Is the association between physical activity and fatigue mediated by physical function or depressive symptoms in symptomatic knee osteoarthritis? The Multicenter Osteoarthritis Study.

Objectives: To examine whether physical activity (PA) was associated with fatigue, and quantify the extent of potential mediation through depressive symptoms or physical function (PF) on the relationship between PA and fatigue in symptomatic knee osteoarthritis (KOA).Method: This longitudinal study used data from the Multicenter Osteoarthritis Study (n = 484), comprising subjects aged ≥ 50 years. Baseline PA was quantified via an ankle-worn accelerometer. The outcome was fatigue, measured using a 0-10 rating scale at 2 year follow-up. Mediators included gait speed as a measure of PF and depressive symptoms at 2 year follow-up. Mediation analysis was carried out after adjustment for baseline confounders. Stratified analysis by baseline fatigue status [no/low (< 4) and high (≥ 4) fatigue] was performed.Results: A significant direct association was found between PA and fatigue at 2 years [unstandardized coefficient (B) = -0.054; 95% confidence interval (CI) -0.107, -0.002, p = 0.041]. The PA-fatigue relationship was not mediated by gait speed (B = -0.006; 95% CI -0.018, 0.001) or depressive symptoms (B = 0.009; 95% CI 0.009, 0.028). In the subgroup with high baseline fatigue, direct associations were found between PA and fatigue (gait speed model:, B = -0.107; 95% CI -0.212, -0.002, p = 0.046; depressive symptoms model: B = -0.110; 95% CI -0.120, -0.020, p = 0.017); but in the no/low baseline fatigue group, no significant association was found between PA and fatigue.Conclusion: In the symptomatic KOA population, higher baseline PA was directly associated with reduced fatigue 2 years later, especially in those with high baseline fatigue. However, this relationship was not mediated by depressive symptoms or PF.

Cox regression survival analysis with compositional covariates: Application to modelling mortality risk from 24-h physical activity patterns.

Survival analysis is commonly conducted in medical and public health research to assess the association of an exposure or intervention with a hard end outcome such as mortality. The Cox (proportional hazards) regression model is probably the most popular statistical tool used in this context. However, when the exposure includes compositional covariables (that is, variables representing a relative makeup such as a nutritional or physical activity behaviour composition), some basic assumptions of the Cox regression model and associated significance tests are violated. Compositional variables involve an intrinsic interplay between one another which precludes results and conclusions based on considering them in isolation as is ordinarily done. In this work, we introduce a formulation of the Cox regression model in terms of log-ratio coordinates which suitably deals with the constraints of compositional covariates, facilitates the use of common statistical inference methods, and allows for scientifically meaningful interpretations. We illustrate its practical application to a public health problem: the estimation of the mortality hazard associated with the composition of daily activity behaviour (physical activity, sitting time and sleep) using data from the U.S. National Health and Nutrition Examination Survey (NHANES).

Characteristics of a protocol to collect objective physical activity/sedentary behaviour data in a large study: Seniors USP (understanding sedentary patterns).

The Seniors USP study measured sedentary behaviour (activPAL3, 9 day wear) in older adults. The measurement protocol had three key characteristics: enabling 24-hour wear (monitor location, waterproofing); minimising data loss (reducing monitor failure, staff training, communication); and quality assurance (removal by researcher, confidence about wear). Two monitors were not returned; 91% (n=700) of returned monitors had 7 valid days of data. Sources of data loss included monitor failure (n=11), exclusion after quality assurance (n=5), early removal for skin irritation (n=8) or procedural errors (n=10). Objective measurement of physical activity and sedentary behaviour in large studies requires decisional trade-offs between data quantity (collecting representative data) and utility (derived outcomes that reflect actual behaviour).

TAxonomy of Self-reported Sedentary behaviour Tools (TASST) framework for development, comparison and evaluation of self-report tools: content analysis and systematic review.

OBJECTIVE: Sedentary behaviour (SB) has distinct deleterious health outcomes, yet there is no consensus on best practice for measurement. This study aimed to identify the optimal self-report tool for population surveillance of SB, using a systematic framework. DESIGN: A framework, TAxonomy of Self-reported Sedentary behaviour Tools (TASST), consisting of four domains (type of assessment, recall period, temporal unit and assessment period), was developed based on a systematic inventory of existing tools. The inventory was achieved through a systematic review of studies reporting SB and tracing back to the original description. A systematic review of the accuracy and sensitivity to change of these tools was then mapped against TASST domains. DATA SOURCES: Systematic searches were conducted via EBSCO, reference lists and expert opinion. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: The inventory included tools measuring SB in adults that could be self-completed at one sitting, and excluded tools measuring SB in specific populations or contexts. The systematic review included studies reporting on the accuracy against an objective measure of SB and/or sensitivity to change of a tool in the inventory. RESULTS: The systematic review initially identified 32 distinct tools (141 questions), which were used to develop the TASST framework. Twenty-two studies evaluated accuracy and/or sensitivity to change representing only eight taxa. Assessing SB as a sum of behaviours and using a previous day recall were the most promising features of existing tools. Accuracy was poor for all existing tools, with underestimation and overestimation of SB. There was a lack of evidence about sensitivity to change. CONCLUSIONS: Despite the limited evidence, mapping existing SB tools onto the TASST framework has enabled informed recommendations to be made about the most promising features for a surveillance tool, identified aspects on which future research and development of SB surveillance tools should focus. TRIAL REGISTRATION NUMBER: International prospective register of systematic reviews (PROPSPERO)/CRD42014009851.