [Google Scholar] [CrossRef]
  9. Nakamura, K.; Ogata, T. Locomotive Syndrome: Definition and Management. Clin. Rev. Bone Miner. Metab. 2016, 14, 56–67. [Google Scholar] [CrossRef]
  10. Akai, M.; Doi, T.; Seichi, A.; Okuma, Y.; Ogata, T.; Iwaya, T. Locomotive Syndrome: Operational Definition Based on a Questionnaire, and Exercise Interventions on Mobility Dysfunction in Elderly People. Clin. Rev. Bone Miner. Metab. 2016, 14, 119–130. [Google Scholar] [CrossRef]
  11. Ikemoto, T.; Arai, Y.C. Locomotive syndrome: Clinical perspectives. Clin. Interv. Aging 2018, 13, 819–827. [Google Scholar] [CrossRef]
  12. Chiba, D.; Tsuda, E.; Wada, K.; Kumagai, G.; Sasaki, E.; Nawata, A.; Nakagomi, S.; Takahashi, I.; Nakaji, S.; Ishibashi, Y. Lumbar spondylosis, lumbar spinal stenosis, knee pain, back muscle strength are associated with the locomotive syndrome: Rural population study in Japan. J. Orthop. Sci. 2016, 21, 366–372. [Google Scholar] [CrossRef]
  13. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef] [PubMed]
  14. Seichia, A.; Hochino, 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] [PubMed]
  15. Arbex, M.C.F.B.; Okazaki, J.E.F.; Tavares, D.R.; Bersani, A.L.F.; Santos, F.C. Locomotive Syndrome is associated with chronic pain and poor quality of life in Brazilian oldest old: LOCOMOV Project. J. Orthop. Sci. 2021, 26, 162–166. [Google Scholar] [CrossRef] [PubMed]
  16. Tavares, D.R.; Santos, F.C. Locomotive syndrome in the elderly: Translation, cultural adaptation, and Brazilian validation of the tool 25-question geriatric locomotive function scale. Rev. Bras. Reumatol. 2016, 57, 56–63. [Google Scholar] [CrossRef]
  17. Carmo, S.N.; Santos, F.C. Locomoção em Idosos Longevos: Um Estudo de Coorte Prospective. Master’s Thesis, Federal University of São Paulo, São Paulo, Brazil, 2019. [Google Scholar]
  18. Alexandre, T.S.; Cordeiro, R.C.; Ramos, L.R. Factors associated to quality of life among elderly with knee osteoarthritis. Fisioter. Pesqui. 2008, 15, 326–332. [Google Scholar] [CrossRef]
  19. Pinheiro, M.M.; Ciconelli, R.M.; Martini, L.A.; Ferraz, M.B. Clinical risk factors for osteoporotic fractures in Brazilian women and men: The Brazilian Osteoporosis Study (BRAZOS). Osteoporos. Int. 2009, 20, 399–408. [Google Scholar] [CrossRef]
  20. Lopes, J.B.; Danilevicius, C.F.; Takayama, L.; Caparbo, V.F.; Menezes, P.R.; Scazufca, M.; Kuroishi, M.E.; Pereira, R.M.R. Prevalence and risk factors of radiographic vertebral fracture in Brazilian community-dwelling elderly. Osteoporos. Int. 2011, 22, 711–719. [Google Scholar] [CrossRef]
  21. Clark, P.; Cons-Molina, F.; Deleze, M.; Ragi, S.; Haddock, L.; Zanchetta, J.R.; Jaller, J.J.; Palermo, L.; Talavera, J.O.; Messina, D.O.; et al. The prevalence of radiographic vertebral fractures in Latin American countries: The Latin American Vertebral Osteoporosis Study (LAVOS). Osteoporos. Int. 2009, 20, 275–282. [Google Scholar] [CrossRef]
  22. Brazilian Society of Geriatrics and Gerontology. Available online: https://www.sbgg-sp.com.br/as-instituicoes-de-longa-permanencia-para-idosos-no-brasil/ (accessed on 20 July 2024).
  23. Araujo, L.M.Q.; Moreira, P.F.D.P.; Almada Filho, C.M.; de Araujo, L.V.; Passos, M.T.; Kirsztajn, G.M.; Neto, M.C.; Sesso, R.C.C.; Cendoroglo, M.S. Functional capacity, renal function and vitamin D in community-dwelling oldest old. Int. Urol. Nephrol. 2019, 51, 713–721. [Google Scholar] [CrossRef]
  24. Fleck, M.; Leal, O.; Louzada, S.; Xavier, M.; Chachamovich, E.; Vieira, G. Application of the Portuguese version of the abbreviated instrument of quality life WHOQOL-bref. Rev. Saude Publica 2000, 34, 178–183. [Google Scholar] [CrossRef]
  25. Theme Filha, M.M.; Szwarcwald, C.L.; Souza Junior, P.R.B. Measurements of reported morbidity and interrelationships with health dimensions. Rev. Saúde Pública 2008, 42, 73–81. [Google Scholar] [CrossRef] [PubMed]
  26. Buatois, S.; Miljkovic, D.; Manckoundia, P.; Gueguen, R.; Miget, P.; Vancon, G. Five times sit to stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older. J. Am. Geriatr. Soc. 2008, 56, 1575–1577. [Google Scholar] [CrossRef] [PubMed]
  27. Figueiredo, I.; Sampaio, R.; Mancini, M.C.; Silva, F.C.M.; Souza, M.A.P. Test of grip strength using the Jamar dynamometer. Acta Fisiatr. 2007, 14, 104–110. [Google Scholar] [CrossRef]
  28. Mazocco, L.; Chagas, P.; Barbosa-Silva, T.G.; Gonzalez, M.C.; Schwanke, C.H.A. Accuracy of SARC-F and SARC-CalF for sarcopenia screening in older women from southern Brazil. Nutrition 2020, 79–80, 110955. [Google Scholar] [CrossRef] [PubMed]
  29. Santos, F.C.; Moraes, N.S.; Pastore, A.; Cendoroglo, M.S. Chronic pain in long-lived elderly: Prevalence, characteristics, measurements and correlation with serum vitamin D level. Rev. Dor 2015, 16, 171–175. [Google Scholar] [CrossRef]
  30. Santos, J.G.; Brito, J.O.; Andrade, D.C.; Kaziyama, V.M.; Ferreira, K.A.; Souza, I.; Teixeira, M.J.; Bouhassira, D.; Baptista, A.F. Translation to Portuguese and validation of the Douleur Neuropathique 4 questionnaire. J. Pain 2010, 11, 484–490. [Google Scholar] [CrossRef]
  31. Yoshimura, N.; Muraki, S.; Nakamura, K.; Tanaka, S. Epidemiology of the locomotive syndrome: The research on osteoarthritis/osteoporosis against disability study 2005–2015. Mod. Rheumatol. 2017, 27, 1–7. [Google Scholar] [CrossRef]
  32. Tanaka, S.; Ando, K.; Kobayashi, K.; Nakashima, H.; Seki, T.; Ishizuka, S.; Machino, M.; Ito, S.; Kanbara, S.; Kanemura, T.; et al. Differences in the prevalence of locomotive syndrome and osteoporosis in Japanese urban and rural regions: The Kashiwara and Yakumo studies. Mod. Rheumatol. 2022, 32, 199–204. [Google Scholar] [CrossRef]
  33. Brazilian Institute of Geography and Statistics. Ministry of Health, Government of Brazil. Nacional Health Survey. 2019. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101764.pdf (accessed on 20 July 2024).
  34. Bohannon, R.W. Reference values for the five-repetition sit-to-stand test: A descriptive meta-analysis of data from elders. Percept. Mot. Ski. 2006, 103, 215–222. [Google Scholar] [CrossRef]
  35. Yamako, G.; Deng, G.; Totoribe, K.; Chosa, E. A novel protocol to test agerelated decreases in sit-to-stand movement abilities in healthy subjects. J. Orthop. Sci. 2016, 21, 517–523. [Google Scholar] [CrossRef]
  36. Munoz-Mendoza, C.L.; Cabrero-García, J.; Reig-Ferrer, A.; Cabanero-Martinez, M.J. Evaluation of walking speed tests as a measurement of functional limitations in elderly people: A structured review. Int. J. Clin. Health Psychol. 2010, 10, 359–378. [Google Scholar]
  37. Akima, H.; Yoshiko, A.; Radaelli, R.; Ogawa, M.; Shimizu, K.; Tomita, A.; Maeda, H.; Neske, R.; Teodoro, J.; Maeda, K.; et al. Comparison of muscle quality and functional capacity between Japanese and Brazilian older individuals. PLoS ONE 2020, 15, e0243589. [Google Scholar] [CrossRef] [PubMed]
  38. Iizuka, Y.; Iizuka, H.; Mieda, T.; Tajika, T.; Yamamoto, A.; Takagishi, K. Populationbased study of the association of osteoporosis and chronic musculoskeletal pain and locomotive syndrome: The Katashina study. J. Orthop. Sci. 2015, 20, 1085–1089. [Google Scholar] [CrossRef] [PubMed]
  39. Goyal, A.K.; Mohanty, S.K. Association of pain and quality of life among middleaged and older adults of India. BMC Geriatr. 2022, 22, 939. [Google Scholar] [CrossRef]
  40. Goes, M.; Lopes, M.; Marôco, J.; Oliveira, H.; Fonseca, C. Psychometric properties of the WHOQOL-BREF(PT) in a sample of elderly citizens. Health Qual. Life Outcomes 2021, 19, 146. [Google Scholar] [CrossRef]
  41. Casamali, F.F.C.; Schuch, F.B.; Scortegagna, S.A.; Legnani, E.; De Marchi, A.C.B. Accordance and reproducibility of the electronic version of the WHOQOL-BREF and WHOQOL-OLD questionnaires. Exp. Gerontol. 2019, 125, 110683. [Google Scholar] [CrossRef]
  42. Pinto, J.M.; Neri, A.L. Trajetórias da participação social na velhice: Uma revisão sistemática da literatura. Rev. Bras. Geriatr. Gerontol. 2017, 20, 260–273. [Google Scholar] [CrossRef]
  43. Sayin Kasar, K.; Karaman, E. Life in lockdown: Social isolation, loneliness and quality of life in the elderly during the COVID-19 pandemic: A scoping review. Geriatr. Nurs. 2021, 42, 1222–1229. [Google Scholar] [CrossRef]
  44. Terai, H.; Tamai, K.; Takahashi, S.; Katsuda, H.; Shimada, N.; Hori, Y.; Kobayashi, Y.; Nakamura, H. Development of locomotive syndrome in elderly population after COVID-19 outbreak: A population-based cross-sectional study with over 12,000 participants. J. Orthop. Sci. 2023, 28, 895–900. [Google Scholar] [CrossRef]
Table 1. Participant characteristics.
Table 1. Participant characteristics.
Without LS (n = 28)With LS (n = 24)Total (n = 52)p-Value
Gender 0.019
Female18 (64.3)22 (91.7)40 (76.9)
Male10 (35.7)2 (8.3)12 (23.1)
Age (years) 0.136 b
Mean ± SD88.6 ± 3.990.2 ± 3.989.3 ± 3.9
Median (Min—Max)89.0 (83.0–101.0)90.5 (84.0–98.0)89.0 (83.0–101.0)
BMI (kg/m2) 0.549 b
Mean ± SD25.3 ± 3.926.0 ± 4.325.7 ± 4.0
Median (Min—Max)25.0 (18.4–34.7)24.8 (18.6–35.8)24.9 (18.4–35.8)
Hypertension 0.728 a
No6 (23.1)4 (16.7)10 (20.0)
Yes20 (76.9)20 (83.3)40 (80.0)
Diabetes mellitus 0.877
No19 (73.1)18 (75.0)37 (74.0)
Yes7 (26.9)6 (25.0)13 (26.0)
Previous stroke 0.103 a
No26 (100.0)21 (87.5)47 (94.0)
Yes0 (0.0)3 (12.5)3 (6.0)
Chronic renal failure 0.182
CKD-EPI ≥ 3018 (64.3)11 (45.8)29 (55.8)
CKD-EPI < 3010 (35.7)13 (54.2)23 (44.2)
Osteoarthritis 0.019
No10 (35.7)2 (8.3)12 (23.1)
Yes18 (64.3)22 (91.7)40 (76.9)
Osteoporosis 0.023
No13 (46.4)4 (16.7)17 (32.7)
Yes15 (53.6)20 (83.3)35 (67.3)
Previous fracture 0.335
No23 (82.1)17 (70.8)40 (76.9)
Yes5 (17.9)7 (29.2)12 (23.1)
Depression 0.031
No21 (75.0)11 (45.8)32 (61.5)
Yes7 (25.0)13 (54.2)20 (38.5)
Falls 0.358 a
<224 (85.7)23 (95.8)47 (90.4)
≥24 (14.3)1 (4.2)5 (9.6)
Polypharmacy 0.054
0 to 410 (35.7)3 (12.5)13 (25.0)
≥518 (64.3)21 (87.5)39 (75.0)
Physical activity 0.157
No20 (71.4)21 (87.5)41 (78.8)
Yes8 (28.6)3 (12.5)11 (21.2)
Walking device 0.006
No22 (78.6)10 (41.7)32 (61.5)
Yes6 (21.4)14 (58.3)20 (38.5)
p—Chi-square test, Fisher’s exact test (a), Student’s t-test (b). CKD-EPI: equation for estimated glomerular filtration rate.
Table 2. Physical performance and pain evaluation.
Table 2. Physical performance and pain evaluation.
Without LS (n = 28)With LS (n = 24)Total (n = 52)p-Value
4-m gait speed 0.002
<0.8 m/s10 (35.7)19 (79.2)29 (55.8)
≥0.8 m/s18 (64.3)5 (20.8)23 (44.2)
5 times sit-to-Stand 0.006 a
˂14.8 s20 (71.4)10 (41.7)30 (57.7)
≥14.8 s8 (28.6)7 (29.2)15 (28.8)
Unable0 (0.0)7 (29.2)7 (13.5)
Chronic pain 0.016
No12 (42.9)3 (12.5)15 (28.8)
Yes16 (57.1)21 (87.5)37 (71.2)
Musculoskeletal chronic pain 0.012
No14 (50.0)4 (16.7)18 (34.6)
Yes14 (50.0)20 (83.3)34 (65.4)
Location of chronic pain 0.021 a
No pain14 (50.0)4 (16.7)18 (34.6)
Lower limbs4 (14.3)12 (50.0)16 (30.8)
Spine6 (21.4)5 (20.8)11 (21.2)
Upper limbs4 (14.3)3 (12.5)7 (13.5)
VRS 0.008
No pain/Mild pain16 (57.1)5 (20.8)21 (40.4)
Moderate/Severe12 (42.9)19 (79.2)31 (59.6)
NRS <0.001 b
Mean ± SD3.5 ± 2.66.5 ± 2.425.7 ± 4.0
Median (Min—Max)4.5 (0.0–8.0)6.5 (0.0–10.0)24.9 (18.4–35.8)
TSS 0.005 b
Mean ± SD0.9 ± 0.200.7 ± 0.30.8 ± 0.3
Median (Min—Max)0.9 (0.6–1.6)0.8 (0.0–1.1)0.9 (0.0–1.6)
TSS Japanese cut-off 0.358 a
<1.124 (85.7)23 (95.8)47 (90.4)
≥1.14 (14.3)1 (4.2)5 (9.6)
Hand-grip strength 0.278 b
Mean ± SD23.9 ± 8.021.5 ± 7.722.8 ± 7.9
Median (Min—Max)23.0 (12.0–48.0)22.0 (2.0–38.0)22.0 (2.0–48.0)
SARC-F 0.039 a
Negative28 (100.0)20 (83.3)48 (92.3)
Positive0 (0.0)4 (16.7)4 (7.7)
p—Chi-square test, Fisher’s exact test (a), Student’s t-test (b). VRS: Verbal Rating Scale. NRS: Numerical Rating Scale. TSS: two-step test. SARC-F: sarcopenia screening tool.
Table 3. Quality of life assessment.
Table 3. Quality of life assessment.
Without LS (n = 28)With LS (n = 24)Total (n = 52)p-Value
WHOQOL—Physical domain <0.001 b
Mean ± SD72.6 ± 15.150.9 ± 17.962.6 ± 19.6
Median (Min—Max)71.4 (42.9–100.0)53.6 (14.3–78.6)64.3 (14.3–100.0)
WHOQOL—Psychological health 0.195 b
Mean ± SD69.8 ± 14.964.1 ± 16.667.1 ± 15.8
Median (Min—Max)75.0 (45.8–95.8)62.5 (29.2–87.5)68.8 (29.2–95.8)
WHOQOL—Social relationships 0.745 b
Mean ± SD79.2 ± 17.277.8 ± 12.778.5 ± 15.2
Median (Min—Max)83.3 (33.3–100.0)75.0 (41.7–100.0)79.2 (33.3–100.0)
WHOQOL—Environmental health 0.515 b
Mean ± SD70.8 ± 14.568.1 ± 14.769.5 ± 14.5
Median (Min—Max)75.0 (37.5–90.6)68.8 (21.9–93.8)71.9 (21.9–93.8)
WHOQOL—Overall QOL and general health 0.992 c
Mean ± SD68.8 ± 16.166.7 ± 20.467.8 ± 18.1
Median (Min—Max)75.0 (50.0–100.0)75.0 (25.0–100.0)75.0 (25.0–100.0)
p—Chi-square test, Student’s t-test (b), Mann-Whitney (c). WHOQOL: World Health Organization Quality of Life Questionnaire-short form—WHOQOL-BREF.
Table 4. Results of the univariate logistic regression model.
Table 4. Results of the univariate logistic regression model.
OR Crude (95%CI)p-Value
Age (years)1.12 (0.96–1.30)0.140
Hypertension1.50 (0.37–6.14)0.573
Diabetes mellitus0.90 (0.25–3.21)0.877
Previous stroke(1)0.999
Chronic renal failure (CKD-EPI < 30)2.13 (0.70–6.48)0.184
Osteoarthritis6.11 (1.18–31.54)0.031
Osteoporosis4.33 (1.17–15.99)0.028
Previous fracture1.89 (0.51–7.00)0.338
Depression3.55 (1.10–11.46)0.034
Obesity1.20 (0.27–5.42)0.813
≥2 falls0.26 (0.03–2.51)0.245
Polypharmacy3.89 (0.93–16.34)0.064
Physical activity0.36 (0.08–1.54)0.167
Gait speed (m/seg)0.01 (0.00–0.13)0.001
Gait speed < 0.8 m/s (ref.≥ 0.8 m/s)6.84 (1.96–23.93)0.003
5 times sit-to-stand (seg)0.91 (0.82–1.01)0.070
5 times sit-to-stand (ref.= 14.8 seg ou menos) 0.687
5 times sit-to-stand > 14.8 seg1.75 (0.49–6.21)0.387
5 times sit-to-stand unable(1)0.999
WHOQOL—Physical domain0.92 (0.87–0.97)0.001
WHOQOL—Psychological health0.98 (0.94–1.01)0.193
WHOQOL—Social relationships0.99 (0.96—1.03)0.740
WHOQOL—Environmental health0.99 (0.95–1.03)0.508
WHOQOL—overall QOL and general health0.99 (0.96–1.02)0.676
Chronic pain5.25 (1.27–21.78)0.022
Musculoskeletal chronic pain5.00 (1.36–18.43)0.016
Location of chronic pain (ref. Sem dor) 0.037
Lower limbs10.50 (2.15–51.28)0.004
Spine2.92 (0.57–14.82)0.197
Upper limbs2.62 (0.41–16.93)0.310
Moderate/Severe NRS5.07 (1.47–17.46)0.010
Two step test0.01 (0.00–0.44)0.015
Two step test ≥ 1.1 (ref.< 1.1)0.26 (0.03–2.51)0.245
Hand-grip strength0.96 (0.89–1.03)0.278
SARC-F(1)0.999
Table 5. Results of initial and final logistic regression models.
Table 5. Results of initial and final logistic regression models.
Initial ModelFinal Model
Adjusted OR (95%CI)p-ValueAdjusted OR (95%CI)p-Value
Osteoarthritis1.88 (0.16–22.45)0.618--
Osteoporosis9.59 (0.64–143.05)0.10110.80 (1.08–108.48)0.043
Depression0.97 (0.10–9.06)0.981--
Polypharmacy0.85 (0.06–13.09)0.909--
4-m gait speed0.05 (0.00–85.73)0.426--
5 times sit-to-stand0.94 (0.77–1.14)0.526--
WHOQOL—Physical Health0.91 (0.84–0.99)0.0230.90 (0.84–0.96)0.003
Chronic pain1.23 (0.04–33.85)0.903--
Moderate/severe pain6.73 (0.49–93.01)0.1558.92 (1.25–63.89)0.029
TSS0.01 (0.00–112.48)0.3290.0007 (0.000002–0.2612)0.016
OR: odds ratio. WHOQOL: World Health Organization Quality of Life Questionnaire-short form—WHOQOL-BREF. TSS: two-step test.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Galiano, J.d.C.; Rodrigues, P.d.C.; dos Santos, F.C.; Trevisani, V.F.M. Risk Factors for Locomotive Syndrome in Brazilian Older Adults: A Nested Case–Control Study. Int. J. Environ. Res. Public Health 2025, 22, 1276. https://doi.org/10.3390/ijerph22081276

AMA Style

Galiano JdC, Rodrigues PdC, dos Santos FC, Trevisani VFM. Risk Factors for Locomotive Syndrome in Brazilian Older Adults: A Nested Case–Control Study. International Journal of Environmental Research and Public Health. 2025; 22(8):1276. https://doi.org/10.3390/ijerph22081276

Chicago/Turabian Style

Galiano, Julia de Carvalho, Patricia de Castro Rodrigues, Fania Cristina dos Santos, and Virginia Fernandes Moça Trevisani. 2025. "Risk Factors for Locomotive Syndrome in Brazilian Older Adults: A Nested Case–Control Study" International Journal of Environmental Research and Public Health 22, no. 8: 1276. https://doi.org/10.3390/ijerph22081276

APA Style

Galiano, J. d. C., Rodrigues, P. d. C., dos Santos, F. C., & Trevisani, V. F. M. (2025). Risk Factors for Locomotive Syndrome in Brazilian Older Adults: A Nested Case–Control Study. International Journal of Environmental Research and Public Health, 22(8), 1276. https://doi.org/10.3390/ijerph22081276

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