Quantified Self: A catalyst for self-improvement
Evaluating the augmented process of reflective learning
The Quantified Self (QS) is a movement to incorporate technology into data acquisition on aspects of a person's daily life. Using this information, the user is able to reflect on their experience and make informed decisions about their lifestyle.
Process Break Down (NIke Fuelband example)
In data collection, we observe the use of software and hardware, like in Nike’s fuelband, in-built accelerometers and GPS tracking via the phone app aided in detecting physical activity and no. of steps taken. For information processing, Nike combines contextual data (eg. perceived activity type) with motion-intensity into an accumulative Nike Fuelpoints© scoring system. In data visualization, users can see their progress of daily goals. Lastly, after our reflection under Action Taking, you can track your daily process, adjust your daily goals, and customize your daily target to suit your day’s activities.
An individual’s reflective process differs from person to person, thus some learn and improve faster. QS tools essentially add new information to facilitate the reflection process. Firstly, data clarity helps remove guesswork and users reflect based on an aggregate between objective (sensor data), self and peers evaluation on the past experience. In contrast to our volatile memory, QS enables reference to past data without losing clarity, enabling analytics that adds meaning through pattern making, data aggregation, averages, and, etc.
We can analyze behavior through Foggs Behavioral Model (FBM) in which the 3 elements: Motivation, Ability, Trigger must converge at the same time before a desired behavior could happen. A successful behavior trigger happens when motivation and ability are above a certain level (activation threshold) at the point of trigger.