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Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial

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Background: Self-monitoring of dietary intake is a valuable component of behavioral weight loss treatment; however, it declines quickly, thereby resulting in suboptimal treatment outcomes. Objective: This study aimed to examine a novel behavioral weight loss intervention that aims to attenuate the decline in dietary self-monitoring engagement. Methods: GoalTracker was an automated randomized controlled trial. Participants were adults with overweight or obesity (n=105; aged 21-65 years; body mass index, BMI, 25-45 kg/m2) and were randomized to a 12-week stand-alone weight loss intervention using the MyFitnessPal smartphone app for daily self-monitoring of either (1) both weight and diet, with weekly lessons, action plans, and feedback (Simultaneous); (2) weight through week 4, then added diet, with the same behavioral components (Sequential); or (3) only diet (App-Only). All groups received a goal to lose 5% of initial weight by 12 weeks, a tailored calorie goal, and automated in-app reminders. Participants were recruited via online and offline methods. Weight was collected in-person at baseline, 1 month, and 3 months using calibrated scales and via self-report at 6 months. We retrieved objective self-monitoring engagement data from MyFitnessPal using an application programming interface. Engagement was defined as the number of days per week in which tracking occurred, with diet entries counted if ≥800 kcal per day. Other assessment data were collected in-person via online self-report questionnaires. Results: At baseline, participants (84/100 female) had a mean age (SD) of 42.7 (11.7) years and a BMI of 31.9 (SD 4.5) kg/m2. One-third (33/100) were from racial or ethnic minority groups. During the trial, 5 participants became ineligible. Of the remaining 100 participants, 84% (84/100) and 76% (76/100) completed the 1-month and 3-month visits, respectively. In intent-to-treat analyses, there was no difference in weight change at 3 months between the Sequential arm (mean -2.7 kg, 95% CI -3.9 to -1.5) and either the App-Only arm (-2.4 kg, -3.7 to -1.2; P=.78) or the Simultaneous arm (-2.8 kg, -4.0 to -1.5; P=.72). The median number of days of self-monitoring diet per week was 1.9 (interquartile range [IQR] 0.3-5.5) in Sequential (once began), 5.3 (IQR 1.8-6.7) in Simultaneous, and 2.9 (IQR 1.2-5.2) in App-Only. Weight was tracked 4.8 (IQR 1.9-6.3) days per week in Sequential and 5.1 (IQR 1.8-6.3) days per week in Simultaneous. Engagement in neither diet nor weight tracking differed between arms. Conclusions: Regardless of the order in which diet is tracked, using tailored goals and a commercial mobile app can produce clinically significant weight loss. Stand-alone digital health treatments may be a viable option for those looking for a lower intensity approach. Trial registration: ClinicalTrials.gov NCT03254953; https://clinicaltrials.gov/ct2/show/NCT03254953 (Archived by WebCite at http://www.webcitation.org/72PyQrFjn).
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Original Paper
Comparing Self-Monitoring Strategies for Weight Loss in a
Smartphone App: Randomized Controlled Trial
Michele L Patel1,2,3, PhD; Christina M Hopkins1,2, BS; Taylor L Brooks1,2, BA; Gary G Bennett1,2, PhD
1Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
2Duke Digital Health Science Center, Duke Global Health Institute, Durham, NC, United States
3Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, CA, United States
Corresponding Author:
Michele L Patel, PhD
Stanford Prevention Research Center
Stanford University School of Medicine
1070 Arastradero Road
Suite 100
Palo Alto, CA, 94304
United States
Phone: 1 650 549 7047
Fax: 1 650 725 6247
Email: michele.patel@stanford.edu
Abstract
Background: Self-monitoring of dietary intake is a valuable component of behavioral weight loss treatment; however, it declines
quickly, thereby resulting in suboptimal treatment outcomes.
Objective: This study aimed to examine a novel behavioral weight loss intervention that aims to attenuate the decline in dietary
self-monitoring engagement.
Methods: GoalTracker was an automated randomized controlled trial. Participants were adults with overweight or obesity
(n=105; aged 21-65 years; body mass index, BMI, 25-45 kg/m2) and were randomized to a 12-week stand-alone weight loss
intervention using the MyFitnessPal smartphone app for daily self-monitoring of either (1) both weight and diet, with weekly
lessons, action plans, and feedback (Simultaneous); (2) weight through week 4, then added diet, with the same behavioral
components (Sequential); or (3) only diet (App-Only). All groups received a goal to lose 5% of initial weight by 12 weeks, a
tailored calorie goal, and automated in-app reminders. Participants were recruited via online and offline methods. Weight was
collected in-person at baseline, 1 month, and 3 months using calibrated scales and via self-report at 6 months. We retrieved
objective self-monitoring engagement data from MyFitnessPal using an application programming interface. Engagement was
defined as the number of days per week in which tracking occurred, with diet entries counted if ≥800 kcal per day. Other assessment
data were collected in-person via online self-report questionnaires.
Results: At baseline, participants (84/100 female) had a mean age (SD) of 42.7 (11.7) years and a BMI of 31.9 (SD 4.5) kg/m2.
One-third (33/100) were from racial or ethnic minority groups. During the trial, 5 participants became ineligible. Of the remaining
100 participants, 84% (84/100) and 76% (76/100) completed the 1-month and 3-month visits, respectively. In intent-to-treat
analyses, there was no difference in weight change at 3 months between the Sequential arm (mean −2.7 kg, 95% CI −3.9 to −1.5)
and either the App-Only arm (−2.4 kg, −3.7 to −1.2; P=.78) or the Simultaneous arm (−2.8 kg, −4.0 to −1.5; P=.72). The median
number of days of self-monitoring diet per week was 1.9 (interquartile range [IQR] 0.3-5.5) in Sequential (once began), 5.3 (IQR
1.8-6.7) in Simultaneous, and 2.9 (IQR 1.2-5.2) in App-Only. Weight was tracked 4.8 (IQR 1.9-6.3) days per week in Sequential
and 5.1 (IQR 1.8-6.3) days per week in Simultaneous. Engagement in neither diet nor weight tracking differed between arms.
Conclusions: Regardless of the order in which diet is tracked, using tailored goals and a commercial mobile app can produce
clinically significant weight loss. Stand-alone digital health treatments may be a viable option for those looking for a lower
intensity approach.
Trial Registration: ClinicalTrials.gov NCT03254953; https://clinicaltrials.gov/ct2/show/NCT03254953 (Archived by WebCite
at http://www.webcitation.org/72PyQrFjn).
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(JMIR Mhealth Uhealth 2019;7(2):e12209) doi:10.2196/12209
KEYWORDS
weight loss; obesity, self-monitoring; technology; mobile app; mobile health; caloric restriction; treatment adherence and
compliance; randomized controlled trial
Introduction
Background
Self-monitoring of dietary intake is a cornerstone of behavioral
weight loss treatment [1], and past research has demonstrated
that the frequency of self-monitoring is positively associated
with weight loss [2]. Despite its utility, dietary self-monitoring
typically declines over the course of treatment [2,3]. Novel
strategies are needed to improve dietary self-monitoring
engagement [4]. One strategy involves enriching self-monitoring
with other theoretically and empirically supported behavior
change techniques such as tailored goals and feedback, action
plans, and skills training [5,6].
A second strategy includes building mastery, self-efficacy, and
self-regulation—key constructs of behavior change in Carver’s
Control Theory [7] and Bandura’s Social Cognitive Theory
[8]— before asking participants to engage in dietary
self-monitoring. Fostering self-regulatory skills may provide
an opportunity for mastery and, in turn, strengthen self-efficacy
[9], which has been linked to greater weight reduction [10].
Behavioral weight loss interventions typically involve
self-monitoring multiple behaviors or outcomes simultaneously
during treatment [2,11,12], which can serve as an efficient
strategy for producing behavior change but may detrimentally
impede performance on each item or result in greater treatment
dropout [13,14]. Failing to develop mastery of a behavior can
weaken self-efficacy [15], leading to worse treatment outcomes.
In addition, when people begin to self-monitor a behavior less
frequently, it is likely that they will also self-monitor a
corresponding behavior less frequently, as demonstrated in an
analysis of self-monitoring patterns [16].
We propose a novel solution that aims to attenuate the decline
in engagement by employing a sequential [17] self-monitoring
approach, wherein individuals track only body weight for a
period of time and then begin to track diet. Tracking body weight
was chosen as it requires minimal effort, provides an opportunity
for habit formation (eg, track every morning upon waking), and
is efficacious for weight loss [18]. We focused on only
self-monitoring of body weight during the first month based on
prior research demonstrating that enhanced engagement in the
first month of treatment may have long-lasting repercussions
[19]. Sequential approaches are based on the premise that
mastery is more easily obtained if only 1 new behavior is
targeted at a time [20,21]. Recent research finds that both
sequential and simultaneous approaches are effective for
behavior change [21] and that focusing on a single component
instead of a multicomponent intervention produces comparable
weight loss [22], suggesting that a simpler approach is sufficient
to produce behavior change.
We tested this sequential strategy using a remotely delivered
intervention that utilizes a popular commercially available
mobile app—MyFitnessPal. Utilizing technology for
self-monitoring dietary intake has been shown to produce greater
adherence and less-pronounced declines in engagement than
traditional paper-based tracking methods [23,24]. As
demonstrated in recent reviews [25-27], smartphone apps can
produce significant weight loss, although most existing trials
that use commercial apps lack fully powered designs or are pilot
studies. Interventions without counseling that utilize commercial
technology for dietary self-monitoring have produced clinically
meaningful weight losses between 2.5 kg and 5.5 kg at 3 months
and beyond in recent studies [28-32].
Objectives
In the current randomized trial, we test a weight loss intervention
among adults with overweight or obesity that targets the
aforementioned strategies of including empirically supported
behavior change techniques, promoting mastery and self-efficacy
through self-monitoring of body weight before diet, and utilizing
a free, commercially available app (MyFitnessPal). We
hypothesize that a sequential approach will produce greater
weight loss and self-monitoring engagement at 3 months
compared with a traditional simultaneous approach and to an
“off-the-shelf” app.
Methods
Study Design Overview
GoalTracker was a 3-arm randomized controlled trial comparing
3 stand-alone weight loss interventions: (1) a Simultaneous
self-monitoring arm in which participants simultaneously tracked
body weight and dietary intake each day and received additional
empirically supported behavior change techniques (see Table
1) via email for the entirety of the intervention, (2) a Sequential
arm, consisting of identical intervention components but
allowing for mastery of 1 skill (ie, self-monitoring of body
weight) before beginning self-monitoring of diet, and (3) an
App-Only arm that tracked only diet with no additional behavior
change components. Study evaluation visits were held at
baseline, 1 month, and 3 months. Self-reported weight was
collected at 6 months. All study procedures were approved by
the Duke University Institutional Review Board (protocol #:
D0822; date of approval: 10/14/16).
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Table 1. Differences in intervention components between treatment arms.
Behavior change technique [5]Theoretical constructApp-Only
arm
Seqb
Sima
Intervention component
Goal setting (outcome)Self-regulation
c
Weight loss goal: 0.5-2.0 lb/week (tailored)
and 5% by 12 weeks
Goal setting (behavior)Self-regulation (delayed)Calorie goal: tailored based on individual
factors and rate of weight loss; minimum
1200 kcal for women, 1500 kcal for men
Prompt self-monitoring of behavioral out-
come
Self-regulation
d
Self-monitoring of body weight: daily via
the app
Prompt self-monitoring of behaviorSelf-regulation (delayed)Self-monitoring of dietary intake: daily via
the app
Set graded tasksSelf-efficacy; self-
regulation; mastery
Facilitate mastery experience by first track-
ing weight then tracking diet
Provide feedback on performanceSelf-regulation; self-
efficacy
In-app real-time feedback
Provide feedback on performance; prompt
review of outcome goals; prompt review of
behavioral goals
Self-regulation; self-
efficacy
Out-of-app summary feedback via weekly
email (tailored)
Provide information on consequences of
behavior in general; prompt generalization
of a target behavior; provide information
on when and where to perform the behavior;
provide instruction on how to perform the
behavior; environmental restructuring; plan
social support/social change; relapse preven-
tion/coping planning
Outcome expectan-
cies; self-efficacy
Skills training via weekly email with struc-
tured behavioral lesson and tips on how to
use features of the app
Action planning; motivational interviewing
[33]; barrier identification/problem solving
[34]; prompt practice; plan social support/so-
cial change
Self-regulation; self-
efficacy
Action plans via weekly email
Prompt review of outcome goals; prompt
review of behavioral goals
Self-regulationReminder of goals
Teach to use prompts/cuesSelf-regulationIn-app automated reminders to track diet
and/or weight sent daily (App-Only received
reminders to track diet, Simultaneous re-
ceived both diet and weight tracking re-
minders, and Sequential received weight
tracking for all 12 weeks and diet tracking
reminders starting in week 5)
aSim: Simultaneous self-monitoring intervention arm.
bSeq: Sequential self-monitoring intervention arm.
cThe component is present.
dThe component is not present.
Participants
Inclusion criteria comprised men and women aged 21 to 65
years with a body mass index (BMI) between 25.0 and 45.0
kg/m2who were interested in losing weight through dietary
change. We required participants to have an iPhone or Android
smartphone, email address, access to a bathroom scale, and
written English fluency. Participants needed to be willing to
download the mobile app on their phone and not track diet or
body weight using any other modality (eg, other health or weight
tracking apps, websites, and paper diaries) for the duration of
the intervention. We excluded participants if they were enrolled
in another weight loss intervention, had used MyFitnessPal to
track diet in the past 6 months, had lost ≥10 lb or used a weight
loss medication in the past 6 months, had previous or planned
bariatric surgery, or if weight loss would be contraindicated (eg,
pregnancy or <12 months postpartum or in need of medical or
psychiatric intervention such as for cancer diagnosis, eating
disorder, uncontrolled hypertension, diabetes mellitus,
cardiovascular event, or congestive heart failure). A total of 2
criteria were amended during the trial recruitment to promote
generalizability of findings: the BMI criteria were expanded to
include participants in the 40.0 to 45.0 kg/m2range, and the
weight change criteria were adjusted to no longer exclude
individuals who gained more than 10 lb in the past 6 months.
The institutional review board approved both amendments.
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Recruitment
Recruitment occurred between April and September 2017 in
central North Carolina via a university-affiliated research
website and listservs, social media postings (Twitter and
Facebook), ClinicalTrials.gov registry, and community
advertisements (Craigslist, Nextdoor, and paper flyers).
Advertisements provided a description of the study and
eligibility criteria. Participants were enrolled on a rolling basis
until we met our intended sample size.
Procedure
We directed interested individuals to a study website with
descriptive information and a screening questionnaire that
assessed all eligibility criteria, including participants’ height
and weight. Study personnel contacted eligible candidates within
3 business days to schedule an in-person baseline visit. During
the baseline visit, trained study staff obtained written informed
consent, confirmed eligibility, collected anthropometric
measurements, and assisted participants in installing and
navigating the MyFitnessPal mobile app; participants then
completed an online survey.
Using simple random assignment, participants were then
randomized by study staff to 1 of 3 treatment arms using Excel’s
random number generator to allocate participants equally (1:1:1)
across conditions. Randomization was revealed to participants
by study personnel; as such, study staff were not blinded to
treatment allocation but were blinded to the allocation sequence.
Participants then reviewed materials describing their treatment
condition and goals (see Intervention Design section below) in
writing and with study staff to reduce contamination.
In-person follow-up visits occurred at 1 month and 3 months.
At 1 month, study staff provided participants with information
on their goals for the remainder of the intervention (see
Intervention Design section below for details). We compensated
participants with Amazon electronic gift cards (US $12 at
baseline, US $6 for each follow-up visit, and US $5 bonus for
completing dietary measures). Questionnaires were administered
in English via a desktop computer. There was no contact with
participants from months 3 to 6, and participants were not asked
to self-monitor in MyFitnessPal during this time (though they
could still do so if desired). At 6 months, study staff contacted
participants via email and text message to collect self-reported
body weight. Data collection ended in March 2018.
Intervention Design
Participants were randomized to 1 of 3 conditions: (1)
Simultaneous, (2) Sequential, or (3) App-Only, as outlined
below and in Table 1. The CALO-RE taxonomy is used to
describe behavior change techniques [5]. The intervention period
lasted 12 weeks.
Common Components
All treatment arms self-monitored dietary intake using
MyFitnessPal, a free commercial app that allows users to log
food and beverages and provides nutritional information from
a database with over 6 million foods [35]. This app has high
acceptability [36]. In-app feedback in both graphical and text
format provides users with real-time progress updates. When
setting up participants’ MyFitnessPal accounts, study staff
entered an end goal weight that corresponded with losing 5%
of their initial body weight by 12 weeks. On the basis of this
goal and the participant’s current weight, a weekly weight loss
goal between 0.5 and 2.0 pounds was calculated. Along with
the Mifflin-St. Jeor equation that factors in basal metabolic rate
[37], this value was used to determine a tailored daily calorie
goal, with a minimum caloric goal of 1200 kcal/day for women
and 1500 kcal/day for men. During the baseline visit, in-app
push-reminders were programmed to be sent each day if tracking
had not occurred by a prespecified time in the evening. No
structured dietary advice (eg, follow a low carbohydrate diet)
was given to participants. Of note, study staff also created a
Fitbit account for each person via the platform’s website and
linked this account with MyFitnessPal. Participants were not
given a Fitbit device and they were never asked to use this Fitbit
account; its sole purpose was for accessing MyFitnessPal’s data
using Fitbit’s application programming interface (API). In the
App-Only arm, MyFitnessPal served as an “off-the-shelf,”
self-guided approach that the general US population can already
access for free in the commercial marketplace.
Both Simultaneous and Sequential Arms
In addition to the common intervention components, participants
in the Simultaneous and Sequential arms were asked to
self-weigh and enter their body weight in the app each day.
Each week, study staff sent participants an email with tailored
feedback that was automatically generated using Microsoft
Word’s Mail Merge feature. This feedback email described the
participants’ overall weight loss progress and their progress on
each goal in the past week, including track weight daily, meet
weekly weight loss goal, track diet daily, and meet daily calorie
goal (the latter 2 goals not given to Sequential participants until
week 5). Feedback on weight outcomes was provided as long
as 1 weight was recorded in the past week. Individuals who did
not track their weight in the past week received a message
stating “Make sure to enter your weight in MyFitnessPal so that
we can give you helpful insights!” Feedback pertaining to the
calorie goal included only days with complete food diaries (≥800
kcal) [38]. This calorie feedback was not given to the Sequential
arm until week 5.
Each week, participants were also sent skills training materials
via email on a different day, including a researcher-designed
tip on using different features of the app (eg, using the barcode
scanner) accompanied by step-by-step screenshots of the app,
a lesson on nutrition or behavior change (eg, reducing sugary
foods and managing food intake on vacations; see Multimedia
Appendix 1for an example) adapted from gold-standard weight
loss curriculum [39,40], and a brief online action plan to
reinforce the weekly lesson. Accessed via a link to a Qualtrics
survey, action plans incorporated motivational interviewing and
problem-solving strategies [33,34] and included the following
types of components: identifying current behaviors and beliefs;
evaluating confidence and reasons for change; thinking about
the when, where, and what of each action; brainstorming
potential barriers that may arise and crafting solutions;
identifying a support person; and reviewing past action plans
(see Tables 2 and 3for lesson and tip topics).
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Table 2. Topics of structured lessons.
Lesson topicWeek
Overview of the program (losing 5% weight, self-monitoring); calorie balancea
1
Red zone foods; green zone foods2
Reading food labelsa
3
Reducing sugar4
Portion control5
Preparing meals at home; shopping tips6
Eating out7
Social support8
Environmental cues; vacations and holidays9
Emotional eating10
Slippery slope; weight loss maintenance; relapse prevention11
aIn these 2 lessons for the Sequential treatment arm, there was no discussion of tracking diet or adhering to a calorie goal. Otherwise, all lesson content
was identical between the arms.
Table 3. Tips for using the MyFitnessPal app.
Tips for Sequential armTips for Simultaneous armWeek
A: How to track body weight; B: How to view weight
progress
A: How to track body weight; B: How to view weight
progress; C: How to track a food item; D: How to view
your calorie goal and the foods you have tracked
Sent after baseline visit
How to delete a weight entryHow to use the barcode scanner1
How to add progress photosHow to use multiadd to speed up food tracking2
How to change reminders to track (weight)How to view nutrition progress3
How to recruit a friend to use MyFitnessPalA: How to delete a weight entry; B: How to add progress
photos
4
A: How to track a food item; B: How to view your calorie
goal and the foods you have tracked
b
Sent after 1MVa
How to use the barcode scannerHow to track food from a restaurant5
How to use multiadd to speed up food trackingA: How to create a meal; B: How to log a meal6
How to view nutrition progressA: How to add a recipe; B: How to log a recipe7
How to track food from a restaurantHow to use the “Complete Diary” feature8
A: How to create a meal; B: How to log a mealHow to change reminders to track (weight and food)9
A: How to add a recipe; B: How to log a recipeHow to customize meal names10
A: How to use the “Complete Diary” feature; B: How to
customize meal names
How to recruit a friend to use MyFitnessPal11
a1MV: 1-month visit.
bData are not applicable (no tips were provided to the Simultaneous arm directly after the 1-month visit).
Sequential Arm’s Self-Monitoring and Feedback
Individuals in the Sequential arm received the same intervention
components as the Simultaneous arm, but they did not begin
self-monitoring dietary intake until week 5 of the intervention.
They did not receive a calorie goal until their 1-month evaluation
visit, nor were their in-app reminders for tracking diet set up
before this time point. The Sequential arm’s weekly feedback
emails did not mention diet tracking or the calorie goal until
they began tracking diet. In addition, their weekly app usage
tips did not describe diet-tracking tips until after the first month
(see Table 3). Like the Simultaneous arm, they were still
encouraged to make healthy dietary changes during the first
month as suggested in the weekly lessons and action plans, but
these lessons did not mention tracking diet or adhering to a
calorie goal.
Outcome Measures
Primary Outcome: Change in Weight
The primary outcome was weight change at 3 months. We
measured body weight using a calibrated electronic scale (SECA
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876) at baseline, 1 month, and 3 months in light clothing with
shoes removed. Height was measured to the nearest 0.1 cm
using a calibrated, wall-mounted stadiometer (SECA 222).
Baseline height was used for calculation of BMI at all time
points. We collected self-reported body weight at 6 months and
asked participants to send a photo with their feet on the scale
displaying the value in either kg or lb. We assessed the
proportion of participants at 3 months who achieved weight loss
of ≥3% and ≥5% from baseline.
Self-Monitoring Engagement Data
We used a software engine developed at Duke—Prompt—to
collect participants’ objective MyFitnessPal self-monitoring
data; Prompt retrieved these data using the API of Fitbit, which
was linked to each participant’s MyFitnessPal account. Primary
outcomes for self-monitoring engagement span from day 1 (the
day after participants’ baseline visit) to day 83 and were
categorized into the first 4 weeks in the intervention (days 1-28),
the final 2 months (days 29-83), and the entire 83-day
intervention period. Exploratory analyses examined engagement
data after the intervention ended up to 6 months (day 183) post
randomization.
For all self-monitoring data, we only counted days with complete
diet entries (ie, recording ≥800 kcal/day [38]). We examined
the median number of days per week that participants
self-monitored weight and diet, as well as the percentage of
days that entries were recorded (ie, number of days with entries
recorded divided by number of days instructed to record an
entry, multiplied by 100).
Engagement in Action Plans
Percentage of action plans completed was examined through
objective Qualtrics survey data: each action plan was coded as
“completed” or “not completed.” The completion status of each
action plan was combined to generate a summary score with a
possible range of 0% to 100% (indicative of 11 out of 11 action
plans completed).
Engagement in Feedback Email
In the 3-month survey, we assessed participants’ self-reported
frequency of reading their weekly feedback email, with the
question “How frequently did you read your weekly Progress
Reports (sent via email), on average?” and 5 response options
(Several times per week, One time per week, Less than 1 time
per week, Less than 1 time per month, or Never).
Sociodemographic and Clinical Characteristics
At baseline, we collected data on participant demographics,
socioeconomic status, and type of smartphone. To assess past
MyFitnessPal use, we asked the Pew Research Center’s
question: “What kind of health apps do you currently have on
your phone?” [41]; if the “Diet, Food, Calorie Counter” response
option was selected, then the open-ended question “What are
the names of the diet, food, or calorie-counting apps that you
used on your phone?” was asked.
We also assessed whether participants had ever been told by a
doctor or other health professional that they had prediabetes or
hypertension. Self-monitoring of weight and self-monitoring
of diet in the month before baseline were each measured with
a 7-point scale ranging from several times per day to never [42].
Statistical Analysis
Sample size was calculated based on power to detect a 3.5 kg
difference in weight change at 3 months between the Sequential
arm and the App-Only arm (our primary comparison) using
3-month results from previous remotely delivered weight loss
interventions for our Sequential arm [43] (results in kilograms
were provided upon request by the author) and our App-Only
arm [44]. Our power analysis (G*Power 3.1.9.2.) determined
that 31 participants per group were needed to achieve 80%
power for a 2-sided test with an alpha level of .05. To account
for attrition of 10% and to obtain equal-size groups, we aimed
to recruit 105 participants (35 per group). In exploratory
analyses, we compared weight change between the Sequential
arm and the Simultaneous arm, although we were not adequately
powered to detect a significant effect.
For the baseline characteristics, we computed descriptive
statistics stratified by treatment arm. To determine whether
baseline characteristics differed by retention status, we used the
Pearson chi-square test for categorical variables, analysis of
variance for continuous variables, and Fisher exact tests with
small cell counts. All analyses were 2-tailed. Participants who
became ineligible during the study period up to 3 months were
excluded from the analyses. Investigators remained blinded to
outcomes until the completion of the 6-month trial.
We used intent-to-treat analyses to test our primary aim using
linear mixed modeling with an unstructured covariance matrix
and restricted maximum likelihood estimates to examine changes
in weight over time by treatment arm. We did not control for
any additional variables, and we assumed missing at random
and used SAS 9.4 PROC MIXED (SAS Institute) for these
analyses. For 6-month weight values sent via photo, we
subtracted 0.172 kg (0.4 lb) to account for participants holding
a device on the scale to take the photo. To account for the
6-month self-reported weight data without photos, we used a
regression model to adjust for age, gender, and race/ethnicity
[45]. Participants who sent a photo of their 6-month weight did
not differ on any measured sociodemographic characteristics
from those who did not send a photo (data not shown). We used
chi-square tests to assess proportion of participants achieving
≥3% and ≥5% weight loss; we assumed noncompleters did not
achieve this clinical threshold.
Given non-normally distributed intervention engagement data,
we reported medians and interquartile ranges (IQR). To examine
differences between treatment arms, we used Wilcoxon
Mann-Whitney Utests (if 2 arms) and the Kruskal-Wallis tests
(if 3 arms). We used Spearman rank correlation coefficients (rs)
to examine the relation between self-monitoring engagement
and change in weight. We also assessed for contamination by
exploring whether participants self-monitored when they were
not expected to do so.
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Results
Participant Enrollment and Retention
Of the 670 individuals who completed the online screen for
eligibility, 58.3% (391) were ineligible, whereas 23.7% (159)
were invited to attend the baseline visit. We enrolled 105
participants and randomized them equally to 3 treatment arms
(n=35, for each; see Figure 1 for Consolidated Standards of
Reporting Trials [CONSORT] diagram). During the trial, 5
participants became ineligible (3 because of pregnancy, 1
because of cancer diagnosis, 1 because of previously undisclosed
eating disorder). Of the remaining 100 participants, 84.0% (84
of 100) completed the 1-month visit and 76.0% (76 of 100)
completed the 3-month visit. Between 3 and 6 months, one
additional participant became ineligible because of pregnancy.
At 6 months, 78% (77 of 99) of participants self-reported their
weight. Participant retention did not differ significantly between
arms at any time point (1 month: P=.84; 3 months: P=.23; 6
months: P=.32). We had no missing self-monitoring engagement
data.
Baseline Characteristics
Table 4 illustrates the baseline characteristics of GoalTracker
participants. At baseline, participants had a mean age (SD) of
42.7 (11.7) years and BMI of 31.9 (4.5) kg/m2and were
predominantly female (84/100) and employed (78/100).
One-third (33/100) were racial or ethnic minorities, most were
married or living with a partner (64/100), and the majority had
at least a college education (83/100). The majority (56/100) did
not track diet in the month before baseline, although most had
experience tracking body weight (87.0%). Completers at 3
months differed from noncompleters in race or ethnic minority
status (P=.03), with 16% (11/67) of non-Hispanic white
participants and 39% (13/33) of racial or ethnic minority
participants missing the visit.
Weight Loss
Figure 2 displays weight change over time by treatment arm.
Weight change was significant over time for all arms (see Table
5). In our primary analysis, the Sequential arm did not
significantly differ from the App-Only arm in weight change
at 1 month (P=.06), 3 months (P=.78), or 6 months (P=.72). In
exploratory analyses, the Sequential arm did not differ from the
Simultaneous arm in weight change at 1 month (P=.36), 3
months (P=.92), or 6 months (P=.45).
The proportion of participants achieving at least 3% weight loss
at 3 months was similar between arms (Sequential: 44%, 15/34
vs App-Only: 29%, 10/34, P=.21; exploratory analysis:
Sequential vs Simultaneous 41%, 13/32, P=.77). Likewise,
weight loss of at least 5% at 3 months occurred in 21% of
Sequential participants (7/34) and 15% of App-Only participants
(5/34), which was not significantly different (P=.52). In
exploratory analyses, the proportion of participants with ≥5%
weight loss did not significantly differ between the Sequential
arm and the Simultaneous arm (31% of participants, 10/32,
P=.32).
Intervention Engagement
As expected, the Sequential arm tracked weight significantly
more days than the App-Only arm (who was not asked to track
weight) over the 12-week intervention; median (IQR) 70%,
(28%-90%) vs 1% (0%-8%), respectively (P<.001). In
exploratory analyses, the frequency of days participants
self-monitored weight did not differ between the Simultaneous
arm (73%; 25%-90%) and the Sequential arm over 12 weeks
(P=.92).
As expected, the frequency with which the Sequential arm and
the App-Only arm tracked diet in weeks 1 to 4 was significantly
different (see Table 6). Between weeks 5 and 12 (once the
Sequential arm began tracking diet), there was no longer a
significant difference between the Sequential and App-Only
arms; 27% (4%-80%) vs 21% (0%-62%), respectively (P=.54).
In exploratory analyses, there were no significant differences
in the frequency of days of self-monitoring diet between the
Simultaneous and App-Only arms over the intervention period;
77% (27%-96%) vs 42% (17%-75%), P=.10. Table 6 displays
additional self-monitoring and action plan completion outcomes
(see Figures 3 and 4for weekly data).
Relation Between Self-Monitoring Frequency and
Weight Change
The percentage of days weight was tracked was significantly
associated with 3-month weight change in both the Simultaneous
arm (rs=−.48, P=.02) and the Sequential arm (rs=−.47, P=.01).
In the same time period, the association between weight change
and the percentage of days with complete diet entries was
significant in the App-Only arm (rs=−.58, P=.003) but not for
the Simultaneous arm (rs=−.25, P=.24). The percentage of days
diet was tracked starting in week 5 for the Sequential arm was
significantly associated with weight change at 3 months
(rs=−.44, P=.02; see Table 7 for additional details).
Contamination
The median (IQR) frequency of days that App-Only participants
tracked weight in the MyFitnessPal app during the 3-month
intervention was 1% (0%-8%), and the frequency of days that
Sequential participants tracked diet during month 1 was 0%
(0%-0%; see Table 6 for absolute values).
Action Plan Completion
In the Simultaneous arm, the median (IQR) number of action
plans completed was 7.7 of 11—70% (14%-91%)—compared
with 3 of 11—27% (9%-82%)—in the Sequential arm; in
exploratory analyses, this difference was not statistically
significant (P=.21). Percent action plan completion was
significantly related to weight change at 3 months in the
Sequential arm (rs=−.60, P<.001) but not in the Simultaneous
group (rs=−.07, P=.75).
Review of Feedback Email
Most participants (35/52; 67%) reported reading their weekly
feedback email at least once per week, whereas 12% (6/52) of
participants reported never reading them. In exploratory
analyses, there were no significant differences between the
Simultaneous and the Sequential arm (P=.90).
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Figure 1. Consolidated Standards of Reporting Trials (CONSORT) flow diagram. BMI: body mass index.
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Table 4. Baseline characteristics by treatment arm.
App-Only (n=34)Sequential (n=34)Simultaneous (n=32)Total (N=100a)Characteristic at baseline
42.3 (12)42.1 (11)43.8 (13)42.7 (11.7)Age (years), mean (SD)
Gender, n (%)
4 (12)4 (12)8 (25)16 (16.0)Male
30 (88)30 (88)24 (75)84 (84.0)Female
Marital status, n (%)
24 (71)18 (53)22 (69)64 (64.0)Married or living with partner
10 (29)16 (47)10 (31)36 (36.0)Not married or living with
partner
Race/ethnicity, n (%)
23 (68)23 (68)21 (66)67 (67.0)Non-Hispanic white
7 (21)6 (18)9 (28)22 (22.0)Non-Hispanic black
1 (3)2 (6)0 (0)3 (3.0)Hispanic (all races)
3 (9)3 (9)2 (6)8 (8.0)Non-Hispanic other
Education, n (%)
4 (12)6 (18)7 (22)17 (17.0)Less than college graduate
30 (88)28 (82)25 (78)83 (83.0)College graduate or above
Employment status, n (%)
20 (59)27 (79)20 (63)67 (67.0)Employed, full-time
7 (21)1 (3)3 (9)11 (11.0)Employed, part-time
7 (21)6 (18)9 (28)22 (22.0)Not employed
Annual household income, in US dollars, n (%)
9 (27)9 (27)8 (25)26 (26.0)$0-$49,999
10 (29)12 (35)14 (44)36 (36.0)$50,000-$99,999
14 (41)11 (32)9 (28)34 (34.0)$100,000 or greater
1 (3)2 (6)1 (3)4 (4.0)Unknown/not reported
88.6 (15)90.8 (17)89.3 (17)89.6 (16.0)Weight, mean (SD), kg
31.7 (4)32.6 (5)31.3 (4)31.9 (4.5)
Body mass index, mean (SD), kg/m2
Body mass index category, n (%)
15 (44)12 (35)13 (41)40 (40.0)
Overweight, 25-29.9 kg/m2
11 (32)13 (38)14 (44)38 (38.0)
Class 1 obesity, 30-34.9 kg/m2
7 (21)6 (18)4 (13)17 (17.0)
Class 2 obesity, 35-39.9 kg/m2
1 (3)3 (99)1 (3)5 (5.0)
Class 3 obesity, 40+ kg/m2
Self-monitoring of diet frequency, n (%)
3 (9)2 (6)1 (3)6 (6.0)Daily
4 (12)4 (12)6 (19)14 (14.0)1 to 6 times per week
9 (27)11 (32)4 (13)24 (24.0)Less than 1 time per week
18 (53)17 (50)21 (66)56 (56.0)Never
Self-monitoring of weight frequency, n (%)
3 (9)2 (6)6 (19)11 (11.0)Daily
15 (44)14 (41)6 (19)35 (35.0)1 to 6 times per week
12 (35)16 (47)13 (41)41 (41.0)Less than 1 time per week
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App-Only (n=34)Sequential (n=34)Simultaneous (n=32)Total (N=100a)Characteristic at baseline
4 (12)2 (6)7 (22)13 (13.0)Never
Type of smartphone, n (%)
21 (62)16 (47)17 (53)54 (54.0)iPhone
13 (38)18 (53)15 (47)46 (46.0)Android
5 (15)9 (27)6 (19)20 (20.0)MyFitnessPal already on phone be-
fore study, n (%)
aFive participants omitted because they became ineligible during the intervention period.
Figure 2. Weight change over time by treatment arm. Data were included for 100 participants; mean (SD) values were estimated using an intention-to-treat
analysis with a linear mixed-model.
Table 5. Change in weight and body mass index (intent-to-treat).
Mean (95% CI)Outcome by time point
Between-group difference
(Seqavs App-Only)b
App-OnlySequentialSimultaneous
Weight change from baseline (kg)
0.97 (−0.03 to 1.97)−1.76 (−2.48 to −1.05)−0.80 (−1.49 to −0.10)−1.25 (−1.97 to −0.53)1 month
−0.24 (−1.97 to 1.49)−2.43 (−3.69 to −1.16)−2.67 (−3.85 to −1.49)−2.75 (−4.01 to −1.49)3 months
−0.38 (−2.46 to 1.71)−1.88 (−3.41 to −0.34)−2.25 (−3.66 to −0.85)−3.05 (−4.57 to −1.52)
6 monthsc
BMIdchange from baseline (kg/m2)
0.35 (0 to 0.69)−0.63 (−0.88 to −0.38)−0.29 (−0.53 to −0.05)−0.46 (−0.71 to −0.21)1 month
−0.08 (−0.69 to 0.53)−0.88 (−1.32 to −0.43)−0.95 (−1.37 to −0.54)−0.99 (−1.44 to −0.55)3 months
−0.15 (−0.88 to 0.59)−0.67 (−1.21 to −0.13)−0.81 (−1.31 to −0.32)−1.06 (−1.60 to −0.52)
6 monthsc
aSeq: Sequential self-monitoring intervention arm.
bThis is the primary comparison; the App-Only arm is the reference group.
cOne additional participant was omitted in analyses (App-Only arm) at 6-months due to becoming ineligible after the intervention period and before
6-months.
dBMI: body mass index.
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Table 6. Self-monitoring engagement by treatment arm.
Median (interquartile range)Self-monitoring engagement by time
period
Pvalue
App-Only (n=34)Sequential (n=34)Simultaneous (n=32)
Baseline to 4 weeks (out of 28 days)
a
0 (0 to 0.75)5.75 (3.50 to 6.50)6.25 (2.63 to 6.75)Number of days per week
tracked weight
5.38 (2.25 to 7.00)0 (0)6.50 (3.88 to 7.00)Number of days per week
tracked diet
.52b; <.001c
0 (0 to 11)82 (50 to 93)89 (37 to 96)Percentage of days tracked
weight
.37d; <.001c
77 (32 to 100)0 (0)93 (55 to 100)Percentage of days tracked diet
5 to 12 weeks (out of 55 days)
0 (0 to 0.25)4.06 (0.75 to 6.63)4.50 (0.69 to 6.13)Number of days per week
tracked weight
1.44 (0 to 4.25)1.88 (0.25 to 5.50)4.88 (0.44 to 6.56)Number of days per week
tracked diet
.95b; <.001c
0 (0 to 4)59 (11 to 95)65 (10 to 89)Percentage of days tracked
weight
.54c; .17e
21 (0 to 62)27 (4 to 80)70 (6 to 95)Percentage of days tracked diet
Entire intervention (out of 83 days)
0.08 (0 to 0.58)4.83 (1.92 to 6.25)5.08 (1.75 to 6.25)Number of days per week
tracked weight
2.92 (1.17 to 5.17)
f
5.33 (1.83 to 6.67)Number of days per week
tracked diet
.92b; <.001c
1 (0 to 8)70 (28 to 90)73 (25 to 90)Percentage of days tracked
weight
.10d
42 (17 to 75)
f
77 (27 to 96)Percentage of days tracked diet
.21b
27 (9 to 82)70 (14 to 91)Percentage of action plans
completed
13 weeks to 6 months (postintervention; out of 99 days)g
0 (0 to 0.14)0.43 (0 to 1.64)0.32 (0 to 0.89)Number of days per week
tracked weight
0 (0 to 0.43)0 (0 to 0.29)0.14 (0 to 1.18)Number of days per week
tracked diet
.78b; .004c
0 (0 to 2)7 (0 to 23)5 (0 to 14)Percentage of days tracked
weight
.96c; .43e
0 (0 to 6)0 (0 to 5)3 (0 to 17)Percentage of days tracked diet
aNot applicable.
bSimultaneous arm versus Sequential arm.
cSequential arm versus App-Only arm.
dSimultaneous arm versus App-Only arm.
eAll arms.
fAs the Sequential arm did not track diet in the first 4 weeks of the intervention, their results for the “Entire intervention” section would be the same as
the results in the “5 to 12 weeks” section above.
gOne additional participant was omitted in analyses (App-Only arm) at 6 months because of becoming ineligible after the intervention period and before
6-months.
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Figure 3. Self-monitoring of weight per intervention week by treatment arm.
Figure 4. Self-monitoring of dietary intake per intervention week by treatment arm.
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Table 7. Spearman rank correlation between engagement metrics and weight change. The table displays correlations and Pvalues for the treatment
arms that were asked to track during the given time period.
Weight change by 3 monthsWeight change by 1 monthEngagement metric
Both: −.41b
a
Percentage of action plans completed
Simc: −.07
Seqd: −.60b
Baseline to 4 weeks
Both: −.40b
Both: −.35b
Percentage of days tracked weight
Sim: −.51e
Sim: −.40e
Seq: −.34Seq: −.29
Both: −.40b
Both: −.42b
Percentage of days tracked diet
Sim: −.30Sim: −.36
App: −.48b
Appf: −.51b
5 to 12 weeks
Both: −.48b
Both: −.44b
Percentage of days tracked weight
Sim: −.49e
Sim: −.37
Seq: −.46e
Seq: −.54b
All: −.42b
Allg: −.37b
Percentage of days tracked diet
Sim: −.27Sim: −.24
Seq: −.44e
Seq: −.50b
App: −.55b
App: −.52b
Entire intervention
Both: −.47b
Both: −.44b
Percentage of days tracked weight
Sim: −.48e
Sim: −.40e
Seq: −.47e
Seq: −.50b
Both: −.42b
Both: −.35b
Percentage of days tracked dieth
Sim: −.25Sim: −.30
App: −.58b
App: −.52b
13 weeks to 6 months (post intervention period)i
Both: −.43b
Both: −.50b
Percentage of days tracked weight
Sim: −.43e
Sim: −.49b
Seq: −.43e
Seq: −.59b
All: −.35b
Both: −.29b
Percentage of days tracked diet
Sim: −.17Sim: −.27
Seq: −.47e
Seq: −.42e
App: −.39App: −.20
aNot applicable.
bP<.01.
cSim: Simultaneous self-monitoring arm.
dSeq: Sequential self-monitoring arm.
eP<.05.
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fApp: App-Only self-monitoring arm.
gAll: All 3 treatment arms.
hAs the Sequential arm did not track diet in the first 4 weeks of the intervention, their results for the “Entire intervention” section would be the same as
the results in the “5 to 12 weeks” section above.
iOne additional participant was omitted in analyses (App-Only arm) at 6 months because of becoming ineligible after the intervention period and before
6-months.
Discussion
Principal Findings
A low-intensity intervention utilizing a commercial app for
self-monitoring resulted in comparable weight loss at 3 months,
with no variability between the Sequential arm and the
“off-the-shelf” App-Only arm. Nevertheless, loss of 3% to 5%
of initial weight has been linked to improved health outcomes
[46,47], suggesting that GoalTracker is an efficacious
intervention for clinically meaningful weight loss.
The addition of evidence-based features such as weekly action
plans, behavioral lessons, and tailored feedback did not
substantially impact outcomes over and above the core
intervention (ie, self-monitoring and in-app feedback) during
our 12-week treatment, which parallels findings from several
digital health weight loss trials [30,48] but not others [6].
Although most commercial weight loss apps do not include
many evidence-based features [49], we suspect that weight loss
might still occur with the inclusion of goals, daily
self-monitoring, and a daily reminder to track. However, we
suspect that had the trial been of a longer duration, the benefit
of these enhanced features (eg, weekly lessons) would have
become apparent in the findings; indeed, by 6 months, trends
suggest continued weight loss at 6 months for the Simultaneous
arm and relatively less weight regain in the Sequential arm
compared with the App-Only arm.
Given that the GoalTracker trial compared 3 multicomponent
interventions, we are unable to isolate the effect of
self-monitoring diet and weight, along with each of the
additional intervention components. Using a factorial design in
consort with the multiphase optimization strategy (MOST) [50]
would allow researchers to investigate the unique impact of
each intervention component and then build and test an
optimized intervention.
We found that self-monitoring engagement was high and that
greater frequency of self-monitoring was related to greater
weight loss. Contrary to our hypothesis, the Sequential arm did
not demonstrate significantly greater engagement in
self-monitoring dietary intake than the App-Only arm. In fact,
once the Sequential arm was instructed to begin tracking diet
after the first month, they tracked only 27% of days. It is
possible that the Sequential arm participants’ minimal weight
loss in the first month may have negatively impacted their future
engagement or that being asked to track diet after a 1-month
period seemed like an additional burden that many were
unwilling to begin (indeed, almost half or 47% of Sequential
participants never or rarely [<20% of days] tracked diet in weeks
5 to 12 [data not shown]). Although the trial was not powered
to detect differences between the Simultaneous and Sequential
arms, it appears that the Simultaneous arm outperforms the
Sequential arm in frequency of tracking diet. This finding
suggests that concurrently tracking diet and weight may have
reinforced use of the app and activated the overarching goal of
losing weight [51], thus leading to high engagement in both
entities.
Future studies could consider framing the initial period before
self-monitoring diet as a time during which to build
self-regulatory skills rather than focus on weight loss, as has
been done previously [9]. We selected weight tracking as the
precursor to diet tracking for the Sequential arm because we
wanted a behavior that could target the theoretical constructs
of mastery—considered the best way to strengthen
self-efficacy—and self-regulation. Self-weighing is a
well-accepted strategy [52] in which mastery can be achieved
[16], and self-regulatory capacity can be strengthened [53]. It
is possible that providing more rationale for using a sequential
approach would have encouraged participants to engage in diet
tracking once asked to do so.
Comparison With Prior Work
Notably, GoalTracker is the first weight loss trial to compare a
sequential self-monitoring approach with a traditional approach
that asks participants to track multiple components
simultaneously. Previous work has compared a simultaneous
approach with either a sequential or single component approach
in other contexts, with mixed results [21,54]; no examination
has focused on self-monitoring or digital approaches for weight
loss.
The App-Only arm in GoalTracker performed better than
expected. Intervention participants in Laing et al’s trial used the
same MyFitnessPal app for diet tracking and lost 0.27 kg at 3
months and 0.03 kg at 6 months and had poor intervention
engagement [44]. Possible explanations for the difference in
weight loss between Laing et al’s trial and GoalTracker include
GoalTracker’s use of specific goals to track diet daily and to
lose 5% of initial weight by a specified end date and usage of
phone-based reminder notifications.
In comparison with other randomized trials of commercial
[28,29,32,55-58] or researcher- designed [24,59,60] apps for
self-monitoring of diet, GoalTrackers Simultaneous arm tended
to have greater adherence to diet tracking, whereas the
Sequential arm had lower adherence. Given that most weight
loss trials of commercial apps are pilot studies and/or were not
powered to detect an effect in weight change between treatment
arms [26,28,29,38,56,61], more fully powered studies are needed
that examine the efficacy of commercial apps for weight loss.
GoalTracker’s Simultaneous arm had a comparable or higher
proportion of participants achieving 5% weight loss compared
with other weight loss interventions that used mobile apps for
self-monitoring dietary intake (range: 26%-35%) [28,59,62,63]
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but lower rates than some interventions including counseling
(range: 42%-44%) [29,60,61].
Strengths
Strengths of this trial include the collection of objective
self-monitoring data for all participants via an API, use of a
popular commercially available smartphone app, and ability to
isolate the effect of a sequential versus simultaneous
self-monitoring approach. In addition, this trial mimicked
real-world weight loss experience (ie, no run-in period,
prebaseline visit, or orientation session); consequently, it is
possible that removal of these treatment barriers allowed for
inclusion of participants with lower motivation and readiness
to change. This design may have greater external validity but
may make it harder to detect an effect between arms. Another
strength was that the trial had little contamination between arms,
which has been a problem in past app-based trials where up to
50% of participants in no-treatment control arms were found to
have used commercial apps during the study period [44,55,63].
Limitations
As this study was powered on superiority rather than
equivalency, we cannot definitively assert that the treatment
arms produce comparable weight loss. In addition, we collected
self-reported weight at 6-months because of logistical reasons;
however, we were encouraged to find that no additional attrition
occurred between 3 and 6 months, despite no contact occurring
during that period and no incentive given to provide a weight
value. As is common in behavioral interventions, we provided
minimal financial compensation to offset costs of attending
study visits. Although we acknowledge that financial
compensation can serve as an incentive for some to
participate—and thus, may result in response bias on self-report
measures—we expect this is unlikely, given that compensation
was appropriately low. In addition, neither study staff nor
participants were blinded to treatment arm, and we required
participants to have access to a bathroom scale, although this
mimics the real-world population who would track weight.
Finally, this study did not include a pure control arm without
an intervention, which may have led to an underestimation of
treatment effects, as could the possibility of data not actually
missing at random.
Conclusions
This study adds to the limited literature of randomized trials
that assess the efficacy of commercially available mobile apps
for weight loss [20,26]. In the GoalTracker trial, all 3 versions
of the intervention produced weight loss and had high
self-monitoring engagement, with no significant impact of
additional features nor differential findings between a sequential
versus simultaneous approach to self-monitoring. These results
suggest that regardless of the order in which diet is tracked,
using tailored weight and calorie goals and a commercial app
can produce clinically significant weight loss in one-third of
individuals. Stand-alone digital health treatments may be a
viable option for those looking for a lower intensity approach
who are willing and able to track.
Acknowledgments
The research described in this paper was supported by a grant to the first author from the American Psychological Association,
the Duke Interdisciplinary Behavioral Research Center, and the Aleane Webb Dissertation Research Award provided by The
Graduate School at Duke University. GGB serves on the scientific advisory board of Nutrisystem and Interactive Health; he holds
equity in Coeus Health. These organizations had no role in study design, data collection, data analysis and interpretation of data,
in the writing of the report, or in the decision to submit the article for publication. The remaining authors have no disclosures.
The authors would like to thank Tia Kelley, Azaria Anderson, and Kendra Pallin for their dedication as research assistants and
are deeply grateful for many members of Duke Digital Health, including Martin Streicher, Sandy Askew, Dori Steinberg, Hira
Ahmed, and Hallie Davis-Penders. Finally, the authors would like to thank all who participated in GoalTracker.
Authors' Contributions
MLP and GGB conceived of and designed the study. MLP, CMH, and TLB delivered the intervention and collected data. MLP
analyzed the results and drafted the manuscript. All authors were involved in writing the paper and had final approval of the
submitted and published versions.
MLP was affiliated with Duke University at the time of the trial and is currently affiliated with the Stanford University School
of Medicine.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Example weekly lesson.
[PDF File (Adobe PDF File), 166KB - mhealth_v7i2e12209_app1.pdf ]
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Multimedia Appendix 2
CONSORT-EHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 2MB - mhealth_v7i2e12209_app2.pdf ]
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Abbreviations
API: application programming interface
BMI: body mass index
CONSORT: Consolidated Standards of Reporting Trials
IQR: interquartile range
Edited by G Eysenbach; submitted 14.09.18; peer-reviewed by B Turner-McGrievy, B Nezami, S Toukhy, M Bardus; comments to
author 29.11.18; revised version received 19.12.18; accepted 06.01.19; published 17.02.19
Please cite as:
Patel ML, Hopkins CM, Brooks TL, Bennett GG
Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial
JMIR Mhealth Uhealth 2019;7(2):e12209
URL: https://mhealth.jmir.org/2019/2/e12209/
doi:10.2196/12209
PMID:
©Michele L Patel, Christina M Hopkins, Taylor L Brooks, Gary G Bennett. Originally published in JMIR Mhealth and Uhealth
(http://mhealth.jmir.org), 17.02.2019. 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
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information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must
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... A total of 69% (11/16) of articles reported data that reflected the sample's socioeconomic status, and 81% (13/16) of articles reported data on the sample's education level. More details on the study characteristics are shown in Table 1, and additional information on socioeconomic status, education level, presence of group differences between participants retained and dropped out, protocol registration, and funding is shown in Multimedia Appendix 3 [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Most included studies (12/16, 75%) covered app-based interventions that were beyond purely food logging, such as real-time self-monitoring of diet and exercise, regular messages tailored according to user progress, timely feedback, smart devices that synchronized activity and weight data to smartphones, personalized progress reports, libraries of diet and physical activity ideas, and remote progress monitoring. ...
... Most included studies (12/16, 75%) covered app-based interventions that were beyond purely food logging, such as real-time self-monitoring of diet and exercise, regular messages tailored according to user progress, timely feedback, smart devices that synchronized activity and weight data to smartphones, personalized progress reports, libraries of diet and physical activity ideas, and remote progress monitoring. Of these 16 articles, 4 (25%) included control conditions that provided app-based food logging [17,[22][23][24]. The intervention duration ranged from 12 weeks to 24 months and the follow-up time points ranged from 8 weeks to 24 months. ...
... The intervention duration ranged from 12 weeks to 24 months and the follow-up time points ranged from 8 weeks to 24 months. The intervention characteristics for each article are detailed in Multimedia Appendix 4 [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Most articles (10/16, 62%) were rated as having an unclear ROB, and 38% (6/16) of articles were rated as having a high ROB (Multimedia Appendix 5 [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]). ...
Article
Full-text available
Background: Evidence on the long-term effects of weight management smartphone apps on various weight-related outcomes remains scarce. Objective: In this review, we aimed to examine the effects of smartphone apps on anthropometric, metabolic, and dietary outcomes at various time points. Methods: Articles published from database inception to March 10, 2022 were searched, from 7 databases (Embase, CINAHL, PubMed, PsycINFO, Cochrane Library, Scopus, and Web of Science) using forward and backward citation tracking. All randomized controlled trials that reported weight change as an outcome in adults with overweight and obesity were included. We performed separate meta-analyses using random effects models for weight, waist circumference, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, blood glucose level, blood pressure, and total energy intake per day. Methodological quality was assessed using the Cochrane Risk of Bias tool. Results: Based on our meta-analyses, weight loss was sustained between 3 and 12 months, with a peak of 2.18 kg at 3 months that tapered down to 1.63 kg at 12 months. We did not find significant benefits of weight loss on the secondary outcomes examined, except for a slight improvement in systolic blood pressure at 3 months. Most of the included studies covered app-based interventions that comprised of components beyond food logging, such as real-time diet and exercise self-monitoring, personalized and remote progress tracking, timely feedback provision, smart devices that synchronized activity and weight data to smartphones, and libraries of diet and physical activity ideas. Conclusions: Smartphone weight loss apps are effective in initiating and sustaining weight loss between 3 and 12 months, but their effects are minimal in their current states. Future studies could consider the various aspects of the socioecological model. Conversational and dialectic components that simulate health coaches could be useful to enhance user engagement and outcome effectiveness. Trial registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42022329197; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329197.
... A recent study on overweight or obese adults, who were advised to self-monitor their dietary intake for 8 weeks with an app, has found that if the frequency of self-monitoring was consistent, weight loss could be achieved in the short term [68]. Another recent study has shown that using tailored weight and calorie goals provided by professionals to track a person's food intake with a mobile app can produce clinically significant weight loss [69]. Thus, by only using the isolated online tracking of food intake, the maintenance of a healthy weight does not seem to be effective, though, previously, it has been shown that electronic dietary records were better than traditional methods for BMI reduction [49]. ...
Article
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An inadequate diet has been shown to be a cause of obesity. Nowadays, digital resources are replacing traditional methods of recording food consumption. Thus, the objective of this study was to analyze a sample of United States of America (USA) residents to determine if the usage of any meal tracker platform to record food intake was related to an improved body mass index (BMI). An analytical cross-sectional study that included 896 subjects with an Instagram account who enrolled to participate in an anonymous online survey was performed. Any meal tracker platform used to record food intake over the last month was employed by 34.2% of the sample. A total of 85.3% of the participants who had tracked their food intake were women (p < 0.001), and 33.3% (p = 0.018) had a doctorate degree. Participants who used any meal tracker platform also had higher BMIs (median: 24.9 (Q1: 22.7–Q3: 27.9), p < 0.001), invested more hours a week on Instagram looking over nutrition or physical activity (median: 2.0 (Q1: 1.0–Q3: 4.0), p = 0.028) and performed more minutes per week of strong physical activity (median: 240.0 (Q1: 135.0–Q3: 450.0), p = 0.007). Conclusions: USA residents with an Instagram account who had been using any meal tracker platform to record food intake were predominantly highly educated women. They had higher BMIs despite the fact they were engaged in stronger exercise and invested more hours a week on Instagram looking over nutrition or physical activity.
... The pursuit of health and well-being extends life quality and expectancy for individuals while fostering positive societal and financial externalities for communities and nations at large. Achieving good health outcomes is challenging in the face of multiple constraints including finances, time, access to services and information [1,2]. While increasing healthcare costs are affecting disproportionally certain individuals and communities, healthcare systems are often supplemented with scattered community programs, targeting particularly disadvantaged communities. ...
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In our project herein, we use the case of farmworkers, an underserved and understudied population at high risk for Type-2 Diabetes Mellitus (T2DM), as a paradigm of an integrated action-oriented research, education and extension approach involving the development of long-term equitable strategies providing empowerment and tailored-made solutions that support practical decision making aiming to reduce risk of T2DM and ensuing cardiovascular disease (CVD). A Technology based Empowerment Didactic module (TEDm) and an Informed Decision-Making enhancer (IDMe) coupled in a smart application (app) for farmworkers aiming to teach, set goals, monitor, and support in terms of nutrition, hydration, physical activity, sleep, and circadian rhythm towards lowering T2DM risk, is to be developed and implemented considering the particular characteristics of the population and setting. In parallel, anthropometric, biochemical, and clinical assessments will be utilized to monitor risk parameters for T2DM and compliance to dietary and wellness plans. The app incorporating anthropometric/clinical/biochemical parameters, dietary/lifestyle behavior, and extent of goal achievement can be continuously refined and improved through machine learning and reprogramming. The app can function as a programmable tool constantly learning, adapting, and tailoring its services to user needs helping optimization of practical informed decision-making towards mitigating disease symptoms and associated risk factors. This work can benefit apart from the direct beneficiaries being farmworkers, the stakeholders who will be gaining a healthier, more vibrant workforce, and in turn the local communities. Citation: Sikalidis, A.K.; Kristo, A.S.; Reaves, S.K.; Kurfess, F.J.; DeLay, A.M.; Vasilaky, K.; Donegan, L. Capacity Strengthening Undertaking-Farm Organized
... [29][30][31] Digital (also called electronic) dietary records generally follow a closed format that prompts the participant to choose from a pre-determined list of foods and beverages and enter the amount consumed. [32][33][34] Also, there is often an option to use qualitative data or images to capture items not in the database. Image-based methods use images of food and beverages captured by participants through applications running on a mobile device. ...
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The effectiveness of the tailored feedback in digital interventions may be limited by the quality of the dietary assessment (DA) upon which it is based. This study systematically reviewed studies reporting the protocols for DA methods used to inform tailored feedback in digital weight loss interventions. The search included: PubMed - National Library of Medicine database, MEDLINE, Cochrane Library of Systematic Reviews, Web of Science, and ProQuest. Search terms were related to five groups: dietary assessment, weight loss, clinical trials, technology and tailoring. Thirteen articles were eligible for inclusion. The most common DA method was a digital dietary record linked to a food database that provided instant feedback on daily energy intake. Only four studies provided feedback on overall diet quality and intake of fruit, vegetables, and fibre. Dietary feedback was provided using text messages, email, mobile applications, and online intervention websites. Most digital dietary feedback focused on reducing energy intake without providing feedback to enhance diet quality. This review highlighted the heterogeneity in DA methods used in tailored weight loss interventions, which may account for the range of outcome results reported. Future interventions should publish the protocols describing how dietary data was collected and used to inform dietary feedback. This article is protected by copyright. All rights reserved.
... Self-monitoring is a common way to diet and achieves weight loss, particularly the self-monitoring of physical activity (PA) and nutritional intake, which are essential components of behavioral weight control programs [11]. Existing research on self-monitoring found a substantial link between self-monitoring and weight loss in some or all three components evaluated in behavioral weight loss studies: diet, workout, and self-weighing [12][13][14][15][16]. Some researchers found that individual dietary self-monitoring has been regarded as one of the most effective techniques for maintaining body weight [11,17,18]. ...
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Nutrition apps for mobile devices such as smartphones are becoming more widely available. They can help ease the arduous chore of documenting intake for nutritional assessment and self-monitoring. This allows people to control food intake, support their participation in physical activities, and promote a healthy lifestyle. However, there remains a lack of research regarding systematic analysis mapping studies in this area. The objective of this study is to identify dietary self-monitoring implementation strategies on a mobile application. This study analyzed 205 journals from the Scopus database using the descriptive-analytic method. The records used in this exploration study were those released between 2007 and 2021 that were collected based on the keywords “dietary self-monitoring,” or “nutrition application,” or “nutrition apps,” and “calorie application.” Data analysis was conducted using the VOSviewer and NVivo software analytical tools. The results show that research studies on dietary self-monitoring increased in 2017. Results also indicated that the country that contributed the most to this topic was China. The study on mobile applications for dietary self-monitoring revealed seven clusters of dominant themes: attitude to improved dietary behaviors, parameters for disease diagnosis, noncommunicable diseases, methods, nutrition algorithms, mobile health applications, and body mass index. This study also analyzed research trends by year. The current research trends are about dietary self-monitoring using a mobile application that can upgrade people’s lifestyles, enable real-time meal recording and the convenience of automatically calculating the calorie content of foods consumed, and potentially improve the delivery of health behavior modification interventions to large groups of people. The researchers summarized the recent advances in dietary self-monitoring research to shed light on their research frontier, trends, and hot topics through bibliometric analysis and network visualization. These findings may provide valuable guidance for future research and perspectives in this rapidly developing field.
... Weight loss maintenance is limited; however, only 20.6% of people sustain decreases for a year [23], and approximately 70% of weight loss is regained within the first 2 years [24]. While digital weight loss programs can be successful [11,22,[25][26][27][28], improvements are needed [16], including a platform that provides comprehensive intervention focusing on both short-term weight loss and long-term maintenance. ...
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Background: Overweight and obesity are serious public health concerns. As the prevalence of excess weight among individuals continues to increase, there is a parallel need for inexpensive, highly accessible, and evidence-based weight loss programs. Objective: This weight loss trial will aim to examine the efficacy of the Noom weight loss program in comparison to a digital control after a 6-month intervention phase and a 24-month maintenance phase, with assessments continuing for 2 years beyond the intervention (to 30 months-after the baseline). The secondary outcomes include quality of life, psychosocial functioning, sleep quality, physical activity, diet, and health status. This trial will also examine the severity of obesity-related functional impairment, weight loss history, and demographic moderators, along with adherence and self-efficacy as mediators of the outcome. Methods: A total of 600 participants were randomized in a parallel-group, controlled trial to either Noom Healthy Weight Program (intervention) or Noom Healthy Weight Control (control) for a 6-month intervention. Both intervention and control groups include diet and exercise recommendations, educational content, daily logging capabilities, and daily weigh-in entries. The Noom Healthy Weight Program also includes a coach support for weight loss. Remote follow-up assessments of eating, physical activity, psychosocial factors, app use data, and weight will be conducted at 1, 4, 6, 12, 18, 24, and 30 months after baseline. Weight is measured at each follow-up point during a Zoom call using the participants' scales. Results: Enrollment began in March 2021 and the 6-month intervention phase ended in March 2022. Data collection for the final assessment will be completed in March 2024. Conclusions: This study tests commercially available digital lifestyle interventions for individuals with overweight and obesity seeking weight loss support. Data obtained from the study will evaluate whether the Noom Healthy Weight Control Program can help individuals overcome weight loss, achieve long-term maintenance, adhere to lifestyle changes, and feature use barriers that are present in other traditional weight loss treatments. Trial registration: ClinicalTrials.gov NCT04797169; https://clinicaltrials.gov/ct2/show/NCT04797169. International registered report identifier (irrid): DERR1-10.2196/37541.
Chapter
A necessary step in the digitalization of our environments is to include the users in the decision loop, following a more human-centric paradigm. Such an aproach will make their interactions with surrounding technology closer to them. Therefore, there is a recurrent need in contemporary technological solutions to create proposals to assist users in a way that is not exclusive to them and makes them feel integrated into the intelligent system. In fact, this is particularly relevant when the proposed technology or system aims to nudge users to form, shape, or change their daily behaviours. In essence, solutions designed for assisting users in that matter need to consider the inclusion of humans in the learning/decision loop and still the literature in the field is scarce. In this work, we identify and address three crucial human requirements that this technology has to integrate to promote a comfortable and long-term use of technology for the effective assistance of behaviour change: trust, engagement, and adaptation. Besides, we propose a collaborative workflow based on hybrid intelligent systems to cover the lack of human requirements and needs of traditional approaches. In essence, this work aims to shed light on how to promote closer collaboration between humans and intelligent agents for behaviour change under the principle that people should not be treated as mere users of technologies and services, but their behaviour should become one of the critical levers for designing and using technologies. That is, creating a closer interaction between these technologies and people.KeywordsHybrid intelligenceBehaviour changeHuman-AI collaborationAdaptive technology
Article
Background Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States. The American Cancer Society (ACS) Nutrition and Physical Activity Guidelines are associated with longer survival among CRC survivors, but few report behaviors consistent with the guidelines. Methods The Tools To Be Fit study, based on the Multiphase Optimization Strategy (MOST) framework, is a full factorial experimental to optimize a remotely delivered 48-week diet and physical activity intervention for non-metastatic CRC survivors. The intervention includes a core component (booklet and personal report). CRC survivors (N = 400) are additionally randomly assigned to one of 16 combinations of four candidate components, each with 2 options: 1) text messaging (on/off); 2) self-monitoring modality (digital/paper); 3) health coaching (on/off); and 4) support person coaching (on/off). Outcomes Our primary outcome is adherence to the ACS guidelines after 48 weeks using a score that includes physical activity from accelerometers, dietary intake from a food frequency questionnaire, and body mass index (BMI) measured by a technician. Secondary outcomes include the ACS score after 24 weeks and score components at 24 and 48 weeks. Exploratory outcomes include adherence and change in Social Cognitive Theory constructs. We will explore moderation by sociodemographic, clinical, and psychological/behavioral factors; and change in the ACS score in relation to change in levels of insulin, insulin sensitivity, inflammation, gut microbiome structure, fatigue, depression, and sleep disturbance. Discussion The proposed study aims to inform a randomized controlled trial to determine whether an optimized intervention reduces risk of recurrence among CRC survivors.
Article
Recall of recent eating episodes is a versatile approach to test influences on amount eaten of specific food. The present experiment explored if reminding current food intake prompts awareness for eating more healthy food. In a randomised between-subjects experiment, 60 healthy young females were offered fruit at breakfast time, which they could eat ad libitum after recalling either eating (n = 31) or non-eating (n = 29) episodes of the day before. Participants in the eating recall condition on average did not eat a larger amount of fruit than participants in the non-eating recall condition (203 ± 62 g vs. 209 ± 69; d = 0.11, t = -0.42, P > 0.68; BF10 = 0.28). So no evidence was found for a stimulatory effect on amount eaten of healthy food from reminding recent food intake. This null result is discussed and guidelines for future research are presented addressing further aspects of memories of eating episodes possibly influencing healthy food ingestion.
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Automated digital interventions for weight loss represent a highly scalable and potentially cost-effective approach to treat obesity. However, current understanding of the active components of automated digital interventions is limited, hindering efforts to improve efficacy. Thus, the current systematic review and meta-analysis (preregistration: PROSPERO 2021-CRD42021238878) examined relationships between utilization of behavior change techniques (BCTs) and the efficacy of automated digital interventions for producing weight loss. Electronic database searches (December 2020 to March 2021) were used to identify trials of automated digital interventions reporting weight loss as an outcome. BCT clusters were coded using Michie’s 93-item BCT taxonomy. Mixed-effects meta-regression was used to examine moderating effects of BCT clusters and techniques on both within-group and between-group measures of weight change. One hundred and eight conditions across sixty-six trials met inclusion criteria (13,672 participants). Random-effects meta-analysis revealed a small mean post-intervention weight loss of -1.37 kg (95% CI, -1.75 to -1.00) relative to control groups. Interventions utilized a median of five BCT clusters, with goal-setting, feedback and providing instruction on behavior being most common. Use of Reward and Threat techniques, and specifically social incentive/reward BCTs, was associated with a higher between-group difference in efficacy, although results were not robust to sensitivity analyses.
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Objective Evaluate the effects of an online commercial weight management program, with and without provision of a ‘smart’ scale with instructions to weigh daily and weekly tailored feedback, on weight loss and the frequency of body‐weight self‐monitoring. Methods Participants (N = 92; body mass index 27–40 kg/m²) were randomized to 6 months of no‐cost access to the Weight Watchers Online (WWO) platform alone, or enhanced with a cellular‐connected ‘smart’ scale, instructions to weigh daily and weekly pre‐scripted email feedback (Weight Watchers Online Enhanced [WWO‐E]). The number of days that weight was self‐monitored (via ‘smart’ scale in WWO‐E and manually in WWO) was recorded automatically across the 6‐month trial. Objective weight was measured at baseline, 3 and 6 months. Results While both groups achieved statistically significant weight loss, mean ± standard error weight loss did not differ between WWO‐E and WWO at 3 months (5.1 ± 0.6 kg vs. 4.0 ± 0.7 kg, respectively; p = 0.257) or 6 months (5.3 ± 0.6 kg vs. 3.9 ± 0.7 kg, respectively; p = 0.116). However, a greater proportion of WWO‐E lost ≥5% of initial body weight at 3 months (52.2% vs. 28.3%; p = 0.033), but not 6 months (43.5% vs. 30.4%; p = 0.280), compared with WWO. Mean ± standard deviation days with self‐monitored weight was higher in WWO‐E (80.5 ± 5.6; 44.7% of days) than WWO (12.0 ± 1.0; 6.7% of days; p < 0.001) across the 6‐month study period. Conclusions This is the first study to show that provision of a ‘smart’ scale with weekly tailored feedback substantially increased the frequency of self‐weighing and the proportion of participants achieving an initial clinically significant ≥5% weight loss (52% vs. 28%) in an online commercial weight management program. Both WWO and WWO‐E produced significant weight loss over 6 months. While mean weight losses were slightly greater in the enhanced group, the difference was not statistically significant in this small sample. This study provides support for the clinical utility of online commercial weight management programs and the potential for supporting technology such as ‘smart’ scales to improve adherence to body‐weight self‐monitoring and clinical outcomes.
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Background The combination of self-tracking and persuasive eCoaching in automated interventions is a new and promising approach for healthy lifestyle management. Objective The aim of this study was to identify key components of self-tracking and persuasive eCoaching in automated healthy lifestyle interventions that contribute to their effectiveness on health outcomes, usability, and adherence. A secondary aim was to identify the way in which these key components should be designed to contribute to improved health outcomes, usability, and adherence. Methods The scoping review methodology proposed by Arskey and O’Malley was applied. Scopus, EMBASE, PsycINFO, and PubMed were searched for publications dated from January 1, 2013 to January 31, 2016 that included (1) self-tracking, (2) persuasive eCoaching, and (3) healthy lifestyle intervention. Results The search resulted in 32 publications, 17 of which provided results regarding the effect on health outcomes, 27 of which provided results regarding usability, and 13 of which provided results regarding adherence. Among the 32 publications, 27 described an intervention. The most commonly applied persuasive eCoaching components in the described interventions were personalization (n=24), suggestion (n=19), goal-setting (n=17), simulation (n=17), and reminders (n=15). As for self-tracking components, most interventions utilized an accelerometer to measure steps (n=11). Furthermore, the medium through which the user could access the intervention was usually a mobile phone (n=10). The following key components and their specific design seem to influence both health outcomes and usability in a positive way: reduction by setting short-term goals to eventually reach long-term goals, personalization of goals, praise messages, reminders to input self-tracking data into the technology, use of validity-tested devices, integration of self-tracking and persuasive eCoaching, and provision of face-to-face instructions during implementation. In addition, health outcomes or usability were not negatively affected when more effort was requested from participants to input data into the technology. The data extracted from the included publications provided limited ability to identify key components for adherence. However, one key component was identified for both usability and adherence, namely the provision of personalized content. Conclusions This scoping review provides a first overview of the key components in automated healthy lifestyle interventions combining self-tracking and persuasive eCoaching that can be utilized during the development of such interventions. Future studies should focus on the identification of key components for effects on adherence, as adherence is a prerequisite for an intervention to be effective.
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Purpose of review: There is a large variability in response to behavioral weight loss (WL) programs. Reducing rates of obesity and diabetes may require more individuals to achieve clinically significant WL post-treatment. Given that WL within the first 1-2 months of a WL program is associated with long-term WL, it may be possible to improve treatment outcomes by identifying and providing additional intervention to those with poor initial success (i.e., "early non-responders"). We review the current literature regarding early non-response to WL programs and discuss how adaptive interventions can be leveraged as a strategy to "rescue" early non-responders. Recent findings: Preliminary findings suggest that adaptive interventions, specifically stepped care approaches, offer promise for improving outcomes among early non-responders. Future studies need to determine the optimal time point and threshold for intervening and the type of early intervention to employ. Clinicians and researchers should consider the discussed factors when making treatment decisions.
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Self-monitoring (SM) of food intake is central to weight loss treatment. Technology makes it possible to reinforce this behavior change strategy by providing real-time feedback (FB) tailored to the diary entry. To test the feasibility of providing 1–4 daily FB messages tailored to dietary recordings via a smartphone, we conducted a 12-week pilot randomized clinical trial in Pittsburgh, PA in US in 2015. We compared 3 groups: SM using the Lose It! smartphone app (Group 1); SM+FB (Group 2); and SM+FB+attending three in-person group sessions (Group 3). The sample (N=39) was mostly white and female with a mean body mass index of 33.76kg/m². Adherence to dietary SM was recorded daily, weight was assessed at baseline and 12weeks. The mean percentage of days adherent to dietary SM was similar among Groups 1, 2, and 3 (p=0.66) at 53.50% vs. 55.86% vs. 65.33%, respectively. At 12weeks, all groups had a significant percent weight loss (p<0.05), with no differences among groups (−2.85% vs. −3.14% vs. −3.37%) (p=0.95); 26% of the participants lost≥5% of their baseline weight. Mean retention was 74% with no differences among groups (p=0.37). All groups adhered to SM at levels comparable to or better than other weight loss studies and lost acceptable amounts of weight, with minimal intervention contact over 12weeks. These preliminary findings suggest this 3-group approach testing SM alone vs. SM with real-time FB messages alone or supplemented with limited in-person group sessions warrants further testing in a larger, more diverse sample and for a longer intervention period.
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Introduction: For women with an increased breast cancer risk, reducing excess weight and increasing physical activity are believed to be important approaches for reducing their risk. This study tested a weight loss intervention that combined commercially available technology-based self-monitoring tools with individualized phone calls. Design: Women were randomized to a weight loss intervention arm (n=36) or a usual care arm (n=18). Setting/participants: Participants were women with a BMI ≥ 27.5 kg/m(2) and elevated breast cancer risk recruited from the mammography clinic at the Moores Cancer Center at the University of California San Diego. Intervention: Intervention participants used the MyFitnessPal website and phone app to monitor diet and a Fitbit to monitor physical activity. Participants received 12 standardized coaching calls with trained counselors over 6 months. Usual care participants received the U.S. Dietary Guidelines for Americans at baseline and two brief calls over the 6 months. Main outcome measures: Weight and accelerometer-measured physical activity were assessed at baseline and 6 months. Data were collected in San Diego, CA, from 2012 to 2014 and analyzed in 2015. Results: Participants (n=54) had a mean age of 59.5 (SD=5.6) years, BMI of 31.9 (SD=3.5), and a mean Gail Model score of 2.5 (SD=1.4). At 6 months, intervention participants had lost significantly more weight (4.4 kg vs 0.8 kg, p=0.004) and a greater percentage of starting weight (5.3% vs 1.0%, p=0.005) than usual care participants. Across arms, greater increases in moderate-to-vigorous physical activity resulted in greater weight loss (p=0.01). Conclusions: Combining technology-based self-monitoring tools with phone counseling supported weight loss over 6 months in women at increased risk for breast cancer.
Article
Unhealthy eating is a major risk factor for chronic disease. However, many current strategies to promote healthy eating are not sustainable over the longer-term. More cost-effective wide-reaching initiatives are urgently needed. Mobile health (mHealth) interventions, delivered via mobile devices, could provide a solution. This systematic review summarized the evidence on the effect of mHealth interventions for promoting healthy eating in adults. A comprehensive systematic search of five scientific databases was conducted using methods adapted from the Cochrane Handbook. Eligible studies were randomized controlled trials (RCTs), published up to 1 July 2016, which examined healthy eating interventions delivered via mobile device. Of 879 articles identified, 84 full text articles were potentially eligible and further assessed, and 23 included. Narrative review results indicated small positive effects of mHealth interventions on healthy eating (5/8 trials) and weight loss (5/13 trials). However, the current evidence base is insufficient (studies are of poor quality) to determine conclusive positive effects. More rigorous RCTs with longer-term (>6months) follow-up are warranted to determine if effects are maintained.
Article
Objective: To determine the effectiveness of various monitoring strategies on weight loss, body composition, blood markers, exercise, and psychosocial indices in adults with overweight and obesity following a 12-month weight loss program. Methods: Two hundred fifty adults with BMI ≥ 27 were randomized to brief, monthly, individual consults, daily self-monitoring of weight, self-monitoring of diet using MyFitnessPal, self-monitoring of hunger, or control over 12 months. All groups received diet and exercise advice, and 171 participants (68.4%) remained at 12 months. Results: No significant differences in weight, body composition, blood markers, exercise, or eating behavior were apparent between those in the four monitoring groups and the control condition at 12 months (all P ≥ 0.053). Weight differences between groups ranged from -1.1 kg (-3.8 to 1.6) to 2.2 kg (-1.0 to 5.3). However, brief support and hunger training groups reported significantly lower scores for depression (difference [95% CI]: -3.16 [-5.70 to -0.62] and -3.05 [-5.61 to -0.50], respectively) and anxiety (-1.84, [-3.67 to -0.02]) scores than control participants. Conclusions: Although adding a monitoring strategy to diet and exercise advice did not further increase weight loss, no adverse effects on eating behavior were observed, and some monitoring strategies may even benefit mental health.
Article
Objective: To examine the use of two different mobile dietary self-monitoring methods for weight loss. Methods: Adults with overweight (n = 81; mean BMI 34.7 ± 5.6 kg/m(2) ) were randomized to self-monitor their diet with a mobile app (App, n = 42) or wearable Bite Counter device (Bite, n = 39). Both groups received the same behavioral weight loss information via twice-weekly podcasts. Weight, physical activity (International Physical Activity Questionnaire), and energy intake (two dietary recalls) were assessed at 0, 3, and 6 months. Results: At 6 months, 75% of participants completed the trial. The App group lost significantly more weight (-6.8 ± 0.8 kg) than the Bite group (-3.0 ± 0.8 kg; group × time interaction: P < 0.001). Changes in energy intake (kcal/d) (-621 ± 157 App, -456 ± 167 Bite; P = 0.47) or number of days diet was tracked (90.7 ± 9.1 App, 68.4 ± 9.8 Bite; P = 0.09) did not differ between groups, but the Bite group had significant increases in physical activity metabolic equivalents (+2015.4 ± 684.6 min/wk; P = 0.02) compared to little change in the App group (-136.5 ± 630.6; P = 0.02). Total weight loss was significantly correlated with number of podcasts downloaded (r = -0.33, P < 0.01) and number of days diet was tracked (r = -0.33, P < 0.01). Conclusions: While frequency of diet tracking was similar between the App and Bite groups, there was greater weight loss observed in the App group.
Article
Objective: To determine the effects on weight loss of three abbreviated behavioral weight loss interventions with and without coaching and mobile technology. Methods: A randomized controlled efficacy study of three 6-month weight loss treatments was conducted in 96 adults with obesity: 1) self-guided (SELF), 2) standard (STND), or 3) technology-supported (TECH). STND and TECH received eight in-person group treatment sessions. SELF and STND used paper diaries to self-monitor diet, activity, and weight; TECH used a smartphone application with social networking features and wireless accelerometer. Results: Weight loss was greater for TECH and STND than SELF at 6 months (-5.7 kg [95% confidence interval: -7.2 to -4.1] vs. -2.7 kg [95% confidence interval: -5.1 to -0.3], P < 0.05) but not 12 months. TECH and STND did not differ except that more STND (59%) than TECH (34%) achieved ≥ 5% weight loss at 6 months (P < 0.05). Self-monitoring adherence was greater in TECH than STND (P < 0.001), greater in both interventions than SELF (P < 0.001), and covaried with weight loss (r(84) = 0.36-0.51, P < 0.001). Conclusions: Abbreviated behavioral counseling can produce clinically meaningful weight loss regardless of whether self-monitoring is performed on paper or smartphone, but long-term superiority over standard of care self-guided treatment is challenging to maintain.
Article
Weight loss interventions are delivered through various mediums including, increasingly, mobile phones. This systematic review and meta-analysis assesses whether interventions delivered via mobile phones reduce body weight and which intervention characteristics are associated with efficacy. The study included randomised controlled trials assessing the efficacy of weight loss interventions delivered via mobile phones. A meta-analysis to test intervention efficacy was performed, and subgroup analyses were conducted to determine whether interventions' delivery mode(s), inclusion of personal contact, duration and interaction frequency improve efficacy. Pooled body weight reduction (d = -0.23; 95% confidence interval = -0.38, -0.08) was significant. Interventions delivered via other modes in addition to the mobile phone were associated with weight reduction. Personal contact and more frequent interactions in interventions were also associated with greater weight reduction. In conclusion, the current body of evidence shows that interventions delivered via mobile phones produce a modest reduction in body weight when combined with other delivery modes. Delivering interventions with frequent and personal interactions may in particular benefit weight loss results.