Individual calibration for estimating free-living walking speed using the MTI monitor

Purpose : This study was conducted to devise a new individual calibration method to enhance MTI accelerometer estimation of free-living level walking speed.

Method : Five female and five male middle-aged adults walked 400 m at 3.5, 4.5, and 5.5 km·h-1, and 800 m at 6.5 km·h-1 on an outdoor track, following a continuous protocol. Lap speed was controlled by a global positioning system (GPS) monitor. MTI counts-to-speed calibration equations were derived for each trial, for each subject for four such trials with each of four MTI, for each subject for the average MTI, and for the pooled data. Standard errors of the estimate (SEE) with and without individual calibration were compared. To assess accuracy of prediction of free-living walking speed, subjects also completed a self-paced, "brisk" 3-km walk wearing one of the four MTI, and differences between actual and predicted walking speed with and without individual calibration were examined.

Results : Correlations between MTI counts and walking speed were 0.90 without individual calibration, 0.98 with individual calibration for the average MTI, and 0.99 with individual calibration for a specific MTI. The SEE (mean ± SD) was 0.58 ± 0.30 km·h-1 without individual calibration, 0.19 ± 0.09 km·h-1 with individual calibration for the average MTI monitor, and 0.16 ± 0.08 km·h-1 with individual calibration for a specific MTI monitor. The difference between actual and predicted walking speed on the "brisk" 3-km walk was 0.06 ± 0.25 km·h-1 using individual calibration and 0.28 ± 0.63 km·h-1 without individual calibration (for specific accelerometers).

Conclusion : MTI accuracy in predicting walking speed without individual calibration might be sufficient for population-based studies but not for intervention trials. This individual calibration method will substantially increase precision of walking speed predicted from MTI counts.

A proposed approach to monitor private-sector policies and practices related to food environments, obesity and non-communicable disease prevention

Private-sector organizations play a critical role in shaping the food environments of individuals and populations. However, there is currently very limited independent monitoring of private-sector actions related to food environments. This paper reviews previous efforts to monitor the private sector in this area, and outlines a proposed approach to monitor private-sector policies and practices related to food environments, and their influence on obesity and non-communicable disease (NCD) prevention. A step-wise approach to data collection is recommended, in which the first (‘minimal’) step is the collation of publicly available food and nutrition-related policies of selected private-sector organizations. The second (‘expanded’) step assesses the nutritional composition of each organization's products, their promotions to children, their labelling practices, and the accessibility, availability and affordability of their products. The third (‘optimal’) step includes data on other commercial activities that may influence food environments, such as political lobbying and corporate philanthropy. The proposed approach will be further developed and piloted in countries of varying size and income levels. There is potential for this approach to enable national and international benchmarking of private-sector policies and practices, and to inform efforts to hold the private sector to account for their role in obesity and NCD prevention.

A proposed approach to systematically identify and monitor the corporate political activity of the food industry with respect to public health using publicly available information.

Unhealthy diets represent one of the major risk factors for non-communicable diseases. There is currently a risk that the political influence of the food industry results in public health policies that do not adequately balance public and commercial interests. This paper aims to develop a framework for categorizing the corporate political activity of the food industry with respect to public health and proposes an approach to systematically identify and monitor it. The proposed framework includes six strategies used by the food industry to influence public health policies and outcomes: information and messaging; financial incentive; constituency building; legal; policy substitution; opposition fragmentation and destabilization. The corporate political activity of the food industry could be identified and monitored through publicly available data sourced from the industry itself, governments, the media and other sources. Steps for country-level monitoring include identification of key food industry actors and related sources of information, followed by systematic data collection and analysis of relevant documents, using the proposed framework as a basis for classification of results. The proposed monitoring approach should be pilot tested in different countries as part of efforts to increase the transparency and accountability of the food industry. This approach has the potential to help redress any imbalance of interests and thereby contribute to the prevention and control of non-communicable diseases.

The double sensor-a non-invasive device to continuously monitor core temperature in humans on earth and in space

The objective of our study was to establish whether rectal temperature recordings in humans could be replaced by a non-invasive skin temperature sensor combined with a heat flux sensor (Double Sensor) located at the forehead to monitor core body temperature changes due to circadian rhythms. Rectal and Double Sensor data were collected continuously for 24h in seven men undertaking strict head-down tilt bed-rest. Individual differences between the two techniques varied between -0.72 and +0.55 degrees C. Nonetheless, when temperature data were approximated by cosinor analysis in order to compare circadian rhythm profiles between methods, it was observed that there were no significant differences between mesor, amplitude, and acrophase (P>0.310). It was therefore concluded that the Double Sensor technology is presently not accurate enough for performing single individual core body temperature measurements under resting conditions at normal ambient room temperature. Yet, it seems to be a valid, non-invasive alternative for monitoring circadian rhythm profiles.

A framework for cloud-based healthcare services to monitor noncommunicable diseases patient

Monitoring patients who have noncommunicable diseases is a big challenge. These illnesses require a continuous monitoring that leads to high cost for patients' healthcare. Several solutions proposed reducing the impact of these diseases in terms of economic with respect to quality of services. One of the best solutions is mobile healthcare, where patients do not need to be hospitalized under supervision of caregivers. This paper presents a new hybrid framework based on mobile multimedia cloud that is scalable and efficient and provides cost-effective monitoring solution for noncommunicable disease patient. In order to validate the effectiveness of the framework, we also propose a novel evaluation model based on Analytical Hierarchy Process (AHP), which incorporates some criteria from multiple decision makers in the context of healthcare monitoring applications. Using the proposed evaluation model, we analyzed three possible frameworks (proposed hybrid framework, mobile, and multimedia frameworks) in terms of their applicability in the real healthcare environment.

Can camera traps monitor Komodo dragons a large ectothermic predator?

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.

A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used.

Utility of the multi-component training distress scale to monitor swimmers during periods of training overload

The purpose of the present study was to determine the efficacy of the multi-component training distress scale (MTDS), in monitoring swimmers prior to national competition. Twenty-one national-level adolescent swimmers completed eight weeks of testing. Once a week participants completed an 8 × 50 m sprint test,
vertical jump test, sit-and-reach test, the MTDS and the
Recovery-Stress Questionnaire for Athletes (REST-Q). All testing
was incorporated into the swimmers’ normal training programme.
The REST-Q accounted for the following variances in performance:
flexibility (14.6%,p= 0.009), power output (17.7%,p= 0.003),swimming speed (15.5%,p
= 0.006) and swimming endurance(17.5%,p= 0.002). In comparison, the MTDS accounted for thefollowing variances in performance:
flexibility (12.1%, p= 0.095),power output (16.4%,p= 0.023), swimming speed (20.5%,p = 0.003) and swimming speed endurance (23.8%,p= 0.001).The findings of the current study suggest that both the REST-Q Sport and the MTDS have the capacity to predict performance on a range of fitness components associated with swimming.

Validation of the SenseWear Mini activity monitor in 5-12-year-old children

OBJECTIVES: This study aimed to validate SenseWear Mini software algorithm versions 2.2 (SW2.2) and 5.2 (SW5.2) for estimating energy expenditure (EE) in children. DESIGN: Laboratory-based validation study. METHODS: 57 children aged 5-12 y completed a protocol involving 15 semi-structured sedentary (SED), light-intensity (LPA), and moderate- to vigorous-intensity (MVPA) physical activities. EE was estimated using portable indirect calorimetry (IC). The accuracy of EE estimates (kcal·min(-1)) from SW2.2 and SW5.2 were examined at the group level and individual level using the mean absolute percentage error (MAPE), Bland-Altman plots and equivalence testing. RESULTS: MAPE values were lower for SW5.2 (30.1±10.7%) than for SW2.2 (44.0±6.2%). Although mean differences for SW5.2 were smaller than for SW2.2 during SED (-0.23±0.22 vs. -0.61±0.20kcal·min(-1)), LPA (-0.69±0.76 vs. -1.07±0.46kcal·min(-1)) and MVPA (-2.22±1.15 vs. -2.57±1.15kcal·min(-1)), limits of agreement did not decrease for the updated algorithms. For all activities, SW2.2 and SW5.2 were not equivalent to IC (p>0.05). Errors increased with increasing intensity. CONCLUSION: The current SenseWear Mini algorithms SW5.2 underestimated EE. The overall improved accuracy for SW5.2 was not accompanied with improved accuracy at the individual level and EE estimates were not equivalent to IC.

A transformation-based multivariate chart to monitor process dispersion

Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then compared to the ones from other existing methods and a real case is presented.

Monitor objectivity with important clients: evidence from auditor opinions around the world

We examine whether monitors are likely to compromise their monitoring objectivity in the face of economically important clients in international business settings. In the context of external auditing and assurance services, we measure monitor objectivity by whether auditors are more (or less) likely to issue to their important clients modified audit opinions, that is, audit opinions provided to outside investors about the firm that demotes explicit areas of concern. Using a large cross-country sample, we document that auditors are more likely to issue modified opinions to their economically important clients relative to other clients. Furthermore, we find that this association is stronger (1) for Big N auditors, (2) for multinational audit clients, and (3) in countries with stronger legal regimes. These results suggest that monitors prioritize the protection of their reputation over lucrative economic relationships, and such information certification function is more pronounced for international auditors, multinational client firms, and in strong legal regimes.