Physical activity and health related quality of life

Copyright @ 2012 Anokye et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.BACKGROUND: Research on the relationship between Health Related Quality of Life (HRQoL) and physical activity (PA), to date, have rarely investigated how this relationship differ across objective and subjective measures of PA. The aim of this paper is to explore the relationship between HRQoL and PA, and examine how this relationship differs across objective and subjective measures of PA, within the context of a large representative national survey from England. METHODS: Using a sample of 5,537 adults (40–60 years) from a representative national survey in England (Health Survey for England 2008), Tobit regressions with upper censoring was employed to model the association between HRQoL and objective, and subjective measures of PA controlling for potential confounders. We tested the robustness of this relationship across specific types of PA. HRQoL was assessed using the summary measure of health state utility value derived from the EuroQol-5 Dimensions (EQ-5D) whilst PA was assessed via subjective measure (questionnaire) and objective measure (accelerometer- actigraph model GT1M). The actigraph was worn (at the waist) for 7 days (during waking hours) by a randomly selected sub-sample of the HSE 2008 respondents (4,507 adults – 16 plus years), with a valid day constituting 10 hours. Analysis was conducted in 2010. RESULTS: Findings suggest that higher levels of PA are associated with better HRQoL (regression coefficient: 0.026 to 0.072). This relationship is consistent across different measures and types of PA although differences in the magnitude of HRQoL benefit associated with objective and subjective (regression coefficient: 0.047) measures of PA are noticeable, with the former measure being associated with a relatively better HRQoL (regression coefficient: 0.072). CONCLUSION: Higher levels of PA are associated with better HRQoL. Using an objective measure of PA compared with subjective shows a relatively better HRQoL.This project was funded by the NIHR Health Technology Assessment programme (project number 08/72/01)

Using Participatory Action Research to explore workplace health promotion strategies using digital technologies for nutrition and physical activity with truckies: An at-risk, hard-to-reach group

Truck drivers have been identified as being at risk for developing chronic disease, due to long periods of sedentary behaviour, irregular and challenging work patterns, and lack of access to nutritional food choices. Our previous research demonstrates workplace health promotion improves truck drivers’ health knowledge, behaviours and outcomes. However, there is little existing research about workplace health promotion for truckies using digital technologies to improve nutrition and physical activity in this hard-to-reach group. This project aims to explore the use of digital technologies as a workplace health promotion strategy to improve nutrition and physical activity with truckies in south-east Queensland

Machine learning models for classifying physical activity in free-living preschool children

Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions

Evolving the validity of a mental toughness measure: Refined versions of the Mental Toughness Questionnaire‐48

The Mental Toughness Questionnaire‐48 (MTQ48) is a 48‐item self‐report instrument to measure one's level of mental toughness. Despite its wide popularity in psychological studies, the questionnaire has been criticized due to its factorial validity. The present study aimed to re‐assess the factorial validity of the instrument and propose alternative models to provide researchers with theoretically and practically useful instruments to measure mental toughness. Two studies were conducted using large samples of university students (Study 1: n = 2186; Study 2: n = 3209). In Study 1, none of one‐, four‐ and six‐factor models with 48 items satisfactorily fit the data set. Instead, two refined 18‐ and 6‐item versions of the questionnaire, covering six aspects of mental toughness, were proposed: the Short MTQ and Very Short MTQ. Both measures demonstrated excellent fit to the data. These results were replicated with a larger independent sample in Study 2. With the Short MTQ, it is possible to represent mental toughness as a multidimensional construct consisting of a global mental toughness factor and six specific factors. The Very Short MTQ is a practical tool for occasions where constraints prevent use of the Short MTQ. The refined questionnaires are promising options to measure and understand individuals' mental toughness with the MTQ

Using Participatory Action Research to the influence of organisational culture on workplace health promotion strategies for nutrition and physical activity for bus drivers

Challenging work patterns, extended periods of sedentariness and limited access to nutritional food choices puts bus drivers at risk for developing chronic disease. The is a small body of evidence about other groups of sitters’ in the road transport industry, however there is a death of existing research about workplace health promotion for bus drivers.This project aims to understand the influence of organisational culture on workplace health promotion strategies for nutrition and physical activity for bus drivers, an at-risk, hard-to-reach group, in south-east Queensland

Sitting-time and 9-year all-cause mortality in older women

Background Studies of mid-aged adults provide evidence of a relationship between sitting-time and all-cause mortality, but evidence in older adults is limited. The aim is to examine the relationship between total sitting-time and all-cause mortality in older women

Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation

Machine learning models to predict energy expenditure (EE) from accelerometer data have traditionally been trained on data from laboratory-based activity trials. However, accuracy is typically attenuated when implemented in free-living scenarios. Currently, no studies involving preschool children have evaluated the accuracy of EE prediction models trained on laboratory (LAB) under free-living conditions. Purpose To evaluate the accuracy of LAB EE prediction models in preschool children completing a free-living active play session. Performance was benchmarked against EE prediction models trained on free living (FL) data. Methods 25 children (mean age = 4.1±1.0 y) completed a 20-minute active play session while wearing a portable indirect calorimeter and ActiGraph GT3X+ accelerometers on their right hip and non-dominant wrist. EE was predicted using LAB models which included Random Forest (RF) and Support Vector Machine (SVM) models for the wrist, and RF and Artificial Neural Network (ANN) models for the hip. Two variations of the LAB models were evaluated; 1) an "off the shelf" model without additional training; 2) models retrained on free-living data, replicating the methodology used in the original calibration study (retrained LAB). Prediction errors were evaluated in a hold-out sample of 10 children. Results Root mean square error (RMSE) for the FL and retrained LAB models ranged from 0.63- 0.67 kcals/min. In the hold out sample, RMSE's for the hip LAB (0.62-0.71), retrained LAB (0.58-0.62) and FL models (0.61-0.65) were similar. For the wrist placement, FL SVM had a significantly higher RMSE (0.73 ± 0.29 kcals/min) than the retrained LAB SVM (0.63 ± 0.30 kcals/min) and LAB SVM (0.64 ± 0.18 kcals/min). The LAB (0.64 ± 0.28), retrained LAB (0.64 ± 0.25), and FL (0.62 ± 0.26) RF exhibited comparable accuracy. Conclusion Machine learning EE prediction models trained on LAB and FL data had similar accuracy under free-living conditions.</p

Nitrate, nitrite and nitrosamines in the global food supply

Inorganic nitrate provided by either nitrate salts or food supplements may improve cardiometabolic health. However, current methods to assess dietary nitrate, nitrite and nitrosamine consumption are inadequate. The purpose of this study was to develop a reference database to estimate the levels of nitrate, nitrite and nitrosamines in the global food supply. A systematic literature search was undertaken; of the 5,747 articles screened, 448 met the inclusion criteria. The final database included data for 1,980 food and beverages from 65 different countries. There were 5,105 unique records for nitrate, 2,707 for nitrite, and 954 for nitrosamine. For ease of use, data were sorted into 12 categories; regarding nitrate and nitrite concentrations in food and beverages, ‘vegetables and herbs’ were most reported in the literature (n = 3,268 and n = 1,200, respectively). For nitrosamines, ‘protein foods of animal origin’ were most reported (n = 398 records). This database will allow researchers and practitioners to confidently estimate dietary intake of nitrate, nitrite and nitrosamines. When paired with health data, our database can be used to investigate associations between nitrate intake and health outcomes, and/or exercise performance and could support the development of key dietary nitrate intake guidelines

Assessing the effectiveness of High Intensity Interval Training (HIIT) for smoking cessation in women: HIIT to quit study protocol

Background: Smoking and physical inactivity are major risk factors for heart disease. Linking strategies that promote improvements in fitness and assist quitting smoking has potential to address both these risk factors simultaneously. The objective of this study is to compare the effects of two exercise interventions (high intensity interval training (HIIT) and lifestyle physical activity) on smoking cessation in female smokers