Attempt 1: logging one month of data to find causes of mood

written by Erik 05/12/2016
measuring factors mood

After some preparations it is time for the first attempt for measuring events during the day to explain changes in mood. Starting May 1st I will use various quantified self-methods for measuring, and in June the analysis will be made. I expect this to be an iterative process with evaluation of the results, necessary adjustments of the method and new measuring. Evaluate, adjust, repeat.

In the list below are the parameters that will be measured in this first attempt category by category, and a brief description on how the tools used. This is not the complete list of measurements presented in this post, but could be seen as a minimal viable product for a first attempt.

  • What is done:
    • Time spent working; measured with a IFTTT recipe tracking your geographic location
    • Time spent on and unlocks of smartphone; measured with Instant
    • Time spent on computer, divided in productive, unproductive and social media time; measured with RescueTime via IFTTT.
    • Amount of movement each day; measured with Fitbit
    • Amount of music listened to; measured with aggregated in Zenobase.
  • How it’s done:
    • Times awake, times restless and minutes awake when sleeping; measured with Fitbit.
    • Subjective sleep quality; logged with rTracker
    • Running intensity; measured with RunKeeper
    • Other excursive, logged with FitBit via Zenobase
    • Food and drinks: logged with MyFitnessPal
    • Total amount of water consumed; logged with iHydrate
    • Perceived stress level; logged with rTracker
    • What type of music is listened to; measured with aggregated in Zenobase
  • How the body responds:
    • Level of stress in the brain; measure with Muse
    • Balance in the autonomic nervous system (Heart rate variability); measured with Polar H7 pulse band and Elite HRV.
    • Breathing pattern; measured with Spire
    • Weight and body fat; measured with a simple bio-impedance scale, logged in FitBit app
    • Bowel movements; measured with TummyLab
    • Heart rate when exercising; measured with Polar H7 and Runkeeper
    • Blood glucose
    • Cognitive capacity; measured with Quantified-Mind
    • Illness and/or injury; measured with rTracker
  • The external influences:
    • Weather; logged via IFTTT
  • How I feel:
    • Mood; logged with MoodNotes
    • Energy level; logged with rTracker
    • Quality of close relationship; logged with rTracker
    • Internal and external stress; logged with rTracker

Will it take over my life?

In order for the measuring not to take over life completely but be as smooth as possible, most of the parameters are measured automatically. There are things where manual logging is necessary,  such as logging food. In order to make the measures as standardized as possible by measuring them at the same time, the routines to be followed looks like this:

  • Morning routine with EEG and HRV measuring, weight, mood and a rTracker questionnaire with info on breakfast, subjective sleep quality, stress, illness & injury, and cognitive mind test.
  • Continuously during the day logging of food, drinks and bowel movements. In the middle of the day a measurement of mood.
  • In the evening complementing facts about food during the day, inner & outer stress, energy levels, and a subjective measure of close relationships, all measured in rTracker. Also another mood log.
  • All the other parameters will all be measured / logged automatically.

Gathering data for analysis

Most of the apps and services has got export functions to get time stamped data in a spreadsheet, and for those that hasn’t there is usually a third party service that can be used to extract the data. In my last post we looked at various aggregators for quantified self data and some of these are used for this purpose, such as Zenobase and IFTTT. No aggregator is however able to collect all the data into one system, so I have created an excel sheet with formulas collecting data from the various outputs.

See you on the other side

I will try to come up with some more reviews and/or scientific blog posts, but method wise the next post will come mid June after the collected data is analyzed and evaluated.

Note: I am not sponsored by or in any way associated with the companies behind the products used.

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