Systems and WBANs for Controlling Obesity

According to World Health Organization (WHO) estimations, one out of five adults worldwide will be obese by 2025. Worldwide obesity has doubled since 1980. In fact, more than 1.9 billion adults (39%) of 18 years and older were overweight and over 600 million (13%) of these were obese in 2014. 42 million children under the age of five were overweight or obese in 2014. Obesity is a top public health problem due to its associated morbidity and mortality. This paper reviews the main techniques to measure the level of obesity and body fat percentage, and explains the complications that can carry to the individual's quality of life, longevity and the significant cost of healthcare systems. Researchers and developers are adapting the existing technology, as intelligent phones or some wearable gadgets to be used for controlling obesity. They include the promoting of healthy eating culture and adopting the physical activity lifestyle. The paper also shows a comprehensive study of the most used mobile applications and Wireless Body Area Networks focused on controlling the obesity and overweight. Finally, this paper proposes an intelligent architecture that takes into account both, physiological and cognitive aspects to reduce the degree of obesity and overweight

How to measure sedentary behavior at work?

Background: Prolonged sedentary behavior (SB) is associated with increased risk for chronic conditions. A growing number of the workforce is employed in office setting with high occupational exposure to SB. There is a new focus in assessing, understanding and reducing SB in the workplace. There are many subjective (questionnaires) and objective methods (monitoring with wearable devices) available to determine SB. Therefore, we aimed to provide a global understanding on methods currently used for SB assessment at work.Methods: We carried out a systematic review on methods to measure SB at work. Pubmed, Cochrane, Embase, and Web of Science were searched for peer-reviewed English-language articles published between 1st January 2000 and 17th March 2019.Results: We included 154 articles: 89 were cross-sectional and 65 were longitudinal studies, for a total of 474,091 participants. SB was assessed by self-reported questionnaires in 91 studies, by wearables devices in also 91 studies, and simultaneously by a questionnaire and wearables devices in 30 studies. Among the 91 studies using wearable devices, 73 studies used only one device, 15 studies used several devices, and three studies used complex physiological systems. Studies exploring SB on a large sample used significantly more only questionnaires and/or one wearable device.Conclusions: Available questionnaires are the most accessible method for studies on large population with a limited budget. For smaller groups, SB at work can be objectively measured with wearable devices (accelerometers, heart-rate monitors, pressure meters, goniometers, electromyography meters, gas-meters) and the results can be associated and compared with a subjective measure (questionnaire). The number of devices worn can increase the accuracy but make the analysis more complex and time consuming

Accuracy of predicted energy expenditure from a Fitbit Inspire HR activity monitor during short- and long-duration exercise

PURPOSE: To determine the accuracy of predicted Energy Expenditure (EE) reported by a wrist-worn activity monitor compared to measured EE during both a long- and short-duration exercise. METHODS: In addition to a VO2max treadmill test, a running speed at approximately 70 - 75% of that VO2max was found during the first visit. The second and third visit was comprised of either a 30-minute or 10-minute run at the speed previously determined. A wrist activity monitor was worn and VO2 and EE were recorded by a metabolic cart. Pearson correlation, paired samples t-test, and repeated measures ANOVAs compared predicted and measured EE. An independent samples t-test determined significant differences in characteristics between fitness groups (p \u3c 0.05). RESULTS: N = 25 (60% male). A significant correlation was found between predicted EE and measured EE for both short and long duration (p \u3c 0.001). The repeated measures ANOVA determined the interactive effect of measurement mode and fitness level was significant. CONCLUSIONS: Overall, there is a strong correlation between criterion and predictive measurement, however, consumers should exercise caution when using predicted measures

Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring

Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst the general population. In fact, emerging evidences identify sedentary behaviour as a ubiquitous characteristic of contemporary lifestyles. This has major implications for the general health of people worldwide particularly for the prevalence of non-communicable conditions (NCDs) such as cardiovascular disease, diabetes and cancer and their risk factors such as raised blood pressure, raised blood sugar and overweight. Moreover, sedentary time appears to be uniquely associated with health risks independent of physical activity intensity levels. However, habitual sedentary behaviour may prove complex to be accurately measured as it occurs across different domains, including work, transport, domestic duties and even lei¬sure. Since sedentary behaviour is mostly reflect as too much sitting, one of the main concerns is being able to distinguish among different activities, such as sitting and standing. Widely used devices such as accelerometer-based activity monitors have a limited ability to detect sedentary activities accurately. Thus, there is a need of a viable large-scale method to efficiently monitor sedentary behaviour. This thesis proposes and demonstrates how a plantar pressure based wearable device and machine learning classification techniques have significant capability to monitor daily life sedentary behaviour. Firstly, an in-depth review of research and market ready plantar pressure and force technologies is performed to assess their measurement capabilities and limitations to measure sedentary behaviour. Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. The proposed model and its algorithm are constructed using a dataset of 20 participants collected at both laboratory-based and free-living conditions. Sitting and standing variations are included in the analysis as well as the addition of a potential novel activities, such as leaning. Video footage is continuously collected using of a wearable camera as an equivalent of direct observation to allow the labelling of the training data for the machine learning model. The optimal parameters of the model such as feature set, epoch length, type of classifier is determined by experimenting with multiple iterations. Different number and location of plantar pressure sensors are explored to determine the optimal trade-off between low computational cost and accurate performance. The model s performance is calculated using both subject dependent and subject independent validation by performing 10-fold stratified cross-validation and leave-one-user-out validation respectively. Furthermore, the proposed model activity performance for daily life monitoring is validated against the current criterion (i.e. direct observation) and against the de facto standard, the activPAL. The results show that the proposed machine learning classification model exhibits excel-lent recall rates of 98.83% with subject dependent training and 95.93% with independent training. This work sets the groundwork for developing a future plantar pressure wearable device for daily life sedentary behaviour monitoring in free-living conditions that uses the proposed ma-chine leaning classification model. Moreover, this research also considers important design characteristics of wearable devices such as low computational cost and improved performance, addressing the current gap in the physical activity and sedentary behaviour wearable market

Novel Methods of Measuring and Visualising Youths’ Physical Activity

Despite the physiological and psychosocial health benefits of youth achieving at least 60 minutes of moderate-to-vigorous physical activity (MVPA) every day, only a small proportion of youth in the UK meet this daily target. While there are several reasons for this failure to achieve the recommended amount of MVPA, recent evidence suggests that many youths lack awareness of their physical activity levels (PAL) and have difficulty interpreting and applying the guidelines to their daily activity. One solution to counteract this problem is to utilise and integrate technology, such as an objective measurement of PAL in combination with personalised feedback, to enhance youth’s awareness and understanding of, and motivation for, physical activity. Whilst accelerometers are the de facto standard in objectively measuring PAL, they have limitations when it comes to assessing non-linear movements, such as turning, that are habitual to youths’ sporadic activity. Study 1, therefore, investigated the energy expenditure of turning in children, finding that the magnitude and frequency of turns completed are important considerations when measuring habitual PAL. Specifically, significant differences in energy expenditure to straight-line walking within speed were established for 2.5 km·hr-1 at 90° turn (~7% increase) and 3.5, 4.5 and 5.5 km·hr-1 for 180° turns (~13%, ~14% and ~30% increase, respectively). Nonetheless, one innovative method that has potential to make physical activity targets more comprehensible and actionable for youths is personalised, 3D-printed feedback that can conceptualise their PAL. Therefore, Study 2 explored youths’ perceptions of, and designs for, 3D-printed visualisations of PAL. The findings revealed that youths understood the concept of visualising physical activity as a 3D object and felt that such feedback could act as a motivational tool to enhance youths PAL. Following youths’ preferences for weekly models represented as abstract and bar-chart designs, two age-specific 3D models were developed to represent MVPA, across a week, with the recommended guideline depicted as a tangible goal. Study 3 sought to validate youths understanding of the age-specific 3D models and intensities of physical activity. Youth were able to correctly interpret the different components of the age-specific 3D models, although showed some misconceptions when defining moderate-intensity activities. Despite this, the age-specific 3D models showed promise to enhance youths understanding of the recommended guideline and associated MVPA intensities. Study 4 subsequently examined the efficacy of the age-specific 3D models within an intervention setting, whereby youth received personal models of their PAL. Over time, the 3D models enhanced youths’ awareness of their PAL and provided a tool to compare their MVPA levels to the recommended guideline. Youths displayed their 3D models in their home environments and utilised the models as a goal-setting strategy to increase their PAL. In conclusion, the nature of the 3D models being a blend of personalised feedback, a reward and a goal-setting tool, may offer a unique strategy for the promotion of PAL and associations to the recommended guideline

Sedentary behaviours, physical activity and cardiovascular health amongst bus and lorry drivers

Prolonged time sitting has been linked to an increased risk of cardiovascular diseases (CVD), cardiovascular mortality (CVM), all-cause mortality, diabetes and some cancers. Workers in occupations where there is no alternative to sitting can best be defined as compulsory sedentary workers , which involve bus and lorry drivers amongst others. Limited research is available on the health behaviours and health profiles of individuals working within these occupations. This thesis adopts a mixed methods approach and fits within the MRC framework for the development of complex interventions to specifically investigate bus and lorry drivers sedentary behaviours and physical activity levels in association with their cardiovascular health. Chapter 3 describes a pilot study, which results showed bus drivers accumulate 12 hours sitting on workdays and presented higher than the recommended ranges for BMI, body fat, waist circumference and blood pressure. Chapter 4 explores the validity of using an ActiGraph accelerometer compared to the activPAL to assess bus drivers sedentary behaviours. Results highlight that compared to the activPAL, the ActiGraph underestimates sedentary time during workdays (151minutes/day) and working hours (172min/day). Chapter 5 phenotypes UK lorry drivers sedentary behaviours and non-sedentary behaviours during workdays and non-workdays and examines lorry drivers markers of cardiovascular health. Lorry drivers accumulate 13 hours sitting on workdays and 8 hours on non-workdays and presented an ill-cardiovascular profile. Chapter 6 examines the effects of an intervention designed to promote PA and reduce sedentary time on a range of cardiovascular risk factors in a sample of lorry drivers. Chapter 7 presents a process evaluation of the Structured Health Intervention for Truckers (SHIFT) programme described in Chapter 6. This thesis highlights that bus and lorry drivers accumulate the highest amount of sitting time reported up to date, together with high levels of physical inactivity and an ill-cardiovascular profile. However, positive changes in cardiovascular risk factors were observed when drivers increased their daily average of step counts. Overall, these results emphasise that targeting bus and lorry drivers health behaviours should be a public health priority