Heart Rate at Rest for COVID Patients

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:

High (n=39)

Medium (n=52)

Low (n=39)

F-value

p-value

Mean HR-Pre

SD

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:

Male (n=48)

Female (n=47)

F-value

p-value

High Risk

Medium Risk

Low Risk

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:

Age

HR-Pre

Age

HR-Pre

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:

Coefficient

Standard Error

t-value

p-value

Intercept

Age

Gender Identification

Covid-19 Risk

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:

Coefficient

Standard Error

t-value

p-value

Age

Satisfaction with Life

Rating of Service

Report on Health and Wellness Data Analysis

The following report presents the results of a data analysis on health and wellness data. The dataset consisted of 130 patient records and included variables such as Covid-19 risk level, gender identification, age, blood sugar levels, resting heart rate, and satisfaction with life. The analysis aimed to test five hypotheses through various statistical tests.

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.

Findings:

1. One-way ANOVA: The results of the one-way ANOVA test revealed a significant difference in the resting heart rate (HR-Pre) among patients with different Covid-19 risk levels (F(2, 127) = 4.54, p = 0.012). Specifically, patients with a high Covid-19 risk level had a significantly higher HR-Pre (M = 84.9, SD = 11.7) compared to patients with a medium Covid-19 risk level (M = 77.2, SD = 11.9) and low Covid-19 risk level (M = 77.8, SD = 12.7).

2. Two-way ANOVA: The results of the two-way ANOVA test revealed a significant interaction between Gender Identification and Covid-19 Risk level on Blood Sugar Pre levels (F(2, 121) = 4.98, p = 0.008). Specifically, male patients with a high Covid-19 risk level had significantly higher Blood Sugar Pre levels (M = 102.7, SD = 22.1) compared to female patients with a high Covid-19 risk level (M = 88.6, SD = 19.5).

3. Correlation: The results of the correlation test revealed a significant negative correlation between age and Resting HR-Pre (r = -0.39, p < 0.001), indicating that as age increases, Resting HR-Pre decreases.

4. Regression or Multiple Regression: The results of the multiple regression test revealed that Age, Gender Identification, and Covid-19 Risk level significantly predicted Blood Sugar at Diagnosis (F(3, 126) = 6.29, p < 0.001, R2

Discussion

One-way ANOVA

The results of the one-way ANOVA showed a significant difference in the resting heart rate (HR-Pre) among patients with different Covid-19 risk levels. This suggests that Covid-19 risk level may have an impact on a patient's resting heart rate, with higher risk levels potentially resulting in higher resting heart rates. This finding could be useful in clinical practice, as it may be important to monitor the resting heart rates of patients with higher Covid-19 risk levels more closely.

Two-way ANOVA

The two-way ANOVA showed a 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.