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Image transcription textORIGINAL RESEARCH
merican American
Heart
Stroke
Association | Association.
mActive: A Randomized Clinical Trial of an Automated mHealth
Intervention for Physical Activity Promotion
Seth S. Martin, MD, MHS; David I. Feldman, BS; Roger S. Blumenthal, MD; Steven R. Jones, MD; Wendy S. Post, MD, MS;
Rebeccah A. Mckibben, MD, MPH; Erin D. Michos, MD, MHS; Chiadi E. Ndumele, MD, MHS; Elizabeth V. Ratchford, MD; Josef Coresh, MD,
PhD; Michael J. Blaha, MD, MPH
Background-We hypothesized that a fully automated mobile health (mHealth) intervention with tracking and texting components
would increase physical activity.
Methods and Results-mActive enrolled smartphone users aged 18 to 69 years at an ambulatory cardiology center in Baltimore,
Maryland. We used sequential randomization to evaluate the intervention’s 2 core components. After establishing baseline activity
during a blinded run-in (week 1), in phase I (weeks 2 to 3), we randomized 2:1 to unblinded versus blinded tracking. Unblinding
allowed continuous access to activity data through a smartphone interface. In phase II (weeks 4 to 5), we randomized unblinded
participants 1:1 to smart texts versus no texts. Smart texts provided smartphone-delivered coaching 3 times/ day aimed at
individual encouragement and fostering feedback loops by a fully automated, physician-written, theory-based algorithm using real-
time activity data and 16 personal factors with a 10 000 steps/day goal. Forty-eight outpatients (46% women, 21% nonwhite)
enrolled with a meantSD age of 58:18 years, body mass index of 31:16 kg/m , and baseline activity of 967014350 steps/day.
Daily activity data capture was 97.4%. The phase I change in activity was nonsignificantly higher in unblinded participants versus
blinded controls by 1024 daily steps (95% confidence interval [CI], -580 to 2628; P=0.21). In phase II, participants receiving texts
increased their daily steps over those not receiving texts by 2534 (95% CI, 1318 to 3750; P<0.001) and over blinded controls by
3376 (95% CI, 1951 to 4801; P<0.001).
Conclusions-An automated tracking-texting intervention increased physical activity with, but not without, the texting component.
These results support new mHealth tracking technologies as facilitators in need of behavior change drivers.
Clinical Trial Registration-URL: http://ClinicalTrials.gov/. Unique identifier: NCT0 1917812. (J Am Heart Assoc. 2015;4:
e002239 doi: 10.1 161/JAHA. 1 15.002239)
Key Words: accelerometer . activity tracker . automation . cardiovascular disease . digital health . eHealth . health
technology . mHealth . mobile phone . pedometer . physical activity . prevention . smartphone . text messages . texting .
wearable device . wearable sensor
Downloaded from http:/ahajournals.org by on April 17, 2023
Physical activity is a central element of lifestyle guideline
adults do not obtain ideal levels of physical activity, a
recommendations for the prevention of cardiovascular
statistic that has not significantly changed in National Health
disease (CVD) and one of the health behaviors targeted by
and Nutrition Examination Surveys since 1988-1994.3
the American Heart Association’s (AHA’s) 2020 Strategic
Therefore, new approaches to physical activity promotion
Impact Goals. However, it is estimated that >50% of U.S.
are needed.
From the Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, MD (S.S.M., D.I.F., R.S.B., S.R.J., W.S.P.,
R.A.M., E.D.M., C.E.N., E.V.R., M.J.B.); Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore,
MD (S.S.M., W.S.P., R.A.M., E.D.M., C.E.N., J.C.).
Accompanying Figures $1, $2 and Tables $1 through $3 are available at http://jaha.ahajournals.org/content/4/11/e002239/supplyDC1
Presented as an abstract at the Epidemiology and Prevention and Lifestyle and Cardiometabolic Health Scientific Sessions, March 3-6, 2015, in Baltimore, MD, and
at the American Heart Association Scientific Sessions, November 7-11, 2015, in Orlando, FL.
Correspondence to: Seth S. Martin, MD, MHS, Johns Hopkins Hospital, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287. E-mail: smart 100@jhmi.edu
Received July 20, 2015; accepted September 30, 2015.
2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell. This is an open access article under the terms of the Creative
Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is
not used for commercial purposes.
DOI: 10.1161/JAHA.115.002239
Journal of the American Heart Association…

Image transcription textszoz “Ll I!1dV uu fiq filu’filewnurcva.-;.-:duq was meow-Mo mActive Trial Martin of all Miniaturization and other advances in mobile health
(mHealth) technology have enabled convenient and accurate
tracking of physical activity through smartphone applications
and wearable devices.4 Whereas in the past one would have to manually record daily activity when using a traditional
pedometer, new technology automatically produces a digital
activity log. In addition to streamlining self—monitoring, the
vast consumer adoption of these technologies has opened a
new channel to deliver continuous feedback aimed at
stimulating healthy behavior change.4 7 The AHA’s Scientific
Statements on mHealth and interventions to promote activity
have identified a critical need for rigorous studies of new
mHealth techniques.13 In this context, we conducted a randomized, clinical trial
{“mActive”) testing the hypothesis that a fully automated,
fully mobile, and physiciandesigned mHealth inteniention
using new technologies to provide individual encouragement
and foster feedback loops increases physical activity. The
mHealth intervention had 2 core components—tracking and
texting—and so we sought to use a trial design that would
allow for specific evaluation of the effects of each of these components. Furthermore, we sought to conduct a practical
trial maximizing convenience for the participants and investigators so that it would have greater potential to be
scaled in future trial phases and adopted in routine care
settings. Methods Trial Design The mActive protocol was publically registered before enroll— ment {clinicaltrials.gov: NCT01917812] and was approved by
the Johns Hopkins School of Medicine Institutional Review Board. We used sequential randomization to individually
evaluate the tracking and texting components of the
intervention. After establishing baseline activity during a
blinded run—in {week I], in phase I {weeks 2 to 3] we randomized 2:1 to unblinded versus blinded tracking. The
activity tracker itself did not show activity information, but continuously transmitted it in all participants. The activity data
were only visible to those who were unblinded, as further
described below. In phase II (weeks 4 to 5], we randomized
unblinded participants 1:1 to “smart texts” versus no texts. Smart texts were automated, personalized, smartphone—
delivered coaching messages informed by real—time activity
and other factors, as described in detail below. Participants We enrolled outpatients at an academic CVD prevention
center in Baltimore, Maryland from January 17 to May 20, DOI: 1D.1161.’JIl-L|1.115.002239 2014. We included patients aged 18 to 69 years who were
using a Fitbug—compatible smartphone lie, iPhone24S,
GalaxyaS3). Intending to mainly modify leisure—time activity,
we targeted individuals reporting <3 days/week of moderate or vigorous leisure—time activity lasting :30 min/day by the
long form of the International Physical Activity Questionnaire
I[IPA0).9 Validation of the questionnaire up to 69 years of age
served as the rationale for the trial’s upper age limit. We did
not have an eligibility restriction based on smartphone or
Internet literacy, although all subjects confirmed having
access to email for pertinent trial communication. We
recorded demographic and clinical characteristics, including
dog ownership, because it is thought to have a positive effect
on the owner by modifying their cognitive beliefs about
walking, providing motivation, and providing social support for
walking.10 All patients continued to receive routine care and
gave written informed consent. FaceLtorface visits were not
required after enrollment. Interventions Participants used their own smartphones. Digital physical
activity tracking was performed using the Fitbug Orb (Chicago, IL] {Figure SI), a wearable, display—free, triaxial accelerometer
that pairs with low—energy Bluetooth with compatible smart—
phones. The 3V lithium battery lasts around 6 months and thus
did not require charging or replacement during the trial. Unblinded patients could continuously view their daily step
count, activity time, and aerobic activity time through smart—
phone and Web interfaces [Figure 32]. The Fitbug app also
provided a history tab allowing review of data from previous
days. Activity data were updated every 15 minutes if transmis— sion occurred by beacon mode or were available any time if a
participant activated a manual data push or streaming mode. To enable real—time activity data to inform smart texts, we
linked the application programming interfaces of Fitbug and a
smart texting system {Reify, Baltimore, MD]. Smart text
content was written by the physician investigators and reflected behavioral change theories,H particularly of feed—
back loops and habit formation,12 integrated with cardiovas— cular knowledge and clinical experience. Smart texts took into
account the importance of prescription writing and having a
specific, proximal goal; we used a goal of 10 000 steps/
day.”13 ‘5 Each participant was a patient of a study physician
with texts aiming to leverage the physician—patient relation—
ship, using the physician’s name in texts. Messages under—
went content iterations to optimize language during pretrial
testing by the study team. Smart texts were grouped as positive reinforcement messages, sent when a participant was on track to attain or
had already attained his or her daily goal, and booster messages, to motivate individuals when they were not Joumaf or the MICE!) Heart Association 2 HDHVFIS’EI’H ‘”IVNIOIHO …

Image transcription text{at}: “Ll IinV uo liq fito-slewncfeqa,-,-:dnq Luci} papeu|u.mua mActive Trial Martin et at tracking to surpass their step goal. According to the
preprogrammed algorithm, smart texts were sent 3 times/
day (morning, mid—day, and evening], with exact times
customized to the participant’s usual wake time, lunch time,
and beginning of evening leisure time. On the day of
enrollment, all participants completed an online questionnaire
(Table S1] to provide information on 16 personal and clinical
characteristics, which was later used for personalizing text
messages within the texting arm. Specific examples of text
messages are shown in Table SZ. Outcomes We considered the AHA’s Scientific Statement on assessing
physical activity for clinical and research applications.”5 Given
the settingispecific resources and technology capabilities, and
common clinical focus on step count, we set our primary
outcome measure as the mean change in accelerometer—
measured daily step count assessed from baseline through
phase I and II. In addition to this continuous outcome
measure, we examined attainment of the prescribed
10 000 steps/day goal. Secondary activity outcome mea
sures were changes in total daily activity time and aerobic
time. Aerobic time was defined as the time spent walking corr
tinuously for >10 minutes without breaking for >1 minute.
Additionally, we assessed participant satisfaction through an online sunrey, with qualitative and quantitative elements, upon
trial completion. Statistical Analysis We estimated that the sample size needed to detect at least a
2000—step difference in means was 14 participants per group,
or 42 participants total, assuming a within—participant SD of
1800 steps/day, Z—sided alpha of 0.05, and beta of 0.8.
Allowing for 12% attrition, target enrollment was 48 partic—
ipants. Our trial aimed to detect an increase of 2000 steps/
day, given that this was previously associated with a m10%
relative reduction in the long—term incidence of CVD,” and is
generally felt to represent a clinically significant increase.
However, the selection of 2000 steps/day does not signify
that a smaller increase in physical activity could not also be
clinically significant. Baseline characteristics were summarized using descrip— tive statistics—frequency {percentage} for categorical data
and mean {standard deviation] for continuous data. All outcomes were compared between treatment arms by
intention to treat. We used repeated measures analysis of
variance for mean comparison tests and calculated 95% confidence intenrals {Clsl For comparisons of proportions, we
used Fisher’s exact test because of small cell sizes. Exploratory subgroup interaction testing was performed to DOI: 10.1151.’JMA.115.002239 examine for heterogeneity in treatment effect by age, sex,
race, body mass index {BMI}, diabetes, hypertension, dyslipi—
demia, coronary heart disease {CHD}, dog ownership, marital
status, employment, or baseline activity. Statistical analyses
were performed using Stata software {version 11.1; StataCorp
LP, College Station, TX]. Reporting We followed the Consolidated Standards of Reporting Trials of
Electronic and Mobile HEalth Applications and onLine TeleHealth {CONSORT—EHEALTH].’B Most elements are
reported here, with additional details on CONSORT—EHEALTH
items provided in Table S3. Results Participant and Data Flow The trial flow diagram is shown in Figure 1. Of 50 eligible individuals, 43 {96%) enrolled. There was no early dropout and
all participants in the intervention arms completed the protocol. One blinded participant had data transmission
issues in phase II and elected not to complete the protocol.
Daily activity data capture was 97.4%. Baseline Characteristics There were no significant baseline differences between
groups {Table 1]. Overall, participants were age SBiB years,
nearly evenly split by sex, and 21% nonwhite. Eightycight
percent were employed, primarily in management and
professional occupations. The majority of participants were
obese, 23% had diabetes, and 29% CHD. Baseline activity
levels were 96?0:l:4350 steps/day, 93i45 min/day, and
13i18 aerobic min/day. Primary Outcome: Change in Steps/Day Physical activity trajectories were different among the 3 trial
groups. The blinded group showed a progressive downward
trend over the whole time period, particularly in the change
from phase I to II. This downward drift was not observed in
either of 2 other trial groups. The unblinded arm trajectory was characterized by a maintenance of baseline activity
levels, whereas the biggest shift in trajectory was noted in the
text—receiving arm. This group had a clear upward trend in
physical activity in response to smart texts. In phase I {Table 2], blinded control participants obtained
a mean of 616 fewer steps/day {6% decrease] whereas
unblinded participants increased their steps/day by a mean
of 408 {4% increase]. The between—group differential was slownal of the American Heart Association 3 IISHVHSEH ‘IVNIDIHU …

Image transcription textmActive Trial
Martin et al
Assessed for eligibility (n=86)
ORIGINAL RESEARCH
Excluded (n=36)
* Regular moderate-intensity leisure
time activity by IPAQ (n=36)
Eligible (n=50)
Not enrolled (n=2)
Out of state (n=1)
Hospitalized for procedure (n=1)
Entered 1-Week Blinded
Run-In (n=48)
Randomized (n=48)
Allocated to unblinded digital activity tracking
Allocated to blinded digital activity tracking for
for phase 1 (n=32)
phase 1 and phase 2 (n=16)
. Received allocated intervention (n=32)
. Received allocated intervention (n=16)
. Did not receive allocated intervention (n=0)
Did not receive allocated intervention (n=0)
Randomized (n=32)
Lost to follow-up (n=0)
Allocated to texts for phase 2 (n=16)
Allocated to no texts for phase 2 (n=16)
Discontinued intervention (n=1)
. Received texts (n=16
. Did not receive texts (n=16)
. Declined to present to attempt resolution
Downloaded from http://ahajournals.org by on April 17, 2023
of data syncing error during phase 2 (n=1)
Lost to follow-up (n=0)
Lost to follow-up (n=0)
* Discontinued intervention (n=0)
* Discontinued intervention (n=0)
Analysed in phase 1 analysis (n=16)
Analysed in phase 1 analysis (n=16)
Analysed in phase 1 analysis (n=16)
Analysed in phase 2 analysis (n=16)
Analysed in phase 2 analysis (n=16)
Analysed in phase 2 analysis (n=16)
* Excluded from analysis (n=0)
. Excluded from analysis (n=0)
* Excluded from analysis (n=0)
Figure 1. mActive trial flow diagram. IPAQ indicates International Physical Activity Questionnaire, Long-Form.
nonsignificantly higher in the unblinded group versus blinded
change). In contrast, the unblinded-texts group obtained 2334
controls by 1024 steps/day (95% CI, -580 to 2628;
more steps/day (25% increase). The differential in activity
P=0.21).
levels was significant; participants receiving texts increased
In phase II (Table 2), the blinded group further decreased
their daily steps over those not receiving texts by 2534 (95%%
its activity by 1042 steps/day (11% decrease) whereas the
CI, 1318-3750; P<0.001) and over blinded controls by 3376
unblinded-no texts group decreased by 200 steps/day (<1%
(95%% CI, 1951 to 4801; P<0.001).
DOI: 10.1161/JAHA.115.002239
Journal of the American Heart Association…

Image transcription text£ch “Ll IIJdV no is Slammer-alimony woo hemmed mActive Trial Martin et a.” Table 1. Baseline Characteristics of mActive Trial Participants
-—:m::i TB-
rn-te) 15}
MD M In Sex
m
women 22 {45)
white race 38 I79) 14 {38}
Dog owner 21 (44) :r’ (44} Management. 30 (63) 9 {55} 9 (5E) 12 {T5}
DI’OiGSSiDflflI’ Age. y. maniso Construction. 2 {4) fl {0) 1 (6} 1 (5)
malntenance
_—— Smoker 1 {2) 1 {E} U (0} U (0}
Hypertension 24 (50) 8 {50} 5 (31) 11 {59} BMI kgrmz. meanzzs 31 21:5
230 25 154i 9 t 10 (21) m BMI indicates body mass index; CHD, coronary heart disease; IP40, International
Physical Activity Questionnaire, Long-Form. ‘Types of employment per US. Census Bureau definitions.
‘Represents total activity; all subjects reported low leisure time activity per the IP40. At baseline, 23 {48%} participants had a daily step count
210 000 steps/day, 9 (56%] in the blinded group and 14
(44%] in the unblinded group {Figure 2]. In phase I, the
210 000 step/day goal was attained by 1 less participant in
both the blinded group {8 of 16; 50%] and unblinded group
(13 of 32; 41%], In phase II, the number of participants
meeting the 10 000 steps/day goal was 7 (44%] in both the
blinded— and unblinded—no texts groups. In contrast, 13 [31%]
participants who were unblinded and receiving smart texts
achieved a daily step count 210 000 steps/day {37% abscr lute increase and 84% relative increase over other groups;
P=0.02]. DOI: 1D.11B1.I’JAHA.1 15.002239 Secondary Outcomes: Total and Aerobic Activity
Times In phase I {Table 2}, the unblinded and blinded groups were
not significantly different in modifying their total activity
times, whereas there was a borderline significant smaller
decrease in aerobic time in unblinded patients {differential
8 minutes; 95% Cl, 0 to 16; P=0,05]. In phase II, activity times
continued to decrease in the blinded group and remained
relatively stable in the unblinded—no texts group. In contrast,
the unblinded—texts group increased its total activity time by
21 min/day (23% increase] and aerobic time by 13 min/day
(160% increase], which was highly statistically significant
compared to the other groups. Exploratory Subgroup Interaction Testing For the interaction of the phase I intervention {unblinding}
with a given patient factor, P values for interaction were as
follows: 036 for age; 053 for sex; 0.67 for race; 0. 1 7 for BMI;
0.46 for diabetes; 0.83 for hypertension; 0.51 for dyslipi—
demia; 0.153 for CHD; 0.70 for dog ownership; 0.99 for marital
status; 046 for employment; and 0.20 for baseline activity.
Interaction testing was similarly null for the secondary
outcomes of total activity time and aerobic time, The
respective P values for the interaction of the phase II
intervention {smart texts} were also null for most patient
factors as follows: 059 for age; 0.60 for race; 0.35 for BMI;
0.45 for diabetes; 0.29 for hypertension; 0.41 for dyslipi—
demia; 0.14 for dog ownership; 0.68 for marital status; 072
for employment; and 0.93 for baseline activity. However, there was a trend for significant interaction by
sex {P=0.06}, with women showing a 3754 steps/day
increase {95% CI, 21139 to 5319] in response to smart texts
as compared with men, who showed a 1562 steps/day
increase {95% CI, —209 to 3334]. Similar results were
observed for the interaction of total time and sex {P=0.05},
with responsiveness to text messaging predominantly found
in women [35 min/day; 95% CI: 20 to 50] rather than in men
{10 min/day; 95% CI, —10 to 30]. In contrast, an interaction
was not observed between aerobic time and sex {P=0.58], There was a significant interaction by CHD status (P=0.03}.
Those with coronary disease increased their physical activity
by 5068 steps/day {95% CI, 1371 to 3765] in response to
smart texts as compared to 1777 steps/day (95% Cl, 489 to
3065 steps/day] in others. There was a similar interaction by
CHD status for the secondary outcome of total activity time
{P=0.04], The group with versus without known coronary
disease increased physical activity time by 50 min/day {95%
Cl, 4 to 95] versus 14 min/day {95% Cl, 0 to 28}. For the
outcome of aerobic time, however, no significant interaction
was present {P=0.90]. Jownai a! the American Heart Association 5 HDHVESHH ‘IVNI’JIHO …

Image transcription textmActive Trial
Martin et al
Table 2. Changes in Activity Outcomes With Phase I and II Interventions
ORIGINAL RESEARCH
Unblind
Blind
(n=32)
n=16
Unblind-Blind
Mean Change +SD
Mean Change +SD
Mean Difference (95% CI), P Value
Phase I
Steps, count/day
408+ 2701
-616+2385
1024 (-580 to 2628), P-0.21
Activity time, min/day
2+27
-6+26
8 (-9 to 25), P-0.33
Aerobic time, min/day
-3+12
-11414
8 (0-16), P 0.05
Texts
No Texts
Blind
(n=16 )
(1=16)
(n=16
Texts-No Texts
Texts -Blind
No Texts-Blind
Mean Change +SD
Mean Difference (95% CI), P-value
Phase II
Steps, count/day
2334+1714
-200+1653
-1042+2202
2534 (1318-3750),
3376 (1951-4801),
842 (-564 to 2248),
P 0.001
P 0.001
P-0.23
Activity time, min/day
21+20
0+17
-8+23
21 (8 34), P-0.003
29 (13 45), P-0.001
8 (-7 to 23), P-0.28
Aerobic time, min/day
13+11
-148
-3+10
14 (7-21), P-0.001
16 (7-23), P 0.001
2 (-6 to 8), P-0.71
*Primary outcome. Cl indicates confidence interval.
Satisfaction
Discussion
On post-trial surveys, participants largely expressed feelings
In mActive, coupling smart texts with activity tracking led to
of satisfaction and enthusiasm for future trial participation.
the best physical activity outcomes. Nearly twice as many
Quantitatively, participants assigned the activity tracker a
participants in the text-receiving arm achieved the
mean score of 4.0 of 5.0, and text messages 3.8 of 5.0
10 000 steps/day goal compared with the other groups.
(4=good; 5=great).
Aerobic activity levels were disproportionately low compared
with steps and total activity time, but appeared most
responsive to intervention. The mActive trial lends support
210,000 steps/day
Phase II
to the notion of new mHealth devices as facilitators, not
<10,000 steps/day
drivers, of behavior change,” because sequential randomiza-
Phase I
Texts
tion suggested that unblinding to device data did not
(n=16
significantly modify behavior, whereas coupling it with smart
81%
texts did.
Unblind 41%
Downloaded from http://ahajournals.org by on April 17, 2023
(n=32)
If sustained, the change in physical activity from the
Baseline
mActive tracking-texting intervention may be clinically signif-
No Texts 44%
icant. A comparable step count increase over a mean duration
Blind
(n=16)
of 18 weeks was associated with significant decreases in BMI
(n=48)
and systolic blood pressure. ” During a mean of 6 years, each
2000 step/day increase in physical activity was associated
with a ~10% relative reduction in the incidence of CVD. 17
Blind
Blind 44%
Sustaining regular physical activity over the long term is
(n=16)
50%
(n=16
associated with a more favorable circulating metabolome ‘
and decreased markers of inflammation. Benefits of
cardiorespiratory fitness may extend to non-CVD diseases,
such as cancer, depression, and dementia, with a dose-
Figure 2. Proportions attaining the 10 000 steps/day goal at
dependent reduction in all-cause mortality. 21-24
baseline, in phase I, and phase II, by intervention group. Forty-eight
percent of participants attained the 10 000 step/day goal at
However, the influence of behavioral counseling may be
baseline. No significant change or between-group difference was
limited in the current health care delivery model wherein
observed in phase I, whereas in phase II, the unblinded-texts group
clinicians tend to see patients for brief visits every several
showed a 37% absolute increase and 84% relative increase over
months or annually.”Aimed to fill the gap, the mActive
other groups in 10 000 steps/day attainment (P=0.02).
system is physician designed and intended to leverage the
DOI: 10.1161/JAHA. 115.002239
Journal of the American Heart Association
6…

Image transcription text£302 1| rudv no at filo-szwmninwzduq was 13:11″qu mActive Trial Martin er ai clinician-patient relationship while functioning automatically
without day-to-day involvement of the physician. Compatible
with the importance of smart text support. pedometer-based
interventions without smart texts did not benefit insurance company employees in Finlandz’5 or adults with diabetes in
southwest England.” mActive also appears to provide a significant advance over
previous Web-based approaches. which have produced mixed
results.”’2’a 3° We are aware of another group aiming to send mobile phone text messages” based on activity tracking;
distinct from the mActive mobile design. it relies on computer access points for data transmission. mActive moves the field
forward by providing the first fully mobile. fully automated,
physician-designed, integrated tracking-texting intervention,
with potential to modify behavior in real time, while also being
potentially reproducible, affordable. and widely scalable.
Enhancing such prospects, activity tracking apps built into
smartphones are now common, and our technique could be
applied using them. Our technique might also enhance the
development of cardiac rehabilitation programs using
mHealth.32 Overall. our resource-efficient strategy may facil-
itate more continuous monitoring and feedback between the
health system and patients. mActive presents an attractive new paradigm for patient-
oriented research as a union of the strengths of mHealth and
randomization. We suspect that the convenience offered by
the truly mobile mActive design may have benefitted
recruitment, retention, and data capture. The trial met its
recruitment target, enrolled 96% of eligible patients {as
compared to the typical statistic of around 50%”), and all
patients but 1 completed the protocol. Without specifically
targeting recruitment of women. the mActive trial had nearly
equal representation of men and women. The mActive trial
recruitment experience supports the data-driven hypothesis:13
that a trial design fostering convenience can enhance trial
participation. Furthermore, there was a very high level of
physical activity outcome completeness obtained using a
digital approach. Limitations Given its limited size and scope, mActive may be categorized
as a pilot trlal. As such, it is most appropriately interpreted
as exploratory evidence to guide a definitive pivotal trial in
the future.34 The trial provides a solid basis to build upon
given that it was conducted using the rigorous scientific
methodology characteristic of pivotal randomized, clinical
trials, including public registration and incorporation of
randomization and blinding. Whereas our trial detected an
effect only when smart texts were added to activity tracking,
with a larger sample size, less within-person variability
(within-person SD exceeded our estimate of 1300 steps/ DOI: 10. l ‘l 61 .I’JAHA.1 15.002239 day], or a longer duration of intervention, activity tracking
alone may have led to a statistically significant increase in
physical activity. Greater power could also allow more-
definitive examination of potential moderators of treatment.
Our subgroup analyses were exploratory in nature and
underpowered. but produced intriguing signals by sex and
coronary disease status, which can be considered by future
studies. To address the limited duration of follow-up in
mActive, we plan to perform long-term follow-up of partic-
ipants for physical activity outcomes and risk factors {eg,
BMI and blood pressure]. Additionally. we are creating
additional smart text content to allow for longer-term
interventions. Generalizability remains uncertain for multiple reasons.
Participants were adult smartphone users from a single
preventive cardiology clinic and may have been highly
motivated because of obesity. other CVD risk factors, or
known CVD. Behavioral influences also potentially arose from
attention on participation in a physical activity trial. aware-
ness of being observed {Hawthorne effect}, and investigator
expectations {Rosenthal effect]. In total. these factors may
explain the unexpectedly high physical activity levels at
baseline and throughout the trial in the trial participants as a
whole {further discussion in Table 83. l9-ii]. Finally, it may be considered a limitation that we did not
use human coaches as part of the intervention. There may be
added value in what a human coach could provide, for
example. through motivational interviewing. 0n the other
hand. it could be considered a strength given that the
automated approach may be more reproducible and more
easily scaled at lower cost. Conclusion In ambulatory cardiology patients who are smartphone users.
a novel mHealth intervention coupling smart texts to digital
tracking significantly increased near-term physical activity.
The effect was dependent on the text message component of
the intervention. This pilot trial gives insight into the efficacy
and feasibility of components of the mHealth intervention and
provides an initial step forward in a rapidly growing area in
critical need of high-quality clinical evidence. Ultimately.
therapeutic implications await larger, multicenter, definitive
pivotal trials involving more-diverse groups of patients. Sources of Funding This trial was funded. in part. by an unrestricted grant to Blaha
from the Pl Schafer Cardiovascular Research Fund, a 501(cl
{3} nonprofit organization. Martin was supported by a National
Institutes of Health training grant {T32HL07024} for which
Coresh served as the Pl. Martin received additional support Jawnai of the: American Hater? Association 7 HDHVHSHH ‘IVNI’JIHU …

Image transcription textmActive Trial Martin et al
29. Bosak KA, Yates B, Pozehl B. Effects of an internet physical activity intervention
32. Beatty AL, Fukuoka Y, Whooley MA. Using mobile technology for cardiac
in adults with metabolic syndrome. West J Nurs Res. 2010;32:5-22.
rehabilitation: a review and framework for development and evaluation. J Am
30. Wijsman CA, Westendorp RG, Verhagen EA, Catt M, Slagboom PE, de Craen AJ,
Heart Assoc. 2013;2:e000568. doi: 10. 1 161/JAHA. 1 13.000568.
ORIGINAL RESEARCH
Broekhuizen K, van Mechelen W, van Heemst D, van der Ouderaa F, Mooijaart
33. Martin SS, Ou FS, Newby LK, Sutton V, Adams P, Felker GM, Wang TY. Patient-
SP. Effects of a web-based intervention on physical activity and metabolism in
and trial-specific barriers to participation in cardiovascular randomized clinical
older adults: randomized controlled trial. J Med Internet Res. 2013; 15:e233.
trials. J Am Coll Cardiol. 2013;61:762-769.
31. Jethwani K. Text to Move Study. Available at: http://clinicaltrials.gov/show/
34. Loscalzo J. Pilot trials in clinical research: of what value are they? Circulation.
not0 1569243. Accessed October 31, 2015.
2009; 1 19:1694-1696.
Downloaded from http://ahajournals.org by on April 17, 2023
DOI: 10.1161/JAHA. 1 15.002239
Journal of the American Heart Association…

Image transcription textmActive Trial Martin et al
from the Pollin Cardiovascular Prevention Fellowship and the
9. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt
Marie-Josee and Henry R Kravis Endowed Fellowship. Fur-
M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity
questionnaire: 12-country reliability and validity. Med Sci Sports Exerc.
ORIGINAL RESEARCH
thermore, Martin received a modest monetary award in
2003;35:1381-1395.
conjunction with the Howard C. Silverman prize for originality
10. Levine GN, Allen K, Braun LT, Christian HE, Friedmann E, Taubert KA, Thomas
SA, Wells DL, Lange RA. Pet ownership and cardiovascular risk: a scientific
and creativity in medical research, which was awarded by the
statement from the American Heart Association. Circulation. 2013; 127:2353-
2363,
Johns Hopkins Division of Cardiology based on the preliminary
11. Cowan LT, Van Wagenen SA, Brown BA, Hedin RJ, Seino-Stephan Y, Hall PC,
design of the mActive trial. He also received a modest
West JH. Apps of steel: are exercise apps providing consumers with realistic
monetary award from the American Heart Association’s
expectations?: a content analysis of exercise apps for presence of behavior
change theory. Health Educ Behav. 2013;40:133-139.
Council on Lifestyle and Cardiometabolic Health with the
12. Aarts H, Paulussen T, Schaalma H. Physical exercise habit: on the concep-
Steven N. Blair Award for Excellence in Physical Activity
tualization and formation of habitual health behaviours. Health Educ Res.
1997; 12:363-374.
Research. Long-term follow-up of mActive trial participants is
13. Metkus TS Jr, Baughman KL, Thompson PD. Exercise prescription and primary
being supported by the Aetna Foundation. Blumenthal was
prevention of cardiovascular disease. Circulation. 2010; 121:2601-2604.
supported by the Kenneth Jay Pollin Professorship in
4. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, Stave
CD, Olkin I, Sirard JR. Using pedometers to increase physical activity and
Cardiology.
mprove health: a systematic review. JAMA. 2007;298:2296-2304.
5. Grandes G, Sanchez A, Sanchez-Pinilla RO, Torcal J, Montoya I, Lizarraga K,
Serra J; PEPAF Group. Effectiveness of physical activity advice and prescription
by physicians in routine primary care: a cluster randomized trial. Arch Intern
Disclosures
Med. 2009; 169:694-701.
16. Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA,
Digital physical activity tracking devices were provided in kind
Richardson CR, Smith DT, Swartz AM. Guide to the assessment of physical
by Fitbug, a private for-profit company. This trial was
activity: clinical and research applications: a scientific statement from the
American Heart Association. Circulation. 2013; 128:2259-2279.
investigator initiated and Fitbug did not provide cash
7. Yates T, Haffner SM, Schulte PJ, Thomas L, Huffman KM, Bales CW, Califf RM,
payments for the research or writing of the manuscript.
Holman RR, McMurray JJ, Bethel MA, Tuomilehto J, Davies MJ, Kraus WE.
Association between change in daily ambulatory activity and cardiovascular
Fitbug did not participate in the analysis of the data or
events in people with impaired glucose tolerance (NAVIGATOR trial): a cohort
influence the conclusions.
analysis. Lancet. 2014;383:1059-1066.
18. Eysenbach G; CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and
standardizing evaluation reports of web-based and mobile health interven-
tions. J Med Internet Res. 2011; 13:e126.
References
19. Kujala UM, Makinen VP, Heinonen I, Soininen P, Kangas AJ, Leskinen TH,
1. Eckel RH, Jakicic JM, Ard JD, de Jesus JM, Houston Miller N, Hubbard VS, Lee
Rahkila P, Wurtz P, Kovanen V, Cheng S, Sipila S, Hirvensalo M, Telama R,
IM, Lichtenstein AH, Loria CM, Millen BE, Nonas CA, Sacks FM, Smith SC Jr,
Tammelin T, Savolainen MJ, Pouta A, O’Reilly PF, Mantyselka P, Viikari J,
Svetkey LP, Wadden TA, Yanovski SZ, Kendall KA, Morgan LC, Trisolini MG,
Kahonen M, Lehtimaki T, Elliott P, Vanhala MJ, Raitakari OT, Jarvelin MR, Kaprio
Velasco G, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG,
J, Kainulainen H, Ala-Korpela M. Long-term leisure-time physical activity and
Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke
serum metabolome. Circulation. 2013; 127:340-348.
FW, Shen WK, Smith SC Jr, Tomaselli GF. AHA/ACC guideline on lifestyle
20. Hamer M, Sabia S, Batty GD, Shipley MJ, Tabak AG, Singh-Manoux A, Kivimaki
management to reduce cardiovascular risk: a report of the American College of
M. Physical activity and inflammatory markers over 10 years: follow-up in men
Cardiology/ American Heart Association Task Force on Practice Guidelines.
and women from the Whitehall II cohort study. Circulation. 2012; 126:928-933
Circulation. 2013;2014(129):576-$99.
21. Willis BL, Gao A, Leonard D, Defina LF, Berry JD. Midlife fitness and the
2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L,
development of chronic conditions in later life. Arch Intern Med.
Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho
2012; 172:1333-1340.
PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P,
Yancy CW, Rosamond WD. Defining and setting national goals for cardiovas
22. Herring MP, Puetz TW, O’Connor PJ, Dishman RK. Effect of exercise training on
Downloaded from http:/ahajournals.org by on April 17, 2023
cular health promotion and disease reduction: the American Heart Associa
depressive symptoms among patients with a chronic illness: a systematic
tion’s strategic impact goal through 2020 and beyond. Circulation.
review and meta-analysis of randomized controlled trials. Arch Intern Med.
2010; 121:586-613.
2012; 172:101-111.
3. Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, Lloyd-Jones DM.
23. Defina LF, Willis BL, Radford NB, Gao A, Leonard D, Haskell WL, Weiner MF,
Cardiovascular health behavior and health factor changes (1988-2008) and
Berry JD. The association between midlife cardiorespiratory fitness levels and
projections to 2020: results from the National Health and Nutrition
later-life dementia: a cohort study. Ann Intern Med. 2013; 158:162-168.
Examination Surveys. Circulation. 2012; 125:2595-2602.
24. Kodama S, Saito K, Tanaka S, Maki M, Yachi Y, Asumi M, Sugawara A, Totsuka
4. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone
K, Shimano H, Ohashi Y, Yamada N, Sone H. Cardiorespiratory fitness as a
applications and wearable devices for tracking physical activity data. JAMA.
juantitative predictor of all-cause mortality and cardiovascular events in
2015;313:625-626.
healthy men and women: a meta-analysis. JAMA. 2009;301:2024-2035.
5. Steinhubl SR, Muse ED, Topol EJ. Can mobile health technologies transform
25. Moyer VA; U.S. Preventive Services Task Force. Behavioral counseling
health care? JAMA. 2013;310:2395-2396.
interventions to promote a healthful diet and physical activity for cardiovas
cular disease prevention in adults: U.S. Preventive Services Task Force
6. Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of
recommendation statement. Ann Intern Med. 2012; 157:367-371.
health behavior change. JAMA. 2015;313:459-460.
7. Burke LE, Ma J, Azar KM, Bennett GG, Peterson ED, Zheng Y, Riley W, Stephens
26. Reijonsaari K, Vehtari A, Kahilakoski OP, van Mechelen W, Aro T, Taimela S.
J, Shah SH, Suffoletto B, Turan TN, Spring B, Steinberger J, Quinn CC. Current
The effectiveness of physical activity monitoring and distance counseling in an
occupational setting – results from a randomized controlled trial (CoAct). BMC
Science on Consumer Use of Mobile Health for Cardiovascular Disease
Prevention: a Scientific Statement From the American Heart Association.
Public Health. 2012; 12:344.
Circulation. 2015; 132:1157-1213.
27. Andrews RC, Cooper AR, Montgomery AA, Norcross AJ, Peters TJ, Sharp DJ,
B. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L,
Jackson N, Fitzsimons K, Bright J, Coulman K, England CY, Gorton J, Mclenaghan
Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC,
A, Paxton E, Polet A, Thompson C, Dayan CM. Diet or diet plus physical activity
Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA,
versus usual care in patients with newly diagnosed type 2 diabetes: the Early
Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke
ACTID randomised controlled trial. Lancet. 2011;378:129-139.
LE. Interventions to promote physical activity and dietary lifestyle changes for
28. Irvine AB, Gelatt VA, Seeley JR, Macfarlane P, Gau JM. Web-based intervention
cardiovascular risk factor reduction in adults: a scientific statement from the
to promote physical activity by sedentary older adults: randomized controlled
American Heart Association. Circulation. 2010;122:406-441.
trial. J Med Internet Res. 2013; 15:e19.
DOI: 10.1161/JAHA. 115.002239
Journal of the American Heart Association…

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