Weight loss as a model for long-term behavior change with technology

Published 2020.8.18

The American healthcare system spends a lot of money to manage patients with chronic disease (including cardiovascular disease [hypertension, lipid disorders], mood disorders [depression, anxiety], diabetes, osteoarthritis, inflammatory joint disorders, COPD and asthma). Enabling individuals to facilitate behavior change to help manage chronic disease might have a broader application than developing new interventions within office or hospital settings. Any proposal to meaningfully reduce healthcare costs needs to include novel strategies to address chronic disease management. The surge in digital health monitoring apps seems to try to address this need. Weight loss might provide insights into how we can facilitate behavior change with or without technological aids.

The National Weight Control Registry is the largest prospective investigation of long-term success weight loss maintenance. The Registry recruits individuals at least 18 years of age and have lost at least 30 pounds for at least a year. Nearly all registry members are white (96%), many are female (80%), and a majority have a college degree (55%). About half lost the weight on their own. Nearly 90% used diet and exercise to lose weight. According to a 2005 publication by registry investigators, the single biggest predictor of risk of regain was how long participants had successfully maintained their weight loss. Participants who had fewer problems with disinhibition, a measure of periodic loss of control of eating, were more likely to maintain their weight loss over one year. Participants with lower levels of depression were more likely to keep their excess weight off. Those who regained weight reported

  • Significant decreases in physical activity
  • Increases in their percentage of calories from fat, and
  • Decreases in dietary restraint

Six general themes appear upon reviewing the participants’ experiences:

  1. Engaging in high levels of physical activity (about an hour a day of moderate-intensity activity [e.g., brisk walking])
  2. Eating a diet low in calories and fat (a more recent meta-analysis of 17 studies suggests that low-carbohydrate diets may be slightly more effective than low-fat diets at helping individuals lose weight)
  3. Eating breakfast
  4. Self-monitoring weight on a regular basis (44% weigh themselves daily and 31% weigh themselves at least once a week)
  5. Maintaining a consistent eating pattern (participants who reported a consistent diet across the week were 1.5 times more likely to maintain their weight within five pounds over the subsequent year than participants who dieted more strictly on weekdays), and
  6. Catching “slips” before they turn into larger regains

Even with the generalizability challenges of the National Weight Control Registry, I believe the focus on behavior change is worth exploring. In addition to committing to a series of behaviors to help keep excess weight off (an hour of exercise daily, eating breakfast, maintaining a consistent eating pattern every day), individuals who successfully lose weight appear to have a process to monitor their weight regularly and then address lapses before weight gain worsens. Extrapolating these ideas into a larger behavioral change model leads to:

One behavior change model
Adjusting behaviors over time

Every new behavior starts with a decision to make a change. If the individual has the knowledge, skills and attitude to adopt new behaviors, they will do so. As they gain more confidence in maintaining these behaviors, they may consider intensifying some behaviors to accelerate change. When a setback occurs, individuals need to learn new behaviors and/or adjust existing behaviors to either reduce the likelihood of the setback occurring again or mitigating the effects of the setback on the durability of the behavior change to achieve the intended goal.

Health information technology could be applied to

  • Measuring knowledge, skills and attitudes,
  • Identify realistic goals,
  • Select behaviors that increase the likelihood of reaching those goals (initially or after a setback),
  • Highlight others who may have been successful in the past or peers who are also looking to adopt a new behavior,
  • Track behavior adherence, and
  • Provide support after a setback

Unfortunately, the existing peer-reviewed literature has not found technology-assisted weight loss tools to be superior to usual care. One study of 471 adults with a BMI between 25 and 40 over two years showed that enhanced monitoring (wearable device with web interface to monitor diet and physical activity) was less effective than usual care (self-monitoring of diet and physical activity using a website). Although both groups lost weight, the usual care group lost more weight (difference 2.4 kg [95% confidence interval, 1.0-3.7 kg]). A more recent meta-analysis of 11 studies supports these findings. The meta-analysis noted 20% or more participants dropped out over the observation period for many of the included studies.

Our early attempts to use technology to support behavior change may not have accounted for the value of connectedness when sustaining engagement on social media. Individuals often use social media to connect with existing friends or look for like-minded people to share information. For health-related behavior change, learning how others have overcome obstacles or achieved a breakthrough may be more important than having others echo one’s own perceptions. Follower counts could be a proxy for “helpfulness,” different from a measure of weight loss or duration of that weight loss. To my knowledge, these social media characteristics have not been studied in much detail in commercial applications.

Using online social groups to promote specific actions may have unintended consequences. One risk of promoting unsupervised online communities is the possible nurturing of maladaptive behaviors. In the weight control arena, these undesirable behaviors include skipping meals, crash diets, purging, laxative and/or diuretic use and even smoking cigarettes. If individuals develop an obsession with reaching or maintaining a particular weight, that obsession may harm their health long-term. Others who are “successful” at losing weight may actually be promoting unhealthy behaviors. The only practice that appears to have some success addressing these unwanted behaviors is the presence of some type of moderator, but the supervision may drive individuals to connect in other ways that are not monitored.

Of all the health behaviors we can promote using technology, achieving a healthy weight might be the best studied. Unlike licit (tobacco, alcohol, marijuana) or illicit drug use, most Americans either have a loved one or they themselves struggle with excess weight. In many cases, losing weight appears to be an attitude and/or skill gap, not a knowledge one. Ideally, health information technology could help individuals lose weight by supporting new goals and habits. The lack of strong efficacy using technology tools to help people lose weight and keep the weight off suggests we need to consider ways to initiate and sustain engagement with online tools to help create and maintain healthy behaviors long-term.