Reframing Perceptions of Hormonal Fluctuations
A nuanced view of premenstrual changes emerged from structured self-observation and AI-assisted reflection.
Context
A 38-year-old artist in Northern Europe experienced significant premenstrual mood shifts, which often led to feelings of frustration and disengagement from her creative work. Her partner frequently noted her withdrawal during these periods, contributing to a sense of predictability and helplessness around her cycle.
The shift
By regularly voicing her daily experiences into a reasoning chat tool, she began to notice recurring patterns in her energy and mood that weren't simply "bad days." This shifted her perspective from passive endurance to active, curious observation of her internal landscape.
Approach (in shape, not in recipe)
For three menstrual cycles, she used a voice recording app to dictate a brief, unstructured daily reflection, focusing on mood, energy levels, and any notable physical sensations. These recordings were then transcribed by a speech-to-text utility. Weekly, she used a reasoning chat tool to summarise and identify themes across the week's transcriptions, building a composite picture of her cycle.
What an honest observer would notice
Her partner observed a reduction in her premenstrual irritability, noting she was more present and communicative, often initiating conversations about her emotional state rather than withdrawing. She found greater agency in managing her creative workflow, scheduling demanding tasks for high-energy phases.
How to apply this
Adapt the shape to your own stack
Vendor-neutral steps. Use whichever AI tools you already trust — the shape of the work matters more than the brand.
- 1
Establish a consistent input routine
Choose a specific time each day to record your observations, focusing on mood, energy, and physical sensations. Consistency is more important than length.
- 2
Utilise a transcription tool
Convert your voice notes into text using a speech-to-text utility. This creates a data source that can be easily analysed.
- 3
Employ a reasoning chat tool for synthesis
Feed weekly or bi-weekly transcripts into a reasoning chat tool. Ask it to identify recurring themes, patterns, or anomalies in your self-reported data.
- 4
Reflect on emerging insights
Review the summaries and patterns identified by the tool. Consider how these insights align with your lived experience and what subtle shifts they suggest in your attention.
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