Does a cold or being sick mess up my insurance video health check?

For developers and architects of digital underwriting platforms, the question is not if an applicant will complete a video health check while feeling unwell, but how the system should interpret the resulting data. A temporary illness like the common cold or flu introduces significant, albeit transient, variance into physiological measurements. A robust risk scoring API must be ableto distinguish between an acute, temporary condition and a chronic health issue to maintain analytical integrity and ensure fair applicant assessments. This is a data science challenge that sits at the core of next-generation predictive underwriting.

"For every 1.8°F (or 1°C) rise in an adult's body temperature, the heart rate can increase by over eight beats per minute." This well-documented physiological response to fever is a primary confounding variable for any health assessment performed during an acute illness.

How being sick affects insurance video health scan performance

A video-based health assessment using remote photoplethysmography (rPPG) works by detecting subtle changes in light reflected from the skin, which correspond to the blood volume pulse. From this optical signal, key vital signs are derived. When an applicant has a common cold, influenza, or other acute infection, the body's systemic response can significantly alter these vitals. Understanding how being sick affects insurance video health scan data is the first step toward building a more resilient underwriting model.

The primary changes are driven by the body's inflammatory response. As detailed by researcher R. Eccles in a 2007 study on the mechanisms of cold and flu symptoms, the immune system's release of cytokines triggers systemic effects like fever, increased metabolic rate, and changes in blood flow. For an rPPG-based system, this manifests as:

• Elevated Heart Rate: Fever and the body's effort to fight infection increase cardiac workload, leading to a higher resting heart rate.
• Increased Respiratory Rate: Respiratory infections can directly impact breathing patterns, leading to faster and sometimes shallower breaths.
• Altered Heart Rate Variability (HRV): The stress of illness typically reduces HRV, indicating a shift in the autonomic nervous system's balance toward a sympathetic (fight-or-flight) state.
• Potential Blood Pressure Fluctuations: Dehydration, fever, and inflammatory responses can cause temporary increases or instability in blood pressure readings.

These are not noise; they are valid physiological signals. The challenge for an underwriting platform is not to ignore them, but to correctly classify them as transient.

| Vital Sign | Baseline State (Healthy) | Acute Illness State (e.g., Common Cold) | | :--- | :--- | :--- | | Resting Heart Rate | 60-100 bpm (stable) | Elevated by 10-20+ bpm; varies with fever | | Respiratory Rate | 12-20 breaths/min (regular) | Elevated (>20 breaths/min); potentially irregular | | Heart Rate Variability (HRV) | Higher, indicating parasympathetic dominance | Lower, indicating sympathetic (stress) dominance | | Blood Oxygen (SpO2) | 95-100% | Generally stable, but can dip with severe respiratory symptoms |

Industry applications for insurtech platforms

For CTOs and system architects, managing this signal variance is a core product feature. A platform that clumsily rejects an applicant due to a temporary cold is not just inaccurate; it's a poor user experience that damages trust.

### differentiating signal from noise

The core technical task is to build models that can differentiate between chronic risk indicators and temporary biological "noise." This involves analyzing the pattern of vitals. For example, a chronically high resting heart rate combined with other risk markers presents a different profile than a high heart rate in the presence of a high respiratory rate and low HRV, a signature more consistent with an acute infection. Advanced algorithms can be trained to recognize these signatures.

### the importance of longitudinal baselines

A single scan is a single data point. The ability to distinguish a transient illness is dramatically improved when the system can compare a scan to a user's own historical baseline. While this is not always possible in a one-time insurance application, it highlights the architectural value of systems that can store and re-analyze data, for instance, when a user is asked to perform a re-scan after a few days. This allows the platform to establish a "normal" for that individual and treat deviations with more context.

### Flagging for Review vs. Auto-Rejection

Instead of a binary approve/reject output, a sophisticated system should use a classification and flagging model. When an rPPG scan produces a signature consistent with acute illness, the API response should not be a 'decline'. Instead, it should be a "flag for review" or "request re-scan" status. This payload can be consumed by the underwriting engine to trigger a workflow, such as sending an automated, empathetic message to the applicant asking them to perform the scan again in 48-72 hours when they're feeling better.

Current research and evidence

The scientific foundation for understanding these transient states is well-established. Research into the pathophysiology of common viral infections provides a clear basis for why vital signs change. The 2007 paper by R. Eccles, "Mechanisms of symptoms of the common cold and influenza," published in the British Journal of Clinical Pharmacology, is a key resource. It explains that systemic symptoms like fever, muscle ache, and malaise are not caused directly by the virus but by the body's own immune response, specifically the release of cytokines like interleukins.

These cytokines signal the hypothalamus in the brain to increase the body's temperature set-point (causing fever) and trigger prostaglandin production, leading to aches. This systemic inflammatory state places a metabolic burden on the body, directly increasing heart rate, respiratory rate, and stress on the cardiovascular system. Therefore, the data captured by a video health scan is a direct measurement of this well-understood immune response. Modern rPPG systems are sensitive enough to pick up these changes, which were previously only observable in a clinical setting.

The future of context-aware underwriting

The future of digital underwriting lies in creating systems that are context-aware. An API that only returns raw numbers is a commodity. An API that returns enriched, contextualized insights provides a durable competitive advantage. The next generation of risk scoring platforms will not just measure vitals but will actively model the state of the applicant. This means incorporating more inputs, potentially even allowing for self-attestation ("Are you currently feeling unwell?") and using machine learning models to identify the likely cause of physiological deviations. The goal is to build a system that understands the difference between a person who is high-risk and a healthy person who is simply having a bad day.

Frequently asked questions

Q: Will having a cold automatically cause my insurance application to be denied? A: Not with a modern underwriting platform. The system should be designed to recognize the typical pattern of a temporary illness. It will likely flag the scan for a follow-up, often asking the applicant to perform the scan again in a few days to establish a more accurate baseline.

Q: How can an API tell the difference between a cold and a chronic health condition? A: It's a matter of pattern analysis. A transient illness creates a specific "signature" in the vital signs, for example, a temporarily elevated heart rate and respiratory rate with a sharp drop in HRV. This signature is different from the patterns associated with long-term cardiovascular conditions. The system analyzes the relationship between multiple data points rather than looking at any single vital in isolation.

Q: Should I wait until I'm completely healthy to do my insurance health check? A: For the most accurate baseline reading, it is always best to perform the scan when you are feeling well and rested. However, underwriting platforms are increasingly built to account for these real-world variations, so a mild illness may not permanently impact your assessment if the system is designed correctly.

The challenge of separating transient physiological states from chronic risk factors is a critical frontier in digital underwriting. As a leader in vitals-based risk scoring, Circadify is developing API-driven solutions that provide this level of analytical depth, allowing platforms to make smarter, fairer, and more contextual decisions. Insurtech CTOs and product leaders looking to build this resilience into their own underwriting funnels can explore our documentation and sandbox at circadify.com/custom-builds.