Reasons for and against self tracking and quantification

A summary of the much more thorough debate and argument map on kialo:

https://www.kialo.com/everyone-should-health-track---self-quantify-49787

Reasons For

Improve the management of health conditions

Sub-clinical issues are issues doctors can only fix with data provided by personal health tracking. Some remedies can only be tested with self tracking. The effects of lifestyle choices like veganism on health may need to be monitored.

Motivate lifestyle change

Gamification seems pretty popular and it requires tracking. Most consumer wearables include lots of motivating information. Just watching the numbers improve or be recorded can be quite motivating. Self tracking can help build healthy habits. Health tracking can once again help find the best method or remedy.

Optimize health, productivity, education, sports training, and cognitive functioning.

Professional coaches do this. QS projects have found interventions that help these like creatine for cognitive functioning.

Contribute your data to medical research.

Variety of literature shows the validity of N-of-1 approaches.

Lifelogging, Introspection

Can be fun. Store important memories.

Reasons Against and Problems

Choosing, setting up, and connecting user's selection of apps and devices can be quite a bit of work. Or expensive.

User could follow someone else's made trail such as reading this wiki instead of researching everything themselves. Custom data sources such as for rare problems that have not been documented here are still a problem. Privacy, data access, data quality are all constraints which make finding a good enough device harder.

Self tracking takes too much time and effort every day.

Manual entry is time consuming though automatic data recording is usually not. Studies and experience show people dropping even fitness trackers.

People make mistakes forget and slack when recording data manually

Self-report is a common concern in studies.

Health tracking presents privacy concerns

GPS can reveal delicate information. Same with emotion detection. Employees already monitor employee health. User has some options for defense. Data protection regulations grow.

Analysis algorithms are either hard to use or too simple

All apps besides OH with any ability to analyze just use linear regression. Just read an article in nature where ML was used on multiple N-of-1 studies but that approach was lacking and is difficult for the average user to set up.

Negative engagement of User

such as becoming obsessed with data tracking.

People do not want to know somethings

Like a genetic predisposition to a disease they can not treat.