Activity Levels Related to Overall Health in Geriatric Populations

1763 words (7 pages) Essay

8th Feb 2020 Health Reference this

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Background & Literature Review

The research I am conducting will analyze the level of physical activity and how it relates to overall health. I would consider physical activity as activity that burns calories over and above what is burned by a person sitting at rest. The National Strength and Conditioning Association (NSCA) recommends that adults get around 120 minutes of moderate physical activity per week. One MET is the energy expended while sitting at rest and moderate physical activity is considered a MET greater than 3.5. Physical activity can provide benefits such as lowering blood pressure, assisting in weight control, reducing stress, and much more. Activity can also improve bone and muscle strength, which reduce naturally as people age.

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There are many different types of physical activity that people can perform every day to help improve their physical health. The type of activity and intensity also influences the benefits received by the exercise. For this research, I intend to study the relationship of general physical activity levels and overall health. This study will be looking at adults in the sixty to eighty-nine age groups.

The lack of appropriate physical inactivity levels contributes to premature deaths from coronary heart disease deaths from other causes (Genin et al., 2017). They researched the effect of establishing a workplace-based physical activity program. Their research found that such programs can improve overall health, exercise levels, and obesity issues. Although such a program requires an investment by the employer, the resulting reduction in absenteeism and increase in productivity makes the investment worthwhile.

Another tool being studied to increase adult physical activity is the use of web-based social networking sites. One research study analyzed the effectiveness of combining the use of pedometers with online social networking applications such as Facebook (Maher et al., 2015). Their research involved a group of over 100 adults with an average age around 35 years (Maher et al., 2015). The study was conducted by giving access to online social networking physical activity intervention applications to one group of adults, along with the use of pedometers (Maher et al., 2015). The second group was not provided access to online social networking physical activity intervention applications, but only the pedometers. The study found that the group with access had significantly increased their total weekly exercise levels as compared to the other group after the eighth week (Maher et al., 2015). Unfortunately, the improvements were not long-lasting and the increased physical activity levels disappeared at the twenty-week mark (Maher et al., 2015). The study showed that pedometers coupled with web-based social networking support can be effective to jump start physical activity for adults. However, to achieve long lasting changes more support may be needed.

Lohne-Seller, Hansen, Kolle, & Anderssen (2014) did a similar study but their study was focused on an older population. The study included giving pedometers to a population of 65-85-year-olds so that a review of their physical activity could be conducted along with an assessment of activity intensity, frequency, and duration (Lohne-Seller, Hansen, Kolle, & Anderssen, 2014). The researchers found that the amount of exercise differed depending on the age of the person. The oldest people in the group exercised significantly less than the younger people in the group. The study also found that exercise levels were much higher for those that self-reported they were in good health as compared to those who reported they had poor health. The study concluded, not surprisingly, that those older people that exercised more had higher levels of good health (Lohne-Seller, Hansen, Kolle, & Anderssen, 2014).

Method

 The study will examine the relationship between the subject’s self-reported activity level and both their self-reported overall health level and measured Body Mass Index (BMI). In order to gather the data needed to perform this analysis the study will use a two-pronged approach. The first prong will involve a short questionnaire that will ask the subject their gender, age, overall health level, and activity level. The subjects will be asked to rate their overall health level from one to four. One is poor, two is fair, three is good, and four is excellent. The subjects will be asked to rate their activity level from one to four. One being sedentary, two being somewhat active, three being active, and four being very active.

 The second prong will involve taking specific measurements of the subjects. The two measurements that will be taken are the subjects height and weight. The study will use a digital scale to record the weight of the subjects and a wall tape measure to measure the height of the subjects. The study will use computer tablets to gather and compile the data.

 Due to the study goal of investigating the specific age group of sixty to eighty-nine-year olds, the study will be conducted at a senior citizen center. People conducting the study will be trained in the proper way to administer the questionnaire. They will be trained in the appropriate way to explain to participants about the different levels of overall health and activity. They also be trained in how to properly operate the digital scale and take an accurate height measurement. The goal is to get thirty subjects to participate and it is anticipated that it will take anywhere from two to four hours to complete the process.

Results

Table 1

Figure 1. Bar Graph showing overall health and activity level by gender.

 

Figure 2. Scatter Plot with Best Fit Line showing overall health v. activity level.

 

Figure 3. Scatter Plot with Best Fit Line showing activity level v. BMI.

Discussion

 The data reflected in Table 1 came from thirty subjects, eighteen male and twelve females. As explained in the methods section the overall health and overall activity levels are self-reported by the participants. The thirty subjects’ range in age from sixty to eight-nine. Figure 1 is a bar graph that represents a comparison of the overall activity levels and overall health of males and females. The mean of the self-reported overall health of males and females is identical at the level of 2.0. Of note is that the self-reported mean activity level of females is higher than that of males, 2.2 to 2.0. I believe that this sample size is too small to draw any conclusions about whether females age sixty to eight-nine are more active to men in the same age group.

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 Figure 2 is a scatter plot that shows the best fit line comparing overall health and activity level. Correlation is defined as an analysis of the direction and strength of the relationship between two sets of data. This is what this plot is showing regarding the data of overall health and activity level. The data produced a correlation coefficient of .77. The closer to the number one, the stronger the relationship is between the data. If the correlation coefficient is in the range of .80 to 1.0 the data is described to have a high correlation. If the correlation coefficient is in the range of .60 to .79 the data is described to have a moderately high correlation. A correlation coefficient of .77 is almost a high correlation and indicates that an active lifestyle can lead to good overall health.

 Figure 3 is a scatter plot that shows the best fit line comparing activity level and BMI. This plot is showing the data of activity level and BMI. The data produced a correlation coefficient of -.46. If the correlation coefficient is in the range of .40 to .59 the data is described to have a moderate correlation. This correlation would indicate that a more active lifestyle leads to a lower BMI. This correlation is something that would be expected prior to conducting this study.

 There are certain results that indicate that further study is warranted. These results showed that females in this age group were more active than males. This conclusion requires further study with a larger sample group. In this study the mean of the female activity was only slightly higher, and the result cannot be called statistically significant. I think it is also of interest that there is a stronger correlation between the two self-reported sets of data (overall health and activity) as compared to the data set that had one self-reported (activity) and one measured (BMI). This prompts the questions as to whether there is some psychological benefit to increased physical activity. Meaning, do those who are more physically active feel better about their overall health? This question also warrants further study.

References

Background & Literature Review

The research I am conducting will analyze the level of physical activity and how it relates to overall health. I would consider physical activity as activity that burns calories over and above what is burned by a person sitting at rest. The National Strength and Conditioning Association (NSCA) recommends that adults get around 120 minutes of moderate physical activity per week. One MET is the energy expended while sitting at rest and moderate physical activity is considered a MET greater than 3.5. Physical activity can provide benefits such as lowering blood pressure, assisting in weight control, reducing stress, and much more. Activity can also improve bone and muscle strength, which reduce naturally as people age.

There are many different types of physical activity that people can perform every day to help improve their physical health. The type of activity and intensity also influences the benefits received by the exercise. For this research, I intend to study the relationship of general physical activity levels and overall health. This study will be looking at adults in the sixty to eighty-nine age groups.

The lack of appropriate physical inactivity levels contributes to premature deaths from coronary heart disease deaths from other causes (Genin et al., 2017). They researched the effect of establishing a workplace-based physical activity program. Their research found that such programs can improve overall health, exercise levels, and obesity issues. Although such a program requires an investment by the employer, the resulting reduction in absenteeism and increase in productivity makes the investment worthwhile.

Another tool being studied to increase adult physical activity is the use of web-based social networking sites. One research study analyzed the effectiveness of combining the use of pedometers with online social networking applications such as Facebook (Maher et al., 2015). Their research involved a group of over 100 adults with an average age around 35 years (Maher et al., 2015). The study was conducted by giving access to online social networking physical activity intervention applications to one group of adults, along with the use of pedometers (Maher et al., 2015). The second group was not provided access to online social networking physical activity intervention applications, but only the pedometers. The study found that the group with access had significantly increased their total weekly exercise levels as compared to the other group after the eighth week (Maher et al., 2015). Unfortunately, the improvements were not long-lasting and the increased physical activity levels disappeared at the twenty-week mark (Maher et al., 2015). The study showed that pedometers coupled with web-based social networking support can be effective to jump start physical activity for adults. However, to achieve long lasting changes more support may be needed.

Lohne-Seller, Hansen, Kolle, & Anderssen (2014) did a similar study but their study was focused on an older population. The study included giving pedometers to a population of 65-85-year-olds so that a review of their physical activity could be conducted along with an assessment of activity intensity, frequency, and duration (Lohne-Seller, Hansen, Kolle, & Anderssen, 2014). The researchers found that the amount of exercise differed depending on the age of the person. The oldest people in the group exercised significantly less than the younger people in the group. The study also found that exercise levels were much higher for those that self-reported they were in good health as compared to those who reported they had poor health. The study concluded, not surprisingly, that those older people that exercised more had higher levels of good health (Lohne-Seller, Hansen, Kolle, & Anderssen, 2014).

Method

 The study will examine the relationship between the subject’s self-reported activity level and both their self-reported overall health level and measured Body Mass Index (BMI). In order to gather the data needed to perform this analysis the study will use a two-pronged approach. The first prong will involve a short questionnaire that will ask the subject their gender, age, overall health level, and activity level. The subjects will be asked to rate their overall health level from one to four. One is poor, two is fair, three is good, and four is excellent. The subjects will be asked to rate their activity level from one to four. One being sedentary, two being somewhat active, three being active, and four being very active.

 The second prong will involve taking specific measurements of the subjects. The two measurements that will be taken are the subjects height and weight. The study will use a digital scale to record the weight of the subjects and a wall tape measure to measure the height of the subjects. The study will use computer tablets to gather and compile the data.

 Due to the study goal of investigating the specific age group of sixty to eighty-nine-year olds, the study will be conducted at a senior citizen center. People conducting the study will be trained in the proper way to administer the questionnaire. They will be trained in the appropriate way to explain to participants about the different levels of overall health and activity. They also be trained in how to properly operate the digital scale and take an accurate height measurement. The goal is to get thirty subjects to participate and it is anticipated that it will take anywhere from two to four hours to complete the process.

Results

Table 1

Figure 1. Bar Graph showing overall health and activity level by gender.

 

Figure 2. Scatter Plot with Best Fit Line showing overall health v. activity level.

 

Figure 3. Scatter Plot with Best Fit Line showing activity level v. BMI.

Discussion

 The data reflected in Table 1 came from thirty subjects, eighteen male and twelve females. As explained in the methods section the overall health and overall activity levels are self-reported by the participants. The thirty subjects’ range in age from sixty to eight-nine. Figure 1 is a bar graph that represents a comparison of the overall activity levels and overall health of males and females. The mean of the self-reported overall health of males and females is identical at the level of 2.0. Of note is that the self-reported mean activity level of females is higher than that of males, 2.2 to 2.0. I believe that this sample size is too small to draw any conclusions about whether females age sixty to eight-nine are more active to men in the same age group.

 Figure 2 is a scatter plot that shows the best fit line comparing overall health and activity level. Correlation is defined as an analysis of the direction and strength of the relationship between two sets of data. This is what this plot is showing regarding the data of overall health and activity level. The data produced a correlation coefficient of .77. The closer to the number one, the stronger the relationship is between the data. If the correlation coefficient is in the range of .80 to 1.0 the data is described to have a high correlation. If the correlation coefficient is in the range of .60 to .79 the data is described to have a moderately high correlation. A correlation coefficient of .77 is almost a high correlation and indicates that an active lifestyle can lead to good overall health.

 Figure 3 is a scatter plot that shows the best fit line comparing activity level and BMI. This plot is showing the data of activity level and BMI. The data produced a correlation coefficient of -.46. If the correlation coefficient is in the range of .40 to .59 the data is described to have a moderate correlation. This correlation would indicate that a more active lifestyle leads to a lower BMI. This correlation is something that would be expected prior to conducting this study.

 There are certain results that indicate that further study is warranted. These results showed that females in this age group were more active than males. This conclusion requires further study with a larger sample group. In this study the mean of the female activity was only slightly higher, and the result cannot be called statistically significant. I think it is also of interest that there is a stronger correlation between the two self-reported sets of data (overall health and activity) as compared to the data set that had one self-reported (activity) and one measured (BMI). This prompts the questions as to whether there is some psychological benefit to increased physical activity. Meaning, do those who are more physically active feel better about their overall health? This question also warrants further study.

References

  • Genin, P.M., Degoutte, F., Finaud, J., Pereira, B., Thivel, D. & Duclos, M. (2017). Effect of a 5-month worksite physical activity program on tertiary employees overall health and fitness. Journal of Occupational and Environmental Medicine, 59(2), e3–e10.
  • Lohne-Seiler, H., Hansen, B. H., Kolle, E., & Anderssen, S. A. (2014). Accelerometer-determined physical activity and self-reported health in a population of older adults (65-85 years): A cross-sectional study. BMC Public Health, 14, 284.
  • Maher, C., Ferguson, M., Vandelanotte, C., Plotnikoff, R., De Bourdeaudhuij., Thomas, S., Nelson-Field, K., & Olds, T. (2015). A web-based, social networking physical activity intervention for insufficiently active adults delivered via Facebook app: Randomized controlled trial. Journal of Medical Internet Research, 17(7), e174.

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