Abstract
Background:
Self-monitoring technology (e.g., smartphone applications) aids weight loss, but its role in weight maintenance remains under studied.
Purpose:
To evaluate use and perceptions of self-monitoring technologies in National Weight Control Registry (NWCR) participants (adults who have maintained a ≥30lbs loss for ≥1 year) who maintained versus regained weight.
Methods:
NWCR participants completed an online survey about self-monitoring technology use and perceptions. Of 1,000 invited participants, 794 completed the survey. Those who reported gaining ≥2.3kg (5lbs) in the past year were categorized as the “regain” group (40.8%); those reporting <2.3kg gain were the “maintain” group (59.2%).
Results:
The sample (n=794) was mostly female, White, middle-aged adults. “Regain” was more interested in technology than paper-based methods to self-monitor weight (p<.01) and diet (p<.01) but not exercise (p=.23) than “maintain”. There were no differences in wearable trackers interest, most valued features, or use barriers, but the “regain” group was more likely to report guilt, discouragement, body image concerns, and anxiety about weight loss when using behavior-tracking technologies (p<.001); rates of discontinuation from these feelings or unhealthy weight control practices were not different between groups.
Conclusions:
This appears to be the first study investigating naturalistic use of self-monitoring technology in a demographically homogenous sample maintaining significant weight loss. The “regain” group was more likely to use self-monitoring technology but reported more tracking-associated negative feelings. Future research must determine how to support individuals emotionally and with weight maintenance when self-monitoring contributes to negative byproducts. Other work should identify the optimal elements of self-monitoring technology for weight loss maintenance.
Keywords: obesity, weight loss, technology, weight trajectory
Self-monitoring is a crucial component of behavioral obesity treatments; it involves observing and recording health-related behaviors such as diet, exercise, and recording weight change (Burke et al., 2011). Individuals benefit from tools that make tracking behaviors easier and more sustainable: ample research has demonstrated that self-monitoring technologies are effective for changing health behaviors, like making healthier dietary choices and increasing physical activity, across populations with heart disease, diabetes, obesity, and other chronic illnesses (Doyle-Delgado & Chamberlain, 2020; Fakih El Khoury et al., 2019; Gandhi et al., 2017; Patel et al., 2021; Ringeval et al., 2020). Self-monitoring technology use, including wearables, mobile/web applications (“apps”), and websites, has increased dramatically, and many people use them to self-monitor during both weight loss and maintenance (Duggan, 2012; Fox & Duggan, 2013; Krebs & Duncan, 2015).
Previous studies have demonstrated the positive influence of continued self-monitoring on weight loss maintenance after substantial weight loss (Butryn et al., 2007; Laitner et al., 2016). Those who maintain formal notes (e.g., pencil-and-paper records, through a technology-based tool) report greater impact than those that track in their head or not at all (Fox & Duggan, 2013). The gradual weight regain that often follows weight loss typically coincides with reduced self-monitoring frequency (Goldstein et al., 2019; Loveman et al., 2011).
Given the relative ubiquity of weight regain after intentional weight loss, studying individuals who have successfully maintained a clinically significant weight loss and their self-monitoring behaviors may reveal strategies that could benefit others. The National Weight Control Registry (NWCR) is the largest registry worldwide of individuals maintaining weight loss with over 10,000 adult members who report on their behaviors and experiences annually for up to 10 years (Wing & Hill, 2001). These individuals have maintained a ≥13.6kg (30lb) weight loss for at least one year. Previous work in the NWCR (Goldstein et al., 2017) demonstrated that adults who are maintaining weight loss use self-monitoring technologies more than the Pew Tracking for Health Survey’s national sample (Fox & Duggan, 2013). However, NWCR participants were less likely to change behavior based on self-monitoring data than Pew participants (Goldstein et al., 2017), suggesting missed opportunities for weight regain prevention. This investigation treated the NWCR sample as a homogenous group, when at any given time NWCR members are working towards different goals (e.g., weight loss, continued maintenance) or experiencing steady or increasing body weight. Yet, individuals’ needs and uses for technology may vary depending on their health goal and weight trajectory. Given these technologies’ emergence, their prevalence among individuals maintaining weight loss, and the dearth of research about self-monitoring preferences and patterns, additional research may help identify how these technologies can best support long-term weight management.
Self-monitoring adherence diminishes over time for many reasons like decreased motivation (Fischer et al., 2020; Williams et al., 1996). These technologies may also engender distress for some, but this is rarely studied as a driver for decreased self-monitoring. Some work limited to undergraduate student samples found a relationship between calorie counting and young adults’ eating disorder symptomology (Hahn, Bauer, et al., 2021; Romano et al., 2018), but other studies conclude that calorie counting does not increase eating disorder risk or have other negative mental health effects (Hahn, Kaciroti, et al., 2021). Research about undesirable byproducts of self-monitoring technology use for tracking weight-related behaviors in individuals who have maintained weight loss is scarce.
Identifying self-monitoring preferences and patterns in a population attempting to maintain weight loss may glean insight into how these technologies can be used most effectively for weight maintenance and coping with regain (which likely presents different challenges than weight loss and represents a greater share of the literature); it can also inform design-related decisions for future self-monitoring technology. This study describes NWCR participants’ self-monitoring technology use and perceptions. Technology access, self-monitoring technology use, and self-monitoring technology perceptions were explored descriptively. Additionally, this study sought to compare use and perceptions of self-monitoring technology among NWCR enrollees who have maintained weight loss and those who are regaining weight. We hypothesized: most NWCR members would own a smartphone and report self-monitoring; individuals maintaining weight would be more likely to report self-monitoring weight, diet, and exercise; individuals experiencing regain would report more technology use barriers while those maintaining weight would endorse more helpful features; and individuals experiencing regain would report more negative emotional reactions to technology. The other analyses were exploratory without corresponding a priori hypotheses.
Method
Participants and Procedures
Adults ≥18 years of age may self-select to enroll in the NWCR if they have lost and maintained a ≥13.6kg (30lbs) weight loss for one year or more. Participants typically learn of the NWCR via reports in lay media and enroll online on the NWCR website. They complete surveys about weight control practices, overall health, and psychosocial functioning annually for 10 years, and they may opt to be contacted for additional one-time surveys on topics of special interest. NWCR participants are not compensated for their participation. For the present study conducted in September-October 2014, a random subset of 1,000 participants who had completed an annual survey in the last year were invited to complete an additional questionnaire on technology and weight management. Of the 1,000 invitations, 794 (79.4%) completed the survey. All procedures were approved by the Institutional Review Board of The Miriam Hospital.
Measures
Demographic Information and Weight History.
Participants provided demographic information (e.g., age, sex, education level), including weight history and current weight goals (maintaining or losing body weight) in a brief survey developed by the research team. Based on self-reported weight change in the last year, participants were assigned to the “maintain” group if they self-reported <2.3kg (5lbs) weight gain in the last year, or they were assigned to the “regain” group if they reported gaining ≥2.3kg in the last year (i.e., “Have you gained more than 5lbs over the past year?).
Technology Access.
Participants were asked to endorse (yes/no) whether they “access the internet on a cell phone, tablet or other mobile handheld device, at least occasionally.” This was used verbatim from the Pew Tracking for Health Survey, a phone-based survey of a nationally representative sample and their technology ownership, self-monitoring of their health (referred to therein as “health tracking”), and methods for self-monitoring (Fox & Duggan, 2013).
Self-monitoring Technology Use.
Participants were asked to report on their: intentions to keep track of weight, diet, exercise (response options: yes/no), preferred methods of self-monitoring (response options: smartphone application or online/paper-based diary/no method or in their head), and specific self-monitoring technologies used (response options of yes/no for each technology: smartphone apps, performance-based feedback tools [e.g., tools that provide narrative feedback on meeting or not meeting a weekly physical activity goal], wearable technologies, social media). The wording for survey questions evaluating intentions to keep track of weight, diet, and exercise and preferred methods of self-monitoring were taken verbatim from the Pew Tracking for Health Survey (Fox & Duggan, 2013). Weight, diet, and exercise were assessed because of relevance to the NWCR and because they were the most commonly tracked health indicators in the Pew Tracking for Health Survey (Fox & Duggan, 2013).
Perceptions of Self-monitoring Technologies.
Participants were asked to rate their interest in using self-monitoring technologies to manage weight, diet, and exercise on a Likert scale of 1 (very disinterested) to 5 (very interested) for each behavior. Helpful/enjoyable features of self-monitoring technologies were assessed by providing a list of 25 features (e.g., calorie tracking, step counting) and asking participants to select which they felt were most helpful/enjoyable. The number of features endorsed was calculated for each participant. Similarly, barriers to using self-monitoring technologies were assessed by providing a list of 8 potential barriers (e.g., expense, motivation) and asking participants to select which were true for them. The number of barriers endorsed was calculated for each participant. Lastly, negative experiences with self-monitoring technologies were assessed via an 8-item questionnaire created for this study that included items such as “Has using an app or online tracker ever made you feel guilty (e.g., while seeing the number of calories you’d consumed in a day)?” and “Has using an app or online tracker ever made you feel discouraged about your weight loss or maintenance?” Response options for all items were yes or no. Perceptions of self-monitoring technologies were evaluated using measures developed by the research team and adapted from the literature for the purposes of this study (Azar et al., 2013; Chudyk et al., 2011; Dennison et al., 2013; Doshi et al., 2003; Lister et al., 2014; Patrick et al., 2014).
Statistical Analysis
Analyses were completed using IBM SPSS Statistics for Windows, Version 24.0 (IBM Corp. Related 2016. Armonk NY: IBM Corp.). Descriptive statistics included means and standard deviations or counts and percentages to describe participant characteristics, technology access, self-monitoring technology use, and perceptions of self-monitoring technology across the sample. To evaluate the effect of weight status (i.e., “maintain” vs. “regain”) on self-monitoring technology use and perceptions, binary logistic regressions and generalized linear models were used on categorical (i.e., self-monitoring technology use variables, negative experiences with self-monitoring technology) and continuous variables (i.e., interest in self-monitoring technologies for weight, diet, and exercise, number of helpful/enjoyable features endorsed, number of barriers to use) respectively. To determine the relevant covariates for these analyses, chi-square tests and independent samples t-tests were used to compare the “maintain” and “regain” groups on categorical and continuous demographic variables respectively. Given the significant between-groups difference in age and employment status (see Table 1), all analyses included these variables as covariates. A significant between-groups difference in BMI (see Table 1) is expected to occur by design and BMI was therefore not added as a covariate.
Table 1.
Participant Characteristics
| Overall (n = 794) | “Regain” (n = 324) | “Maintain” (n = 470) | p-value | ||||
|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | ||
| Age | 54.2 | 11.3 | 52.5 | 11.3 | 55.4 | 11.2 | <.001** |
| BMI (kg/m 2 ) | 27.4 | 5.5 | 29.4 | 4.6 | 25.9 | 4.9 | <.001** |
| Number | % | Number | % | Number | % | ||
| Sex | .80 | ||||||
| Female | 612 | 77.1 | 252 | 77.8 | 360 | 76.6 | |
| Male | 172 | 21.6 | 69 | 21.3 | 103 | 21.9 | |
| Did Not Answer | 10 | 1.3 | 3 | 0.9 | 7 | 1.5 | |
| Race + | .66 | ||||||
| White | 754 | 94.9 | 309 | 95.4 | 445 | 94.7 | |
| Black or African American | 26 | 3.3 | 12 | 3.7 | 14 | 3.0 | |
| Asian or Pacific Islander | 9 | 1.1 | 3 | 0.9 | 6 | 1.3 | |
| Native American/American Indian |
9 | 1.1 | 5 | 1.5 | 4 | 0.9 | |
| Ethnicity | 0.95 | ||||||
| Not Hispanic or Latino | 779 | 98.1 | 318 | 98.1 | 461 | 98.1 | |
| Hispanic or Latino | 15 | 1.9 | 6 | 1.9 | 9 | 1.9 | |
| Education | 0.31 | ||||||
| Junior High School or Less | 1 | 0.1 | 1 | 0.3 | 0 | 0.0 | |
| Attended or Graduated from High School or Earned GED |
19 | 2.4 | 9 | 2.8 | 10 | 2.1 | |
| Vocational Training (beyond High School | 17 | 2.2 | 3 | 0.9 | 14 | 3.0 | |
| Some College but No Degree, Community College, Vocational School, or Associate’s Degree |
85 | 10.7 | 37 | 11.4 | 48 | 10.2 | |
| College or University Degree | 259 | 32.6 | 108 | 33.3 | 151 | 32.1 | |
| Graduate or Professional Education (e.g., MBA, MS, MA, PhD, MD, JD) |
413 | 52.0 | 166 | 51.3 | 247 | 52.6 | |
| Marital Status | 0.53 | ||||||
| Married | 556 | 70.1 | 224 | 69.1 | 332 | 70.7 | |
| Separated | 6 | 0.8 | 4 | 1.2 | 2 | 0.4 | |
| Divorced | 75 | 9.4 | 35 | 10.8 | 40 | 8.5 | |
| Widowed | 24 | 3.0 | 7 | 2.2 | 17 | 3.6 | |
| Never Married | 105 | 13.2 | 43 | 13.3 | 62 | 13.2 | |
| Living With a Partner (Not Married) | 28 | 3.5 | 11 | 3.4 | 17 | 3.6 | |
| Income | 0.11 | ||||||
| Under $50,000 | 98 | 12.3 | 48 | 14.8 | 50 | 10.6 | |
| $50,000-$99,999 | 240 | 30.2 | 99 | 30.6 | 141 | 30.0 | |
| $100,000 or Higher | 345 | 43.5 | 128 | 39.5 | 217 | 46.2 | |
| Prefer Not to Answer | 108 | 13.6 | 48 | 14.8 | 60 | 12.8 | |
| Do Not Know | 3 | 0.4 | 1 | 0.3 | 2 | 0.4 | |
| Employment Status | .03* | ||||||
| Employed Full-Time | 427 | 53.8 | 187 | 57.8 | 240 | 51.1 | |
| Employed Part-Time | 67 | 8.4 | 32 | 9.9 | 35 | 7.4 | |
| Self-Employed | 75 | 9.4 | 22 | 6.8 | 53 | 11.3 | |
| Not Currently Employed or Not Employed for Pay | 42 | 5.3 | 21 | 6.5 | 21 | 4.5 | |
| Retired | 177 | 22.3 | 59 | 18.2 | 118 | 25.1 | |
| Student | 6 | 0.8 | 3 | 0.9 | 3 | 0.6 | |
p<.05
p<.001
Select all that apply, frequencies may not add up to 100%
Results
Participant Characteristics
The sample was predominantly female, White, college-educated, non-Hispanic, and middle-aged (see Table 1). While all 794 participants who began the survey completed it, data were not available for all 794 participants at every question due to survey skip logic, “select all that apply” responses, and non-forced responses (e.g., gender, race). The total number of valid participant responses are therefore reported for each analysis below. More than half of the sample (59.2%; n=470/794) were classified as the weight “maintain” group based on self-reported weight gain in the last year of <2.3kg (5lbs), and 40.8% (n=324/794) were classified as the “regain” group based on self-reported weight gain of >2.3kg (5lbs) in the last year.
Technology Access and Self-monitoring Technology Use
Most of the sample (89.8%, n=712/793) indicated that they have access to the internet on a mobile phone, tablet, or other mobile handheld device at least occasionally. Most (80.3%, n=637/793) also reported owning a smartphone. Very few (1.5%, n=12/793) reported not having a mobile phone.
Overall, (93.1%, n=737/792) participants reported keeping track of their weight, diet, or exercise. More than half of the full sample reported having smartphone apps for self-monitoring or managing health (58.1%, n=454/781), and just under half reported having used performance-based feedback tools (47.1%, n=364/773). A small proportion endorsed using wearable technologies (26.6%, n=208/781) and social media for weight control (11.0%, n=86/780).
Among the subgroup that reported their goal was to lose weight (n=369), over half (56.1%, n=207) were using an app or online system, 19.2% (n=71) were using paper diaries, 24.4% (n=90) reported not using a self-monitoring system or tracking in their head, and 0.3% (n=1) did not respond to any choices. For those in the sample whose stated focus (current pursuit of weight maintenance versus loss) was weight loss maintenance (n=413), 30.9% (n=128) were using an app or online system, 21.1% (n=87) were using paper diaries, 46.0% (n=190) reported not using a self-monitoring system or tracking in their head, and 1.9% (n=8) did not respond to any choices.
Self-monitoring Technology Use: Comparing the Weight “Regain” Group to the Weight “Maintain” Group
See Table 2 for results of binary logistic regressions regarding the effect of researcher-calculated weight status on self-monitoring technology use. “Regain” was significantly less likely to report keeping track of weight, diet, or exercise (89.8%, n=291/324) when compared to “maintain” (95.3%, n=446/468). Results revealed that the odds of reporting that they keep track of weight, diet, or exercise was reduced by 56% among “regain” compared to “maintain.” However, “regain” was more likely to have smartphone apps for self-monitoring or managing health (65.4%, n=210/321) than “maintain” (53.0%, n=244/460), such that the “regain” group had 50% greater odds of reporting that they have apps for self-monitoring compared to “maintain.” “Regain” was also more likely to have used performance-based feedback tools (52.5%, n=166/316) compared to “maintain” (43.3%, n=198/457) but was not any more likely to rate the feedback from these tools as helpful (88.6%, n=140/158) compared to “maintain” (85.7%, n=162/189). Approximately one quarter of “regain” (28.3%, n=90/318) and “maintain” (25.5%, n=118/463) reported using wearable technologies, with no statistically significant differences between groups. A small proportion of “regain” (11.9%, n=38/318) and “maintain” 10.3%, n=48/462) reported using social media for weight control with no statistically significant differences between groups.
Table 2.
Odds Ratios (OR) and 95% Confidence Intervals (Cis) for “Maintain” v. “Regain” Groups on Self-Monitoring Technology Use +
| Self-monitoring technology use (n=number of valid responses) | B | SE B | Wald χ2 (df) | OR | 95% CI | p |
|---|---|---|---|---|---|---|
| Do you currently keep track of your own weight, diet, or exercise routine? (n=792) | −0.83 | 0.29 | 8.2(1) | 0.44 | [0.25, 0.77] | .004** |
| On your cell phone, do you have apps to track or manage your health? (n=781) | 0.38 | 0.16 | 5.7(1) | 1.5 | [1.1,2.0] | .01* |
| Have you ever used health tracking technology that provides performance-based feedback for longer than a week? (n=773) | 0.33 | 0.15 | 4.9(1) | 1.4 | [1.0, 1.9] | .03* |
| Was this feedback helpful? (n=347) | 0.39 | 0.33 | 1.4(1) | 1.48 | [0.77, 2.8] | .24 |
| Do you use a wearable monitor like a Fitbit? (n=781) | 0.07 | 0.17 | 0.17(1) | 1.1 | [0.77, 1.5] | .68 |
| Do you use social media (facebook, twitter, blogs, myspace) to lose or maintain weight? (n=780) | 0.10 | 0.24 | 0.19(1) | 1.1 | [0.7,1.8] | .65 |
Reference group: “maintain”
p<.05;
p<.01
Perceptions of Self-monitoring Technologies: Comparing the Weight “Regain” Group to the Weight “Maintain” Group
Helpful Features of Self-monitoring Technologies and Barriers to Use.
“Regain” was reported being more interested in using self-monitoring technologies to manage weight (M=3.70 out of 5), SD=1.43, n=317) compared to “maintain” (M=3.26 out of 5, SD=1.52, n=460), F(1,773)=9.97, p<.01, ηp2=.013. “Regain” also reported more interest in using self-monitoring technologies to manage their diet (M=3.60 of out 5, SD=1.43, n=316) than “maintain” (M=3.19 out of 5, SD=1.55, n=454), F(1,766)=8.74, p=<.01, ηp2=.011. There were no differences between “regain” (M=3.68 of of 5, SD=1.42, n=316) and “maintain” (M=3.45 out of 5, SD=1.52, n=460) in interest for self-monitoring technologies to manage exercise, F(1,772)=1.47, p=.23, ηp2=.002.
Participants were asked to endorse which features of self-monitoring technologies (of 25) were most helpful/enjoyable (see Table 3 for frequencies and percentages). On average, participants endorsed 5.53 (SD=3.48) features as helpful/enjoyable and there were no group differences in the number of helpful features endorsed by weight status (n=234 “regain” reported an average of 5.82 helpful features (SD=3.54) v. n=296 “maintain” reported an average of 5.29 helpful features (SD=3.41)), F(1,526)=2.63, p=.11, ηp2=.005. Similarly, participants were asked to endorse which barriers to accessing and/or using self-monitoring technologies (of 8) they have experienced (see Table 3 for frequencies and percentages of barriers endorsed; percentages are calculated out of the total respondents that endorsed that feature or reported that barrier (overall number column)). The most often endorsed helpful features were logging exercise (46.9%), calorie counters (44.0%), and progress graphs (41.1%). On average, each participant endorsed 1.11 (SD=1.21) barriers, and there were no group differences in the number of barriers endorsed by weight status (n=323 “regain” group members reported an average of 1.22 barriers (SD=1.19) v. n=467 “maintain” group members reported an average of 1.04 barriers (SD=1.22)), F(1,786)=3.46, p=.06, ηp2=.004. The most endorsed barriers were being time intensive (32.6%) and expense of technology (26.3%).
Table 3.
Helpful/Enjoyable Features Of, And Barriers to Using, Self-monitoring Technologies
| Overalla | “Regain”b | “Maintain”b | ||||
|---|---|---|---|---|---|---|
| Number | % | Number | % | Number | % | |
| Helpful/Enjoyable Features of Self-monitoring Technologies | ||||||
| Calorie Counter | 349 | 44.0 | 166 | 47.6 | 183 | 52.4 |
| Steps Counter | 218 | 27.5 | 94 | 43.1 | 124 | 56.9 |
| Logging Exercise | 372 | 46.9 | 168 | 45.2 | 204 | 54.8 |
| Wearable Compatible | 188 | 23.7 | 80 | 42.6 | 108 | 57.4 |
| Reminders/Alarms | 107 | 13.5 | 41 | 38.3 | 66 | 61.7 |
| Progress Graphs | 326 | 41.1 | 158 | 48.5 | 168 | 51.5 |
| Exercise/Calorie Recommendations | 101 | 12.7 | 49 | 48.5 | 52 | 51.5 |
| Record Goals | 186 | 23.4 | 96 | 51.6 | 90 | 48.4 |
| GPS Tracking | 221 | 27.8 | 100 | 45.2 | 121 | 54.8 |
| Record Water | 126 | 15.9 | 57 | 45.2 | 69 | 54.8 |
| Tracking Multiple Behaviors | 117 | 14.7 | 64 | 54.7 | 53 | 45.3 |
| Offered Prizes | 58 | 7.3 | 26 | 44.8 | 32 | 55.2 |
| Offered Health Info | 83 | 10.5 | 38 | 45.8 | 45 | 54.2 |
| Exercise Videos | 65 | 8.2 | 28 | 43.1 | 37 | 56.9 |
| Appointment Calendar | 19 | 2.4 | 12 | 63.2 | 7 | 36.8 |
| Latest Research News | 47 | 5.9 | 21 | 44.7 | 26 | 55.3 |
| Weather | 22 | 2.8 | 8 | 36.4 | 14 | 63.6 |
| Recorded Resting Heart Rate | 41 | 5.2 | 24 | 58.5 | 17 | 41.5 |
| Explore Nearby Healthy Things | 16 | 2.0 | 10 | 62.5 | 6 | 37.5 |
| Option for Status Updates | 24 | 3.0 | 16 | 66.7 | 8 | 33.3 |
| Posted to Social Media | 30 | 3.8 | 14 | 46.7 | 16 | 53.3 |
| Boosted Motivation | 85 | 10.7 | 46 | 54.1 | 39 | 45.9 |
| Hosted on Social Media | 24 | 3.0 | 8 | 33.3 | 16 | 66.7 |
| Recipes | 84 | 10.6 | 36 | 42.9 | 48 | 57.1 |
| Relaxation Exercises | 38 | 4.8 | 16 | 42.1 | 22 | 57.9 |
| Never used/Did not like any features | 146 | 18.4 | 47 | 32.2 | 99 | 67.8 |
| Barriers to Using Self-monitoring Technologies | ||||||
| Technology is Expensive | 209 | 26.3 | 101 | 48.3 | 108 | 51.7 |
| Difficulty to Learn | 71 | 8.9 | 24 | 33.8 | 47 | 66.2 |
| Too Many Options | 148 | 18.6 | 52 | 35.1 | 96 | 64.9 |
| Time Intensive | 259 | 32.6 | 111 | 42.9 | 148 | 57.1 |
| Uses Too Much of Data Plan | 28 | 3.5 | 10 | 35.7 | 18 | 64.3 |
| Low Motivation | 163 | 20.5 | 95 | 58.3 | 68 | 41.7 |
| Data Plans/Smartphone Too Expensive | 35 | 4.4 | 21 | 60.0 | 14 | 40.0 |
| Hard to Stick With | 72 | 9.1 | 37 | 51.4 | 35 | 48.6 |
Note: Data are ‘select all that apply’.
Percentages based on overall sample n=794
Percentages based on overall number of participants that endorsed that feature or barrier.
Potential Negative Impacts of Self-monitoring Technologies.
See Table 4 for results of binary logistic regressions regarding the effect of weight status on reports of negative experiences resulting from using self-monitoring technologies. The “Regain” group had 130% greater odds of reporting feeling guilty because of using self-monitoring technologies (58.6%, n=135/230) when compared to “maintain” (36.8%, n=110/299). “Regain” also had 50% greater odds of reporting feeling discouraged when using self-monitoring technologies (50.0%, n=115/230) compared to “maintain” (37.8%, n=113/299) and were 80% more likely to report poor body image concerns when using self-monitoring technologies (27.4%, n=63/230) compared to “maintain” (16.4%, n=49/299). Lastly, “regain” had 100% greater odds of reporting feeling anxious about weight control efforts from using self-monitoring technologies (46.9%, n=108/230) when compared to “maintain” (29.1%, n=87/299). There were no between-group differences on likelihood of engaging in unhealthy weight control practices such as binge eating (6.5% of “regain” endorsed [n=15/230] v. 4.6% of “maintain” endorsed [n=14/299]), restricting (13.9% of “regain” endorsed [n=32/230] v. 8.4% of “maintain” endorsed [n=25/299]), or over exercising (4.3% of “regain” endorsed [n=10/230] v. 3.0% of “maintain” endorsed [n=9/299]). Only 32.5% of “regain” reported discontinuing self-monitoring technologies due to negative feelings (n=64/197), compared to 25.9% (n=54/208) of “maintain” who discontinued self-monitoring technologies for the same reason, and this difference was not statistically significant.
Table 4.
Odds Ratios (OR) and 95% Confidence Intervals (CIs) for “Maintain” v. “Regain” on Negative Experiences Using Self-monitoring Technologiesa
| Self-reported negative experiences (n=number of valid responses) | B | SE B | Wald χ2 (df) | OR | 95% CI | p |
|---|---|---|---|---|---|---|
| Have health tracking technologies ever made you feel guilty (e.g., while seeing the number of calories consumed)? (n=529) | 0.84 | 0.19 | 19.5(1) | 2.3 | [1.6, 3.4] | <.001*** |
| Has using health tracking technologies ever made you exercise to the point that you hurt yourself? (n=529) | 0.45 | 0.48 | 0.88(1) | 1.6 | [0.62,4.0] | .35 |
| Has using health tracking technologies ever made you feel discouraged about your weight loss or maintenance? (n=529) | 0.43 | 0.19 | 5.4(1) | 1.5 | [1.1, 2.2] | .02* |
| Has using health tracking technologies ever prompted you to binge? (n=529) | 0.24 | 0.39 | 0.39(1) | 1.3 | [0.59, 2.7] | .53 |
| Has using health tracking technologies ever made you feel poorly about your body shape or size? (n=529) | 0.61 | 0.22 | 7.52(1) | 1.8 | [1.19, 2.8] | .006** |
| Has using health tracking technologies ever prompted you to restrict your calories below what was healthy for you? (n=529) | 0.55 | 0.29 | 3.5(1) | 1.7 | [0.9,3.1] | .06 |
| Has using health tracking technologies ever made you feel anxious about your weight control? (n=529) | 0.71 | .19 | 14.1(1) | 2.0 | [1.4,2.9] | <.001*** |
| Did these negative experiences cause you to use the health-tracker technology less? (n=529) | −0.29 | 0.22 | 1.75(1) | 0.74 | [0.48,1.2] | .19 |
Reference group: “maintain”
p<.05
p<.01
p<.001
Discussion
Most NWCR participants in this sample reported using self-monitoring tools naturalistically to reduce or maintain their body weight, regardless of whether they were classified as maintaining their weight loss (60%) or regaining weight (40%). Consistent with stated hypotheses, most participants owned smartphones (80.3%) and reported self-monitoring via any medium resulting in formal records (93.1%). However, one-quarter of those currently attempting to lose weight denied self-monitoring or only doing so in their head. This study points to the need for further investigation of barriers to self-monitoring among individuals who are regaining body weight. Given self-monitoring’s demonstrated effectiveness for weight loss, future research should evaluate the optimal time to prompt resumed self-monitoring during a lapse and how to make self-monitoring easy but effective.
As expected, the “maintain” group was more likely to report self-monitoring weight, diet, and exercise. Individuals experiencing regain reported greater likelihood of having apps for self-monitoring or managing health and using tools that deliver performance-based feedback (e.g., feedback on meeting a dietary goal), but they did not report the feedback as more helpful. Since performance-based feedback relies on accurate and complete data and only 67.4% indicated self-monitoring on a regular basis, individuals experiencing regain may require support for frequent, accurate self-monitoring to optimize automatically generated feedback. Further, the quality and delivery of feedback may have improved since the data were collected in part due to advances in microrandomized trials and personalization based on contextual factors (e.g., location and weather near the participant; Schembre et al., 2018). Receiving performance-based feedback from technology based on accurate and complete data and sharing it with a healthcare provider may improve the likelihood of success, but these points are not often stressed in weight loss maintenance. Individuals can be guided towards meaningful behavior changes based on their data and received feedback, but both require resultant behavior change to be maximally effective.
One primary goal was to understand the technological features that were valued or perceived as deleterious. It was anticipated that “maintain” would report more helpful features while individuals who are regaining weight would report more barriers to using technology for self-monitoring, but these hypotheses were not confirmed. Both found logging exercise and food and seeing progress graphs most helpful and agreed that technology being “time intensive” was the biggest barrier. This corroborates previous human computer/device interaction research, which demonstrated that poor user interface, difficulty adding information, being time intensive, and low motivation to self-monitor were the primary barriers to using a nutrition app for weight (Scerri et al., 2015). Adaptive intervention designs that can toggle features on/off based on participant response and progress (Almirall et al., 2014) may be helpful for self-monitoring in weight management; customization can allow participants to utilize helpful features, hide less preferred features, and change preferences amidst evolving needs as they progress towards their goals. Although previous research suggests that human computer/device interaction research can reduce barriers to using technology for weight management (Scerri et al., 2014), published research in this area as applied to biobehavioral clinical trials is limited. More research is available in user-centered design practices and weight management. One recent study by Asbjørnsen and colleagues found that individuals maintaining weight loss expressed preference for a combination of persuasive design principles (e.g., self-management, personalization, autonomy; Torning & Oinas-Kukkonen, 2009) and behavior change techniques based on Michie’s taxonomy (e.g., goals, planning, feedback, monitoring; Michie et al., 2013) in pursuing sustainable weight loss maintenance (Asbjørnsen et al., 2020). The underlying principles behind the present study’s most commonly endorsed helpful features (e.g., monitoring calories and exercise, personalization of progress graphs) are consistent with Asbjørnsen and colleagues’ findings. Ideally, future collaborations between health technology developers and behavioral scientists will identify features worthy of inclusion and upkeep, remove “dead weight” features that reduce effectiveness or usability and include design principles to encourage sustainability.
As expected, individuals experiencing regain reported more detrimental effects from self-monitoring tools; use in this group was more likely to be associated with guilt, discouragement, experiencing body image concerns when using technology, and feeling anxious about weight control efforts. Despite these adverse effects, they were not more likely to stop self-monitoring as a result: this is inconsistent with a recent systematic review of qualitative studies (Hartmann-Boyce et al., 2018) and findings from one weight loss trial (Tanenbaum et al., 2016). The negative emotions reported in the present study may not have been very strong, hence why individuals who were regaining weight were not deterred from using the technology, but the present investigation did not account for this. This research does not identify which parts of self-monitoring are distressing and which are critical for maintenance, nor does it assess the contextual factors that influence self-monitoring individual behaviors or overall weight maintenance (Butryn et al., 2020; Thomas et al., 2022). Future studies may investigate the intensity and impact of these experiences, as well as the causality of the relationship between emotional distress and self-monitoring. Despite these experiences, participants still reported self-monitoring benefits and denied unhealthy weight control practices. This highlights these tools’ potential and the need to optimize them. Moreover, while technology is ever evolving, tracking technology has remained evergreen since collection of these data. The questions about how individuals responded to self-monitoring technology continue to be applicable today regardless of specific devices used, technology features, and targeted behaviors. The relative consistency of self-monitoring technology confers broad generalizability of the findings and continued need for improvement.
This study is limited by the sample’s homogenous demographics. NWCR participants are often white, non-Hispanic/Latinx, and report high household incomes (Goldstein et al., 2017), but obesity disproportionately affects racial and ethnic minority populations (Flegal et al., 2016; Wang & Beydoun, 2007). If self-monitoring technology design is based on predominantly white, wealthy participants’ perceptions and preferences, then future technology may systematically underserve other potential users. Additionally, participants did not provide data on their total weight losses, regain, and comorbidities in this cross-sectional study; this would have enriched characterization of the sample. When asked about their preferred self-monitoring method, the question’s wording, which combined mentally self-monitoring or not self-monitoring at all into one response option, limits further contextualization. As noted above, the intensity of negative psychological effects of self-monitoring (e.g., guilt, poor body-image) was not assessed. In addition, rates of mental health concerns extending beyond technology use was not measured. Rates of negative impacts of using technology are not well known in the general public and thus cannot be compared. The sample may also suffer from self-selection bias.
Nevertheless, this study has important strengths. To our knowledge, this is the first study to investigate potential psychological and emotional impacts of self-monitoring technology in a sample using technology naturalistically to maintain clinically significant weight loss. It was also the first to evaluate perception of whether specific features help or harm weight loss or maintenance in individuals maintaining and regaining weight. By investigating the potentially distressing effects of these tools, as well as their barriers and facilitators, we can optimize self-monitoring technologies and programs.
Altogether, this work illuminates numerous areas for future research including increasing engagement with these tools for individuals experiencing regain. These tools could be tailored to alleviate some of the potential emotional distress of using self-monitoring technology and be further tailored to medical comorbidities, mental health status, substance use, and confounding life events that may affect weight maintenance and regain. Furthermore, prior research demonstrates that bolstering motivation to lose weight, increasing social support and coping capabilities, increasing self-efficacy and autonomy, and interventions that improve psychological strength and stability can reduce distress associated with weight gain and regain (Elfhag & Rössner, 2005). Support for developing these skills can be delivered via technology during maintenance. Also, although participants attributed feeling distressed to using self-monitoring technology, more work should evaluate if self-monitoring contributes to distress or if individuals experiencing negative outcomes look to technology for support. Alternatively, results of a recent Delphi study suggest that individuals may reduce their self-monitoring burden by only tracking less often consumed but higher calorie intake, self-weighing as the only self-monitoring strategy, or self-monitoring intake for fewer days each week (Krukowski et al., 2022). Individuals distressed by self-monitoring but find it to be key to their weight management could employ these strategies to mitigate iatrogenic effects. Individuals utilizing technology should be educated about how to ensure that their technology provides useful data and minimizes distress, how to self-evaluate positive and negative effects of technology use, and how to devise a plan for promoting wellbeing and self-monitoring across diverse samples.
Despite self-monitoring technology’s popularity, the findings from this cross-sectional survey of NWCR participants suggest that these tools can be improved. Self-monitoring technology continues to demonstrate immense promise as a cost-effective and widely available toolset that can aid individuals in making meaningful behavioral changes in the service of improving their health, but these tools must continue to be optimized to reach their full public health potential.
Acknowledgements:
The authors wish to acknowledge the members of the National Weight Control Registry for their time and commitment to the Registry.
Declarations:
This work was supported by NHLBI, NIGMS, and NIDDK. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. JGT also reports personal fees from Medifast, Inc. and Lumme Health Inc., and RRW reports serving on the Scientific Advisory Board of Noom Inc., outside the submitted work. These interests are unrelated to the science reported in this paper.
Funding Declaration:
The authors wish to acknowledge the funding sources that make their effort on this work possible: K23 HL136845 (CMG), K23 HL136845-05S1 (CMG), P20 GM139767 (CMG), R01 DK132210 (SPG), and R01 HL153543 (SPG).
Footnotes
Financial Interests: All authors declare that they have no financial interests.
Ethics Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Institutional Review Board of The Miriam Hospital.
Consent to Participate: Informed consent was obtained from all individual participants included in the study.
The other authors have no conflicts of interest to declare.
Publisher's Disclaimer: This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s41347-024-00448-0. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/openresearch/policies/accepted-manuscript-terms.
Data Availability:
Deidentified study data representing reported findings may be available on reasonable request from the corresponding author, CMG.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Deidentified study data representing reported findings may be available on reasonable request from the corresponding author, CMG.