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Fatigue and getting sick: cause, or the first symptom?

Does being worn out make a cold more likely, or is the tiredness already the infection starting? What virus-challenge studies and wearable data show, and why looking back rarely settles it.

Imagine you feel flat on Monday. Heavy legs, slow head, no appetite for the gym. On Wednesday you have a sore throat. Was Monday's tiredness the reason the virus got a foothold, or was it the virus, already at work, before you had any other sign of it?

Those are two different questions, and they get mixed up all the time. The first asks whether being worn out makes you more likely to catch something. The second asks whether the tiredness is itself an early symptom. Each has its own evidence, and neither answer tells you much about a particular Monday. This is informational material, not medical advice. Fatigue that lasts for weeks without an obvious reason is worth discussing with a doctor, since it has many possible causes, and so are symptoms that are severe or getting worse.

Question one: does being worn out make you more likely to get sick?

The best-known test is a quarantine experiment. In the 1991 study, 394 healthy volunteers first filled in questionnaires about stress in their lives. Then they were given nasal drops containing one of five respiratory viruses (rhinovirus types 2, 9 or 14, respiratory syncytial virus, or coronavirus 229E). Another 26 people got saline. Everyone was kept in quarantine and monitored. A "clinical cold" meant symptoms plus infection confirmed by isolating the virus or by a rise in antibodies (Cohen et al., 1991). The design is the same one behind the sleep study article: exposure is the same for everyone, so differences in outcome are easier to read.

The rates of infection and of clinical colds rose steadily with the stress score. Across the stress levels, infection went from about 74% to about 90%, and clinical colds from about 27% to 47%. Adjusting for age, sex, education, allergies, weight, season, how many volunteers were housed together during the trial, whether those housemates were infected, and antibodies at the start did not change this. The pattern looked similar for all five viruses. Smoking, alcohol, exercise, diet, sleep quality, white-cell counts and total immunoglobulin did not account for it either.

The authors read this as a difference in infection, not in symptoms after infection: more stressed people were more likely to be infected, not just to feel worse once they were.

One caution matters here. What was measured was psychological stress, by questionnaire. It was not fatigue, and it was not a bad week. Feeling worn out and being under stress overlap, but they are not the same thing, and this study cannot tell you what a tired Monday does.

A proposed mechanism, with support

Why would stress matter to a virus? A 2012 paper from the same research tradition proposes a chain. Long-lasting stress makes cells less responsive to the body's own anti-inflammatory stress-hormone signal (glucocorticoid receptor resistance). With that signal muffled, inflammation is not switched off as it should be (Cohen et al., 2012).

Two pieces of data support the model. In a group of 276 healthy adults, those who had recently been through a long-term threatening stressful experience showed this resistance, and those with more of it were at higher risk of developing a cold after exposure to a rhinovirus. In a second group of 79 people, greater resistance went with more local inflammatory signalling in the nasal secretions of those who were infected.

This is a proposal with some evidence behind it, not a settled pathway. And note what neither study tested: whether lowering stress lowers anyone's risk. They observed people as they were. They offer no basis for stress-management advice framed as protection, and this article does not give any.

Question two: is the tiredness already the infection?

Here the biology points the other way. When the body meets an infection, immune cells of the innate system release inflammatory signalling molecules (cytokines), and these act on the brain. The result is what researchers call sickness behaviour: the familiar run-down, sleepy, withdrawn state of being ill (Dantzer et al., 2008). The review makes a broader argument about inflammation and mood, which we leave aside. The part that matters for a Monday: feeling run-down is a normal part of the body's response to an infection, so tiredness is a plausible early sign of one.

Wearables give a window onto it. Two studies are worth knowing, and both come with sharp limits.

First, a smartwatch study of COVID-19. Researchers followed a cohort of nearly 5,300 people. Thirty-two of them got COVID-19, and 26 of those 32 (81%) showed changes in heart rate, daily steps or time asleep. Of 25 cases with symptom information, 22 were picked up before or at the onset of symptoms, and four at least nine days earlier. Looking back at the data, 63% of the cases could have been flagged before symptoms in real time by a two-tier alert built on extreme rises in resting heart rate relative to the person's own baseline (Mishra et al., 2020). It was small, retrospective, covered one infection, and used a research algorithm.

Second, a population study of flu-like illness. Acute infections can raise resting heart rate and change daily routines. Using more than 13.3 million resting-heart-rate and sleep measurements from 47,249 Fitbit users in five US states (2016 to 2018), researchers added the share of users with raised resting heart rate and more sleep to their models. State-level estimates of flu-like illness improved in all five states, with correlations against CDC rates of 0.84 to 0.97 (Radin et al., 2020). That is surveillance of a population. It says nothing about spotting illness in one person.

Together they support a modest statement: across a population, resting heart rate and sleep data follow flu-like illness, and in a small retrospective set of COVID-19 cases some people's numbers had shifted before they felt ill.

What MeteoHealth does and does not do

MeteoHealth does not do what the research algorithm above did. It does not detect, diagnose, predict or warn about infections, and it has no alert of that kind. It has no entries for cough, sore throat or fever either.

What it does: it reads resting heart rate, HRV and sleep from Apple Health, and it logs local outdoor weather (pressure, temperature, relative humidity, wind speed and UV index) next to the symptom entries you make. Fatigue is one of its nine fixed symptoms, along with headache, drowsiness, dizziness, anxiety, irritability, nausea, joint pain and insomnia. It looks for relationships in your own records with Pearson correlation and ANOVA, with false-discovery-rate correction so that chance patterns are less likely to pass for findings.

So the bridge from the research to your phone is plain. You can look at your own resting heart rate, HRV, sleep and logged fatigue over time. If your resting heart rate has sat above its usual range for a few days, that is a reason to pay attention to how you feel, not a diagnosis. The reverse holds as well: an ordinary reading does not mean you are well. For background on what HRV is and why your own trend matters more than a single number, see HRV explained.

Why looking back rarely settles it

Take the Monday and Wednesday from the start. Three stories fit them equally well.

  • A stretch of stress or poor recovery lowered your resistance, and the virus took hold.
  • The infection started on Sunday and the tiredness was its first sign.
  • Both: you were run down, and the infection then made it worse.

The virus-challenge studies avoid this confusion by design. The stress score came before the virus was given, and the researchers knew exactly when exposure happened. Real life gives you no such record. You find out about the exposure only after the symptoms, and by then the tiredness has to be sorted into cause or warning from memory.

The same problem sits inside your own data. A logged fatigue entry, a higher resting heart rate and a short night can precede an illness or belong to it, and a pattern that repeats over a season is easier to trust than one bad week. That is also why a single episode is a weak test of any of this. An app that finds correlations in your history can show what tends to go together for you, not what caused what.

A last practical note, on the app and on yourself. Do not grade a rough week. Watch your own trend, and if tiredness persists for weeks or arrives with symptoms that worry you, bring the records to a doctor. For the season itself, the winter article covers what the weather may and may not do to infection.

Sources [1..5]
  1. Psychological stress and susceptibility to the common cold — Cohen et al., New England Journal of Medicine, 1991.
  2. Chronic stress, glucocorticoid receptor resistance, inflammation, and disease risk — Cohen et al., PNAS, 2012.
  3. From inflammation to sickness and depression: when the immune system subjugates the brain — Dantzer et al., Nature Reviews Neuroscience, 2008.
  4. Pre-symptomatic detection of COVID-19 from smartwatch data — Mishra et al., Nature Biomedical Engineering, 2020.
  5. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study — Radin et al., The Lancet Digital Health, 2020.