Physical Activity Epidemiology

1. Physical Activity Epidemiology • “The cure for this ill is not to sit still, • Or frowst with a book by the fire; • But to take a large hoe and a shovel also, • And dig till you gently perspire.” • Rudyard Kipling, 1890

2. Chapter 2Concepts and Methods in Physical Activity Epidemiology. • Epidemiology: Latin-epi (upon) and demo (the people). The study of the distribution and determinants od disease in a population. Who have the characteristic compared • The application of the scientific method to the study of the distribution and dynamics of disease in a population for the purposes of identifying factors that affect the distribution and then modifying these risk factors to reduce the frequency of morbidity and mortality from the disease. • Risk factor: a characteristic, that if present, increases the probability of disease in a group of individuals who have the characteristic compared with a group of individuals who do not have the characteristic.

3. The Scientific Method • The scientific method is a way to ask and answer scientific questions by making observations and doing experiments. The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge.[1] To be termed scientific, a method of inquiry must be based on empirical and measurable evidence subject to specific principles of reasoning.[2] The Oxford English Dictionary defines the scientific method as: "a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses."[ • The steps of the scientific method are to: • Ask a Question • Do Background Research • Construct a Hypothesis • Test Your Hypothesis by Doing an Experiment • Analyze Your Data and Draw a Conclusion • Communicate Your Results • It is important for your experiment to be a fair test. A "fair test" occurs when you change only one factor (variable) and keep all other conditions the same.

4. Epidemiology is the use of the scientific method to study the distribution of disease in a population, to identify risk factors that likely cause the disease, and then to change the risk factors in order to reduce sickness and death from the disease.

5. Incident cases: new occurrences of events (disease) in the study population during the period of interest. Alive to dead, injured to not injured, alive to dead—during period of observation. • Prevalent cases: number of people in population who have a certain disease or condition at some specific point in time. • Prevalence of disease is a function of both incidence and duration. • Rate = Number of cases / Average population size. • Crude rates: rates based on a total population without consideration of any characteristics such as distribution of age, sex and ethnicity. • Specific rates: rates calculated separately for population subgroups (age, sex, ethnicity). • Standardized rates: crude rates that have been standardized for some population characteristic such as age or sex to allow valid comparisons of rates among populations where the distribution of the given characteristic may be quite different.

6. Observational study: when change occurs as the result of natural history. • Experimental: When change in the IV is manipulated by the investigator. • Longitudinal/Prospective: when the IV and DV are observed or manipulated across a period of time. • Retrospective: when the study looks back in time after the occurrence of injury, disease, or death in an attempt to reconstruct an influencing factor such as physical activity habits.

7. Research Design • Independent Variables (IV) & Dependent Variables (DV) • In an experiment, the independent variable is the variable that is varied or manipulated by the researcher, and the dependent variable is the response that is measured. • An independent variable is the presumed cause, whereas the dependent variable is the presumed effect. • The IV is the antecedent, whereas the DV is the consequent. • In experiments, the IV is the variable that is controlled and manipulated by the experimenter; whereas the DV is not manipulated, instead the DV is observed or measured for variation as a presumed result of the variation in the IV. • "In nonexperimental research, where there is no experimental manipulation, the IV is the variable that 'logically' has some effect on a DV. For example, in the research on cigarette-smoking and lung cancer, cigarette-smoking, which has already been done by many subjects, is the independent variable." (Kerlinger, 1986, p.32) • When reseaerchers are not able to actually control and manipulate an IV, it is technically referred to as a status variable (e.g., gender, ethnicity, etc.). Even though researchers do not actually control or manipulate status variables, researchers can, and often do, treat them as IVs (Heppner, Kivlighan & Wampold, 1999).

8. Ecological study: cross-sectional survey in which the frequency of some risk factor (sedentary behavior) is compared with an outcome measure (Obesity) in a particular geographic region. • Risk difference: the risk of disease in a group exposed to the risk factor minus the risk of disease in an unexposed group. • Attributable risk: an estimate of the amount of risk attributed to the risk factor. • Relative risk/risk ratio: the ratio of the risk in the exposed group to the risk in the unexposed group. • Odds ratio: the likelihood of an event between 2 groups. • Confidence interval: the 95% confidence level gives an estimate of the lowest and highest values that might be expected 95 times.

9. Study Designs • The research design used determines whether it is reasonable to infer that physical inactivity was a direct, or the only, explanation for the occurrence of an injury, disease or death. • Cross-Sectional Surveys: Measure both risk factors and the presence or absence of disease at some point in time. • Case-Control Studies: Subjects are selected based on the presence of a disease of interest and matched with controls without disease. • Prospective Cohort Study: A group of people are selected at random from a population, baseline information is collected and the individuals are followed over time to track the incidence of disease between those exposed and those not exposed to the risk factors of interest. • Randomized Controlled Trial: The gold standard of research designs. Participants selected and randomly assigned to receive either experimental or control condition. Dta form experimental is compared to control.

10. Confidence Interval • A survey is a valuable assessment tool in which a sample is selected and information from the sample can then be generalized to a larger population. Surveying has been likened to taste-testing soup – a few spoonfuls tell what the whole pot tastes like. • The key to the validity of any survey is randomness. Just as the soup must be stirred in order for the few spoonfuls to represent the whole pot, when sampling a population, the group must be stirred before respondents are selected. It is critical that respondents be chosen randomly so that the survey results can be generalized to the whole population. • How well the sample represents the population is gauged by two important statistics – the survey’s margin of error and confidence level. They tell us how well the spoonfuls represent the entire pot. For example, a survey may have a margin of error of plus or minus 3 percent at a 95 percent level of confidence. These terms simply mean that if the survey were conducted 100 times, the data would be within a certain number of percentage points above or below the percentage reported in 95 of the 100 surveys. • In other words, Company X surveys customers and finds that 50 percent of the respondents say its customer service is “very good.” The confidence level is cited as 95 percent plus or minus 3 percent. This information means that if the survey were conducted 100 times, the percentage who say service is “very good” will range between 47 and 53 percent most (95 percent) of the time.

11. Attributable Risk: the amount of disease burden in a population that results from a potentially modifiable risk factor. (How much CHD mortality is the result from sedentary behavior?) (smoking with cancer risk?).

12. Models in Physical Activity Epidemiology • 3 Traditional models used by epidemiologists to understand the independent and interactive causes of disease, injury, and death. They provide a framework for considering the association among variables in an attempt to determine cause and effect. • (1) The Epidemiologic Triangle: agent, host & environment. First and most common model. Host—person; environment—physical, social; agent—physical activity or fitness. • (2) The Web of Causation. Says that no disease has a single, isolated cause. Such as physical activity and fitness as risk factors for a disease must consider how they interact with other potential causes of the disease. Its complexity leads to difficulty with regard to understanding the etiology of disease and predicting health outcomes. • (3) The Wheel. The most valid model because it views the development of the host as intertwined with the environment, and it recognizes that the host develops from a genetic core that is modifiable to varying degrees by the biological, physical, and social environments to which the host is exposed.

13. Heredity • Genotype: genetic inheritance. • Phenotype: a person’s observable traits. The set of observable characteristics of an individual resulting from the interaction of its genotype with the environment. • (1) The physicalappearance or biochemicalcharacteristic of an organism as a result of the interaction of its genotype and the environment. • (2) The expression of a particular trait, for example, skin color, height, behavior, etc., according to the individual’s genetic makeup and environment.

14. Inferring Cause in Epidemiologic Studies • Confounding: Confounding occurs when the experimental controls do not allow the experimenter to reasonably eliminate plausible alternative explanations for an observed relationship between independent and dependent variables. • Consider this example. A drug manufacturer tests a new cold medicine with 200 volunteer subjects - 100 men and 100 women. The men receive the drug, and the women do not. At the end of the test period, the men report fewer colds. • This experiment implements no controls at all! As a result, many variables are confounded, and it is impossible to say whether the drug was effective. For example, gender is confounded with drug use. Perhaps, men are less vulnerable to the particular cold virus circulating during the experiment, and the new medicine had no effect at all. Or perhaps the men experienced a placebo effect. • This experiment could be strengthened with a few controls. Women and men could be randomly assigned to treatments. One treatment could receive a placebo, with blinding. Then, if the treatment group (i.e., the group getting the medicine) had sufficiently fewer colds than the control group, it would be reasonable to conclude that the medicine was effective in preventing colds

15. Effect Modification: An interaction among multiple possible cause-and-effect relationships, where the estimate of the effect of one factor on a disease process depends on other factors in the study. Example—age as an effect modifier. Mortality decreases linearly with higher levels of physical activity. However, the slope of the decrease is much steeper with older people. The least active older person has a much higher mortality rate than does the least active younger person. But the mortality rates of older people become increasingly similar to those of young people at higher levels of physical activity. In other words, low activity is a far bigger problem for older people, but high activity protects against mortality regardless of age.

16. Mill’s Canons • Describes the modern-day principles for inferring causality from observation in a population. Ultimately, the strength of the evidence for concluding the existence of a cause and effect is judged by Mill’s canons. • Temporal sequence: Exposure to the risk factor must precede development of the disease with sufficient time to account for the disease progression. • Strength of association: There is a large and clinically meaningful difference in disease risk between those exposed and not exposed to the risk factors. • Consistency: The observed association is always observed if the risk factor is present (e. g., regardless of sex, race, age, or methods of measurement). • Dose response: The risk of disease associated with the risk factor is greater with stronger exposure to the risk factor. • Biological plausibility: The observed association is explainable by existing knowledge about possible biological mechanisms of the disease, which may be alterable (e. g., by physical activity).