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Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors

Authors:

Abstract

Background: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet, it is unknown how the implementation of BCTs differs between these behaviors. Objective: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers (self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type of BCTs differ between behaviors. Methods: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a 1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the number of BCTs implemented was assessed using kappa statistics. Results: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and sedentary behavior (n=6). Conclusions: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown.
Original Paper
Activity Trackers Implement Different Behavior Change
Techniques for Activity, Sleep, and Sedentary Behaviors
Mitch Duncan1, PhD; Beatrice Murawski1, BSc, MSc; Camille E Short2, PhD; Amanda L Rebar3, PhD; Stephanie
Schoeppe3, PhD; Stephanie Alley3, PhD; Corneel Vandelanotte3, PhD; Morwenna Kirwan4, PhD
1School of Medicine & Public Health, Priority Research Centre for Physical Activity and Nutrition, Faculty of Health and Medicine, The University of
Newcastle, Callaghan, Australia
2Freemasons Foundation Centre for Men’s Health, School of Medicine, University of Adelaide, Adelaide, Australia
3Physical Activity Research Group, School of Medical, Health and Applied Sciences, Central Queensland University, Rockhampton, Australia
4School of Science and Health, Western Sydney University, Campbelltown, Australia
Corresponding Author:
Mitch Duncan, PhD
School of Medicine & Public Health, Priority Research Centre for Physical Activity and Nutrition
Faculty of Health and Medicine
The University of Newcastle
University Drive
Callaghan, 2308
Australia
Phone: 61 024921 7805
Fax: 61 2 4921 2084
Email: Mitch.Duncan@newcastle.edu.au
Abstract
Background: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between
different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet,
it is unknown how the implementation of BCTs differs between these behaviors.
Objective: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers
(self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type
of BCTs differ between behaviors.
Methods: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a
1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The
Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation
to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the
number of BCTs implemented was assessed using kappa statistics.
Results: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for
activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for
sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and
sedentary behavior (n=6).
Conclusions: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and
sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term
effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown.
(Interact J Med Res 2017;6(2):e13) doi:10.2196/ijmr.6685
KEYWORDS
health behavior; public health; exercise; sleep; behavior change; fitness trackers; adult, mobile applications
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Introduction
Higher levels of moderate-to-vigorous intensity activity, lower
levels of sedentary behavior, and sufficient sleep on a daily
basis are key components of maintaining a healthy lifestyle that
is associated with improved quality of life, reduced risk of
cardiovascular disease, and diabetes [1-3]. Yet, many adults are
not sufficiently active for health benefits, spend considerable
amounts of time in sedentary activities, and do not obtain sleep
that is of a sufficient duration or quality [4-7]. There are
numerous published intervention studies that aim to improve
physical activity, sedentary, and sleep behaviors [8-11], and
although many are effective, few are disseminated to the broader
public [12].
Burgeoning technological innovations mean that mobile devices
(smartphone or tablets) and wearable technology such as wrist
worn activity trackers, now have increasingly sophisticated
capabilities to capture, analyze, and provide feedback to users
on their daily physical activity, sleep, and sedentary behaviors.
Public interest in this technology is substantial, and adoption
of this technology exceeds that of many interventions. Mobile
device ownership is increasing, with nearly 80% of people
owning a smartphone and 47% owning a tablet [13] and 10%
of US adults owning an activity tracker [14]. Studies that critique
the potential effectiveness of apps and websites to change
behavior conclude that the majority of apps and websites do not
contain features or functionality, which are thought to be
effective in changing behaviors [15-18]. These critiques have
been guided by the availability of behavior change techniques
(BCTs) that are potentially effective in changing health
behaviors such as goal-setting and self-monitoring [18-21].
The combination of apps, websites, and wearable trackers which
synchronize data between them provides a “self-monitoring
system,” allowing users to self-monitor their physical activity,
sleep, and sedentary behaviors. Despite existing self-monitoring
systems providing information on all three behaviors, previous
reviews of self-monitoring systems have focused on a single
behavior, in most cases physical activity [15,16,18,21-23]. As
a result, it is unknown if the approaches implemented by
self-monitoring systems to change behavior differ between
physical activity, sleep, and sedentary behaviors. In addition,
although there is emerging evidence regarding the potential of
BCTs to promote behavior change, there is also debate
concerning how the number of BCTs and the cooccurrence of
BCTs can influence behavior change [20,24-27]. Therefore,
examining differences in the number or type of BCTs included
in self-monitoring systems for physical activity, sleep, and
sedentary behaviors is a first step toward describing the
differences in the potential effectiveness of the systems to
change these behaviors. Therefore, the purpose of this study
was to examine how the number and type of BCTs implemented
in self-monitoring systems targeting activity, sleep, and
sedentary behaviors differs for each behavior.
Methods
Self-Monitoring System Inclusion Criteria and
Descriptions
Self-monitoring systems included in this review were the Fitbit
Charge HR, Garmin Vivosmart2, and Jawbone UP3 and their
respective mobile phone apps and websites. The Fitbit and
Jawbone systems were selected for inclusion based on a 2014
review, which indicated that these systems included the highest
number of BCTs in relation to physical activity of the 13 systems
evaluated [18]. The Garmin system was not included in the prior
review but was included in this review as the system includes
a “vibration alert.” This feature is also included in the Jawbone
and can be used to alert wearers to the fact that they have not
taken any steps in the previous hour, which may be useful in
assisting wearers to reduce their sedentary behavior. Inclusion
criteria were that the self-monitoring systems include a wearable
activity tracker that measured physical activity levels, sedentary
behavior, and sleep; and an app and/or website that provided
the user with information on their behaviors. The activity tracker
in all three systems was worn on the wrist. This represents a
comprehensive monitoring system. The Jawbone system
included an activity tracker and app only and did not include a
website that provided feedback to users on their behaviors,
whereas the Fitbit and Garmin systems included all three
components. This study did not require ethics committee
approval, and no informed consent was required as it did not
involve participants.
Coding and Data Extraction
Two trackers for each system were available, so two authors
(MJD and BM) could concurrently use each system for a 1-week
period. This included wearing the activity tracker and using the
app and website (if available). Each author wore the same model
of activity tracker, used the same version—the most recent
version available at the time of wearing—of the app software
on an Apple-based device (mobile phone and tablet), over the
same 1-week period. Each activity tracker was worn during all
daytime and sleep periods, except for when engaged in
water-based activities, if the units were not water proof. At the
end of each wear period, the features and content of the systems
were independently coded using the Coventry, Aberdeen, and
London-Refined (CALO-RE) taxonomy that contains a list of
40 BCTs [19]. The presence or absence of each BCT was coded
specifically for the behavior of interest. For coding purposes,
physical activity was defined as steps and/or
moderate-to-vigorous intensity physical activity; sleep was
defined as sleep quality, sleep duration, and/or sleep timing;
and sedentary behavior was defined as sitting or standing
stationary. This definition of sedentary behavior differs to other
definitions which would not classify standing stationary as
sedentary [28]; however, this operational definition was
necessary as previous experience using the systems showed that
standing stationary is classified as sedentary by the systems.
For instance, to be coded as allowing users to set goals for
sedentary behavior, the system had to allow the user to
specifically set goals for that behavior (eg, maximum amount
of sedentary behavior performed each day or hour). Agreement
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between coders on the number of BCTs present for each
behavior within each system was calculated using Kappa
statistics, and the magnitude of agreement was interpreted using
the following criteria: 0.00=poor, 0.01-0.20=slight,
0.21-0.40=fair, 0.41-0.60=moderate, 0.61-0.80=substantial, and
0.81-1.00=almost perfect [29]. The coders then met to discuss
any discrepancies in coding, and all discrepancies were resolved
to produce a coding summary that is presented in Tables 1 and
2. All use of the systems and coding was conducted in August
2015.
Results
Summary of Behavior Change Techniques (BCTs)
Implemented
Table 1 summarizes the number of BCTs coded as present for
each behavior within each system. The version of the software
used for each system is detailed in Table 1 footnotes.
Between-rater agreement ranged from 0.64 to 1.00, representing
substantial to almost perfect agreement. From a possible range
of 40 BCTs, the number of BCTs present for physical activity
ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to
29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for
sedentary behavior. When averaged across systems,
self-monitoring systems implemented the highest number of
BCTs for physical activity (n=26), a smaller number of BCTs
were implemented for sleep (n=14), and the fewest BCTs were
implemented for sedentary behavior (n=6). The total number
of BCTs included within a system also varied (Table 1). The
system that included the highest number of BCTs (n=69) across
the three behaviors was Jawbone, followed by Fitbit (n=35),
and then Garmin (n=33).
Table 1. Summary of the number of behavior change techniques (BCTs) implemented in relation to physical activity, sleep, and sedentary behaviors.
CALO-REa
System and behavior
Summary No. BCTKappaRater 2 No. BCT
Rater 1 No. BCTb
Fitbit Charge HRc
250.682723Activity
100.751210Sleep
01.0000Sedentary
Garmin Vivosmart2d
190.951918Activity
40.6442Sleep
101.001010Sedentary
Jawbone Up3e
331.003333Activity
290.822829Sleep
71.0077Sedentary
aCALO-RE: Coventry, Aberdeen, and London-Refined.
bBCT: behavior change technique.
cFitbit app version 84.
dGarmin app version 2.13.1.
eJawbone Up3 app version 4.7.0.121.
Activity
Table 2 displays which of the 40 BCTs were present within
each system for monitoring physical activity. All three systems
implemented the following 18 BCTs: providing information
about others’ approval, providing normative information about
others behavior, goal setting (behavioral and outcome), goal
review (behavioral and outcome), prompt rewards contingent
on progress toward goal, prompt rewards contingent on
successful behavior, shaping, self-monitoring (behavior and
outcome), prompting focus on past success, providing feedback
on performance, agreeing to behavioral contracts, facilitate
social comparison, plan social support, prompt identification
of role model, and relapse prevention. Table 2 also details the
6 BCTs that were not implemented in any of the three
self-monitoring systems. These were model or demonstrate the
behavior, prompt anticipated regret, prompt self-talk, fear
arousal, prompt use of imagery, and general communication
skills training.
The activity tracker for all three systems measured physical
activity, which was then integrated into the app and/or website
to provide users with additional feedback on activity levels. For
example, self-monitoring systems frequently implemented BCTs
related to social support and social comparisons by allowing
peers to offer each other social support through the use of app
messaging systems and emoji (Figure 1), peer leader boards
(Figure 1), and/or challenges which displayed to users a history
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(amount or pattern) of their peer’s physical activity (Figure 1).
Challenges may offer users a “behavioral contract”; this can be
used to prevent relapse, as physical activity is required to be
performed over multiple days and plan necessary actions to
achieve this (Figure 1). Self-monitoring, goal setting, evaluating
activity in relation to goals, providing rewards on past success,
progress toward goals (Figure 1), and providing feedback were
typically delivered by graphical display of the volume of
physical activity performed on a daily basis in comparison to a
specified physical activity goal (Figures 1). Feedback to users
on achieving a goal was typically highlighted by changing the
color or pattern of a progress bar or adding a unique identifying
feature to the progress bar (eg, a “star” or textured bar graph).
In addition, the activity trackers of all systems vibrated and
provided visual feedback to users’ when the daily activity goal
was achieved. The Garmin system automatically generated a
goal for the user, whereas the Fitbit and Jawbone systems
allowed users to set their own goal. The Garmin and Jawbone
systems also automatically (Garmin) prompted a user (Jawbone)
to increase their activity goal if they reached it consistently.
Figure 1. Screenshots of the app or website displaying how various behavior change techniques (BCTs) related to physical activity were implemented.
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Table 2. Presence of specific behavior change techniques (BCTs) in relation to activity, sleep, and sedentary behavior.
TotalJawbone UP3 Garmin VivosmartFitbit Charge HR
Behavior change technique
Seden-
try
SleepActivi-
ty
Seden-
try
SleepActivi-
ty
Seden-
try
SleepActivi-
ty
Seden-
try
SleepActivi-
ty
011011000000Provide information on consequences
of behavior in general
011011000000Provide information on consequences
of behavior to the individual
013011001001Provide information about others’
approval
013011001001Provide normative information about
others’ behavior
123111001011Goal-setting (behavior)
123111001011Goal-setting (outcome)
012011000001Action Planning
011011000000Barrier identification or problem
solving
012011000001Set graded tasks
123011101011Prompt review of behavioral goals
123011101011Prompt review of outcome goals
113011101001Prompt rewards contingent on effort
or progress toward behavior
123011101011Provide rewards contingent on suc-
cessful behavior
003001001001Shaping
001001000000Prompting generalization of a target
behavior
233111111011Prompt self-monitoring of behavior
233111111011Prompt self-monitoring of behavioral
outcome
133011111011Prompting focus on past success
233111111011Provide feedback on performance
012011000001Provide information on where and
when to perform the behavior
012011000001Provide instruction on how to perform
the behavior
000000000000Model or demonstrate the behavior
212111100001Teach to use prompts or cues
010010000000Environmental restructuring
003001001001Agree behavioral contract
212111100001Prompt practice
011011000000Use of follow-up prompts
013011001001Facilitate social comparison
013011001001Plan social support or social change
003001001001Prompt identification as role model
or position advocate
000000000001Prompt anticipated regret
000000000000Fear arousal
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TotalJawbone UP3 Garmin VivosmartFitbit Charge HR
Behavior change technique
Seden-
try
SleepActivi-
ty
Seden-
try
SleepActivi-
ty
Seden-
try
SleepActivi-
ty
Seden-
try
SleepActivi-
ty
000000000000Prompt self talk
000000000000Prompt use of imagery
013011001001Relapse prevention or coping plan-
ning
011011000000Stress management or emotional
control training
001001000000Motivational interviewing
011011000000Time management
000000000000General communication skills training
022011001010Stimulate anticipation of future re-
wards
174377729331041901025Total
Sleep
All three systems implemented the following four BCTs: prompt
self-monitoring (behavior and outcome), prompt focus on past
success, and provide feedback on performance (Table 2). In
terms of how these BCTs were implemented in each system,
the activity tracker component of all systems provided a measure
of sleep volume and quality. This information was then used to
generate feedback to users, focus on past success, and providing
feedback were implemented by providing graphical display on
the volume and quality of sleep (Figure 2). In addition, Fibit
and Jawbone systems also implemented the following 6 BCTs:
goal setting (behavior and outcome), prompting review of goals
(behavior and outcome), providing rewards contingent on
successful behavior, and stimulate anticipation of future rewards.
These were operationalized by identifying whether the volume
and/or quality of sleep (Figure 2) met a user’s goal or not (Figure
2) and by altering the graphical feedback provided by changing
the color or pattern of a progress bar or adding a unique
identifying feature to the progress bar (eg, a “star” or textured
bar graph). The Jawbone system also implemented action
planning, prompting, relapse prevention, time management
(Figure 2), and environmental restructuring (Figure 2). Table 2
details the 11 BCTs that were not implemented by any of the
self-monitoring systems.
Sedentary Behavior
The Fitbit system did not implement any BCT in relation to
sedentary behavior. The Garmin and Jawbone systems both
applied the following five BCTs: prompt self-monitoring
(behavior and outcome), provide feedback on performance,
teach prompts, and prompt practice. These BCTs were
implemented by the activity tracker monitoring periods of no
physical activity or steps and then displaying this information
to users in terms of the volume of sedentary behavior (Figure
3) and specifically identifying periods of “long” sedentary
behavior (Figure 3). Both Garmin and Jawbone units provided
feedback to users via the activity tracker, vibrating to indicate
if they had been sedentary for a “long” period of time. The
Garmin system had a default setting of 1 hour of sedentary
activity, which could not be altered by users, whereas the
Jawbone system allowed this to be defined by the user. This
difference resulted in the Garmin being coded as absent for goal
setting (behavioral and outcome) in relation to sedentary
behavior, whereas the Jawbone was coded as present. Table 2
displays the 28 BCTs that were not implemented by any of the
systems in relation to sedentary behavior.
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Figure 2. Screenshots of the app or website displaying how various behavior change techniques (BCTs) related to sleep were implemented.
Figure 3. Screenshots of the app or website displaying how various behavior change techniques (BCTs) related to sedentary behaviour were implemented.
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Discussion
Principal Findings
This study assessed the number and type of BCT that three
self-monitoring systems implemented to support users in
changing their physical activity, sleep, and sedentary behaviors
and summarized how the most prevalent BCTs were
implemented. The number of BCTs implemented varied between
these behaviors. On average, the greatest number of BCTs were
implemented in relation to physical activity, followed by sleep
and sedentary behavior. All three systems provided
self-monitoring of physical activity and sleep and provided
feedback to allow the user to focus on their previous success
with changing the behavior.
The type of BCT implemented in each system for monitoring
physical activity was similar to that observed in other studies
[18]. A major difference in the BCT implemented between
physical activity, sleep, and sedentary behaviors was the use of
challenges, leader boards, and peer to peer “messaging” for
physical activity and not for sleep or sedentary behavior. These
features operationalize BCTs related to action planning,
providing information about others behaviors, social support,
shaping, peer approval, and relapse prevention, which may be
useful in changing behaviors [19]. These differences may reflect
the inherent differences between behaviors and approaches to
changing them. For example, the more physical activity people
perform, the greater their health benefits [30], and this lends
itself to the concept of leader boards and challenges, which can
involve frequent peer-to-peer interactions. Yet, for sleep
duration, more is not always better as sleep duration has a
U-shaped curve in relation to health [31,32], and the concept
of “good” sleep is highly individualistic resulting from a
complex interaction between the duration, timing, and quality
of sleep [1]. As such, whereas goal setting and feedback can be
implemented in relation to sleep duration and quality as observed
in the systems evaluated (see Figure 2), if leader board and
challenge concepts are implemented in relation to sleep, they
likely need to be configured around parameters of sleep that are
more under the control of the individual, such as sleep hygiene
behaviors. For example, the number of days or nights a person
went to sleep and woke up at times that “matched” their goals
for these behaviors. Alternatively, the concept of leader boards
may not be appropriate for sleep. Furthermore, it is important
to implement any BCT that seek to improve sleep behaviors in
ways which do not increase worry and anxiousness regarding
sleep, as this may be detrimental to improving sleep [33].
For sedentary behavior, in light of growing evidence that regular
activity breaks are beneficial in comparison with continuous
sitting, it may be useful to configure the concept of leader boards
and challenges around this premise [34,35]. Leader boards and
challenges were implemented in all three systems evaluated in
this study in relation to physical activity and are also
increasingly implemented in physical activity promotion
websites [16]. These features were coded as BCT related to
social support, shaping, and relapse prevention; yet, it is
unknown how this type of electronic social support compares
with in-person peer support and how this influences the efficacy
of these strategies. A review of “online social networks”
concluded that there was only modest evidence regarding their
efficacy to increase physical activity, and continued research is
required to clarify their efficacy [36]. Similarly, the evaluated
systems implemented “badges” to reward users on their
accomplishments, as do many physical activity promotion
websites [16]. To date, little is known about how users perceive
these features and their effectiveness to change behaviors.
Sleep hygiene education is an effective strategy to improve sleep
behaviors in populations with clinical sleep disorders and is
also thought to be useful in a public health context to improve
the sleep for those people who have sleep complaints but do
not have a clinical sleep disorder [37,38]. The Jawbone system
implemented the greatest number of BCTs in relation to sleep
and did so in a way that was broadly consistent with sleep
hygiene guidelines on the timing of sleep, stress reduction, and
restructuring the sleep environment to promote sleep [37,38].
It achieved this by measuring sleep and providing feedback on
goals using the mobile device notification system to prompt the
user to begin getting ready for bed and that their goal time to
sleep was approaching. When combined with further education
and strategies, these features could help users initiate prebed
routines including relaxation techniques to reduce stress and
also achieve regularity in the timing of sleep. There is some
evidence of the efficacy of these approaches in the literature
[37-39], yet, their effectiveness when implemented as part of
self-monitoring systems is unknown. These are examples of the
BCTs implemented within the Jawbone system that were not
implemented within the other systems and highlight how the
number and type of BCT implemented vary between the
evaluated self-monitoring systems for given behaviors.
Two of the three systems (Jawbone and Garmin) included a
vibration alert in the wrist worn activity to alert the user that
they had been sedentary for a period of time. This may be a
useful prompt to engage in physical activity and reduce sitting
time and similar strategies have been implemented as part of
ongoing interventions [40]. Although this is an example of a
behavioral prompt, it is was not coded as present for goal-setting
in the Garmin unit as the user could not set the timing of this
feature and therefore adjust their goal. It must also be
acknowledged that the two systems that provided feedback on
sedentary behavior did so from the perspective of a lack of
stepping or movement behavior which does not align with
recommended definitions of sedentary behavior [41] and is a
function of the technical limitations associated with the activity
trackers being worn on the wrist and may have influenced the
implementation of BCT for this behavior. Goal-setting is a BCT
that is frequently implemented in interventions and is associated
with behavior change [8,19,24-26,42]. The Garmin system
automatically created a step-based goal for individuals based
on the activity level (low, medium, and high) entered when
creating a user profile and adjusts the goal based on activity
levels the previous day. The Garmin system also used a default
1 hour goal for sedentary behavior, which could not be adjusted
by the user, and the inability to adjust this goal was why it was
coded as absent for this behavior in the Garmin system. It is
unclear how the activity level specified when creating a user
profile is translated into a step goal, as is how this automatically
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created goal relates to existing step-based recommendations
[43,44]. The Fitbit and Jawbone systems allowed users to specify
their own goals. However, a more useful approach to goal setting
for self-monitoring systems may be to provide users with
information on the level of behavior for optimum health,
information on the goal setting process (eg, promote attainable
goals and prompt revision of goals in light of performance), and
engage them in the goal setting process (eg, personalized goals)
to facilitate users setting goals that move them toward improved
health, are attainable, and meaningful to the individual. This
approach could be translated to activity, sedentary, and sedentary
behaviors in efforts to enhance the way in which goal-setting
strategies are implemented.
A total of 18 BCTs were implemented by all three systems in
relation to physical activity (Table 2), including those previously
associated with increased physical activity, such as providing
information on the consequences of the behavior (individual
and general), goal setting (behavior and outcome), prompt
self-monitoring (behavior and outcome), facilitate social support,
prompt practice, and prompt rewards contingent on effort or
progress [25,42]. Setting behavioral goals, providing unspecified
forms of social support, and adding objects to the environment
have been identified as promising BCTs for reducing sedentary
time [8]. BCTs related to social support were not present in any
of the systems in relation to sedentary behavior and provides
an opportunity to expand the capability of the systems to include
BCTs that are promising to reduce sedentary time. We are
unaware of any previous studies examining BCTs in relation to
changes in sleep in either self-monitoring systems or intervention
studies; therefore, the insights provided in this study are novel.
Although the systems included a number of BCTs which are
associated with improved behaviors, to date, there is limited
effectiveness surrounding the use of self-monitoring systems
to improve these target behaviors [45,46].
A number of BCTs were not implemented in any of the
evaluated systems for any of the behaviors (Table 2), and many
of these same BCTs are also absent from interventions on other
lifestyle behaviors [20,24,26]. Several possibilities may explain
this. Designers of self-monitoring systems may simply be
unaware of the BCTs literature and implement features guided
by the functionality of the system (eg, activity trackers measure
amount of movement so systems focus on provided feedback
on this), features based on app or website design principles, or
features desired by users. Alternatively, omissions of certain
BCTs may reflect decisions to implement fewer BCTs as
effectively as possible rather than to implement as many as
possible in a less effective manner. Furthermore, there is a
debate concerning dose-response relationships between the
number of BCTs and behavior change and if specific clusters
of BCTs are more efficacious than other clusters or if certain
BCTs are required to cooccur to maximize potential behavior
change [20,24-26]. In light of this, decisions on BCT inclusion
and implementation in interventions or self-monitoring systems
should be based on addressing the specific behavioral
determinants of a behavior. Furthermore, it is unknown how
the different combinations of BCTs present in the
self-monitoring systems for a specific behavior are related to
behavior change. This may also explain differences in the
number of BCTs implemented between behaviors, as there is a
richer literature on the determinants of physical activity
compared with sedentary behavior and sleep [8,47-49].
Furthermore, the mere presence of a BCT does not indicate the
way in which it is implemented, which has important
implications for behavior change.
Limitations
Limitations of this study include using a behavior change
taxonomy that is directed toward changing physical activity and
dietary behaviors to assess sleep and sedentary behaviors.
Although this was offset by coding the presence or absence of
a BCT specifically to the behavior in question. Furthermore,
this study did not assess the features and functionality of the
systems in relation to sleep hygiene recommendations which
are useful in changing sleep behaviors [37,38]. There are many
systems currently available, and it is unknown how systems not
included in this study compare on their use and implementation
of BCTs. All systems were only used over a 1-week period,
which is consistent with previous evaluations [18], and a longer
period of use may have resulted in a different user experience
resulted in additional BCTs being coded as present. However,
there are currently no recommendations regarding how long an
intervention or self-monitoring system should be used for before
coding.
Conclusions
In conclusion, the number and type of BCT implemented varied
between the evaluated self-monitoring systems and the number
and type of BCT varied between activity, sleep, and sedentary
behaviors. The greatest number of BCTs was implemented in
relation to physical activity, followed by sleep and sedentary
behavior. However, the number of BCTs does not reflect how
a BCT is implemented and presented to users, or the
cooccurrence of a particular BCT with other BCT, which may
influence the potential effectiveness of the self-monitoring
system to actually change behavior [27]. It is important to note
that this study was evaluating the “potential” of these
self-monitoring systems to change activity, sleep, and sedentary
behaviors and further research is required to establish their
effectiveness to change these behaviors. Such evaluations could
also examine the actual usage patterns of these devices and the
different types of BCTs that users make use of.
Acknowledgments
The use of brand names is for identification purposes only and does not constitute endorsement by the authors. MJD is supported
by a Future Leader Fellowship (ID 100029) from the National Heart Foundation of Australia. CV is supported by a Future Leader
Fellowship (ID 100427) from the National Heart Foundation of Australia. CES (1090517), SS (GNT1125586) and AR
(GNT1105926) are supported by a National Health and Medical Research Council Early Career Fellowship. SS (ID 101240) is
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also supported by a Postdoctoral Fellowship from the Australian Heart Foundation of Australia. All data used in this study is
provided in Table 2 and is accessible as part of this article.
Authors' Contributions
All authors provided critical review of the manuscript. MJD, CV, and MK conceptualized the study, MJD and BM undertook
data collection.
Conflicts of Interest
None declared.
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Abbreviations
BCTs: behavior change techniques
CALO-RE: Coventry, Aberdeen, and London-Refined
Edited by G Eysenbach; submitted 22.09.16; peer-reviewed by K Grindrod, D Glance, M Alcañiz, J Bort-Roig, A Gray; comments to
author 08.01.17; revised version received 06.06.17; accepted 23.06.17; published 14.08.17
Please cite as:
Duncan M, Murawski B, Short CE, Rebar AL, Schoeppe S, Alley S, Vandelanotte C, Kirwan M
Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors
Interact J Med Res 2017;6(2):e13
URL: http://www.i-jmr.org/2017/2/e13/
doi:10.2196/ijmr.6685
PMID:
©Mitch Duncan, Beatrice Murawski, Camille E Short, Amanda L Rebar, Stephanie Schoeppe, Stephanie Alley, Corneel
Vandelanotte, Morwenna Kirwan. Originally published in the Interactive Journal of Medical Research (http://www.i-jmr.org/),
14.08.2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in the Interactive Journal of Medical Research, is properly cited. The complete
bibliographic information, a link to the original publication on http://www.i-jmr.org/, as well as this copyright and license
information must be included.
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... At that time, EAMs were starting to proliferate commercially and in research. Other researchers have continued to investigate implemented BCTs in EAMs [15][16][17] but it is difficult for research to keep up with advances in technology. Most devices that were included in the early publications are either no longer available or are now obsolete. ...
... The principal distinction between devices was the battery life which ranged from 1-2 days to more than 7 days. [12,[15][16][17]. This increase in average BCTs may be the result of previous reviews using the CALO-RE taxonomy for coding [12,15,17]. ...
... [12,[15][16][17]. This increase in average BCTs may be the result of previous reviews using the CALO-RE taxonomy for coding [12,15,17]. The CALO-RE outlines 40 BCTs that are significantly correlated to PA [11] while the current study utilized the complete 93-item behavior change taxonomy [14] in order to translate the results to the BCW. ...
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The aim of this study was to perform a content analysis of electronic activity monitors that also evaluates utility features, code behavior change techniques included in the monitoring systems, and align the results with intervention functions of the Behaviour Change Wheel program planning model to facilitate informed device selection. Devices were coded for the implemented behavior change techniques and device features. Three trained coders each wore a monitor for at least 1 week from December 2019–April 2020. Apple Watch Nike, Fitbit Versa 2, Fitbit Charge 3, Fitbit Ionic—Adidas Edition, Garmin Vivomove HR, Garmin Vivosmart 4, Amazfit Bip, Galaxy Watch Active, and Withings Steel HR were reviewed. The monitors all paired with a phone/tablet, tracked exercise sessions, and were wrist-worn. On average, the monitors implemented 27 behavior change techniques each. Fitbit devices implemented the most behavior change techniques, including techniques related to the intervention functions: education, enablement, environmental restructuring, coercion, incentivization, modeling, and persuasion. Garmin devices implemented the second highest number of behavior change techniques, including techniques related to enablement, environmental restructuring, and training. Researchers can use these results to guide selection of electronic activity monitors based on their research needs.
... Wearables and companion apps were coded using the BCT Taxonomy Version 1 (BCTTv1), which was previously employed in similar studies [11][12][13]. The BCTTv1 is explained in detail by Michie et al [14]. ...
... The number of incorporated BCTs in this study is in line with previous research examining incorporated BCTs within earlier versions of the wearables tested herein [11][12][13]. In studies comparing different wearables, Fitbit incorporated a higher number of BCTs than those incorporated by the Garmin [12] or Polar [11,13] wearables. ...
... The number of incorporated BCTs in this study is in line with previous research examining incorporated BCTs within earlier versions of the wearables tested herein [11][12][13]. In studies comparing different wearables, Fitbit incorporated a higher number of BCTs than those incorporated by the Garmin [12] or Polar [11,13] wearables. ...
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Background Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA. Objective The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users’ PA behavior. Methods The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature. Results The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA. Conclusions Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.
... Selfmonitoring has been well established as an effective behaviour change technique in promoting adoption of targeted health behaviours such as physical activity [13]. When wearable physical activity trackers are combined with an accompanying app and/or website platform, this 'self-monitoring system' provides an individual with access to a range of features and functions that have been shown to align with up to 26 different behaviour change techniques that are known to be effective [13][14][15]. ...
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Background There has been increasing interest in using wearable activity trackers to promote physical activity in youth. This study examined the short- and longer-term effects of a wearable activity tracker combined with digital behaviour change resources on the physical activity of adolescents attending schools in socio-economically disadvantaged areas. Methods The Raising Awareness of Physical Activity (RAW-PA) Study was a 12-week, multicomponent intervention that combined a Fitbit Flex (and accompanying app), and online digital behaviour change resources and weekly challenges delivered via Facebook. RAW-PA was evaluated using a cluster-randomised controlled trial with 275 adolescents (50.2% female; 13.7 ± 0.4 years) from 18 Melbourne secondary schools (intervention n = 9; wait-list control group n = 9). The primary outcome was moderate- to vigorous-intensity physical activity (MVPA), measured using hip-worn ActiGraph accelerometers. The secondary outcome was self-reported physical activity. Data were collected at baseline, 12-weeks (immediately post-intervention), and 6-months post-intervention (follow-up). Multilevel models were used to determine the effects of the intervention on daily MVPA over time, adjusting for covariates. Results No significant differences were observed between intervention and wait-list control adolescents’ device-assessed MVPA immediately post-intervention. At 6-months post-intervention, adolescents in the intervention group engaged in 5 min (95% CI: − 9.1 to − 1.0) less MVPA per day than those in the wait-list control group. Males in the intervention group engaged in 11 min (95% CI: − 17.6 to − 4.5) less MVPA than males in the wait-list control group at 6-months post-intervention. No significant differences were observed for females at either time point. For self-reported physical activity, no significant effects were found at 12-weeks and 6-months post-intervention. Conclusions Combining a wearable activity tracker with digital behaviour change resources and weekly challenges did not increase inactive adolescents’ accelerometer-derived and self-reported physical activity levels immediately post-intervention. This contrasts previous research that has suggested wearable activity tracker may increase youth physical activity levels in the short-term. Lower engagement in MVPA 6-months post-intervention was observed for males but not for females, though it is unclear why this finding was observed. The results suggest wearable activity trackers, in combination with supporting materials, may not be effective for increasing physical activity levels in adolescents. Trial registration ACTRN12616000899448 . Australian and New Zealand Clinical Trials Registry. Registered 7 July 2016.
... Interestingly, another stream of research focused on WD capabilities and features that enable users to realise health improvement goals. These studies focused on WD behavioural interventions, such as alarms, rewards, and behaviour demonstration, and how these interventions motivate users to improve their activity level (Duncan et al., 2017;Lentferink et al., 2017;Mercer et al., 2016). These interventions are similar to our definitions of affordances in this study. ...
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Healthcare is an area that has benefitted from the developments in wearable device technology. Seniors, who usually suffer from multiple comorbidities, are among the target users of these devices, and research has shown potential health benefits for seniors when they use these devices effectively. However, the adoption rate of wearable devices is low, especially among seniors, preventing the full utilisation of their data in healthcare. In this study, we interviewed forty-four seniors across North America and collected data from their wearable devices to develop a theoretical affordance network-based model to explain seniors’ effective use of wearable devices. Our model indicates that despite the apparent simplicity of wearable devices, they have multiple affordances that help seniors achieve several goals, including activity monitoring, activity planning, and activity improvement. Furthermore, we identified factors that enable seniors to actualise the affordances of wearable devices and achieve their goals. The results of this study suggest a strong relationship between seniors’ mental and physical capabilities and their willingness to use and benefit from wearable devices. We join other researchers in their call for a contextual study on consumer technology use.
... Thanks to the large adoption of wearable devices able to measure and store data about PA and to provide contingent feedbacks and reminders, a large-scale health revolution has begun [14][15][16]. The availability of smartphone applications has contributed to a better understanding of human health by allowing us to assess precious, contingent data for medical and fitness area [14,[16][17][18] taking advantages of some improvements in app technology (e.g., a built-in camera for heart rate assessment, accelerometers) that have proved useful [19][20][21][22] and supported by current reviews of mHealth (healthcare practice supported by mobile devices). Further, the capability of mobile technology to improve access to a large number of people living far from clinical centers, reduce costs, and enhance health outcomes for management of chronic health condition represents the core feature of these apps, connected with their potential and effectiveness for remote monitoring of clinical parameters, such as cardiovascular disease (CVD) risk factors [18], and for implementing behavioral change strategies aimed to promote healthy habits, in particular regarding compliance to therapies, diet, and physical activity. ...
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The potential benefits of promotion of physical activity for whole populations and at-risk individuals have become a well-established agenda for public health with various meta-analyses that have suggested physical fitness to be an important predictor of morbidity and mortality in adults. Lifestyle behaviors, such as diet and physical activity, are modifiable risk factors associated with many noncommunicable diseases, and chronic pathologies and health condition, such as obesity. To date, many intervention programs targeting physical activity and dietary changes have had modest effects and their long-term effectiveness is not well established. Thus, public health researchers have begun to examine novel approaches to deliver behavior change interventions. Mobile and wireless technology (health) is a growing area in the prevention and management of obesity that holds potential to deliver health-related behavior change interventions.
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Objective: The purpose of this qualitative study was to explore working men’s perspectives about sleep health and the intersecting influences of gender and work, describing participant’s views on current and potential programming and organizational support to promote sleep health. Methods: Twenty men employed in male-dominated industries in the north-central region of Alberta, Canada, participated in 4 consultation group discussions addressing motivators, facilitators and barriers to sleep health. Results: Participants reported sleeping an average of 6.36 (SD ±1.1) hours per night, and the majority worked more than 40 hours per week. Data were analyzed using an inductive approach. The findings provided important insights. In normalizing sleep deprivation and prioritizing the need to “just keep going” on six or less hours of sleep, the men subscribed to masculine ideals related to workplace perseverance, stamina and resilience. Workplace cultures and practices were implicated including normative dimensions of overtime and high productivity and output, amid masculine cultures constraining emotions and conversations about sleep, the sum of which muted avenues for discussing, let alone promoting sleep. Challenges to good sleep were primarily constructed around time constraints, and worry about meeting work and home responsibilities. Men’s preferences for workplace support included providing and incentivizing the use of sleep health resources, designing work for sleep health (e.g., shift schedules, overtime policies) and getting advice from experienced coworkers and experts external to the workplace organization. Conclusion: These findings hold potential for informing future gender-sensitive programming and organizational practices to support sleep health among working men.
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Background Older cancer survivors are at risk of the development or worsening of both age- and treatment-related morbidity. Sedentary behavior increases the risk of or exacerbates these chronic conditions. Light-intensity physical activity (LPA) is more common in older adults and is associated with better health and well-being. Thus, replacing sedentary time with LPA may provide a more successful strategy to reduce sedentary time and increase physical activity. Objective This study primarily aims to evaluate the feasibility, acceptability, and preliminary efficacy of a home-based mobile health (mHealth) intervention to interrupt and replace sedentary time with LPA (standing and stepping). The secondary objective of this study is to examine changes in objective measures of physical activity, physical performance, and self-reported quality of life. Methods Overall, 54 cancer survivors (aged 60-84 years) were randomized in a 1:1:1 allocation to the tech support intervention group, tech support plus health coaching intervention group, or waitlist control group. Intervention participants received a Jawbone UP2 activity monitor for use with their smartphone app for 13 weeks. Tech support and health coaching were provided via 5 telephone calls during the 13-week intervention. Sedentary behavior and physical activity were objectively measured using an activPAL monitor for 7 days before and after the intervention. Results Participants included survivors of breast cancer (21/54, 39%), prostate cancer (16/54, 30%), and a variety of other cancer types; a mean of 4.4 years (SD 1.6) had passed since their cancer diagnosis. Participants, on average, were 70 years old (SD 4.8), 55% (30/54) female, 24% (13/54) Hispanic, and 81% (44/54) overweight or obese. Malfunction of the Jawbone trackers occurred in one-third of the intervention group, resulting in enrollment stopping at 54 rather than the initial goal of 60 participants. Despite these technical issues, the retention in the intervention was high (47/54, 87%). Adherence was high for wearing the tracker (29/29, 100%) and checking the app daily (28/29, 96%) but low for specific aspects related to the sedentary features of the tracker and app (21%-25%). The acceptability of the intervention was moderately high (81%). There were no significant between-group differences in total sedentary time, number of breaks, or number of prolonged sedentary bouts. There were no significant between-group differences in physical activity. The only significant within-group change occurred within the health coaching group, which increased by 1675 daily steps (95% CI 444-2906; P=.009). This increase was caused by moderate-intensity stepping rather than light-intensity stepping (+15.2 minutes per day; 95% CI 4.1-26.2; P=.008). Conclusions A home-based mHealth program to disrupt and replace sedentary time with stepping was feasible among and acceptable to older cancer survivors. Future studies are needed to evaluate the optimal approach for replacing sedentary behavior with standing and/or physical activity in this population. Trial Registration ClinicalTrials.gov NCT03632694; https://clinicaltrials.gov/ct2/show/NCT03632694
Article
Background Electronic sensors measuring biological and behavioral aspects of health and the environment are becoming ubiquitous and, with advances in data science and ehealth technology, provide opportunities for new inquiry and innovative approaches to nursing research. Purpose To conceptualize the use of sensor technology from the perspective of nursing science. Methods This review reports the keynote presentation from the Expanding Science of Sensor Technology in Nursing Research Conference presented by the Council for Advancement of Nursing Science in 2019 Results Electronic sensors enable collection, recording, and transmission of data in real time in real life settings, remote monitoring, self-monitoring, and communication between health care professionals and patients. A deliberative approach to selecting and applying electronic sensors and analyzing and interpreting the data is needed for successful research. Conclusions Electronic sensors have high potential to advance nursing science.
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In the last two decades, whole-body vibration training (WBVT), involving exercising on a vibrating platform, emerged as an alternative exercise modality for the treatment of obesity. In this chapter, the possible clinical use of WBVT in obese individuals is addressed, involving its effect on body composition, muscle strength, and cardiovascular function.
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The aim of this chapter is to present a practical overview of the most common equipment for patient handling and rehabilitation technologies for a clinical setting, focusing the attention on devices suited for obese individuals. In details, the equipment, devices, aids, and resources designed as alternative to manual handling are described. We have reviewed the equipment related to lifting, transferring, repositioning, moving, and mobilizing of obese patients ensuring that patients are cared for safely preventing consequences of immobility, while maintaining a safe work environment for employees.
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Aim This study examines associations between the variability in bed/rise times, usual bed/rise time and dietary quality, physical activity, alcohol consumption, sitting time, sleep insufficiency and a composite index of behaviors. Subject and methods A random sample of Australian adults drawn from an online Panel cohort in 2013 completed a cross-sectional online survey. A total of 1,317 participants, median age 57 (IQR = 20) completed the survey. Bed- and wake times, variability in bed- and wake-times, dietary quality, physical activity, alcohol consumption, sitting time, sleep insufficiency and socio-demographics were assessed using a questionnaire. Associations were examined with generalized linear models. Results Having bed - times that varied by >30 min were associated with lower dietary quality, higher alcohol consumption, higher sitting time, more frequent insufficient sleep and poorer overall pattern of lifestyle behaviors. Greater variability in wake times, usual bed times and usual wake times were inconsistently associated with lifestyle behaviours. Conclusions Greater bed-time variability is associated with a less healthy pattern of lifestyle behaviors. Greater consistency in sleep timing may contribute to, or be reflective of, a healthier lifestyle.
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Sedentary behaviour - i.e., low energy-expending waking behaviour while seated or lying down - is a health risk factor, even when controlling for physical activity. This review sought to describe the behaviour change strategies used within interventions that have sought to reduce sedentary behaviour in adults. Studies were identified through existing literature reviews, a systematic database search, and hand-searches of eligible papers. Interventions were categorized as 'very promising', 'quite promising', or 'non-promising' according to observed behaviour changes. Intervention functions and behaviour change techniques were compared across promising and non-promising interventions. Twenty-six eligible studies reported 38 interventions, of which 20 (53%) were worksite-based. Fifteen interventions (39%) were very promising, 8 quite promising (21%), and 15 non-promising (39%). Very or quite promising interventions tended to have targeted sedentary behaviour instead of physical activity. Interventions based on environmental restructuring, persuasion, or education, were most promising. Self-monitoring, problem solving, and restructuring the social or physical environment were particularly promising behaviour change techniques. Future sedentary reduction interventions might most fruitfully incorporate environmental modification and self-regulatory skills training. The evidence base is however weakened by low-quality evaluation methods; more RCTs, employing no-treatment control groups, and collecting objective data, are needed. Supplemental_PRISMA_checklist.pdf Supplemental References.pdf Supplemental Table_1.pdf Supplemental Table_2.pdf Supplemental Table_3.pdf Supplemental Table_4.pdf Supplemental Table_5.pdf.
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Direct-to-consumer mHealth devices are a potential asset to behavioral research but rarely tested as intervention tools. This trial examined the accelerometer-based Fitbit tracker and website as a low-touch physical activity intervention. The purpose of this study is to evaluate, within an RCT, the feasibility and preliminary efficacy of integrating the Fitbit tracker and website into a physical activity intervention for postmenopausal women. Fifty-one inactive, postmenopausal women with BMI ≥25.0 were randomized to a 16-week web-based self-monitoring intervention (n=25) or comparison group (n=26). The Web-Based Tracking Group received a Fitbit, instructional session, and follow-up call at 4 weeks. The comparison group received a standard pedometer. All were asked to perform 150 minutes/week of moderate to vigorous physical activity (MVPA). Physical activity outcomes were measured by the ActiGraph GT3X+ accelerometer. Data were collected and analyzed in 2013-2014. Participants were aged 60 (SD=7) years with BMI of 29.2 (3.5) kg/m(2). Relative to baseline, the Web-Based Tracking Group increased MVPA by 62 (108) minutes/week (p<0.01); 10-minute MVPA bouts by 38 (83) minutes/week (p=0.008); and steps by 789 (1,979) (p=0.01), compared to non-significant increases in the Pedometer Group (between-group p=0.11, 0.28, and 0.30, respectively). The Web-Based Tracking Group wore the tracker on 95% of intervention days; 96% reported liking the website and 100% liked the tracker. The Fitbit was well accepted in this sample of women and associated with increased physical activity at 16 weeks. Leveraging direct-to-consumer mHealth technologies aligned with behavior change theories can strengthen physical activity interventions. Copyright © 2015. Published by Elsevier Inc.
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There is a need for development of more effective interventions to achieve healthy eating, enhance healthy ageing, and to reduce the risk of age-related diseases. The aim of this study was to identify the behaviour change techniques (BCTs) used in complex dietary behaviour change interventions and to explore the association between BCTs utilised and intervention effectiveness. We undertook a secondary analysis of data from a previous systematic review with meta-analysis of the effectiveness of dietary interventions among people of retirement age. BCTs were identified using the reliable CALO-RE taxonomy in studies reporting fruit and vegetable (F and V) consumption as outcomes. The mean difference in F and V intake between active and control arms was compared between studies in which the BCTs were identified versus those not using the BCTs. Random-effects meta-regression models were used to assess the association of interventions BCTs with F and V intakes. Twenty-eight of the 40 BCTs listed in the CALO-RE taxonomy were identified in the 22 papers reviewed. Studies using the techniques ‘barrier identification/problem solving’ (93 g, 95% confidence interval (CI) 48 to 137 greater F and V intake), ‘plan social support/social change’ (78 g, 95%CI 24 to 132 greater F and V intake), ‘goal setting (outcome)’ (55 g 95%CI 7 to 103 greater F and V intake), ‘use of follow-up prompts’ (66 g, 95%CI 10 to 123 greater F and V intake) and ‘provide feedback on performance’ (39 g, 95%CI −2 to 81 greater F and V intake) were associated with greater effects of interventions on F and V consumption compared with studies not using these BCTs. The number of BCTs per study ranged from 2 to 16 (median = 6). Meta-regression showed that one additional BCT led to 8.3 g (95%CI 0.006 to 16.6 g) increase in F and V intake. Overall, this study has identified BCTs associated with effectiveness suggesting that these might be active ingredients of dietary interventions which will be effective in increasing F and V intake in older adults. For interventions targeting those in the peri-retirement age group, ‘barrier identification/problem solving’ and ‘plan for social support/social change’ may be particularly useful in increasing the effectiveness of dietary interventions.
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Background: Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. Objective: The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Methods: Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. Results: All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Conclusions: Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.
Article
Background Despite the increasing popularity of activity trackers, little evidence exists that they can improve health outcomes. We aimed to investigate whether use of activity trackers, alone or in combination with cash incentives or charitable donations, lead to increases in physical activity and improvements in health outcomes. Methods In this randomised controlled trial, employees from 13 organisations in Singapore were randomly assigned (1:1:1:1) with a computer generated assignment schedule to control (no tracker or incentives), Fitbit Zip activity tracker, tracker plus charity incentives, or tracker plus cash incentives. Participants had to be English speaking, full-time employees, aged 21–65 years, able to walk at least ten steps continuously, and non-pregnant. Incentives were tied to weekly steps, and the primary outcome, moderate-to-vigorous physical activity (MVPA) bout min per week, was measured via a sealed accelerometer and assessed on an intention-to-treat basis at 6 months (end of intervention) and 12 months (after a 6 month post-intervention follow-up period). Other outcome measures included steps, participants meeting 70 000 steps per week target, and health-related outcomes including weight, blood pressure, and quality-of-life measures. This trial is registered at ClinicalTrials.gov, number NCT01855776. Findings Between June 13, 2013, and Aug 15, 2014, 800 participants were recruited and randomly assigned to the control (n=201), Fitbit (n=203), charity (n=199), and cash (n=197) groups. At 6 months, compared with control, the cash group logged an additional 29 MVPA bout min per week (95% CI 10–47; p=0·0024) and the charity group an additional 21 MVPA bout min per week (2–39; p=0·0310); the difference between Fitbit only and control was not significant (16 MVPA bout min per week [–2 to 35; p=0·0854]). Increases in MVPA bout min per week in the cash and charity groups were not significantly greater than that of the Fitbit group. At 12 months, the Fitbit group logged an additional 37 MVPA bout min per week (19–56; p=0·0001) and the charity group an additional 32 MVPA bout min per week (12–51; p=0·0013) compared with control; the difference between cash and control was not significant (15 MVPA bout min per week [–5 to 34; p=0·1363]). A decrease in physical activity of −23 MVPA bout min per week (95% CI −42 to −4; p=0·0184) was seen when comparing the cash group with the Fitbit group. There were no improvements in any health outcomes (weight, blood pressure, etc) at either assessment. Interpretation The cash incentive was most effective at increasing MVPA bout min per week at 6 months, but this effect was not sustained 6 months after the incentives were discontinued. At 12 months, the activity tracker with or without charity incentives were effective at stemming the reduction in MVPA bout min per week seen in the control group, but we identified no evidence of improvements in health outcomes, either with or without incentives, calling into question the value of these devices for health promotion. Although other incentive strategies might generate greater increases in step activity and improvements in health outcomes, incentives would probably need to be in place long term to avoid any potential decrease in physical activity resulting from discontinuation. Funding Ministry of Health, Singapore.
Article
Background: Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults. Objective: The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population. Methods: Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa. Results: The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce. Conclusions: Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults.
Article
The ineffectiveness of sleep hygiene as a treatment in clinical sleep medicine has raised some interesting questions. If it is known that, individually, each specific component of sleep hygiene is related to sleep, why wouldn't addressing multiple individual components (i.e., sleep hygiene education) improve sleep? Is there still a use for sleep hygiene? Global public health concern over sleep has increased demand for sleep promotion strategies accessible to the population. However, the extent to which sleep hygiene strategies apply outside clinical settings is not well known. The present review sought to evaluate the empirical evidence for sleep hygiene recommendations regarding exercise, stress management, noise, sleep timing, and avoidance of caffeine, nicotine, alcohol, and daytime napping, with a particular emphasis on their public health utility. Thus, our review is not intended to be exhaustive regarding the clinical application of these techniques, but rather to focus on broader applications. Overall, though epidemiologic and experimental research generally supported an association between individual sleep hygiene recommendations and nocturnal sleep, the direct effects of individual recommendations on sleep remains largely untested in the general population. Suggestions for clarification of sleep hygiene recommendations and considerations for the use of sleep hygiene in nonclinical populations are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.