One size does not fit all - Application of accelerometer thresholds in chronic disease

This is the author accepted manuscript. The final version is available from Oxford University Press via the DOI in this record National Institute for Health Research (NIHR

Factors Associated with Objectively Assessed Physical Activity Levels of Heart Failure Patients

This is the final version. Available on open access from Longdom Publishing via the DOI in this recordAim: To determine the level of objectively measured moderate-to-vigorous physical activity (MVPA) in patients with heart failure (HF), and to assess the association between MVPA and patient sociodemographic, exercise capacity, and health status factors. Methods: Baseline MVPA data was available in 247 HF patients with 7-day wrist-worn accelerometry from two randomized controlled trials. Associations between MVPA and patient sociodemographic, exercise capacity, and health status factors were assessed using univariate and multivariable linear regression models. Results: 247 patients (28% female, mean age 71 ± 10 years) with HF with reduced ejection fraction (n=198) and preserved ejection fraction (n=49) were included in the analysis. Average MVPA was 283. 3 min/week and ranged widely from a minimum of 0 mins/week to maximum of 2626. 7 mins/week (standard deviation: 404. 1 mins/week). 111 (45%) of patients had a level of PA that met current guidelines of at least 150 minutes/week of MVPA. Multivariable regression showed patient’s age, body mass index, employment status, smoking status, New York Heart Association class, NT-proBNP and exercise capacity to be strongly associated (p<0. 001) with the level of MVPA (p<0. 001). Conclusion: Whilst 45% of HF patients had objectively measured levels of MVPA that met current PA recommendations, we observed a wide range in the level of MVPA across this patient sample. As a number of factors were found to be associated with MVPA our findings provide important information for future interventions aiming to increase MVPA in HF patients.University of ExeterNational Institute for Health Research (NIHR

Individual, social and area level factors associated with older people\u27s walking: analysis of a UK household panel study (Understanding Society)

BackgroundAmong older people, walking is a popular and prevalent activity and is key to increasing physical activity levels and resulting physical and mental health. In the context of rapidly ageing populations, it is important to better understand what factors are associated with walking among older people, based on the socioecological model of health.MethodsWe used data from Understanding Society (n:6450), a national panel survey of UK adults aged 65 years and over living in Great Britain. Slope Indices of Inequality (SII) were calculated for weekly walking hours for those according to individual, social and area characteristics. These include health, loneliness and social isolation, previous walking and activity, residential self-selection, contact with neighbours, number of close friends and social activity, and spatial area-level data describing local area crime, walkability, and proximity to retail, greenspace, and public transport amenities.ResultsResults from multivariable models indicated that poor health, particularly requiring help with walking, was the strongest predictor of weekly walking hours (SII (95% CI) comparing those needing help vs. no help: -3.58 (-4.30, -2.87)). However, both prior sporting activity (most vs. least active: 2.30 (1.75, 2.88)) and walking for pleasure (yes vs. no: 1.92 (1.32, 2.53)) were strongly associated with increased walking several years later. Similarly having close friends (most vs. fewest, 1.18 (0.72, 1.77)) and local retail destinations (any vs. none: 0.93 (0.00, 1.86)) were associated with more weekly walking.ConclusionsPast engagement in physical activity and walking for pleasure are strong predictors of walking behaviour in older people, underscoring the importance of implementing and sustaining walking interventions across the lifespan to ensure continued engagement in later years and the associated health benefits. However, poor health significantly impedes walking in this demographic, emphasising the need for interventions that offer both physical assistance and social support to promote this activity

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

\ua9 2024 The AuthorsBackground: It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Methods: Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n = 1,825,289), UK Biobank (n = 502,363), and the UK Household Longitudinal Study (UKHLS) (n = 49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using &lt;2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL). Findings: Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the “Pain+” cluster in the age-group 18–36 years (mortality IRR = 4.47, hospitalisation IRR = 1.84; GP use IRR = 2.87) and the “Hypertension, Diabetes &amp; Heart disease” cluster in the age-group 37–54 years (mortality IRR = 4.52, hospitalisation IRR = 1.53, GP use IRR = 2.36). In UK Biobank, the “Cancer, Thyroid disease &amp; Rheumatoid arthritis” cluster in the age group 37–54 years had the largest association with mortality (IRR = 2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18–36 years. Interpretation: Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. Funding: This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)—NIHR202020)