As a multifactorial condition, investigations in the area of obesity can focus on a wide range of different types of behaviors, including those associated with physical activity, nutritional intake, sedentary behavior, and sometimes weighing behavior. Studies which require collecting accurate data on such behaviors will impose notoriously difficult and burdensome tasks to participants, which negatively affects their adherence.
Even when observed, it can be challenging to infer the motivation or intentions behind behaviors. For example, the reasons underlying a lack of physical activity can be a feeling of lack of social support, concerns regarding physical safety, an injury, or perception of adverse weather. We are also frequently interested in the role of exposures (to built environments, social environments, food environments), knowledge, attitudes and beliefs related to such behaviors, as well as dynamics of outcomes such as weight, BMI, motivation, and downstream factors such as injuries, including those of a subclinical nature. Such factors are again frequently difficult to measure accurately and with sufficient temporal resolution without overwhelming participants.
Given the multiple causal pathways involved, when interpreting outcomes in the context of interventions, it can be highly desirable understand which behaviors have been successfully altered (e.g., physical activity) and whether there are compensatory or simultaneous induced changes in other pathways (e.g., increases in caloric intake, or sedentary behavior).
Ethica can play a key role in easing such data collection, linking together knowledge of fine—grained exposures, dynamics across different particular pathways, and their time—varying effects on outcomes. By linking physical measures in the form of sensor data for factors such as physical activity, sedentary behavior, exposures to environments using unobtrusive ecological momentary assessments (EMAs) and crowdsourcing, such understanding can span our knowledge on exposures, attitudes, and ideation. The potential for linking such information with physical measures recorded from smart watches and data from GIS, weather and other databases further emphasizes the potential for discovery.
How much and how soon does the intervention affect different pathways involving risk factors (e.g., diet, physical activity, sedentary behavior, care seeking, etc.)
With a particle filtered dynamic model receiving a stream of accelerometer readings, weight measurements, possibly caloric consumption estimates, anticipating what lies ahead.
How would interventions to improve diet or physical activity on the part of one person be likely to affect their own trajectory, and that of their family and broader network?