Insights on digital health and preventive medicine
Clinical reflections, evidence and a vision for the future. From the IKI Health team for professionals who want to lead the change.
Women's Health Is an Iceberg: Why the Invisible Matters More
Hot flashes, weight changes and fatigue are only the visible tip of the iceberg of women's health. Beneath the surface, hormonal transition, cardiovascular and metabolic risk, sleep, stress, daily habits and treatment adherence are the true drivers of long-term wellbeing. Studies such as SWAN (Study of Women's Health Across the Nation) and WHO reports on treatment adherence show why a single lab test can't capture what happens to a woman between visits. This article reviews the evidence behind each layer and explains why continuous monitoring, not just the isolated appointment, is the key to earlier intervention and personalized clinical care over time.
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Why data doesn't change behavior: the science of adherence
A recurring comment in the debate over longevity investment sums it up well: the industry has solved measurement, but not the 30 seconds between seeing a risk signal and acting on it. This article reviews what behavior change science actually says about that gap — from Eysenbach's 'law of attrition' in digital health, to Michie's COM-B model, just-in-time adaptive interventions (JITAIs), and the evidence on health coaching. The conclusion: closing the gap between data and action doesn't require more sensors, it requires a layer of interpretation and support designed for the exact moment the decision is made.
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The Silence Between Visits: Why Prevention Fails Even When Information Exists
A patient stops following the plan, motivation shifts and symptoms evolve — and the professional often finds out weeks or months later. Prevention doesn't fail for lack of information; it fails for lack of visibility at the exact moment intervention is still possible. Concepts such as clinical inertia, the \"law of attrition\" in digital health, and the predictable decay of adherence over time explain why so many lifestyle medicine programs quietly lose their patients. This article reviews the evidence on why disengagement rarely announces itself, and why continuous clinical intelligence, not just scheduled visits, is what makes early detection possible.
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From reactive to proactive care: prioritizing patient follow-up with data
In patient follow-up, one of the biggest challenges is not doing more, but knowing who to attend to first. This article explores how a dynamic status (Red, Orange, Green) helps prioritize clinical attention based on key indicators — sleep, exercise, nutrition, and emotional wellbeing through FeelTrack™ — and how alerts for prolonged high-risk states enable earlier intervention. We also review the evidence on remote monitoring, treatment adherence, and lifestyle medicine behind this shift from reactive to proactive follow-up, without ever replacing clinical judgment.
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