Research Question:

For this project, I explored the question: “How do my walking habits impact my mood throughout the day?” This research stems from my interest in understanding the relationship between physical activity, specifically walking, and my emotional well-being.

Dataset:

The dataset I collected is entirely self-reported. It focuses on my walking behaviors and mood changes from October 24, 2024, to November 6, 2024. I meticulously recorded data for each walk, resulting in 52 unique entries.

For each walk, I documented the following variables:

1. Walk ID: A unique identifier for each walk
2. Day: The date and time when the walk began
3. Duration (Minutes): The length of the walk
4. Location: The type of area where the walk took place (e.g., Neighborhood, Park, Waterfront)
5. Borough: The New York City borough where the walk occurred
6. Mood Before: My self-assessed mood on a scale of 1-10 before the walk
7. Mood After: My self-assessed mood on a scale of 1-10 after the walk
8. Weather: The weather conditions during the walk (Sunny or Clear)
9. Company: Whether I walked alone (FALSE) or with someone (TRUE)

To collect this data, I used my smartphone’s health app to track the duration and a simple note-taking app to record the other variables immediately before and after each walk. I consciously tried to be consistent and accurate in my self-assessments, remarkably when rating my mood.

Interpretation of Visualizations:

Mood Change Analysis:

The visualization “How Walks Impact My Mood Ratings Over Time” presents a comprehensive view of how my mood fluctuated in relation to my walking habits over the two weeks. This line chart effectively illustrates the impact of each walk on my emotional state.

Y-Axis: This axis depicts the mood scale, ranging from 1 to 10, where 1 represents the lowest mood and 10 is the highest.

Dual Line Representation: The chart uses two distinct lines to represent my mood

X-Axis: This axis represents the chronological sequence of walks, labeled from 1 to 52, corresponding to each unique walk in my dataset.

After analyzing my walking data over the two weeks, I’ve uncovered some fascinating insights about how these walks affected my mood. The general trend is overwhelmingly positive – out of my 52 recorded walks, 41 resulted in mood improvement, 7 had no change, and only 4 led to a decrease in mood. The impact of walking on my mood varied significantly.

My most dramatic improvement was on November 2nd when a 90-minute evening walk in Forest Park, Queens, lifted my mood from a 2 to a 7. On the other hand, sometimes the change was as subtle as a single point increase, which happened several times. I noticed that I went for walks regardless of how I initially felt. My pre-walk moods ranged from 1 to 8, averaging around 4.9. Interestingly, even when I started in a good mood (6 or higher), 15 out of 20 of these walks still boosted my spirits.

There were a few exceptions worth noting. My mood decreased after a walk on four occasions (October 26th, 27th, November 1st, and 4th). These walks tended to be shorter, lasting 15-30 minutes, mainly in the evening or at night.

Despite these daily fluctuations, my average mood remained relatively stable throughout the two weeks. My pre-walk average was 4.9, while my post-walk average rose to 6.9. Perhaps most interestingly, I found that walks had the most significant impact when I started in a lower mood. For walks where I began with a mood of 3 or lower, I saw an average improvement of 3 points.

This self-study has provided valuable insights into how walking affects my emotional well-being. While the overall trend is positive, the data also highlights the complexity of the relationship between physical activity and mood.

Location Impact:

The second chart illustrates how different walking locations affected my mood. Waterfront walks consistently led to the most substantial mood improvements, followed closely by park walks. While still generally positive, neighborhood walks had a less pronounced effect on my mood.

Insights from the Map:

This visualization illustrates the geographical aspect of my walking habits and their impact on my mood:

1. It clearly shows my preference for walking in Manhattan, possibly due to convenience, personal preference, or where I spend most of my time.
2. The varying intensities of color provide a quick visual cue about the general mood impact of walks in each borough. Manhattan appears to be the most beneficial for my mood.
3. The map’s representation of Brooklyn correlates with my limited data for this borough, with only six recorded walks, visually reinforcing the need for more data points in this area for a more comprehensive analysis.

This map visualization adds a spatial dimension to my analysis, helping to contextualize the walking data within the urban landscape of New York City. It communicates the frequency of walks and their mood impacts across different parts of the city, providing an intuitive and geographically grounded perspective on the dataset.

Conclusion:

This self-quantification study revealed several interesting patterns in how walking impacts my mood:

1. Longer walks (60-90 minutes) have the most significant positive effect on my mood.
2. Walking in natural settings, particularly near water or parks, consistently improves my mood more than in urban neighborhoods.
3. Morning walks are most beneficial for my mood, while late-night walks have mixed effects.
4. Walking with company generally leads to more significant mood improvements than solo walks.

However, it’s important to note some limitations of this study:

1. The dataset is relatively small (52 entries). It covers only two weeks, which may not capture long-term trends or account for external mood factors, such as the general election results.
2. As a self-reported study, there’s potential for bias in mood assessments, especially since I was aware of the study’s purpose.
3. The study doesn’t account for other variables influencing mood, such as diet, sleep quality, or work stress.

For future research, it would be beneficial to:

1. Extend the data collection period to capture more long-term trends.
2. Include additional variables like sleep quality, diet, and stress levels to provide a more comprehensive picture.
3. Use objective mood measurement tools in addition to self-reporting to reduce potential bias.
4. Compare this data with other individuals to see if these patterns are personal or more universal.

This project has provided valuable insights into how walking affects my mood, and I hope it inspires others to explore their patterns of physical activity and emotional well-being.