Tools to survey symptoms and states

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Tool Infobox
Related topics Mood

State and symptom surveying is about user answering and filling out values for amount something has happened in a just passed time period. Examples include tracking mood now, and back pain over the past 5 hours. This type of tool can be used to track almost anything but is vulnerable to the issues inherent in self assessment. Self questionnaire is a type of Manual input. Compared to journaling and note taking, survey and assessment is less flexible but much easier to analyze.

Apps[edit | edit source]

  • Best Life: Health Diary[1], includes a mobile app and web app to track moods, symptoms, life events, environmental details
  • Mysymptoms Easy and quick to fill out but sampling is when user remembers to do it.
  • Quantified Flu, a web-based tool focused on infections, sends daily reminders
  • Track & Graph
  • reporter-app.com
  • doentry.com
  • nomie.app
  • bearable.app
  • KeepTrack
  • Loop Habit Tracker
  • Live Learn Innovate Foundation's PDD
  • chrono.me
  • Spread sheet like Excel

Self Assessment[edit | edit source]

The whole act of manually trying to turn experience, qualia,[2] into a number. Not considered as reliable as concrete data because people can just make it up. Not self report because you are not reporting to anyone but yourself. Users will avoid recording embarrassing things, even if they are only hiding from themselves. According to the wikipedia, this process is called something else[3][4], not self assessment[5].

Mood, psychological factors, focus, and interest in the issue can all complicate the data. For example Psychogenic pain[6].

Timing[edit | edit source]

User can do the assessment right after an incident or as an aggregate of a recently passed time period. Most things user would want to measure do not occur as easily defined and measured incidents so aggregation will need to be used. Unfortunately, having to remember a state over many hours requires effort and creates ambiguity. In addition, the method of aggregation (mean, mode, extremes) loses information, take a bit of effort, and can be a cause of drift. By information loss I mean that if a weighed mean is used to represent a time series the distribution is lost. For example, pain that spikes to high values for a few minutes every hour can have the same number as just pain that stays the same throughout the time period.

Assessments of aggregated intervals can also be presented at random intervals or constantly in small pieces or at user's convenience e.x. before going to bed.

Scales[edit | edit source]

Personal Science (book) recommends simpler scales to stop user from wasting time on details. DG (talk) recommends somewhat wider scales that split positive and negative into 'definitely' and 'extremely/unusually' and the neutral state into two to let user guess which side is more likely.

Ratings drift is when user adjusts to some new average over time and that average becomes their new neutral. Sometimes this is caused by user not being to set in the ways they measure the phenomenon and exactly what ratings mean what. This is a problem because it creates false positives for tests and makes comparing between long time periods difficult. It is possible, but not recommended, to look at distribution, especially extremes, and guess at how ratings may have drifted.

Anchored Scales. User could connect each level of a scale to specific set of physical characteristics, for example how disruptive it was to user's every day work. This technique stops drift, is less ambiguous, and therefore faster too. In addition this means comparisons can be done between different variables that have the same scale.

Simplify. Consider all the situations that may occur, information that is compressed into, and things that may influence the scale. The more complicated the variable the more ambiguity in it and effort it will require. It may be easier to split one variable into multiple different variables.

Linked content on this wiki[edit source]

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Projects that use this tool  
Self researchers who used this tool  
We talked about this tool in the following meetings