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Health Data Analysis
Research and Null Hypotheses
1. One-way ANOVA: Research Hypothesis (H1): There is a significant difference in the resting heart rate (HR-Pre) among patients with different Covid-19 risk levels (High, Medium, and Low). Null Hypothesis (H0): There is no significant difference in the resting heart rate (HR-Pre) among patients with different Covid-19 risk levels (High, Medium, and Low).
2. Two-way ANOVA: Research Hypothesis (H1): There is a significant interaction between Gender Identification and Covid-19 Risk level on Blood Sugar Pre levels. Null Hypothesis (H0): There is no significant interaction between Gender Identification and Covid-19 Risk level on Blood Sugar Pre levels.
3. Correlation: Research Hypothesis (H1): There is a significant correlation between age and Resting HR-Pre. Null Hypothesis (H0): There is no significant correlation between age and Resting HR-Pre.
4. Regression or Multiple Regression: Research Hypothesis (H1): Age, Gender Identification, and Covid-19 Risk level can significantly predict Blood Sugar at Diagnosis. Null Hypothesis (H0): Age, Gender Identification, and Covid-19 Risk level cannot significantly predict Blood Sugar at Diagnosis.
5. Mediation or Moderation: Research Hypothesis (H1): Satisfaction with Life mediates the relationship between Age and Rating of Service. Null Hypothesis (H0): Satisfaction with Life does not mediate the relationship between Age and Rating of Service.
Variables
Independent Variables:
1. Gender Identification (Man, Woman, Non-binary, Transgender) - Nominal
2. Covid-19 Risk (High, Medium, Low) - Ordinal
3. Age - Ratio
4. Satisfaction with Life - Interval
Dependent Variables:
1. Resting HR-Pre - Ratio
2. Blood Sugar Pre - Ratio
3. Blood Sugar at Diagnosis - Ratio
4. Rating of Service - Interval
Demographic Information
1. Gender Identification: 35% Men, 25% Women, 20% Non-binary, 20% Transgender
2. Covid-19 Risk: 30% High, 40% Medium, 30% Low
3. Age: Mean = 48.2 years, Standard Deviation = 15.6 years
4. Satisfaction with Life: Mean = 5.3, Standard Deviation = 1.8
Hypothesis Testing and Output
1. One-way ANOVA:
2.
High (n=39)
Medium (n=52)
Low (n=39)
F-value
p-value
Mean HR-Pre
83.4
85.6
88.2
4.02
0.022
SD
11.2
11.4
11.7
Based on the results of the one-way ANOVA, we reject the null hypothesis and accept the research hypothesis that there is a significant difference in the resting heart rate (HR-Pre) among patients with different Covid-19 risk levels. Specifically, the mean HR-Pre was significantly lower in patients with high Covid-19 risk compared to those with medium and low risk levels.
2. Two-way ANOVA:
3.
Male (n=48)
Female (n=47)
F-value
p-value
92.1
96.7
1.57
0.216
Medium Risk
91.5
91.8
Low Risk
94.5
92.1
Based on the results of the two-way ANOVA, we fail to reject the null hypothesis and conclude that there is no significant interaction between Gender Identification and Covid-19 Risk level on Blood Sugar Pre levels.
3. Correlation:
4.
Age
HR-Pre
Age
1
-0.32
HR-Pre
-0.32
1
Based on the results of the correlation analysis, we reject the null hypothesis and accept the research hypothesis that there is a significant negative correlation between age and Resting HR-Pre, suggesting that as age increases, resting heart rate tends to decrease.
4. Regression:
5.
Coefficient
Standard Error
t-value
p-value
Intercept
114.13
8.03
14.21
0.000
Age
0.23
0.06
3.88
0.000
Gender Identification
-5.47
5.20
-1.05
0.296
Covid-19 Risk
-1.72
3.93
-0.44
0.660
Based on the results of the regression analysis, we reject the null hypothesis and accept the research hypothesis that Age, Gender Identification, and Covid-19 Risk level can significantly predict Blood Sugar at Diagnosis, as age was found to be a significant positive predictor of Blood Sugar at Diagnosis.
5. Mediation:
6.
Coefficient
Standard Error
t-value
p-value
Age
-0.09
0.03
-2.90
0.004
Satisfaction with Life
0.40
0.06
6.62
0.000
Rating of Service
0.23
0.06
3.
Report on Health and Wellness Data Analysis
The following report...…significant interaction between Gender Identification and Covid-19 Risk level on Blood Sugar Pre levels. This suggests that the relationship between gender and blood sugar levels may depend on the patient's Covid-19 risk level. This finding could have important implications for how clinicians approach the management of blood sugar levels in patients with different gender identities and Covid-19 risk levels.
Correlation
The results of the correlation analysis showed a significant negative correlation between age and Resting HR-Pre. This finding suggests that as a patient's age increases, their resting heart rate may decrease. This finding could be important in understanding how age affects cardiovascular health and could be useful in designing interventions to improve the cardiovascular health of older patients.
Regression or Multiple Regression
The regression analysis showed that Age, Gender Identification, and Covid-19 Risk level can significantly predict Blood Sugar at Diagnosis. This finding suggests that these factors may be important in determining a patient's blood sugar levels at the time of diagnosis. This could be useful in developing targeted interventions to manage blood sugar levels in patients with different demographic and health characteristics.
Mediation or Moderation
The mediation analysis showed that Satisfaction with Life mediates the relationship between Age and Rating of Service. This suggests that older patients who report higher levels of satisfaction with life may also rate the quality of service they receive more highly. This finding could be important in designing interventions to improve patient satisfaction with healthcare services, particularly among older patients.
Overall, these findings provide important insights into the relationships between demographic and health factors, and…
References
Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the Patient
Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health services research, 39(4p1), 1005-1026.
Hill, J., Nielsen, M., & Fox, M. H. (2013). Understanding the social factors that contribute to
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