General Anxiety Disorder – Dosage, Mood, Caffeine, Alcohol

Since May 2021, I’ve participated in monthly meetings with a nurse practitioner over the phone about the medication prescribed to me for generalized anxiety disorder. The same questions are asked in every monthly session:

• How is your sleep?
• How is your appetite?
• How is your anxiety?
• Have you had any intense episode of negative emotions lately?
• What percentage of you day do you spend ruminating?
• On average, how often are you missing doses?
• How is it compared to last month? The month before that? A year ago before medication?

Even though I anticipate the same questions every month, I still struggle to provide accurate answers. When an intense emotional episode occurred, it was usually followed by my menstrual cycle– which made me believe it wasn’t related to the medication but in fact my hormones changing once a month. How often did I get mood swings as I neared the cycle? Should that affect my medication? And does that indicate how stable my mental health is? In 2020, the therapist I was seeing at the time asked if I ever noticed my mood swings occurring right before the start of a new menstrual cycle. As I tracked my cycle, I still couldn’t find a consistent correlation between intense mood and menstruation.

The nurse practitioner advised to avoid caffeine, as that heightens anxiety, as well as alcohol, as it can counter the benefits of antidepressants. In order to keep track of mood and anxiety, I recorded in my phone application cycles of menstruation, caffein intake, alcohol intake, days I missed a dose, and mood.

The project is intended to explore how one can self-reflect on their behavior and mood and how it impacts their daily life on a larger scope. Such practices are encouraged in group therapy through “Chain Analysis of Problem Behavior”, albeit in a narrower scope. In an effort to understand what led to a problem behavior, it is encouraged to describe in chronological order the labels:

1. Vulnerability
2. Prompting Event
3. Links 
   1. Actions
   2. Body Sensations
   3. Cognitions
   4. Events
   5. Feelings
4. Problem Behavior
5. Consequences

Although my personal data may be insignificant to others, it seems to be a common theme in psychiatric fields to self-reflect and psychoanalyze our own behaviors in order to fix bad coping mechanisms one engages in.

“In the effort to establish a working definition of affect/emotion, Aristotle offers a useful starting place. He defines the emotions as “those feelings that so change men as to affect their judgments, and that are also attended by pain or pleasure. Such are anger, pity, fear, and the like, with their opposites. In this understanding, emotions describe a moment when one’s experience of the world is altered in a way that affects one’s judgment of that world. Together, the emotions constitute one of our basic ways of establishing value, of assessing or judging our world, often prior to cognition or will.”

– FLATLEY, JONATHAN. “Glossary: Affect, Emotion, Mood (Stimmung), Structure of Feeling.” Affective Mapping, Harvard University Press, 2008, pp. 11–27. JSTOR, http://www.jstor.org/stable/j.ctt13x0m3t.4. Accessed 23 Oct. 2022.

Jaime Snyder, an Assistant Professor in the Information School at the University of Washington, is currently working on a project that visualizes Bipolar Disorder for clients to self-report their symptoms and behaviors. In the publication Towards personal stress informatics: Comparing minimally invasive techniques for measuring daily stress in the wild, the abstract reads as follows:

Identifying episodes of significant stress is a challenging problem with implications for personal health and interface adaptation. We present the results of a study comparing multiple modalities of minimally intrusive stress sensing in real-world environments, collected from seven participants as they carried out their everyday activities over a ten-day period. We compare the data streams produced by sensors and self-report measures, in addition to asking the participants, themselves, to reflect on the accuracy and completeness of the data that had been collected. Finally, we describe the range of participant experiences—both positive and negative—as they reported their everyday stress levels. As a result of this study, we demonstrate that voice-based stress sensing tracks with electrodermal activity and self-reported stress measures in real-world environments and we identify limitations of various sensing approaches.

NYC Social Distancing Reports

  On March 22, 2020 NYC Mayor de Blasio issued COVID-19 guidance to New Yorkers. All non-essential businesses in NYC will be closed, with exception to essential businesses, such as grocery stores, pharmacies, internet providers, food delivery, mass transit, banks, and financial institutions. As of 6:00PM on March 20, 2020 there were a reported 5,683 positive cases of COVID-19 and 43 fatalities. However, the businesses with essential functions that continue to operate must do so under these guidelines, in which the NYPD will help enforce these policies. The policy is as follows:

No non-essential gatherings; any concentration of people outside their home must be limited to workers providing essential services

• Practice social distancing in public (6 feet or more)
• Individuals should limit outdoor recreational activities to non-contact.
• Limit use of public transportation to only when absolutely necessary.
• Sick individuals should not leave home except to receive medical care.

Between the dates of March 01st, 2020 and July 31st, 2020, there have been a recorded number of 61,008 complaints that fall under the descriptor “Social Distancing”. These complaints are categorized by time created, borough, location type where the incident occurred, and the resolution that followed by either the NYPD or Department of Parks and Recreation.

 

Location types are categorized as follows:

• Park/Playground
• Residential Building/House
• Store/Commercial
• Street/Sidewalk

From the data shown, it’s clear that majority of reports were for Store/Commercial locations, given that there were a few that essential stores could be operating at that time. Even though the restrictions went into affect March 22, majority of the reports took place between April through June.

The graph here illustrates how each complaint was handled after the report, divided by borough. Majority of the complaints are categorized as “The Police Department responded to the complain and took action to fix the condition”, with about 9,219 of those reports being located in Brooklyn.

The second most popular categorization is “The Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.” There have been a recorded 11 resolutions that resulted in the arrest due to a complaint.

The gradient of the squares slowly increase to a dark red, representing emergency and high-risk– very fitting during those months.

The graph explains the amount of reports over the five month period as a line graph, with each line color-coded by borough. It illustrates the increase of reports made over this period of time, and the decrease that follows after the month of May.

I decided to create the graph with a dark background in order to make the colors of the bars and lines pop out. The colors chosen are similar to the NYC Subway map, in my opinion, although I cannot guarantee if others get a similar impression.

Some limitations I encountered were trying to incorporate all 4 variables into a single graph: 1. Borough 2: Month 3: Location Type 4: Resolution Description. While trying to combine all 4, I felt the information got a little bit crowded, and it would be hard to the viewer to understand the visuals at hand. Another set of data I would have like to included would the populations of each borough. Number of reports in contrast to population of borough would be more telling; however I suppose that would be hard to find considering the number of deaths that occurred each day in NYC due to Coronavirus.

Ideally, I’d like to take this project further by incorporating the median income and racial groups of the borough, considering that social distancing is a privilege to those who have the luxury to work from home, or to move out of NYC into a new home or an already pre-existing home. However, I’m not sure what kind of data can be extracted from 311 on peoples socio-economic background, so I’d have to use another database. I’d mostly be interested in data of those who continued to work throughout the pandemic as front-line workers and how they dealt with the COVID risks due too the nature of their job or their situation at hand.