The Need for an mHealth Technology to Monitor Intake : The Bite Counter as a Case Study

Mobile health (mHealth) technologies are envisioned as self-monitoring tools of health behaviors (Kumar et al., Computer 46:28–35, 2013). They are meant to empower the individual to make sustainable behavior change that leads to better health. They are intended to be used long-term, with minimal to no supervision. This is in contrast to laboratory and clinical testing tools which are typically used shortterm by physicians and researchers under strict patient constraints to resolve urgent conditions. Because of the individual-empowered focus, mHealth technologies need to meet the following design criteria: low user burden; low-cost; and long-term usability under free-living conditions. mHealth technologies present an interesting opportunity because of the high quantity of inexpensive data generated, which is far, far greater than what is typically provided by sporadic and expensive laboratory tests. In this chapter, we discuss this opportunity in the context of the development of the Bite Counter. The Bite Counter uses sensors embedded into a watch-like device to automatically track wrist motion to count bites. The device provides the user intake feedback during a meal, allowing them to self-monitor intake anywhere and anytime. The behavior change goal is to reduce intake in a way that results in healthy weight loss. Key Questions for mHealth Technologies A mobile health (mHealth) technology is intended to afford individual selfmonitoring of a health behavior in such a way that empowers the individual to change their behavior and improve health outcomes [1]. In the development of mHealth technologies, three questions should be considered: (1) what health behavior is to be monitored; (2) what behavior is intended to be changed; and (3) what health outcome is targeted for improvement? In contrast to typical clinical E.R. Muth ( ) Department of Psychology, Clemson University, Clemson, SC, USA e-mail: muth@clemson.edu A. Hoover Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA e-mail: ahoover@g.clemson.edu © Springer International Publishing AG 2017 J.M. Rehg et al. (eds.), Mobile Health, DOI 10.1007/978-3-319-51394-2_6 101 102 E.R. Muth and A. Hoover tools, mHealth tools are intended to be inexpensive and available for long-term use by the individual with little supervision. Further, in traditional medical technology development, the behavior being monitored is usually a clinical measurement, e.g., blood pressure, and there is an obvious gold standard against which the developed tool is compared, e.g., the mercury sphygmomanometer. However, with mHealth technologies, a gold standard for long-term monitoring may not exist. This is because most clinical measurements are made over very short periods and sporadically, while mHealth technologies measure over long periods and often continuously. Further, traditional medical technology is developed to be used under controlled conditions by an expert. However, mHealth tools are often used in freeliving, constantly changing conditions, by laypersons. Therefore, the validity and accuracy standards for assessing the answers to the above three questions must be measured in light of the low-cost, long-term, low level of user expertise and use in uncontrolled conditions design criteria. This chapter discusses these design challenges in developing an mHealth technology to measure a health behavior using the Bite Counter as a case study. The Need for an mHealth Technology to Monitor Intake: The Bite Counter as a Case Study The World Health Organization reported that in 2008, 1.4 billion adults (age 20C) were overweight (BMI > 25) and 500 million adults were obese (BMI > 30) [2]. Obesity is a major risk factor for diabetes, heart disease, high blood pressure, stroke and cancer [3], and has become one of the largest preventable causes of death [4, 5]. Behavior change programs are still the most cost-effective treatment for individuals with BMIs <40 kg/m2 [6, 7]. More consistent self-monitoring of energy intake is associated with improved dietary adherence and weight loss and maintenance [8, 9]. Self-monitoring of energy intake and expenditure as well as knowledge of current body weight relative to a future body weight goal is required for successful regulation of body weight. The problem is that individuals are notoriously bad at self-monitoring their intake. One study found that individuals underestimate the caloric content of individual foods by an average of 17% [10]. Even with training and 20 weeks of practice using a calorie measurement technique, another study found the underestimation still averages 19% [11]. This underreporting bias has been found to range from 15 to 50% depending on the foods selected, and the measurement technique used [12, 13]. Furthermore, regardless of whether individuals use manual or cell phone based methods to selfmonitor their intake, monitoring adherence is often low [14] because users find the process to be burdensome and overly time consuming [15]. The Bite Counter (see Fig. 1) is a new mHealth tool for self-monitoring of intake. The wrist-worn device monitors intake by counting the number of times a person puts food or drink into their mouth, i.e., takes a bite. The device simply has to Designing Mobile Health Technologies for Self-Monitoring: The Bite Counter. 103 Fig. 1 The commercially available Bite Counter, now marketed as the ELMM (eat less, move more) watch; see: http://www.myelmm. com. (a) Bite Counter close-up. (b) Bite Counter in siteu be turned on at the start of eating and off at the end of eating. During eating it displays bite count for the current eating activity (EA) in real-time. Between meals, the device has a user review button which when pressed will display the bite count for the last EA and a total bite count for the day. A time-stamped log of bite count data is stored in memory for download to a PC to generate a calendar of EAs for longer term analysis of eating behavior. Hence, the device provides data for realtime self-monitoring of intake during a meal, daily intake self-monitoring, and long term analysis of week-to-week and month-to-month EAs. The behavior change goal is for the individual to use the bite count feedback to decrease the daily portion of food they are eating. This allows for behavior change to be targeted at the single meal, e.g., a cue to stop eating before overeating, as well as at longer term eating patterns, e.g., eliminating overeating on weekends compared to weekdays. Hence, for a modest upfront cost, the device has the potential to support sustainable intake self-monitoring with the goal of reducing intake through portion control, and improve health through weight loss. However, it is important to note that the story of the Bite Counter told in the above paragraph was developed over time, and is closer to the ending than the beginning. Design is an iterative process. Liedtka and Ogilvie [16] discuss design as answering a series of four questions: “what is, what if, what wows and what works? ” In the case of mHealth technologies, the what-is phase represents an assessment of the current state of the tools available for measuring the behavior of interest. The whatif phase is the phase of discovery where a variety of new solutions are considered, 104 E.R. Muth and A. Hoover prototyped and tested. The what-wows phase focuses on choosing a solution that will promote adoption by the potential customers. The what-works phase fully tests out a solution to see if it measures the intended behavior, if the solution can lead to behavior change, and if the behavior change leads to the intended health outcome. In the remainder of the chapter, we discuss our design journey in terms of these four phases. We use our journey to illustrate how mHealth tools can move from the bench to the breakfast table when informed by human factors design principles