Creación de la App “FLEXION BOX”

The second methodological approach used to identify dietary patterns is known as thea posterioriapproach. In the literature, it is also referred to as the exploratory/ empirically derived approach. It does not take into account prior scientific evi- dence of dietary pattern recommendations and as such is not limited by current

knowledge in nutrition (Allès et al., 2012). The a posteriori approach utilises

statistical methods such as principal component analysis (PCA), factor analysis or cluster analysis and reduced rank regression to derive dietary patterns collected from study participants (Gu & Scarmeas, 2011).

I Principal Component Analysis/Factor Analysis: Perhaps the most widely

used dietary pattern identification technique in the literature. It searches for underlying traits that explain most of the variation in the data. In the case of food patterns, when a factor is heavily loaded with foods and nutrients, it means that these components of the diet are significantly qualifying it (i.e. where there is a large number of food variables that have been reduced to a smaller set of variables that capture the major dietary traits in the popula- tion) (Reedy et al., 2010). For a given individual, the higher a factor pattern score is, the closer the diet of this individual matches this pattern (Allès et al., 2012). Main critiques of this method are the seemingly arbitrary nature in determining the number of factors extracted and their interpretation and the relatively small percentage of variance in food intake explained by the

extracted components (Hoffmann et al., 2004).

II Cluster Analysis: Results in subjects being grouped into clusters that min- imise the sum of squares distances from each subject to the cluster mean – homogeneous non-overlapping groups (Allès et al., 2012). Each cluster can be qualified as a food pattern. Food choices common to all contribute less to cluster formation than those choices made by some and not by others (Reedy et al., 2010). The main limitation of the approach is its sensitivity to small

sample size changes and that it involves thea prioridefinition of the number

of clusters (Allès et al., 2012).

III Reduced Rank Regression (RRR): Determines linear functions of a first

set of variables called predictors (typically food groups) by maximising the explained variation of a set of response variables (nutrients/biomarkers). The production of factor scores (similar to that used in PCA) can be interpreted as dietary patterns and are further related to health outcomes (Allès et al., 2012). Perhaps, the biggest strength of this approach is the ability to incorporate prior knowledge gained from studies and the possibility of

researchers postulate that application of RRR in nutritional epidemiology has several limitations including the fact that it is subject to considerable

measurement error (Hoffmann et al., 2004).

Overall, while each has its strengths and limitations, the method employed in analyses should depend on the research question that needs to be answered. One limitation that applies to all methods though is the relatively small percentage of variation in food intake explained by the patterns identified which can make it

difficult to derive dietary patterns that are predictive of disease (Hoffmann et al.,

2004). Appendix A.2 highlights the key characteristics of the various approaches to examining dietary patterns.

3.5

Chapter Conclusion

The examination of diet-disease relationships has evolved from examining the

effect of single nutrients to examining the effect of dietary patterns. While much

is known about the relationship between diet and cognition, there is still much more left to be determined. For example, clarification of the relationship between B-vitamins and cognition and determining whether the purported benefits of

the MeDi can and should be applied to different ethnic and cultural groups still

warrant further investigation. Much more research is needed to examine these and other inconsistent findings between dietary intake and cognitive endpoints.

Chapter 4

Physical Activity and Cognitive Im-

pairment

4.1

Chapter Summary

Physical activity refers to energy expending skeletal movement that promotes health and wellbeing. When reference is made to physical activity, a number of factors must be considered including the type, level of intensity, and duration.

Different types of physical activities are performed in order to target particular

body systems. For example, aerobic activities cause the lungs to work harder and the heart to beat faster thereby promoting cardiovascular health. The type of physical activity that is undertaken also influences the size of benefit received. Research indicates that moderate and vigorous activities such as climbing and walking uphill accrue more health benefits than sedentary activities that require far less energy such as reading.

In order to make recommendations at a population level, some surveillance data are needed. This data can be obtained through the use of questionnaires that typically ask an individual to recall their activity levels over a period of time, via direct observation and measurement using electronic devices, for example, pedometers or videotaping or using activity diaries where all activities undertaken and the time spent engaging in them is recorded in a log book.

The benefits of physical activity in the prevention of disease has been widely studied with evidence highlighting reduction in risks of developing multiple diseases including cancers and other non-communicable diseases. Research has also focused on the role of physical activity in neurodegeneration. Studies suggest that adults who engage in physical activity have a reduced risk of CIm and have a higher functional status due to improved strength, endurance, and balance (Paterson, Jones, & Rice, 2007; Warburton, Charlesworth, Ivey, Nettlefold, &

Bredin, 2010). There is at present insufficient evidence to conclude that for

dementia sufferers physical activity is beneficial and also to conclusively state

the relationship between frailty (which affects an individual’s ability to engage in

physical activity) and cognitive decline (Prince et al., 2014).

Global recommendations have been issued for physical activity by the World Health Organisation in light of global trends toward physical inactivity as our lifestyles become more westernised and technologically driven (World Health Organisation, 2010).