As part of research, I am particularly interested in adherence to weight loss plans wearable technology devices. I will break this research down into three parts, the first focusing on goal framing.
Despite public awareness efforts on weight, obesity continues to increase in the United States. Roughly 36.5% of the population is clinically diagnosed obese (defined as Body Mass Index [BMI] ≥30 kg/m2) (Ogden, et al. 2015). The reason for the continued increase is currently unknown, although some experts hypothesize a causal relationship between well-being elements (life, work, health) and obesity. Recent data illustrates a direct correlation between weight and well-being: obese individuals reported the lowest ratings of well-being (Gallup, 2014). For the purposes of this paper, well-being will be discussed from a cognitive psychology perspective instead of from an effective or psychomotor perspective. Cognitive well-being is defined by a person’s needs and satisfaction (Gilboa & Schmeidler, 1999).
Most recent efforts to combat obesity concentrate on diet and exercise to produce weight loss without addressing aspects of cognitive well-being. Although evidence based research shows that diet and exercise are important for weight loss, evaluating “underlying causes of obesity through a better understanding of all elements of well-being can help more Americans achieve a long-term healthier weight (Gallup, 2014).”
Digital health is the convergence of the digital revolution with health, healthcare, living, and society (Sonnier, 2015). This industry is creating new ways to empower individuals to lead healthy lives. One type of digital health, wearable technology, has created new ways for individuals to self-monitor their health. Wearable devices like Jawbone UP, FitBit, and the Apple Watch allow individuals to track nutrition, exercise, medication, sleep, etc. in order provide data about health and behavioral patterns. Many people purchase these devices to aid in adherence to a diet and exercise plan, and there is strong evidence to suggest that frequent self-tracking produces positive effects on weight-loss (Burke, et al. 2011).
Relationship to HCI and Cognitive Psychology
One in every ten Americans over the age of 18 now owns some type of wearable device. However, research shows adherence to device use quickly decreases over time. People that purchased a wearable device only remained compliant for an average of 6 months (Endeavor Partners, 2014). Additional report an even faster decline in tracking as early as 3 – 5 weeks (Yu, et al. 2015). Wearable devices provide utility in self-monitoring but lack solutions promoting long-term adherence to a weight loss plan. Using wearable devices as a medium to increase individual cognitive well-being will increase user engagement to the device and adherence to a long-term weight loss plan. By further evaluating wearable devices from a cognitive psychology perspective, we can better understand what is needed to create this improvement in individual well-being.
Goal framing to increase weight loss plan adherence
Much of the weight-loss process entails the ability to comply with a weight loss program through the creation of individual goals. Bandura’s social cognitive theory of self-regulation (SCT) states that much of what is learned by an individual occurs through observation. People self-regulate through goal setting, and continually align motivation and actions with an established goal. In the context of weight loss, observations about new habits (EG: walking 30 minutes a day) may be difficult to observe, quantify, and mentally index. Wearable devices provide a way to operationalize goal performance through tracked data (EG: tracking heart rate and calories burned from walking 30 minutes a day) that is presented to the user in a understandable fashion.
Continued feedback from a goal gives the ability to track progress and enforces individual commitment to a goal (EG: a new exercise regiment) (Locke & Latham, 2006). An individual’s commitment and motivation towards a goal has a direct effect on attention, memory, and goal interference (Neuberg, et al. 2010). Producing an effectively framed goal at the beginning of a weight loss program is key to long-term user engagement.
Some researchers have found that device programs are largely shallow at the goal setting level. In the study “Rebranding exercise: closing the gap between values and behavior,” Segar et al. attempt to understand goal setting and weight loss behavior. Researches were concerned with understanding superordinate goals, which are considered to be self-regulatory guides for behavior. Superordinate goals are long-term, and can include smaller focal goals, which are more short-term. They hypothesized that superordinate weight loss goals related to individual well-being would promote stronger adherence to an exercise plan as compared to smaller focal goals related only to only the physical body. Their findings were that those participants who set superordinate goals of well-being or quality of life had better adherence to an exercise plan than those who set focal goals relating to physical weight loss or disease prevention.
They attribute much of how individuals frame goals to socialization, “the process by which individuals learn what to value and pursue.” Consumers have been “socialized [by advertising] to consider exercise primarily for health-related and body-shaping benefits.” Cognitive preferences are developed when exercise is marketed primarily as a weight control solution. Such product branding changes individuals’ perceptions and expectations of exercise.
Similarly, wearable technology interfaces focus mainly on the physical body, instead of promoting well-being. Since superordinate goals reflect principles that individuals value, framing weight loss goals to fit in an individual’s larger objectives would increase adherence to a wearable device weight loss program. Goals that include well-being will increase user engagement, but only if the design continually promotes the user’s superordinate goals. Focal goals on wearable devices are much easier to frame because they can be quantified. For example, it is much easier for a device to report on weight loss progress by tracking weight through a Wi-Fi enabled scale than it is to evaluate quality of life, which includes all emotional, social, and physical aspects of the individual’s life.
Burke L.E., Wang J., Sevick, M.A.. (2011, January) Self-monitoring in weight loss: a systematic review of the literature. Journal of the Academy of Nutrition and Dietetics. Volume 111, Issue 1, Pages 92–102. http://dx.doi.org/10.1016/j.jada.2010.10.008
Gallup Poll, “U.S. Obesity Rate Inches Up to 27.7% in 2014.” (2014, January 26). http://www.gallup.com/poll/181271/obesity-rate-inches-2014.aspx
Gilboa, I., Schmeidler, D. (2001) A cognitive model of individual well-being. Social Choice and Welfare. Volume 18, Issue 2 , Pages 269–288. http://dx.doi.org/10.1007/s003550100103
Ogden, C., Carroll, M., Fryar, C., Flegal, K. (2015). Prevalence of Obesity Among Adults and Youth: United States, 2011 – 2014. NCHS Data Brief, No. 219. http://www.cdc.gov/nchs/data/databriefs/db219.htm
Sonnier, P. “Definition of Digital Health. The Story of Digital Health. 2015. http://storyofdigitalhealth.com/definition
Ledger, D., McCaffrey, D. (2014). How the Science of Human Behavior Change Offers the Secret to Long-Term Engagement.” Inside Wearables.http://endeavourpartners.net/assets/Endeavour-Partners-Wearables-and-the-Science-of-Human-Behavior-Change-Part-1-January-20141.pdf
Locke, E., Latham, G. (2006, October). New Directions in Goal-Setting Theory Current. Directions in Psychological Science. Volume 15, Pages 265–268, http://cdp.sagepub.com/content/15/5/265
Kenrick, D. T., Neuberg, S. L., Griskevicius, V., Becker, D. V., & Schaller, M. (2010). Goal-Driven Cognition and Functional Behavior: The Fundamental-Motives Framework. Current Directions in Psychological Science, 19(1), 63–67. http://doi.org/10.1177/0963721409359281
Yu, Z., Sealey-Potts, C., Rodriguez, J. (2015, December) Dietary Self-Monitoring in Weight Management: Current Evidence on Efficacy and Adherence, Journal of the Academy of Nutrition and Dietetics, Volume 115, Issue 12, Pages 1931-1933, 1934-1938. http://dx.doi.org/10.1016/j.jand.2015.04.005.