Towards Hormone Health: An Autoethnography of Long-Term Holistic Tracking to Manage PCOS

Research Background and Problem

• Problem and Challenges: This paper focuses on Polycystic Ovary Syndrome (PCOS), a condition affecting 11-13% of women of reproductive age worldwide, characterized by diverse symptoms such as irregular menstruation, acne, and obesity. Existing health tracking tools are primarily designed for women without underlying health conditions and lack support for personalized and long-term comprehensive tracking of complex chronic diseases like PCOS.
• Significance: The complexity and individual characteristics of PCOS necessitate a holistic tracking approach to help patients manage symptoms more effectively and develop health strategies. Addressing this issue can significantly improve patients' quality of life and drive the design and development of targeted technologies.
• Research Motivation and Related Work: The authors observed that existing tools, such as period tracking apps, often fail to capture critical health data related to PCOS, such as hormonal fluctuations, acne locations, and weight changes. Furthermore, PCOS patients often struggle to understand the complexity of their multidimensional health data or track irregular patterns using current tools.

Solution

• Method and Innovation: The authors employed an autoethnographic approach, documenting the first author's detailed tracking experience as a PCOS patient over ten months. This included monitoring 55 variables (e.g., hormone levels, weight, diet, sleep) and utilizing various tracking tools such as wearable devices, mobile apps, and test kits.
• Key Implementation Steps:
  1. Initial Tracking: The author used Google Sheets to record data across seven categories, including symptoms, diet, weight, and sleep.
  2. Turning Point: Collaborated with a nutritionist, psychologist, and primary healthcare provider to integrate professional feedback into the tracking process and began using basal body temperature and hormone monitoring tools (e.g., Inito).
  3. Later Stage: Transitioned to specialized apps (e.g., Femometer, Inito) for more accurate hormone tracking and data analysis.
• Technology Utilization: The study leveraged apps paired with digital thermometers, urine test devices (e.g., Inito), and manual input in Google Sheets to provide both flexibility and precision in data capture.

Research Findings

• Specific Findings:
  1. Identified challenges in long-term tracking across five dimensions: medical (false positives/interpretation of abnormal hormone levels), sociocultural (food tracking constrained by cultural context), temporal (tracking interruptions due to travel), technical (data inconsistencies), and spatial (influence of home environment).
  2. Proposed design insights for improving tracking technologies, including shifting the focus from fertility to metabolic health, offering more detailed tracking options, supporting multimodal input, integrating cross-platform data, and leveraging smart home systems to support behavior change.
• Strengths: By employing an autoethnographic approach, the study provides deep, personalized insights that capture experiential details often overlooked by traditional research methods. These perspectives help the HCI community understand the needs of underserved groups.
• Experimental or Evaluation Results: The authors effectively demonstrated how data-driven behavioral interventions can optimize health management while highlighting the technical, functional, and visual design shortcomings of existing tools.
• Limitations and Future Directions:
  • Limitations: The study is based on the experience of a single author, which may not fully represent the needs of all PCOS patients. It also does not encompass perspectives from diverse cultural and socioeconomic backgrounds.
  • Future Directions: The authors suggest expanding the study to a broader population through participatory design and exploring community-based autoethnographic approaches to gain deeper insights into the challenges faced by users.

Conclusion

This study reveals the complexities of PCOS management from an ethnographic perspective and proposes several technological improvements to enhance the inclusivity and utility of tracking tools. The authors' exploration not only aids in understanding the needs of PCOS patients but also provides valuable insights for designing socially meaningful health tools in the HCI domain.