Beyond the Cycle: PCOD/PCOS Health Monitoring and Wellness Management System

Abstract

Polycystic Ovary Syndrome (PCOS) is a complex and multifactorial condition that affects reproductive, metabolic, and overall wellness health. Its management extends beyond menstrual irregularities and involves symptoms, nutrition, physical activity, stress, lifestyle patterns, and environmental factors. With the increasing use of digital healthcare and artificial intelligence, various approaches have been proposed for PCOD/PCOS monitoring, mobile health applications, clinical assessment, and intelligent classification. This review paper presents a comparative analysis of five existing studies covering digital PCOS health tracking, mobile application evaluation, comparative clinical literature, explainable multimodal deep learning, and the evolutionary relationship between PCOS, lifestyle, and environmental factors. The reviewed studies are examined based on their research approach, working methodology, contributions, and limitations. The analysis shows that existing approaches address different aspects of PCOS independently, with digital systems primarily focusing on tracking, mobile applications emphasizing usability and information quality, clinical studies concentrating on disease understanding and management, and artificial intelligence approaches focusing mainly on classification. The review identifies a gap in comprehensive approaches that integrate continuous health monitoring, lifestyle assessment, intelligent analysis, and personalized wellness management. The findings emphasize the importance of moving beyond menstrual-cycle tracking toward a broader and more holistic approach to PCOD/PCOS health and wellness management.

Introduction

PCOS is a complex endocrine and reproductive condition involving reproductive, hormonal, metabolic, and potentially psychological factors. Because its manifestations and management needs extend beyond menstrual irregularities, long-term monitoring may benefit from considering symptoms, nutrition, physical activity, stress, metabolic health, lifestyle, and environmental influences.

The reviewed literature demonstrates several approaches to PCOS management. Kajale et al. (2026) developed a digital PCOD/PCOS health tracker for recording menstrual cycles, symptoms, lifestyle habits, and basic health indicators, but with limited predictive and personalized capabilities. Arabkermani et al. (2025) evaluated PCOS mobile applications using the Mobile App Rating Scale (MARS), identifying important usability and quality characteristics while not developing an integrated wellness platform. Disha and Ashok Kumar (2025) provided a comparative clinical review of PCOD and PCOS and emphasized lifestyle management, but did not introduce continuous digital monitoring. Lakshmi and Pushpa (2026) proposed an explainable multimodal deep-learning approach combining ultrasound images and clinical information for PCOS classification; however, its primary objective is diagnosis rather than continuous wellness management. Parker et al. (2021) synthesized evidence concerning genetics, metabolism, lifestyle, microbiome, and environmental factors to develop an evolutionary perspective on PCOS, but did not implement these factors in a digital monitoring system.

Literature gap

The studies collectively reveal a gap between individual PCOS monitoring/diagnostic functions and a comprehensive, personalized wellness-management system. Existing approaches can broadly be grouped as:

Overall research gap

The proposed “Beyond the Cycle” system addresses this gap by aiming to combine:

Menstrual tracking + symptom monitoring + lifestyle data + wellness trends + personalized recommendations + continuous health monitoring

Conclusion

Research Area Finding from Literature Identified Gap Menstrual & symptom tracking Digital tracking can improve health-record management. Limited intelligent interpretation and prediction. Mobile health applications Engagement, usability, functionality, and information quality are important. Limited integration of comprehensive personalized wellness management. Clinical management Nutrition, exercise, stress management, and lifestyle modification are important. Limited continuous digital monitoring of these factors. Artificial intelligence Multimodal AI can support PCOS classification. Requires clinical datasets and is not designed primarily for everyday wellness monitoring. Lifestyle & environment Lifestyle, metabolic, genetic, and environmental factors may influence PCOS. These factors are rarely integrated into one continuous digital management framework.

References

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Copyright

Copyright © 2026 Miss. Sarika Gajanan Khalpe, Miss. Krushnai Yadavrao Pingale, Mr. Hitesh Raju Ghotkar, Mr. Aniket Shivajirao Hole, Prof. Kalyani D. Dahikar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.