Quantified Self Experiments / Summary

Valuable findings

Which factors influence by sleep.

Caffeine, bedtime start, sickness, vitamin D3 and negative emotions significantly and independently influence my sleep in restorative phases. I'll lower my caffeine intake, will go sleep earlier, take D3 every other morning, and try to reduce stress. Detailed analysis is here.
Outdated: The later i'm going to sleep, the less total sleep time i'll get. My optimal sleep time around 21:30 - 22:30 and will stick to it. Explained here.
Outdated: Average intake of 68 mg of caffeine between 8:30 and 13:00 is enough to destroy ~30 minutes of my sleep in restorative phases. Explained here

Increasing Deep Sleep

Pink noise stimulations increase my Deep sleep by ~11 minutes per night. Additional ~1h20m of DEEP sleep per week seems to be a significant effect. Explained here

Physical activity increases mood

Physical activity significantly increases my positive emotions and decrease negative emotions. 1 hour training / 10k steps is enough to get a valuable effect. Explained here

Tracking body temperature

Oura ring temperature seems to be accurate and reliable proxy to core body temperature and sensitive to body temperature spikes caused by sickness. Explained here

HRV and sickness

Standing RMSSD seems to be a useful tool which provides insights about illness onset and recovery and helps make a decision of resuming physical activity. Explained here

Increasing HRV with physical activity

Increase in physical activity leads to increase in HRV. Weekly standing HRV more sensitive to physical activity. Explained by weekly and daily analysis.

Relaxation and HRV

I have found that relaxations like massage acutely boost HRV.

HRV and Deep (N3) sleep

I was able to reproduce some studies which tried to asess HRV during Deep (N3, SWS) sleep by using ECG only data. HRV during Deep sleep asessed with ECG only data were comparable with HRV assess with both ECG + EEG. It seems ECG signal is enough to assess HRV during Deep sleep.

Arrythmia

I weared Shimmer ECG for long term and was able to detect arrythmia (Premature Atrial Contractions, PACs). It occurs 200-400 times per day, sometimes even 1000+.

Circadian rhythm

I've checked my body temperature circadian rhythm with iButton and it seems to be strong enough. Fitbit Charge 4 wrist temperature data also was accurate enough to reveal diurnal changes. Also i've found and measured power of resting HR diurnal changes.

Sleep health

Comparison to healthy population my sleep is fairly good. But i need to focus on lowering awake time during night.

Accuracy

Fitbit Charge 5 and 4 have a low agreement for absolute heart rate values, but their trends are pretty consistent. Both devices agree pretty well on steps.
Oura sleep staging seems to be in weak agreement compared to EEG device.
Withings Sleep staging have a moderate agreement and impressive long term averaged values.
Fitbit Charge 4 sleep staging have a moderate agreement (better than Oura / Withings) and shows impressive long term averaged values.
Fitbit Charge 5 sleep staging looks same as Charge 4.
Manual assessment seems to be a good estimate of total sleep time.

Changelog

I've added changelog to find out fresh news / milestones /  changes in my QS framework.

Coming soon

Data already collected, just need to finalize.

Interesting resources

Take into account that most QS experimenters doesnt bother with checking accuracy of their devices, blinding, statistical significance, confidence intervals, data distribution analysis, adjusting p-values for multiple comparisons, prone to observer/confirmation bias and other biases. So just add a ton of salt to the QS stories.

Books recommendations

General health and longevity

If you want to know how to improve decision making and account for cognitive biases, increase rationality

Data analysis

Data collection in progress

New! I've setup BrainBay with ZMax for hrv/neurofeedback sessions. Also i plan to assess what happens with my brain during NBack, Anki and Psychomotor Vigilance Task.

In addition to wearables i'm using: