Actigraph GT3X: validation and determination of physical activity intensity cut points

The aims of this study were: to compare energy expenditure (EE) estimated from the existing GT3X accelerometer equations and EE measured with indirect calorimetry; to define new equations for EE estimation with the GT3X in youth, adults and older people; and to define GT3X vector magnitude (VM) cut points allowing to classify PA intensity in the aforementioned age-groups. The study comprised 31 youth, 31 adults and 35 older people. Participants wore the GT3X (setup: 1-s epoch) over their right hip during 6 conditions of 10-min duration each: resting, treadmill walking/running at 3, 5, 7, and 9 km · h⁻¹, and repeated sit-stands (30 times · min⁻¹). The GT3X proved to be a good tool to predict EE in youth and adults (able to discriminate between the aforementioned conditions), but not in the elderly. We defined the following equations: for all age-groups combined, EE (METs)=2.7406+0.00056 · VM activity counts (counts · min⁻¹)-0.008542 · age (years)-0.01380 ·  body mass (kg); for youth, METs=1.546618+0.000658 · VM activity counts (counts · min⁻¹); for adults, METs=2.8323+0.00054 · VM activity counts (counts · min⁻¹)-0.059123 · body mass (kg)+1.4410 · gender (women=1, men=2); and for the elderly, METs=2.5878+0.00047 · VM activity counts (counts · min⁻¹)-0.6453 · gender (women=1, men=2). Activity counts derived from the VM yielded a more accurate EE estimation than those derived from the Y-axis. The GT3X represents a step forward in triaxial technology estimating EE. However, age-specific equations must be used to ensure the correct use of this device.

Validity of the Physical Activity Questionnaires IPAQ-SF and GPAQ for Cancer Survivors: Insights from a Spanish Cohort

Regular physical activity (PA) decreases mortality risk in survivors of breast and colorectal cancer. Such impacts of exercise have prompted initiatives designed both to promote and adequately monitor PA in cancer survivors. This study examines the validity of 2 widely used self-report methods for PA determination, the International Physical Activity Questionnaire short version (IPAQ-SF) and Global Physical Activity Questionnaire (GPAQ). Both instruments were compared with the triaxial accelerometry (Actigraph) method as an objective reference standard. Study participants were 204 cancer survivors (both sexes, aged 18-79 years). Compared with accelerometry, both questionnaires significantly overestimated PA levels (across all intensities) and underestimated physical inactivity levels. No differences were detected between the 2 questionnaires except for a shorter inactivity time estimated by GPAQ (p=0.001). The Bland and Altman method confirmed that both questionnaires overestimated all PA levels. Receiver operating characteristic (ROC) analysis classified IPAQ and GPAQ as fair and poor predictors, respectively, of the proportions of survivors fulfilling international PA recommendations (≥150 min·week^-1 of moderate-vigorous PA). IPAQ-SF showed a higher sensitivity but lower specificity than GPAQ. Our data do not support the use of IPAQ-SF or GPAQ to determine PA or inactivity levels in cancer survivors.

Effects of allopurinol on exercise-induced muscle damage: new therapeutic approaches?

Intensive muscular activity can trigger oxidative stress, and free radicals may hence be generated by working skeletal muscle. The role of the enzyme xanthine oxidase as a generating source of free radicals is well documented and therefore is involved in the skeletal muscle damage as well as in the potential transient cardiovascular damage induced by high-intensity physical exercise. Allopurinol is a purine hypoxanthine-based structural analog and a well-known inhibitor of xanthine oxidase. The administration of the xanthine oxidase inhibitor allopurinol may hence be regarded as promising, safe, and an economic strategy to decrease transient skeletal muscle damage (as well as heart damage, when occurring) in top-level athletes when administered before a competition or a particularly high-intensity training session. Although continuous administration of allopurinol in high-level athletes is not recommended due to its possible role in hampering training-induced adaptations, the drug might be useful in non-athletes. Exertional rhabdomyolysis is the most common form of rhabdomyolysis and affects individuals participating in a type of intense exercise to which they are not accustomed. This condition can cause exercise-related myoglobinuria, thus increasing the risk of acute renal failure and is also associated with sickle cell trait. In this manuscript, we have reviewed the recent evidence about the effects of allopurinol on exercise-induced muscle damage. More research is needed to determine whether allopurinol may be useful for preventing not only exertional rhabdomyolysis and acute renal damage but also skeletal muscle wasting in critical illness as well as in immobilized, bedridden, sarcopenic or cachectic patients.

A Bayesian network model for predicting cardiovascular risk

BACKGROUND AND OBJECTIVE

Cardiovascular diseases are the leading death cause in Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the management and control of cardiovascular diseases. Based on a Bayesian network built from a large population database and expert judgment, this work studies interrelations between cardiovascular risk factors, emphasizing the predictive assessment of medical conditions, and providing a computational tool to explore and hypothesize such interrelations.

METHODS

We implement a Bayesian network model that considers modifiable and non-modifiable cardiovascular risk factors as well as related medical conditions. Both the structure and the probability tables in the underlying model are built using a large dataset collected from annual work health assessments as well as expert information, with uncertainty characterized through posterior distributions.

RESULTS

The implemented model allows for making inferences and predictions about cardiovascular risk factors. The model can be utilized as a decision- support tool to suggest diagnosis, treatment, policy, and research hypothesis. The work is complemented with a free software implementing the model for practitioners' use.

CONCLUSIONS

Our implementation of the Bayesian network model facilitates answering public health, policy, diagnosis, and research questions concerning cardiovascular risk factors.

Colorectal cancer risk mapping through Bayesian networks

BACKGROUND AND OBJECTIVE

Only about 14% of eligible EU citizens finally participate in colorectal cancer (CRC) screening programs despite it being the third most common type of cancer worldwide. The development of CRC risk models can enable predictions to be embedded in decision-support tools facilitating CRC screening and treatment recommendations. This paper develops a predictive model that aids in characterizing CRC risk groups and assessing the influence of a variety of risk factors on the population.

METHODS

A CRC Bayesian Network is learnt by aggregating extensive expert knowledge and data from an observational study and making use of structure learning algorithms to model the relations between variables. The network is then parametrised to characterize these relations in terms of local probability distributions at each of the nodes. It is finally used to predict the risks of developing CRC together with the uncertainty around such predictions.

RESULTS

A graphical CRC risk mapping tool is developed from the model and used to segment the population into risk subgroups according to variables of interest. Furthermore, the network provides insights on the predictive influence of modifiable risk factors such as alcohol consumption and smoking, and medical conditions such as diabetes or hypertension linked to lifestyles that potentially have an impact on an increased risk of developing CRC.

CONCLUSION

CRC is most commonly developed in older individuals. However, some modifiable behavioral factors seem to have a strong predictive influence on its potential risk of development. Modeling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.