The health benefits of fruit and vegetables are well established. Studies have shown that people who consume more fruit and vegetables have better cardiovascular health, reduced risk of some cancers and greater longevity. There is also growing evidence that people who eat more fruit and vegetables have better mental health, including lower incidence of depression and anxiety, higher life satisfaction and greater socio-emotional well-being. This suggests that the importance of what food we eat goes beyond the already established physical health benefits. A recent study1 tested the psychological benefits of a 14-day clinical intervention designed to increase fruit and vegetable (FV) consumption among low FV consuming adults, identified as anyone aged 18 to 25 who is eating fewer than 3 combined serves of fruit and vegetables per day. Study participants were 171 students at the University of Otago, New Zealand. There were 56 men and 115 women, with majority of participants (64%) identifying themselves as of European descent. Recruited volunteers were included in the study if they were young adults (18-25 in age), had an internet enabled smartphone, were low FV consuming (no more than 3 combined serves of FV per day), without any known FV allergies and not on any anti-depressant medication. Once formally enrolled in the study, students provided written consent to participate and were randomly assigned to three intervention conditions. The control condition was diet as usual. Those assigned to the ecological momentary intervention (EMI) condition were given a challenge to increase their FV consumption to at least 5 serves a day. They received a voucher for a local green grocer and were explicitly told to use this voucher to assist in the challenge. These participants also received two textmessages a day at targeted times for maximum impact (e.g., before typical meal times to influence food choices). Finally, participants assigned to the fruit and vegetable intervention (FVI) condition were supplied with a bag with enough fruit and vegetables for one serve of each for every day of the intervention. It was a mixture of large carrots, apples, oranges and kiwi fruit. Fruit and vegetables were selected based on freshness, seasonal availability, and their vitamin C (oranges, kiwi fruit) and carotenoids content (carrots). These participants were asked to consume one additional serve of fruit and one additional serve of vegetables every day, in addition to their normal consumption
Are there any significant differences in the composition of study groups? Random assignment to treatments can sometimes lead to an unbalanced split of subjects. We want to check this using Gender, Ancestry and BMI. Answer the questions that follow. In each case, you will need to decide on the most appropriate hypothesis test to perform. Variable descriptions are given in Appendix A, Table A.1. It will help you to first decide which variables are categorical and which are numerical. The decision tree from Week 7 workshop called ‘Which statistical test?’ will also help.
(a) (6 marks) Is there is a statistically significant relationship between the variables Gender and Intervention? Formulate and perform an appropriate hypothesis test at a 5% significance level. Use Minitab to obtain the test statistic and the P-value. For full marks, include appropriate Minitab output and check the requirements. Use the STATE-FORMULATE-SOLVE-CONCLUDE procedure and perform follow-up analysis if appropriate.
(b) (8 marks) Now test to see if there is a statistically significant relationship between Ancestry and Intervention. Formulate and perform an appropriate hypothesis test at a 5% significance level. Use Minitab to obtain the test statistic and the P-value. For full marks, include appropriate Minitab output and check the requirements. Use the STATE-FORMULATE-SOLVE-CONCLUDE procedure and perform follow-up analysis if appropriate.
(c) (6 marks) Are there statistically significant differences in BMI among the three groups? Formulate and perform an appropriate hypothesis test at a 5% significance level. Use Minitab to obtain the test statistic and the P-value. For full marks, include appropriate Minitab output and check the requirements. Use the STATE-FORMULATE-SOLVE-CONCLUDE procedure and perform follow-up analysis if appropriate.
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