
==== Front
J Bone Miner Res
J Bone Miner Res
jbmr
Journal of Bone and Mineral Research
0884-0431
1523-4681
Oxford University Press

38995943
10.1093/jbmr/zjae114
zjae114
Research Article
AcademicSubjects/MED00010
AcademicSubjects/MED00160
AcademicSubjects/MED00250
High physical activity is associated with greater cortical bone size, better physical function, and with lower risk of incident fractures independently of clinical risk factors in older women from the SUPERB study
Johansson Lisa Sahlgrenska Osteoporosis Centre, Institute of Medicine, University of Gothenburg, 431 80 Mölndal, Sweden
Region Västra Götaland, Department of Orthopedics, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden

Litsne Henrik Sahlgrenska Osteoporosis Centre, Institute of Medicine, University of Gothenburg, 431 80 Mölndal, Sweden

https://orcid.org/0000-0002-4118-6038
Axelsson Kristian F Sahlgrenska Osteoporosis Centre, Institute of Medicine, University of Gothenburg, 431 80 Mölndal, Sweden
Region Västra Götaland, Närhälsan Norrmalm Health Centre, 549 40 Skövde, Sweden

https://orcid.org/0000-0003-0749-1431
Lorentzon Mattias Sahlgrenska Osteoporosis Centre, Institute of Medicine, University of Gothenburg, 431 80 Mölndal, Sweden
Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria 3065, Australia
Geriatric Medicine, Institute of Medicine, Sahlgrenska Academy, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden

Corresponding author: Mattias Lorentzon, Geriatric Medicine, Institute of Medicine, Sahlgrenska Academy, Building K, 6th Floor, Sahlgrenska University Hospital, Mölndal 431 80, Sweden (mattias.lorentzon@medic.gu.se).
9 2024
12 7 2024
12 7 2024
39 9 12841295
21 11 2023
07 3 2024
02 4 2024
18 8 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

The Physical Activity Scale for the Elderly (PASE) is a validated test to assess physical activity in older people. It has not been investigated if physical activity, according to PASE, is associated with fracture risk independently from the clinical risk factors (CRFs) in FRAX, bone mineral density (BMD), comorbidity, and if such an association is due to differences in physical performance or bone parameters. The purpose of this study was to evaluate if PASE score is associated with bone characteristics, physical function, and independently predicts incident fracture in 3014 75–80-yr-old women from the population-based cross-sectional SUPERB study. At baseline, participants answered questionnaires and underwent physical function tests, detailed bone phenotyping with DXA, and high-resolution peripheral quantitative CT. Incident fractures were X-ray verified. Cox regression models were used to assess the association between PASE score and incident fractures, with adjustments for CRFs, femoral neck (FN) BMD, and Charlson comorbidity index. Women were divided into quartiles according to PASE score. Quartile differences in bone parameters (1.56% for cortical volumetric BMD and 4.08% for cortical area, Q4 vs Q1, p = .007 and p = .022, respectively) were smaller than quartile differences in physical performance (27% shorter timed up and go test, 52% longer one leg standing time, Q4 vs Q1). During 8 yr (median, range 0.20–9.9) of follow-up, 1077 women had any fracture, 806 a major osteoporotic fracture (MOF; spine, hip, forearm, humerus), and 236 a hip fracture. Women in Q4 vs. Q1 had 30% lower risk of any fracture, 32% lower risk of MOF, and 54% lower risk of hip fracture. These associations remained in fully adjusted models. In conclusion, high physical activity was associated with substantially better physical function and a lower risk of any fracture, MOF and hip fracture, independently of risk factors used in FRAX, FN BMD, and comorbidity.

physical activity
physical function
bone geometry
fracture prevention
older women
Swedish Research Council 10.13039/501100004359 Sahlgrenska University Hospital 10.13039/501100005754
==== Body
pmcIntroduction

Fragility fractures are associated with morbidity, mortality, and high societal costs.1 It is of great importance to identify patients at risk for fracture and, when appropriate, initiate medication according to treatment guidelines.2 Several fracture risk assessment tools have been developed the past 20 yr, integrating several risk factors for fracture.3 FRAX (the fracture risk assessment tool) is the most used worldwide and provides information on the 10-yr fracture probability for hip or major osteoporotic fracture (MOF; hip, wrist, humerus or clinical vertebral fracture) based on age, sex, body mass index (BMI), and clinical risk factors (CRFs), with or without bone mineral density (BMD).4

In a cohort of 1044 older Swedish women followed for 10 yr, standing balance test and gait speed were independently predictive for hip fracture.5 In another Swedish cohort of women aged 75–80 yr, a slow (>12 s) Timed Up and Go (TUG) time and a low (<10 s) one leg standing (OLS) time were independently associated with an increased risk of MOF and hip fracture.6,7 Even though physical function (performance) tests have been studied for their predictive ability of fractures, physical activity as a predictor of incident fracture has been less examined in older women. In 2008, Department of Health and Human Services compiled a report on physical activity guidelines.8 The conclusion was that physical activity is inversely associated with fracture risk and that a greater volume of physical activity confers greater risk reduction. However, out of the 21 studies, the report was based on only 8 studies that included only women, of which 5 studies had hip fracture as outcome. The only study reporting any incident fractures or fragility fractures was cross-sectional and evaluated fracture risk in older and elderly women who participated in exercise classes at least 1 h per week for at least 20 yr, compared with age-matched women.9 In 1985, Caspersen et al. defined physical activity as “any bodily movement produced by skeletal muscle that requires energy expenditure,” and exercise was defined as a subcategory of physical activity “that is planned, structured, repetitive, and purposive to improve or maintain components of physical fitness.”10 However, despite these definitions, comparisons across studies are limited due to the variation in the methods used to assess physical activity. In 1993, Washburn et al. developed the Physical Activity Scale for the Elderly (PASE).11 It is a brief, reliable, and valid questionnaire for the assessment of physical activity over the last 7 d in persons aged 65 yr and older.12 PASE has been validated in several countries, and it has been used in studies on, for example, osteoarthritis, Parkinson’s disease, sarcopenia, lung cancer, frailty, and mortality. To date, to the best of our knowledge, the PASE scale has only been used in one previous study investigating incident fractures.13 Studies on whether physical activity and exercise affects bone geometry and strength are inconclusive.14,15 In a subset of the SUPERB cohort, current but not previous physical activity was independently associated with thicker cortex and higher bone strength at the distal tibia, indicating that physical activity at old age may decrease cortical bone loss in weight-bearing bone in elderly women.16 Even though physical activity has been associated with maintenance of bone mass, improved balance, and reduction of falls, the question remains if physical activity prevents fractures independently of these parameters. If physical activity is associated with incident fracture, it is valuable to understand whether this association is due to physical performance or bone geometry and bone strength. Also, women who are affected by morbidity may have low physical activity, which could confound the association with incident fractures. Finding additional risk factors for fractures could improve fracture risk assessments with the potential to reduce the burden of fractures. Therefore, the aim of this study was to evaluate if PASE score predicts incident fracture in older women, independently of CRFs in FRAX, femoral neck (FN) BMD, comorbidities, and whether these associations are mainly dependent on differences in physical function and/or bone geometry.

Materials and methods

Study participants

The SUPERB study is a prospective cross-sectional population-based study of 3028 older Swedish women from the greater Gothenburg area, recruited via the Swedish population register between March 2013 and May 2016.17 Out of 3028 women in the SUPERB study, 3014 women had answered all the questions in the PASE questionnaire and were therefore included in this study. Included women were 75 to 80 yr of age at baseline and were followed until November 2022 to March 2023. All of the included women were ambulant and could understand Swedish. The inclusion process has been described earlier.18 At baseline, the participants underwent dual x-ray absorptiometry (DXA) and high resolution peripheral quantitative computed tomography (HR-pQCT) of distal radius and distal tibia, and physical function tests were performed. At baseline, all participants completed a self-administered questionnaire regarding lifestyle factors, physical activity, medical history, medication, and prior fractures. Anthropometrics were measured using standardized equipment. All the study participants gave their informed consent, and the ethical review board at the University of Gothenburg approved the study. The examinations took place at the Clinical Osteoporosis Research Department, Sahlgrenska University Hospital in Mölndal, Sweden.

Dual X-ray absorptiometry

DXA was used to measure BMD (g/cm2) at the FN and lumbar spine (LS) L1 to L4, and trabecular bone score (TBS) of L1 to L4 (Discovery A; Hologic). LS BMD and TBS were calculated as the mean of at least 2 assessable vertebrae L1 to L4, and any fractured and/or osteosynthesis-contained vertebra was excluded. The coefficient of variation (CV) for FN BMD, LS BMD, and TBS was 1.3%, 0.7%, and 2.12%, respectively.

HR-pQCT

All participants’ distal radius and distal tibia (non-dominant side) were measured using a HR-pQCT device (XtremeCT; Scanco Medical AG). The first CT slice started 9.5 mm and 22.5 mm proximal to the articular surface of the distal radius and distal tibia, respectively, and labeled the standard site.19 A more proximal site (at 14% of the bone length from the articular surface) was used to measure mainly cortical bone. At each scan site, 110 cross-sectional images were obtained, and the quality of the images was graded from 1 (best) to 5 (worst), and only images with adequate quality, 1 to 3, were used in the analyses, as recommended by the manufacturer (Scanco Medical AG). Parameters obtained were: trabecular bone volume fraction (BV/TV, %), trabecular number (mm−1), trabecular thickness (mm), trabecular separation (mm), cortical volumetric BMD (Ct.vBMD) (mg/cm3), cortical area (mm2), and total volumetric BMD (mg/cm3). To obtain CVs, duplicate measurements on 30 older women were made. The CVs for measurement of trabecular parameters in distal radius and distal tibia were 0.4% to 2.5% and 0.8% to 2.6%, respectively. CVs for measurement of cortical parameters in distal radius and distal tibia were 0.1% to 0.9%. With software (Image Processing Language; IPL v5.08b) from the manufacturer (Scanco Medical AG), the cortical bone was separated from the trabecular bone and extraosseous soft tissue by automatically placed contours at the periosteal and endosteal surfaces of the bone, which could be corrected by the operator if needed. From this image, the cortical parameters measured were: cortical pore volume (Ct.Po.V; mm3), cortical bone volume (Ct.BV; mm3), and cortical porosity (Ct.Po; %), which was calculated as Ct.Po.V/(Ct.Po.V + Ct.BV).20 CVs for measurement of cortical porosity at the distal radius and distal tibia were 5.3% to 13.3% and 0.9% to 4.1%, respectively.

Finite element (FE) analysis was performed using models provided in software from Scanco Medical AG (version V5.11/FE-V01.15), by converting the voxels to brick elements of the same size. A simulated uniaxial compression was applied, and failure load was calculated as the load at which at least 2% of the bone elements surpassed 7000 microstrain.21 Stiffness, the resistance against deformation, was reported. A Young’s modulus of 10 GPa and a Poisson ratio of 0.3 were used in the FE models for all participants, as previously described.21 The CVs for these measurements ranged from 0.5% to 1.5% in the radius and 0.6% to 1.2% in the tibia.

Questionnaires

At baseline, data on life-style factors, medical history, medication, and prior fractures were collected from all participants by a self-administered questionnaire. Current physical activity over the last 7 d prior to assessment was quantified by PASE, a validated self-administered questionnaire constructed for individuals over 65 years old.11 The participants estimated the time (seldom: 1–2 d, sometimes: 3–4 d or often: 5–7 d) and duration (<1 h, 1–2 h, 2–4, or > 4 h) spent doing different sports or leisure activities, recreation, household work, and gardening. A total score was computed by multiplying the amount of time spent on each activity (hours per week) or participation (yes/no) in an activity by empirically derived weights and summarized. Higher scores represent greater physical activity.

Physical function tests

OLS tests balance.22 The women were timed for how long time they could stand on one leg (or until the allowed maximum time reached 30 s), with eyes open and arms across the chest. The test was performed twice for both legs, and the maximum value was used in the analyses. The TUG tests mobility and balance.23 The test measures the time in seconds it takes to rise from a chair, walk 3 m at normal pace, turn around, walk back, and then sit down again. Participants were allowed to use walking aids if needed. The 30-s chair-stand test measures lower body strength.24 The women were asked to rise from a chair without armrests, with their arms crossed over their chest, and sit down again and repeat this as many times as possible in 30 s. Gait velocity is a measurement that can predict adverse events in healthy older people.25 For the 10-m walk test, the women were asked to walk 10 m at a pace of their own choice. Time-keeping started after 2 m and ended at 8 m to exclude the time for acceleration and deceleration.26 The mean value of 2 repeated tests was recorded in meters per second. Grip strength in the dominant hand was measured with a Saehan hydraulic hand dynamometer (model SH5001; Saehan Corporation). With the elbow flexed at 90° and the lower arm resting on a table, 2 attempts were made and the average strength, measured in kilograms, was used in the analyses.27

Incident fractures

Incident fractures were X-ray verified and identified using a regional X-ray archive that included all 49 municipalities (covering an area of 25 000 square kilometers) in the Västra Götaland region surrounding Gothenburg.17 Five research nurses reviewed all the radiology reports from baseline to the end of March 2023. All reported fractures were recorded. All radiographs without available radiology reports or reports with uncertain fracture diagnosis were manually reviewed by an orthopedic surgeon (L.J.).

Ascertainment of morbidity data and mortality

Data regarding diseases at baseline were collected from a self-administered questionnaire and from the National Patient Register using ICD-10 codes (within 5 yr prior to the recruitment date), provided at hospitals in Sweden. The Charlson comorbidity index was calculated to summarize and quantify comorbidity.28 Mortality data were obtained from the regional population registry (Västfolket).

Statistical analyses

Physical activity (PASE score) showed a skewed distribution and was therefore divided into quartiles used in further analyses. Continuous variables at baseline were analyzed by ANOVA followed by the least significant difference (LSD) post hoc test to compare means between women with lowest PASE score with increasing PASE score. Results are presented as mean ± SD. Categorical data were analyzed by the Chi-square test and presented as numbers (percentage). Fisher’s exact test was used if the number of categorical observations was small. p-values less than .05 were considered significant. The incidence per 1000 person-years was calculated as the number of events divided by the total follow-up time (until fracture, death, or study end (the date of archive search for fractures)) per 1000 yr. Cox proportional hazard models were used to investigate the association between physical activity level (PASE score) and incident fracture, with adjustments for confounders. Each participant’s maximum follow-up time was used in the Cox regression model. Cox analyses were performed for any fracture, MOF (fracture of the hip, spine, forearm, proximal humerus), hip fracture, and death. The first endpoint per person was counted for each relevant outcome. Adjustments for confounders were performed in 3 steps, with increasing numbers of covariates included, to establish associations independent of CRFs and FN BMD. Model 1 was adjusted for age, height, and weight, whereas model 2 was also adjusted for Charlson comorbidity index and CRFs used in FRAX including self-reported previous fracture (after the age of 50 yr, fractures of the skull, and face excluded), family history of hip fracture, current smoking, oral glucocorticoid use (daily treatment with at least 5 mg prednisolone or equivalent for 3 mo or more ever), rheumatoid arthritis, excessive alcohol intake (21 or more standard units per week), and secondary osteoporosis (diabetes mellitus (type 1 and 2), menopause before 45 yr of age, inflammatory bowel disease, and chronic liver disease), but without FN BMD. In model 3, FN BMD was also included. The results are presented as hazard ratios (HRs) with 95% CI. By visually reviewing the log (−log[survival]) vs log(time) curves for each outcome (any fracture, MOF, hip fracture), the Cox models satisfied the proportionality assumption. Interaction was tested using multivariable adjusted Cox model, with interaction term for PASE (continuous) and Charlson comorbidity index. For quartile 4 (compared to quartile 1), post hoc statistical power analyses were performed, demonstrating >80% power (with an alpha of 0.05) for incident any fracture, MOF, and hip fracture. Subgroup analyses were performed on participants with (>0) and without (=0) comorbidities according to Charlson, by repeating the cox regression model as described. All statistical analyses were performed with SPSS Statistics Version 25 (IBM Corporation). Statistical imputation using the MICE-package in R-studio (Multivariate imputation by Chained Equations) was utilized for missing CRFs in FRAX using 20 iterations with Nelson–Aalen estimates for all the outcomes. Imputation was performed for 210 (7.0%) women with missing data on CRFs concerning 226 data points (46 regarding missing information on parental history of hip fracture, 4 for rheumatoid arthritis, 6 for previous fragility fracture, 1 for current smoking, 4 for oral glucocorticoids, and 165 had missing data for any of the secondary osteoporosis components). A subgroup analysis was performed on the complete cases without imputation for missing CRFs. To assess the potential impact of death as a competing risk, the subdistribution HRs for fracture were analyzed using a Fine and Gray model with death as the competing risk (the finegray command in R survival package).29

Results

Characteristics of the cohort

Baseline characteristics of the women are presented in Table 1. In total, 3014 women completed the PASE assessment and were divided into quartiles according to PASE score (PASE score median (range) Q1 52.1 (0.0–65.0), Q2 78.6 (65.1–96.1), Q3 113.9 (96.3–134.2), Q4 164.8 (134.2–385.3)). Increasing PASE quartiles were associated with lower age, BMI, and Charlson comorbidity index (p < .001) (Table 1). Significantly fewer women in Q2–4 had fallen within the last year and fewer smoked compared to women in Q1. Significantly fewer women in Q4 had secondary osteoporosis, rheumatoid arthritis, comorbidity, and back pain and they ranked their health-related quality of life significantly higher compared to Q1 (Table 1). There was no quartile difference in 10-yr FRAX probabilities for MOF or hip fracture, glucocorticoid use, or self-reported number of fractures after 50 yr of age (Table 1). The type and frequency of different physical activities according to PASE quartile are presented in Table S1.

Table 1 Characteristics of older women in quartiles of physical activity according to PASE.

	Quartile 1 n = 754	Quartile 2 n = 753	Quartile 3 n = 753	Quartile 4 n = 754	p-value a	
 Age (yrs)	78.1 ± 1.6	77.8 ± 1.6§	77.8 ± 1.6¤	77.5 ± 1.6#, ^, £	<.001	
 Height (cm)	161.2 ± 6.3	162.0 ± 5.4§	161.8 ± 6.0	162.3 ± 5.8#	.005	
 Weight (kg)	72.2 ± 13.9	68.9 ± 11.6$	68.0 ± 11.1¤	66.0 ± 10.8#,  ^,£	<.001	
 BMI (kg/m2)	27.8 ± 5.0	26.2 ± 4.2§	26.0 ± 4.1¤	25.1 ± 3.9#,  ^,  £	<.001	
 PASE score, median (range)	52.1 (0.0–65.0)	78.6 (65.1–96.1)	113.9 (96.3–134.2)	164.8 (134.2–385.3)		
 FRAX MOF without BMD (%)	33.4 ± 13.7	33.5 ± 13.1	33.0 ± 12.3	34.1 ± 13.3	.455	
 FRAX MOF with BMD (%)	23.8 ± 12.2	23.1 ± 12.2	22.7 ± 11.4	22.5 ± 11.5	.122	
 FRAX hip without BMD (%)	20.3 ± 14.0	20.2 ± 13.7	19.7 ± 12.6	21.3 ± 13.9	.168	
 FRAX hip with BMD (%)	11.8 ± 11.4	11.11 ± 11.7	10.6 ± 10.6	10.7 ± 10.7	.160	
 Fall within the last year, % (n)	34.6 (261)	28.4 (214)	27.4 (206)	28.1 (212)	.007	
 Self-reported prior fracture, % (n)b	35.7 (269)	37.6 (283)	37.5 (282)	36.6 (276)	.860	
 Family history of hip fracture, % (n)	18.0 (136)	17.3 (130)	16.1 (121)	18.8 (142)	.540	
 Current smoking, % (n)	9.0 (68)	4.5 (34)	4.1 (31)	3.2 (24)	<.001	
 Excessive alcohol consumption, % (n)c	0.8 (6)	0.5 (4)	0.5 (4)	0.4 (3)	.796d	
Medications						
 Glucocorticoid use, % (n)e	4.1 (31)	2.9 (22)	2.9 (22)	3.6 (27)	.526	
 Osteoporosis medication, % (n)f	13.0 (98)	10.0 (75)	8.4 (63)	11.0 (83)	.028	
Medical history						
 Secondary osteoporosis, % (n)	30.1 (227)#	27.1 (204)	23.8 (179)	23.3 (176)	.008	
 Rheumatoid arthritis, % (n)	6.0 (45)#	3.6 (27)	3.6 (27)	2.7 (20)	.007	
 Hyperthyroidism, % (n)	6.4 (48)	4.2 (32)	5.2 (39)	4.5 (34)	.245	
 Osteoporosis, % (n)	22.9 (173)	20.2 (152)	17.9 (135)	20.4 (154)	.118	
 Charlson comorbidity index = 0, % (n)	40.6 (306)#	52.3 (394)	54.1 (407)	59.5 (449)	<.001	
 Charlson comorbidity index = 1, % (n)	23.6 (178)	20.3 (153)	21.1 (159)	18.6 (140)	.112	
 Charlson comorbidity index = 2, % (n)	20.6 (155)#	16.6 (125)	15.8 (119)	14.2 (107)	.008	
 Charlson comorbidity index ≥3, % (n)	15.3 (115)#	10.8 (81)	9.0 (68)	7.7 (58)	<.001	
 Self-reported back pain % (n)	71.2 (537)#	63.1 (475)	61.4 (462)	58.2 (439)	<.001	
SF-12						
 PCS12	38.8 ± 11.6	45.0 ± 10.3§	47.4 ± 9.9¤,  *	49.3 ± 8.8#,  ^,  £	<.001	
 MCS12	51.1 ± 10.9	53.9 ± 9.1§	53.8 ± 8.8¤	55.2 ± 8.8	<.001	
Unadjusted characteristics values are presented as mean ± SD for continuous variables and as percentage and number for categorical variables. PASE = Physical Activity Scale for the Elderly. Significance was defined by a p-value <.05 and significant values are presented in bold. SF-12 = 12-Item Short Form Survey, PCS = physical component summary, MCS = mental component summary.

a Differences between groups tested by ANOVA followed by least significant difference post hoc test for continuous variables or by χ2 for categorical variables.

§ Second quartile vs first quartile.

¤ Third quartile vs first quartile.

# Fourth quartile vs first quartile.

*Third quartile vs second quartile.

^ Fourth quartile vs third quartile.

£ Fourth quartile vs second quartile.

b After 50 yr of age, fractures of the skull and face are excluded.

c Twenty-one or more units per week.

d Fisher’s Exact Test.

e Daily oral treatment with at least 5 mg for 3 mo or more ever.

f Current treatment with bisphosphonates, teriparatide, or denosumab.

Association between PASE and bone parameters

Baseline characteristics of bone parameters (measured by DXA and HR-pQCT) are presented in Table 2.

Table 2 Characteristics of bone parameters in quartiles of physical activity according to PASE quartiles in older women.

	Quartile 1 n = 754	Quartile 2 n = 753	Quartile 3 n = 753	Quartile 4 n = 754	p-value a	
 PASE score, median (range)	52.1 (0.0–65.0)	78.6 (65.1–96.1)	113.9 (96.3–134.2)	164.8 (134.2–385.3)		
DXA						
 FN BMD (g/cm2)	0.66 ± 0.12	0.66 ± 0.10	0.66 ± 0.11	0.66 ± 0.10	.881	
 FN BMD (T-score)	−1.65 ± 0.96	−1.65 ± 0.86	−1.63 ± 0.88	−1.63 ± 0.85	.896	
 LS BMD (g/cm2)	0.97 ± 0.18b	0.94 ± 0.17§	0.94 ± 0.17¤	0.93 ± 0.17#	<.001	
 TBS	1.20 ± 0.12b	1.21 ± 0.10§	1.21 ± 0.11¤	1.22 ± 0.11#,£	<.001	
HR-pQCT	Quartile 1 n = 570	Quartile 2 n = 611	Quartile 3 n = 626	Quartile 4 n = 626	p-value a	
Radius						
 Tb.BV/TV (%)	10.1 ± 3.6	10.0 ± 3.3	10.1 ± 3.4	10.0 ± 3.5	.844	
 Tb.N (1/mm)	1.72 ± 0.46	1.72 ± 0.42	1.72 ± 0.43	1.69 ± 0.45	.474	
 Tb.Th (mm)	0.06 ± 0.01	0.060.01	0.06 ± 0.01	0.06 ± 0.01	.758	
 Tb.Sp (mm)	0.57 ± 0.2	0.57 ± 0.2	0.57 ± 0.2	0.59 ± 0.2	.290	
 Cortical porosity standard site (%)	4.5 ± 2.1c	4.4 ± 2.2d	4.5 ± 2.2e	4.4 ± 2.2f	.848	
 Cortical vBMD, standard site  (mg/cm3)	771.2 ± 79.0	773.6 ± 79.7	765.4 ± 79.0	769.0 ± 78.5	.309	
 Cortical area, standard site (mm2)	37.8 ± 12.2	37.8 ± 11.5	36.9 ± 11.2	37.4 ± 11.5	.512	
 Total vBMD, standard site (mg/cm3)	241.4 ± 65.4	242.1 ± 60.6	238.9 ± 62.6	238.2 ± 61.5	.641	
 Stiffness, standard site (kN/mm)	55±14r	54±12s	55±12t	55±12u	.737	
 Failure load, standard site (N)	2815±671r	2782±581s	2791±583t	2811±594u	.747	
HR-pQCT	Quartile 1 n = 685	Quartile 2 n = 671	Quartile 3 n = 706	Quartile 4 n = 708	p-value a	
Radius						
 Cortical vBMD, 14% site (mg/cm3)	1002.0 ± 40.2	1006.8 ± 37.8§	1001.2 ± 37.8*	1003.2 ± 38.9	.038	
 Cortical area, 14% site (mm2)	59.6 ± 10.3	58.9 ± 9.5	58.8 ± 9.3	59.3 ± 9.6	.450	
 Cortical porosity, 14% site (%)	2.5 ± 2.1g	2.2 ± 1.8h,§	2.4 ± 1.9i	2.4 ± 0.02j	.038	
 Total vBMD, 14% site (mg/cm3)	531.9 ± 105.1	539.2 ± 98.2	527.5 ± 101.0	529.1 ± 104.1	.151	
HR-pQCT	Quartile 1 n = 724	Quartile 2 n = 721	Quartile 3 n = 735	Quartile 4 n = 733	p-value a	
Tibia						
 Tb.BV/TV (%)	12.2 ± 3.0	12.1 ± 2.9	12.2 ± 3.0	12.2 ± 2.9	.975	
 Tb.N (1/mm)	1.81 ± 0.36	1.77 ± 0.36§	1.78 ± 0.36	1.76 ± 0.35#	.021	
 Tb.Th (mm)	0.067 ± 0.01	0.069 ± 0.0§	0.069 ± 0.01¤	0.07 ± 0.01#	.006	
 Tb.Sp (mm)	0.50 ± 0.12	0.52 ± 0.14§	0.52 ± 0.13	0.52 ± 0.13#	.028	
 Cortical porosity, standard site (%)	12.4 ± 4.1k	12.2 ± 3.9l	12.3 ± 3.9m	12.1 ± 0.04n	.543	
 Cortical vBMD, standard site  (mg/cm3)	731.6 ± 75.2	739.7 ± 66.4§	741.7 ± 65.7¤	743.0 ± 68.4#	.007	
 Cortical area, standard site (mm2)	75.9 ± 24.9	78.1 ± 22.7	79.4 ± 22.4¤	79.0 ± 23.1#	.022	
 Total vBMD, standard site (mg/cm3)	223.8 ± 50.1	225.5 ± 47.3	227.5 ± 48.0	226.4 ± 47.4	.503	
 Stiffness, standard site (kN/mm)	161±32v	163±28x	165±27y	164±28z	.105	
 Failure load, standard site (N)	8223±1550v	8289±1368x	8380±1327y	8355±1374z	.139	
HR-pQCT	Quartile 1 n = 738	Quartile 2 n = 739	Quartile 3 n = 738	Quartile 4 n = 749	p-value a	
Tibia						
 Cortical vBMD, 14% site (mg/cm3)	912.6 ± 42.6	916.8 ± 40.7	916.1 ± 41.6	916.4 ± 42.5	.200	
 Cortical area, 14% site (mm2)	145.2 ± 25.4	147.0 ± 23.8	149.0 ± 22.6¤	149.1 ± 23.4#	.003	
 Cortical porosity, 14% site (%)	5.6 ± 2.6o	5.2 ± 2.4m,§	5.3 ± 2.5p	5.3 ± 2.5q	.047	
 Total vBMD, 14% site (mg/cm3)	377.6 ± 80.1	386.4 ± 78.5§	387.5 ± 77.1¤	387.1 ± 77.6#	.046	
Unadjusted bone parameter values are presented as mean ± SD for continuous variables. PASE, Physical Activity Scale for the Elderly; TBS, trabecular bone score. Significance was defined by a p-value <.05, and significant values are presented in bold.

§ Second quartile vs first quartile.

¤ Third quartile vs first quartile.

# Fourth quartile vs first quartile.

£ Fourth quartile vs second quartile.

*Third quartile vs second quartile.

a Continuous variables are analyzed using one-way ANOVA followed by LSD post hoc test, HR-pQCT = high- resolution peripheral quantitative computed tomography; Tb.N, trabecular number; Tb.Th, trabecular thickness; Tb.Sp, trabecular separation; standard site, standard measurement site; 14% site, measurement at 14% of radius or tibia length; vBMD=volumetric BMD.

b 740.

c 571.

d 609.

e 624.

f 626.

g 679.

h 669.

i 703.

j 706.

k 723.

l 721.

m 736.

n 731.

o 737.

p 732.

q 745.

r 571.

s 608.

t 626.

u 627.

v 724.

x 720.

y 736.

z 733.

Different numbers regarding HR-pQCT are due to the fact that only images with adequate quality, 1 to 3, were used in the analyses.

Dual X-ray absorptiometry

There was no difference in FN BMD between Q1 and Q4, while LS BMD (L1–L4) was 4% lower (p < .001) and TBS (L1–L4) 1.7% higher (p < .001) in Q4 compared to Q1 (Table 2).

HR-pQCT

There were no differences between Q1 and Q4 regarding trabecular and cortical parameters at the standard site of the distal radius (Table 2). Ct.vBMD at 14% site (radius) was 0.5% higher and cortical porosity 12% lower in Q2 compared to Q1 (p = .038). At the distal tibia (standard site), women in Q4 had greater Ct.vBMD and cortical area than women in Q1, with quartile differences corresponding to 1.56% for Ct.vBMD (p = .007) and 4.08% for cortical area (p = .022). Also, at the 14% tibial site, women in Q4 had greater cortical area than women in Q1 (2.7%, p = .003), and total volumetric BMD was 2.5% higher in Q4 than in Q1 (p = .046) (Table 2).

Associations between PASE and physical performance

The women in Q4 performed better than women in Q1 in all of the physical function tests, including grip strength, OLS, TUG, 30-s chair stand test, and walking speed, with quartiles Q4 vs Q1 differences of 24%, 52%, 27%, 49%, and 25% (p < .001), respectively (Table 3).

Table 3 Characteristics of physical function in quartiles of physical activity according to PASE in older women.

	Quartile 1 n = 754	Quartile 2 n = 753	Quartile 3 n = 753	Quartile 4 n = 754	p-value a	
 PASE score, median (range)	52.1 (0.0–65.0)	78.6 (65.1–96.1)	113.9 (96.3–134.2)	164.8 (134.2–385.3)		
Physical function tests						
 Grip strength (kg)	13.0 ± 5.6b	14.5 ± 5.6c, §	15.2 ± 5.2d,¤,*	16.1 ± 5.2e, #,^,£	<.001	
 OLS (s)	10.4 ± 8.6f	13.7 ± 9.5g, §	14.7 ± 9.4h,¤,*	15.8 ± 9.9i,#,^,£	<.001	
 TUG (s)	10.6 ± 4.7j	8.7 ± 2.7k, §	8.1 ± 2.2¤	7.7 ± 1.9#,^,£	<.001	
 30-s chair stand test (n)	8.2 ± 4.7e	10.4 ± 4.0k, §	11.3 ± 3.9l,¤,*	12.2 ± 3.8m,#,^,£	<.001	
 Walking speed (m/s)	1.10 ± 0.27n	1.26 ± 0.23l,§	1.31 ± 0.22¤,*	1.37 ± 0.20o,#,^,£	<.001	
Values are presented as mean ± SD for continuous variables. PASE, Physical Activity Scale for the Elderly. Significance was defined by a p-value <.05, and significant values are presented in bold.

OLS, one leg standing; TUG, timed up and go.

a Continuous variables one-way ANOVA followed by LSD post hoc test

§ Second quartile vs first quartile.

¤ Third quartile vs first quartile.

# Fourth quartile vs first quartile.

*Third quartile vs second quartile.

^ Fourth quartile vs third quartile.

£ Fourth quartile vs second quartile.

b 717.

c 721.

d 724.

e 742.

f 462.

g 602.

h 638.

i 695.

j 735.

k 749.

l 748.

m 745.

n 738.

o 753.

Associations between PASE and the risk of incident fracture

Associations between quartiles of PASE and 3 different categories of first incident fracture: any fracture, MOF, and hip fracture are presented in Table 4. During a median follow-up time of 8.0 yr (range min-max 0.20–9.9), 562 died, 1077 women sustained any fracture, and 806 women sustained a MOF. Of those with an incident MOF, 236 women sustained a hip fracture. The proportion of women with incident any fracture was 40.5% (n = 305) in Q1 and decreased to 36.8% (n = 277), 33.3% (n = 251), and 32.4% (n = 244) in Q2, Q3, and Q4, respectively. The same trend was seen regarding incident MOF (30.4% (n = 229), 27.4% (n = 206), 26.0% (n = 196), 23.2% (n = 175), Q1–Q4, respectively) and incident hip fracture (10.6% (n = 80), 7.6% (n = 57), 7.0% (n = 53), 6.1% (n = 46), Q1–Q4, respectively).

Table 4 Associations between quartiles of physical activity according to PASE and fracture risk in older women.

	Quartile 1  n= 754	Quartile 2  n= 753	Quartile 3 n = 753	Quartile 4 n = 754	
Any fracture					
 No. (%)	305 (40.5)	277 (36.8)	251 (33.3)	244 (32.4)	
 Per 1000 person-years	69.4	58.2	50.6	48.6	
 Time at risk, median (Q3–Q1), years	6.94 (4.76)	7.16 (3.99)	7.38 (3.21)	7.50 (3.40)	
 HR (95% CI)					
 Model 1	1 [Reference]	0.83 [0.71–0.98]	0.72 [0.61–0.86]	0.70 [0.59–0.83]	
 Model 2	1 [Reference]	0.85 [0.72–1.00]	0.74 [0.63–0.88]	0.72 [0.61–0.86]	
 Model 3a	1 [Reference]	0.86 [0.73–1.01]	0.76 [0.64–0.90]	0.75 [0.63–0.89]	
MOF					
 No. (%)	229 (30.4)	206 (27.4)	196 (26.0)	175 (23.2)	
 Per 1000 person-years	48.6	40.6	37.7	33.2	
 Time at risk, median (Q3–Q1), years	7.25 (4.31)	7.45 (2.83)	7.61 (2.34)	7.65 (2.05)	
 HR (95% CI)					
 Model 1	1 [Reference]	0.83 [0.69–1.01]	0.77 [0.64–0.94]	0.68 [0.56–0.84]	
 Model 2	1 [Reference]	0.85 [0.70–1.03]	0.80 [0.66–0.96]	0.71 [0.58–0.87]	
 Model 3 a	1 [Reference]	0.87 [0.72–1.05]	0.82 [0.68–1.00]	0.75 [0.61–0.92]	
Hip					
 No. (%)	80 (10.6)	57 (7.6)	53 (7.0)	46 (6.1)	
 Per 1000 person-years	14.9	10.0	9.2	7.9	
 Time at risk, median (Q3–Q1), years	7.80 (2.07)	7.93 (1.76)	7.87 (1.73)	7.84 (1.64)	
 HR (95% CI)					
 Model 1	1 [Reference]	0.61 [0.43–0.86]	0.55 [0.39–0.79]	0.46 [0.31–0.66]	
 Model 2	1 [Reference]	0.63 [0.45–0.89]	0.60 [0.42–0.85]	0.49 [0.34–0.72]	
 Model 3 a	1 [Reference]	0.66 [0.47–0.93]	0.63 [0.44–0.90]	0.55 [0.37–0.80]	
Death					
 No. (%)	218 (28.9)	123 (16.3)	117 (15.5)	104 (13.8)	
 Per 1000 person-years	38.8	20.9	19.8	17.5	
 Time at risk, median (Q3–Q1), years	7.98 (1.86)	7.99 (1.74)	7.99 (1.66)	7.94 (1.54)	
 HR (95% CI)					
 Model 1	1 [Reference]	0.55 [0.44–0.68]	0.51 [0.41–0.64]	0.47 [0.37–0.59]	
 Model 2	1 [Reference]	0.60 [0.48–0.75]	0.59 [0.47–0.75]	0.55 [0.43–0.71]	
 Model 3	1 [Reference]	0.62 [0.49–0.77]	0.61 [0.49–0.77]	0.58 [0.45–0.74]	
Model 1 = Adjusted for age, height, weight. Model 2 = model 1 + clinical risk factors (smoking, parental hip fracture, excessive alcohol intake, oral glucocorticoid use, rheumatoid arthritis, previous fracture, secondary osteoporosis) + Charlson comorbidity index. Model 3 = model 2+ FN BMD.

MOF, major osteoporotic fracture; PASE, Physical Activity Scale for the Elderly; Q, quartile.

a Number of participants with FN BMD in Q1, Q2, Q3, and Q4 was 749, 749, 751, 752, respectively. Associations were examined using Cox proportional hazard models. Hazard ratios (HRs) and 95% CIs are presented.

A Cox regression model, adjusted for age, height, and weight showed that higher PASE score (Q4 vs Q1) was associated with 30%, 32%, and 54% lower risk of sustaining an incident any fracture, MOF, and hip fracture, respectively (Table 4). These associations were independent of Charlson comorbidity index and other CRFs included in FRAX and FN BMD (Table 4 and Figure 1A–C). PASE was also significantly associated with all fracture outcomes and death when analyzed as a continuous variable in Cox models (Table S2). A subgroup analysis was performed using only complete cases (excluding cases with any imputed CRF). Using complete cases in the analysis did not materially change the associations between PASE and fracture outcomes (Table S3).

Figure 1 The cumulative hazard for any fracture (A), MOF (B), hip fracture (C), and death (D) in older women divided by PASE quartiles (Q1 lowest score–Q4 highest score) at baseline, adjusted for age, height, weight, previous fracture, family history of hip fracture, current smoking, oral glucocorticoid use, rheumatoid arthritis, excessive alcohol intake, secondary osteoporosis, Charlson comorbidity index, and FN BMD (PASE = physical activity scale for the elderly).

The number of women reporting a fall within the last year was lower in Q4 compared with Q1 (28.1% vs 34.6%, p < .007, respectively) (Table 1). Therefore, a previous fall was included in fully adjusted (CRFs and comorbidity) Cox regression model. The lower fracture risk observed in Q4 for any fracture, MOF, and hip fracture was largely similar as when history of falls was included (HR 0.73 [95% CI, 0.61–0.87] vs HR 0.72 [95% CI, 0.61–0.86], HR 0.72 [95% CI, 0.59–0.88] vs HR 0.71 [95% CI, 0.58–0.87], HR 0.50 [95% CI, 0.34–0.73] vs HR 0.49 [95% CI, 0.34–0.72], respectively.)

A Cox regression model was also performed with bone parameters (Ct.vBMD and cortical area in the distal tibia) or physical function test (TUG) added to the fully adjusted model (Table 5). When bone parameters were included in the model, the associations between PASE (Q4 vs Q1) and incident any fracture, and hip fracture were largely unchanged (Table 5). In contrast, when TUG was added to the fully adjusted models, the associations with fracture outcomes were weaker, reaching 16% lower risk for any fracture, 18% for MOF, and 39% of hip fracture, of which only the association with hip fracture remained significant (Table 5).

Table 5 Associations between quartiles of physical activity according to PASE and fracture risk in older women adjusted for bone parameters and physical function.

	Quartile 1 n = 754	Quartile 2 n = 753	Quartile 3 n = 753	Quartile 4 n = 754	
Any fracture					
 No. (%)	305 (40.5)	277 (36.8)	251 (33.3)	244 (32.4)	
 Per 1000 person-years	69.4	58.2	50.6	48.6	
 Time at risk, median (Q3–Q1), years	6.94 (4.76)	7.16 (3.99)	7.38 (3.21)	7.50 (3.40)	
 HR (95% CI)					
Model 3* + Ct.vBMD and cortical area distal tibia a	1 [Reference]	0.88 [0.75–1.05]	0.78 [0.65–0.92]	0.78 [0.65–0.93]	
Model 3* + TUG b	1 [Reference]	0.93 [0.78–1.10]	0.84 [0.71–1.01]	0.84 [0.70–1.00]	
MOF					
 No. (%)	229 (30.4)	206 (27.4)	196 (26.0)	175 (23.2)	
 Per 1000 person-years	48.6	40.6	37.7	33.2	
 Time at risk, median (Q3–Q1), years	7.25 (4.31)	7.45 (2.83)	7.61 (2.34)	7.65 (2.05)	
 HR (95% CI)					
Model 3* + Ct.vBMD and cortical area distal tibia c	1 [Reference]	0.88 [0.72–1.07]	0.82 [0.67–1.01]	0.77 [0.63–0.95]	
Model 3* + TUG d	1 [Reference]	0.92 [0.76–1.12]	0.89 [0.73–1.09]	0.82 [0.66–1.01]	
Hip					
 No. (%)	80 (10.6)	57 (7.6)	53 (7.0)	46 (6.1)	
 Per 1000 person-years	14.9	10.0	9.2	7.9	
 Time at risk, median (Q3–Q1), years	7.80 (2.07)	7.93 (1.76)	7.87 (1.73)	7.84 (1.64)	
 HR (95% CI)					
Model 3* + Ct.vBMD and cortical area distal tibia e	1 [Reference]	0.70 [0.49–1.00]	0.67 [0.47–0.97]	0.58 [0.39–0.85]	
Model 3* + TUG f	1 [Reference]	0.71 [0.50–1.01]	0.70 [0.48–1.01]	0.61 [0.41–0.90]	
Death					
 No. (%)	218 (28.9)	123 (16.3)	117 (15.5)	104 (13.8)	
 Per 1000 person-years	38.8	20.9	19.8	17.5	
 Time at risk, median (Q3–Q1), years	7.98 (1.86)	7.99 (1.74)	7.99 (1.66)	7.94 (1.54)	
 HR (95% CI)					
Model 3* + Ct.vBMD and cortical area distal tibia g	1 [Reference]	0.66 [0.52–0.83]	0.63 [0.50–0.80]	0.59 [0.46–0.76]	
Model 3* + TUG h	1 [Reference]	0.76 [0.60–0.96]	0.79 [0.62–1.00]	0.74 [0.57–0.96]	
Ct.vBMD, cortical volumetric BMD; MOF, major osteoporotic fracture; PASE, Physical Activity Scale for the Elderly; Q, quartile; TUG, timed up and go.

Model 3 = Adjusted for age, height, weight, CRFs (smoking, parental hip fracture, excessive alcohol intake, oral glucocorticoid use, rheumatoid arthritis, previous fracture, secondary osteoporosis), Charlson comorbidity index + FN BMD.

*Number of participants with FN BMD in Q1, Q2, Q3, and Q4 was 749, 749, 751, 752, respectively. Associations were examined using Cox proportional hazard models. Hazard ratios (HRs) and 95% CIs are presented.

a Number of total events (n) =1030.

b n = 1062.

c n = 768.

d n = 795.

e n = 223.

f n = 230.

g n = 536.

h n = 543.

There was no interaction between PASE (continuous) and Charlson comorbidity index regarding any fracture (fully adjusted model) (p = 1.00), MOF (p = .96), or hip fracture (p = .70). To further explore the potential interaction between comorbidity and physical activity (PASE), we also performed a subgroup analysis on women without comorbidity (Charlson comorbidity index = 0). Higher PASE score (Q4 vs Q1) was significantly associated with lower fracture risk; 26% (HR 0.74 [95% CI, 0.58–0.96]), 28% (HR 0.72 [95% CI, 0.54–0.97), and 52% (HR 0.48 [95% CI, 0.29–0.82]) for any fracture, MOF, and hip fracture, respectively, which remained significant for any fracture and hip fracture in fully adjusted models (Table S4). Highly similar results were observed in another subgroup analysis on women with comorbidity (Charlson comorbidity index ≥1) (Table S5).

Mortality and competing risk

During follow-up, the number of deaths in Q1–Q4 was 218 (28.9%), 123 (16.3%), 117 (15.5%), and 104 (13.8%), respectively. Mortality rates increased with decreased PASE score (Table 4). In fully adjusted models, women in Q2–Q4 had a significantly lower risk of death compared with Q1 with quartiles differences of 44%, 46%, and 52%, respectively (Table 4 and Figure 1D). Adjusted subhazard ratios for the association between women in Q2–Q4 vs women in Q1 and any fracture, MOF, and hip fracture calculated using Fine and Gray with death as a competing risk were similar to the HRs calculated using Cox regression (Table S6).

Discussion

In this population-based cross-sectional study of older women, physical activity, assessed by PASE divided into quartiles, was associated with any fracture, MOF, and hip fracture, independently of FN BMD, comorbidities, and CRFs used in FRAX. Overall, greater PASE quartile differences were observed for physical performance measurements than for bone microstructure or bone geometry measurements, indicating that poor physical performance, rather than effects on bone, is the most important cause of the increased fracture risk found with low physical activity. This finding was supported by the attenuated associations between PASE, any incident fracture, and MOF, when further adjusting the models for physical performance, an attenuation not seen when additionally adjusting for bone parameters. To our knowledge, this is the first study evaluating associations between physical activity, assessed by a questionnaire valid and reliable for persons aged 65 yr or over, and incident fractures, with adjustments for confounders known to affect skeletal health, falls, and fracture risk.

Recently, we showed that having a slow TUG time (> 12 s) or a low OLS test (<10 s) had a substantial impact on the 10-yr probability of MOF and hip fracture, and increased the risk of hip fracture 3-fold, in the SUPERB cohort of older women, and it was concluded that TUG and OLS should be considered when evaluating fracture risk in older women.6,7 Cummings et al. found that the inability to stand from a chair doubled the risk of hip fractures in older women.30 However, Alajlouni et al. did not find any associations between baseline muscle performance measurements (TUG, 5 times repeated sit-to-stand, and gait speed) and fracture risk in women, although associations were found in men.31 To examine whether physical performance mediated the association between PASE and incident fractures, we added TUG to the fully adjusted models. OLS was the physical function test with greatest difference between PASE quartiles, but the number of participants who managed to perform OLS was lower than for TUG (n = 2397 vs n = 2991, respectively), which was the reason for performing additional adjustments for TUG. With TUG in the fully adjusted model, the reduction in fracture risk was slightly lower for any fracture, MOF, and hip fracture (16% vs 25%, 18% vs 25% and 39% vs 45%, respectively), and only remained significant for hip fracture. Even though physical function (performance) influenced the association between PASE (physical activity) and fracture risk, we found that also in a fully adjusted model with TUG included, PASE predicted hip fracture risk, indicating that PASE may have other benefits, which we were not able to capture with the used confounders in our analysis.

In this study, the comprehensive examination of bone geometry, volumetric BMD, and microstructure showed small differences between PASE quartiles regarding trabecular and cortical parameters at the distal tibia, but not at the distal radius, which may be due to PASE-associated loading effects on weight-bearing bones not seen on unloaded bones. In addition, there was no difference in FN BMD between PASE quartiles, although TBS was higher in Q2–Q4 compared with Q1, suggesting that any physical activity effects, captured by PASE, on bone are absent or small. A possible explanation as to why LS BMD was higher in Q1 than Q2–Q4 could be the occurrence of more degenerative changes and/or scoliosis resulting in a falsely high LS BMD, an hypothesis supported by the higher prevalence of self-reported back pain the last year in Q1 compared to Q4, 71.2%, and 58.2%, respectively (p < .001)(Tables 1 and 2). Recently, a meta-analysis of 5 studies (of which 4 were randomized controlled trials) examining the effects of exercise on trabecular microarchitecture (distal radius and distal tibia) in older adults using HR-pQCT did not show any significant effects on trabecular microarchitecture in postmenopausal women, although the results should be interpreted with caution.32 However, a systematic review and meta-analysis of randomized controlled trials concluded that exercise in postmenopausal women may decrease bone loss by maintaining trabecular (distal tibia) and cortical (tibia shaft) volumetric BMD.33 In this study, we examined whether bone parameters confound the associations between PASE and incident fractures by adding Ct.vBMD and cortical area (distal tibia) to the multiadjusted model (Table 5). With cortical parameters added to the Cox model, the associations between PASE quartiles and any fracture, MOF, and hip fracture were only slightly attenuated compared to the model without Ct.vBMD and cortical area. These results indicate that the lower fracture risk seen in higher quartiles compared to the lowest quartile (PASE) is independent of bone geometry and density.

Physical function is associated with falls, and history of a fall is an independent risk factor for fracture.5,34 It is known that 35%–40% of those older than 65 yr and living at home fall at least once a year and that falls account for 87% of all fractures in the elderly.34 In this study, 35% of women in Q1 reported a fall within the last year, which was significantly more than women in Q2–Q4 (Table 1). In a sensitivity analysis with fall history included in the fully adjusted (CRFs and comorbidity) Cox regression model, the fracture risk reduction in Q4 of any fracture, MOF, and hip fracture was largely unaltered as when history of fall was not included, indicating that physical activity in this cohort of older women predicts fractures independently of falls.

Comorbidities increase the risk of mortality. In 1987, Charlson et al. defined which clinical conditions predicted 1-yr mortality and developed the tool Charlson comorbidity index as a prognostic indicator for mortality depending on the disease burden.28 In this prospective study of older women, the results may be confounded by the fact that women might die before any event (fracture) or end of study. We therefore included Charlson comorbidity index as a covariate in the Cox regression model together with CRFs used in FRAX (Table 4).28 To further investigate whether comorbidities confound the associations between physical activity and fractures, we performed subgroup analyses, showing that women in Q4 (compared with women in Q1) had a similar association to fracture irrespective of general health status. In another sensitivity analysis, with death as a competing risk using the Fine and Gray model, the subdistribution HRs for fracture were largely similar to HRs observed in the main analysis.29 These results suggest that high level of physical activity is associated with lower risk of fracture, independently of comorbidities and competing risk of death, in this cohort of older women.

In this study, we chose PASE to assess physical activity, as it is reliable and valid in older people.12 In 1990, it was shown that questionnaires designed for assessment of physical activity in younger people (ie age-neutral) are inaccurate when used in older people.35 Older people may have difficulties in answering if the time-frame is too long (months, years), and if the questions used are too open (eg how many minutes per week the respondent spent in a specific activity), and light activities as walking, light-moderate housework, gardening are usually not included in age-neutral questionnaires. In the elderly, light-intensity physical activities occupy a large part of daily physical activity, exceeding time spent on moderate and strenuous intensity physical activities, in terms of total daily energy expenditure,36 making it suitable for the assessment of older persons’ physical activity. Eckert et al. examined the content of 18 different physical activity questionnaires (PAQs) for the elderly.37 According to the International Classification of Functioning, Disability and Health (ICF), developed by the World Health Organization, the 4 ICF components are body functions, body structures, activity and participation, and environmental factors. Eckert et al. linked the items (n = 414) from the 18 PAQs to a code in the ICF. Only 5, of which 1 was PASE, out of the 18 PAQs contained items referring to all 4 domains, and PASE was the only PAQ developed to target persons 65 yr and older.37 Another advantage with PASE is that it covers a broad range of activities, taking into account the large activity variation among older individuals, differences which could be dependent on working or retirement. In this study, we did not isolate or analyze the specific type of physical activity performed. However, we do present data regarding the frequency of different physical activity types per quartile (Table S1) to facilitate the interpretation of which activity level is associated with the lower fracture risk observed in the fourth quartile of PASE. For example, the median PASE score in Q4 was 165. To achieve this score, a woman in the cohort would have to, for example, walk 3–4 d between 1 and 2 h each time, perform strenuous sports such as hiking or jogging, 3–4 d, 2–4 h each time, train muscle strength/endurance 1–2 d, 1–2 h each time, perform light housework as dishwashing, heavy housework as vacuuming, lawn work/yard care, and outdoor gardening at least once during the last 7 d.

In the “Physical Activity Guidelines Advisory Committee Report” from 2008, the studies the report was based on assessed physical activity in different ways.8,9,38-43 In some studies, the participants were asked to report the time spent in different activities during the previous year,38,39 and in others the physical activity was assessed at different ages.40-42

Recently Cauley et al. performed a review on 18 prospective observational studies examining the association between physical activity and incident fractures in individuals aged 40 yr or older.44 Out of 18 studies, only one used PASE and that study was performed on men.13 Seven studies included women only and out of these 2 studies adjusted for skeletal strength and physical function (quantitative ultrasound/TUG and BMD/grip strength, respectively) to test whether the association between incident fractures and physical activity was independent of bone strength and physical performance.45,46 Only 2 studies adjusted for comorbidities, even though health status is an important confounding variable in studies of physical activity in older people.47,48 To our knowledge, this is the first study using PASE for assessment of physical activity in older women investigating the impact of physical activity on fracture risk, including CRFs used in FRAX, BMD, comorbidities, falls, physical function, and bone measurements.

The incident fractures were divided into any fracture, MOF, and hip fracture. Regarding any fracture, we chose to include all fractures. This decision was based on a recent retrospective nationwide cohort study of nearly 3.5 million subjects, 50 yr or older, which found that all fractures, regardless of fracture site, confer an increased risk of subsequent fracture, indicating that not only MOFs should be considered for secondary prevention.49 Also, we did not exclude any possible high-trauma fractures due to that high-trauma and low-trauma fractures show similar relationships with low BMD and future fracture risk.50

There are some strengths of the present study. It is a large, prospective and population-based study. Physical activity was assessed by PASE, a reliable and valid questionnaire in older people. The cohort was evaluated with a wide range of physical performance tests; balance, grip strength, walking speed, and muscle strength, and bone measurements by DXA and HR-pQCT, and comprehensive self-administered questionnaires (PASE, SF-12, lifestyle factors, prior fractures and falls, medication, and comorbidities). Another strength is that incident fractures were identified with high accuracy through X-ray verification. A limitation is that only ambulatory, Swedish, older women, 75 to 80 yr old, were included, and therefore the results cannot be generalized to women of other ages, other origins, or men. With the cross-sectional design, a causal relationship cannot be assured. Another limitation is that PASE scores may increase with outdoor temperature.11 However, the inclusion of the 3028 women in the SUPERB study took place throughout the year, so the possible impact on the results is considered to be negligible. In this study, we did not isolate the role of physical activity, and therefore we cannot draw any conclusions about which role of physical activity (sport, leisure activities, recreation, housework or gardening) that have the greatest impact on fracture reduction. It would improve fracture risk assessment further if this question could be evaluated in future studies, and probably enhance the applicability of physical activity in the clinical practice. Another limitation is that we did not adjust for multiple comparisons. However, the number of comparisons was not overly high (n = 36) (Tables 2 and 3) and if the adjustment of multiple comparisons had been done all the physical function tests would remain significant, although the HR-pQCT parameters would not. Thus, adjustment for multiple comparisons would further strengthen the message that differences in physical function, rather than in bone parameters, are the main underlying reason for the association between physical activity and fracture risk. Finally, when using X-ray archives in prospective studies, there is always a possibility of data loss. However, the proportion of individuals in this age group that immigrate is very low and this potential loss to follow-up is unlikely to influence the results.

In conclusion, in this prospective, population-based study on older ambulant Swedish women, high physical activity was associated with a lower risk of any fracture, MOF, and hip fracture, independently of risk factors used in FRAX, FN BMD, comorbidities, history of falls, physical function, and bone geometry. These results suggest that fracture risk assessments in older women may be improved if physical activity level is considered.

Supplementary Material

Supplemental_table_1_2024_03_05_zjae114

Supplemental_table_2_2024_03_05_zjae114

V2_Supplemental_table_3-2024_03_05_zjae114

V2_Supplemental_table_4_2024_03_05_zjae114

V2_Supplemental_table_5_2024_03_05_zjae114

V2_Supplemental_Table_6_2024_03_24_zjae114

Author contributions

Study design: Lisa Johansson and Mattias Lorentzon. Study conduct: Lisa Johansson, Kristian Axelsson, and Mattias Lorentzon. Data collection: Lisa Johansson, Henrik Litsne, Kristian Axelsson, and Mattias Lorentzon. Data analysis and interpretation: Lisa Johansson, Kristian Axelsson, and Mattias Lorentzon. Drafting manuscript: Lisa Johansson. Revising manuscript content: Lisa Johansson, Henrik Litsne, Kristian Axelsson, and Mattias Lorentzon. Approving final version of manuscript: Lisa Johansson, Henrik Litsne, Kristian Axelsson, and Mattias Lorentzon. Lisa Johansson and Mattias Lorentzon take responsibility for the integrity of the data analysis.

Lisa Johansson (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing—original draft), Henrik Litsne (Data curation, Formal analysis, Methodology, Software, Writing—review & editing), Kristian Axelsson (Data curation, Formal analysis, Investigation, Methodology, Writing—review & editing), and Mattias Lorentzon (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—review & editing)

Funding

This study was funded by the Swedish Research Council (VR), the ALF/LUA grant from the Sahlgrenska University Hospital.

Conflicts of interest

K.A. has received lecture fees from Lilly, Meda/Mylan, and Amgen, all outside the submitted work. L.J. has received lecture fees from UCB Pharma, all outside the submitted work. M.L. has received lecture fees from Astellas, Amgen, UCB Pharma, and Viatris, Parexel International, all outside the submitted work. All other authors have no conflicts of interest.

Data availability

Data cannot be made publicly available for ethical and legal reasons. Such information is subject to legal restrictions according to national legislation. Specifically, in Sweden, confidentiality regarding personal information in studies is regulated in the Public Access to Information and Secrecy Act (SFS 2009:400). The data underlying the results of this study might be made available upon request, after an assessment of confidentiality. There is thus a possibility to apply to get access to certain public documents that an authority holds. In this case, the University of Gothenburg is the specific authority that is responsible for the integrity of the documents with research data. Questions regarding such issues can be directed to the head of the Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Contact information can be obtained from medicin@gu.se.
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