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Accounting for how end users engage with technologies is imperative for designing an efficacious mobile behavioral intervention.
This mixed methods analysis examined the translational potential of user-centered design and basic behavioral science to inform the design of a new mobile intervention for obesity and binge eating.
A total of 22 adults (7/22, 32% non-Hispanic White; 8/22, 36% male) with self-reported obesity and recurrent binge eating (≥12 episodes in 3 months) who were interested in losing weight and reducing binge eating completed a prototyping design activity over 1 week. Leveraging evidence from behavioral economics on choice architecture, participants chose treatment strategies from 20 options (aligned with treatment targets composing a theoretical model of the relation between binge eating and weight) to demonstrate which strategies and treatment targets are relevant to end users. The process by which participants selected and implemented strategies and their change in outcomes were analyzed.
Although prompted to select one strategy, participants selected between 1 and 3 strategies, citing perceived achievability, helpfulness, or relevance as selection reasons. Over the week, all practiced a strategy at least once; 82% (18/22) struggled with implementation, and 23% (5/22) added a new strategy. Several themes emerged on successes and challenges with implementation, yielding design implications for supporting users in behavior change. In postexperiment reflections, 82% (18/22) indicated the strategy was helpful, and 86% (19/22) planned to continue use. One-week average within-subject changes in weight (–2.2 [SD –5.0] pounds) and binge eating (–1.6 [SD –1.8] episodes) indicated small clinical improvement.
Applying user-centered design and basic behavioral science yielded design insights to incorporate personalization through user choice with guidance, which may enhance engagement with and potential efficacy of digital health interventions.
Experimental therapeutics and the Science of Behavior Change program at the National Institutes of Health focus on measuring whether experimentally manipulated, hypothesized targets of an intervention lead to behavior change and improved clinical outcomes [
User-centered design provides a methodology for engaging deeply with end users about their needs, goals, and preferences to yield discoveries about the user experience and generate evidence for designing interventions [
However, the approach to date of simply translating an evidence-based face-to-face intervention to a digital format has not worked; the process relies on what clinicians think users need, the way in which in-person services are delivered does not align with how people engage with their phones [
This paper aims to demonstrate the application of user-centered design and basic behavioral science to inform the design of a new mobile behavioral intervention that addresses both obesity and recurrent binge eating, an eating disorder behavior characterized by eating a large amount of food while experiencing a sense of loss of control over eating [
Theoretical model depicting the relation between binge eating and weight gain. The model integrates treatment targets (white boxes) in evidence-based treatments for obesity or binge eating [
To this end, we applied user-centered design methods to understand how end users select strategies that could address the treatment targets and how they implement these strategies over 1 week. Although this brief period may be insufficient for an end user to determine if a strategy would work for them longer term, it allowed users to answer design questions about how to make strategies within an intervention relevant and engaging without devoting substantial resources to intervention development and deployment. To avoid the shortcomings of prior, overly prescriptive digital interventions, we had users choose their strategy from multiple options. Behavioral economics suggests that leveraging choice architecture, such as using active choice (ie, forced selection among relevant options), may improve engagement and facilitate behavior change [
Taken together, this study employed a mixed methods approach to understand how end users would select and implement a strategy and where they would struggle in the implementation process. Findings inform design implications to increase intervention engagement and, in turn, clinical impact. This work is an example of how user-centered design and basic behavioral science can be leveraged to inform the design of a mobile intervention within an overarching program of research to establish an evidence-based intervention for obesity and binge eating. Such efforts are imperative because few publications have documented the use of design methods to evaluate digital interventions for eating disorders [
Participants were recruited using dscout (dscout Inc), an online/mobile qualitative and market research platform. Although small for behavioral science research, this sample size is consistent with research in the field of human-computer interaction [
Interested individuals were invited to complete an online screening questionnaire in dscout titled “Struggles with eating and weight” that advertised a $100 reward and 25 openings for participation and gave a brief study overview. The 15 screening questions were developed for this study to confirm eligibility; demographic data (ie, age, gender, race, city of residence) were already captured in the profile of each dscout user. Eligible participants screened positive for obesity (BMI ≥30, based on self-reported height and weight) and self-reported recurrent binge eating (≥12 episodes in the past 3 months). For reporting weight, instructions stated, “What is your current weight? Please tell us this number based on when you measure your weight wearing light indoor clothing and without shoes.” For reporting binge eating, instructions stated: “Binge eating is when someone eats an unusually large amount of food and feels a sense of loss of control while eating.” These instructions were written to align with the definition of binge eating in the
This study was approved by the Northwestern University Institutional Review Board. All enrolled participants provided online informed consent. Of those eligible, all participants who were invited (n=25) began the study. Participation was ended early for 3 individuals who stopped completing study procedures. No reasons for discontinuation were provided. Only completers (n=22) were compensated, all of whom received the $100 compensation.
All study procedures occurred online via the diary study feature of dscout [
Schematic of participant flow through the study activities, including prototyping, the focus of this analysis, and the three research prompts it comprises. Of the 25 participants who enrolled, 22 began and completed prototyping.
An assessment guide was created that specified the research prompts that would be administered to evaluate user experiences. Questions were drafted by AKG with input from SAM, MR, DCM, and JEW. These individuals are researchers with expertise in the treatment of eating disorders and obesity, digital interventions, and/or user-centered design. AKG and SWN (an undergraduate student) then practiced answering the questions by submitting mock entries in dscout for internal testing prior to launch with participants. AKG oversaw study administration and data collection with participants. Study procedures and assessment items were consistent for all participants. After participants’ entries were submitted, SWN edited the transcribed videorecordings for accuracy and deidentification.
At the start of the week, participants were prompted to submit their first entry. In this entry, participants were asked to report their weight and number of binge eating episodes in the previous week and were then prompted to select one strategy from 20 options to practice for the week. The instructions did not indicate a limit to how many strategies participants could select. The strategies aligned with the putative intervention targets of the intervention’s theoretical model (
Over the next week, participants submitted 3 entries showing moments in which they practiced or were struggling to practice their strategy. For those struggling, participants were asked to share what was getting in the way. For each entry, participants submitted a video and an open-ended response describing the experience.
At the end of the week, participants were prompted to submit their final entry. They recorded a video reflecting on how the experience went, if it matched what they guessed would happen, and the evidence they collected on whether the strategy was helpful. Responses were coded as “planned,” “somewhat as planned,” and “not as planned.” Participants answered a yes/no question on whether they would continue using the strategy in the future and reported their weight and number of binge eating episodes over the past week.
Analyses focused on the process by which participants selected and implemented strategies and their change in outcomes. Qualitative analyses were conducted using Dedoose (SocioCultural Research Consultants), a qualitative data analysis software. Qualitative data from the baseline strategy selection process were coded separately from the weekly entries on implementation. Qualitative data were analyzed using thematic analysis based on the methodology of Braun and Clark [
A total of 22 participants completed all study procedures and were included in the analyses.
At screening, average BMI was 37.1 (SD 5.4, range 30.3 to 49.4), and average number of binge eating episodes over the previous 3 months was 20.5 (SD 7.3, range 12 to 35). All participants endorsed previous attempts to lose weight, and 91% (20/22) endorsed previous attempts to stop binge eating.
Study participant demographics.
ID | Sex | Age | Race/ethnicity | State of residence |
1 | Male | 42 | Asian or Pacific Islander | California |
3 | Female | 45 | African American/Black | Arizona |
4 | Female | 27 | Hispanic/Latino | California |
5 | Female | 35 | Hispanic/Latino | California |
6 | Male | 42 | Hispanic/Latino | Pennsylvania |
7 | Male | 43 | African American/Black | New York |
8 | Female | 43 | Prefer not to respond | South Carolina |
9 | Female | 45 | Hispanic/Latino | Illinois |
10 | Female | 36 | White | South Carolina |
11 | Male | 47 | White | Texas |
12 | Female | 62 | White | California |
13 | Male | 30 | White | Texas |
14 | Female | 39 | Asian or Pacific Islander | Illinois |
15 | Female | 20 | White | Illinois |
16 | Female | 43 | African American/Black | California |
19 | Male | 30 | Hispanic/Latino | California |
20 | Female | 30 | White | Virginia |
21 | Female | 22 | African American/Black | Illinois |
22 | Male | 39 | Hispanic/Latino | Florida |
23 | Female | 26 | White | Ohio |
24 | Female | 22 | African American/Black | North Carolina |
25 | Male | 45 | African American/Black | New Jersey |
Participants selected 15 unique strategies, shown in
Selection of strategies.
Putative intervention target and strategy | Times selected | Went as planned? | Helpfula % | ||||
|
|
Yes | Some | No |
|
||
Dietary intake | |||||||
|
Eat meals and snacks at the same time each day. | 2 | ✓ | ✓ | —b | 100 | |
|
Avoid eating snacks that you didn’t plan to eat. | 2 | ✓ | — | ✓ | 50 | |
|
Plan for the meals you’ll eat this week. | 6 | ✓ | ✓✓✓✓ | ✓ | 83 | |
|
Find a buddy who will help you eat more healthfully. | 2 | ✓✓ | — | — | 100 | |
|
Eat smaller portions. | 3 | ✓✓✓ | — | — | 100 | |
|
Eat more fruits and vegetables. | 2 | — | — | ✓✓ | 50 | |
|
Eat less fast food. | 2 | ✓ | ✓ | — | 100 | |
|
Total selections of this target | 19 | — | — | — | — | |
Physical activity | |||||||
|
Regularly (approximately 3 times per week) do physical activity like walking, riding a bike, or going to the gym (unless a doctor has said it is not appropriate/healthy for you to exercise right now). | 4 | ✓✓ | ✓✓ | — | 100 | |
|
Have less screen time: watch less television and spend less time on your computer, tablet, or phone. | 1 | ✓ | — | — | 100 | |
|
Find a buddy who will help you be more physically active. | 1 | — | ✓ | — | 100 | |
|
Total selections of this target | 6 | — | — | — | — | |
Overvaluation of weight and/or shape | |||||||
|
When you notice yourself criticizing something about your body, stop yourself. Ask yourself: What is the evidence that the criticism is true or not true? Then think of a more balanced conclusion you can draw about your body. | 0 | — | — | — | — | |
|
List things you like and value about yourself as a person. Remind yourself of things that are more important to you than how your body looks or how much you weigh. | 1 | ✓ | — | — | 100 | |
|
Avoid spending time in front of the mirror pointing out what you think of as your “flaws.” | 2 | ✓ | ✓ | — | 100 | |
|
Stop yourself when you dwell on “feeling fat.” Tell yourself that “fat” is not a feeling and instead say something to yourself that is not self-blaming or self-shaming. | 0 | — | — | — | — | |
|
Total selections of this target | 3 | — | — | — | — | |
Unhealthy weight control practices | |||||||
|
Avoid skipping meals or going for long stretches of time without eating. | 1 | — | ✓ | — | 100 | |
|
Avoid “dieting” and cutting out certain types of foods. | 0 | — | — | — | — | |
|
Try eating one serving of a food that you’ve been avoiding because you consider it a “trigger” food for binge eating. | 1 | — | ✓ | — | 0 | |
|
Total selections of this target | 2 | — | — | — | — | |
Negative affect | |||||||
|
Do activities that make you happy and do not involve food. | 0 | — | — | — | — | |
|
Notice times when you’re feeling down and find something that makes you feel a bit better about the situation. | 0 | — | — | — | — | |
|
Ask a friend or loved one to do something enjoyable together or repair a relationship in which you had a disagreement or falling out. | 1 | ✓ | — | — | 100 | |
|
Total selections of this target | 1 | — | — | — | — |
aIndicates the percentage of participants who rated their selected strategy as helpful to them.
bNot applicable.
Participants gave a variety of reasons for selecting versus not selecting strategies. Some participants indicated they selected a strategy because it was something they already were pursuing, whereas for others, this was the reason they did not select that strategy. Participants also indicated they selected strategies they perceived to be attainable and easy to complete or adjust to doing, that they perceived would be most helpful to them in managing their eating or weight, or that were new to them and therefore were perceived to be worth trying. Participants did not select strategies that they described as having been unhelpful in the past or that they believed would be unhelpful for achieving their goals around weight and binge eating. Some said certain strategies did not apply to them and thus would be challenging to achieve. Finally, participants did not select strategies because they thought they would fail in the implementation.
Participants who selected multiple strategies indicated their selections were motivated by a desire to capitalize on recent successes with those strategies or make progress with recently planned goals or because the strategies were perceived as serving a similar purpose. Two participants did not provide a rationale for why they selected multiple strategies.
While selecting strategies, all but 2 participants set a plan and/or identified ways to implement the strategy. Most participants (19/22, 86%) listed the benefits of maintaining a plan. Participants guessed their strategy would help them manage binge eating or weight (21/22, 95%), address eating-related triggers (17/22, 77%; eg, cravings, unplanned snacking, negative self-talk), or improve a related area (10/22, 45%; eg, increase self-esteem, happiness, or productivity; be more present with children; or improve their immune system).
Over the week, all participants reported they practiced implementing a strategy at least once; 82% (18/22) described moments in which they struggled with implementation. A total of 23% (5/22) of participants indicated they focused on a new or additional strategy. Participants changed strategies for 2 reasons. One reason was because they realized they would be unable to work on their originally selected choice (ie, scheduling difficulties prevented exercising with a buddy). The other reason was to address a more pressing and/or relevant problem area that had presented (eg, reduce binge eating by avoiding eating unplanned snacks, increase physical activity to avoid overeating, plan for meals and snacks to reduce overeating when very hungry, or eat less fast food after a recent increase in this behavior).
Participants experienced successes and challenges with implementation; overarching themes from these results are described here, and specific details for each strategy are detailed in
The ways in which participants were successful ranged from using individually focused techniques (eg, changing a routine) to collaborating (eg, using resources and others for support); participants used techniques that best fit their needs. Participants were successful when they planned in advance, including when to eat (eg, setting a schedule), what to eat (eg, meal planning, packing healthy snacks), or how much to eat (eg, ordering smaller portions). Participants described ways they avoided triggers and unhealthy behaviors, such as avoiding eating unplanned snacks, reducing stress, or doing alternative activities to avoid overeating or triggers for overeating (eg, engaging in physical activity, referencing a list of alternative responses in the face of triggers, or using flash cards with positive statements to combat negative thoughts about their body). A total of 18% (4/22) of participants described changing their routine to engage in healthy behaviors; 18% (4/22) practiced moderation with their strategy (eg, using smaller dishes, eating smaller amounts more frequently, or eating unhealthy snacks in moderation); 23% (5/22) leveraged resources (eg, social media, commercial entities like Weight Watchers) and other people for support; 9% (2/22) found new outlets for physical activity, like being active with pets or doing chores; and 23% (5/22) described ways they challenged negative thinking, engaged in positive self-talk, and practiced motivation-enhancing techniques to support progress. During the week, 9% of participants (2/22) reflected on the results of implementing their strategy.
Participants also described challenges that affected implementation. Participants faced challenges with integrating healthy behaviors into their schedules. They had difficulties practicing their strategy in unforeseen situations (eg, when away from home or in unplanned circumstances) and challenges with sufficient planning. A total of 32% (7/22) of participants reported challenges associated with their home environment (eg, others in the home do not support healthy eating, ordering fast food is more convenient than cooking, or being at home triggered binge eating); 27% (6/22) shared how preferences for other behaviors (eg, low desire to eat healthy foods despite planning to do so or using birthdays to justify unhealthy eating) and changes in motivation affected implementation; and 14% (3/22) described the impact of stress, low mood or energy, and financial difficulties on implementation. Finally, 23% (5/22) conveyed they misunderstood the strategy and how it should be applied.
In postimplementation reflections, 41% (9/22) said implementation went as planned, 41% (9/22) said it went somewhat as planned, and 18% (4/22) said it did not go as planned. A total of 82% (18/22) endorsed their strategy as helpful.
At the start of the week, participants reported an average weight of 225.9 (SD 34.6, range 162 to 307) pounds and engaged in an average of 3.4 (SD 2.1, range 0 to 8) binge eating episodes over the prior week. After implementation, participants reported an average weight of 223.7 (SD 36.8, range 159 to 320) pounds and average of 1.7 (SD 1.2, range 1 to 5) binge eating episodes. Average within-subject changes in weight and binge eating were –2.2 (SD –5.0, range –11 to 13) pounds and –1.6 (SD –1.8, range –6 to 1) episodes, respectively.
User-centered design has the potential to improve engagement with and efficacy of behavioral interventions [
In this prototyping activity, offering a choice in selecting strategies seemed to be successful given the variation of strategies participants selected. Although we did not compare active choice to no choice and therefore cannot conclude that active choice is better than assigning strategies to participants, evidence from behavioral economics shows that prompting people to make choices from several options (ie, active choice [
With that said, a second, complementary design implication is to reduce or scaffold the number of strategies presented. Research on decision making shows that offering too many choices leads to choice overload [
The third design implication is to define strategies when they are presented. Participants were not given definitions for the 20 options (to learn how users interpret the strategies) nor did they receive feedback as they shared their experiences throughout the week. As a result, seemingly straightforward strategies were interpreted in multiple ways. The variation in how participants interpreted strategies was particularly notable given that most selected strategies focused on the main intervention targets of behavioral weight loss treatment, changing dietary intake and physical activity, and 100% of participants endorsed prior weight loss attempts. This suggests that digital intervention designers cannot make assumptions about what and how much users know about eating and weight management; they need to educate users about strategies so they are positioned for success. Further, for nearly 60% of participants, implementation did not go as planned, likely because the strategies lacked specificity in how they should be implemented. Consequently, some participants abandoned their strategy or reported feelings of failure, disheartenment, and decreased willingness to practice that strategy again in the future—opposite the intention of offering choice to increase engagement. Based on these findings, an intervention architecture may need to include descriptions of what the strategy is, why the strategy is relevant to managing eating and weight, and how the strategy could be implemented. Presenting these details could help avoid misinterpretations, make unfamiliar strategies seem less daunting, and offer structure and scaffolding for their implementation.
However, delivering only instructional content on how to implement a strategy is likely insufficient. A challenge for technology-mediated services is moving users from qualitative, often distant goals to something concrete and actionable [
Guidance and support could be delivered through coaching and content or app designs that model how to implement strategies. Throughout implementation, timely feedback on progress would be helpful, too, as this is an important component of health-related behavior change strategies [
Much can be learned from this study in terms of applying user-centered design to drive progress for health-related behavioral interventions. This low-fidelity prototyping activity used qualitative and quantitative data to understand why and how users engage with aspects of an intervention—in this case, selecting and implementing strategies. The data collection platform and design methods enabled gathering in-the-moment perspectives from diverse participants who were matched to intended intervention users. End user perspectives were rapidly gathered with low participant burden, given that each entry required only a few minutes to complete and could be submitted from participants’ smartphones. Researcher burden was also minimized through the use of remote recruitment, remote data collection with multiple response types, and automatic video transcription. The design methods generated insights for intervention design without spending time or money developing a mobile intervention or creating high-fidelity prototypes. Further, these insights were gleaned from relatively few participants.
However, limitations should be noted. First, because procedures occurred remotely using an existing platform (ie, dscout), the research team was unable to ask clarifying or follow-up questions about participant entries, which may have limited the number and depth of insights generated. Second, the lack of definitions for each of the strategies may have influenced strategy selection and adherence and therefore generalizability and clinical relevance of the findings. Third, the study design makes it difficult to disentangle how participant improvements in the implementation process were affected by having to submit multiple entries about their progress, as longitudinal design research itself can affect behavior [
Results of this study highlight the translational potential of applying user-centered design and basic behavioral science to inform the design of a mobile behavioral intervention for obesity and binge eating. Discovering ways to make digital technologies relevant to end users is imperative to ensure these tools fit into the fabric of users’ lives and therefore are used in the moments and contexts when they are needed most. Such efforts can substantially improve engagement with and potential efficacy of digital health-related behavioral interventions.
Participant successes and challenges implementing each selected strategy.
This work was supported by grants K01 DK116925 and P50 MH115837 from the National Institutes of Health.
AKG reports a grant from the National Institute of Diabetes and Digestive and Kidney Diseases during the study and personal fees from Actualize Therapy outside the submitted work. SAM reports a grant from the National Institute of Mental Health during the study and personal fees from Apple Inc outside the submitted work. BS reports personal fees from Apple and Actigraph outside the submitted work. DCM reports personal fees from Otsuka Pharmaceuticals, Apple Inc, Pear Therapeutics, One Mind Foundation, and Adaptive Health Inc outside the submitted work. MR, SWN, EAG, AC, and JEW have no disclosures.