Abstract
Background
There is little understanding of men’s weight loss outcomes and behaviors in self-directed contexts, such as digital commercial mobile weight management programs. This is an especially pressing question given that men often express disinterest in weight management programs and it is unknown how that manifests in self-directed environments. Aims. Two studies fill this gap by retrospectively observing how men lose weight and engage in weight loss behaviors (Study 1) and their perceptions of improvements and gained knowledge (Study 2) when participating in the full length of a commercial mobile behavior change program called Noom.
Method
In Study 1, repeated-measures linear mixed modeling was used to examine whether weight loss was statistically significant from baseline to 16 weeks and how engagement behaviors predicted weight in a sample of 7,495 male Noom users. In Study 2, 971 male Noom users completed an exploratory survey on the impact of the behavior change education in the program.
Results
In Study 1, men who remained in the full length of the program lost statistically significant weight from baseline to 16 weeks. 63% achieved clinically meaningful (5% or more) weight loss. Engagement in weight loss behaviors on the program predicted the amount of weight lost. In Study 2, men reported learning most about practical application and psychological aspects relating to food and psychology.
Discussion and Conclusion
This is the first study to observe men’s weight loss outcomes, behaviors, and perceptions of what they learned in a self-directed behavior change program. Our findings have important implications for more effective health promotion for the many men who choose to self-direct their weight loss.
Approximately 35% of adult men in the United States are affected by obesity (Ogden et al., 2014). Behavioral lifestyle modification resulting in modest weight loss may significantly reduce risk of weight-related health complications in men (Blumenthal et al., 2000; Franklin et al., 2020: Wadden et al., 2020; Wadden & Foster, 2000). It is estimated that the vast majority of individuals attempt to lose weight either on their own or in commercial programs (Brownell & Rodin, 1994; Stubbs, Whybrow, et al., 2011). Most previous work on men’s weight loss, however, has been in university or clinic settings. An important distinction may be that unlike clinical or research settings or in-person commercial programs, mobile commercial programs are self-directed; individuals direct their enrollment and engagement in their own homes at their own convenience (Gudzune et al., 2015; Hwang et al., 2013; Nikolaou & Lean, 2017). There is a paucity of research on men’s weight loss outcomes, behaviors, and attitudes in self-directed programs.
Outside of self-directed programs, there are mixed findings on men’s weight loss. On one hand, men are underrepresented in both commercial programs and clinical trials (Bye et al., 2005; Pagoto et al., 2012; Robertson et al., 2014). This could be due to socially constructed ideas of “masculinity” (Addis & Mahalik, 2003; Courtenay, 2000; Evans et al., 2011; Gough, 2018; Gough & Conner, 2006). While there is increasing recognition that there is not one type of masculinity, there is an idealized form of masculinity in Western contexts (“hegemonic masculinity”) that values traits like independence, autonomy, and rationality (Evans et al., 2011; Gough, 2018; Gough & Conner, 2006). Because of these values, men tend to believe that their bodies are strong and do not require help nor should be controlled, whether from clinicians or from engaging in healthy behaviors (Courtenay, 2000). Therefore, due to hegemonic masculinities, men can be drawn toward health-averse behaviors such as eating unhealthy food and away from healthy behaviors (Addis & Mahalik, 2003; Campos et al., 2020; Connell, 1995; Connell & Messerschmidt, 2005; Courtenay, 2000; de Visser & McDonnell, 2013; Gough & Connor, 2006; Kruger et al., 2004; Wardle et al., 2004). They tend to distance themselves from weight control and modifying food behaviors more specifically (Elliott et al., 2020; Kiefer et al., 2005; Sloan et al., 2010). They view in-person weight loss programs as “feminine” spaces, and tend to feel neglected by service providers more broadly (Budden et al., 2020; Elliott et al., 2020; Hunt et al., 2014; Kirwan et al., 2013; Monaem et al., 2007).
On the other hand, there is evidence of substantial male engagement and weight loss in in-person clinical trials as well as in commercial programs (Bye et al. 2005; Crane et al., 2018; Johnson & Wardle, 2011; Robertson et al., 2014; Rounds & Harvey 2019; Stubbs, Pallister, et al., 2011). Previous work has even found that men lost more weight than women and had similar engagement in these programs (Elliott et al., 2020; Stubbs et al. 2015; Stubbs, Pallister, et al., 2011). Researchers have reconciled these conflicting streams of evidence by concluding that even if men are less likely to join a program, once they join, they actively engage in weight loss behaviors (Robertson et al., 2014; Rounds & Harvey, 2019).
However, it is unclear if these findings would apply to self-directed commercial programs, since managing one’s own participation could be a different experience than formal study settings. In-person assessments, clear minimum participation requirements, and other aspects of clinical or in-person settings could influence weight loss behaviors in different ways than in self-directed environments (Hampl et al., 2011; Mallyon et al., 2010; MacNeill et al., 2016; McCambridge et al., 2014; Muñoz et al., 2017). This is a particularly important question for men because many studies highlight their lack of interest in engaging in weight loss behaviors (Ahlgren et al., 2016; Gray et al., 2011; Jeffery et al., 2004; Monaghan, 2007). There is little, if any, current understanding of men’s weight loss and engagement on a weight loss program in a self-directed context; this could inform future health promotion efforts for men. To our knowledge, there is only one prior study of a self-directed program, which found that male subscribers of a digital commercial program who actively logged weight at least twice in 28 days showed significant weight loss and engagement (Johnson & Wardle, 2011). However, this study investigated 642 men from 2005 to 2008 and the program focused on food and exercise diaries, rather than education and support surrounding behavior change. Therefore, in two studies, we examined men’s weight loss, engagement, and attitudes in a self-directed commercial behavior change program using a large sample and more recent observations.
Study 1
Study 1 observes men’s weight loss outcomes and behaviors in a self-directed mobile behavior change program from baseline to the end of the program (16 weeks) in a single-arm design. We conducted a retrospective analysis by extracting data from the program database after 16 weeks. We focused on men who remained in the entire length of the program to explore their weight loss and engagement even after they have committed to remain in a self-directed program.
Method
Intervention
Noom is a behavior change intervention that has been found to effectively aid in clinically significant weight reduction (Michaelides et al., 2016; Toro-Ramos et al., 2020). The Noom program is based on cognitive behavioral therapy, motivational interviewing, and behavior change techniques, which all have demonstrated effects on weight reduction (Alimoradi et al., 2016; Armstrong et al., 2011; Michie et al., 2013). The program provides daily articles; food, exercise, and weight logging features; and a virtual one on one coach and group, all of which are well-validated components (Khaylis et al., 2010; Kim et al., 2017). The articles cover topics on eating, exercise, weight, and behavior change. Coaches are trained on CBT and motivational interviewing techniques to support users and set health goals (Kim et al., 2020).
Noom has additional features that previous work suggests would bolster men’s weight loss experiences. Noom has a color scheme categorizing food options by caloric density, encouraging a set proportion of green (low caloric density), yellow (medium caloric density), and red (high caloric density) foods. This aligns with work showing that men prefer having autonomy over their food choices and not having to fully restrict their foods during weight loss (Archibald et al., 2015; Sabinsky et al., 2007). Noom’s individualized coaching allows men to set their own goals, which aligns with men’s preferences for increased autonomy and individualized weight loss programs (Kim et al., 2020; Sabinsky et al., 2007).
Users are not given any specific requirements to remain in the program. They are encouraged to read an article and log their food daily, as well as weigh in and message their coaches weekly, but can do as little or as much as they prefer. Users also do not typically receive push notifications based on lack of engagement. If users opt in for push notifications, they are reminded to log a meal at their preferred frequency and notified when there is a new coach or group message. Articles cover a variety of topics, which allows users to focus on specific program aspects or individualized needs. For example, young men who are more focused on energy drinks and building muscle can talk to their coach about muscle composition goals and barriers to their workouts; log their protein and energy drinks; or focus on the articles that are about workouts, strength training, muscle, and protein.
Participants
Participants were users who had voluntarily signed up for Noom. During program sign-up, all participants had provided informed consent for their deidentified data or follow-up survey responses to be analyzed in research and were given the opportunity to opt out. This study and consent process received prior institutional review board approval. First, data for participants meeting inclusion criteria were extracted from Noom’s database and deidentified. Eligible participants: (1) remained in the program for 16 weeks, defined as one in-program interaction per week for 16 weeks, (2) were male, (3) were located in the United States, (4) signed up during January 2018 to June 2019, and (5) weighed in at least once a week to ensure complete data for modeling. There were 11,569 participants who were initially eligible. Participants were excluded from analysis if they had one or more of the following criteria: (1) they were above 65 years old, (2) their baseline body mass index (BMI) was underweight (<18 kg/m2) or healthy (18.5–24.9 kg/m2), (3) they did not report their height and/or (4) their BMI changed more than 3.5 units within 1 month (Jacobs et al., 2017). This led to a final sample of 7,495 men (64.8% of eligible participants; see Figure 1).
Flowchart of inclusion in Study 1 and Study 2.
Measures
The primary outcome was participants’ self-reported weight each week (Weeks 1–16). Age and gender were self-reported at program sign-up. Two types of engagement scores were created (DeLuca et al., 2020). First, to create individual variables of engagement, frequency counts were calculated for 16 weeks. The following engagement variables, which have been found to predict weight loss, were measured: the total number of self-logged eating occasions and exercises, and recorded steps, messages sent to the coach, articles read, and days with one weigh-in each week. Eating occasions were defined as an instance in which at least one food was logged. Steps were measured both by automatic recording through smartphone sensors and/or wearable devices, as well as manual input by participants. A composite engagement measure was also calculated. Each engagement variable was given a score of 0 or 1, with 1 constituting meeting or exceeding the 75% percentile for that variable for the week. For example, if a participant sent more messages to the coach than 75% of the sample, he received a score of 1 for coach messages. The dichotomized variables were summed to create a composite engagement measure (0-low engagement to 6-highest engagement) each week, over a total of 16 weeks.
Statistical Analysis
Descriptive statistics are expressed in means and standard deviations. Analyses of variance were used to assess whether engagement significantly differed across three categories of weight loss: meaningful, moderate, and no weight loss. Participants who had achieved clinically meaningful weight loss (National Heart, Lung, and Blood Institute, National Institutes of Health, 2013) were classified into the “meaningful weight loss” group (at least 5%). The “moderate weight loss” group consisted of participants who had achieved some weight loss but below clinical standards (above 0% to below 5%). All other participants were classified into the “no weight loss group.”
Linear mixed modeling was used to evaluate significant changes in weight from baseline to 16 weeks, as well as the impact of engagement on weight. Linear mixed models provide robustness to bias compared with other methods for repeated measurements (Brauer & Curtin, 2018). Time, entered as a continuous variable, was a fixed effect to evaluate weight over time. Time and the intercept for each participant were random effects. First, time, the composite engagement measure, and their interaction were added in the initial model construction. Next, age and baseline BMI were added to the model. Significance tests were two-sided, except for a one-sided goodness-of-fit test, with an α of .05.
Results
Demographics and Baseline Characteristics
Participant demographics are displayed in Table 1. The sample’s average baseline BMI was 31.23 kg/m2 (SD = 4.82), with an average baseline weight of 111.59 kg (SD = 18.36). The mean age was 49.34 (SD = 10.16). Weight loss groups did not significantly differ in age, F(1, 7494) = 0.02, p = .88 or height, F(1, 7494) = 0.29, p = .59, but only in baseline BMI, F(1, 7494) = 6.47, p = .011, and baseline weight, F(1, 7494) = 7.27, p = .007.
Descriptive Statistics of Participant Characteristics.
Note. BMI = body mass index.
Positive values indicate greater weight loss.
Weight Over Time
By Week 16, weight declined to an average of 104.75 kg (SD = 18.17) and BMI to an average of 29.12 kg/m2 (SD = 4.81 kg/m2). Average weight loss was 7.69 kg (SD = 5.34), which constituted 6.94% body weight loss; 63% of participants achieved at least 5% body weight loss.
Linear mixed model results showed time to be a significant predictor of weight, Table 2, b = −.44, p < .001. On average, participants lost .44 kg each week over the 16 weeks.
Summary of Linear Mixed Model Results.
Note. BMI = body mass index.
Engagement
The interaction of time and engagement was significant, b = −.04, t(7493) = −49.82, p < .001. Over the 16 weeks, higher engagement predicted greater weight loss, where a one-unit increase in engagement was associated with a 0.04 kg decrease in weight over time.
There were significant differences across weight loss groups for every engagement variable: eating occasions logged, weigh-ins, articles read, coach messages, exercises logged, and recorded steps (p < .001; see Table 3). Post hoc tests revealed that all groups significantly differed from each other on all engagement variables except one: coach messages for the moderate weight loss group compared with the no weight loss group became not significant when adjusted for multiple comparisons using the Bonferroni correction.
Summary of Engagement Indicators Between Weight Loss Groups.
Note. Medians are displayed, with interquartile ranges (IQR) in parentheses. Shared subscripts represent statistically significant differences below p < .05, adjusting for multiple comparisons with Bonferroni correction.
Study 2
The results of Study 1 raise the question of how men who remained in the program perceived of program benefits, what they learned, and what they found to be most useful. Given a lack of prior research on men who use self-directed programs, in Study 2, we used an exploratory survey to examine men’s perceptions and whether they differ by weight loss outcome. We use the framework of hegemonic masculinity to contextualize this study. It is important to note that men are not a homogeneous group; their constructions of masculinity are shaped by a variety of sociodemographic factors, which create and reflect disparities in outcomes (Bridges & Pascoe, 2014; Galdas, 2009; Thorpe et al., 2015). However, we focus here on hegemonic masculinity given that it likely influences the Western, middle-aged users on this type of program (Evans et al., 2011; Mitchell et al., 2021). It is unclear what men would find most effective in a self-directed commercial program. On one hand, men with constructions of hegemonic masculinities are less likely to seek out information on healthy behavior or to find it helpful (Courtenay, 2000; Evans et al., 2011; Robinson & Robertson, 2010). They are also more likely to report lacking knowledge or interest in modifying their eating behaviors in healthier ways (Carroll et al., 2019; Gough & Conner, 2006; Maclean et al., 2014; Sabinsky et al., 2007; Sloan et al., 2010). Therefore, men may not find food-related information or components to be helpful, particularly if the focus is on modifying their eating behaviors.
Alternatively, the self-directed nature of the program may mean that men who participate in the full length of the program are interested in most relevant behaviors and topics, including food. Previous investigations have found that Western men who engage in weight management-related behaviors justify them ways that align with hegemonic masculinity (Carroll et al., 2019; Sloan et al., 2010). Therefore, men on this program might actually value learning about food consumption, preparation, and monitoring, given shifts toward such behaviors documented in recent years (Carroll et al., 2019; Elliott, 2019; MacLean et al., 2014; Newcombe et al., 2012; Sloan et al., 2010). Additionally, despite traditional hegemonic constructions of men as rational and unemotional, men might find psychological information to be useful, as it could help them exert control of their behavior and emotions (Lefkowich et al., 2017; Sloan et al., 2010).
Method
Participants
Of the 7,495 men in Study 1, 7,055 had opted in to receive emails from the Noom organization. An invitation to participate in this survey study was sent by email to each of these 7,055 men after they had completed 16 weeks of the program. The survey was online and self-administered. This study received prior IRB approval. As part of the approved process, along with providing consent in Study 1, participants were informed that their participation was voluntary and had the option to opt out at any time of Study 2. Participants were given the chance to win a $200 Amazon gift card for their participation. Participation was confidential and all data were deidentified prior to data analysis. Full survey measures are included in the Supplemental Material.
Measures
Demographics and perceptions of improvement
Marital status, education level, and ethnicity were measured, as well as perceptions of areas of improvement in their life as a result of losing weight, satisfaction with their weight loss, control over eating, body positivity, and perceived energy.
Perceptions of intervention
Measures included the most important tool or skill used on the program for healthy eating (closed-ended) and knowledge or strategies learned from the program (open-ended).
Content Analysis
Content analysis was employed for the open-ended responses to create categories and calculate frequencies for each category (Kondracki et al., 2002). Since it was unclear a priori what participants would report in the open-ended question, a multistep process was used (Bankauskaite & Saarelma, 2003). First, the open-ended responses were first read in full by a coder blind to the study’s research questions. Then, the coder created initial categories for the overall topic of the response. From the initial categories, two specific categories were formed: the content of the knowledge or strategies (Psychological aspects, such as mindfulness; Practical application, such as portion control or calorie density, Habit formation, and Other) and the domain of the knowledge or strategies (Food/eating, Weight, Cognitive/psychology, Exercise, General health, and Other). This was done to better distinguish between the domain and content (e.g., psychological aspects about food vs. psychological aspects about weight). All responses were coded by the same coder blind to research questions into these two categories such that every response had two codes (one for content and one for topic). A second coder coded 10% of the responses for calculation of interrater reliability. Cohen’s kappa was .73 for content and .62 for topic, indicating acceptable interrater reliability (Landis & Koch, 1977). Example responses are displayed in Table 4.
Example Responses of Knowledge and Strategies Learned From Noom.
Note. Each response was given two codes, one content code and one topic code.
Statistical Analysis
Descriptive statistics were conducted using means, frequencies, percentages, and standard deviations. To evaluate whether responses differed by weight loss, Fisher’s exact tests were used for questions with categorical response options and one-way analyses of variance were used for questions with numerical response options. Categories of weight loss (meaningful, moderate, and no weight loss) were calculated using the same method as Study 1.
Results
Survey Completion
Survey completion rates were significantly different across weight loss groups. Indeed, 15.1% of the meaningful weight loss group, 9.1% of the moderate weight loss group, and 11.3% of the no weight loss group completed the survey, χ2(2) = 50.81, p < .001. A total of 971 men (13.76%) completed the survey and were included in the analyses below.
Demographics
Demographic characteristics across weight loss groups are displayed in Table 5. The majority of men were Caucasian, married, and had a 4-year degree. The average age was 51.70. There were no significant differences in marital status or age across weight loss groups. However, there were significant differences in education level (p = .008) and ethnicity (p = .005), with more participants who lost moderate or meaningful weight by program end reporting higher education and Caucasian ethnicity.
Demographic Characteristics and Perceptions Across Weight Loss Groups.
Note. Frequencies with corresponding percentages are displayed, unless otherwise noted, in which means with standard deviations in parentheses are displayed. Shared subscripts represent statistically significant differences between groups for continuous variables below p < .05, adjusting for multiple comparisons with Bonferroni correction.
Perceptions of Improvement
Differences across weight loss groups in perceptions of improvement as a result of weight loss are displayed in Table 5. Distributions differed across weight loss groups (p = .01). The most prevalent responses varied across groups. They were “more energy to do things” (32.8%, 31.8%) and “better body image” (14.4%, 13.5%) for the meaningful weight loss and moderate weight loss groups, but “more energy to do things” (31.6%) and “better life quality” (15.8%) for the no weight loss group.
The groups significantly differed in other perceptions of improvement. The highest cognitive restraint emerged in the meaningful weight loss group (M = 6.11, SD = 1.63), compared with the moderate weight loss group and to the no weight loss group, M = 5.65, SD = 1.74; t(353) = 3.50, p = .0005; M = 5.37, SD = 1.68; t(41) = 2.65, p = .01; F(1, 969) = 17.77, p < .001. The moderate weight loss and no weight loss groups did not differ, t(51) = 0.94, p = .35. Similarly, perceived energy, positive feelings toward one’s body, and satisfaction with one’s weight loss significantly differed across weight loss groups, F(1, 969) = 17.77, p < .001; F(1, 969) = 46.07, p < .001; F(1,969) = 160.5, p < .001. As with cognitive restraint, the meaningful weight loss and moderate weight loss groups differed from each other, and so did the meaningful weight loss and no weight loss groups, perceived energy: t(324) = 5.21, p <. 001; t(41)=2.65, p = .01; body positivity: t(329)=7.49, p < .001; t(39)=3.63, p < .001; satisfaction with weight loss: t(329)=7.49, p < .001; t(39)=5.50, p < .001. There were no significant differences between the moderate and no weight loss groups.
Perceptions of the Intervention
Most important component used for healthy eating
The distribution of responses pertaining to the most important tool or skill used with the program for healthy eating marginally significantly differed across weight loss groups (see Table 5, p = .054). Unlike perceptions of improvement, the most prevalent responses were similar across all groups. The calorie budget (meaningful: 39.3%, moderate: 30.0%, no weight loss: 36.8%), “had the freedom to choose what I wanted to eat” (18.4%, 18.4%, 13.2%), and the green/yellow/red food color scheme (16.9%, 16.1%, 28.9%) were the three most prevalent responses in all three weight loss groups.
Knowledge and strategies learned
The distributions of both the content and topic of knowledge and strategies mentioned significantly differed across weight loss groups (p = .002; p = .03; Table 5). Though the relative distributions differed, the most prevalent types of knowledge and strategies were similar across groups. The most prevalent types of content across all groups were practical application (meaningful: 53.9%, moderate: 42.2%, no weight loss: 47.4%) and psychological aspects (28%, 32.7%, and 34.2%). The domain of food/eating was mentioned most (71.4%, 66.8%, and 68.4%), followed by cognitive/psychology (13.5%, 11.2%, and 13.2%).
Overall Discussion
The bulk of understanding of men’s weight loss is derived from clinical trials or studies that involve in-person assessments. However, many men lose weight in a self-directed manner, but we have limited understanding of this process (Stubbs, Whybrow, et al., 2011). These two studies provide important understanding of men’s weight loss behaviors and outcomes in a large sample of men enrolled in a mobile self-directed program.
In Study 1, a single-arm study, we retrospectively observed weight loss and engagement as they occurred on the program (i.e., extracted data after participation) in a large population of male users in the United States. We found that by 16 weeks, men who participated in the full program lost 7.69 kg, which constituted 6.95% body weight loss. Since this is one of the first studies to examine men’s weight loss in a self-directed program, there is no exact comparison in the literature. Similar amounts of weight loss in men have been found in previous retrospective examinations, such as 5.6 kg (5.5%) in a self-directed digital food and exercise diary service and 6.5 kg (5.7%) an in-person commercial program (Johnson & Wardle, 2011; Stubbs et al. 2015); however, these studies were not based on program completers. Similarly, weight loss was 8.1 kg at 3 months in a male-tailored intervention from a controlled trial when using similar engagement and completion criteria (Morgan et al., 2009; Petrella et al., 2017), though the study context and intervention are not directly comparable.
There was evidence of substantial and beneficial engagement in weight loss behaviors, even beyond the minimal engagement required for inclusion in the study, which was one in-app action and one weigh-in per week. Engagement was relatively high across all groups. For example, per week, an average of 14.13 to 17.99 eating occasions were logged, and 35,788 to 43,807 steps were recorded. Furthermore, engagement predicted weight loss, which aligns with prior work with primarily female samples (Burke et al., 2011).
Our results suggest that men show significant weight loss and engagement even on a self-directed digital weight loss program, providing ecologically valid insight into how men lose weight and engage in this type of program. The program did not specifically target men in advertising, nor was the program built for a male-only audience. Therefore, the program most likely appealed most to men who were already interested in managing weight and who were at least open to the possibility of learning about behavior change, rather than solely food and exercise guidelines. Future work should investigate the appeal of this program for men who have health risks due to their weight, but do not feel the need to control their weight, and thus are not likely to sign up for a weight loss program. In addition, as a first step toward tailoring support to the disparities and specific needs of various men, we explored outcomes on this generalized program that men could customize for individualized needs. Men could focus on the information and features that fit their preferences, such as those related to muscle building and fitness, or those related to general health. Future research should directly compare outcomes of self-directed generalized programs to tailored male interventions, for which men have shown enthusiasm and significant outcomes (Blunt et al., 2017; Hunt et al., 2014). In Study 2, an exploratory survey was used to understand men’s perceptions of a self-directed digital behavior change weight loss program. Men’s perceptions of indirect benefits, such as more energy, better health, and better body image, differed depending on weight lost. The most important tools or skills on the program were the calorie budget, having the freedom to choose what they wanted to eat, and the food color scheme. The most common types of strategies and knowledge learned were practical application and psychological aspects of food/eating and general psychology.
Theories of hegemonic masculinity have traditionally posited that men express disinterest at the idea of learning about and engaging in healthy eating behaviors (Archibald et al., 2015; Sabinsky et al., 2007; Sloan et al., 2010). On the contrary, we found that men found food-related information and program components most useful. This aligns with work showing that men gained more knowledge and appreciation of healthy eating behaviors in a weight loss intervention (Gray et al., 2009; Maclean et al., 2014). This result also corroborates sociological masculinities work showing that in recent years, men have shifted from avoiding healthy eating toward actively engaging in healthy eating behaviors (Carroll et al., 2019; Elliott, 2019; MacLean et al., 2014; Newcombe et al., 2012; Sloan et al., 2010). However, our results could also be due to the fact that most of the survey items assessed perceptions related to food or eating. Future qualitative work should explore how these men justified focusing on eating behaviors in ways that perpetuate or challenge their constructions of masculinities.
Additionally, in line with work showing that men prefer learning factual information in weight loss interventions, we found that across all weight loss groups, men most often reported learning about practical application of strategies and knowledge (Robertson et al., 2014). This aligns with hegemonic ideals of rationality (Gough, 2018; Gough & Conner, 2006; Sloan et al., 2010). Additionally, another hegemonic masculine ideal is autonomy, or feeling in control of one’s life and choices (Carroll et al., 2019; Sloan et al., 2010). Men who desire to have autonomy can feel reluctant to lose control over their food choices (Carroll et al., 2019; Sabinsky et al., 2007). Along these lines, we found that men commonly found “the freedom to choose what I want to eat” to be helpful no matter how much weight they lost. It is important to note that not all men may have the ability, opportunity, or preference to assert such autonomy due to disparities, or if autonomy does not align with their particular constructions of masculinity. Future qualitative work should more deeply investigate how men make sense of their behaviors on this type of program in terms of rationality and autonomy.
After practical application, men most reported gaining knowledge on psychological aspects. This was not restricted to topics directly related to weight (“mindful eating”) but also included general psychology (“forgiving myself”). This is notable given that because of hegemonic masculinity concerns, men have expressed disinterest in psychological components in weight loss interventions. The finding corroborates work showing improvements in men’s psychological outcomes (e.g., self-efficacy) after an intervention (Crane et al., 2016; de Visser & McDonnell 2013; Egger & Mowbray 1993; Lefkowich et al., 2017; Seidler, 2007). It is also notable that this occurred on a mobile self-directed behavior change program, even without additional researcher contact, and regardless of weight lost. Future work should examine whether this occurs on other self-directed settings.
Limitations
In Study 1, we used a retrospective single-arm design to measure real-world self-directed use of the program as much as possible. However, this design poses limitations. For example, the primary outcome was self-reported weight via the program. Self-reported weight can be inaccurate due to error or bias, such as underreporting from social desirability concerns (Bowman & Delucia, 1992; Larson, 2000). There could have been less potential for bias in this study since participants were not reporting their weight to researchers, and social desirability underreporting is less likely among men than women (Bowring et al., 2012; Larson, 2000). Still it is important for future research to assess the reliability and validity of self-reported weight. Another limitation is that there was no control or comparison group, and an on-treatment instead of intent-to-treat approach was used. Therefore, results should be interpreted in light of possible threats to validity, such as selection bias. In both Studies 1 and 2, the use of a self-selected sample limits generalizability to those who finished the program, and it is unknown how weight loss and engagement might differ in men who chose not to join or finish the program. In addition, only men who completed one in-app action and one weigh-in each week for the full length of the program (Study 1) or completed the survey (Study 2) were included. The response rate, and therefore participation rate, was relatively low (14%) in Study 2. While this is within the range for online survey studies (Daikeler et al., 2019), our results may not generalize to men who did not feel motivated to take the survey and could correspond to a particularly motivated sample. Future research should build on this study by using strategies to boost response rates from as many men as possible who participated in this type of program, and should also compare weight loss and behaviors in self-directed programs with control groups. Future research should also examine retention on this type of program.
Conclusions
Our results, while limited by their exploratory and retrospective nature, suggest implications for health researchers and practitioners. Results of Study 1 align with the notion that men are not “hard to engage,” as they showed significant weight loss and engagement even when self-directing their weight loss on a mobile program without in-person assessments or minimum participation requirements (Robinson & Robertson, 2010). Effort may instead need to be allocated toward reaching, recruiting men, encouraging them to participate in the full length of the program, and educating them on associated health benefits. To confirm, future studies should use randomized controlled trials or control groups. Additionally, men may find a focus on practical application of knowledge surrounding food or eating, as well as learning about psychological aspects, to be effective.
Supplemental Material
sj-docx-1-heb-10.1177_10901981211055467 – Supplemental material for Men’s Weight Loss Outcomes, Behaviors, and Perceptions in a Self-Directed Commercial Mobile Program: Retrospective Analysis
Supplemental material, sj-docx-1-heb-10.1177_10901981211055467 for Men’s Weight Loss Outcomes, Behaviors, and Perceptions in a Self-Directed Commercial Mobile Program: Retrospective Analysis by Heather Behr, Annabell Suh Ho, Qiuchen Yang, Ellen Siobhan Mitchell, Laura DeLuca, Noa Greenstein and Andreas Michaelides in Health Education & Behavior
Footnotes
Authors’ Note
We affirm that this study is original research, has not been previously published, and has not been submitted for publication elsewhere nor previously considered for publication.
Declaration of Conflicting Interests
The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Authors HB, AH, EM, QY, LD, SB, NG, & AM are employees at Noom Inc. and have received salary and stock options for their employment.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
References
Supplementary Material
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