Can physical activity modulate pancreatic cancer risk?:a systematic review and meta-analysis

Numerous epidemiological studies have examined the association between physical activity and pancreatic cancer; however, findings from individual cohorts have largely not corroborated a protective effect. Among other plausible mechanisms, physical activity may reduce abdominal fat depots inducing metabolic improvements in glucose tolerance and insulin sensitivity, thereby potentially attenuating pancreatic cancer risk. We performed a systematic review to examine associations between physical activity and pancreatic cancer. Six electronic databases were searched from their inception through July 2009, including MEDLINE and EMBASE, seeking observational studies examining any physical activity measure with pancreatic cancer incidence/mortality as an outcome. A random effects model was used to pool individual effect estimates evaluating highest vs. lowest categories of activity. Twenty-eight studies were included. Pooled estimates indicated a reduction in pancreatic cancer risk with higher levels of total (five prospective studies, RR: 0.72, 95% CI: 0.52-0.99) and occupational activity (four prospective studies, RR: 0.75, 95% CI: 0.59-0.96). Nonsignificant inverse associations were seen between risks and recreational and transport physical activity. When examining exercise intensity, moderate activity appeared more protective (RR: 0.79, 95% CI: 0.52-1.20) than vigorous activity (RR: 0.97, 95% CI: 0.85-1.11), but results were not statistically significant and the former activity variable incorporated marked heterogeneity. Despite indications of an inverse relationship with higher levels of work and total activity, there was little evidence of such associations with recreational and other activity exposures.

UNISTEP (University Students Exercise and Physical Activity) Study: A Pilot Study of the Effects of Accumulating 10,000 Steps on Health and Fitness Among University Students.

BACKGROUND: Recent public health initiatives have promoted accumulating 10,000 steps per day. Little previous research has evaluated its effects in young adults. The aim of this study was to determine the effects of taking 10,000 steps per day on fitness and cardiovascular risk factors in sedentary university students. METHODS: Healthy, sedentary students (mean age 21.16 ± SD 6.17) were randomly allocated to take 10,000 steps per day or to a control group who maintained their habitual activity. Members of the 10,000 step group wore a pedometer and reported daily step count in a diary. Outcome measurements (20-meter multistage shuttle run, BMI, and blood pressure) were measured before and after 6 weeks. RESULTS: There were no significant differences between the groups at baseline. After 6 weeks, the 10,000 steps group were taking significantly more steps (8824.1 ± SD 5379.3 vs. 12635.9 ± SD 6851.3; P = .03).No changes were observed in fitness, or BMI (P > .05). Significant reductions in blood pressure (P = .04) in the 10,000 step group. CONCLUSIONS: A daily target of 10,000 steps may be an appropriate intervention in sedentary university students to increase their physical activity levels. The positive health benefits of simple everyday physical activity should be promoted among health professionals.

Job strain and leisure-time physical activity in female and male public sector employees

Background. High work stress could decrease physical activity but the evidence of the relationship has remained equivocal, The present study examined the association between job strain and leisure-time physical activity in a large sample of employees.

An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.