As a PI, I don\u2019t think of trust as a vague, warm feeling. I think of it as something that is earned through a set of concrete behaviors over time.<\/p>\n
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Trustworthy Use of Your Data in Digital Health Research<\/p><\/div>\n
What participants should be able to expect<\/h2>\n
If we take trust seriously, then people who share their health data with us should be able to expect at least a few basics. Here is what I believe belongs on that list. You should be able to expect:<\/p>\n
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- Clarity. You can understand what the study is about, what you will be asked to do, and what will happen with your data.<\/li>\n
- Purpose. Data collection feels connected to clear scientific questions, not like we are simply gathering everything we can \u201cjust in case.\u201d<\/li>\n
- Boundaries. Your data are used and shared only in the ways that are described in the consent materials and approved through the proper regulatory and institutional channels.<\/li>\n
- Protection. Technical and procedural safeguards are in place to protect privacy and confidentiality, in line with institutional and regulatory requirements.<\/li>\n
- Choice. You can say no, or change your mind later, without being penalized or losing access to clinical care or other services you would otherwise receive outside of that study.<\/li>\n
- Responsiveness. When you ask questions or raise concerns, you get direct, honest answers.<\/li>\n
- Respect. Your time, your energy, and your experiences are treated as valuable, not as something we are entitled to.<\/li>\n<\/ul>\n
These are not about making a system risk\u2011free\u2014that\u2019s not realistic\u2014but about building trustworthy systems where the way we handle risk is transparent and thoughtful, and where we don\u2019t hide important details in fine print.<\/p>\n
Research vs. \u201cterms and conditions\u201d<\/h2>\n
In Post 1 of this series, I talked about the difference between an informed consent process and the \u201cI agree\u201d box you see in many apps and wearables. That difference becomes even more important when we think about trustworthy data use. In a research study, the expectation is that we:<\/p>\n
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Define specific goals and methods,<\/p>\n<\/li>\n
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Submit our plans to independent review,<\/p>\n<\/li>\n
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Explain what we are doing in language you can understand, and<\/p>\n<\/li>\n
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Invite you to make a free, informed choice about joining.<\/p>\n<\/li>\n<\/ul>\n
In many consumer contexts, you may see broad terms and conditions that:<\/p>\n
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Allow for open\u2011ended data use or sharing,<\/p>\n<\/li>\n
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Can change with new versions of the app, and<\/p>\n<\/li>\n
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Often appear when you\u2019re just trying to get to the service you want to use.<\/p>\n<\/li>\n<\/ul>\n
That doesn\u2019t mean all research is perfect or all companies are careless; reality is more complicated than that. But it does mean that the underlying model of agreement is different.<\/p>\n
What this looks like in a study like CPP Tracker<\/h2>\n
In CPP Tracker, we ask participants to share information about chronic pelvic pain and related symptoms over time, often alongside app\u2011based or wearable\u2011based measures of daily life. The technical details live in our protocols, which are reviewed and approved by our IRBs and other compliance offices, but the values underneath them guide our everyday decisions. Some examples of how we try to translate \u201ctrustworthy use of data\u201d into practice:<\/p>\n
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When we design our questionnaires and app prompts, we think carefully about what we are asking, how often, and why, so participation is meaningful but not overwhelming.<\/p>\n<\/li>\n
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When we decide which data streams to include, we think not just about what is technically possible, but about what is scientifically justified and respectful of your privacy.<\/p>\n<\/li>\n
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When we set up data storage and access, we use secure, approved systems and limit access based on people\u2019s roles, not their curiosity.<\/p>\n<\/li>\n
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When we plan analyses and publications, we keep the focus on group\u2011level patterns rather than shining a spotlight on individual stories without consent.<\/p>\n<\/li>\n
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When participants or potential participants ask questions, we strive to answer them in plain language, even when the answer is \u201cwe don\u2019t fully know yet, and this is part of why we\u2019re doing the study.\u201d<\/p>\n<\/li>\n<\/ul>\n
None of these things are glamorous. Most of them happen in the background and do not get any spotlight or headlines. But together, they help a digital health study feel like a trustworthy partnership.<\/p>\n
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Different ways data agreements can be structured.<\/p><\/div>\n
Trust in digital health is not just about data security<\/h2>\n
It\u2019s natural to think about trust mainly in terms of security: encryption, firewalls, access logs, and so on. Those are critical, and they\u2019re part of our everyday reality when we work with health data. But trust in digital health research also depends on factors that are less technical and more human, such as:<\/p>\n
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- Being honest about trade\u2011offs. For example, the more frequently we ask you to report symptoms, the richer the dataset, but also the more time and energy it costs you.<\/li>\n
- Listening when people tell us something feels too invasive, too confusing, or too burdensome.<\/li>\n
- Acknowledging history. Many communities have good reasons to be cautious about how institutions have used their health information in the past. We can\u2019t erase that history, but we can let it inform how we show up now.<\/li>\n
- Sharing back. Whenever possible, giving participants and communities access to what we\u2019re learning, and in formats that are actually understandable, helps show that their contribution is valued.<\/li>\n<\/ul>\n
Strong passwords and secure servers are necessary, but they\u2019re not sufficient. Trustworthy use of health data requires both solid infrastructure and a consistent, participant\u2011centered way of thinking.<\/p>\n
A future worth building toward<\/h2>\n
When I think about the future of digital health research, I hope a world where:<\/p>\n
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People understand enough about how their data might be used to make real choices,<\/p>\n<\/li>\n
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Research participation feels like a partnership, not a transaction,<\/p>\n<\/li>\n
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Digital tools are designed with privacy and respect in mind from the beginning,<\/p>\n<\/li>\n
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The benefits of research reach the people whose data made it possible.<\/p>\n<\/li>\n<\/ul>\n
These are the principles that guide how we design and conduct our research studies. For conditions like chronic pelvic pain, endometriosis, fibroids, and other areas of women\u2019s health that have been under\u2011studied and under\u2011served, we urgently need better data and better science. But we can\u2019t afford to ignore the how while we chase the what. How we collect, protect, analyze, and talk about data is part of the science.<\/p>\n
Closing: The Pondering Professor Corner<\/h2>\n
Across these three posts in The Pondering Professor Corner, we\u2019ve moved from why we start with informed consent, to what happens to your data once you join a study, to what trustworthy use of health data should look like in digital health research. If there\u2019s one idea I hope you carry with you, it\u2019s this: digital health research depends on participant trust\u2014and trust depends on clear, honest communication and responsible data stewardship. If you\u2019ve shared your experience with us in CPP Tracker or any of our other studies: thank you. If you\u2019re considering joining in the future, I hope this series helps you feel better equipped to ask questions, set boundaries, and decide what feels right for you.<\/p>\n
This post is meant to give a general overview of how we think about trustworthy use of health data in studies like ours. It does not replace the specific consent form, HIPAA authorization, or study information you receive for any individual research study.<\/em><\/p>\n
\nAbout the author: Ipek Ensari, PhD, is an Assistant Professor at the Icahn School of Medicine at Mount Sinai Department of AI and Human Health and principal investigator of the CPP Tracker study on chronic pelvic pain. Her research focuses on validation and reliability evaluation of data from wearables and apps, with a focus on women\u2019s health and conditions like endometriosis, adenomyosis, and fibroids. Her recent talks and writings cover accuracy and reliability of digital health data and AI methods for women\u2019s health, and how to responsibly use wearable and smartphone data in clinical research and real\u2011world settings.<\/p>\n
<\/p>\n
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In the first two posts of this series, I focused on two stages in a digital health study like CPP Tracker: the moment you\u2019re deciding whether to join (and why we start with informed consent), and what actually happens to your data once you\u2019ve said \u201cyes.\u201d For this third post, we will zoom out. Instead […]<\/p>\n","protected":false},"author":640,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[42,36,40,1],"tags":[46],"class_list":["post-5093","post","type-post","status-publish","format-standard","hentry","category-data-trust","category-digital-data","category-digital-trust","category-womens-health","tag-epistemic-justice"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts\/5093","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/users\/640"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/comments?post=5093"}],"version-history":[{"count":7,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts\/5093\/revisions"}],"predecessor-version":[{"id":5143,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts\/5093\/revisions\/5143"}],"wp:attachment":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/media?parent=5093"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/categories?post=5093"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/tags?post=5093"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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