Overeat today, skip the scale tomorrow: An examination of caloric intake predicting nonadherence to daily self-weighing

Initial weight loss and early intervention adherence predict long-term weight loss during the Promoting Successful Weight Loss in Primary Care in Louisiana lifestyle intervention

OBJECTIVE

This study tested whether initial weight change (WC), self-weighing, and adherence to the expected WC trajectory predict longer-term WC in an underserved primary-care population with obesity.

METHODS

Data from the intervention group (n = 452; 88% women; 74% Black; BMI 37.3 kg/m2 [SD: 4.6]) of the Promoting Successful Weight Loss in Primary Care in Louisiana trial were analyzed. Initial (2-, 4-, and 8-week) percentage WC was calculated from baseline clinic weights and daily at-home weights. Weights were considered adherent if they were on the expected WC trajectory (10% at 6 months with lower [7.5%] and upper [12.5%] bounds). Linear mixed-effects models tested whether initial WC and the number of daily and adherent weights predicted WC at 6, 12, and 24 months.

RESULTS

Percentage WC during the initial 2, 4, and 8 weeks predicted percentage WC at 6 (R2  = 0.15, R2  = 0.28, and R2  = 0.50), 12 (R2  = 0.11, R2  = 0.19, and R2  = 0.32), and 24 (R2  = 0.09, R2  = 0.11, and R2  = 0.16) months (all p < 0.01). Initial daily and adherent weights were significantly associated with WC as individual predictors, but they only marginally improved predictions beyond initial weight loss alone in multivariable models.

CONCLUSIONS

These results highlight the importance of initial WC for predicting long-term WC and show that self-weighing and adherence to the expected WC trajectory can improve WC prediction.

Do monthly coaching calls influence proximal participant adherence in a behavioral weight loss program?

Participants who receive continued coach contact following behavioral weight loss treatment are more successful in maintaining their weight loss long-term. The current study examines whether these contacts have dynamic effects, such that participants are most adherent to the prescribed weight loss behaviors in the days after the call, when motivation and goal salience may be heightened, than they are as time goes on. The current study examined the trajectory of calorie intake, physical activity, weight, and self-monitoring behavior in the fourteen days after a monthly coaching call among participants completing the maintenance phase of a behavioral weight loss trial. For physical activity outcomes, caloric intake, and weight, there were no changes across time. Participants did have the highest adherence and quality of dietary self-monitoring immediately after the call, which diminished over time. Coach contact may continually renew commitment to this burdensome but critical behavior. Likelihood of self-weighing showed an opposite trend, where participants were more likely to weigh themselves in the days more distal from the coach call. Results can inform the timing and content of future coach contact to promote weight control.

Patterns and Predictors of Engagement With Digital Self-Monitoring During the Maintenance Phase of a Behavioral Weight Loss Program: Quantitative Study

BACKGROUND

Long-term self-monitoring (SM) of weight, diet, and exercise is commonly recommended by behavioral weight loss (BWL) treatments. However, sustained SM engagement is notoriously challenging; therefore, more must be learned about patterns of engagement with digital SM tools during weight loss maintenance (WLM). In addition, insight into characteristics that may influence SM engagement could inform tailored approaches for participants at risk for poor adherence.

OBJECTIVE

This study explored patterns of digital SM of weight, diet, and exercise during WLM (aim 1) and examined timing, patterns, and rates of disengagement and reengagement (aim 2). This study also assessed relationships between individual-level factors (weight-related information avoidance and weight bias internalization) and SM engagement (aim 3).

METHODS

Participants were 72 adults enrolled in a BWL program consisting of a 3-month period of weekly treatment designed to induce weight loss (phase I), followed by a 9-month period of less frequent contact to promote WLM (phase II). Participants were prescribed daily digital SM of weight, diet, and exercise. At baseline, self-report measures assessed weight-related information avoidance and weight bias internalization. SM adherence was objectively measured with the days per month that participants tracked weight, diet, and exercise. Repeated-measures ANOVA examined differences in adherence across SM targets. Multilevel modeling examined changes in adherence across phase II. Relationships between individual-level variables and SM adherence were assessed with Pearson correlations, 2-tailed independent samples t tests, and multilevel modeling.

RESULTS

During WLM, consistently high rates of SM (≥50% of the days in each month) were observed for 61% (44/72) of the participants for exercise, 40% (29/72) of the participants for weight, and 21% (15/72) of the participants for diet. Adherence for SM of exercise was higher than that for weight or diet (P<.001). Adherence decreased over time for all SM targets throughout phase II (P<.001), but SM of exercise dropped off later in WLM (mean 10.07, SD 2.83 months) than SM of weight (mean 7.92, SD 3.23 months) or diet (mean 7.58, SD 2.92 months; P<.001). Among participants with a period of low SM adherence (ie, <50% of the days in a month), only 33% (17/51 for weight, 19/57 for diet) to 46% (13/28 for exercise) subsequently had ≥1 months with high adherence. High weight-related information avoidance predicted a faster rate of decrease in dietary SM (P<.001). Participants with high weight bias internalization had the highest rates of weight SM (P=.03).

CONCLUSIONS

Participants in BWL programs have low adherence to the recommendation to sustain daily SM during WLM, particularly for SM of diet and weight. Weight-related information avoidance and weight bias internalization may be relevant indicators for SM engagement. Interventions may benefit from innovative strategies that target participants at key moments of risk for disengagement.

The Effects of Implicit Theories on Body Weight Information Avoidance

Regular self-weighing is associated with more effective weight control, yet many individuals avoid weight-related information. Implicit theories about weight, or perceptions of how malleable weight is, predict more effortful weight management and may also influence weight-related information avoidance. Participants (N = 209) were randomly assigned to read an article stressing an incremental theory of weight (i.e., weight is malleable), an article stressing an entity theory (i.e., weight is fixed), or to a control condition. We then examined their self-reported preference to avoid their body composition (i.e., body fat, weight, and muscle composition), their willingness to have their body composition measured during the lab visit, and their eating and exercise intentions. There were no notable differences across conditions, but higher self-reported incremental beliefs predicted less self-reported avoidance of body composition. The findings suggest that implicit theories may influence weight-related information avoidance, but a brief manipulation is not powerful enough to create meaningful change.

Behaviors and psychological states associated with transitions from regaining to losing weight

OBJECTIVE

Little is known about week-to-week recovery from regains following a behavioral weight loss intervention (BWLI). This study examined changes in behaviors, cognitions, and moods associated with transitioning from weight regain to weight loss during the nine-month weight loss maintenance period after a three-month Internet-based BWLI.

METHOD

Participants (n = 68) self-weighed daily via "smart" scales and answered 40 weekly questionnaires about their weight-related behaviors and psychological states. Mixed models were used to (a) determine whether weight gain in a given week predicted changes in weight, behaviors (e.g., self-monitoring), and psychological states (e.g., mood, temptation) the following week and to (b) compare back-to-back weeks when individuals recovered from weight gain (gained in the first week and lost in the second) versus those in which they gained both weeks.

RESULTS

Weight gain in a given week predicted greater weight gain and greater report of behaviors and psychological states associated with weight gain the following week. Back-to-back weeks when individuals switched from gaining to losing were few (5%) compared with weeks when individuals continued gaining (60%). Switching from gaining to losing was associated with greater physical activity during the initial weight gain week and greater self-reported behaviors/cognitions consistent with weight loss (e.g., greater calorie self-monitoring, greater importance of "staying on track") during the following week.

CONCLUSIONS

Engagement in more weight loss consistent behaviors and more favorable ratings of key psychological variables were associated with the rare shifts from gaining to losing. Future research should investigate interventions to help individuals quickly recover from weight regain. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Consistency With and Disengagement From Self-monitoring of Weight, Dietary Intake, and Physical Activity in a Technology-Based Weight Loss Program: Exploratory Study

BACKGROUND

Digital self-monitoring tools offer promise to improve adherence to self-monitoring of weight and weight-related behaviors; however, less is known regarding the patterns of participant consistency and disengagement with these tools.

OBJECTIVE

This study characterizes the consistency of use and time to disengagement with digital self-monitoring tools during a 6-month weight loss intervention and investigates whether the provision of phone-based intervention improved self-monitoring adherence.

METHODS

Participants were 54 adults with overweight or obesity (mean age 49.6 years, SD 12.4 years; mean BMI 32.6 kg/m², SD 3.2 kg/m²) enrolled in a pilot trial assessing the impact of self-monitoring technology (Fitbit Zip, Aria scale, and smartphone app), with and without additional interventionist contact, on weight loss. All participants received weight loss education and were asked to self-monitor weight, dietary intake, and physical activity daily throughout the 6-month program. Consistency was defined as the number of weeks that participants adhered to self-monitoring recommendations (7 out of 7 days). Disengagement was defined as the first of 2 consecutive weeks that the 7-day self-monitoring adherence goal was not met. Wilcoxon signed-rank tests were used to examine differences in consistency and disengagement by behavioral targets. t tests (2-tailed) and Cox proportional hazards models were used to examine whether providing additional interventionist contact would lead to significant improvements in consistency and time to disengagement from self-monitoring tools, respectively. Linear regressions were used to examine associations between consistency, time to disengagement, and weight loss.

RESULTS

Participants consistently self-monitored physical activity for more weeks (mean 17.4 weeks, SD 8.5 weeks) than weight (mean 11.1 weeks, SD 8.5 weeks) or dietary intake (mean 10.8 weeks, SD 8.7 weeks; P<.05). Similarly, participants had a significantly longer time to disengagement from self-monitoring of physical activity (median 19.5 weeks) than weight (4 weeks) or dietary intake (10 weeks; P<.001). Participants randomized to receive additional interventionist contact had significantly greater consistency and longer time to disengagement for self-monitoring of dietary intake compared with participants who did not (P=.006); however, there were no statistically significant differences between groups for self-monitoring of weight or physical activity (P=.24 and P=.25, respectively). Greater consistency and longer time to disengagement were associated with greater weight loss for self-monitoring of weight and dietary intake (P<.001 and P=.004, respectively) but not for physical activity (P=.57).

CONCLUSIONS

Results demonstrated that self-monitoring adherence differed by behavioral target, with greater consistency and longer time to disengagement associated with lower-burden tools (ie, self-monitoring of physical activity). Consistent with supportive accountability theory, additional interventionist contact improved consistency and lengthened time to disengagement from self-monitoring of dietary intake. Given the observed associations between consistency, disengagement, and weight loss outcomes, it is important to identify additional methods of increasing consistency and engagement with digital self-monitoring tools.

The relationship between objective app engagement and medication adherence in asthma and COPD: a retrospective analysis

Digital health tools can promote disease self-management, but the association of smartphone app engagement and medication adherence is unclear. We assessed the relationship between objective smartphone app engagement and controller medication use in adults with asthma and COPD. We retrospectively analyzed data from participants enrolled in a digital self-management platform for asthma and COPD. Eligible adults had a smartphone and a paired electronic medication monitor (EMM). Longitudinal, mixed-effects logistic regressions estimated the relationship between daily app engagement (app opens, session duration) and daily controller medication use. Data from 2309 participants (71% asthma; 29% COPD) was analyzed. Opening the app (vs. not opening the app) was associated with significantly greater odds (OR (95% CI)) of using controller medications in asthma (2.08 (1.98, 2.19)) and COPD (1.61 (1.49, 1.75). Longer session duration was also associated with greater odds of using controller medications in asthma and COPD, but the odds of use attenuated with longer session duration in COPD. This study presents a novel assessment of the relationship between objectively-measured smartphone app engagement and controller medication use in asthma and COPD. Such insights may help develop targeted digital health tools and interventions.

Weight-Related Information Avoidance Prospectively Predicts Poorer Self-Monitoring and Engagement in a Behavioral Weight Loss Intervention

BACKGROUND

Self-monitoring is a key component of behavioral weight loss (BWL) interventions. Past research suggests that individuals may avoid self-monitoring in certain contexts (e.g., skipping self-weighing after higher-than-usual calorie intake). However, no studies have attempted to quantify individuals' inclination to avoid information about their weight control ("weight-related information avoidance"; WIA) or prospectively examined its implications for treatment engagement and outcomes in BWL programs.

PURPOSE

Characterize WIA using a validated questionnaire among adults enrolled in BWL treatment and examine whether WIA prospectively predicts self-monitoring adherence, session attendance, treatment discontinuation, or weight loss.

METHODS

Participants (N = 87; MBMI = 34.9 kg/m2, 83% female) completed a measure of WIA prior to starting a 12 week, group-based BWL intervention. Participants were given digital self-monitoring tools and instructed to self-monitor their food intake daily, physical activity daily, and body weight weekly (Weeks 1-10) and then daily (Weeks 11-12). Session attendance and treatment discontinuation were recorded. Weight was measured in-clinic pretreatment and posttreatment.

RESULTS

While mean WIA was low (M = 2.23, standard deviation [SD] = 0.95; potential scale range: 1-7), greater WIA predicted poorer attendance (r = -.23; p = .03) and poorer self-monitoring of physical activity (r = -.28; p = .009) and body weight (r = -.32; p = .003). WIA did not predict food monitoring (p = .08), treatment discontinuation (p = .09), or 12 week weight loss (p = .91).

CONCLUSIONS

Greater WIA, as assessed via a brief questionnaire, may place individuals at risk for poorer self-monitoring and treatment engagement during BWL. Further research on the implications of WIA in the context of weight management is warranted, including evaluation of correlates, moderators, and mechanisms of action of WIA.

CLINICAL TRIAL REGISTRATION

NCT03337139.

Examination of the relationship between lapses and weight loss in a smartphone-based just-in time adaptive intervention

We developed a smartphone-based just-in-time adaptive intervention (JITAI), called OnTrack, that provides personalized intervention to prevent dietary lapses (i.e., nonadherence from the behavioral weight loss intervention diet). OnTrack utilizes ecological momentary assessment (EMA; repeated electronic surveys) for self-reporting lapse triggers, predicts lapses using machine learning, and provides brief intervention to prevent lapse. We have established preliminary feasibility, acceptability, and efficacy of OnTrack, but no study has examined our hypothesized mechanism of action: reduced lapse frequency will be associated with greater weight loss while using OnTrack. This secondary analysis investigated the association between lapse frequency and the weekly percentage of weight loss. Post hoc analyses evaluated the moderating effect of OnTrack engagement on this association. Participants (N = 121) with overweight/obesity (MBMI = 34.51; 84.3% female; 69.4% White) used OnTrack with a digital weight loss program for 10 weeks. Engagement with OnTrack (i.e., EMA completed and interventions accessed) was recorded automatically, participants self-reported dietary lapses via EMA, and weighed weekly using Bluetooth scales. Linear mixed models with a random effect of subject and fixed effect of time revealed a nonsignificant association between weekly lapses and the percentage of weight loss. Post hoc analyses revealed a statistically significant moderation effect of OnTrack engagement such that fewer EMA and interventions completed conferred the expected associations between lapses and weight loss. Lapses were not associated with weight loss in this study and one explanation may be the influence of engagement levels on this relationship. Future research should investigate the role of engagement in evaluating JITAIs.

Frequency of Self-Weighing and Weight Change: Cohort Study With 10,000 Smart Scale Users

Background

Frequent self-weighing is associated with successful weight loss and weight maintenance during and after weight loss interventions. Less is known about self-weighing behaviors and associated weight change in free-living settings.

Objective

This study aimed to investigate the association between the frequency of self-weighing and changes in body weight in a large international cohort of smart scale users.

Methods

This was an observational cohort study with 10,000 randomly selected smart scale users who had used the scale for at least 1 year. Longitudinal weight measurement data were analyzed. The association between the frequency of self-weighing and weight change over the follow-up was investigated among normal weight, overweight, and obese users using Pearson’s correlation coefficient and linear models. The association between the frequency of self-weighing and temporal weight change was analyzed using linear mixed effects models.

Results

The eligible sample consisted of 9768 participants (6515/9768, 66.7% men; mean age 41.5 years; mean BMI 26.8 kg/m2). Of the participants, 4003 (4003/9768, 41.0%), 3748 (3748/9768, 38.4%), and 2017 (2017/9768, 20.6%) were normal weight, overweight, and obese, respectively. During the mean follow-up time of 1085 days, the mean weight change was –0.59 kg, and the mean percentage of days with a self-weigh was 39.98%, which equals 2.8 self-weighs per week. The percentage of self-weighing days correlated inversely with weight change, r=–0.111 (P<.001). Among normal weight, overweight, and obese individuals, the correlations were r=–0.100 (P<.001), r=–0.125 (P<.001), and r=–0.148 (P<.001), respectively. Of all participants, 72.5% (7085/9768) had at least one period of ≥30 days without weight measurements. During the break, weight increased, and weight gains were more pronounced among overweight and obese individuals: 0.58 kg in the normal weight group, 0.93 kg in the overweight group, and 1.37 kg in the obese group (P<.001).

Conclusions

Frequent self-weighing was associated with favorable weight loss outcomes also in an uncontrolled, free-living setting, regardless of specific weight loss interventions. The beneficial associations of regular self-weighing were more pronounced for overweight or obese individuals.

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e., deviations from the BWL diet) occur frequently and can adversely affect weight loss outcomes. Research indicates that lapse behavior is heterogenous; there are many eating behaviors that could constitute a dietary lapse, but they are rarely studied as distinct contributors to weight outcomes. This secondary analysis aims to evaluate six behavioral lapse types during a 10-week mobile BWL program (eating a large portion, eating when not intended, eating an off-plan food, planned lapse, being unaware of caloric content, and endorsing multiple types of lapse). Associations between weekly behavioral lapse type frequency and weekly weight loss were investigated, and predictive contextual characteristics (psychological, behavioral, and environmental triggers for lapse) and individual difference (e.g., age, gender) factors were examined across lapse types. Participants (N = 121) with overweight/obesity (M_BMI = 34.51; 84.3% female; 69.4% White) used a mobile BWL program for 10 weeks, self-weighed weekly using Bluetooth scales, completed daily ecological momentary assessment of lapse behavior and contextual characteristics, and completed a baseline demographics questionnaire. Linear mixed models revealed significant negative associations between unplanned lapses and percent weight loss. Unplanned lapses from eating a large portion, eating when not intended, and having multiple "types" were significantly negatively associated with weekly percent weight loss. A lasso regression showed that behavioral lapse types share many similar stable factors, with other factors being unique to specific lapse types. Results add to the prior literature on lapses and weight loss in BWL and provide preliminary evidence that behavioral lapse types could aid in understanding adherence behavior and developing precision medicine tools to improve dietary adherence.

Predictors and Outcomes of Digital Weighing and Activity Tracking Lapses Among Young Adults During Weight Gain Prevention

OBJECTIVES

Self-monitoring is critical for weight management, but little is known about lapses in the use of digital self-monitoring. The objectives of this study were to examine whether lapses in self-weighing and wearing activity trackers are associated with weight and activity outcomes and to identify objective predictors of lapses.

METHODS

Participants (N = 160, BMI = 25.5 ± 3.3 kg/m2 , 33.1 ± 4.6 years old) were drawn from a sample of young adults in the Study of Novel Approaches to Prevention-Extension (SNAP-E) weight gain prevention trial. Analyses evaluated associations between weighing and tracker lapses and changes in weight and steps/day during the first 90 days after receiving a smart scale and activity tracker.

RESULTS

On average, participants self-weighed 49.6% of days and wore activity trackers 75.2% of days. Every 1-day increase in a weighing lapse was associated with a 0.06-lb gain. Lapses in tracker wear were not associated with changes in steps/day or weight between wear days. Weight gain predicted a higher likelihood of starting a lapse in weighing and tracker wear, whereas lower steps predicted a higher likelihood of a tracker lapse.

CONCLUSIONS

Weight gain may discourage adherence to self-monitoring. Future research could examine just-in-time supports to anticipate and reduce the frequency or length of self-monitoring lapses.

Week-to-week predictors of weight loss and regain

OBJECTIVES

Despite increased interest in the development of individually tailored weight management programs, little is known about what factors proximally predict weight change.

METHOD

The current study investigated proximal (week-to-week) predictors of weight loss and regain in 74 adults during a 3-month, Internet-based behavioral weight loss program followed by a 9-month "maintenance" period (during which no additional intervention was provided). Participants were asked to self-weigh daily using scales that transmitted weight via the cellular network and to answer a brief questionnaire each week querying mood, behaviors, and cognitions hypothesized to be associated with weight loss and regain.

RESULTS

Longitudinal multilevel models demonstrated that weight loss during initial intervention was proximally predicted by (a) greater frequency of self-monitoring weight and caloric intake, consistency between eating choices and weight loss goals, and importance of "staying on track" with these goals and (b) less negative mood, boredom with weight control efforts, hunger, and temptation to eat foods "not on plan" (ps < .05). Greater weight regain after intervention was also proximally predicted by these factors (with effects in the opposite direction) and additionally by less physical activity, less positive mood, more stress, greater temptation to skip planned physical activity, and higher ratings of the amount of effort required to stay on track (ps < .05).

CONCLUSIONS

Results confirmed the importance of self-monitoring for weight loss and maintenance and identified other key week-to-week predictors of weight change. Results also supported efforts to develop intervention approaches specifically focused on weight loss maintenance. Future research should investigate whether using identified predictors to tailor intervention content and timing can improve weight outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Associations between self-monitoring and weight change in behavioral weight loss interventions

OBJECTIVE

The current study is a secondary analysis of the Live SMART trial, a randomized controlled trial comparing a behavioral weight loss (BWL) condition delivered via smartphone (SMART) to a group-based BWL condition (GROUP) and a control condition (CONTROL). Given the established importance of self-monitoring for weight loss, the aims were to evaluate bidirectional associations between adherence to self-monitoring and weight change and to examine the moderating effect of treatment condition on these associations.

METHOD

Adults with overweight/obesity (n = 276; 83% women; 92.8% White; Mage = 55.1 years; Mbody mass index = 35.2 kg/m2) were instructed to self-monitor dietary intake, daily weight, and physical activity minutes via paper diaries in GROUP and CONTROL and via a smartphone application in SMART. All participants were weighed monthly at the research center. Adherence to self-monitoring was assessed via examination of self-monitoring records.

RESULTS

Generalized linear mixed models revealed that adherence to self-monitoring of dietary intake, self-weighing, and physical activity for each month was associated with weight change throughout that month, such that increased frequency of self-monitoring led to greater weight loss (ps < .001). For the GROUP condition only, poorer weight losses in 1 month were prospectively associated with poor adherence to self-monitoring the following month (ps ≤ .01).

CONCLUSIONS

Results provide evidence of a bidirectional association between self-monitoring and weight change. Better self-monitoring was consistently associated with better weight loss across intervention and tracking modalities. Poorer weight loss was prospectively associated with poorer self-monitoring in group treatment, suggesting that social influences could drive adherence in this form of treatment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Accuracy of Self-Report Versus Objective Smart-Scale Weights During a 12-Week Weight Management Intervention

OBJECTIVE

Greater frequency of self-weighing has been associated with greater weight loss in weight management interventions, but little is known regarding the accuracy of self-reported weight data.

METHODS

Agreement between objective smart-scale and self-reported weight data was assessed in 74 adults (age = 50.7 years; BMI = 31.2 kg/m² ) enrolled in a 12-week, Internet-based weight management program. Participants were asked to self-weight daily using a study-provided smart scale and to self-report weights via the study website.

RESULTS

There was strong agreement between smart-scale and self-reported weight values (intraclass correlation = 0.982) but only moderate agreement regarding frequency of self-weighing assessed via each method (κ = 0.491; P < 0.0001). Greater self-weighing frequency was associated with greater weight loss across measures (all P < 0.001). Compared with days when participants did both, weights were 0.66 kg higher on days when participants self-weighed via the smart scale but did not self-report weight (8% of days) and 0.58 kg higher on days when they self-reported weight but did not self-weigh via the smart scale (4% of days; all P < 0.0001).

CONCLUSIONS

Results suggest that self-reported weight values are similar to smart-scale measurements; however, either method alone may underestimate self-weighing frequency. Furthermore, missing self-weighing data should not be treated as ignorable because weights may be higher than those observed on nonmissing days.

Aggressive clinical approach to obesity improves metabolic and clinical outcomes and can prevent bariatric surgery: a single center experience

BACKGROUND

The number of bariatric procedures has exponentially increased in the past decade, as a result of the lack of successful clinical weight-loss interventions. The main reasons for the failure of clinical obesity management are: (1) anti-obesity medications are administered as monotherapies (or pre-combined drugs); (2) lack of combination between pharmacotherapy and non-pharmacological modalities; (3) short duration of pharmacotherapy for obesity; (4) lack of weight-loss maintenance strategies; (5) misunderstanding of the complex pathophysiology of obesity; and (6) underprescription of anti-obesity medications. We developed a protocol that can potentially overcome the drawbacks that may lead to the failure of clinical therapy for obesity. The aim of this study is therefore to report the clinical and metabolic effects of our proposed obesity-management protocol over a 2-year period, and to determine whether this more intensive approach to obesity management is feasible and a possible alternative to bariatric surgery in patients with moderate-to-severe obesity.

METHODS

This retrospective study involved 43 patients in whom bariatric surgery was indicated. Patients underwent an intensive anti-obesity protocol that included pharmacotherapy with multiple drugs; intense surveillance with monthly body analysis by air-displacement plethysmography, electrical bioimpedance, and 3D body scans; weekly psychotherapy; diet planning with a dietician every 2 months; and exercises at least 3 times a week with exercises prescribed by a personal trainer at least once a month. Body weight (BW), total weight excess (TWE), obesity class, body mass index, fat weight, muscle weight, waist circumference, and visceral fat were analyzed. Markers of lipid and glucose metabolism, liver function, and inflammation were also evaluated. Therapeutic success was defined as >20% BW loss or >50% decrease in TWE after 1 year.

RESULTS

Significant improvements were observed in all clinical and metabolic parameters. Thirty-eight (88.4%) patients achieved 10% BW loss, and 32 (74.4%) achieved 20% BW loss. TWE decreased by >50% in 35 (81.4%) patients. Forty (93.0%) patients were able to avoid bariatric surgery.

CONCLUSION

An intensive clinical approach to obesity management can be an effective alternative to bariatric surgery, although further randomized controlled studies are necessary to validate our findings.