Content uploaded by Meelim Kim
Author content
All content in this area was uploaded by Meelim Kim on Jun 08, 2021
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
Original Paper
Multidimensional Cognitive Behavioral Therapy for Obesity Applied
by Psychologists Using a Digital Platform: Open-Label
Randomized Controlled Trial
Meelim Kim1,2, MA; Youngin Kim3,4, MD; Yoonjeong Go1; Seokoh Lee1, BS; Myeongjin Na2, BA; Younghee Lee1,
BS; Sungwon Choi2*, PhD; Hyung Jin Choi1*, MD
1Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
2Department of Psychology, Duksung Women’s University, Ssangmun-Dong, Dobong-Gu, Republic of Korea
3Noom Inc, New York City, NY, United States
4Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
*these authors contributed equally
Corresponding Author:
Hyung Jin Choi, MD
Department of Biomedical Sciences
Seoul National University College of Medicine
28 Yungun-Dong, Chongno-Gu
Seoul
Republic of Korea
Phone: 82 2 740 8204
Email: hjchoi@snu.ac.kr
Abstract
Background: Developing effective, widely useful, weight management programs is a priority in health care because obesity is
a major health problem.
Objective: This study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching
program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile
services Noom Coach and InBody.
Methods: This was an open-label, active-comparator, randomized controlled trial. A total of 70 female participants with BMI
scores above 24 kg/m2and no clinical problems besides obesity were randomized into experimental and control groups. The
experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that
provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but
practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8
weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated
using independent ttests and a per-protocol framework.
Results: Mean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (–3.1%, SD
4.5, vs –0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline
in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not
at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group
(–6.3%, SD 8.8, vs –0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly
reduced compared to the control group (–15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and –7.1%, SD 35.1, vs 14.4%, SD 41.2,
P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at
8 weeks was significantly improved compared to the control group (–2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack
calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9
kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted
long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term
clinical outcomes.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 1http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Conclusions: These findings confirm that technology-based interventions should be multidimensional and are most effective
with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT
approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions.
Trial Registration: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306
(JMIR Mhealth Uhealth 2020;8(4):e14817) doi: 10.2196/14817
KEYWORDS
obesity; digital health care; cognitive behavioral therapy; mobile phone
Introduction
One of the major concerns of the health care industry is to find
effective and widely practical solutions for weight management,
given that obesity is one of the dominant public health problems
of the 21st century. It is well known that weight reduction is
highly correlated with reductions in the incidence of type 2
diabetes, as well as other medical weight-related comorbidities
and psychosocial issues, and that it improves the quality of life
[1].
Accordingly, various types of treatments for obesity have been
developed. Several drugs have been proposed as
pharmacotherapy for obesity since the 1990s, but most have
demonstrated a lack of efficacy and unfavorable risks [2].
Bariatric surgery is another obesity treatment that has been used
for over 50 years. Because the prevalence of obesity is rapidly
rising, the number of patients who believe that bariatric surgery
is an effective treatment to cure their obesity is also increasing
[3]. Additionally, patients may believe that surgical intervention
to overcome obesity will ultimately lead to behavioral changes
sustaining weight loss [3], which may increase the risk of weight
regains after the surgery. To date, the most effective standard
obesity treatment is weight-loss lifestyle modification based on
a combination of behavioral and cognitive approaches and
nutrition and physical education.
Clinical psychological treatment approaches are pivotal and
involve engaging patients in lifestyle modification and
motivating them to successfully lose weight with the help of a
multidisciplinary team [4]. Cognitive behavioral therapy (CBT)
for obesity is aimed at not only losing weight but also preventing
weight regain, thereby avoiding the dissatisfactory long-term
results of earlier behavioral treatments. It firmly distinguishes
between weight loss and weight maintenance, allowing patients
to practice effective weight-maintenance strategies (eg, avoiding
unrealistic weight goals and addressing obstacles to weight
maintenance) [5]. One study applied a 12-week CBT program
for obese people, resulting in a 6% reduction in body fat relative
to the control group [6]. Moreover, a 20-week CBT intervention
involving a 10-week main program followed by a 10-week
less-intensive care program significantly improved body
composition and improved soft drink consumption habits
compared to the control group [7].
Although cognitive behavioral programs involving weekly clinic
visits are known to be the most effective treatments for obesity,
they place high demands due to time, cost, distance, status of
endorsement, and difficulties securing child care [8]. A previous
study found that people would prefer cost-effective and
time-saving methods to lose weight [9]. Researchers have thus
explored alternative methods for carrying out weight-loss
programs, such as television, computers, and smartphone apps,
to meet individual needs and to make obesity treatment more
accessible. Among these, self-monitoring via smartphone apps
has shown the greatest potential to make diet tracking easier
and engaging because of its convenience and accessibility [10].
Despite the use of smartphone apps for self-monitoring, a law
of attrition in digital health interventions still holds, whereby
users stop using technology-based components over time.
Because the effectiveness of treatments via digital tools is
closely associated with the user’s extent of engagement [11], a
high attrition rate is a critical issue in the assessment of the
efficacy of digital intervention programs. Therefore, based on
behavioral modification principles, periodic prompts that
encourage healthy behaviors are one method to remind and
motivate people to change their health behaviors. A systematic
review of the use of technology tools to send periodic
notifications about users’ behavior changes found them to be
more effective than nontechnological notifications or no
notifications [12]. However, this review only focused on the
effectiveness of digital interventions for behavior change as a
whole and did not investigate how to enhance engagement with
the intervention.
The goal of this study was to test a novel approach to losing
weight and maintaining the new weight after participation in
an intensive and comprehensive human coaching program based
on CBT modules via digital tools, such as the Noom Coach app
and InBody Dial. The Noom Coach app is one of the most
popular smartphone apps currently available; it has received
higher quality assessment scores than other smartphone apps
[13]. It allows participants to log their food intake, exercise
activities, and weight, and to engage in in-app group activities,
read in-app articles, and interact with a human coach via in-app
messages. In-app group activity lets participants communicate
with other participants and share their experience of healthy
lifestyle trials. In-app articles deliver practical information about
healthy lifestyles written by physicians, nutritionists, and clinical
psychologists. In-app messages enable participants to receive
individualized feedback from human coaches based on their
own records presented on the Web-based dashboard. A
Web-based dashboard is provided to the coaches to monitor
participants’data. InBody Dial is a body composition analyzer
for the home linked to a mobile app, allowing users to
conveniently measure their body composition. Furthermore, we
addressed the self-sustainability of the promoted lifestyle change
after the intervention. We hypothesized that individuals
randomized to the digital CBT group would lose weight and
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 2http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
better maintain their weight loss than individuals in the control
group.
Methods
Participants
A total of 70 female subjects were recruited between September
and October 2017 through both online and offline boards of a
university campus in Seoul, South Korea, and a social network
service. Eligibility criteria included the following: 18-39 years
of age, body mass index of 25-40 kg/m2, smartphone usage,
and scores in the highest 40% (ie, scores above 68 out of 112
total) on the Situational Motivation Scale (SIMS). Participants
were ineligible if they had a history of major medical problems,
such as diabetes, angina, or stroke; a major psychiatric disorder
involving hospitalization or medication in the past; and a current
or planned pregnancy within the next 6 months. The flow of
participants from recruitment to final assessment at 24 weeks
is shown in Figure 1.
Figure 1. Digital cognitive behavioral therapy (CBT) CONSORT (Consolidated Standards of Reporting Trials) flow diagram. SIMS: Situational
Motivation Scale.
The Institutional Review Board of Seoul National University
Hospital approved the study (approval number
H-1707-122-872). All study participants provided written
informed consent. This study was conducted to examine the
clinical efficacy of the obesity digital CBT model and find
factors predicting its efficacy. The study was registered with
ClinicalTrials.gov (NCT03465306).
Study Design
This was an open-label, active-comparator, randomized
controlled trial (RCT). Following initial screening, all
participants were asked to attend an orientation session where
the study was described in more detail. Written informed consent
and baseline measurements were obtained in person. Blood
samples were taken in the morning after overnight fasting to
avoid daily variations in activities. The basics of the tutorial
and log-in procedures for both the Noom app and the InBody
H20B (InBody Co) body composition analyzer were
demonstrated to all participants during the orientation session
of the study. The Noom app was mainly used to keep a food
diary and deliver messages between the therapist and
participants, while the InBody H20B analyzer was used to
monitor and collect the body composition data of the
participants. The randomization was designed to randomly
assign 75 participants in total to a control (app only) group or
a digital CBT (app + human CBT) group at a ratio of 1:2 in
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 3http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
order to deliver a more powerful trial within resource constraints
and to maximize the statistical power of predictor analysis (ie,
within-group analysis) [14]. Randomization was performed by
the project manager by drawing lots. The digital CBT group
was given daily feedback and assignments from a psychologist,
based on the CBT modules, for 8 weeks and could access the
digital tools from the intervention period to the 24-week
follow-up. The control group was instructed to use only a food
diary without therapist intervention until the 24-week follow-up
but was given the same digital tools and instruction as the digital
CBT group. Thus, the control group underwent the same
standard-of-care trial as the digital CBT group, except that it
was asked to practice self-care. All participants were asked to
visit at baseline, 8 weeks, and 24 weeks for objective
measurements and completion of questionnaires, and they were
each paid US $4 for attending each of the appointments. This
study was conducted from September 2017 to April 2018.
Assessment
The primary outcome was change in body weight. Other
measures, such as change in BMI and body fat mass, were
secondary outcomes. Anthropometric measurements were
assessed by the InBody H20B analyzer at baseline, 8 weeks,
and 24 weeks in light street clothing and without socks and
shoes. For secondary outcomes, blood samples were collected
at baseline and 8 weeks after a 10-hour fast. We examined serum
insulin, leptin, glucose concentrations, aspartate
aminotransferase, alanine aminotransferase, gamma-glutamyl
transferase, total cholesterol, and triglyceride levels to assess
the changes in these indices in relation to the change in body
weight. The engagement criteria of the program were completing
actions, such as responding to the daily assessment (responses
per day), logging meals (meals per week), consuming green
foods as defined by Noom [15] (logged per week), performing
exercise (times per week), registering exercise time (minutes
per week), recording steps taken (steps per week), logging
weigh-ins (times per week), reading articles (articles per week),
completing group posts (posts per week), posting group
comments (comments per week), sending messages to the coach
(messages per week), and liking group posts (likes per week).
These criteria were used to assess the use of the app by each
participant with objective measures.
Participants’ situational motivation toward the weight-loss
program was assessed using an adapted version of the SIMS.
The SIMS typically measures four types of motivation—intrinsic
motivation, identified regulation, external regulation, and
amotivation—to engage in a task (ie, the weight-loss program)
at a specific point in time, with four items per subscale. The
SIMS has demonstrated acceptable levels of reliability and
validity in past research. The Body Shape Questionnaire-8C
(BSQ-8C) is a brief version of the Body Shape Questionnaire
(BSQ) consisting of eight items extracted from the full version
measuring the extent of psychopathology of concerns about
body shape. Higher values on the BSQ indicated more body
dissatisfaction. Depression was assessed using the Korean
version of the Beck Depression Inventory-II (K-BDI-II) scoring
system. A total score of 0-9 indicated no depression, 10-15
indicated mild depression, 16-23 indicated moderate depression,
and 24-63 indicated severe depression. Anxiety was measured
using the 20-item Trait Anxiety Inventory (TAI) of the
State-Trait Anxiety Inventory, with higher scores indicating
greater trait anxiety. The Rosenberg Self-Esteem Scale (RSES)
measure of self-esteem was used in this research with a 10-item
scale consisting entirely of negatively worded items. Thus,
higher scores implied lower self-esteem. Eating behavior notions
were measured with the Dutch Eating Behavior Questionnaire
(DEBQ), which identifies three distinct psychologically based
eating behaviors: restrained eating, emotional eating, and
external eating. It contains 33 items, with higher scores
indicating a greater tendency to present subscale behavior. The
frequency of occurrence of automatic negative thoughts
associated with depression was assessed by the Automatic
Thoughts Questionnaire (ATQ-30). The scores ranged from 30
to 150, where higher scores indicated more frequent automatic
negative thoughts. All the psychological questionnaires were
in Korean.
Interventions
The intervention of this study was a multifactorial, daily-based
personalized coaching program implemented by a psychologist
using CBT modules via the digital platform. The digital CBT
contents were based on programs proposed to clinicians [16]
as a guide. We monitored and assessed various factors related
to the behavior, cognition, mood, and motivation of each
participant assigned to the digital CBT group.
The following were assessed every day using responses to
questions and scores from the questionnaires: eating behaviors
(eg, Where did you eat? What type of food did you have? How
fast did you eat? and What time did you eat?), automatic
thoughts (eg, What came to your mind when you were eating
or thinking of food?), mood (eg, Score your mood from 0 to
100 regarding each type of negative mood: irritated, lonely,
anxious, bored, and depressed), and motivation (eg, Score your
status from 0 to 10 based on the following items: willingness
to lose weight, importance of losing weight, assurance of losing
weight, and helpfulness of this program to lose weight). Scores
were used to individually track the daily patterns of the four
factors—eating behaviors, automatic thoughts, mood, and
motivation—and provide individualized interventions. As such,
participants in the digital CBT group received daily self-report
assessments in a Google survey form via text message on their
phone. Participants were also instructed to log their dietary
intake and physical exercise on a daily basis. Additionally, they
were asked to measure their weight, BMI, and fat mass twice a
week with the InBody H20B analyzer as soon as they woke up
in the morning and were instructed to log their meals and
physical activity by self-report on the Noom Coach app on a
weekly basis.
After participants’ responses to the components related to the
four factors were collected, digital mobile tools collected the
data to allow the therapist to securely monitor participants’
progress through a Web-based dashboard. The participants
received at least three individual messages from the coach every
day, except on weekends and holidays, via the Noom Coach
app. Furthermore, the therapist individually sent a daily report,
a weekly report, and a midweek report (ie, Week 4) to the
participants for the purpose of goal setting and to strengthen
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 4http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
their motivation. Weekly group missions were provided to the
digital CBT group based on the expectation that social supports
(eg, communicating needs and building positive support) would
intensify the motivation. When the participants were inactive
for more than 3 consecutive days or asked for thorough
counseling, the therapist phoned them and conducted
motivational interviews. The motivational interviews could be
implemented only once a week per person. The duration of the
phone call did not exceed 15 minutes.
All contents of the coaching messages, group missions, and
articles were managed by a supervisor of the digital health care
coach, who has a master-level degree in clinical psychology.
She has trained as a behavioral therapist using CBT modules,
such as self-monitoring, goal setting, problem solving,
nutritional and physical activity education, stimulus control,
challenging automatic thoughts, thought restructuring, and
relapse prevention. Throughout the intervention, we expected
the participants in the digital CBT group to experience a lifestyle
change by finding a healthy pattern of living that fit each
participant’s context. The diagram of the digital CBT process
and features of the digital platform are presented in Figure 2
and Figure 3, respectively.
Figure 2. Diagram of the digital cognitive behavioral therapy (CBT) process.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 5http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Figure 3. Screenshots of the digital platform (ie, mobile apps) for the participants (top) and screenshots of the digital platform (ie, dashboard) for the
therapist (ie, clinical psychologist) (bottom).
Statistical Analysis
The sample size was selected to provide the study with a
statistical power of 80% to detect clinically meaningful mean
differences in weight loss of 5 kg with an SD of 7 kg in
treatment effect, based on previous studies [17]. Assuming an
average attrition rate of 10%, a sample of at least 70 subjects
was selected. For differences in baseline characteristics,
independent-sample ttests were used for continuous variables
and a chi-square test of independence was used for categorical
data assessing the demographic patterns of subjects.
We conducted the analysis following per-protocol principles.
The participants who attended at either 8 or 24 weeks were
included in the analysis of the applicable period without missing
imputations. There were no outliers in the dataset. To investigate
differences in the outcomes between the two groups, changes
in the outcomes of weight, BMI, and fat mass were analyzed
using an independent-sample ttest. To investigate statistical
differences between baseline and postintervention within a
group, a paired ttest was used. To detect statistical differences
of the proportion within the thresholds and engagement rates
between groups, a chi-square test was used. Correlation analysis
using the Pearson correlation coefficient was used to investigate
which variables at the baseline had a predictive role in changes
in anthropometrics at 8 and 24 weeks. Receiver operating
characteristic (ROC) curve analysis was undertaken to identify
the optimum trade-off between sensitivity and specificity for
cutoffs in weight-change distribution. For the ROC analysis in
this study, we set a cutoff of 3% loss of initial body weight as
a good response at 24 weeks for the digital CBT group data.
The Youden index was used for the optimal cutoff. The results
regarding the proportion of people who reached 5% weight-loss
threshold are also reported to permit comparison with other
previous studies. All analyses were conducted using SPSS
Statistics for Windows, version 20 (IBM Corp), and statistical
significance of two-tailed Pvalues were set at .05. For multiple
comparison correction, a threshold of P<.001 was used (ie, the
Pvalue threshold of .05 divided by 42, corresponding to two
different time periods and 21 phenotypes).
Results
Overview
There were no significant differences between the randomization
groups on key demographic characteristics (see Table 1).
However, the DEBQ emotional eating scale (DEBQ-EM)
(P=.001) and the DEBQ external eating scale (DEBQ-EX)
(P=.049) scores of the two groups did differ at baseline. These
differences between the groups were found after lots were drawn
for the randomized control procedure. Participants had a mean
age of 21.8 years (SD 3.3) and a mean BMI of 28.0 kg/m2(SD
3.2).
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 6http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Table 1. Baseline characteristics of participants in both groups.
Digital CBTa(ie, app + human CBT)
(n=45)
Control (ie, app only)
(n=25)Characteristic
22.3 (3.5)21.0 (2.7)Age (years), mean (SD)
Anthropometric measures, mean (SD)
74.5 (9.0)71.9 (7.7)Weight (kg)
28.2 (3.4)27.7 (2.9)
BMI (kg/m2)
30.2 (6.8)29.3 (6.0)Fat mass (kg)
40.4 (5.4)40.5 (4.8)Fat percent (%)
24.0 (2.6)23.8 (3.3)Lean body mass (kg)
Blood measures, mean (SD)
87.3 (7.4)87.0 (8.1)Fasting glucose (mg/dL)
93.2 (42.6)92.2 (35.9)Triglyceride (mg/dL)
191.1 (30.4)184.7 (24.9)Total cholesterol (mg/dL)
15.3 (11.9)12.7 (6.9)Alanine aminotransferase (U/L)
16.9 (4.8)17.0 (4.7)Aspartate aminotransferase (U/L)
21.3 (32.8)15.3 (8.5)Gamma-glutamyl transpeptidase (U/L)
42.5 (15.3)37.5 (14.7)Leptin (ng/mL)
16.1 (9.1)12.6 (6.1)Fasting insulin (µU/mL)
3.5 (2.1)2.8 (1.5)
Homeostasis Model for Assessment of Insulin Resistanceb, mean (SD)
Scale or questionnaire (score), mean (SD)
76.1 (5.7)77.0 (5.8)Situational Motivation Scale
36.2 (7.5)34.8 (8.9)Body Shape Questionnaire-8C
13.6 (9.0)14.7 (9.6)Beck Depression Inventory-II in Korean
48.0 (10.4)47.8 (11.0)Trait Anxiety Inventory
19.8 (5.6)21.9 (6.4)Rosenberg Self-Esteem Scale
29.9 (6.6)30.6 (7.3)
DEBQcrestrained eating scale
38.0 (10.1)29.1 (11.6)
DEBQ emotional eating scaled
34.9 (4.8)32.0 (7.0)
DEBQ external eating scaled
57.2 (22.3)57.6 (26.0)Automatic Thoughts Questionnaire
3.0 (1.7)2.2 (1.7)Yale Food Addiction Scale
Residence status, n (%)
27 (60)10 (40)Living with family
8 (18)8 (32)Living alone
9 (20)7 (28)Living with roommates
1 (2)0 (0)Others
Number of attempts to lose weight by different methods, n (%)
1 (2)0 (0)None
4 (9)3 (12)Once
15 (33)12 (48)Twice
13 (29)3 (12)Three times
8 (18)4 (16)Four times
4 (9)2 (8)Five times
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 7http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Digital CBTa(ie, app + human CBT)
(n=45)
Control (ie, app only)
(n=25)Characteristic
0 (0)1 (4)Six times
aCBT: cognitive behavioral therapy.
bInsulin resistance = (insulin [µU/mL] × glucose [mg/dL]) / 405.
cDEBQ: Dutch Eating Behavior Questionnaire.
dThere was a statistical difference between the two groups at baseline.
Primary Outcome of Weight Change and
Anthropometric Outcomes
The primary outcome (ie, weight change) was assessed at two
time points—immediately after lifestyle change with digital
CBT (8 weeks) and at the long-term follow-up without digital
CBT (24 weeks)—to investigate the self-sustaining effect of
lifestyle change induced by 8 weeks of digital CBT. Of the 70
randomized participants, 65 (93%) were assessed for the primary
outcome—body weight—at 24 weeks and 5 (7%) were lost to
follow-up. Figures 4 and 5represents the mean weight change
along with other anthropometric measures—BMI, body fat
mass, and body lean mass—at each study time point. Participants
in the digital CBT group showed significant changes in mean
body weight at 8 weeks compared to the control group (–3.1%,
SD 4.5, vs –0.7%, SD 3.4, P=.04) but not at 24 weeks. The
proportion of subjects who showed good response was 45%
(17/38) in the digital CBT group and 29% (6/21) in the control
group at 8 weeks (P=.22), while at 24 weeks it was 54% (22/41)
in the digital CBT group and 42% (10/24) in the control group
(P=.35). In addition, the number reaching the conventional 5%
weight loss from the baseline in the digital CBT group was
significantly higher than in the control group at 8 weeks (12/38,
32%, vs 1/21, 4%, P=.02) but not at 24 weeks (18/41, 44%, vs
7/24, 29%, P=.24). Changes in mean BMI (–3.1%, SD 4.6, vs
–0.7%, SD 3.5, P=.04) and body fat mass (–6.3%, SD 8.8, vs
–0.8%, SD 8.1, P=.02) of the digital CBT group were also
significant compared to the control group at 8 weeks but not at
24 weeks (see Multimedia Appendix 1, Table MA1-1). Body
lean mass did not significantly differ between the two groups
at both 8 and 24 weeks. Examining within-group changes, only
the digital CBT group achieved significant weight changes, as
well as BMI and body fat mass, at both 8 and 24 weeks; the
digital CBT group achieved significant changes in lean body
mass at 24 weeks but not at 8 weeks (see Multimedia Appendix
1, Tables MA1-2 and MA1-3).
Figure 4. Patterns of changes in mean body weight (A), BMI (B), body fat mass (C), and lean body mass (LBM) (D). CBT: cognitive behavioral
therapy. *P<.05; **P<.01.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 8http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Figure 5. Weight change based on individual data from the experimental group at the 8-week follow-up (A), from the experimental group at the 24-week
follow-up (B), from the control group at the 8-week follow-up (C), and from the control group at the 24-week follow-up (D). CBT: cognitive behavioral
therapy.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 9http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Figure 6. Changes in meal calories between experimental and control groups during the intervention period, as well as the contrast of mean energy
intake between groups. *P<.05; ** P<.01.
Secondary Outcomes: Metabolic and Psychological
Outcomes
Multimedia Appendix 1, Table MA1-4, shows a comparison of
the metabolic outcomes from baseline to 8 weeks in each group
and by intervention condition. The mean decreases in leptin
(–15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01), insulin (–4.4%,
SD 35.2, vs 15.4%, SD 35.1, P=.048), and Homeostatic Model
Assessment for Insulin Resistance (HOMA-IR) (–7.1%, SD
35.1, vs 14.4%, SD 41.2, P=.04) were significantly greater in
the digital CBT group than in the control group. For
within-group analysis, the changes in glucose (–2.91%, P=.04)
and leptin (–15.82%, P=.003) of the digital CBT group were
significant. No significant outcome changes were found in the
control group. The mean percentage changes in psychological
outcomes are shown in Multimedia Appendix 1, Table MA1-5,
by intervention condition. There was no significant difference
between the groups regarding the number of changes in
psychological outcomes except for the change in the DEBQ-EM
from baseline to 8 weeks (P=.048). Paired ttest analysis showed
significant changes in the BSQ-8C and DEBQ-EX scores at 8
and 24 weeks in both groups. However, the changes in the scores
of the DEBQ restrained eating scale (DEBQ-RE) (P<.001) at
8 weeks, and those of the K-BDI-II (P=.001), TAI (P=.04),
RSES (P=.03), and ATQ-30 (P=.02) at 24 weeks, appeared to
be significant only in the digital CBT group (see Multimedia
Appendix 1, Tables MA1-6 and MA1-7). Behavioral outcomes,
measured via the Noom app, are represented as the amount of
calorie intake and the pattern of weekly changes between the
groups and the average energy intake of each group, as presented
in Figure 3. Mean snack calories (P=.02) significantly differed
between the two groups, and total calories (P=.06) had a
tendency toward critical difference by intervention condition
(see Figure 6 and Multimedia Appendix 1, Table MA1-8).
Lastly, the digital CBT group had a higher engagement rate
when using digital tools than the control group, though it
declined over time in both groups (see Figure 7and Multimedia
Appendix 1, Table MA1-9).
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 10http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Figure 7. Patterns of changes in engagement rate of the experimental and control groups during the intervention period. *P<.05.
Predictors of the Primary Outcome, Weight Change
Correlations Between the Primary Outcome and the
Baseline Characteristics
The baseline motivation, as measured by the SIMS, was
significantly correlated with weight change at 8 weeks (P=.009)
and 24 weeks (P=.003). Depression, as measured by the
K-BDI-II (P=.03); anxiety, as measured by the TAI (P=.008);
and self-esteem, as measured by the RSES (P=.002) at baseline
also showed a significant correlation with weight change at 24
weeks but not at 8 weeks. Depression, anxiety, self-esteem,
restrained eating behavior, external eating behavior, and
automatic thoughts at baseline were significantly correlated
with BMI change at 24 weeks. Lastly, lean body mass, anxiety,
and self-esteem at baseline were significantly correlated with
change in body fat mass at 24 weeks. Figure 8 illustrates the
significant correlations between the predictive markers and the
change of the anthropometric measures at 24 weeks. All the
results of the correlation analysis are presented in detail in
Multimedia Appendix 1, Table MA1-10. Multimedia Appendix
1, Figure MA1-1, also illustrates the correlations between
predictive markers and the change of BMI.
Figure 8. The correlation between weight change at the long-term follow-up period (24 weeks) and the level of motivation, self-esteem, depression,
and anxiety at baseline. Also shown are the correlation between BMI change at the long-term follow-up and the level of motivation at baseline, and the
correlation between fat mass change at the long-term follow-up and lean body mass at baseline. K-BDI: Korean version of the Beck Depression Inventory;
RSES: Rosenberg Self-Esteem Scale; SIMS: Situational Motivation Scale; TAI: Trait Anxiety Inventory.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 11http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Receiver Operating Characteristic Analysis Determining
the Optimal Cutoff Scores of the Predictive Markers of
Success in Weight Loss by Digital Cognitive Behavioral
Therapy
Multimedia Appendix 1, Table MA1-11, shows the sensitivity
and specificity of the baseline psychological characteristics
showing significant correlations with weight change, the primary
outcome. The definition of optimal statistical prediction
threshold is weight loss of more than 3% of the initial body
weight. This is an important threshold because our treatment
was CBT as a lifestyle modification without any biological
intervention. Both motivation and self-esteem had the greatest
area under the curve (AUC) (0.63). The AUCs of depression
and anxiety were 0.61 and 0.62, respectively. To predict a good
response, the cutoff for motivation (SIMS score=76.5) provided
a good trade-off between sensitivity (59%) and specificity
(74%). Additionally, the cutoff for depression (K-BDI-II
score=7.5), anxiety (TAI score=41.5), and self-esteem (RSES
score=24.5) provided optimal sensitivity and specificity to
predict a good response. Overall, motivation showed the best
predictive performance.
Clinical Efficacy of Digital Cognitive Behavioral
Therapy Based on the Optimal Cutoff Scores of the
Predictive Markers in the Clinical Setting
The high-motivation subgroup (SIMS scores >76.5) showed a
65% (13/20) probability of successful 3% weight loss, whereas
the low-motivation subgroup (SIMS scores <76.5) showed a
36% (9/25) probability of successful 3% weight loss. Optimal
predictive performance was achieved by combining both
motivation and depression scores. The high-motivation plus
low-depression subgroup (SIMS scores >76.5 and K-BDI-II
scores <7.5) showed a 100% (6/6) probability of successful 3%
weight loss. Other subgroups showed a lower probability of
successful 3% weight loss: 55% (5/9) of the low-motivation
and low-depression subgroup, 50% (7/14) of the high-motivation
and high-depression subgroup, and 25% (4/16) of the
low-motivation and high-depression subgroup (see Figure 9).
Figure 9. The clinical efficacy of digital cognitive behavioral therapy (CBT) by applying the optimal cutoff scores of the predictive markers in the
clinical setting. The pink line represents the threshold for successful weight loss.
Even when the strict statistical threshold for multiple comparison
corrections was applied, changes in weight, BMI, and fat mass
from baseline to 8 weeks in the digital CBT group were
considered significant (P<.001). The changes in the scores of
the DEBQ-RE from baseline to 8 weeks, and in the K-BDI-II
and DEBQ-EX scores from baseline to 24 weeks, in the digital
CBT group were also significant after multiple corrections
(P<.001). Furthermore, the changes in the scores of the BSQ-8C
from baseline to 8 weeks and 24 weeks in both the digital CBT
and control groups were considered significant after multiple
corrections (P<.001).
Discussion
Principal Findings
This study successfully examined the efficacy of a newly
developed, multifactorial, and daily-based personalized CBT
model conducted by a psychologist via a digital platform for
managing body weight, BMI, and body fat mass and showed a
legacy effect even after the intervention terminated. This was
performed by comparing this group to the active comparators
using only the app as the control group. Furthermore, this study
successfully explored the predictors for the efficacy of digital
CBT from the baseline characteristics and recommended them
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 12http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
as precision medicine biomarkers, namely, depression, anxiety,
self-esteem, and motivation.
Among mobile health (mHealth) RCTs for obesity, this study
has unique implications regarding the application of CBT
strategies by a human coach in the intervention. This study,
therefore, contributes to the broader literature on weight-loss
treatments that involve human factors. There have been
widespread studies of mHealth approaches to weight-loss
programs [18-33]. There are several studies of obesity treatments
that did not investigate CBT settings; these include studies of
human-based mHealth RCTs [18,21,24,29,34,35], human-based
mHealth but without RCT design [17,26,30], and mHealth not
based on human factors but with RCT design
[19,20,22,25,27,28,32,33]. There are also several studies of
human-based RCT designs, including CBT settings for obesity
but not mHealth procedures (ie, telephone, website, face-to-face,
and others) [16,36-40].
This study is comparable to other mHealth RCTs. The mean
percentage weight loss of our study was 4% of initial body
weight, and previous mHealth RCTs reported a mean percentage
weight loss ranging from 1% to 3% [24,29,32,34]. Moreover,
this study successfully showed weight maintenance. Most
interventions for obesity have shown a tendency to regain weight
after discontinuing the treatment [11,16,24,29,32,41-43], but
our digital CBT intervention showed a sustained trend of further
decrease even up to 16 weeks after cessation of the 8-week
intervention. This affords solid support for the assumption that
digital CBT promotes an overall healthy lifestyle. However,
because we do not have data beyond a 1-year period, a direct
comparison with previous studies is not feasible. Preventing
weight regain at 24 weeks is closely related with a decrease in
body fat mass and an increase in lean body mass at 8 weeks,
which are relevant to physical activity rate and nutrition status
[44,45]. Indeed, an improvement in both physical activity and
diet, representing changes in lifestyle, leads to healthy body
composition. Therefore, the patterns of changes in not only
body weight but also body fat mass and lean body mass may
imply that the participants in the digital CBT group experienced
self-sustainable transitions in daily decision making for a healthy
life.
With regard to the appropriate threshold, previous behavioral
weight-loss studies often reported 5% weight loss in the majority
of participants [16,26,30]. Conventionally, several studies
adopted a 5% threshold as a clinically significant threshold
[16,19,26,29]. However, in contrast to the conventional 5%
threshold, we adopted a tempered 3% weight-loss threshold as
the good response threshold for two main reasons. First, the
duration of the active intervention period in this study was
shorter than in other studies and only persisted transiently for
the initial 2 months. The majority of previous behavioral studies
had a full 6-month active intervention design [16,36,37].
However, the duration of the active intervention period in our
study was only 8 weeks (2 months). There was no intervention
delivered after 8 weeks (2 months) until the 6-month time point.
Thus, the subjects did not receive the intervention during the
remaining 4 months after the initial 2-month active intervention.
Second, the components of the intervention in this study did
not include extreme restrictions or requirements in either diet
or exercise. The main goal of our intervention was to implement
sustainable weight management skills by learning an appropriate
behavioral process as well as establishing new cognitive
processes. Therefore, the weight loss per se could be weaker
than with the stringent diet restrictions and exercise requirements
of a behavioral program during the intervention. In addition to
the 3% threshold, we also reported the results based on the
conventional 5% threshold to allow a direct comparison of
clinical efficacy between studies.
Regarding personalization, our digital CBT was fully tailored
to each participant’s characteristics in multifactorial domains:
the behavioral, cognitive, emotional, motivational, and physical
domains. The therapist in our study altered the feedback styles
based on data from five types of domains for every participant
and conducted intensive daily monitoring. Most of the previous
RCTs on mHealth interventions for obesity—those not based
on human factors—considered one or two factors of individual
symptoms that led to the implementation of homogeneous
interventions [18,19,22,31]. Although there are some
interventions that use custom algorithms to provide
individualized feedback, they only focus on diet, physical
activity, weight loss, or any two of these [27,28,32,46].
Furthermore, some earlier mHealth RCTs for obesity based on
human factors only dealt with diet and physical activities
[21,24]. One study managed three domains for the intervention:
diet, physical activities, and eating behaviors [29]. However,
instructions on behavior change strategies were not delivered
by smartphone but by attending weekly group sessions for the
first phase of the intervention. The study was deficient in other
principal factors, such as emotional, cognitive, and motivational
domains, implying insufficient potentiality for long-term
lifestyle change. Because cognitive conceptualization and
emotional regulation process are naturally associated with
behavioral patterns, consideration of all these components can
allow changes in one’s lifestyle and ultimately solve problems
related to obesity [47]. Therefore, it is important to address the
respective multifactorial domains so as to conduct tailored
treatment for individuals with fully integrated techniques. Our
digital CBT strategies operate in a fully comprehensive system
that deals with behavioral, cognitive, emotional, motivational,
and physical factors and allows integrated mediation to
successfully manage obesity.
After examining aspects of temporal strategies for intervention,
we arranged three different time points (ie, daily, weekly, and
monthly points) and initiated a daily human-agent intervention
in an mHealth RCT for obesity. All previous face-to-face,
electronic health (eHealth), and mHealth RCTs for obesity
treatment have been either weekly- or monthly-based
interventions delivered by therapists [7,16,21,24,25,29,36-38].
Temporal strategies can influence the engagement rate, which
is closely related to treatment outcomes [48]. Unfortunately,
according to a systematic review, mHealth RCTs related to
weight-loss programs suffer from a high attrition rate of more
than 30% [49]. Our digital CBT trial, however, showed high
in-app activity rates as well as engagement in the intervention
program. Only one participant in the digital CBT group had to
withdraw for personal reasons; 80% of the participants were
active until the end of the treatment session. One possible reason
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 13http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
for these outcomes is that our digital CBT intervention
effectively managed participants’ motivation to lose weight as
well as participate in the program. Our intervention did this by
delivering individualized messages every day, based on data
from the in-app database and daily assessment of various
psychological factors using CBT modules, as well as facilitating
real-time access to the therapist. Additionally, the midreport,
employed as a monthly intervention, allowed personalized
precision treatment based on initial psychological conditions
to keep participants motivated. Thus, the engagement rate of
the digital CBT group improved from 91% to 93%, whereas
the engagement rate in the control group dropped from 84% to
76% between Week 4 and Week 5.
Through our digital CBT, changes in biological indexes, leptin,
insulin, and HOMA-IR indicated that factors related to physical
health can be successfully improved. Moreover, we also
successfully managed motivation, emotion, cognition, and
behavior. The level of self-body-image satisfaction and external
eating behaviors was improved in both groups. This indicates
that simply including the standard mHealth treatment in the
control group in our study was practical for improving body
image perception and external eating habits. Digital CBT
improved the level of depression, anxiety, self-esteem, and
automatic thoughts related to depression. In fact, the DEBQ-EM
and DEBQ-EX scores showed a significant difference between
the two groups at baseline but were not notably correlated with
the primary measures at baseline. This may be considered a
random circumstance of randomization. Therefore, these
differences can be interpreted as not affecting the main outcomes
of our study. Furthermore, a significant difference in reported
snack calorie intake between the two groups suggests that our
digital CBT intervention had an impact on managing snack
calories compared to other meals. Stress is highly correlated
with the frequency of snacks [50]. Thus, it is possible that our
digital CBT intervention affected snack calorie intake by finding
individualized stress coping strategies, restructuring cognitive
structures of automatic negative eating or weight-related
thoughts, and developing regular and balanced eating behaviors.
Therefore, this provides evidence that the participants in the
digital CBT group changed their lifestyle to constantly manage
their weight.
This study can be considered a practical one because it explored
clinical markers that predict the effect of digital CBT and
suggested plausible criteria that can be applied to clinical
settings. The follow-up results at 24 weeks in this study showed
that the levels of motivation, depression, anxiety, and
self-esteem were the predictive markers of weight loss based
on the digital CBT intervention. Some of our results regarding
the predictors of weight control conflict with the findings of
previous research [51], but they are consistent with recent
findings that the level of motivation is the strongest predictive
trait for weight control [52,53]. We defined people who lost
less than 3% of their baseline weight as poor responders to the
treatment. Thus, people with a SIMS score lower than 76.5 are
recommended to find and pursue their own way of enhancing
their motivation to lose weight before they undertake digital
CBT. Furthermore, a person whose score is higher than 7.5 on
the K-BDI-II, 41.5 on the TAI, or 24.5 on the RSES is
encouraged to handle the relevant issue before, or at the same
time as, digital CBT. This will prevent further distress from
repeated failure to control weight, save limited resources, and
allow better concentration in individuals with a higher chance
of success in weight control.
Considering the comparator of this study as the best active
comparator without human coaching, digital CBT is a competent
intervention for obesity in the current situation in the digital
health care industry. We provided education on how to log
meals and exercise as well as how to use InBody Dial and the
mobile app, not only to the digital CBT group but also to the
control group during the orientation. Thus, the control group in
this study can be defined as an active group as in previous
studies [19,22,31]. As expected, the control group in this study
showed favorable results. Therefore, the results of this study
are superior and significant compared to those of previous
studies of digital health care interventions.
Limitations
While the results are highly promising, the study is not without
limitations. First of all, the participants were limited to those in
their 20s and 30s, resulting in limited generalizability. Second,
since this is not a blinded study, an observer bias could have
been generated. Thus, an implication of this study that should
be noted is that it tested the digital CBT and did not validate it.
Third, the sample size was relatively small (N=70). Therefore,
most of the results did not pass the strict
multiple-comparison-corrected Pthreshold. Fourth, the
follow-up period needs to be extended to increase the reliability
and validity of our results. Accordingly, we recommend that
future studies examine more information on personal
characteristics, such as single nucleotide polymorphisms (SNPs)
and daily patterns of digital phenotypes for individuals within
in-app data, in order to enhance the interpretation of the efficacy
of digital-based interventions. Fifth, the total amount of food
calories in the app might have been underestimated because the
amount per serving for diverse types of food was not precise
and people may have miscalculated their food intake. The
primary reason for errors in food records is that most people
have difficulties in estimating food portions [54]. The
discrepancy in food choice between the food diary and actual
meals (ie, recording similar but not exact menus, skipping
reports of foods eaten, or logging foods not offered) could
explain the remainder of the total miscalculation [55]. Thus, we
suggest that a direct assessment of food choice and intake, such
as buffet tests, should be performed in parallel with logging
intake in the food diary on the app for future research. In
addition, it should be noted that it is necessary to involve
dietitians on multidisciplinary health care teams for obesity
CBT, as their evaluations of dietary assessment and nutritional
advice would greatly strengthen the efficacy of the intervention.
Lastly, there is a feasibility issue regarding the digital CBT of
this study since it is intensive and costly, requiring daily
intervention by therapists trained in both physical and mental
health care. Therefore, more research involving human factors
in technology-based treatments should be conducted to collect
enough data to create automatic functions, thereby decreasing
the burdens of therapists in the future.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 14http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
Conclusions
For the first time, we discovered that human-based digital CBT
is capable of treating obesity using digital tools. Anthropometric
measures, such as body weight and body compositions, were
comparably improved by the digital CBT model as well as
physiological indices and obesity-related psychological factors.
There was no relapse in weight change after the end of the
intervention. We also found predictable psychological markers
to estimate the efficacy of the digital CBT treatment for obesity.
This will open up new aspects of digital precision remedies for
obesity in the digital health care industry.
Acknowledgments
We would like to thank Hyunjae Kim, the chief executive manager of LookinBody, for supporting this study by sharing his
company’s advanced digital technologies. Also, I would like to thank Ms Younghyun Yun from the Department of Anatomy and
Cell Biology, Seoul National University College of Medicine, for the preparation of the excellent illustrations and graphic design.
This study was supported by a grant from the National Research Foundation of Korea (NRF), funded by the Korean Government,
Ministry of Science and ICT (MSIT) (No. NRF-2018R1A5A2025964), and was supported by the Creative-Pioneering Researchers
Program through Seoul National University (SNU).
Noom provided the funding to conduct this research and InBody provided body composition analyzer devices for this research.
Representatives of InBody had no role in the management, analysis, and interpretation of the data; preparation, review, or approval
of the manuscript; and decision to submit the manuscript for publication. YK, an employee of Noom, participated in the generation
of the study design and in data collection.
Authors' Contributions
MK conceptualized and designed the clinical infrastructure for the digital CBT intervention during the implementation phase.
HJC and SC gave valuable research insights when designing the digital CBT intervention. MK, HJC, YK, YG, MN, SL, and YL
contributed to the study design and data collection. MK, HJC, YG, and SC analyzed and interpreted the data. MK wrote the
manuscript and edited the contents of the manuscript. HJC and SC reviewed the manuscript. All authors approved the final version
of the manuscript for submission.
Conflicts of Interest
YK is an employee of Noom.
Multimedia Appendix 1
Changes in outcomes from baseline, correlations, and predicting efficacy of digital cognitive behavioral therapy.
[DOCX File , 200 KB-Multimedia Appendix 1]
Multimedia Appendix 2
CONSORT-eHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 1466 KB-Multimedia Appendix 2]
References
1. Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JP, et al. Effect of low-fat vs low-carbohydrate
diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: The
DIETFITS randomized clinical trial. JAMA 2018 Feb 20;319(7):667-679 [FREE Full text] [doi: 10.1001/jama.2018.0245]
[Medline: 29466592]
2. Wyatt HR. Update on treatment strategies for obesity. J Clin Endocrinol Metab 2013 Apr;98(4):1299-1306 [FREE Full
text] [doi: 10.1210/jc.2012-3115] [Medline: 23443815]
3. Sjöström L, Narbro K, Sjöström CD, Karason K, Larsson B, Wedel H, Swedish Obese Subjects Study. Effects of bariatric
surgery on mortality in Swedish obese subjects. N Engl J Med 2007 Aug 23;357(8):741-752. [doi: 10.1056/NEJMoa066254]
[Medline: 17715408]
4. Castelnuovo G, Pietrabissa G, Manzoni GM, Cattivelli R, Rossi A, Novelli M, et al. Cognitive behavioral therapy to aid
weight loss in obese patients: Current perspectives. Psychol Res Behav Manag 2017;10:165-173 [FREE Full text] [doi:
10.2147/PRBM.S113278] [Medline: 28652832]
5. Cooper MJ, Wells A, Todd G. A cognitive model of bulimia nervosa. Br J Clin Psychol 2004 Mar;43(Pt 1):1-16. [doi:
10.1348/014466504772812931] [Medline: 15005903]
6. Brennan L. Cognitive Behavioural Evaluation and Treatment of Adolescent Overweight and Obesity [doctoral thesis].
Melbourne, Australia: RMIT University; 2006 Aug 31. URL: http://researchbank.rmit.edu.au/eserv/rmit:6333/Brennan.pdf
[accessed 2006-08-31]
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 15http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
7. Tsiros MD, Sinn N, Brennan L, Coates AM, Walkley JW, Petkov J, et al. Cognitive behavioral therapy improves diet and
body composition in overweight and obese adolescents. Am J Clin Nutr 2008 May;87(5):1134-1140. [doi:
10.1093/ajcn/87.5.1134] [Medline: 18469231]
8. Steele RM, Mummery WK, Dwyer T. A comparison of face-to-face or internet-delivered physical activity intervention on
targeted determinants. Health Educ Behav 2009 Dec;36(6):1051-1064. [doi: 10.1177/1090198109335802] [Medline:
19502534]
9. Sherwood NE, Morton N, Jeffery RW, French SA, Neumark-Sztainer D, Falkner NH. Consumer preferences in format and
type of community-based weight control programs. Am J Health Promot 1998;13(1):12-18. [doi: 10.4278/0890-1171-13.1.12]
[Medline: 10186930]
10. Klasnja P, Pratt W. Healthcare in the pocket: Mapping the space of mobile-phone health interventions. J Biomed Inform
2012 Feb;45(1):184-198 [FREE Full text] [doi: 10.1016/j.jbi.2011.08.017] [Medline: 21925288]
11. Yon BA, Johnson RK, Harvey-Berino J, Gold BC. The use of a personal digital assistant for dietary self-monitoring does
not improve the validity of self-reports of energy intake. J Am Diet Assoc 2006 Aug;106(8):1256-1259. [doi:
10.1016/j.jada.2006.05.004] [Medline: 16863723]
12. Fry JP, Neff RA. Periodic prompts and reminders in health promotion and health behavior interventions: Systematic review.
J Med Internet Res 2009 May 14;11(2):e16 [FREE Full text] [doi: 10.2196/jmir.1138] [Medline: 19632970]
13. Chen J, Cade JE, Allman-Farinelli M. The most popular smartphone apps for weight loss: A quality assessment. JMIR
Mhealth Uhealth 2015 Dec 16;3(4):e104 [FREE Full text] [doi: 10.2196/mhealth.4334] [Medline: 26678569]
14. Hey SP, Kimmelman J. The questionable use of unequal allocation in confirmatory trials. Neurology 2014 Jan 07;82(1):77-79
[FREE Full text] [doi: 10.1212/01.wnl.0000438226.10353.1c] [Medline: 24306005]
15. Michaelides A, Major J, Pienkosz E, Wood M, Kim Y, Toro-Ramos T. Usefulness of a novel mobile diabetes prevention
program delivery platform with human coaching: 65-week observational follow-up. JMIR Mhealth Uhealth 2018 May
03;6(5):e93 [FREE Full text] [doi: 10.2196/mhealth.9161] [Medline: 29724709]
16. Cooper Z, Doll HA, Hawker DM, Byrne S, Bonner G, Eeley E, et al. Testing a new cognitive behavioural treatment for
obesity: A randomized controlled trial with three-year follow-up. Behav Res Ther 2010 Aug;48(8):706-713 [FREE Full
text] [doi: 10.1016/j.brat.2010.03.008] [Medline: 20691328]
17. Mao AY, Chen C, Magana C, Caballero Barajas K, Olayiwola JN. A mobile phone-based health coaching intervention for
weight loss and blood pressure reduction in a national payer population: A retrospective study. JMIR Mhealth Uhealth
2017 Jun 08;5(6):e80 [FREE Full text] [doi: 10.2196/mhealth.7591] [Medline: 28596147]
18. Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional
versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an
mHealth weight loss program. J Am Med Inform Assoc 2013 May 01;20(3):513-518 [FREE Full text] [doi:
10.1136/amiajnl-2012-001510] [Medline: 23429637]
19. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website
and paper diary: Pilot randomized controlled trial. J Med Internet Res 2013 Apr 15;15(4):e32 [FREE Full text] [doi:
10.2196/jmir.2283] [Medline: 23587561]
20. Spring B, Duncan JM, Janke EA, Kozak AT, McFadden HG, DeMott A, et al. Integrating technology into standard weight
loss treatment: A randomized controlled trial. JAMA Intern Med 2013 Jan 28;173(2):105-111 [FREE Full text] [doi:
10.1001/jamainternmed.2013.1221] [Medline: 23229890]
21. Allen JK, Stephens J, Dennison Himmelfarb CR, Stewart KJ, Hauck S. Randomized controlled pilot study testing use of
smartphone technology for obesity treatment. J Obes 2013;2013:151597 [FREE Full text] [doi: 10.1155/2013/151597]
[Medline: 24392223]
22. Laing BY, Mangione CM, Tseng C, Leng M, Vaisberg E, Mahida M, et al. Effectiveness of a smartphone application for
weight loss compared with usual care in overweight primary care patients: A randomized, controlled trial. Ann Intern Med
2014 Nov 18;161(10 Suppl):S5-S12 [FREE Full text] [doi: 10.7326/M13-3005] [Medline: 25402403]
23. Wharton CM, Johnston CS, Cunningham BK, Sterner D. Dietary self-monitoring, but not dietary quality, improves with
use of smartphone app technology in an 8-week weight loss trial. J Nutr Educ Behav 2014;46(5):440-444. [doi:
10.1016/j.jneb.2014.04.291] [Medline: 25220777]
24. Godino JG, Merchant G, Norman GJ, Donohue MC, Marshall SJ, Fowler JH, et al. Using social and mobile tools for weight
loss in overweight and obese young adults (Project SMART): A 2 year, parallel-group, randomised, controlled trial. Lancet
Diabetes Endocrinol 2016 Sep;4(9):747-755 [FREE Full text] [doi: 10.1016/S2213-8587(16)30105-X] [Medline: 27426247]
25. Jun MK, Ha JY. Effect of smartphone apps applying BodyThink program on obesity in adolescent girls [Article in Korean].
J Korean Acad Nurs 2016 Jun;46(3):390-399. [doi: 10.4040/jkan.2016.46.3.390] [Medline: 27411766]
26. Michaelides A, Raby C, Wood M, Farr K, Toro-Ramos T. Weight loss efficacy of a novel mobile diabetes prevention
program delivery platform with human coaching. BMJ Open Diabetes Res Care 2016;4(1):e000264 [FREE Full text] [doi:
10.1136/bmjdrc-2016-000264] [Medline: 27651911]
27. Mummah SA, Mathur M, King AC, Gardner CD, Sutton S. Mobile technology for vegetable consumption: A randomized
controlled pilot study in overweight adults. JMIR Mhealth Uhealth 2016 May 18;4(2):e51 [FREE Full text] [doi:
10.2196/mhealth.5146] [Medline: 27193036]
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 16http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
28. Turner-McGrievy GM, Wilcox S, Boutté A, Hutto BE, Singletary C, Muth ER, 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 Aug;25(8):1336-1342 [FREE Full text] [doi: 10.1002/oby.21889] [Medline: 28600833]
29. Spring B, Pellegrini CA, Pfammatter A, Duncan JM, Pictor A, McFadden HG, et al. Effects of an abbreviated obesity
intervention supported by mobile technology: The ENGAGED randomized clinical trial. Obesity (Silver Spring) 2017
Jul;25(7):1191-1198 [FREE Full text] [doi: 10.1002/oby.21842] [Medline: 28494136]
30. Toro-Ramos T, Lee D, Kim Y, Michaelides A, Oh TJ, Kim KM, et al. Effectiveness of a smartphone application for the
management of metabolic syndrome components focusing on weight loss: A preliminary study. Metab Syndr Relat Disord
2017 Nov;15(9):465-473. [doi: 10.1089/met.2017.0062] [Medline: 29035677]
31. Turner-McGrievy G, Tate D. Tweets, apps, and pods: Results of the 6-month Mobile Pounds Off Digitally (Mobile POD)
randomized weight-loss intervention among adults. J Med Internet Res 2011 Dec 20;13(4):e120 [FREE Full text] [doi:
10.2196/jmir.1841] [Medline: 22186428]
32. Burke LE, Styn MA, Sereika SM, Conroy MB, Ye L, Glanz K, et al. Using mHealth technology to enhance self-monitoring
for weight loss: A randomized trial. Am J Prev Med 2012 Jul;43(1):20-26 [FREE Full text] [doi:
10.1016/j.amepre.2012.03.016] [Medline: 22704741]
33. Shaw RJ, Bosworth HB, Silva SS, Lipkus IM, Davis LL, Sha RS, et al. Mobile health messages help sustain recent weight
loss. Am J Med 2013 Nov;126(11):1002-1009 [FREE Full text] [doi: 10.1016/j.amjmed.2013.07.001] [Medline: 24050486]
34. Nezami BT, Ward DS, Lytle LA, Ennett ST, Tate DF. A mHealth randomized controlled trial to reduce sugar-sweetened
beverage intake in preschool-aged children. Pediatr Obes 2018 Nov;13(11):668-676. [doi: 10.1111/ijpo.12258] [Medline:
29119719]
35. Spring B, Pellegrini C, McFadden HG, Pfammatter AF, Stump TK, Siddique J, et al. Multicomponent mHealth intervention
for large, sustained change in multiple diet and activity risk behaviors: The Make Better Choices 2 randomized controlled
trial. J Med Internet Res 2018 Jun 19;20(6):e10528 [FREE Full text] [doi: 10.2196/10528] [Medline: 29921561]
36. Tate DF, Wing RR, Winett RA. Using internet technology to deliver a behavioral weight loss program. JAMA 2001 Mar
07;285(9):1172-1177. [doi: 10.1001/jama.285.9.1172] [Medline: 11231746]
37. Harvey-Berino J, Pintauro S, Buzzell P, Gold EC. Effect of internet support on the long-term maintenance of weight loss.
Obes Res 2004 Feb;12(2):320-329 [FREE Full text] [doi: 10.1038/oby.2004.40] [Medline: 14981225]
38. Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored
counseling, and no counseling in an internet weight loss program. Arch Intern Med 2006;166(15):1620-1625. [doi:
10.1001/archinte.166.15.1620] [Medline: 16908795]
39. Stapleton P, Bannatyne A, Chatwin H, Urzi K, Porter B, Sheldon T. Secondary psychological outcomes in a controlled
trial of Emotional Freedom Techniques and cognitive behaviour therapy in the treatment of food cravings. Complement
Ther Clin Pract 2017 Aug;28:136-145. [doi: 10.1016/j.ctcp.2017.06.004] [Medline: 28779921]
40. Zerwas SC, Watson HJ, Hofmeier SM, Levine MD, Hamer RM, Crosby RD, et al. CBT4BN: A randomized controlled
trial of online chat and face-to-face group therapy for bulimia nervosa. Psychother Psychosom 2017;86(1):47-53 [FREE
Full text] [doi: 10.1159/000449025] [Medline: 27883997]
41. Wadden TA, Hollander P, Klein S, Niswender K, Woo V, Hale PM, NN8022-1923 Investigators. Weight maintenance and
additional weight loss with liraglutide after low-calorie-diet-induced weight loss: The SCALE Maintenance randomized
study. Int J Obes (Lond) 2013 Nov;37(11):1443-1451. [doi: 10.1038/ijo.2013.120] [Medline: 23812094]
42. Srivastava G, Buffington C. A specialized medical management program to address post-operative weight regain in bariatric
patients. Obes Surg 2018 Aug;28(8):2241-2246. [doi: 10.1007/s11695-018-3141-z] [Medline: 29464536]
43. Pareek M, Schauer PR, Kaplan LM, Leiter LA, Rubino F, Bhatt DL. Metabolic surgery: Weight loss, diabetes, and beyond.
J Am Coll Cardiol 2018 Feb 13;71(6):670-687 [FREE Full text] [doi: 10.1016/j.jacc.2017.12.014] [Medline: 29420964]
44. Vehrs PR, Hager R. Assessment and interpretation of body composition in physical education. J Phys Educ Recreat Dance
2013 Jan 26;77(7):46-51 [FREE Full text] [doi: 10.1080/07303084.2006.10597907]
45. Kyle UG, Schutz Y, Dupertuis YM, Pichard C. Body composition interpretation. Contributions of the fat-free mass index
and the body fat mass index. Nutrition 2003;19(7-8):597-604. [doi: 10.1016/s0899-9007(03)00061-3] [Medline: 12831945]
46. Kim EK, Kwak SH, Jung HS, Koo BK, Moon MK, Lim S, et al. The effect of a smartphone-based, patient-centered diabetes
care system in patients with type 2 diabetes: A randomized, controlled trial for 24 weeks. Diabetes Care 2019 Jan;42(1):3-9.
[doi: 10.2337/dc17-2197] [Medline: 30377185]
47. Fabricatore AN. Behavior therapy and cognitive-behavioral therapy of obesity: Is there a difference? J Am Diet Assoc 2007
Jan;107(1):92-99. [doi: 10.1016/j.jada.2006.10.005] [Medline: 17197276]
48. Alkhaldi G, Hamilton FL, Lau R, Webster R, Michie S, Murray E. The effectiveness of prompts to promote engagement
with digital interventions: A systematic review. J Med Internet Res 2016 Jan 08;18(1):e6 [FREE Full text] [doi:
10.2196/jmir.4790] [Medline: 26747176]
49. Byambasuren O, Sanders S, Beller E, Glasziou P. Prescribable mHealth apps identified from an overview of systematic
reviews. NPJ Digit Med 2018;1:12 [FREE Full text] [doi: 10.1038/s41746-018-0021-9] [Medline: 31304297]
50. Jeong EK, Kim KN. Influence of stress on snack consumption in middle school girls. Nutr Res Pract 2007;1(4):349-355
[FREE Full text] [doi: 10.4162/nrp.2007.1.4.349] [Medline: 20368961]
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 17http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
51. Teixeira PJ, Going SB, Sardinha LB, Lohman TG. A review of psychosocial pre-treatment predictors of weight control.
Obes Rev 2005 Feb;6(1):43-65. [doi: 10.1111/j.1467-789X.2005.00166.x] [Medline: 15655038]
52. Teixeira PJ, Going SB, Houtkooper LB, Cussler EC, Martin CJ, Metcalfe LL, et al. Weight loss readiness in middle-aged
women: Psychosocial predictors of success for behavioral weight reduction. J Behav Med 2002 Dec;25(6):499-523. [doi:
10.1023/a:1020687832448] [Medline: 12462956]
53. Teixeira PJ, Going SB, Houtkooper LB, Cussler EC, Metcalfe LL, Blew RM, et al. Pretreatment predictors of attrition and
successful weight management in women. Int J Obes Relat Metab Disord 2004 Sep;28(9):1124-1133. [doi:
10.1038/sj.ijo.0802727] [Medline: 15263921]
54. Teixeira V, Voci SM, Mendes-Netto RS, da Silva DG. The relative validity of a food record using the smartphone application
MyFitnessPal. Nutr Diet 2018 Apr;75(2):219-225. [doi: 10.1111/1747-0080.12401] [Medline: 29280547]
55. Beasley J, Riley WT, Jean-Mary J. Accuracy of a PDA-based dietary assessment program. Nutrition 2005 Jun;21(6):672-677.
[doi: 10.1016/j.nut.2004.11.006] [Medline: 15925290]
Abbreviations
ATQ-30: Automatic Thoughts Questionnaire
AUC: area under the curve
BSQ: Body Shape Questionnaire
BSQ-8C: Body Shape Questionnaire-8C
CBT: cognitive behavioral therapy
DEBQ: Dutch Eating Behavior Questionnaire
DEBQ-EM: Dutch Eating Behavior Questionnaire emotional eating scale
DEBQ-EX: Dutch Eating Behavior Questionnaire external eating scale
DEBQ-RE: Dutch Eating Behavior Questionnaire restrained eating scale
eHealth: electronic health
HOMA-IR: Homeostatic Model for Assessment of Insulin Resistance
K-BDI-II: Korean version of the Beck Depression Inventory-II
mHealth: mobile health
NRF: National Research Foundation of Korea
RCT: randomized controlled trial
ROC: receiver operating characteristic
RSES: Rosenberg Self-Esteem Scale
SIMS: Situational Motivation Scale
SNP: single nucleotide polymorphism
SNU: Seoul National University
TAI: Trait Anxiety Inventory
Edited by G Eysenbach; submitted 25.05.19; peer-reviewed by S Smith, J Chen, A Pfammatter, M Gaman; comments to author 30.09.19;
revised version received 13.10.19; accepted 09.02.20; published 30.04.20
Please cite as:
Kim M, Kim Y, Go Y, Lee S, Na M, Lee Y, Choi S, Choi HJ
Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized
Controlled Trial
JMIR Mhealth Uhealth 2020;8(4):e14817
URL: http://mhealth.jmir.org/2020/4/e14817/
doi: 10.2196/14817
PMID: 32352391
©Meelim Kim, Youngin Kim, Yoonjeong Go, Seokoh Lee, Myeongjin Na, Younghee Lee, Sungwon Choi, Hyung Jin Choi.
Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 30.04.2020. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and
uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/,
as well as this copyright and license information must be included.
JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 4 | e14817 | p. 18http://mhealth.jmir.org/2020/4/e14817/ (page number not for citation purposes)
Kim et alJMIR MHEALTH AND UHEALTH
XSL
•
FO
RenderX
