Falls are a major cause of disability and death in older people, particularly women. Cross-sectional surveys suggest that some risk factors associated with a history of falls may be sex-specific, but whether risk factors for incident falls differ between the sexes is unclear. We investigated whether risk factors for incident falls differ between men and women. Participants were 3298 people aged ≥60 who took part in the Waves 4–6 surveys of the English Longitudinal Study of Ageing. At Wave 4, they provided information about sociodemographic, lifestyle, behavioural and medical factors and had their physical and cognitive function assessed. Data on incident falls during the four-year follow-up period was collected from them at Waves 5 and 6. Poisson regression with robust variance estimation was used to derive relative risks (RR) for the association between baseline characteristics and incident falls. In multivariable-adjusted models that also controlled for history of falls, older age was the only factor associated with increased risk of incident falls in both sexes. Some factors were only predictive of falls in one sex, namely more depressive symptoms (RR (95% CI) 1.03 (1.01,1.06)), incontinence (1.12 (1.00,1.24)) and never having married in women (1.26 (1.03,1.53)), and greater comorbidity (1.04 (1.00,1.08)), higher levels of pain (1.10 (1.04,1.17) and poorer balance, as indicated by inability to attempt a full-tandem stand, (1.23 (1.04,1.47)) in men. Of these, only the relationships between pain, balance and comorbidity and falls risk differed significantly by sex. There were some differences between the sexes in risk factors for incident falls. Our observation that associations between pain, balance and comorbidity and incident falls risk varied by sex needs further investigation in other cohorts.

Regular physical activity (PA) is associated with reduced risk of the development and progression of musculoskeletal, metabolic and vascular disease. However, PA declines with age and this can contribute to multiple adverse outcomes. The aims of this study were to describe the relationship between accelerometer-determined PA, body composition and sarcopenia (the loss of muscle mass and function with age). Seven-day PA was measured using the GENEactiv accelerometer among 32 men and 99 women aged 74–84 years who participated in the Hertfordshire Sarcopenia Study. We measured mean daily acceleration and minutes/day spent in non-sedentary and moderate-to-vigorous physical activity (MVPA) levels. Body composition was measured by dual-energy X-ray absorptiometry, muscle strength by grip dynamometry and function by gait speed. Sarcopenia was defined according to the EWGSOP diagnostic algorithm. Men and women spent a median (inter-quartile range) of 138.8 (82, 217) and 186 (122, 240) minutes/day engaging in non-sedentary activity but only 14.3 (1.8, 30.2) and 9.5 (2.1, 18.6) min in MVPA, respectively. Higher levels of PA were associated with reduced adiposity, faster walking speed and decreased risk of sarcopenia. For example, a standard deviation (SD) increase in mean daily acceleration was associated with an increase in walking speed of 0.25 (95% CI 0.05, 0.45) SDs and a reduction in the risk of sarcopenia of 35% (95% CI 1, 57%) in fully adjusted analyses. PA was not associated with hand grip strength. Community-dwelling older adults in this study were largely sedentary but there was evidence that higher levels of activity were associated with reduced adiposity and improved function. PA at all intensity levels in later life may help maintain physical function and protect against sarcopenia.

The UK population is ageing; improved understanding of risk factors for hospital admission is required. Linkage of the Hertfordshire Cohort Study (HCS) with Hospital Episode Statistics (HES) data has created a multiple-failure survival dataset detailing the characteristics of 2,997 individuals at baseline (1998–2004, average age 66 years) and their hospital admissions (regarded as ‘failure events’) over a 10 year follow-up. Analysis of risk factors using logistic regression or time to first event Cox modelling wastes information as an individual’s admissions after their first are disregarded. Sophisticated analysis techniques are established to examine risk factors for admission in such datasets but are not commonly implemented. We review analysis techniques for multiple-failure survival datasets (logistic regression; time to first event Cox modelling; and the Andersen and Gill [AG] and Prentice, Williams and Peterson Total Time [PWP-TT] multiple-failure models), outline their implementation in Stata, and compare their results in an analysis of housing tenure (a marker of socioeconomic position) as a risk factor for different types of hospital admission (any; emergency; elective; >7 days). The AG and PWP-TT models include full admissions histories in the analysis of risk factors for admission and account for within-subject correlation of failure times. The PWP-TT model is also stratified on the number of previous failure events, allowing an individual’s baseline risk of admission to increase with their number of previous admissions. All models yielded broadly similar results: not owner-occupying one’s home was associated with increased risk of hospital admission. Estimated effect sizes were smaller from the PWP-TT model in comparison with other models owing to it having accounted for an increase in risk of admission with number of previous admissions. For example, hazard ratios [HR] from time to first event Cox models were 1.67(95 % CI: 1.36,2.04) and 1.63(95 % CI:1.36,1.95) for not owner-occupying one’s home in relation to risk of emergency admission or death among women and men respectively; corresponding HRs from the PWP-TT model were 1.34(95 % CI:1.15,1.56) for women and 1.23(95 % CI:1.07,1.41) for men. The PWP-TT model may be implemented using routine statistical software and is recommended for the analysis of multiple-failure survival datasets which detail repeated hospital admissions among older people.

Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk. Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods. Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74. These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.

Sarcopenia and osteoporosis are associated with poor health outcomes in older people. Relationships between muscle and bone have typically been reported at a functional or macroscopic level. The aims of this study were to describe the relationships between muscle morphology and bone health among participants of the Hertfordshire Sarcopenia Study (HSS). 105 older men, mean age 72.5 (SD 2.5) years, were recruited into the HSS. Whole body lean mass as well as appendicular lean mass, lumbar spine and femoral neck bone mineral content (BMC) and bone mineral density (BMD) were obtained through dual-energy X-ray absorptiometry scanning. Percutaneous biopsy of the vastus lateralis was performed successfully in 99 participants. Image analysis was used to determine the muscle morphology variables of slow-twitch (type I) and fast-twitch (type II) myofibre area, myofibre density, capillary and satellite cell (SC) density. There were strong relationships between whole and appendicular lean body mass in relation to femoral neck BMC and BMD (r ≥ 0.43, p < 0.001). Type II fibre area was associated with both femoral neck BMC (r = 0.27, p = 0.01) and BMD (r = 0.26, p = 0.01) with relationships robust to adjustment for age and height. In unadjusted analysis, SC density was associated with whole body area (r = 0.30, p = 0.011) and both BMC (r = 0.26, p = 0.031) and area (r = 0.29, p = 0.017) of the femoral neck. We have demonstrated associations between BMC and changes in muscle at a cellular level predominantly involving type II myofibres. Interventions targeted at improving muscle mass, function and quality may improve overall musculoskeletal health. Larger studies that include women are needed to explore these relationships further.