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.