Manal Almalki Manal Almalki is a university computer science lecturer currently on a Saudi Arabian Government scholarship at the Health and Biomedical Informatics Unit at the University of Melbourne. She is undertaking PhD studies in the field of personal informatics for self-quantification.
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Health and wellness as the basic organising concept.
Fit within a comprehensive framework for describing self-tracking practices (e.g. tools and technologies, data and measurements, time and location, etc.).
Reference to pre-existing classification systems developed to account for conventional and unconventional observations of potential influences on a health condition.
The proposed classification model consists of three domains (Figure 1). Each domain has several categories as follows: 1. Body structures and functions domain which includes: mental functions, sensory functions, sensation of pain, voice and speech functions, cardiovascular system, haematological system, immunological system, respiratory system, digestive system, metabolic system, endocrine system, genitourinary functions, reproductive functions, skeletal system, muscular system, nervous system, skin, hair, nails, genome (DNA, RNA and genes), and microbes categories. 2. Body actions and activities domain which includes: learning and applying knowledge, communication, mobility, self-care, domestic life, interpersonal interactions, education, work and employment, economic life, recreation and leisure, and religion and spirituality categories. 3. Around body domain which includes: relationships and attitudes, products or substances for personal consumption, products and technology for use, and natural environment and human-made changes to environment categories.
This classification model describes these domains as interactive and dynamic rather than linear or static. It is applicable to all people, whatever
their health condition. It is also relevant to all self-tracking and quantification practice and technologies identified in the authors’ prior review
Our data classification model can be used for describing the vast array of measurements generated in self-tracking. If we think of self-quantification as a way of investigating factors which affect health and fitness, we can see that we need to describe three main components as illustrated in Figure 2. The component number one provides the investigation questions or hypothesises. The second component sets the main attributes of a particular study, the study’s sample, the assays, and describes the instruments used in the study. Such instruments are classified into two categories: primary and secondary self-quantification systems (SQS). This SQS taxonomy is explained in detail in Almalki, Martin-Sanchez, and Gary (2013). Also, the second component explains the measurements – this is where our model provides a way to classify such data and their types. The third component is the data generated from the investigation.
CONCLUSION Self-quantification produces big data, and has the potential to advance healthcare knowledge. However, it lacks a formal architecture for describing the data that are generated. Our CDA-SQS model for classifying such data overcomes this problem and enables more systematic research in this field. 1. The proposed CDA -SQS model 2. Self-quantification investigation components. {Numbers are used for illustration only}.