[et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_row column_structure=”1_3,2_3″ _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”||-2px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-content\/uploads\/sites\/503\/2026\/06\/nyas_talk_pic1_sub-scaled.jpg” title_text=”Ipek Ensari” force_fullwidth=”on” _builder_version=”4.27.4″ _module_preset=”default” background_image=”https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-content\/uploads\/sites\/503\/2026\/06\/nyas_talk_pic1_sub-scaled.jpg” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF90aXRsZSIsInNldHRpbmdzIjp7ImJlZm9yZSI6IiIsImFmdGVyIjoiIiwiZW5hYmxlX2h0bWwiOiJvZmYifX0=@” _builder_version=”4.27.4″ _dynamic_attributes=”title” _module_preset=”default” title_text_align=”center” module_alignment=”center” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
We build digital evidence to make overlooked women\u2019s health conditions more visible, measurable, and actionable.<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”]<\/p>\n The Ensari Lab studies how patient-generated digital data, wearable technologies, and AI can improve the understanding and care of understudied women\u2019s health conditions. Our work focuses on conditions such as endometriosis, adenomyosis, fibroids, chronic pelvic pain, and related disorders that can profoundly affect daily functioning and quality of life, yet remain under-documented, poorly managed, and too often dismissed.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ background_enable_image=”off” custom_margin=”-1px||-1px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Many women\u2019s health conditions remain “elusive” not because they are rare, but because they are often under-documented, lack clear diagnostic markers, and are poorly captured in conventional clinical data. The result is a gap between lived experience and the data used to support diagnosis, treatment, and research.<\/p>\n The Ensari Lab was built to help close that gap. We use mobile health tools, wearable data, and analytic methods to make these data-poor conditions more visible, more measurable, and more actionable in both research and real-world care.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” module_alignment=”center” global_colors_info=”{}”]<\/p>\n We believe women\u2019s health research and care are limited when important parts of daily lived experience remain invisible in the data used to understand them. We see this, in part, as a question of epistemic justice: ensuring that patients\u2019 lived experience is treated not as anecdotal noise, but as meaningful evidence that can inform research, interpretation, and care.<\/p>\n Better care does not come from more technology alone; it comes from tools and models that are accurate, interpretable, clinically meaningful, and responsive to the realities of living with chronic and reproductive health conditions.<\/p>\n Our work is grounded in the idea that patient-generated data can reveal patterns<\/a> that are otherwise missed, support earlier recognition of poorly understood conditions, and open the door to more personalized and equitable care.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-content\/uploads\/sites\/503\/2026\/06\/generated-image-4.png” title_text=”digital evidence on womens health” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”2_5,3_5″ _builder_version=”4.27.4″ _module_preset=”default” module_alignment=”center” border_color_all=”RGBA(255,255,255,0)” border_style_all=”none” global_colors_info=”{}”][et_pb_column type=”2_5″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Our work is driven by a practical question: how do we ensure that digital data from apps, wearables, and AI-based tools are accurate, reliable, and clinically useful for women\u2019s health?<\/p>\n To answer that question, we design real-world studies using a variety of supervised<\/a> and unsupervised learning<\/a> approaches for patient-generated mHealth data to understand how they perform in everyday life.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Validate<\/a> digital health methods<\/strong> [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Interpret patient-generated mHealth data<\/strong> [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Study supportive lifestyle strategies<\/strong> [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-content\/uploads\/sites\/503\/2026\/06\/hpi_workshop_2026_ipek.jpeg” title_text=”ipek ensari digital health innovation” _builder_version=”4.27.4″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”Leadership” _builder_version=”4.27.4″ _module_preset=”default” title_level=”h2″ title_font=”|–et_global_heading_font_weight|||||||” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” module_alignment=”center” global_colors_info=”{}”]<\/p>\n The lab is led by Dr. Ipek Ensari, whose work sits at the intersection of women\u2019s health, digital health, and AI. Her research focuses on how patient-generated data from mobile tools and wearables can improve disease characterization, symptom monitoring<\/a>, and supportive care for conditions that have historically been understudied or poorly documented<\/a>.<\/p>\n Under her leadership, the lab brings together interdisciplinary perspectives to ask not only what digital tools can measure (validity and reliability), but also how those measurements should be interpreted and when they can meaningfully support patients, clinicians, and real-world care.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The lab welcomes engagement from collaborators, trainees, clinicians, and community members interested in advancing women\u2019s health through thoughtful, evidence-driven use of digital tools and patient-generated data.<\/p>\n We value interdisciplinary collaboration and work that connects research design, clinical relevance, data interpretation, implementation, and public understanding.<\/p>\n To inquire about trainee opportunities, collaborations, or related work, please contact Dr. Ensari at\u00a0ipek.ensari@mssm.edu<\/span><\/a>.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n In addition to peer-reviewed publications<\/a>, we share ongoing findings and reflections through Data Bytes<\/a>, our lab blog for research participants and the broader community. We see knowledge sharing as part of the research itself: a way to make digital health science more transparent, more interpretable, and more connected to the people it is meant to serve. This reflects a broader commitment to making women\u2019s health research not only more rigorous, but also more interpretable, transparent, and publicly useful.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" We conduct studies on mobile health (mHealth) technologies and machine learning methods for complex patient-generated data toward improving chronic disease characterization and patient self-management. Our research is grounded in female reproductive health conditions (e.g., endometriosis, fibrods, pelvic pain) and lifestyle factors for longevity such as physical activity. For inquiries and questions, please reach out to our lab PI (ipek.ensari@mssm.edu).<\/p>\n","protected":false},"author":545,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":" Often deemed \u201celusive\u201d due to under-documentation and lack of clear diagnostic markers, chronic pelvic pain (CPP) related conditions such as endometriosis, uterine fibroids, and menstrual disorders inflict significant patient burden and contribute to rising healthcare costs. Our work in the Ensari Lab confronts these prevalent yet poorly-understood women\u2019s health challenges through research-based mobile health tools and AI methodologies. We strive to illuminate these \"hidden\" diseases, accelerate diagnosis, and personalize care, directly addressing health disparities that have too often left vulnerable populations behind.<\/p> Our interdisciplinary team pursues three strategic pillars to fulfill this mission:<\/p> Validating Mobile Health Technologies:<\/strong> Testing whether data from apps, wearables, and AI-based health technologies are reliable, valid, and clinically meaningful for reproductive and pelvic pain conditions.<\/p><\/li> AI-Driven Personalized Lifestyle Interventions:<\/strong> Studying exercise and other lifestyle-based strategies to improve daily functioning, quality of life<\/a>, and long-term health for people living with chronic pain conditions.<\/p><\/li> Clinical Actionability of Digital Data:<\/strong> Developing ways for patients and clinicians to summarize and interpret symptom, behavior, and physiological data in ways that support better conversations, better decisions, and more agency.<\/p><\/li><\/ul> Currently active research studies focus on:Why we exist<\/h1>\n
\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nOur point of view<\/h2>\n
How we work<\/h2>\n
We study whether apps, wearables, and AI-enabled tools can generate reliable, valid, and clinically meaningful data for phenotyping female reproductive and pelvic pain disorders.<\/p>\n
We develop ways for patients and clinicians to better summarize and interpret<\/a> longitudinal symptom, behavioral, and physiological data in support of better conversations, better decisions, and more agency.<\/p>\n
We study exercise<\/a> and other lifestyle-based approaches that may improve daily functioning<\/a>, quality of life, and long-term health for people living with chronic pain conditions.<\/p>\nCollaborations<\/strong><\/h2>\n
How we share knowledge<\/h2>\n
1) AI-driven personalized lifestyle interventions for symptom management in CPP Disorders, with a focus on exercise<\/a>
2) Investigation of large language models for predictive modeling of infrequently-documented diagnoses and conditions for improving diagnostic performance under real-life clinical settings.
3) Integrating genomic markers and genetic data into mHealth and clinical data for improving model performance.<\/p>