Wellness Achieved Through Changing Habits: A Randomized Controlled Trial of an Acceptance-Based Intervention for Adolescent Girls With Overweight or Obesity

Abstract

Background:

Obesity prevalence among adolescent girls continues to rise. Acceptance-based therapy (ABT) is effective for weight loss in adults and feasible and acceptable for weight loss among adolescents. This pilot randomized controlled trial (RCT) assessed effectiveness of an adolescent-tailored ABT intervention on decreasing weight-related outcomes and improving psychological outcomes compared with enhanced care.

Methods:

In this 6-month, two-arm pilot RCT, participants were randomized to the ABT intervention or to enhanced care. The ABT intervention condition attended 15 virtual, 90-minute group sessions. The enhanced care comparison received 15 healthy lifestyle handouts and virtually met twice with a registered dietitian. The primary outcome assessed was change in BMI expressed as a percentage of the 95th percentile (%BMIp95).

Results:

Participants included 40 girls (ages 14–19) assigned to ABT (n = 20) or enhanced care (n = 20). A decrease in %BMIp95 was observed within the ABT intervention [d = −0.19, 95% confidence interval, CI: (−0.36 to −0.02)], however, not within the enhanced care comparison [d = −0.01, 95% CI: (−0.09 to 0.07)]. The ABT group showed slight changes in psychological flexibility [d = −0.34, 95% CI: (−0.62 to −0.06)] over enhanced care [d = −0.11, 95% CI: (−0.58 to 0.37)]. There was no significant intervention effect noted between groups.

Conclusion:

In this pilot RCT, the ABT intervention was as effective as enhanced care for weight loss. However, previous ABT studies occurred in person, and this study was conducted virtually due to COVID-19. Thus, future research investigating the potential effectiveness of ABT in-person among adolescents and optimization of virtual interventions is needed.

Introduction

Rates of obesity among adolescents continue to rise, with a higher prevalence among girls.¹ Obesity is complex, with behavioral, psychological, biological, environmental, and social mechanisms that contribute to disease development and maintenance.² Adverse mental and physical health outcomes associated with obesity include increased cardiometabolic risk, depression, and anxiety.^3,4 Patients with obesity during adolescence are likely to have the disease in adulthood, therefore early and effective treatments are necessary.⁵

Behavioral interventions are the first step in treating obesity.⁶ Acceptance-based therapy (ABT) is a behavioral intervention that includes traditional techniques such as self-monitoring and controlling stimuli.^7,8 However, ABT uniquely focuses on psychological flexibility, which includes processes such as cognitive fusion and defusion, experiential avoidance and acceptance, and identification of values.⁹ Specifically, ABT encourages participants to identify and act in accordance with chosen values, even when it is difficult.

The ABT teaches self-regulatory skills, assisting patients in accepting uncomfortable internal states that may discourage participation in activities that align with their goals.^7,8 The ABT has led to weight loss in groups diverse in race and ethnicity, sex, and education.^10–13 Clinically meaningful weight loss is defined as a loss of 5%–10% of body weight.¹⁴ Notably, in one study, participants randomized to an ABT intervention lost 13.3% over the course of a year, which is higher than the 5%–8% body weight loss observed in other studies.^13,15,16

Acceptance-based interventions have been used to treat chronic pain, risky sexual behavior, anorexia, and mental well-being among adolescents.^17–22 The ABT is a particularly relevant intervention for obesity as it focuses on self-regulatory skills and teens with obesity exhibit worse self-regulatory skills compared with teens without obesity.²³ Due to the success of ABT interventions for other health issues among adolescents, and its success in treating obesity among adults, further understanding the effectiveness of an ABT intervention for adolescents with obesity is critical.

To address this gap, our team created WATCH (Wellness Achieved Through Changing Habits), an ABT intervention for adolescent girls with overweight or obesity. The present study and formative work were designed, utilizing the Obesity-Related Behavioral Intervention Trials (ORBIT) process for intervention development.²⁴ Formative work revealed differences in perceived barriers and facilitators among males and females, and a desire for sex stratified and tailored interventions.^25,26 Therefore, this intervention was developed specifically for adolescent girls. Adolescent citizen scientists, teenagers with the lived experience of overweight/obesity who were hired to provide feedback and served as research partners, were included and compensated during the development and implementation of the curriculum used in this intervention.^25,26

We conducted focus groups and gathered feedback from adolescent citizen scientists to finalize intervention components.^25,26 In addition, an acceptability and feasibility open trial found that the intervention used in the current study resulted in decreased BMI z-scores, improvements in quality of life, psychological flexibility, and depression.²⁷ This two-arm, pilot randomized controlled trial (RCT) aimed at determining the effectiveness of an ABT intervention on the primary outcome of BMI expressed as a percentage of the 95th percentile (%BMIp95) and secondary outcomes of quality of life, depression, and anxiety sensitivity compared with enhanced care.

Materials and Methods

Participants

Adolescent girls aged 14–19 who had a BMI ≥85th percentile for sex and age were eligible. Exclusion criteria included weight loss of 5% or more of body weight in the past 6 months for any reason except post-partum weight loss, having recently begun or changed the dosage of any medication known to affect appetite or body composition, any condition prohibiting physical activity, or diagnosed eating disorders. Additional exclusion criteria included active cancer, chronic infections, cardiovascular disease, kidney disease, lung disease, or diabetes (type 1 or 2). Participants were recruited in clinics or via social media.

Protocol

Interested adolescents were screened via telephone. After determining eligibility, a videoconferencing session was scheduled where the research coordinator (D.M.) reviewed the consent form with potential participants and their guardians (if participant <18 years). After consent/assent was obtained, the baseline surveys were emailed. All survey data were collected via Research Electronic Data Capture (REDCap 9.3.4, Vanderbilt University 2020).

After participants consented, materials, including a BodyTrace scale and an instruction sheet, were shipped. Participants kept scales during the entire study. At the conclusion of the study, surveys were emailed again. Participants in both conditions could receive up to $180 in compensation for participation. This study was approved by the University of Florida's Institutional Review Board (IRB201701609) and is reported on ClinicalTrials.gov (NCT04484831).

ABT Intervention Condition and Enhanced Care Comparison

In this two-arm pilot RCT, all participants received an initial nutrition consultation via videoconferencing software with the principal investigator of the study, a registered dietitian with a doctorate degree in nutrition sciences (M.I.C.). Participants were then randomized, using blocked randomization in block sizes of two and four, to the ABT intervention or enhanced care comparison for the duration of the 6-month study.

The ABT intervention attended 15 sessions based on ABT practices.^7,8 The first eight sessions focused on “Control What You Can” behaviors, emphasizing change within modifiable aspects of the participants' lives. These sessions included content on navigating a grocery store, participating in physical activity, navigating challenging environments, and managing internal experiences. The next three sessions focused on “Accept What You Can't” skills, explaining aspects of their weight management journeys that cannot be voluntarily controlled (e.g., thoughts, feelings, and physical sensations) and thus must be accepted while continuing with behavior change.

These sessions included content on willingness, identifying values, and defusion (separating thoughts and feelings from actions). The final four sessions involved integration of skills previously learned. Parents, or guardians, were invited to three sessions to honor experiences adolescents described in focus groups (parents/guardians were either described as supportive or a hindrance).²⁶ Participants were encouraged to self-monitor between sessions by self-weighing and tracking food intake in the FatSecret^© app.

This app is an effective self-monitoring tool; however, because the name could be upsetting or offensive, a debrief was held during the first session.^28,29 Participants were also asked to use TEAM (time, eat, activity, method) sheets to monitor how many days they planned to be physically active and log meals into the app.

Sessions ran every week for the first 2 months, bi-weekly for the second 2 months, and monthly for the last 2 months and generally lasted 90 minutes. Each session consisted of individual check-ins, discussing session content, completing a short YouTube^© physical activity video, and covering a healthy snack recipe. Sessions were led either by the Principal Investigator (M.I.C.) or by a doctoral student (F.A.N.). Both instructors had previous lived experience with weight management, which was preferred by adolescents.²⁶

The research team opted to refer to the comparison group as enhanced care as they were receiving more than the usual care for pediatric obesity. Enhanced care received 15 healthy lifestyle handouts, designed by the research team for this study. Content included topics such as reading a nutrition label, problem solving, managing stress, sleeping an adequate amount, and self-monitoring. These handouts were emailed to participants each time ABT met. In addition to the initial nutrition consultation, enhanced care also virtually met with a registered dietitian (M.I.C.) at mid-point (3 months).

Outcomes

The primary outcome for this study was change in (%BMIp95) from baseline to 6 months. Height and weight to calculate these statistics were captured and entered into the REDCap (9.3.4, Vanderbilt University 2020). Height was captured through self-report. Weight was captured through the BodyTrace online platform, where the research team could access measurements taken with a Wi-Fi scale at each time point. Secondary outcomes included changes in quality of life, depression, anxiety sensitivity, psychological flexibility, and behavioral outcomes at 6 months.

The Pediatric Quality of Life Inventory Assessment assessed quality of life.³⁰ This assessment covers four dimensions: physical, emotional, social, and school functioning. Participants are asked questions such as “I miss school because of not feeling well,” or “I cannot do things that other kids my age can do,” and they are asked to respond using a 5-point Likert scale (0 = Never, 5 = Almost Always). Scales are transformed on a scale of 0–100. Higher scores indicate higher quality of life.

Depression was assessed through the Beck Depression Inventory II (BDI-II).^31,32 The BDI-II assesses several domains, including sadness, self-dislike, agitation, and tiredness or fatigue. Participants receive a sum-score, which correlates with a certain classification and level of depression.

Anxiety sensitivity was assessed through the valid Short Scale Anxiety Sensitivity Index (ASI-3).^33,34 The ASI-3 consists of five questions that provide a sum score that ranges from 0 to 20. Higher scores indicate higher anxiety-sensitivity.

Psychological flexibility was assessed through the Acceptance and Action Questionnaire for Weight Related Difficulties (AAQW).³⁵ The AAQW consists of 22 questions and asks participants to respond using a 7-point Likert scale (1 = Never true, 7 = Always True). Examples of questions include “I am in control of how much physical activity I do,” and “In order to eat well and do physical activity, I need to feel like it.” Participants receive a sum score ranging from 22 to 154. Lower scores indicate lower levels of experiential avoidance and more psychological flexibility.

Behavioral outcomes included diet quality, assessed through dietary recalls. Trained research assistants collected three, 24-hour dietary recalls from each participant at baseline and post-intervention using the National Cancer Institute's Automated Self-Administered dietary assessment tool (ASA-24).^36,37 Recalls were collected for 1 weekend day and 2 weekdays. Data from the dietary recalls were used to calculate a Healthy Eating Index (HEI) score, a measure of diet quality.³⁸

Statistical Analyses

The means of baseline demographics and baseline measures in cardiometabolic factors, health-related behaviors, and psychological factors were compared between the two groups via t-tests for continuous variables and Fisher's exact tests for categorical variables. Statistical significance was defined by α = 0.05. Cohen's d and 95% confidence intervals (95% CIs) statistics were calculated to provide standardized effect sizes for changes between the two groups and within group over time for the primary outcome change in (%BMIp95) and additional outcomes. All statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC).

Results

Demographics

We enrolled and randomized 40 adolescent girls (ages 14–19) to two conditions—ABT intervention and enhanced care comparison. Table 1 includes demographics and baseline characteristics for all enrolled participants (n = 40), ABT (n = 20) and enhanced care (n = 20). The mean age was 15.78 ± 1.54 and baseline BMI was 33.67 ± 6.7 kg/m² for all enrolled participants. Race and ethnicity were captured separately. Of the enrolled participants, 77.50% self-identified as White, 15.00% identified as Black, and 10.00% identified as Hispanic/Latina.

Table 1.

Demographics

Variable	All enrolled participants (n = 40)	Intervention (n = 20)	Control (n = 20)	p ^a
Age (in years), mean (SD)	15.78 (1.54)	15.50 (1.54)	16.05 (1.54)	0.2654
Race, N (%)				0.5421
White	31 (77.50)	14 (70.00)	17 (85.00)
Black	6 (15.00)	4 (20.00)	2 (10.00)
Other	3 (7.50)	2 (10.00)	1 (5.00)
Ethnicity, N (%)				0.6050
Hispanic/Latina	4 (10.00)	1 (5.00)	3 (15.00)
Not Hispanic/Latina	36 (90.00)	19 (95.00)	17 (85.00)
Highest parental education, N (%)				0.8320
Some High School/High School Diploma/GED	5 (12.50)	2 (10.00)	3 (15.00)
Some college/Associate degree	9 (22.50)	5 (25.00)	4 (20.00)
Completed Tech or Vocational School/College Graduate	8 (20.00)	3 (15.00)	5 (25.00)
Graduate or professional degree	18 (45.00)	10 (50.00)	8 (40.00)
BMI, mean (SD)	33.67 (6.71)	32.66 (5.53)	34.68 (7.73)	0.3477

p-Values from t-test for continuous variables and Fisher's exact test for categorical variables.

SD, standard deviation.

Most enrolled participants reported the highest level of parent education to be some college/associate degree or higher, and only 12.50% reported some high school/high school diploma/GED. There was not any significant difference in baseline demographics between the two conditions.

Intervention Fidelity

In the Fall cohort, the average attendance among the 12 participants was 99.44%. Among the Spring cohort, one participant withdrew. The average attendance among the remaining seven participants was 84.76%. Participants were marked as attending a session if they logged on for the main videoconferencing session, or if they attended a make-up session with the interventionist.

Baseline Measures in Cardiometabolic Factors, Health-Related Behaviors, and Psychological Factors

The mean baseline BMI z-score was 1.97 ± 0.42, and the (%BMIp95) was 117.71 ± 24.32 kg/m²·s. The mean baseline HEI, psychological flexibility score, and depression score were 50.16 ± 12.32, 79.98 ± 22.91, and 18.38 ± 12.96, respectively. The two conditions did not differ in baseline measures (Table 2).

Table 2.

Baseline Measures in Cardiometabolic Factors, Health-Related Behaviors, and Psychological Factors

Variable	All enrolled participants (n = 40), mean (SD)	Intervention (n = 20), mean (SD)	Control (n = 20), mean (SD)	p
Weight	88.29 (17.41)	85.31 (12.88)	91.27 (20.92)	0.2863^a
Height	162.01 (6.15)	161.96 (6.01)	162.06 (6.44)	0.9588
BMI z-score	1.97 (0.42)	1.94 (0.41)	2.00 (0.44)	0.6725
%BMIp95	117.71 (24.32)	115.01 (19.80)	120.40 (28.40)	0.4904
Healthy Eating Index	50.16 (12.32)	49.43 (14.65)	50.89 (9.79)	0.7120
Quality of life	65.57 (14.37)	64.33 (16.78)	66.81 (11.80)	0.5927
Psychological flexibility	79.98 (22.91)	80.75 (29.47)	79.20 (14.41)	0.8342^a
Depression	18.38 (12.96)	20.10 (15.50)	16.65 (9.91)	0.4069
Perceived stress	22.15 (3.05)	22.80 (2.40)	21.50 (3.53)	0.1812
Anxiety sensitivity	4.75 (4.16)	5.20 (4.01)	4.30 (4.35)	0.5005

t-Test p-value allowing for unequal variances.

%BMIp95, BMI expressed as a percentage of the 95th percentile.

Effects Between Two Conditions

Results in Table 3 represent the mean of change from baseline to post-intervention for all enrolled participants, and Cohen's d for the intervention effects between the two conditions. Reduction was observed in the (%BMIp95) in ABT [mean change: −1.11 ± 5.93, 95% CI: (−4.69 to 2.48)] compared with enhanced care [mean change: −0.08 ± 8.05, 95% CI: (−4.54 to 4.38)], and a small marginal effect d = −0.18, 95% CI: (−0.36 to 0.01) with respect to the change score between two conditions. Other changes remained at similar levels, and no effects were observed between two conditions.

Table 3.

Changes in Cardiometabolic Factors, Health-Related Behaviors, and Psychological Factors

Variable	Change from baseline to post-intervention mean (SD), (95% CI)			Cohen's d (95% CI)^a
Variable	All enrolled participants (n = 39)	Intervention (n = 19)	Control (n = 20)	Cohen's d (95% CI)^a
Weight	0.52 (4.35) (−0.97 to 2.02)	0.26 (3.54) (−1.63 to 2.14)	0.75 (5.03) (−1.67 to 3.17)	−0.08 (−0.23 to 0.08)
Height	0 (0) (0 to 0)	0 (0) (0 to 0)	0 (0) (0 to 0)	0 (0) (0 to 0)
BMI z-Score	−0.06 (0.24) (−0.16 to 0.03)	−0.03 (0.13) (−0.11 to 0.05)	−0.10 (0.30) (−0.26 to 0.07)	0.11 (−0.19 to 0.42)
%BMIp95	−0.56 (7.04) (−3.29 to 2.17)	−1.11 (5.93) (−4.69 to 2.48)	−0.08 (8.05) (−4.54 to 4.38)	−0.18 (−0.36 to 0.01)
Healthy Eating Index	−1.62 (13.77) (−6.35 to 3.11)	−3.10 (17.63) (−12.17 to 5.96)	−0.22 (9.08) (−4.74 to 4.29)	−0.15 (−0.90 to 0.60)
Quality of life	3.03 (10.84) (−0.81 to 6.88)	2.94 (10.86) (−2.85 to 8.73)	3.12 (11.15) (−2.62 to 8.85)	−0.01 (−0.49 to 0.47)
Psychological flexibility	−3.42 (16.94) (−9.43 to 2.58)	−4.81 (14.27) (−12.41 to 2.79)	−2.12 (19.48) (−12.14 to 7.90)	−0.23 (−0.78 to 0.32)
Depression	−1.00 (11.88) (−5.21 to 3.21)	−2.81 (11.58) (−8.98 to 3.36)	0.71 (12.26) (−5.60 to 7.01)	−0.30 (−0.91 to 0.31)
Perceived stress	−0.82 (4.10) (−2.27 to 0.63)	−0.94 (3.30) (−2.69 to 0.82)	−0.71 (4.83) (−3.19 to 1.78)	−0.14 (−0.87 to 0.60)
Anxiety sensitivity	1.00 (4.70) (−0.67 to 2.67)	1.00 (3.65) (−0.95 to 2.95)	1.00 (5.63) (−1.90 to 3.90)	0.10 (−0.53 to 0.72)

For the difference between two groups with respect to the change scores.

CI, confidence interval.

Effects Within Group

We found that enhanced care gained weight from baseline to post-intervention [d = 0.15, 95% CI: (0.06 to 0.24)] whereas ABT did not [d = 0.07, 95% CI: (−0.06 to 0.20)]. A small within-condition effect in (%BMIp95) was observed in ABT [d = −0.19, 95% CI: (−0.36 to −0.02)]. We also observed a small within-condition effect in psychological flexibility in ABT [d = −0.34, 95% CI: (−0.62 to −0.06)] but not in enhanced care [d = −0.11, 95% CI: (−0.58 to 0.37)].

Both conditions had a similar effect in BMI z-score, ABT [d = −0.21, 95% CI: (−0.40 to −0.01)] and enhanced care [d = −0.32, 95% CI: (−0.55 to −0.08)]. No effects with respect to the change scores were observed in HEI, quality of life, depression, perceived stress, or anxiety sensitivity in both conditions (Table 4). A visual representation of changes in physiological and psychosocial outcomes can be found in Figures 1A and 1B. Additionally post-treatment measures can be viewed in Table 5.

Figure 1.

(A) Changes in physiological outcomes; (B) Changes in psychosocial outcomes.

Table 4.

Within-Group Effect Size in Cardiometabolic Factors, Health-Related Behaviors, and Psychological Factors

Variable	Cohen's d (95% CI)^a
Variable	Intervention (n = 19)	Control (n = 20)
Weight	0.07 (−0.06 to 0.20)	0.15 (0.06 to 0.24)
Height	0 (0 to 0)	0 (0 to 0)
BMI z-Score	−0.21 (−0.40 to −0.01)	−0.32 (−0.55 to −0.08)
%BMIp95	−0.19 (−0.36 to −0.02)	−0.01 (−0.09 to 0.07)
Healthy Eating Index	−0.18 (−0.82 to 0.46)	−0.02 (−0.42 to 0.37)
Quality of life	0.27 (−0.05 to 0.59)	0.28 (−0.08 to 0.64)
Psychological flexibility	−0.34 (−0.62 to −0.06)	−0.11 (−0.58 to 0.37)
Depression	−0.24 (−0.64 to 0.15)	0.06 (−0.41 to 0.53)
Perceived stress	−0.28 (−0.90 to 0.34)	−0.15 (−0.54 to 0.25)
Anxiety sensitivity	0.27 (−0.08 to 0.63)	0.18 (−0.34 to 0.69)

For the within-group difference with respect to the change scores.

Table 5.

Post-Treatment Measures in Cardiometabolic Factors, Health-Related Behaviors, and Psychological Factors

Variable	Overall		Intervention		Control
Variable	Mean	SD	Mean	SD	Mean	SD
Weight	88.39	19.83	85.66	13.80	90.69	23.91
Height	162.1	5.96	161.8	5.13	162.3	6.72
BMI z-Score	1.90	0.55	1.90	0.39	1.90	0.67
%BMIp95	117.5	29.76	113.8	18.97	120.7	37.10
Healthy Eating Index	48.25	11.05	46.43	11.17	49.98	10.97
Quality of Life	69.43	16.87	68.61	17.86	70.20	16.40
Psychological Flexibility	75.82	24.19	74.31	26.63	77.24	22.38
Depression	16.52	13.34	16.31	12.86	16.71	14.17
Perceived stress	21.18	4.87	22.06	2.69	20.35	6.25
Anxiety sensitivity	5.70	5.56	5.75	5.58	5.65	5.71

Additional Analyses

The study consisted of two, sequentially run cohorts in Fall 2020 and Spring 2021. The mean weight change differed by cohort in the ABT intervention. For the first ABT cohort, the mean weight change was −0.96 kg [standard deviation (SD) = 3.27, 95% CI = −3.16 to 1.24] whereas the mean weight change for the second was +2.93 kg (SD = 2.67, 95% CI = −0.39 to 6.25). As an exploratory analysis, a t-test was used to compare the difference between the two cohorts (p = 0.0362). However, no significant difference in mean weight change between cohorts was observed in the enhanced care condition (p = 0.9387); the mean change in weight was +0.66 kg (SD = 3.41, 95% CI = −1.63 to 2.95) in the first enhanced care cohort and +0.87 (SD = 6.95, 95% CI = −4.94 to 6.68) in the second enhanced care cohort. In addition, the mean change in (%BMIp95) and BMI z-score did not differ by cohort in both conditions.

Discussion

This study assessed the effectiveness of an ABT intervention for adolescent girls with overweight and obesity. The intervention was developed in collaboration with adolescents, and formative work revealed high acceptability and feasibility.^25–27 This study found small effects in (%BMIp95) and psychological flexibility within ABT; however, there was no evidence that ABT delivered virtually was superior to enhanced care for weight loss.

Contextually, considering COVID-19 is important. During the first year of the pandemic, adolescents exhibited accelerated weight gain, increases in snacking and screen-time, decreases in physical activity, and sleep disruption.^40–43 These changes occurred concurrently with the study, thus potentially impacting effects of the ABT intervention and influencing behaviors of participants in enhanced care. In addition, other behavioral interventions among pediatric patients were affected by COVID-19.

One study found significantly different responses pre-pandemic (BMI z-scores decreased) compared with during the first year of the pandemic (BMI zscores increased).⁴⁴ Thus, these unexpected circumstances must be considered when interpreting the results of this study, though both the ABT intervention and enhanced care conditions were exposed to the pandemic.

Similarly, the remote delivery of the intervention must be considered. During intervention delivery, participants were likely spending more time online due to school closures. In addition to mental health impacts, it is also possible that fatigue induced by computer-mediated communication also impacted adolescents.^45,46 Considering the impacts of videoconferencing, the study team opted to allow participants to keep cameras on or off during sessions.

Although efforts were made to keep adolescents engaged, it is possible that distractions were present that would not have occurred in person. Preliminary work assessing the acceptability and feasibility of the intervention, completed in person, found improvements in BMI z-scores [a mean decrease of −0.15 (SD = 0.34, Cohen's d = −0.44)], (%BMIp95) (d = −0.35), psychological flexibility (d = −0.86), and depression (d = −0.86).²⁷

These findings differ from the current study, which found a slight decrease in BMI z-scores [−0.03 SD = (0.13)], decreases in (%BMIp95) [d = −0.19, 95% CI: (−0.36 to −0.02)], and slight changes in psychological flexibility [d = −0.34, 95% CI: (−0.62 to −0.06)] among ABT. These results further support that the pandemic may have had negative effects.

Group characteristics and dynamics could have influenced outcomes. There was a difference in weight loss between the two ABT cohorts. Anecdotally, the first cohort was more talkative, kept cameras on, and appeared to form friendships outside of sessions. Higher group attraction is associated with weight loss outcomes.⁴⁷ Social support is helpful for individuals during weight management.^48–51 Therefore, although group bonding was encouraged during and in-between sessions via check-ins and a group messaging system, it is possible that social dynamics differed between groups, thus potentially influencing outcomes.

Strengths of this intervention include heavy involvement of adolescents during curriculum development. Focus groups and feedback from adolescent citizen scientists revealed barriers and facilitators to a healthy lifestyle and preferences for behavioral weight management programs.^25,26 An additional strength includes the flexibility of the intervention. If participants were unable to make a session, they could schedule a time to meet with the facilitator to cover the material. For adolescents who cited time as a restriction, flexibility in scheduling allowed exposure to session content even when they could not attend sessions.^26,49

Limitations of this study include the remote delivery of the RCT, despite the fact formative work was completed in-person.^25–27 An additional limitation is that the study only included individuals who identified as female, thus limiting generalizability beyond that gender. Further, for the sake of brevity, another limitation includes that details on physical activity are not reported in this article.

With increased interest in telehealth, future studies on behavioral interventions should assess best practices for engaging teenagers virtually. Future research should also assess group dynamics while focusing on facilitating comradery and support among cohorts. Finally, as noted, formative work for this study revealed that adolescents prefer sex-stratified interventions. Therefore, the development of an intervention tailored for adolescent boys is also needed.

Conclusion

This study assessed the effectiveness of an ABT intervention on BMI change and improving psychological outcomes among adolescent girls with overweight and obesity compared with enhanced care. This study found small effects in (%BMIp95) and psychological flexibility within ABT; however, there is no evidence of an intervention effect, and ABT delivered virtually was not superior to enhanced care. Future research should investigate in-person delivery of an ABT intervention for adolescents, and best practices for optimizing virtual interventions.

Footnotes

Impact Statement

Considering the high prevalence of obesity among adolescents, effective treatments are needed. This study contributes to the knowledge of behavioral interventions for teenagers with obesity by revealing that an acceptance-based intervention delivered virtually was as effective for weight loss as enhanced care.

Authors' Contributions

All people who meet authorship criteria are listed as authors. M.I.C. contributed to the conceptualization. M.I.C., D.M., F.A.N., and A.M.L. contributed to data curation. M.J.G. and X.C. contributed to formal analysis. M.I.C., D.M., M.L.B., and D.M.J. contributed to funding acquisition. M.I.C., M.J.G., M.L.B., and D.M.J. contributed to the study design. D.M., A.M.L., F.A.N., and M.I.C. contributed to data collection and issuing the intervention. S.M. revised the manuscript for intellectual content and contributed to manuscript revisions. All authors contributed to the interpretation of the analysis and revisions of the manuscript and approved the final version.

Disclaimer

The research presented in this article is that of the authors and does not reflect the official policy of the NIH.

Funding Information

This work was supported by the National Institutes of Health National Heart, Lung, and Blood Institute K01HL41535 and R25HL126146 and the National Center for Advancing Translational Sciences of the National Institutes of Health UL1TR001427.

Author Disclosure Statement

F.A.N. reports personal fees from Novo Nordisk, outside the submitted work. M.I.C. reports grants from NIH NHLBI, grants from WellCare Health Plans, Inc., during the conduct of the study; personal fees and income from WW International, Inc., personal fees for consulting from Amazon, consulting with NovoNordisk where personal fees were not accepted, outside the submitted work. All other authors have no competing financial interests.

References

Skinner

, Ravanbakht

, Skelton

, et al. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics, 2018; 141(3):e20173459; doi: 10.1542/peds.2017-3459

Cardel

, Atkinson

, Taveras

, et al. Obesity treatment among adolescents: A review of current evidence and future directions. JAMA Pediatr, 2020; 174(6):609–617; doi: 10.1001/jamapediatrics.2020.0085

Franssen

WMA

, Beyens

, Hatawe

, et al. Cardiac function in adolescents with obesity: Cardiometabolic risk factors and impact on physical fitness. Int J Obes (Lond), 2019; 43(7):1400–1410; doi: 10.1038/s41366-018-0292-x

Puder

, Munsch

. Psychological correlates of childhood obesity. Int J Obes (London), 2010; 34(S2):S37–S43; doi: 10.1038/ijo.2010.238

Daniels

, Arnett

, Eckel

, et al. Overweight in children and adolescents. Circulation, 2005; 111(15):1999–2012; doi: 10.1161/01.CIR.0000161369.71722.10

Looney

, Raynor

. Behavioral lifestyle intervention in the treatment of obesity. Health Serv Insights, 2013; 6:15–31; doi: 10.4137/HIS.S10474

Forman

, Butryn

. A new look at the science of weight control: How acceptance and commitment strategies can address the challenge of self-regulation. Appetite, 2015; 84:171–180; doi: 10.1016/j.appet.2014.10.004

Forman

, Butryn

, Manasse

, et al. Acceptance-based behavioral treatment for weight control: A review and future directions. Curr Opin Psychol, 2015; 2:87–90; doi: 10.1016/j.copsyc.2014.12.020

Levin

, Hilderbrandt

, Lillis

, et al. The impact of treatment components suggested by the psychological flexibility model: A meta-analysis of laboratory-based component studies. Behav Ther, 2012; 43(4):741–756; doi: 10.1016/j.beth.2012.05.003

10.

Forman

, Butryn

, Juarascio

, et al. The mind your health project: A randomized controlled trial of an innovative behavioral treatment for obesity. Obesity (Silver Spring), 2013; 21(6):1119–1126; doi: 10.1002/oby.20169

11.

Forman

, Manasse

, Butryn

, et al. Long-term follow-up of the mind your health project: Acceptance-based versus standard behavioral treatment for obesity. Obesity (Silver Spring), 2019; 27(4):565–571; doi: 10.1002/oby.22412

12.

Butryn

, Forman

, Lowe

, et al. Efficacy of environmental and acceptance-based enhancements to behavioral weight loss treatment: The ENACT trial. Obesity (Silver Spring), 2017; 25(5):866–872; doi: 10.1002/oby.21813

13.

Forman

, Butryn

, Manasse

, et al. Acceptance-based versus standard behavioral treatment for obesity: Results from the mind your health randomized controlled trial. Obesity (Silver Spring), 2016; 24(10):2050–2056; doi: 10.1002/oby.21601

14.

Wing

, Lang

, Wadden

, et al. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care, 2011; 34(7):1481–1486; doi: 10.2337/dc10-2415

15.

Heymsfield

, Wadden

. Mechanisms, pathophysiology, and management of obesity. N Engl J Med, 2017; 376(3):254–266; doi: 10.1056.NEJMra1514009

16.

Jensen

, Ryan

, Apovian

, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol, 2014; 63(25 Pt B):2985–3023; doi: 10.1161/01.cir.0000437739.71477.ee

17.

Wicksell

, Melin

, Lekander

, et al. Evaluating the effectiveness of exposure and acceptance strategies to improve functioning and quality of life in longstanding pediatric pain—A randomized controlled trial. Pain, 2009; 141(3):248–257; doi: 10.1016/j.pain.2008.11.006

18.

Metzler

, Biglan

, Noell

, et al. A randomized controlled trial of a behavioral intervention to reduce high-risk sexual behavior among adolescents in STD clinics. Behav Ther, 2000; 31(1):27–54; doi: 10.1016/S0005-7894(00)80003-9

19.

Heffner

, Sperry

, Eifert

, et al. Acceptance and commitment therapy in the treatment of an adolescent female with anorexia nervosa: A case example. Cogn Behav Pract, 2002; 9(3):232–236; doi: 10.1016/S1077-7229(02)80053-0

20.

Hayes

, Boyd

, Sewell

. Acceptance and commitment therapy for the treatment of adolescent depression: A pilot study in a psychiatric outpatient setting. Mindfulness, 2011; 2(2):86–94; doi: 10.1007/s12671-011-0046-5

21.

Livheim

, Hayes

, Ghaderi

, et al. The effectiveness of acceptance and commitment therapy for adolescent mental health: Swedish and Australian pilot outcomes. J Child Fam Stud, 2015; 24:1016–1030; doi: 10.1007/s10826-014-9912-9

22.

Hayes

, Ciarrochi

. Using Acceptance and Commitment Therapy to Help Young People Develop and Grow to Their Full Potential. In: Kirkcaldy

(ed), Promoting Psychological Well-Being in Children and Families, pp. 102–123. London: Palgrave Macmillan, 2015.

23.

Thamotharan

, Lange

, Zale

, et al. The role of impulsivity in pediatric obesity and weight status: A meta-analytic review. Clin Psychol Rev, 2013; 33(2):253–262; doi: 10.1016/j.cpr.2012.12.001

24.

Czajkowski

, Powell

, Adler

, et al. From ideas to efficacy: The ORBIT model for developing behavioral treatments for chronic diseases. Health Psychol, 2015; 34(10):971–982; doi: 10.1037/hea0000161

25.

Cardel

, Szurek

, Dillard

, et al. Perceived barriers/facilitators to a healthy lifestyle among diverse adolescents with overweight/obesity: A qualitative study. Obes Sci Pract, 2020; 6(6):638–648; doi: 10.1002/osp4.448

26.

Lee

, Szurek

, Dilip

, et al. Behavioral weight loss intervention preferences of adolescents with overweight/obesity. Child Obes, 2021; 17(3):160–168; doi: 10.1089/chi.2020.0296

27.

Cardel

, Lee

, Chi

, et al. Feasibility/acceptability of an acceptance-based therapy intervention for diverse adolescent girls with overweight/obesity. Obes Sci Pract, 2021; 7(3):291–301; doi: 10.1002/osp4.483

28.

Turner-Mcgrievy

, Wilcox

, Boutté

, et al. The Dietary Intervention to Enhance Tracking with Mobile Devices (DIET Mobile) study: A 6-month randomized weight loss trial. Obesity (Silver Spring), 2017; 25(8):1336–1342; doi: 10.1002/oby.21889

29.

Fallaize

, Zenun Franco

, Pasang

, et al. Popular nutrition-related mobile apps: An agreement assessment against a UK reference method. JMIR Mhealth Uhealth, 2019; 7(2):e9838; doi: 10.2196.mhealth.9838

30.

Varni

, Seid

, Kurtin

. PedsQL™ version 4.0 generic core scales in healthy and patient populations. Med Care, 2001; 39(8):800–812; doi: 10.1097/00005650-200108000-00006

31.

Beck

, Steer

, Brown

Manual for the Beck Depression Inventory-II. Psychological Coporation: San Antonio, TX, 1996.

32.

Osman

, Kopper

, Barrios

, et al. Reliability and validity of the Beck Depression Inventory-II with adolescent psychiatric inpatients. Psychol Assess, 2004; 16(2):120–132; doi: 10.1037/1040–3590.16.2.120

33.

Zvolensky

, Garey

, Fergus

, et al. Refinement of anxiety sensitivity measurement: The Short Scale Anxiety Sensitivity Index (SSASI). Psychiatry Res, 2018; 269:549–557; doi: 10.1016.j.psychres.2018.08.115

34.

Dehon

, Weems

, Stickle

, et al. A cross-sectional evaluation of the factorial invariance of anxiety sensitivity in adolescents and young adults. Behav Res Ther, 2005; 43(6):799–810; doi: 10.1016/j.brat.2004.06.008

35.

Lillis

, Hayes

. Measuring avoidance and inflexibility in weight related problems. Int J Behav Consult Ther, 2008; 4(4):348–354; doi: 10.1037/h0100865

36.

Hughes

, Summer

, Ollberding

, et al. Comparison of an interviewer-administered with an Automated Self-Administered 24h (ASA24) dietary recall in adolescents. Public Health Nutr, 2017; 20(17):3060–3067; doi: 10.1017/S1368980017002269

37.

Kirkpatrick

, Subar

, Douglass

, et al. Performance of the Automated Self-Administered 24-hour recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am J Clin Nutr, 2014; 100(1):233–240; doi: 10.3945.ajcn.114.083238

38.

National Cancer Institute Division of Cancer Control & Population Sciences. ASA-24^® Resources Related to the Healthy Eating Index (HEI) 2020. Available from: https://epi.grants.cancer.gov/asa24/resources/hei.html [Last accessed September 2022 ].

39.

Cardel

, Szurek

, Dillard

, et al. Perceived barriers/facilitators to a healthy lifestyle among diverse adolescents with overweight/obesity: A qualitative study. Obes Sci Pract, 2020; 6(6):638–648; doi: 10.1002/osp4.448

40.

Stavridou

, Kapsali

, Panagouli

, et al. Obesity in children and adolescents during COVID-19 pandemic. Children (Basel), 2021; 8(2):135; doi: 10.3390/children8020135

41.

Neshteruk

, Zizzi

, Suarez

, et al. Weight-related behaviors of children with obesity during the COVID-19 pandemic. Child Obes, 2021; 17(6):371–378; doi: 10.1089/chi.2021.0038

42.

Chang

T-H

, Chen

Y-C

, Chen

W-Y

, et al. Weight gain associated with COVID-19 lockdown in children and adolescents: A systematic review and meta-analysis. Nutrients, 2021; 13(10):3668; doi: 10.3390/nu13103668

43.

Jenssen

, Kelly

, Powell

, et al. COVID-19 and changes in child obesity. Pediatrics, 2021; 147(5):e2021050123; doi: 10.1542/peds.2021–050123

44.

Appelhans

, French

, Martin

, et al. Attenuated efficacy of pediatric obesity treatment during the COVID-19 pandemic. Obesity (Silver Spring), 2022; 30(1):45–49; doi: 10.1002/oby.23313

45.

Lee

Mental health effects of school closures during COVID-19. Lancet Child Adolesc Health, 2020; 4(6):421; doi: 10.1016/S2352–S4642(20)30109-7

46.

Nadler

Understanding “Zoom fatigue”: Theorizing spatial dynamics as third skins in computer-mediated communication. Comput Compos, 2020; 58:102613; doi: 10.1016/j.compcom.2020.102613

47.

Nackers

, Dubyak

, Lu

, et al. Group dynamics are associated with weight loss in the behavioral treatment of obesity. Obesity (Silver Spring), 2015; 23(8):1563–1569; doi: 10.1002/oby.21148

48.

Verheijden

, Bakx

, Van Weel

, et al. Role of social support in lifestyle-focused weight management interventions. Eur J Clin Nutr, 2005; 59(S1):S179–S186; doi: 10.1038/sj.ejcn.1602194

49.

Hwang

, Ottenbacher

, Green

, et al. Social support in an Internet weight loss community. Int J Med Inform, 2010; 79(1):5–13; doi: 10.1016/j.ijmedinf.2009.10.003

50.

Kiernan

, Moore

, Schoffman

, et al. Social support for healthy behaviors: Scale psychometrics and prediction of weight loss among women in a behavioral program. Obesity (Silver Spring), 2012; 20(4):756–764; doi: 10.1038/oby.2011.293

51.

Dutton

, Nackers

, Dubyak

, et al. A randomized trial comparing weight loss treatment delivered in large versus small groups. Int J Behav Nutr Phys Act, 2014; 11:123; doi: 10.1186/s12966-014-0123-y