American Journal of Epidemiology
© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: [email protected].
Vol. 181, No. 5
DOI: 10.1093/aje/kwu260
Advance Access publication:
February 10, 2015
Systematic Reviews and Meta- and Pooled Analyses
Mobile Phone Intervention and Weight Loss Among Overweight and Obese Adults:
A Meta-Analysis of Randomized Controlled Trials
Fangchao Liu, Xiaomu Kong, Jie Cao, Shufeng Chen, Changwei Li, Jianfeng Huang, Dongfeng Gu*,
and Tanika N. Kelly*
* Correspondence to Dr. Dongfeng Gu, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Department of Epidemiology,
National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
(e-mail: [email protected]); or Dr. Tanika N. Kelly, Department of Epidemiology, School of Public Health and Tropical Medicine,
Tulane University, 1440 Canal Street, Suite 2000, New Orleans, LA 70112 (e-mail: [email protected]).
Initially submitted May 24, 2014; accepted for publication September 2, 2014.
We conducted a meta-analysis of randomized controlled trials to examine the association of mobile phone intervention with net change in weight-related measures among overweight and obese adults. We searched electronic
databases and conducted a bibliography review to identify articles published between the inception date of each
database and March 27, 2014. Fourteen trials (including 1,337 participants in total) that met the eligibility criteria
were included. Two investigators independently abstracted information on study characteristics and study outcomes. Net change estimates comparing the intervention group with the control group were pooled across trials
using random-effects models. Compared with the control group, mobile phone intervention was associated with
significant changes in body weight and body mass index (weight (kg)/height (m)2) of −1.44 kg (95% confidence
interval (CI): −2.12, −0.76) and −0.24 units (95% CI: −0.40, −0.08), respectively. Subgroup analyses revealed
that the associations were consistent across study-duration and intervention-type subgroups. For example, net
body weight changes were −0.92 kg (95% CI: −1.58, −0.25) and −1.85 kg (95% CI: −2.99, −0.71) in trials of shorter
(<6 months) and longer (≥6 months) duration, respectively. These findings provide evidence that mobile phone intervention may be a useful tool for promoting weight loss among overweight and obese adults.
behavioral intervention; mobile phones; obesity; overweight; weight loss
Abbreviations: BMI, body mass index; CI, confidence interval; ITT, intention-to-treat; MMS, multimedia message service; PRISMA,
Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RCT, randomized controlled trial; SMS, short message
service; WC, waist circumference.
related morbidity, mortality, and health-care costs (6–9).
For example, 1 kg of weight loss has been associated with a
13% reduction in the risk of incident diabetes (10). Behavioral
changes, including increased physical activity, improved diet
quality, and restriction of caloric intake, are important components of weight management. However, randomized controlled trials (RCTs) of these types of interventions have
shown limited success in sustaining weight loss due to a lack
of long-term compliance (11–13). Intervention by mobile
telephone, including contact by short message service (SMS)
(text messages) and multimedia message service (MMS)
(pictures or other multimedia materials), that delivers frequent reminders of nutritional and physical activity goals
Overweight/obesity is a major global health challenge due
to its high prevalence and associated increases in chronic disease morbidity and mortality (1–3). In 2005, an estimated 1.3
billion adults were either overweight or obese globally, with
estimates projected to increase to 3.3 billion by 2025 (2). As
one of the leading risk factors for mortality and morbidity
worldwide, high body mass index (BMI) was responsible for
approximately 3.4 million deaths and 3.8% of global disabilityadjusted life-years in 2010 (4). Furthermore, overweight/obesity
exerts a substantial economic toll, accounting for approximately 7% of total health-care costs worldwide (5).
Studies have shown that even modest levels of sustained
weight loss could yield substantial reductions in weight337
Am J Epidemiol. 2015;181(5):337–348
338 Liu et al.
Records Identified Through Database
Searching (n = 626)
Medline (n = 83)
Embase (n = 80)
CENTRAL (n = 68)
Web of Science (n = 395)
Records Identified From Review
and Reference List (n = 20)
Records Remaining After Removal
of Duplicates (n = 497)
Records Excluded by Title and Abstract
Review (n = 456)
Records Remaining After Title and
Abstract Review (n = 41)
Records Excluded by Full-Text Review (n = 27)
Duplicate reports (n = 4)
≤80% overweight or obese participants (n = 2)
No mobile intervention (n = 6)
Non-RCTs (n = 3)
No outcome of interest (n = 12)
Studies Included in the MetaAnalysis (n = 14)
Figure 1. Selection of eligible randomized controlled trials (RCTs) examining the association of mobile phone intervention with weight-related
measures, 2004–2013. CENTRAL, Cochrane Central Register of Controlled Trials.
or recommendations may be a convenient and potentially
cost-effective method of encouraging people to maintain
healthy behavior and lose weight. Studies of mobile phone
interventions have shown promise in reducing diabetes risk
(14), increasing physical activity (15), and promoting smoking
cessation (16). However, the associations of mobile phone intervention with weight loss are still controversial. Some RCTs
have found text messaging to be a useful method of promoting
weight loss in overweight adults (17, 18), while others have not
shown benefits (19, 20). In addition, many of these trials have
had limited statistical power to detect moderate but meaningful body weight reductions associated with mobile phone intervention (18, 21). Given the conflicting results and limitations of
previous trials, we determined that a meta-analysis of RCTs
would allow for more precise estimation of the association
of mobile phone interventions with weight loss.
The purpose of the current meta-analysis of RCTs was to
estimate the association of mobile phone intervention with
weight-related health measures, including body weight, BMI,
waist circumference (WC), waist:hip ratio, and body fat percentage, among overweight and obese adults. In addition, we
explored whether the relationship between weight-related
measures and mobile phone intervention varied by trial duration (shorter (<6 months) vs. longer (≥6 months)) or by type
of intervention (SMS alone vs. SMS combined with MMS).
METHODS
We utilized a standardized written protocol for conducting
the literature search, selecting studies, extracting data, and
synthesizing results, following the outlines of the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) statement (22). The PRISMA checklist and flow
diagram were used for reporting the results of this analysis
(22).
Search strategy and study selection
We conducted a systematic literature search of Medline
(via PubMed; National Library of Medicine, Bethesda,
Maryland) (1966–March 27, 2014), the Excerpta Medica Database (Embase; Elsevier B.V., Amsterdam, the Netherlands)
Am J Epidemiol. 2015;181(5):337–348
Am J Epidemiol. 2015;181(5):337–348
Table 1. Characteristics of 14 Randomized Controlled Trials That Examined the Association of Mobile Phone Intervention With Net Change in Weight-Related Measures Among Overweight
and Obese Adults, 2004–2013
First Author,
Year
(Reference No.)
No. of
Participants
Country
Weight
Loss as
Primary
Outcome
Type of Intervention
No
Noc
SMS + usual care
Usual care
9 weeks
No
Nod
SMS + PA
monitoring
Study
Design
Study
Population
Duration of
Study
Clustered
RCT
Persons
with HT
6 months
RCT
GP
Blinding
Feedback
Requirement
Outcome
Anthropometric
Measurement
Intentionto-Treat
Analysisa
Jadad
Scoreb
2 times/week
No
BW
PM
No
3
PA monitoring
As appropriate
Yes
BMIe,
body
fat %
PM
Yes
3
Control Group
Treatment
Intervention
Frequency
67
Spain
Hurling,
2007 (15)
77
United Kingdom
Faridi,
2008 (31)
30
United States
Clustered
RCT
Persons
with DM
3 months
No
Nof
SMS
Standard selfmanagement
Once per day
Yes
BW, BMI
PM
Yes
3
Helsel,
2009 (32)
30
United States
RCT
GP
16 weeks
NR
Yes
IPWP + SMS
IPWP
3 times/day
NR
BW
NR
Yes
2
Haapala,
2009 (17)
125
Finland
RCT
GP
12 months
No
Yes
SMS
No intervention
As appropriate
Yes
BW, WC
PM
Yes
3
Patrick,
2009 (18)
65
United States
RCT
GP
16 weeks
No
Yes
SMS + MMS +
Paper materials
paper materials
2–5 times/day
Yes
BW
PM
Yes
3
Yoo,
2009 (19)
111
South Korea
RCT
Persons
with HT
and DM
12 weeks
No
Nog
SMS
Usual outpatient
treatment
3 times/day
Yes
BW,
BMI,
WC
Selfmeasurement
No
3
Zuercher,
2009 (33)
177
United States
RCT
GP
1 month
No
Yes
SMS + standard
care
Standard care
Once per day
Yes
BW, BMI
Self-report
No
3
Lombard,
2010 (34)
250
Australia
Clustered
RCT
GP
12 months
Single
Yes
SMS +
Group sessions + Once per
pedometer +
pedometer
month
group sessions
No
BW, WC
PM
No
5
Turner-McGrievy,
2011 (20)
102
United States
RCT
GP
6 months
Single
Yes
SMS + podcast
No support
≥2 times/day
No
BW
PM
Yes
3
Shapiro,
2012 (35)
170
United States
RCT
GP
12 months
No
Yes
SMS + MMS +
monthly
e-newsletters
Monthly
e-newsletters
4 times/day
Yes
BW
PM
Yes
3
Orsama,
2013 (21)
48
Canada
RCT
Persons
with HT
and DM
10 months
No
Noc,f
SMS + education
Education
As appropriate
Yes
BW
Self-report
No
3
Napolitano,
2013 (36)
35
United States
RCT
GP
8 weeks
No
Yes
SMS + Facebookh Facebook
Once per day
Yes
BW
PM
No
3
Steinberg,
2013 (37)
50
United States
RCT
GP
6 months
No
Yes
SMS + usual care
≥1 time per
day
Yes
BW, BMI
PM
Yes
3
Usual care
Abbreviations: BMI, body mass index; BW, body weight; DM, diabetes mellitus; GP, general population; HT, hypertension; IPWP, in-person weight-loss program; MMS, multimedia message service; NR, not reported; PA, physical
activity; PM, physician measurement; RCT, randomized controlled trial; SMS, short message service; WC, waist circumference; WHR, waist:hip ratio.
a
Identified by the extracted data.
b
Based on the 5-item Jadad scale (24).
c
The primary outcome was lowering blood pressure.
d
The primary outcome was increasing PA.
e
Weight (kg)/height (m)2.
f
The primary outcome was lowering blood glucose level.
g
The primary outcome was chronic disease self-management.
h
Facebook, Inc. (Menlo Park, California).
Mobile Phone Intervention and Weight Loss 339
Márquez
Contreras,
2004 (30)
Intervention Group
First Author,
Year
(Reference No.)
No. of
Persons
Mean Age,
years
(SD)
No.
Male Sex
%
Mean
BMIa
(SD)
Control Group
Mean Body
Weight, kg
(SD)
Mean
WC, cm
(SD)
No. of
Persons
Am J Epidemiol. 2015;181(5):337–348
Márquez Contreras,
2004 (30)
34
52.3 (10.2)
18
52.9
Hurling,
2007 (15)
47
40.5 (7.1)
17
36.2
Faridi,
2008 (31)
15
55.3 (8.7)
6
40.0
Helsel,
2009 (32)b,c
15
44.2 (3.9)
Haapala,
2009 (17)
62
38.1 (4.7)
13
21.0
30.6 (2.7)
87.5 (12.6)
Patrick,
2009 (18)
33
47.4 (7.1)
8
24.2
32.8 (4.3)
89.8 (17.2)d
Yoo,
2009 (19)
57
57.0 (9.1)
30
52.6
25.6 (3.5)
66.4 (12.5)
Zuercher,
2009 (33)
88
23.6 (3.5)
0
0
26.1 (5.2)
72.5 (15.6)
Lombard,
2010 (34)
127
40.6 (4.8)
0
0
27.5 (5.1)
73.2 (13.8)
Turner-McGrievy,
2011 (20)
47
42.6 (10.7)
11
23.0
32.9 (4.8)
Shapiro,
2012 (35)
81
43.1 (11.1)
27
33
32.4 (4.2)
Orsama,
2013 (21)
27
62.3 (6.5)
13
54.0
30.7 (4.5)
Napolitano,
2013 (36)c
18
20.5 (2.2)
5
13.5
31.4 (5.3)
86.5 (17.1)
17
Steinberg,
2013 (37)
26
37.6 (7.4)
0
0
35.8 (6.1)
102.0 (16.6)
24
Mean Age,
years
(SD)
No.
Male Sex
%
Mean
BMI
(SD)
Mean Body
Weight, kg
(SD)
82.0 (10.9)
33
59.4 (10.9)
16
47.6
26.2 (2.8)
75.1 (11.7)
30
40.1 (7.7)
9
30.0
26.5 (4.1)
73.9 (10.2)
34.3 (7.4)
93.7 (24.1)
15
56.7 (10.6)
5
33.3
36.9 (12.5)
100.5 (28.8)
63
38.0 (4.7)
15
24.0
30.4 (2.8)
86.4 (12.5)
32
42.4 (7.5)
5
15.6
33.5 (4.5)
88.0 (13.1)d
54
59.4 (8.4)
35
64.8
25.5 (3.3)
67.7 (10.8)
89
24.0 (3.4)
0
0
27.1 (7.5)
73.8 (21.1)
123
40.3 (4.8)
0
0
28.1 (5.8)
74.6 (16.1)
49
43.2 (11.7)
13
27.0
89
40.9 (12.1)
32
64
32.0 (4.0)
26
61.5 (9.1)
13
54.0
33.5 (8.0)
39.0 (9.0)
0
0
34.6 (5.8)
31.2 (2.4)
Mean
WC, cm
(SD)
79.8 (13.6)
15
91.6 (17.2)
98.5 (10.3)
89.5 (9.7)
94.8 (12.6)
Abbreviations: BMI, body mass index; SD, standard deviation; WC, waist circumference.
a
Weight (kg)/height (m)2.
b
This conference poster stated only that the total sample size was 30; we assumed there were 15 participants in each group.
c
Only average age, number of males, and average body weight for all participants were reported.
d
For participants analyzed in terms of body weight change.
32.2 (4.5)
92.9 (17.9)
96.0 (23.1)
96.6 (10.4)
91.3 (7.5)
96.8 (14.6)
340 Liu et al.
Table 2. Characteristics of Participants in 14 Randomized Controlled Trials That Examined the Association of Mobile Phone Intervention With Net Change in Weight-Related Measures Among
Overweight and Obese Adults, 2004–2013
Mobile Phone Intervention and Weight Loss 341
Table 3. Average Change in Body Weight–Related Measures Among Participants From 14 Randomized Controlled Trials, 2004–2013
Intervention Group
First Author, Year
(Reference No.)
Márquez Contreras, 2004 (30)
Mean Weight
Change, kg (SD)
a
Mean BMI
Change (SD)
Mean WC
Change, cm (SD)
Mean Weight
Change, kg (SD)
−5.16 (4.30)
−0.05 (2.45)
Helsel, 2009 (32)
−4.9 (5.4)
Haapala, 2009 (17)
−3.1 (4.9)
1.41 (3.40)
−2.10 (2.99)
−1.10 (4.88)
−0.50 (1.36)
−0.5 (2.5)
−0.2 (0.9)
−4.5 (5.3)
−0.7 (4.7)
−2.70 (3.78)
−1.30 (3.06)
0 (2.2)
Lombard, 2010 (34)
−0.20 (3.70)
−2.57 (2.60)
−2.45 (4.39)
Shapiro, 2012 (35)
−1.65 (5.45)
−1.03 (4.26)
Orsama, 2013 (21)
−2.10 (3.75)
−1.27 (6.51)
−1.6 (4.5)
−0.40 (3.00)
Turner-McGrievy, 2011 (20)
Steinberg, 2013 (37)
1.00 (3.49)
−2.6 (3.4)
Patrick, 2009 (18)
−2.4 (2.5)
Mean WC
Change, cm (SD)
0.10 (0.77)
0 (0.41)
Yoo, 2009 (19)
Napolitano, 2013 (36)
Mean BMI
Change (SD)
−0.20 (3.86)
−0.24 (0.75)
Hurling, 2007 (15)
Faridi, 2008 (31)
Zuercher, 2009 (33)
Control Group
−1.30 (5.99)
−0.50 (0.94)
−2.20 (2.16)
0 (0.8)
−0.12 (5.73)
0.83 (3.76)
0.40 (3.75)
−0.63 (2.40)
−0.47 (2.42)
1.14 (2.53)
0.42 (0.90)
Abbreviations: BMI, body mass index; SD, standard deviation; WC, waist circumference.
a
Weight (kg)/height (m)2.
(1947–March 27, 2014), the Cochrane Central Register of
Controlled Trials (CENTRAL; The Cochrane Collaboration,
Oxford, United Kingdom) (March 2014 issue), and Web of
Science (Thomson Reuters, New York, New York) (1976–
March 27, 2014) to identify trials examining the association
of mobile phone intervention with weight-related measures.
Details on the search strategy are presented in the Web Appendix (available at http://aje.oxfordjournals.org/). Briefly, our
literature search strategy combined all synonyms for the intervention “cellular phone” with all synonyms for the outcomes
“weight loss,” “body mass index,” “waist circumference,”
“waist-to-hip ratio,” or “body fat.” The search was limited to
adults aged ≥18 years, with no language restrictions. A manual search of all references from eligible articles, reviews, systematic reviews, and meta-analyses was performed.
To determine eligibility for the meta-analysis, 2 investigators independently reviewed each of the articles generated by
the literature search after removal of duplicates. BMI was calculated as kilograms of weight per square meter of height. To
be included in the meta-analysis, a study had to meet the following criteria: 1) participants were at least 18 years of age;
2) at least 80% of the study participants were overweight or
obese (the BMI cutpoint for overweight/obesity was ≥25
units (23)); 3) the application of mobile phone messaging
was part of the intervention; 4) the only difference between
intervention and control groups was the mobile phone intervention; 5) mobile phone intervention included delivery of
information on healthy eating, physical activity, or weight
loss; 6) changes in body weight, BMI, WC, waist:hip ratio,
or body fat percentage or data with which to calculate the
changes in these measures were provided, along with a measure of variance or confidence interval; and 7) allocation to
the intervention or control group was random. If the results
Am J Epidemiol. 2015;181(5):337–348
of a study had been published more than once, only the article
with the most complete and up-to-date information was included in the analysis. Any between-reviewer discrepancies
regarding study eligibility were resolved by reviewer discussion and consensus.
Data extraction
A standardized data abstraction form was used to obtain information from selected articles (see Web Appendix). Two
investigators independently abstracted the following items
from eligible articles: general study information (including
title, authors, name of the trial, and year of publication), study
characteristics (including study design, primary outcome of
the study, randomization, blinding, outcome measurement,
and statistical analysis methods), participant characteristics
(including age, sex, race/ethnicity, and comorbid conditions),
information about the intervention (including intervention
type, duration, frequency, and feedback requirements), and
study outcome measures (including body weight, BMI, WC,
waist:hip ratio, or body fat percentage). Abstraction results
were compared, and discrepancies were resolved by discussion
and consensus. If any data were missing or incomplete, the
original study authors were contacted (20).
Quality assessment of individual trials
A standardized 5-point Jadad scale was used to examine
the quality of selected studies, which included assessment
of the following items: randomization, blinding, description
of dropout and withdrawal, and evaluation of randomization
and blinding (24). Two investigators assessed the articles independently, and discrepancies were resolved by discussion
342 Liu et al.
A)
First Author, Year (Reference No.)
Net Change (95% CI)
Márquez Contreras, 2004 (30)
–4.96 (–6.92, –3.00)
6.58
Faridi, 2008 (31)
–1.46 (–3.58, 0.66)
6.01
Haapala, 2009 (17)
–2.40 (–4.09, –0.71)
7.66
Zuercher, 2009 (33)
–0.50 (–1.20, 0.20)
12.66
Helsel, 2009 (32)
–2.30 (–5.53, 0.93)
3.39
Patrick, 2009 (18)
–1.70 (–3.16, –0.24)
8.73
0.20 (–1.33, 1.73)
8.40
Lombard, 2010 (34)
–1.03 (–2.02, –0.04)
11.13
Turner-McGrievy, 2011 (20)
–0.12 (–1.57, 1.33)
8.76
Shapiro, 2012 (35)
–0.62 (–2.08, 0.84)
8.70
Napolitano, 2013 (36)
–1.77 (–3.44, –0.10)
7.74
Steinberg, 2013 (37)
–2.41 (–5.19, 0.37)
4.22
Orsama, 2013 (21)
–2.50 (–4.62, –0.38)
6.01
Overall (I 2 = 59.2%, P = 0.003)
–1.44 (–2.12, –0.76)
100.00
Yoo, 2009 (19)
–9.0
0.0
Weight, %
9.0
Net Change in Body Weight, kg
B)
Net Change (95% CI)
Weight, %
Hurling, 2007 (15)
–0.34 (–0.60, –0.08)
36.03
Faridi, 2008 (31)
–1.00 (–5.42, 3.42)
0.13
Zuercher, 2009 (33)
–0.20 (–0.41, 0.01)
54.07
0.00 (–0.51, 0.51)
9.45
Steinberg, 2013 (37)
–2.41 (–5.19, 0.37)
0.32
Overall (I 2 = 0.0%, P = 0.409)
–0.24 (–0.40, –0.08)
First Author, Year (Reference No.)
Yoo, 2009 (19)
–6.0
100.00
6.0
0.0
Net Change in Body Mass Index
Figure 2 continues
and consensus. Quality assessment details and the score for
each trial are given in Web Table 1.
Data synthesis and statistical analysis
For each RCT, if the net effect size was not provided, the
net effect size was calculated as the change in body weight–
related measures resulting from treatment (T) (from baseline
(B) to the end of intervention) in the intervention group minus
the change in body weight–related measures in the control
(C) group: (XTT − XTB) – (XCT – XCB). For studies without
variance data, we calculated the variance from confidence intervals or test statistics. If the variance for change between
baseline and the end of the intervention (σΔ) was not reported,
Am J Epidemiol. 2015;181(5):337–348
Mobile Phone Intervention and Weight Loss 343
C)
Net Change (95% CI)
Weight, %
Haapala, 2009 (17)
–2.90 (–11.41, 5.61)
13.29
Yoo, 2009 (19)
–0.50 (–4.08, 3.08)
75.21
Lombard, 2010 (34)
–1.18 (–10.32, 7.96)
11.51
Overall (I 2 = 0.0%, P = 0.876)
–0.90 (–4.00, 2.20)
100.00
First Author, Year (Reference No.)
–12.0
0.0
12.0
Net Change in Waist Circumference, cm
Figure 2. Average net change in body weight (kg) (A), body mass index (weight (kg)/height (m)2) (B), and waist circumference (cm) (C) in randomized controlled trials comparing persons receiving weight-related mobile phone intervention with a control group, 2004–2013. The size of each
square is proportional to the percent weight that each study contributed in the pooled estimate. The pooled effect size is indicated by the diamond.
Bars, 95% confidence intervals (CI).
it was calculated from the following equation (25):
σ2Δ ¼ σ2pre þ σ2post 2ρσpre σpost ;
where σpre corresponds to the variance at baseline, σpost corresponds to the variance at the end of intervention, and ρ is
the correlation coefficient for correlations between measurements taken at baseline and the end of intervention (ρ estimates of 0.925 for the intervention group and 0.959 for the
control group were imputed based on reported data (26)).
Since a ρ value of 0.5 is frequently imputed, we also conducted a sensitivity analysis using this ρ value (13).
Random-effects models were used to pool net change estimates across trials. The heterogeneity of net changes was assessed across studies using the Cochrane Q and I 2 statistics
(27). We also conducted analyses using 2 a priori-defined
subgroups to examine the associations of mobile phone intervention with weight loss on the basis of study duration
(<6 months or ≥6 months) and type of intervention (SMS
only or both SMS and MMS). We also performed a metaregression analysis examining study duration as a continuous
measure. To further assess the robustness of our findings, we
performed several sensitivity analyses by restricting the data
on the basis of quality parameters—for example, restricting
the analyses to high-quality trials (Jadad score ≥3 (24)), trials
with weight control as the primary outcome, trials using
intention-to-treat (ITT) analysis, trials with an intervention
frequency of ≥1 message/day, trials with outcome measurement by a physician, trials requiring feedback, or trials with a
sample size of ≥40—or excluding trials with normal-weight
participants or clustered RCTs. We also removed each trial
Am J Epidemiol. 2015;181(5):337–348
sequentially to determine the magnitude of its influence on
the overall pooled estimates.
Publication bias was evaluated using visual inspection
of funnel plots which plotted the standard errors against the
net change for each study and also using Egger’s test to assess the asymmetry of the funnel plot (28). The Duval and
Tweedie nonparametric “trim-and-fill” method (29) was used
to examine any influence of publication bias on meta-analysis
findings. All of the analyses were conducted using Stata software, version 12.0 (StataCorp LP, College Station, Texas). A
2-sided P value less than 0.05 was considered statistically
significant.
RESULTS
Of the 497 relevant citations retrieved, 14 trials of 1,337
randomized participants were included in the current metaanalysis (Figure 1). Characteristics of the 14 trials in the
current meta-analysis are shown in Table 1, and detailed information on types of mobile phone intervention is presented
in Web Table 2. The trials, published between 2004 and 2013,
varied in size from 30 participants to 250 participants. Study
durations ranged from 8 weeks to 12 months. Most of the
studies had been conducted in the United States or European
countries, while 1 study was conducted in South Korea (19).
Participants in intervention groups received mobile phone interventions, including SMS or MMS. Intervention frequency,
feedback requirements (requiring participant response to the
delivery service), and outcome measurements varied across
trials. Of the 14 trials, 8 were conducted to assess the primary
outcome of weight loss, 7 applied ITT analysis, and 13 were
categorized as high-quality (Jadad score ≥3).
Body Mass Indexa
Body Weight
Analysis
All studies
No. of
Studies
Net
Change,
kg
Heterogeneity
95% CI
13
−1.44
−2.12, −0.76
I ,%
P Value
No. of
Studies
59.2
0.003
5
2
Waist Circumference
Heterogeneity
Net
Change
95% CI
−0.24
−0.40, −0.08
Net
Change,
cm
95% CI
Heterogeneity
I ,%
P Value
No. of
Studies
I ,%
P Value
0
0.409
3
−0.90
−0.40, 2.20
0
0.876
2
−2.10
−8.33, 4.13
0
0.787
2
2
Subgroup analysis
Study duration
<6 months
6
−0.92
−1.58, −0.25
20.9
0.276
3
−0.27
−0.51, −0.04
≥6 months
7
−1.85
−2.99, −0.71
70.1
0.003
2
−0.85
−2.83, 1.12
0
0.487
58.5
0.121
Type of intervention
SMS only
SMS + MMS
11
−1.53
−2.34, −0.73
64.8
0.002
2
−1.16
−2.22, −0.10
4.9
0.305
Sensitivity analysis
High-quality trialsb
12
−1.41
−2.11, −0.71
62.0
0.002
5
−0.24
−0.40, −0.08
0
0.409
3
−0.90
−0.40, 2.21
0
0.876
Exclusion of studies with
normal-weight
participants
10
−1.27
−1.91, −0.63
21.5
0.245
2
−0.85
−2.83, 1.12
58.5
0.121
2
−0.86
−4.16, 2.44
0
0.610
Weight control was the
primary outcome
9
−1.05
−1.55, −0.54
16.4
0.296
2
−0.54
−1.65, 0.57
21.0
0.282
2
−2.10
−8.33, 4.13
0
0.787
Use of ITT analysis
7
−1.30
−1.99, −0.60
4.6
0.392
3
−0.49
−1.23, −0.24
8.6
0.335
Frequency of contact ≥1
time/day
9
−0.79
−1.28, −0.29
5.7
0.388
3
−0.58
−2.03, 0.87
32.3
0.228
0.787
Am J Epidemiol. 2015;181(5):337–348
Physician measurement
9
−1.68
−2.53, −0.84
58.7
0.013
4
−0.24
−0.44, −0.04
22.4
0.276
2
−2.10
−8.33, 4.13
0
Feedback requirement
9
−1.20
−1.83, −0.57
33.5
0.150
5
−0.24
−0.40, −0.08
0
0.409
2
−0.86
−4.16, 2.44
0
0.610
Sample size ≥40
10
−1.40
−2.20, −0.60
68.0
0.001
4
−0.27
−0.40, −0.10
1.8
0.383
3
−0.90
−0.40, 2.20
0
0.876
Exclusion of clustered
RCTs
10
−1.10
−1.71, −0.49
34.6
0.131
4
−0.24
−0.44, −0.08
22.4
0.276
2
−0.86
−4.16, 2.44
0
0.610
Exclusion of nonpeer-reviewed
studies
11
−1.56
−0.78, −2.34
59.6
0.006
4
−0.29
−0.52, −0.05
18.8
0.297
3
−0.90
−0.40, 2.20
0
0.876
Abbreviations: CI, confidence interval; ITT, intention-to-treat; MMS, multimedia message service; RCT, randomized controlled trial; SMS, short message service.
a
Weight (kg)/height (m)2.
b
Jadad score ≥3 (24).
344 Liu et al.
Table 4. Results From Subgroup Analyses and Sensitivity Analyses of Net Change in Body Weight, Body Mass Index, and Waist Circumference in a Meta-Analysis of 14 Randomized
Controlled Trials, 2004–2013
Mobile Phone Intervention and Weight Loss 345
Am J Epidemiol. 2015;181(5):337–348
A)
SE of Net Change
0.0
0.5
1.0
1.5
2.0
–6
–4
–2
0
2
Net Change in Body Weight, kg
B)
0.0
SE of Net Change
0.5
1.0
1.5
2.0
2.5
–4
–2
0
2
4
Net Change in Body Mass Index
C)
0.0
1.0
SE of Net Change
Table 2 shows characteristics of participants at baseline in
the intervention and control groups, respectively. The average age ranged from 20.5 years to 57.0 years. The percentage
of males ranged from 0% to 54% in the intervention groups
and from 0% to 65% in the control groups. Average BMI
ranged from 25.6 to 35.8 in intervention groups and from
25.5 to 36.9 in control groups. Five trials included participants with hypertension or type 2 diabetes.
Among the 14 trials, 13 reported comparisons of body
weight (17–21, 30–37), 5 reported comparisons of BMI
(15, 19, 31, 33, 37), 3 reported comparisons of WC (17,
19, 34), and 1 reported a comparison of body fat percentage
(15) (Table 3). Body weight change ranged from −5.16 kg
to −0.05 kg in intervention groups and from −2.60 kg to
1.41 kg in control groups; BMI change ranged from −0.50
units to 0 units in intervention groups and from −0.50 units
to 1.00 unit in control groups; and WC change ranged from
−4.50 cm to −1.30 cm in intervention groups and from
−2.20 cm to −0.12 cm in control groups.
Pooled estimates of the net changes in body weight, BMI,
and WC are presented in parts A–C of Figure 2, respectively.
On average, compared with the control group, mobile phone
intervention resulted in significant decreases in body weight,
with a net body weight change of −1.44 kg (95% confidence
interval (CI): −2.12, −0.76; P < 0.001, I 2 = 59.2%). Similarly, BMI was also significantly decreased in response to
the mobile phone intervention, with a net BMI change of
−0.24 units (95% CI: −0.40, −0.08; P = 0.41, I 2 = 0). In
comparison with the control group, no statistically significant
difference in WC was associated with mobile phone intervention, with a pooled net change estimate of −0.90 cm (95% CI:
−4.00, 2.20; P = 0.88, I 2 = 0), but this result was based on
only 3 trials.
Subgroup analyses for body weight, BMI, and WC were
generally consistent with overall findings (Table 4), showing
no significant differences in weight-related measures between
trials of shorter and longer duration (P for difference = 0.33) or
trials employing only SMS versus those employing SMS plus
MMS (P for difference = 0.71). Furthermore, meta-regression
analysis revealed no association between trial duration as a
continuous variable and net change estimates (data not shown;
P = 0.62). In order to examine the robustness of our findings,
we also conducted sensitivity analyses restricting the data on
the basis of quality parameters. The pooled net change estimates were not substantially different from the overall estimates (Table 4). In addition, the net changes were similar to
the overall estimates when we imputed a ρ value of 0.5 to estimate variance in the 2 studies that did not present variance
estimates for the change measures. Results of the influence
analysis did not identify any trials whose removal would have
significantly altered the findings (Web Figure 1).
The funnel plot demonstrated that the distribution of net
body weight change estimates for individual studies was somewhat asymmetrical (Figure 3A), and Egger’s test indicated
significant publication bias (P = 0.04). Application of the nonparametric trim-and-fill method to assess the potential influence of publication bias on our findings resulted in virtually
unchanged net body weight change. In addition, there was little
evidence of publication bias for BMI change (Figure 3B) and
WC change (Figure 3C) (P = 0.42 and P = 0.40, respectively).
2.0
3.0
4.0
5.0
–10
–5
0
5
10
Net Change in Waist Circumference, cm
Figure 3. Funnel plots from a meta-analysis of the association of
mobile phone intervention with body weight change (kg) (A), body
mass index change (weight (kg)/height (m)2) (B), and waist circumference change (cm) (C), 2004–2013. SE, standard error.
DISCUSSION
The current meta-analysis, incorporating data from 14 published and unpublished RCTs on more than 1,300 participants,
346 Liu et al.
documented significant decreases of approximately 1.44 kg in
body weight and 0.24 units in BMI associated with mobile
phone intervention. Body weight decreases were not significantly different between shorter- and longer-duration trials, although statistical power for confirming possibly larger net
changes with longer-duration trials was limited. Furthermore,
the association of mobile phone intervention with weight loss
did not differ by type of mobile phone intervention (SMS or
SMS and MMS combined), and findings were robust across
sensitivity analyses. Although there was no association between
mobile phone intervention and waist circumference, this negative finding may be explained by the very limited number of trials assessing this body weight–related measure. In aggregate,
our findings indicated that mobile phone intervention could be
an important strategy for promoting weight loss.
We identified significant reductions in body weight and
BMI of 1.44 kg and 0.24 units, respectively, due to mobile
phone intervention in comparison with the control group.
In a previous systematic review, Stephens and Allen (38) suggested that mobile phone intervention is effective for increasing physical activity and/or reducing overweight/obesity. The
current meta-analysis provides important quantitative evidence of the benefits of mobile phone intervention in body
weight and BMI reduction from the accumulation of RCT
results in this area.
Of particular importance was our finding of nonsignificant
differences between the weight reductions observed in trials
of shorter duration and those of longer duration. While some
previous studies have found that lifestyle intervention can result in meaningful, sustained weight loss (39), several others
have observed body weight regain after 6 months’ intervention (13, 26, 40, 41). Such results may be due to reduced
intervention compliance. Mobile phone intervention is convenient for study participants, and the widespread use of and
accessibility of mobile phones facilitates high compliance.
Findings from the current meta-analysis indicate that methods such as mobile phone intervention, which delivers frequent reminders about healthy eating and physical activity
and other information related to weight loss, may be a potentially useful tool for achieving successful long-term weight
management (42–44).
To our knowledge, the current study is the first metaanalysis of RCTs to have assessed the association of mobile
phone intervention with weight loss. One strength of this
study was the inclusion of only RCTs, reducing the likelihood that the observed association between mobile phone
intervention and overweight/obesity-related traits can be explained entirely by bias and confounding. In addition, only 6
of the 14 trials of body weight and only 1 of the 5 trials of
BMI had individually statistically significant results, highlighting the benefits of meta-analysis for identifying important effect sizes with increased statistical power.
Still, some limitations of this study should be addressed.
Although we searched for and included “gray literature” in
the current meta-analysis, there was some indication of possible publication bias for the body weight trait. However, the
results were virtually unchanged upon application of the trimand-fill method for assessment of the potential influence of
publication bias. Furthermore, restricting the meta-analysis to
only larger trials had no influence on the findings. In addition,
there was considerable heterogeneity in the pooled estimates of
net change in body weight (59%). Heterogeneity was substantially reduced when we restricted the meta-analysis to trials
using ITT analysis and trials with an intervention frequency
of ≥1 message/day, suggesting that these factors could contribute to inconsistency between studies. While we found evidence
that weight loss could be sustained in trials of longer duration,
there were no trials of over 1 year’s duration. Therefore, more
research is needed to determine whether mobile phone intervention can maintain its benefits over longer periods of time.
In addition, investigators in 6 of the 14 trials did not apply ITT
analysis. However, sensitivity analyses restricted to only studies using ITT analysis produced findings similar to the overall
pooled estimate. Moreover, nearly all trials included in the current meta-analysis were conducted in high-income countries in
North America and Europe. The paucity of data from low- and
middle-income nations limits the generalizability of metaanalysis findings to these areas.
While numerous studies examined the association of
mobile phone intervention with body weight, fewer studies
examined the associations with BMI and WC. Subgroup and
sensitivity analyses were probably underpowered and, under
some circumstances, could not be conducted for BMI and
WC traits. It is also likely that the main WC analysis was substantially underpowered to detect the seemingly modest benefits of mobile phone intervention. In addition, no studies
examined the associations of mobile phone intervention with
waist:hip ratio, and only 1 study examined its benefit in reducing body fat percentage. Therefore, these traits could not
be examined in the meta-analysis. Future studies are needed
to assess the impact of mobile phone intervention on these important overweight/obesity-related measures.
In conclusion, this meta-analysis provides evidence that
mobile phone intervention is an effective strategy for promoting weight loss. While these findings are encouraging, additional trials are required to examine the long-term (>1 year)
association of mobile phone intervention with weight loss.
Furthermore, studies designed to better understand the associations of mobile phone intervention with WC, waist:hip
ratio, and body fat percentage are warranted. Research will
also be needed to assess the cost-effectiveness of mobile phone
intervention (45–47). In aggregate, the results of the current
meta-analysis are promising, suggesting that mobile phone intervention could contribute to meaningful reductions in body
weight and BMI at the population level. While the current
meta-analysis focused on the individual benefits of mobile
phone intervention, combining this approach with other behavioral and lifestyle modifications could prove especially
beneficial for achieving weight loss success and helping to
curb the worldwide overweight/obesity epidemic.
ACKNOWLEDGMENTS
Author affiliations: State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Department of Epidemiology,
National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
Beijing, China (Fangchao Liu, Jie Cao, Shufeng Chen,
Am J Epidemiol. 2015;181(5):337–348
Mobile Phone Intervention and Weight Loss 347
Jianfeng Huang, Dongfeng Gu); Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane
University, New Orleans, Louisiana (Fangchao Liu, Xiaomu
Kong, Jie Cao, Shufeng Chen, Changwei Li, Tanika N.
Kelly); and Department of Endocrinology, China-Japan
Friendship Hospital, Beijing, China (Xiaomu Kong).
This research was not funded by any specific grant. F.L.,
X.K., J.C., and S.C. were supported by the Fogarty International Center of the US National Institutes of Health (award
D43TW009107).
We thank Dr. Bonnie Spring and H. Gene McFadden for
their help in calculating ρ values using their own data (26).
We also thank Dr. Gabrielle Turner-McGrievy for providing
more information on the study by Turner-McGrievy and
Tate (20).
Conflict of interest: none declared.
REFERENCES
1. Abelson P, Kennedy D. The obesity epidemic [editorial].
Science. 2004;304(5676):1413.
2. Kelly T, Yang W, Chen CS, et al. Global burden of obesity in
2005 and projections to 2030. Int J Obes (Lond). 2008;32(9):
1431–1437.
3. Lopez AD, Mathers CD, Ezzati M, et al. Global and regional
burden of disease and risk factors, 2001: systematic analysis
of population health data. Lancet. 2006;367(9524):
1747–1757.
4. Lim SS, Vos T, Flaxman AD, et al. A comparative risk
assessment of burden of disease and injury attributable to 67
risk factors and risk factor clusters in 21 regions, 1990–2010: a
systematic analysis for the Global Burden of Disease Study
2010. Lancet. 2012;380(9859):2224–2260.
5. Withrow D, Alter DA. The economic burden of obesity
worldwide: a systematic review of the direct costs of obesity.
Obes Rev. 2011;12(2):131–141.
6. Klein S, Burke LE, Bray GA, et al. Clinical implications of
obesity with specific focus on cardiovascular disease: a
statement for professionals from the American Heart
Association Council on Nutrition, Physical Activity,
and Metabolism: endorsed by the American College of
Cardiology Foundation. Circulation. 2004;110(18):
2952–2967.
7. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in
the incidence of type 2 diabetes with lifestyle intervention or
metformin. N Engl J Med. 2002;346(6):393–403.
8. Kaukua J, Pekkarinen T, Sane T, et al. Health-related
quality of life in obese outpatients losing weight with
very-low-energy diet and behaviour modification—a 2-y
follow-up study. Int J Obes Relat Metab Disord. 2003;
27(10):1233–1241.
9. Ryan DH, Kushner R. The state of obesity and obesity research
[editorial]. JAMA. 2010;304(16):1835–1836.
10. Kriska AM, Delahanty LM, Pettee KK. Lifestyle intervention
for the prevention of type 2 diabetes: translation and future
recommendations. Curr Diab Rep. 2004;4(2):113–118.
11. Shaw K, O’Rourke P, Del Mar C, et al. Psychological
interventions for overweight or obesity. Cochrane Database
Syst Rev. 2005;2:CD003818.
12. Wadden TA, Butryn ML. Behavioral treatment of obesity.
Endocrinol Metab Clin North Am. 2003;32(4):981–1003, x.
Am J Epidemiol. 2015;181(5):337–348
13. Dansinger ML, Tatsioni A, Wong JB, et al. Meta-analysis: the
effect of dietary counseling for weight loss. Ann Intern Med.
2007;147(1):41–50.
14. Franklin VL, Waller A, Pagliari C, et al. A randomized controlled
trial of Sweet Talk, a text-messaging system to support young
people with diabetes. Diabet Med. 2006;23(12):1332–1338.
15. Hurling R, Catt M, Boni MD, et al. Using Internet and mobile
phone technology to deliver an automated physical activity
program: randomized controlled trial. J Med Internet Res. 2007;
9(2):e7.
16. Rodgers A, Corbett T, Bramley D, et al. Do u smoke after txt?
Results of a randomised trial of smoking cessation using mobile
phone text messaging. Tob Control. 2005;14(4):255–261.
17. Haapala I, Barengo NC, Biggs S, et al. Weight loss by mobile
phone: a 1-year effectiveness study. Public Health Nutr. 2009;
12(12):2382–2391.
18. Patrick K, Raab F, Adams MA, et al. A text message-based
intervention for weight loss: randomized controlled trial. J Med
Internet Res. 2009;11(1):e1.
19. Yoo HJ, Park MS, Kim TN, et al. A ubiquitous chronic disease
care system using cellular phones and the Internet. Diabet Med.
2009;26(6):628–635.
20. Turner-McGrievy G, Tate D. Tweets, apps, and pods: results of
the 6-month Mobile Pounds Off Digitally (Mobile POD)
randomized weight-loss intervention among adults. J Med
Internet Res. 2011;13(4):e120.
21. Orsama AL, Lähteenmäki J, Harno K, et al. Active assistance
technology reduces glycosylated hemoglobin and weight in
individuals with type 2 diabetes: results of a theory-based
randomized trial. Diabetes Technol Ther. 2013;15(8):662–669.
22. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement
for reporting systematic reviews and meta-analyses of studies
that evaluate health care interventions: explanation and
elaboration. J Clin Epidemiol. 2009;62(10):e1–e34.
23. WHO Expert Consultation. Appropriate body-mass index for
Asian populations and its implications for policy and
intervention strategies. Lancet. 2004;363(9403):157–163.
24. Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of
reports of randomized clinical trials: is blinding necessary?
Control Clin Trials. 1996;17(1):1–12.
25. Thiessen Philbrook H, Barrowman N, Garg AX. Imputing
variance estimates do not alter the conclusions of a
meta-analysis with continuous outcomes: a case study of
changes in renal function after living kidney donation. J Clin
Epidemiol. 2007;60(3):228–240.
26. Spring B, Duncan JM, Janke EA, et al. Integrating technology
into standard weight loss treatment: a randomized controlled
trial. JAMA Intern Med. 2013;173(2):105–111.
27. DerSimonian R, Laird N. Meta-analysis in clinical trials.
Control Clin Trials. 1986;7(3):177–188.
28. Egger M, Davey Smith G, Schneider M, et al. Bias in
meta-analysis detected by a simple, graphical test. BMJ. 1997;
315(7109):629–634.
29. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based
method of testing and adjusting for publication bias in
meta-analysis. Biometrics. 2000;56(2):455–463.
30. Márquez Contreras E, de la Figuera von Wichmann M,
Gil Guillén V, et al. Effectiveness of an intervention to
provide information to patients with hypertension as short text
messages and reminders sent to their mobile phones
(HTA-Alert) [in Spanish]. Aten Primaria. 2004;34(8):399–405.
31. Faridi Z, Liberti L, Shuval K, et al. Evaluating the impact of
mobile telephone technology on type 2 diabetic patients’
self-management: the NICHE pilot study. J Eval Clin Pract.
2008;14(3):465–469.
348 Liu et al.
32. Helsel DL, Jakicic JM. Effect of adding telephone text message
prompts to in-person weight loss program [abstract 788-P].
Obesity. 2009;17(suppl 2):S263.
33. Zuercher JL. Developing Strategies for Helping Women
Improve Weight-Related Health Behaviors [doctoral
dissertation]. Chapel Hill, NC: University of North Carolina at
Chapel Hill; 2009.
34. Lombard C, Deeks A, Jolley D, et al. A low intensity,
community based lifestyle programme to prevent weight gain in
women with young children: cluster randomised controlled
trial. BMJ. 2010;341:c3215.
35. Shapiro JR, Koro T, Doran N, et al. Text4Diet: a randomized
controlled study using text messaging for weight loss behaviors.
Prev Med. 2012;55(5):412–417.
36. Napolitano MA, Hayes S, Bennett GG, et al. Using Facebook
and text messaging to deliver a weight loss program to college
students. Obesity (Silver Spring). 2013;21(1):25–31.
37. Steinberg DM, Tate DF, Bennett GG, et al. The efficacy of a
daily self-weighing weight loss intervention using smart scales
and e-mail. Obesity (Silver Spring). 2013;21(9):1789–1797.
38. Stephens J, Allen J. Mobile phone interventions to increase
physical activity and reduce weight: a systematic review.
J Cardiovasc Nurs. 2013;28(4):320–329.
39. Wadden TA, Neiberg RH, Wing RR, et al. Four-year weight
losses in the Look AHEAD Study: factors associated with
long-term success. Obesity (Silver Spring). 2011;19(10):
1987–1998.
40. van Wier MF, Dekkers JC, Hendriksen IJ, et al. Effectiveness of
phone and e-mail lifestyle counseling for long term weight
41.
42.
43.
44.
45.
46.
47.
control among overweight employees. J Occup Environ Med.
2011;53(6):680–686.
Champagne CM, Broyles ST, Moran LD, et al. Dietary intakes
associated with successful weight loss and maintenance during
the Weight Loss Maintenance Trial. J Am Diet Assoc. 2011;
111(12):1826–1835.
Milsom VA, Middleton KM, Perri MG. Successful long-term
weight loss maintenance in a rural population. Clin Interv
Aging. 2011;6:303–309.
Shuger SL, Barry VW, Sui X, et al. Electronic feedback in a
diet- and physical activity-based lifestyle intervention for
weight loss: a randomized controlled trial. Int J Behav Nutr
Phys Act. 2011;8:41.
Tate DF, Jackvony EH, Wing RR. Effects of Internet behavioral
counseling on weight loss in adults at risk for type 2 diabetes: a
randomized trial. JAMA. 2003;289(14):1833–1836.
Hebden L, Balestracci K, McGeechan K, et al. ‘TXT2BFiT’ a
mobile phone-based healthy lifestyle program for preventing
unhealthy weight gain in young adults: study protocol for a
randomized controlled trial. Trials. 2013;14:75.
Batch BC, Tyson C, Bagwell J, et al. Weight loss intervention
for young adults using mobile technology: design and rationale
of a randomized controlled trial—Cell Phone Intervention for
You (CITY). Contemp Clin Trials. 2014;37(2):333–341.
Duncan MJ, Vandelanotte C, Rosenkranz RR, et al.
Effectiveness of a website and mobile phone based physical
activity and nutrition intervention for middle-aged males: trial
protocol and baseline findings of the ManUp Study. BMC
Public Health. 2012;12:656.
Am J Epidemiol. 2015;181(5):337–348