Original Paper
Comparing a Tailored Self-Help Mobile App With a Standard
Self-Monitoring App for the Treatment of Eating Disorder
Symptoms: Randomized Controlled Trial
Jenna Tregarthen
1
, MA; Jane Paik Kim
2
, PhD; Shiri Sadeh-Sharvit
2
, PhD; Eric Neri
2
, MA; Hannah Welch
2
, BA;
James Lock
2
, MD, PhD
1
Recovery Record, San Francisco, CA, United States
2
Stanford University School of Medicine, Stanford, CA, United States
Corresponding Author:
Jenna Tregarthen, MA
Recovery Record
2190 Beach Street
San Francisco, CA, 94123
United States
Phone: 1 6504047098
Email: jenna@recoveryrecord.com
Abstract
Background: Eating disorders severely impact psychological, physical, and social functioning, and yet, the majority of individuals
with eating disorders do not receive treatment. Mobile health apps have the potential to decrease access barriers to care and reach
individuals who have been underserved by traditional treatment modalities.
Objective: The objective of this study was to evaluate the effectiveness of a tailored, fully automated self-help version of
Recovery Record, an app developed for eating disorders management. We examined differences in eating disorder symptom
change in app users that were randomized to receive either a standard, cognitive behavioral therapy–based version of the app or
a tailored version that included algorithmically determined clinical content aligned with baseline and evolving user eating disorder
symptom profiles.
Methods: Participants were people with eating disorder symptoms who did not have access to traditional treatment options and
were recruited via the open-access Recovery Record app to participate in this randomized controlled trial. We examined both
continuous and categorical clinical improvement outcomes (measured with the self-report Eating Disorder Examination
Questionnaire [EDE-Q]) in both intervention groups.
Results: Between December 2016 and August 2018, 3294 Recovery Record app users were recruited into the study, out of
which 959 were considered engaged, completed follow-up assessments, and were included in the analyses. Both study groups
achieved significant overall outcome improvement, with 61.6% (180/292) of the tailored group and 55.4% (158/285) of the
standard group achieving a clinically meaningful change in the EDE-Q, on average. There were no statistically significant
differences between randomized groups for continuous outcomes, but a pattern of improvement being greater in the tailored group
was evident. The rate of remission on the EDE-Q at 8 weeks was significantly greater in the group receiving the tailored version
(d=0.22; P≤.001).
Conclusions: This is the first report to compare the relative efficacy of two versions of a mobile app for eating disorders. The
data suggest that underserved individuals with eating disorder symptoms may benefit clinically from a self-help app and that
personalizing app content to specific clinical presentations may be more effective in promoting symptomatic remission on the
EDE-Q than content that offers a generic approach.
Trial Registration: ClinicalTrials.gov NCT02503098; https://clinicaltrials.gov/ct2/show/NCT02503098.
(JMIR Ment Health 2019;6(11):e14972) doi: 10.2196/14972
KEYWORDS
mobile health; smartphone; mobile apps; eating disorders; cognitive behavioral therapy; mental health; intervention study
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Introduction
Background
The need for scalable delivery of eating disorder (ED) care
services that are clinically effective and broadly accessible is
now a major public health priority. EDs are common mental
disorders that are both psychologically debilitating and
physically threatening. Approximately 13% of young women
and 1.93% to 6% of adults will meet the criteria for an ED in
their lifetime, and 3% to 3.5% of men also struggle with an ED
[1-4]. Despite the severity and burden of EDs, they often remain
undetected, and the majority of individuals with EDs do not
seek or receive mental health care [5,6]. Recent systematic
reviews found that as few as 23% of people with a diagnosable
ED seek conventional treatment [5], and about only 1 in 10
individuals with this illness receive treatment [7].
There are significant barriers to access to ED treatments,
including high cost of care, inadequate insurance coverage [8,9],
paucity of trained clinicians [10,11], and experiences of shame
or fear of stigmatization [12]. One study found that it took
individuals, on average, 3.6 years to acknowledge that they were
suffering from an ED and a further 4.2 to 6.3 years to seek
treatment [13]. Unfortunately, these delays are costly, as over
time, EDs become more severe and less responsive to treatment
[12,14]. There is evidence that the duration of ED is adversely
associated with the treatment outcome [15,16]. However, even
if all people with EDs were to seek conventional treatment, the
current models of treatment delivery would be insufficient to
meet the enormous need. A major shift in intervention practice
is warranted with a focus on reaching more individuals in a
more cost-effective manner, while at the same time achieving
clinically meaningful improvement. Mobile health (mHealth)
apps will almost certainly play a role because of their reach and
breadth of functionality.
At least 271 million people in the United States or 94% of the
population own a mobile phone, and smartphone use has reached
77% population penetration, with uptake spanning all
socioeconomic groups [17]. mHealth apps have the potential
to decrease the aforementioned treatment access gap for EDs
and reach individuals who have traditionally been underserved
by existing treatment modalities. By offering anonymous,
accessible, affordable, and engaging interventions, barriers to
receiving care can be reduced. The convenience of an
intervention that can be accessed in moments of need at any
location may enhance acceptability, and the scalable nature of
technology holds promise for delivering support in a
cost-effective manner [18].
Objectives
One example of an mHealth app for EDs is Recovery Record
(RR). RR has established population-level reach and user
acceptability [19]. Although RR was initially developed as an
adjunctive tool to support clinical treatment, a large portion of
app users access the tool without the accompanying forms of
traditional face-to-face treatment. A 2014 case report surveyed
over 100,000 RR app users and found that 46% were not
receiving clinical treatment, and 33% of users reported that they
had not told anyone about their ED [19]. The study further found
that 80% of users had experienced symptoms for 5 to 10 years,
and 58.3% had Eating Disorder Examination Questionnaire
(EDE-Q) global scores of 2 or more SDs above community
norms [20]. Hence, the RR app was found to be successful at
reaching and engaging many people with severe and enduring
ED symptoms who were otherwise not receiving care.
Incorporated into RR app’s core functionality are cognitive
behavioral therapy (CBT)–based eating and symptom
monitoring, CBT-style coping skills, goal setting, and
motivational messaging. Self-help CBT can be an effective
intervention for some EDs, and preliminary data suggest that
RR might be effective as a stand-alone self-help intervention.
Data from 1178 RR app users who were not receiving clinical
treatment revealed that after using RR for 1 month, 28% of
participants no longer scored in the clinical range on the EDE-Q
and 39% were clinically improved [21]. These response rates
approximate those observed in studies of therapist-assisted
internet-based treatments for EDs [22,23]. Another study found
that RR users naturally clustered into 5 clinical groups that could
be mapped onto the existing Diagnostic and Statistical Manual
of Mental Disorders ED categories [24]. Of further interest, a
signal detection analysis revealed that RR intervention response
was not homogenous across the sample and that outcome varied
by clinical presentation. For example, those with binge eating
and purging symptoms were found to be more likely to respond
to the RR app than those with mostly restrictive behaviors [21].
Overall, these data indicate that there are distinct RR user groups
who already utilize the app and may derive greater clinical
benefit from a personalized intervention that targets their
specific clinical needs [25]. As a next step, a new tailored
version of the RR app was developed, including an 8-week
program of personalized content specifically addressing baseline
and evolving clinical characteristics. A pilot study demonstrated
the feasibility of deploying the tailored version of the app to a
sample of 189 app users and validated acceptability of the new
intervention developed by the study team [26].
The purpose of this study was to examine whether a personalized
app for EDs would be superior to the universal app in reducing
negative outcomes when used in self-help capacity. Specifically,
we were interested in studying the differences in symptom
change in users of RR that were randomized to either the
standard RR app (RR-S) or the tailored version of RR (RR-T),
which included algorithmically determined content aligned with
user baseline ED symptom profiles. Our primary hypothesis
was that those who received RR-T would demonstrate greater
clinical improvements compared with those who received RR-S.
Methods
Participants
RR app is free and publicly available via the Google Play
(Android) and iTunes (iPhone) app stores. Potential participants
were recruited from within the app registration system. All users
were asked to provide consent. Users were eligible for inclusion
if they (1) had downloaded the app on their iPhone, (2) were
located in the United States, and (3) recorded at least three
self-monitoring entries before being contacted about the study.
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The focus of this study was on underserved populations who
might not have access to best practice treatment options. As
such, individuals were considered ineligible to join the study if
they were using RR linked with a treatment provider or indicated
that they were receiving treatment at least weekly from a
specialist ED provider. The study received Institutional Review
Board approval, and participants did not receive any payment
for completing assessments.
Study Design
Randomization
Participants randomized to RR-T were probabilistically assigned
to 1 of the 5 clusters based on their baseline demographic
characteristics and EDE-Q scores. Each participant was
randomly assigned to a cluster with a probability inversely
proportional to his or her distance to each cluster mean. This
distance was defined as the Euclidean distance between a
participant’s coordinates (ie, all baseline measures) and the
cluster mean. This method meant that participants were more
likely to be assigned to the symptom cluster they were most
similar to.
Tailored Intervention
Details on the app and the development of the tailored
intervention have been described in earlier reports [24,26].
Informed by baseline cluster assignment and existing knowledge
about CBT-based strategies for addressing ED symptoms and
cognitive distortions, novel and tailored content was developed
for each baseline symptom cluster group. Descriptions and
examples of each key feature are provided in Table 1. The
tailored intervention took the form of an 8-week program that
delivered tailored content to complement the standard app.
Specifically, the tailored app is configured with cognitive
behavioral self-monitoring questions that are differentiated
according to user baseline symptom cluster assignment. Users
in the tailored group were also invited to complete a progress
review on a weekly basis. Components of the progress review
included the following: a summary of recovery-oriented
milestones achieved (see Figure 1); a self-guided review of goal
progress and perceived helpfulness of coping skills (see Figure
1); the selection of new goals and coping strategies from a
curated, tailored list for the week ahead; and, finally, the
identification of possible obstacles to achieving chosen goals
(see Figure 1).
The weekly goal selection was designed to encourage task
practice of specific activities each day and then to facilitate
rating of activities on the degree of mastery and/or pleasure in
the weekly progress review. Goal options follow the CBT for
EDs framework and aim to disrupt mechanisms that may be
maintaining symptoms reflected in baseline profiles and ongoing
meal logs. Skill-based components of the tailored version of
the app provide the opportunity to learn and implement strategies
for managing distorted cognitions that fuel both the emotional
and behavioral responses to engage in unhealthy eating or weight
loss practices. Given the limited presentation capacity of a
smartphone, content is skill- and goal-based rather than
psychoeducational and aims to maximize user engagement and
generalizability rather than present large amounts of data.
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Table 1. Key Recovery Record tailored app features.
ExampleDescriptionFeature
If a participant endorsed binge eating in their baseline ques-
tionnaire, then the questions “Did you binge eat?” and “Do
you have an urge to binge eat?” are included in meal logs.
Self-monitoring questions are customized based on
baseline symptoms. Participants can also optionally
enable additional questions if relevant to their needs.
Customized self-monitoring
questions
If a participant indicated in a meal log that they were experi-
encing an urge and, in the same entry, endorsed the use of a
coping strategy, the following weekly milestone would be
displayed: “You discovered <number> new coping strategies
for responding to a difficult feeling or urge.”
Each week the app displays 4 to 7 user achievements
based on participants’ daily self-monitoring entries.
Participants can also optionally enter additional
achievements not captured by the app.
Weekly milestones
If a user had previously selected a goal to preplan their meals,
they would be asked how they are progressing toward the goal,
with the following response options: “I haven’t thought about
it yet,” “I have thought about it,” “I have a plan and will put
it into action today,” “I did this several days this week,” and
“I did this every day.”
On a weekly basis, the app displays the SMART
a
-style
goals that the participant had selected in the prior week
and prompts them to evaluate goal progress.
Goal progress review
If a user had selected “Mindful Eating” in the prior week, they
would be asked how many times they tried the technique, with
0, 1, 2 to 3, and ≥4 response options, and to evaluate how
much the skill helped on a Likert scale.
Following the goal progress review, the app displays
coping skills selected in the prior week and prompts
the participants to evaluate their utility and helpfulness.
Coping skill review
If a user has baseline dietary restriction symptoms, they may
be presented with the optional goal to keep track of their trig-
gers: “I will notice and record dietary restriction triggers in
Recovery Record. To identify triggers, I will ask, ‘what set
me off?’Triggers amplify eating disordered thinking and make
me more vulnerable to relapse. Examples: Feeling unwell,
drinking alcohol, certain emotions, body comments, negative
self-talk, weight gain, confrontation, financial stress, lack of
sleep.”
An 8-week program of SMART-style goals was devel-
oped for each baseline symptom cluster group. Each
week, 4 to 6 goals are presented to the participants
who are invited to select at least two goals to work on
each day of the upcoming week. Users are prompted
on a daily basis during the week, at a time they select,
to review their progress.
Weekly goal selection
If a participant has baseline binge eating symptoms and intru-
sive thoughts, they may be presented with the “Questioning
the Evidence” skill to: “Catch the actual thoughts you are
thinking when you’re in a situation that upsets you. Examine
them to see if they’re valid. Ask: Where’s the evidence for
this? What do you get if you ‘buy’into that thought? Where
does it leave you and does it bring you closer to your best self?
Consider these example thoughts: ‘If I keep X food in the
house, I can prove I am strong enough to recover,’‘My eating
problem has already ruined X,’ ‘What do I have to gain from
recovering now?’.”
An 8-week program of coping skills was developed
for each baseline symptom cluster group to comple-
ment the program of goals. Each week, 4 to 6 coping
skills are presented to the participants who are invited
to select at least two skills to try out in the upcoming
week. Users are prompted to utilize their selected skills
in real time when they self-monitor relevant symptoms.
Weekly coping skill selection
If a participant selected a goal of eating something at every
meal and snack, a suggested barrier to action might be “Having
to give up the short-term reward of meal skipping.”
A list of potential barriers or obstacles that participants
may experience when trying to achieve their goals is
presented. Participants select obstacles that are relevant
to them and identify actions they can take to overcome
them.
Obstacle identification
a
SMART: 8-week program of coping skills for each symptom cluster group.
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Figure 1. Select recovery record adaptive application features.
Standard App Intervention
Users randomly assigned to the standard app were also prompted
to complete meal and symptom self-monitoring in an
evidence-based CBT format that has been described previously
[19]; however, they did not have access to the weekly progress
review, including tailored milestone feedback, coping skill and
goal content, or obstacle identification. Both versions of the
app also included psychoeducation regarding skills to increase
distress tolerance and overcome urges to engage in disordered
behaviors and included textual and image affirmation content
targeting motivational enhancement (see Figure 1).
Clinical Outcomes
The EDE-Q is a self-report measure of ED psychopathology
and behaviors that has been shown to have good reliability
[20,27]. We examined both continuous and categorical outcomes
related to clinical improvement in ED psychopathology in the
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randomized groups at baseline, 4 weeks, and 8 weeks. At the
relevant time intervals, participants were prompted with a banner
on the home screen within the app to complete the in-app
EDE-Q assessment. An automated email was also delivered to
participants to notify them when an assessment was available
within the app.
Primary Outcome
The primary dichotomous outcome of a response, that is,
clinically meaningful change, was defined as an improvement
(ie, decrease) in the EDE-Q global score by a 0.5 SD. A
secondary outcome of remission on the EDE-Q was defined as
being within the range of 1 SD around the mean, based on the
global EDE-Q (community norm of 1.55) [20].
Secondary Analysis
Frequencies of objective binges, vomiting, and excessive
exercise over the previous 28 days were derived from EDE-Q
questions 14, 16, and 18, respectively. The categorical outcomes
for abstinence were defined as whether the participant endorsed
0 instances of binge eating (or purging or excessive exercise)
at follow-up. We also examined continuous outcomes defined
as the differences in the EDE-Q item 14 between baseline, week
4, and week 8. We repeated this outcome analysis using items
16 and 18 on the EDE-Q.
Statistical Analysis
Primary Analysis
To address the primary hypothesis that RR-T improves EDE-Q
total score, a complete case analysis was used. All participants
randomized to the 2 treatment conditions and who had outcome
data (week 4 or 8) were included in the analysis. To determine
whether a clinical improvement in the RR-T arm occurred at 4
and 8 weeks, two-sample z tests for proportions were used.
Effect sizes (ie, success rate differences) were reported. All tests
were 2-sided and performed at the 0.05 level of significance.
We note that complete case analysis will only be unbiased under
the missing completely at random (MCAR) assumption, that
is, it is valid only when the missingness probability does not
depend on the outcome [28].
Covariate adjustment was performed to address a secondary
hypothesis of whether there was conditional independence
between the treatment assignment and clinical improvement,
given other variables, that is, we tested a secondary hypothesis
of whether there was a treatment effect within strata defined by
the variables mentioned above. This covariate adjustment
analysis addresses a different null hypothesis than the primary
hypothesis of testing the unconditional treatment effect.
Covariate adjustment was performed using generalized linear
mixed models and linear mixed models as appropriate, with the
treatment assignment indicator, treatment by time interaction,
and other variables including baseline severity and duration of
app usage. Gender and treatment frequency were not used
because of sparsity in groups.
Secondary Analysis
A sensitivity analysis was conducted using clinical end points
defined by a change in EDE-Q global score by 0.75 SD and by
0.25 SD. We conducted an analysis using the outcome of
remission as defined above. Outcomes of remission were binary
and remission rates, that is, proportions were computed for each
arm at each time point. Differences between the remissions rates
observed in RR-T and RR-S arms at weeks 4 and 8 were
evaluated by z tests for proportions, with a significance level
of 0.05. We also constructed graphical summaries of the
proportion of remitters over time per arm.
A per-protocol analysis was performed, excluding subjects who
failed to submit logs over a duration of less than 35 days (out
of 69 possible days). The threshold for the inactive period, that
is, 35 days, was determined via exploratory data analysis
including histograms. To determine whether a clinical
improvement in the RR-T arm occurred at 4 and 8 weeks as the
clinical end points, z tests for proportions were used. All tests
were 2-sided and performed at the 0.05 level of significance.
Subgroup analyses were performed for ED behaviors such as
objective binge eating, vomiting, and excessive exercise as
indicated by items 14, 16, and 18 on the EDE-Q, respectively.
We performed a subgroup analysis among participants who
endorsed nonzero instances of binge eating, purging, and
excessive exercise, as indicated by items 14, 16, and 18,
respectively, on the EDE-Q at baseline. Participants who did
not endorse such behaviors at baseline were excluded from this
analysis. To compare proportions of abstainers across
randomized groups, an intention-to-treat (ITT) analysis was
used. To determine whether group differences in eating
behaviors (with respect to binge eating, purging, and over
exercise) occurred at 4 and 8 weeks, z tests for proportions were
used. Proportions of individuals who experienced a worsening
of the raw global EDE-Q score were assessed at weeks 4 and
8. It should be noted that in the absence of a known cut point
for clinically meaningful negative change in the EDE-Q global
score, any negative directional change in this score was included
in this portion of the analysis.
Results
Sample Characteristics
A total of 3440 RR users met eligibility criteria between the
months of December 2016 and August 2018 and were invited
to complete an in-app EDE-Q self-assessment as per current
procedure (see Figure 2 for a Consolidated Standards of
Reporting Trials diagram). Of these, 146 declined to participate
in the study, leaving 3294 who were randomized: 1665
participants were randomized to the standard, fully automated
self-help intervention (RR-S) and 1629 participants were
randomized to the personalized, tailored self-help intervention
(RR-T). Chance imbalances in the randomized group numbers
are attributable to our use of a simple randomization procedure.
A total of 123 participants reported 1 of the following exclusion
criteria after the start of the trial: dizziness, hospitalization,
fainting, or suicidal ideation—requiring them to be withdrawn
from the study. There were 15 participants (13 RR-S and 2
RR-T) who were excluded at 4 weeks because their EDE-Q was
completed outside of a 7-day window from the expected
completion at day 30. There were 39 participants (6 RR-S and
33 RR-T) who were excluded at 8 weeks because their EDE-Q
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was completed outside of a 7-day window from the expected
completion at day 60.
Table 2 presents a summary of demographic and usage
characteristics of the sample at week 4. All demographic
characteristics were balanced between groups. Moreover, 93%
(426/458) participants in the standard group were female and
95% (455/501) participants in the tailored group were female,
with 4.6% (21/458) [2.6% (13/501) tailored] reporting male
gender and 2.4% (11/458) [2.2% (11/501) tailored] reporting
other. The mean age of the participants was 34 (SD 12.3) years
in the standard group and 34.9 (SD 12.5) years in the tailored
group. Quartiles of the global EDE-Q score were all severe
(Quartile 1: [0.35, 3.12]; Quartile 2: [3.12,3.84]; Quartile 3:
[3.84,4.58]; Quartile 4: [4.58,5.95]).
Figure 2. Consolidated Standards of Reporting Trials diagram. There were 15 excluded (13 RR-S and 2 RR-T) at 4 weeks because their EDE-Q was
completed outside of a 7-day window from the expected completion at day 30. There were 39 excluded (6 RR-S and 33 RR-T) at 8 weeks because their
EDE-Q was completed outside of a 7-day window from the expected completion at day 60. EDE-Q: Eating Disorder Examination Questionnaire; RR-S:
standard Recovery Record app; and RR-T: tailored version of Recovery Record app.
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Table 2. Demographic characteristics of participants.
Tailored version of Recovery Record app
(RR-T; n=501)
Standard Recovery Record app (RR-S;
n=458)
Demographical descriptors
34.9 (12.5)34.0 (12.3)Age (years), mean (SD)
Gender, n (%)
477 (95.2)426 (93.0)Female
13 (2.6)21 (4.6)Male
11 (2.2)11 (2.4)Other
Race or ethnicity, n (%)
407 (81.2)385 (84.1)White
22 (4.4)14 (3.1)Hispanic or Latino
20 (4.0)13 (2.8)Asian
13 (2.6)13 (2.8)Black or African American
1 (0.2)1 (0.2)American Indian or Alaska Native
22 (4.4)29 (6.3)Multiple race or ethnicity
16 (3.2)3 (0.7)Unknown
Eating problem—how long? (years), n (%)
20 (4.0)19 (4.1)0
113 (22.6)130 (28.4)1-5
102 (20.4)80 (17.5)6-10
58 (11.6)57 (12.4)11-15
90 (18.0)70 (15.3)15-25
118 (23.6)102 (22.3)≥25
28.7 (8.6)
c
29.0 (8.9)
b
Body mass index
a
, mean (SD)
Treatment history, n (%)
249 (49.7)239 (52.2)I have never received treatment for an eating disorder
169 (33.7)145 (31.7)I have received treatment for an eating disorder in the
past
83 (16.6)74 (16.2)I am currently receiving treatment for an eating disorder
Treatment frequency (for those currently receiving treatment for an eating disorder), n (%); (N=74 RR-S, N=83 RR-T)
57 (68.7)46 (62.2)2-3 times per month
19 (22.9)19 (25.7)Monthly or less
7 (8.4)9 (12.2)Occasionally or as needed
Treatment types (participants could choose more than one; N=74), n (%); (N=74 RR-S, N=83 RR-T)
66 (79.5)64 (86.5)Licensed mental health professional
46 (55.4)36 (48.6)Dietitian or nutritionist
5 (6.0)2 (2.7)Life coach or mentor
13 (15.7)9 (12.2)Support group or advocacy organization
5 (6.0)5 (6.8)Other
a
Excluded 2 standard and 3 tailored Recovery Record app participants with body mass index >65.
b
n=427.
c
n=469.
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Analyses
Unadjusted Analysis
The responder proportions in the tailored and standard groups
were moderately large. At week 4, approximately half (51.5%;
227/441) of the tailored group achieved a clinically meaningful
change in EDE-Q, compared with 46.2% (156/338) of the
standard group. At week 8, the proportion of treatment
responders was slightly greater, with 61.6% (180/292) of the
tailored group achieving a clinically meaningful change,
compared with 55.4% (158/285) of the standard (see Figure 3).
Responder proportions were not statistically different across
treatment and control groups at week 4 or 8 (P=.16 or P=.15;
effect sizes=0.05 and 0.06, respectively). Both groups
experienced slight improvements in the global EDE-Q score
from baseline to week 4 (−0.8 and −0.7 for treatment and control
groups, respectively) and from baseline to week 8 (−0.99 and
−1.0 for treatment and control groups, respectively).
Figure 3. Proportions of responders at weeks 4 and 8. EDE: Eating Disorder Examination; RR-S: standard Recovery Record app; RR-T: tailored version
of Recovery Record app.
Sensitivity Analysis
We repeated the unadjusted analysis replacing the outcome of
clinically meaningful change based on a 0.25 SD change and
0.75 SD change, respectively. Figure 3 presents the sensitivity
analysis. There were no statistically significant differences
between randomized groups found in this analysis.
Covariate Adjustment
The covariate-adjusted treatment effect is consistent with the
ITT analysis (conditional odds ratio [OR] 1.2; P=.59). Results
from generalized linear mixed-effects model estimates showed
that subjects with a higher baseline severity (>3 global EDE-Q)
were more likely to achieve a clinically meaningful change
(conditional OR 3.5; P<.001). Although treatment and
comparison groups did not differ over time, the effect of time
was significant (conditional OR 1.12; P=.01): users were 12%
more likely to achieve improvement for each additional week
of being in the study, holding group assignment constant.
Remission Analysis
Figure 4 presents the remission analysis. At week 4, the
proportions of users reporting symptoms within community
norms in both groups increased; however, the difference between
groups also widened: 44.8% (198/441) of participants receiving
the tailored app were remitters, and 35.5% (120/338) of
participants receiving the standard app were remitters (P value
for z test of proportions=.008; effect size=0.09). At week 8, the
proportion of participants receiving the tailored app meeting
the community norms criteria increased to 53.3% (137/257),
whereas that of participants receiving the standard app slightly
decreased to 31.1% (70/225; P value for z test of proportions
≤.001; effect size=0.22).
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Figure 4. Proportions of individuals whose EDE-Q scores were within community norms at weeks 4 and 8. EDE-Q: Eating Disorder Examination
Questionnaire; RR-S: standard Recovery Record app; RR-T: tailored version of Recovery Record app.
Per-Protocol Analysis
Among the tailored group, 57% (166/290) achieved a clinically
meaningful change in EDE-Q at week 4, compared with 48%
(47/98) in the standard group (P=.16; effect size=0.09). At week
8, the proportion of responders was slightly greater, with 63%
(138/219) of the tailored group achieving a clinically meaningful
change, compared with 53% (62/118) of the standard (P=.08;
effect size=0.10).
Subgroup Analyses
Table 3 presents the proportions of abstainers. At baseline, the
number of participants who endorsed any binge episodes did
not vary significantly by group: 1390 participants in the tailored
versus 1407 in the standard arm endorsed some binge eating
(tailored=409 and standard=422 endorsed purging; tailored=705
and standard=753 endorsed excessive exercise). At week 4, the
proportion of abstainers for binge eating was 14% (51/359) and
13% (38/287) of the tailored and standard groups, respectively.
For purging, abstainers comprised 28% (27/96) and 35% (28/81)
of the tailored and standard groups, respectively. For excessive
exercise, higher proportions were observed— 40.6% (73/180)
and 29.5% (44/149) in the tailored and standard groups. At week
8, the proportion of abstainers slightly increased with respect
to binging [20% (49/241) vs 18% (40/227)] and purging [42%
(21/52) vs 40% (26/64)], but the proportion of abstainers for
excessive exercise decreased [45% (47/104) vs 40% (47/116)].
There were no significant differences between groups on any
of these variables.
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Table 3. Eating behaviors of subgroups of participants who endorsed eating behaviors at baseline.
P valueTailored version of Recovery Record appStandard Recovery Record appEating behaviors
ValuesTotal number of participants
Values
a
Total number of participants
Week 4
Objective binge
b
.8138 (13)28751 (14.2)359
Abstinent
c
, n (%)
.87−4.2 (10.8)287−4.3 (9.4)359
Change in score
d
, mean
(SD)
Purge
e
.4128 (34.6)8127 (28.1)96Abstinent, n (%)
.55−2.8 (6.4)81−2.0 (10.3)96Change in score, mean (SD)
Objective binge and purge
.9937 (13)29547 (13)373Abstinent, n (%)
.67−4.8 (11.4)295−4.4 (12.1)373Change in score, mean (SD)
Excessive exercise
f
.0544 (29.5)14973 (40.6)180Abstinent, n (%)
.12−3.4 (2.4)149−5.1 (2.9)180Change in score, mean (SD)
Week 8
Objective binge
.3740 (17.6)22749 (20.3)241Abstinent, n (%)
.11−7.0 (9.8)227−5.5 (11.0)241Change in score, mean (SD)
Purge
.9926 (40.6)6421 (42.0)52Abstinent, n (%)
.54−3.7 (8.1)64−4.6 (7.9)52Change in score, mean (SD)
Objective binge and purge
.7142 (17.6)23848 (19.3)249Abstinent, n (%)
.14−7.6 (11.5)238−6.0 (11.7)249Change in score, mean (SD)
Excessive exercise
.5747 (40.5)11647 (45.2)104Abstinent, n (%)
.57−4.4 (7.7)116−5.0 (7.9)104Change in score, mean (SD)
a
Values: Values refer to “n (%)”; or “mean (SD)” as appropriate.
b
Objective Binge: participant report of eating what other people would regard as an unusually large amount of food and experiencing a sense of loss of
control while eating.
c
Abstinent: participants who abstained from behavior.
d
Change in Score: the difference in the binge (or purge) items from the EDE-Q questionnaire.
e
Purge: participant report of making oneself sick (vomit) as a means of controlling shape and weight.
f
Excessive exercise: participant report of exercising in a “driven” or “compulsive” way as a means of controlling weight, shape or amount of fat or to
burn off calories
Worsening of Pathology in Terms of Raw Eating
Disorder Examination Questionnaire Global Score
At week 4, we observed that 16% (59/374) of the tailored group
experienced a directional worsening of raw EDE-Q global score,
compared with 23% (67/296) of the standard group (P=.03,
before multiple comparison correction). After correcting for
multiple comparisons, the difference at week 4 was not
significant. At week 8, 15% (39/250) of the tailored group
experienced a directional worsening of raw EDE-Q global score,
compared with 19% (47/238) of the standard group (P=.28). In
the absence of a known cut point for clinically meaningful
negative change in the EDE-Q global score, any negative
directional change in this score was included in this portion of
the analysis.
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Discussion
Individuals with EDs are in urgent need of more affordable,
accessible, empirically supported, and engaging interventions.
This study is important because it is the first randomized
controlled trial to evaluate the efficacy of a personalized app
for the self-management of EDs. The study makes an important
contribution to the field in its focus on an under-researched and
underserved population—people with ED symptoms who may
not otherwise have access to traditional treatment options.
Principal Findings
Although there were no statistical differences (including in the
sensitivity analyses) between randomized groups for continuous
outcomes, the pattern of improvement was greater in the
personalized, tailored version of the app. However, participants
in both the tailored and standard app groups achieved a high
overall level of response, with more than 50% of participants
in each group achieving clinically meaningful change on the
EDE-Q at week 8. These response rates indicate that both
versions of the app may be beneficial. It should be noted that
as yet, there is no standard definition of clinically meaningful
change in EDE-Q global scores [29]. As such, a moderate effect
size was utilized in this primary analysis.
When examining remission status on the EDE-Q as a categorical
outcome, we detected a statistically significant difference
between the groups associated with a small effect size. In this
study, remission was defined as a score within 1 SD of the
community norm, which suggests that symptoms are no longer
in the clinical range. These results are encouraging as many app
users do not have access to therapists or other treatments, and
the tailored version moves more of them out of the clinical range
than the standard app.
Contrary to previous research findings [21], we did not find
substantial evidence that individuals with mostly restrictive
behaviors are less likely to respond to the RR app. Given the
transdiagnostic approach to EDs, adults with restrictive
symptoms may benefit from a CBT-focused app [30]. This is
an important contribution to the literature, given that there are
very few studies of self-help for anorexia nervosa. Clinical
improvement instead appeared to be related to symptom severity.
Participants with higher baseline severity were more likely to
achieve clinically meaningful change. It is noteworthy, however,
that according to baseline EDE-Q scores, the sample as whole
was extremely ill. Therefore, although clinical change was
greatest in the most severe group, it might be less dramatic in
the groups, overall [30]. It is also possible that there are
attributes of participants with high symptom severity not
captured in this study that moderated outcome. We also
examined changes in objective binge eating, purging, and
exercise in the 2 randomized groups (see Table 3). There were
improvements in these behaviors across the sample, with no
differences between the 2 groups. Finally, we examined whether
some participants worsened using the app. We found that
approximately 15% to 20% of the participants experienced a
directional worsening of their EDE-Q global score during the
study app, with no differences between the groups. In the
absence of validated negative change cut point for the EDE-Q
scores, it is difficult to determine what portion of these
individuals experienced clinically meaningful deterioration.
Strengths and Limitations
Several novel aspects of the study should be emphasized: design
of the intervention components; use of a tailored randomization
scheme for the tailored arm, that is, probabilistically assigning
people to clusters; naturalistic recruitment within the app’s
existing user pool; and all screening, recruitment, randomization,
and assessments being completed within the app. Nevertheless,
there are significant limitations of the study. As the intervention
is disseminated through an app, our study inherits a host of
challenges that come with the large-scale usage of mobile
devices in intervention research. Among the challenges
addressed during the study were the implementation of the
intervention and recruitment of nonpatient participants, strategies
to assess compliance and engagement, and problems related to
study retention in the absence of the accountability that in-person
recruitment affords.
Although we attempted to obtain complete records to the extent
possible through the delivery of reminder emails and a lottery
for a gift card, it should be noted that of those who were initially
randomized, 23.6% (779/3294) provided outcome data at week
4 and even fewer at week 8 (577/3294, 17.5%). Given the high
proportion of missing data, an imputation approach would have
forced the reliance on an imputation model for 67% of the data
and thus presented an infeasible option. To handle the missing
data issue, we used a complete case analysis (see the study by
Little and Rubin [28] for more details on this approach). A
limitation of the complete case analysis is that the unbiasedness
of a complete case analysis is predicated on the validity of the
MCAR assumption. Although not without limitations, we
deemed that it was the most reasonable analytic strategy, given
the percentage of data observed. This limitation should be noted
in our instance, and the inferences are based on a subset of
participants who adhered to the assessment completion. This
result, in fact, provided a point to consider for future work, in
that a brief period of app usage assessment before randomization
should be incorporated in other or future randomized studies to
address this data problem.
With regard to study retention, there is a known high variability
of dropout rates in studies using self-help treatments for EDs,
ranging from 0% to 62% [31]. High dropout rates are common
in patients with EDs, even in face-to-face therapy [32].
Within-app recruitment may have additionally contributed to
attrition and/or lack of adherence during the study, although
attrition rates did not differ based on treatment allocation. These
challenges may also have been related to the population of
interest, that is, individuals lacking in adjunctive support
structures outside of the app. Therefore, the attrition rate
observed in this study should not be surprising, considering the
in-app recruitment and realities of smartphone app compliance
[33]. In fact, the observed rate could offer a perspective to the
literature as it provides evidence for appropriate and realistic
considerations for power that should be taken into account at
the early stages of study design. We should note that we
accounted for the probability of a high attrition rate when
designing the study, such that our resultant power calculations
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were based on the number needed in each group to detect a
difference of at least 80% power.
Another important limitation is that follow-up data on
maintenance of treatment effects is limited because of the short
8-week follow-up period. The effect of time was significant in
the study, with users 12% more likely to achieve improvement
for each additional week of being in the study. This raises the
question of optimal intervention duration. Future studies should
aim to assess duration of treatment effects and whether this
relates to user characteristics such as symptoms, severity,
demographics, motivation or compliance, and/or app content
during the intervention period. Diversity across ethnic groups
represented in the sample was a limitation of the study. An
additional avenue for future research may be to explore the
relative effect of ethnicity on outcome.
Conclusions
The results of this study suggest that a significant proportion
of ED app users benefit from using a self-help version of the
RR app; however, overall clinical improvements may be greater
and symptomatic remission may be significantly greater, with
a more specific matching of content to specific clinical
groupings as in the tailored version of the app used in this study.
More research should be conducted on how app-based self-help
can be integrated into a stepped care model of ED interventions,
thereby closing the treatment gap. The results suggest that apps
that use tailored contents are feasible to use; likely effective for
many in improving clinical symptoms; scalable; and, thus, may
reduce disease burden in those with EDs at low cost.
Acknowledgments
This study was supported by a Small Business Research grant awarded by the National Institute of Mental Health (R44MH108221).
Conflicts of Interest
The authors declare the following potential conflicts of interest with respect to the research, authorship, and/or publication of this
paper: JT is a cofounder of and shareholder in RR Inc. Although JT was involved in the design of the interventions (the app and
the tailored version of the app), the study design, the acquisition of funding, collection of data, and writing the paper, she was
not involved in data analysis. Owing to the possibility of perceived conflicts of interest as an owner of RR, all data analysis
procedures were conducted independently by the researchers at Stanford University. There are no other conflicts of interest to
declare.
Multimedia Appendix 1
CONSORT-EHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 2420 KB-Multimedia Appendix 1]
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Abbreviations
CBT: cognitive behavioral therapy
ED: eating disorder
EDE-Q: Eating Disorder Examination Questionnaire
ITT: intention-to-treat
MCAR: missing completely at random
mHealth: mobile health
OR: odds ratio
RR: Recovery Record
RR-S: standard RR app
RR-T: tailored version of RR app
Edited by J Torous, G Eysenbach; submitted 10.06.19; peer-reviewed by Z Cooper, S Grande; comments to author 28.06.19; revised
version received 12.08.19; accepted 03.10.19; published 21.11.19
Please cite as:
Tregarthen J, Paik Kim J, Sadeh-Sharvit S, Neri E, Welch H, Lock J
Comparing a Tailored Self-Help Mobile App With a Standard Self-Monitoring App for the Treatment of Eating Disorder Symptoms:
Randomized Controlled Trial
JMIR Ment Health 2019;6(11):e14972
URL: http://mental.jmir.org/2019/11/e14972/
doi: 10.2196/14972
PMID: 31750837
©Jenna Tregarthen, Jane Paik Kim, Shiri Sadeh-Sharvit, Eric Neri, Hannah Welch, James Lock. Originally published in JMIR
Mental Health (http://mental.jmir.org), 21.11.2019. 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 Mental Health, is properly cited. The complete
bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license
information must be included.
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