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ORIGINAL PAPER
Mobile Mindfulness Meditation: a Randomised Controlled Trial
of the Effect of Two Popular Apps on Mental Health
Jayde A. M. Flett
1
&Harlene Hayne
1
&Benjamin C. Riordan
1
&Laura M. Thompson
1
&Tamlin S. Conner
1
#Springer Science+Business Media, LLC, part of Springer Nature 2018, corrected publication November/2018
Abstract
We present the results of a pre-registered randomised controlled trial (RCT) that tested whether two smartphone-based mindful-
ness meditation applications (apps) lead to improvements in mental health. University students (n= 208, aged 18 to 49) were
randomly assigned to use one of the three apps: Headspace, Smiling Mind, or Evernote (control group). Participants were
instructed to use their assigned app for 10 min each day for 10 days, after which they received an extended 30 days’access to
continue practicing at their discretion. Participants completed measures of depressive symptoms, anxiety, stress, college adjust-
ment, flourishing, resilience, and mindfulness at baseline, after the 10-day intervention, and after the 30-day continued access
period. App usage was measured by self-report. Mindfulness app usage was high during the 10-day period (used on 8 of 10 days),
but low during the 30-day extended use period (less than 20% used the app 2+ times per week). Mindfulness app users showed
significant improvements in depressive symptoms, college adjustment, resilience (Smiling Mind only), and mindfulness
(Headspace only) from baseline to the end of 10 days relative to control participants. Participants who continued to use the
app frequently were more likely to maintain improvements in mental health, e.g. in depressive symptoms and resilience
(Headspace only), until the end of the 30-day period. Thus, brief mobile mindfulness meditation practice can improve some
aspects of negative mental health in the short term and may strengthen positive mental health when used regularly. Further
research is required to examine the long-term effects of these apps.
Keywords Mindfulness .Applications .Mobile phones .Mental health
Mindfulness meditation is an increasingly popular practice
aimed at improving mental health in clinical and non-clinical
populations (Creswell 2017; Hofmann et al. 2010). Practicing
mindfulness meditation and mindfulness-based therapies like
mindfulness-based stress reduction (MBSR) (Kabat-Zinn
1982) and mindfulness-based cognitive therapy (MBCT)
(Teasdale et al. 2000) have been associated with a number of
beneficial effects including reductions in depressive symptoms,
negative affect, stress, and anxiety, as well as increases in pos-
itive affect, life satisfaction, and vitality (Creswell 2017;
Hofmann et al. 2010). Despite these benefits, many
mindfulness meditation programmes remain inaccessible be-
cause they require the presence of highly qualified instructors
(Kabat-Zinn 2003), involve multiple in-person and group train-
ing sessions (e.g. MBCT and MBSR 8–12 weeks of 2–2.5 h
sessions; Carmody and Baer 2009), and can incur significant
financial costs to the meditator (Cavanagh et al. 2014).
The recent development of mobile applications (apps) for
smartphones presents a promising opportunity to overcome a
number of the barriers associated with typical mindfulness
meditation training (Cavanagh et al. 2014;Manietal.2015;
Plaza et al. 2013). For example, mindfulness meditation de-
livered via a mobile app allows an experienced instructor to
deliver high quality guided meditation training to far more
people than face-to-face training can practically allow
(Cavanagh et al. 2014). Further, the portable nature of the
mobile phone can reduce geographical, social and financial
barriers to access (Cavanagh et al. 2014). Smartphone owner-
ship is also increasing rapidly. In 2016, 77% of Americans
owned a smartphone, up from just 35% in 2011 (Smith
2017). For some populations, namely younger adults, non-
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s12671-018-1050-9) contains supplementary
material, which is available to authorized users.
*Jayde A. M. Flett
jflett@psy.otago.ac.nz
1
Department of Psychology, University of Otago, P.O. Box 56,
Dunedin 9054, New Zealand
Mindfulness
https://doi.org/10.1007/s12671-018-1050-9
white, and lower-income Americans, smartphones are the on-
ly means of access to the Internet at home (Anderson and
Horrigan 2016) and they are the preferred method of contact
for young people (Oliver et al. 2005). These socio-
demographic factors also coincide with populations that are
less likely to seek or receive treatment for mental health issues
(Prins et al. 2008). Thus, mobile apps can overcome barriers to
introducing mindfulness meditation practice to a wide range
of people.
Although there are hundreds of mindfulness meditation
apps available with collectively millions of downloads, there
is a relatively high turnover rate for mindfulness meditation
apps in app stores (Larsen et al. 2016; also see Bakker et al.
2016 on Donker et al. 2013). The relative newness of mind-
fulness meditation apps, combined with the high turnover rate
of app technology, means that few studies have rigorously
examined the effectiveness of mobile mindfulness apps for
improving mental health (Mani et al. 2015; Plaza et al.
2013). Given that mindfulness meditation practice is increas-
ingly used in mental health care settings (Brody et al. 2017)
and these apps are promoted as delivering health benefits (e.g.
Bcreating healthier, happier and more compassionate people
around Australia and across the globe^Smiling Mind 2017),
the evidence gap is particularly concerning because people
may turn to mindfulness meditation apps in the absence of
contact with a health professional.
While researchers are making inroads in testing web-based
mindfulness meditation interventions (see: Spijkerman et al.
2016 for review and meta-analysis), to our knowledge, there
are now eight published studies that have tested whether
mindfulness meditation apps improve mental health
(Ahtinen et al. 2013; Bostock et al. 2018; Carissoli et al.
2015;Economidesetal.2018; Howells et al. 2016;Lim
et al. 2015; Wen et al. 2017;Yangetal.2018). In a
randomised, controlled trial, Howells et al. (2016)testedthe
mindfulness meditation app Headspace for 10 days against an
attention control app in a non-clinical adult sample of 121
Bself-selected …happiness-seekers^who were recruited to a
study that Baimed to enhance well-being^(Howells et al.
2016, p. 169). Participants randomly assigned to use
Headspace showed significant improvements in depressive
symptoms and positive affect measured after the 10 days of
app use when compared to the attention-matched control
group. No changes were found for satisfaction with life,
flourishing, or negative affect. These findings suggest that
using Headspace for 10 days reduces depressive symptoms
and improves happiness, albeit over a short period of time
and in those motivated to become happier.
In another study, researchers supplied 45 sessions of
Headspace to 123 employed adults from the UK (Bostock
et al. 2018). Headspace users reported significant improve-
ments in depressive symptoms, anxiety, global well-being
and several measures of job strain at 8 weeks post-baseline
compared to a waitlist control group (n= 55). Further, those
who used Headspace more frequently during the 45 days
showed greater improvements in the psychological outcomes
than those who used the app less frequently (Bostock et al.
2018). Improvements in well-being and job strain were also
sustained at a 16-week follow-up. Other studies with smaller
sample sizes have also demonstrated some benefit. For exam-
ple, in one study, the 27 adults who received 2 weeks of
Headspace-based mindfulness training showed improvements
in compassionate prosocial responding to an injured confed-
erate compared to 29 adults who received 2 weeks of
Luminosity-based cognitive training (Lim et al. 2015). In a
one-arm pilot study, 30 medical residents who were given
4 weeks of access to Headspace reported higher mindfulness
and positive affect when accounting for frequency of app use
(Wen et al. 2017). Carissoli et al. (2015)comparedtheuseofa
‘mindfulness-inspired’meditation app (Bit’stimetorelax^)to
listening to music or a waitlist control in 56 employed Italian
adults over a 3-week period. Both the meditation app users
and music listeners showed some improvements in coping
with psychological stress.
In a more recent study conducted by Headspace, use of
Headspace by 41 adults was associated with a significant im-
provement in irritability, affect, and stress resulting from exter-
nal pressure when compared to 28 adults assigned to an audio-
book educational control (Economides et al. 2018). Similarly,
45 medical students who were randomised to use Headspace
for 30 days reported significant improvements in perceived
stress over time and improvements in general well-being when
compared to a waitlist control group (Yang et al. 2018). Finally,
in a feasibility study, 15 adult users of the Finnish app Oiva
(grounded in Acceptance and Commitment Therapy [ACT]
incorporating mindfulness training) showed significant im-
provements in stress and satisfaction with life following
1 month of use (Ahtinen et al. 2013).
On the basis of the previous app-based research, we believe
that there may be benefits of app-based mindfulness medita-
tion training, but the conclusions are by no means definitive
and consensus on the effectiveness of mindfulness training
remains elusive in the broader mindfulness literature (Van
Dam et al. 2017). Of the eight mobile mindfulness meditation
studies, only one of the published studies used an app-based
attention control as the comparison condition (Howells et al.
2016). The rest have used waitlist, alternate attention control,
or no controlled comparison conditions, which can only pro-
vide insight into whether mobile mindfulness interventions
have an impact over and above no treatment. Such designs
also do not control for digital placebo effects, that is, the po-
tential for placebo-like non-specific treatment effects in mo-
bile interventions resulting from the high levels of mental and
physical attachment, trust, affinity, and reliance on our mobile
phones (Clayton et al. 2015; Torous and Firth 2016). Further,
with the exception of Bostock et al. (2018), Wen et al. (2017),
Mindfulness
and Yang et al. (2018), most of these studies have been rela-
tively short—i.e. they deliver less than 2 weeks’mindfulness
meditation practice. Given that the end goal of these tools is to
provide a training platform to establish long-term mindfulness
practice, it would be useful to know whether these apps are
effective over longer periods of time. Also, it is important to
address whether use of these tools improves mental health
under more naturalistic conditions (e.g. when people use the
app at their own discretion) and not just under optimal condi-
tions (e.g. when people are requested to use the app for a
specific period of time).
One challenge in testing mindfulness meditation apps is
choosing the apps to test. Although a small number of
mHealth apps are responsible for over 90% of consumer
downloads (Aitken and Lyle 2015), the majority of research-
validated apps have already disappeared from the app stores
(see Bakker et al. 2016 on Donker et al. 2013). For these
reasons, we agree with Meinlschmidt et al.’s(2016)assertion
that there is little value in testing a single new app. Unlike
Meinlschmidt et al. (2016), we instead believe there is value in
identifying and testing popular and currently available tools
that have a committed user-base and are likely to remain in use
in the near future.
In the present study, we conducted a pre-registered
randomised, controlled trial of the effectiveness of two popu-
lar and currently available mindfulness meditation apps—
Headspace and Smiling Mind—on changes in mental health
compared to an attention placebo control app. We selected
Headspace and Smiling Mind based on several factors: both
apps have a high download rate (over 100,000 downloads at
the time of writing, suggesting a higher user-base), similar
introductory mindfulness meditation practices that have been
well-received by users (e.g. Headspace: 4.7 stars v. Smiling
Mind 3.7 stars in Google Play Store at the time of writing),
and high-quality ratings as assessed by the Mobile
Application Rating Scale (MARS; Stoyanov et al. 2015;4.0
and 3.7 out of 5.0, respectively; median MARS score of 23
apps was 3.2 out of 5.0; Mani et al. 2015). A key differential is
cost; Headspace incurs a fee after a brief trial, whereas Smiling
Mind is free. While neither app is necessarily comparable with
more rigorous and evidence-based programmes like MBSR
(Kabat-Zinn 1982), these apps do provide a number of med-
itations consistent with common meditation practices (Kabat-
Zinn 2013). We also incorporated an app-based attention pla-
cebo control condition (Evernote) to help account for digital
placebo and treatment expectancies. We examined changes in
mental health outcomes over a short-term adherence-request-
ed period (10 days) and over a medium-term discretionary use
period (30 days) to mimic both optimal and naturalistic use
and uptake of the app. We predicted that individuals using the
mindfulness apps would report improved mental health out-
comes from baseline to the 10-day follow-up, compared to
those using the attention control app. We also predicted that
app usage would be higher during the initial 10days of access,
and much lower during the subsequent 30 days of access. This
prediction is based on evidence that health and fitness app
usage typically drops off to below 50% user retention after
1 month (Farago 2012). Finally, in line with Bostock et al.
(2018); pilot data first available in Bostock and Steptoe
2013), we predicted that sustained improvement in mental
health outcomes for mindfulness app users (but not attention
control app users) following the 30 days of discretionary use
would only occur for those who continued to use the app.
Method
Participants
Participants were a convenience sample of 208 undergraduate
students between 18 and 49 years old (M= 20.08 years, SD =
2.8 years) from the University of Otago, Dunedin, New
Zealand. An additional two participants did not complete the
required study measures and thus were excluded from analysis
(see Fig. 1. CONSORT diagram for further details). At an
alpha level of .05, power of .80, and expected effect sizes of
between d=.36 (informed by the depressive symptoms effect
size we calculated for Howells et al. 2016—an app-based
mindfulness intervention) and d= .62 (informed by the .41
and .62 effect sizes for depression/anxiety and stress, respec-
tively, in Cavanagh et al. 2013—an online mindfulness-based
intervention), we registered a recruitment target of 80 partici-
pants per condition (i.e. between 42 [d= .62] and 125
[d= .356] participants per condition). We fell short of this
recruitment number by approximately 10 people per
condition.
Participants were recruited from April–August 2015
through the Department of Psychology’sonlinepsychology
research participation pool where research participation could
be applied to a small component of their undergraduate
Psychology course grade. Participants were largely of New
Zealand European descent (73.6%; 12.0% Asian; 5.8%
Māori or Pacific Islander; 8.6% other) which reflected demo-
graphics of the wider university community. Informed consent
was obtained from all individual participants included in the
study. It was recommended that participants access the
University’s primary healthcare provider should they have
any concerns about their feelings before, during, or after the
study. This study was approved by the University of Otago
Human Ethics Committee (D15/063).
Procedure
Design The study consisted of a pre-registered three-arm
randomised controlled trial (RCT) that tested the effect of
two mobile mindfulness apps (Headspace and Smiling
Mindfulness
Mind) on changes in mental health, relative to a control
app (Evernote). The trial and outcome measures were reg-
istered prior to recruitment with the Australia and New
Zealand Clinical Trials Registry (#368325).The pre-
registered mental health outcome measures were depressive
symptoms, anxiety, and stress as primary outcome mea-
sures, and mindfulness, resilience, flourishing, and college
adjustment as secondary outcome measures. The mental
health measures were completed at three time points: at
baseline before the intervention (time 0), following a 10-
day trial of their randomly assigned app (time 1), and fol-
lowing a further 30-day access to their assigned app to use
at their own discretion (time 2).
Materials Headspace is a smartphone application that offers
hundreds of hours of guided and unguided mindfulness med-
itations delivered by Andy Puddicombe (a novice monk in the
Theravada Tradition and fully ordained Tibetan Buddhist
monk in the Karma Kagyu Lineage; Puddicombe 2017). At
the time of this research, the app was structured so that partic-
ipants must complete the Foundation Level 1 training (previ-
ously known as ‘Take 1 0’; available free of charge) before
accessing other content in the app (available following pur-
chase). Foundation Level 1 is a 10-day introduction to mind-
fulness with formal meditation practices such as mindful
breathing (i.e. using the breath as an attentional object of in-
tense focus), body scan (systematically focusing on certain
Fig. 1 CONSORT diagram
showing the flow of subjects
through the trial
Mindfulness
parts of the body), sitting meditation, practice of non-
judgement of thoughts and emotions, and other guided med-
itations that vary the orientation (inward vs. outward vs. no
orientation), spatial focus (fixed vs. moving), and aperture of
attention (narrow vs. diffuse). Headspace is currently available
on iOS and Android platforms and is also accessible via the
Headspace website. Participants randomised to the Headspace
condition were instructed to download the app and complete
the Foundation Level 1 training across 10 days. Following the
introductory period, participants were invited to access the
other meditation tracks for the next 30 days using a pre-paid
Headspace voucher to access the paid content.
Smiling Mind is a smartphone application developed
by psychologists and educators that offers hundreds of
hours of guided and unguided mindfulness meditation
practices across several different mindfulness programmes
targeting different age groups (e.g. 16–18 years, adults)
and themes (e.g. mindfulness in the classroom, workplace,
or to complement sports training). The meditations vary in
durationfrom1to45min.SmilingMindisavailablefree
of charge. It is currently available on iOS and Android
platforms and through the Smiling Mind website.
Participants randomised to the Smiling Mind condition
were instructed to download the app and use the ‘For
adults’programme for 10 min each day for 10 days.
The ‘For adults’programme features practices such as
mindful breathing, body scan, mindful eating (i.e. focus-
ing intently on the thoughts, feelings, and experiences of
eating a piece of food), sitting meditation, practice of non-
judgement of thoughts and emotions, and other guided
meditations that vary the orientation (inward vs. outward
vs. no orientation), spatial focus (fixed vs. moving), and
aperture of attention (narrow vs. diffuse). If participants
ran out of content on that programme during the 10 days,
they were instructed to revisit the content they enjoyed or
to try out a different programme. Following the 10 days,
participants were invited to continue using Smiling Mind
for the next 30 days.
Evernote (the attention placebo control application) is an
organisation application with a variety of free and pay-for
features. Participants randomised to the control condition
were instructed to download the Evernote app and use the
note-taking function to Bjot down all the things you can re-
member doing on this day last week^for 10 min every day,
following a similar control procedure as used in Howells et al.
(2016) and Lyubomirsky et al. (2011). Unlike previous re-
search, we provided a more elaborate cover story by describ-
ing this activity as a means to practice ‘organisational remi-
niscing’(See Supplemental Materials: A for precise scripts).
In doing so, we aimed to account for placebo and digital pla-
cebo effects (Torous and Firth 2016). As with the other con-
ditions, following the initial 10-day period, participants were
invited to continue using Evernote for the next 30 days.
Testing Participants met with a researcher in groups of one to
five people in a psychology laboratory to complete study en-
rolment. There were up to three study enrolment sessions per
day. Using a random number generator, each enrolment ses-
sion was randomly assigned to an app condition meaning that
all participants in a shared session were assigned to the same
condition. Following online informed consent, participants
completed a survey of basic demographic information (age,
gender, and ethnicity), the time 0 mental health measures (de-
pressive symptoms, anxiety, stress, resilience, flourishing, col-
lege adjustment, and mindfulness), and perceptions of their
assigned practice (mindfulness meditation or control task) on
computers in private cubicles. Next, they received instructions
for their randomly assigned condition and were asked to
download the app prior to leaving the lab. Starting that day,
participants completed a 10-day trial of their randomly
assigned app (Headspace n= 72, Smiling Mind n= 63, or at-
tention control app n= 73). Participants were encouraged to
complete one 10-min session on their app each day during the
first 10 days.
Starting the first night of the 10-day trial, participants com-
pleted a short daily online survey (sent via SMS) of activity
adherence. Following the 10-day trial, participants completed
a follow-up survey that was sent to their email address (time
1). Excluding demographic questions, this survey was identi-
cal to the one they completed at time 0. Finally, all participants
were given an additional 30-day access to their assigned app
to use at their own discretion. For Headspace users, this in-
volved emailing them a ‘user code’voucher which they could
apply online to gain an addition 30 days of access to the
content; for Smiling Mind and attention control app users, this
involved emailing them to inform them that their access had
rolled over. All participants were told to use the apps at their
own discretion and that their app use would not be actively
tracked. Participants completed a final survey (time 2; identi-
cal to the time 1 survey) at the end of their 30-day access. All
participants were debriefed about the nature of the study via
email approximately 2 weeks after the time 2 survey.
Participants could receive a small amount of course credit
upon completion of a worksheet about the study, but no remu-
neration was tied to app adherence.
Measures
Depressive Symptoms Symptoms of depression were assessed
using the 20-item Center for Epidemiological Studies
Depression Scale (CES-D; Radloff 1977). Participants rated
how much they had experienced a range of depressive symp-
toms over the past week (e.g. I was bothered by things that do
not usually bother me) on a 4-point scale ranging from 0
[Rarely or none of the time (< 1 day)] to 3 [Most or all of
the time (5–7 days)]. Items were summed to create a score
ranging from 0 to 60, with higher scores indicating more
Mindfulness
symptoms of depression. Although we analysed the CES-D as
a continuous measure, a score of 16 or higher is frequently
used to identify individuals experiencing significant symp-
toms of depression (Lewinsohn et al. 1997). This scale has
high internal consistency (αs.89,.91,and.93attime0,time
1, and time 2 survey points in this study, respectively, similar
to α.85 in Radloff 1977) and acceptable concurrent and dis-
criminant validity (Radloff 1977).
Anxiety Anxiety was assessed using the Hospital Anxiety and
Depression Scale–Anxiety Subscale (HADS-A; Zigmond and
Snaith 1983). Participants rated how much they agreed with 7
statements (e.g. I feel tense or ‘wound up’) over the past week
using a Likert scale from 0 [e.g. Not at all] to 3 [e.g. Most of
the time]. Responses were summed over the 7 items to obtain
a score for each participant ranging from 0 to 21, with higher
scores indicating greater anxiety. This scale has high internal
consistency (αs .81, .81, .83 at time 0, time 1, time 2, respec-
tively) and good to very good concurrent validity (Bjelland
et al. 2002).
Stress Perceived stress was assessed using the 10-item
Perceived Stress Scale (PSS; Cohen and Williamson 1988).
Participants rated how often they experienced 10 statements
over the past month (e.g. In the last month, how often have
you felt confident about your ability to handle your personal
problems?) using a Likert scale from 0 [Never] to 4 [Very
often]. Responses were summed over the 10 items to obtain
a score for each participant ranging from 0 to 40, with higher
scores indicating greater perceived stress. This scale has high
internal consistency (αs .87, .89, .90 at time 0, time 1, time 2,
respectively) and acceptable convergent and divergent validi-
ty (Roberti et al. 2006).
Resilience Resilience was assessed using the 6-item Brief
Resilience Scale (Smith et al. 2008), a measure of the ability
to bounce back or recover from stress. Participants rated how
much they generally agreed with 6 statements (e.g. I tend to
bounce back quickly after hard times) using a Likert scale from
1 [Strongly disagree] to 5 [Strongly agree]. Responses were
averaged across the 6 items to obtain a score for each partici-
pant ranging from 1 to 5, with higher scores indicating greater
resilience. This scale has high internal consistency (αs .88, .87,
.89 at time 0, time 1, time 2, respectively, similar to αs.80to
.91 in Smith et al. 2008), good test-retest reliability and accept-
ably convergent and divergent validity (Smith et al. 2008).
Flourishing Flourishing was assessed using the 8-item
Flourishing Scale (Diener et al. 2010), a measure of perceived
achievement in areas such as relationships, self-esteem, pur-
pose, and optimism that is commonly used as a proxy for
psychological well-being. Participants rated how much they
agreed with 8 statements in general (e.g. I lead a purposeful
and meaningful life) using a Likert scale from 1 [Strongly
disagree] to 7 [Strongly agree]. Responses were summed over
the 8 items to obtain a score for each participant ranging from
8 to 56, with higher scores indicating greater well-being. This
scale has high internal consistency (αs .88, .94, .95 at time 0,
time 1, time 2, respectively, similar to α.87 in Diener et al.
2010, and .91 and .81 in Hone et al. 2014), good test-retest
reliability (Diener et al. 2010) and adequate convergent and
divergent validity (Hone et al. 2014).
College Adjustment College adjustment was assessed using
the 19-item College Adjustment Test (Pennebaker et al.
1990), which captures experiences related to adjusting to col-
lege life in terms of positive affect, negative affect, and home-
sickness. Participants rated to what extent they had experi-
enced 19 statements in the past week (e.g. worried about
how you will perform academically at college) on a scale of
1 [Not at all] to 7 [A great deal]. Items were reverse scored
where appropriate and summed to create a score from 19 to
133, with higher values indicating greater adjustment to col-
lege. This scale has high internal consistency (αs .83, .85,
.86 at time 0, time 1, time 2, respectively, similar to α.79 in
Pennebaker et al. 1990) and good test-retest reliability
(Pennebaker et al. 1990).
Mindfulness Mindfulness was assessed using the 12-item
Cognitive Affective Mindfulness Scale–Revised (Feldman
et al. 2007), a scale consisting of several facets of mindfulness
including attention, present focus, awareness, and acceptance.
Participants rated how much each statement applied to them in
general (e.g. it is easy for me to concentrate on what I am
doing) using a Likert scale from 1 [Rarely/Not at all] to 4
[Almost always]. Responses were summed over the 12 items
to obtain a score for each participant ranging from 12 to 48,
with higher scores indicating a greater level of the mindfulness
facets: attention, present focus, awareness, and acceptance.
This scale has acceptable internal consistency (αs .78, .84,
.86 at time 0, time 1, time 2, respectively, similar to αs.74
and .77 in Feldman et al. 2007) and acceptable convergent and
discriminant validity (Feldman et al. 2007).
Expectation and Perceptions of App Use Participants were
asked to rate their randomly assigned practice of either mind-
fulness meditation (Headspace, Smiling Mind) or
organisational reminiscing (Control; Evernote) through two
questions (‘How useful do you think mindfulness meditation
/ organisational reminiscing would be for yourself [Time 0]/
has been for yourself [Time 1, Time 2]?’and ‘How effective
do you think mindfulness / organisational reminiscing would
be for yourself [Time 0]/ has been for yourself [Time 1, Time
2]?’) on a scale from 1 [Not at all] to 4 [Very useful]. Scores on
these two items were highly correlated (rs > .76) and averaged
at each time point. Higher scores indicated a higher overall
Mindfulness
positive expectation (at time 0) or perception (at time 1 and
time 2) of utility and effectiveness their assigned activity.
App Adherence App adherence during the first 10 days was
assessed via an online daily diary sent directly as a hyperlink
to participants’mobile phones at 7 pm every day for 10 days.
Participants were asked if they had completed a session using
their app today (yes/no), and yesterday (yes/no) to capture app
use after the previous day’s survey and/or account for missing
surveys. These questions were embedded in a number of daily
measures outside of the scope of this article. Daily diaries
were lagged to account for missing diaries to create an overall
measure of activity adherence as a sum from 0 (app used on
zero days) to 10 (app used on all 10 days). App adherence for
the 30-day open access to the app was assessed using a single
question in the final survey asking BHow often did you use the
app in the last 30 days?^with answer options including 0
[Never], 1 [Once], 2 [2–4 times a month], 3 [2–3 times a
week], 4 [4 or more times a week], and 5 [Daily or almost
daily].
Data Analyses
Prior to our main analyses, we conducted between-group
comparisons of baseline characteristics (demographic and
time 0 mental health measures) to test for equivalency of con-
ditions at trial outset. We also computed changes in mental
health within each condition separately in order to show the
percentage change, effect sizes (within Cohen’sd; between
Hedges’gwith correction factor) and achieved power within
each condition (using Cohen’sdz) prior to conducting our
main analyses. This was done using paired ttests to measure
changes in mental health from time 0 to time 1 (baseline to the
end of the 10-day period) and, from time 0 to time 2 (from
baseline to the end of the 30-day discretionary period, approx-
imately 40 days after baseline) within each condition
separately.
For our main analyses, we used a multiple regression ap-
proach to compare the changes in the mental health outcomes
over time between the three conditions using dummy codes. A
regression approach was used instead of ANOVA because it
provided more flexibility, precision, and consistency in
analysing group differences and patterns of moderation by
app use. The first set of regressions compared changes in
mental health from time 0 to time 1 (baseline to the end of
the 10-day period) between the three conditions: The outcome
measure was mental health after the 10 days of app use (e.g.
depressive symptoms at time 1), which was predicted from
mental health at baseline (e.g. depressive symptoms at time
0), plus two condition dummy codes with the control condi-
tion as the reference group [Dummy 1 = Control (0),
Headspace (1), Smiling Mind (0); Dummy 2 = Control (0),
Headspace (0), Smiling Mind (1)].
We conducted these analyses a second time using
Headspace as the reference group in order to establish whether
there were any differences between the two mindfulness apps
[Dummy Code 2 = 0 (Control), 0 (Headspace), 1 (Smiling
Mind); Dummy Code 3 = 1 (Control), 0 (Headspace), 0
(Smiling Mind)]. The second set of regressions compared
changes in mental health from time 0 to time 2 (baseline to
the 30-day follow-up) between the three conditions following
a similar process as with testing from time 0 to time 1. For
analyses of the time 2 mental health measures, the sample size
was reduced from 208 to 192 because 16 participants did not
complete the time 2 survey (17.7% attrition; see Fig. 1.
CONSORT diagram for details).
The third set of regressions consisted of moderation analy-
ses to determine whether frequency of app use during the 10-
day and 30-day discretionary period moderated the effect of
condition on changes in mental health from time 0 to time 1,
and from time 0 to time 2, respectively. This was done by
adding self-reported app use (10-day or 30-day) (centred),
plus the cross-product interaction terms between app use
(centred) and the group dummy codes (e.g. app use centred
× Dummy Code 1 and app use centred × Dummy Code 2;
then, separately, app use centred × Dummy Code 2 and app
use centred × Dummy Code 3) to the regression models.
Lastly, in all models, we had originally controlled for partici-
pants’age, gender, ethnicity, and previous experience with
mindfulness/organisational reminiscing, but removed them
from the final models because they did not affect the results.
However, the final models controlled for app expectation
scores (average of expected usefulness and effectiveness at
time 0, mean centred, in time 0 to time 1 analyses) or app
perception scores (average of perceived usefulness and effec-
tiveness at time 1, mean centred, in time 0 to time 2 analyses).
Results
Descriptive Statistics and Baseline Characteristics
Chi-square tests and one-way ANOVAs showed no significant
differences between conditions in any of the baseline mea-
sures (all ps > .25). Where cut-offs exist, participants’average
scores fell into the commonly accepted normative ranges
(depressive symptoms, Radloff 1977; anxiety, Snaith 2003;
resilience, Smith et al. 2013). Supplemental Table 1presents
the demographic characteristics and baseline mental health
measures for the sample overall and separately for the three
conditions.
At baseline, a subset of participants from each condition (con-
trol: 31.5%, Headspace: 25.0%, Smiling Mind: 27.0%) reported
that they had previous experience with their assigned activity
(mindfulness meditation or the attention placebo control activity,
organisational reminiscing). There were no significant
Mindfulness
differences between conditions in reports of previous experience
with mindfulness or organisational reminiscing nor perceived
usefulness or effectiveness of the tool (all ps > .20), suggesting
that the control task was a theoretically feasible attention placebo.
However, there were differences at time 1 and time 2 for per-
ceived usefulness and effectiveness whereby participants in the
mindfulness app conditions reported that the tool was more use-
ful and effective than participants in the control condition (all ps
< .05) suggesting that in practice, the mindfulness tasks were
more convincing than the control task. (See Supplementary
Tab le 2for descriptive statistics).
Participants reported high app adherence between time
0 to time 1, using their app on 8.24 days for Headspace
users (SD = 2.02, range 2–10), on 8.00 days for Smiling
Mind users (SD = 2.03, range 3–10)andon8.74daysfor
attention control app users (SD =1.76,range2–10), which
did not differ between conditions (F(2,205) = 2.63,
p= .075). App use during the 30-day open access period
was much lower. Nearly half of all participants reported
‘never’using their app again during that 30-day open
access period (41.8% Headspace; 50.0% Smiling Mind;
53.7% Control app) (F(2,188) = .026, p= .975). In fact,
only 16.4% of Headspace, 15.4% of Smiling Mind, and
17.9% of the control app users reported using their app
two or more times per week during that open access pe-
riod; again, there were no differences between the condi-
tions (F(2,188) = .053, p= .948).
Changes in Mental Health within Conditions
Tab le 1presents the paired ttest results and descriptive statis-
tics for the mental health measures at all three time points for
the separate conditions. Headspace users reported significant
reductions in depressive symptoms, anxiety, and stress, and
significant improvements in college adjustment and mindful-
ness, but not flourishing or resilience, from baseline to the end
of the 10-day period. These changes were mostly maintained
until the final time point 40 days later, with the exception of
depressive symptoms. Resilience showed a different pattern,
exhibiting a significant increase only at the final time point,
but not immediately after the 10 days for Headspace users,
suggesting a ‘sleeper effect’. Smiling Mind users reported
significant reductions in depressive symptoms and anxiety,
but not stress, and significant improvements in resilience
and college adjustment, but not flourishing or mindfulness,
from baseline to the end of the 10-day period. These changes
were only maintained for anxiety and college adjustment at
the final time point 40 days later. By contrast, control app
users reported small but significant increases in depressive
symptoms and stress, and significant decreases in flourishing
from baseline to the end of the 10-day period, which were
mostly maintained 40 days later.
Changes in Mental Health Between Conditions
Table 2presents the multiple regression results comparing
changes in mental health outcomes over time between the
three conditions. Users of Headspace and Smiling Mind re-
ported significantly reduced depressive symptoms over time
compared to the control group at both time points. The size of
the coefficients suggested that both mindfulness apps reduced
depressive symptoms by approximately 3–3.5 points (equiv-
alent to approximately a .4 standard deviation change in de-
pressive symptoms), bringing mindfulness app users below
the cut-off for significant symptoms of depression (cut-off
16; Lewinsohn et al. 1997). There were no significant differ-
ences in changes in depressive symptoms between the two
mindfulness app’s users. Headspace and Smiling Mind users
showed improvements in college adjustment at time 1 (equiv-
alent to approximately a .3 standard deviation change in col-
lege adjustment), but these changes did not last through to
time 2. Headspace users had significant improvements in trait
mindfulness at time 1 compared to both Smiling Mind and
control app users (equivalent to approximately a .25 standard
deviation change in trait mindfulness), although the trait mind-
fulness changes did not last through to time 2. Finally,
Headspace users had significant improvements in resilience
that emerged at time 2 (equivalent to approximately a .3 stan-
dard deviation change in resilience), but not at time 1, com-
pared to the control group. By contrast, Smiling Mind users
reported significant improvements in resilience at time 1 rela-
tive to the control group (equivalent to approximately a .2
standard deviation change in resilience), but this change did
not last through to time 2.
Moderation by App Use
Frequency of app use during the 10 days did not show clear
patterns of moderation (see Supplementary Table 3and
Supplementary Figure 1). This is likely because usage was quite
high and consistent during the 10 days (overall M(SD) =
8.34(1.95) days). However, app use during the 30-day discretion-
ary period significantly moderated the effect of condition on
changes in depressive symptoms, anxiety, college adjustment,
and mindfulness from time 0 to time 2 (see Supplementary
Tab le 4). Figure 2shows this pattern of moderation by 30-day
app use for depressive symptoms (panel A), anxiety (panel B),
college adjustment (panel C), and mindfulness (panel D) with
significant simple slopes indicated (Aiken and West 1991).
Participants who used the mindfulness meditation apps more
frequently during the discretionary period showed statistically
greater improvements in college adjustment and mindfulness
[Smiling Mind only] compared to participants who did not use
the mindfulness apps as frequently or who used the control app.
Similar but weaker patterns were found for depressive symptoms
and anxiety. By contrast, participants who used the control app
Mindfulness
Table 1 Descriptive statistics and paired ttests comparing changes from time 0 (baseline) to time 1 (follow-up) or time 2 (final) for each condition separately
Time 0 Time 1 Time 2 Time 0 to time 1 Time 0 to time 2
Condition Outcome MSDMSDMSD% change Hedges’g
(between
a
)
Cohen’sd
(within
b
)
Power
(within
c
)
t% change Hedges’g
(between
d
)
Cohen’sd
(within
b
)
Power
(within
c
)
t
Headspace (time
0+1n=72;
time 2 n=67)
Depressive symptoms 15.56 10.00 13.00 9.38 13.63 10.22 −16.5 .23 .26 .87 3.17** −10.9 .17 .17 .46 1.91
Anxiety 6.754.275.744.145.874.13−15.0 .08 .24 .79 2.78** −13.9 .15 .23 .59 2.20*
Stress 16.89 6.14 15.29 6.26 15.04 6.24 −9.5 .19 .26 .96 3.71*** −10.8 .17 .30 .90 3.28**
Resilience 3.20 .72 3.28 .73 3.46 .72 2.5 −.08 −.11 .33 −1.55 7.1 −.27 −.32 .92 −3.44**
Flourishing 45.60 6.87 44.58 8.90 44.57 8.12 −2.2 .08 .13 .22 1.23 −2.6 .01 .17 .28 1.39
College adjustment 80.27 16.77 85.00 15.72 84.80 16.54 5.9 −.30 −.29 .97 3.98*** 6.4 −.17 −.31 .91 −3.35**
Mindfulness 31.17 4.97 33.03 5.58 32.54 5.45 6.0 −.34 −.35 .99 4.62*** 4.4 −.28 −.26 .73 −2.63*
Smiling Mind
(time 0 + 1
n= 63; time 2
n=58)
Depressive symptoms 15.52 9.21 13.51 9.44 14.14 9.83 −13.0 .17 .22 .59 2.25* −8.4 .12 .13 .22 1.25
Anxiety 7.084.265.984.126.033.99−15.5 .02 .26 .84 3.00** −16.7 .12 .29 .64 2.37*
Stress 18.02 6.47 17.02 6.61 16.62 6.62 −5.5 −.08 .15 .38 1.68 −6.7 −.05 .18 .41 1.72
Resilience 3.09 .80 3.29 .73 3.23 .83 6.5 −.09 −.26 .97 −3.89*** 4.2 .02 −.16 .44 −1.86
Flourishing 45.10 6.91 44.40 8.12 45.09 7.97 −1.5 .12 .09 .26 1.36 −.1 −.07 .01 .05 .08
College adjustment 78.40 15.02 82.32 16.57 81.91 16.12 5.0 −.12 −.25 .92 −3.45** 4.7 .00 −.24 .55 −2.12*
Mindfulness 30.52 5.53 31.14 6.25 31.48 6.53 2.0 .01 −.11 .26 −1.36 2.9 .09 −.15 .30 −1.44
Control (time
0+1n=73;
time 2 n=67)
Depressive symptoms 13.36 6.99 15.05 8.47 15.43 11.40 12.7 −.22 .72 −2.59* 19.8 −.28 .62 −2.26*
Anxiety 6.363.676.053.486.564.81−4.9 .09 .20 1.10 6.5 −.09 .14 −.88
Stress 17.79 6.06 16.52 6.40 16.27 8.14 −7.1 .20 .74 2.68** −6.3 .15 .31 1.48
Resilience 3.22 .80 3.22 .74 3.25 .80 .0 .00 .05 −.07 −.3 .01 .05 .16
Flourishing 46.77 4.20 45.21 5.68 44.51 8.79 −3.3 .31 .86 3.10** −5.2 .36 .73 2.72**
College adjustment 81.02 14.07 80.36 15.05 81.98 16.86 −.8 .05 .08 .50 .2 −.01 .05 −.09
Mindfulness 30.70 5.13 31.19 5.29 30.90 6.00 1.6 −.09 .22 −1.20 .4 −.02 .05 −.19
M,mean;SD, standard deviation
*p< .05, **p< .01, ***p< .001
a
Effect size comparing mindfulness app to control app at time 1 using Hedges’g= [mean intervention −mean control / pooled standard deviation] * Hedges’correction factor [1 –3/(4N−9)]
b
Effect size within condition over time
c
A priori power achieved for ttest comparisons using Cohen’sdz
d
Effect size comparing mindfulness app to control app at time 2 using Hedges’gwith correction factor
Mindfulness
most frequently reported poorer mental health than those who
used the control app less frequently.
Discussion
In line with our hypotheses, mindfulness meditation app users
reported greater improvements in several mental health out-
comes than did attention placebo control app users. The im-
provements varied by the mindfulness app, timeframe, fre-
quency of use, and reference point (i.e. comparing to self-
baseline vs. comparing to control app users) but the results
provide preliminary evidence of the mental health utility of
mobile mindfulness meditation apps, which warrants further
investigation. The most consistent improvements were ob-
served for depressive symptoms and college adjustment,
where both Headspace and Smiling Mind users reported small
but significant improvements after 10 days of requested app
use relativeto the control condition. These improvements may
represent a clinically meaningful change given that a 3–3.5
point change in depressive symptoms brings mindfulness app
users under the cut-off for significant symptoms of depression,
unlike the control app users. This change in depressive symp-
toms relative to control was further maintained after 30 days
of discretionary use. Thus, our patterns for depressive
symptoms replicated the work of Bostock et al. (2018)and
Howells et al. (2016) who also found reductions in depressive
symptoms after Headspace mindfulness meditation app use.
Changes in mental health outcomes were also largely de-
pendent upon frequency of app use. People who used their
mindfulness apps more frequently during the 30 days of dis-
cretionary use showed the greatest benefits in terms of their
college adjustment and mindfulness, and to a lesser extent,
depressive symptoms and anxiety. Importantly, this benefit
for frequent app users only occurred for those randomly
assigned to use a mindfulness app, suggesting that the mental
health benefits of mobile mindfulness apps were not explained
by digital placebo effects. By contrast, users of the control app
Evernote reported mostly poorer mental health outcomes with
more frequent use. We are not sure what explains this finding.
Although it is possible that excessive ‘organisational remi-
niscing’across an extended period of time could have trig-
gered rumination or an awareness of unmet goals, which could
be distressing, it could also simply be that the more people use
an ineffective tool, the worse they feel—because the tool is
not designed to improve mental health. Either way, the pat-
terns suggest that the Evernote app might not have been a
completely neutral control condition. Although this does not
take away from our findings, it suggests that future research
should carefully consider the nature of the control condition.
Table 2 Multiple regression analysis of changes in mental health
outcomes by experimental condition from time 0 to time 1 (left
columns), and from time 0 to time 2 (right columns). The time 0
outcome and expectation scores were controlled in time 0 to time 1
analyses. The time 0 outcome and time 1 perception scores were
controlled in time 0 to time 2 analyses
Time0totime1 Time0totime2
Control Control vs.
Headspace
1
Control vs.
Smiling Mind
2
Headspace vs
Smiling Mind
3
Control Control vs.
Headspace
1
Control vs.
Smiling Mind
2
Headspace vs
Smiling Mind
3
Outcome Constant ß
4
ß
5
ßConstant ßß ß
(SE) (SE) (SE) (SE) (SE) (SE) (SE) (SE)
Depressive symptoms 16.18 −3.74*** −3.35** .39 16.44 −3.21* −3.00* .21
(.72) (1.02) (1.06) (1.06) (1.02) (1.47) (1.48) (1.45)
Anxiety 6.32 −.61 .62 .02 6.88 −1.05 −1.18 −.14
(.30) (.43) (.44) (.44) (.44) (.63) (.64) (.62)
Stress 16.33 −.48 .28 .77 15.98 −.36 .31 .67
(.47) (.66) (.69) (.69) (.66) (.95) (.95) (.94)
Resilience 3.19 .07 .16* .10 3.22 .20* .07 −.13
(.05) (.07) (.08) (.08) (.07) (.10) (.10) (.10)
Flourishing 44.38 .41 .86 .46 43.94 .61 1.82 1.21
(.61) (.89) (.90) (.90) (.84) (1.21) (1.23) (1.20)
College adjustment 79.51 5.40** 4.09* −1.32 81.14 3.48 1.98 −1.50
(1.17) (1.66) (1.73) (1.74) (1.55) (2.24) (2.26) (2.23)
Mindfulness 31.28 1.42* .15 −1.27* 31.14 1.00 .51 −.49
(.60) (.57) (.59) (.60) (.55) (.79) (.79) (.78)
*p<.05, **p<.01, ***p< .001.
1
Dummy coded 0 (control), 1 (Headspace), 0 (Smiling Mind), and entered with
2
dummy coded 0 (control), (0)
Headspace, (1) Smiling Mind.
3
Dummy coded 0 (control), 0 (Headspace), 1 (Smiling Mind) and entered with dummy coded 1 (control), 0 (Headspace),
0(SmilingMind)
Mindfulness
When looking at our secondary pre-registered outcomes,
we found that mindfulness practice was beneficial for increas-
ing adjustment to college life after 10 days of requested app
usewithadditionaltimeinconsistentimprovementsinresil-
ience by app. Smiling Mind users reported immediate but not
sustained improvements in resilience, whereas, Headspace
users reported a lagged improvement in resilience after the
30-day discretionary use period. Previous research has linked
mindfulness with increases in adaptive stress responses and
coping resources (Weinstein et al. 2009). Given that the tran-
sition to college life can be tumultuous (Fisher and Hood
1987) and young adults are heavily reliant on their mobile
phones (Oliver et al., 2005; Smith, 2017), mobile mindfulness
may present apromising tool toimprove adjustment to college
life, build resilience, and enhance the ability to cope with
stressors in incoming college students. Thus, these findings
may have valuable applications for colleges that are
implementing mindfulness programmes (Swain 2016).
Mobile mindfulness users did not report significant chang-
es in flourishing. Improvements in trait well-being can be
extremely difficult to achieve (Weiss et al. 2016). Therefore,
the low-intensity of this intervention (i.e. 10 min meditations,
once a day) may not have been sufficient to produce changes
in flourishing. In addition, although we attempted to investi-
gate the notion of a digital placebo effect (Torous and Firth
2016) by implementing an attention placebo control condi-
tion, control participants did not report that their app was as
useful or effective as the mindfulness apps, suggesting that
this was not an adequate comparison condition to tease apart
the relationship between app use in general and the therapeutic
benefits of mindfulness, specifically. Nonetheless, when con-
trolling for participants’expectations (time 0 to time 1) and
their subsequent perceptions following use of their apps (time
0 to time 2), we still found some improvements in mental
health (namely, depressive symptoms at both time points
and college adjustment at time 1 only).
Changes in mindfulness were much less consistent than the
other mental health measures. Interestingly, those who used
Headspace showed sustained small increases in mindfulness
over the course of the intervention (at least as we measured it,
using the Cognitive and Affective Mindfulness Scale–Revised;
Feldman et al. 2007), whereas Smiling Mind users did not
(although more frequent Smiling Mind users were equivalent
in mindfulness to more frequent Headspace users at the end of
the 30-day discretionary period). Given that both apps provide
similar introductory mindfulness training content (e.g. body
scan, breathing exercises), we believe that differences in the
app interface may be responsible for any observed differences
here. Previous research has established that interactive, beauti-
ful, and well-designed apps are more appealing and encourage
more loyalty from their users (Cyr et al. 2006). In ongoing
research, we are qualitatively addressing questions about dif-
ferences in user experience with Headspace and Smiling Mind
app users where initial themes suggest interface differences
0
5
10
15
20
Low Med High
Depressive Symptoms
Frequency of App Use
a
Control
Headspace
Smiling Mind
†
*
0
2
4
6
8
10
Low Med High
Anxiety
Frequency of App Use
b
Control
Headspace
Smiling Mind
***
*
†
72
74
76
78
80
82
84
86
88
90
Low Med High
College Adjustment
Frequency of App Use
c
Control
Headspace
Smiling Mind
*
27
28
29
30
31
32
33
34
Low Med High
Mindfulness
Frequency of App Use
d
Control
Headspace
Smiling Mind
†
Fig. 2 The relationship between frequency of app use during the 30-day
self-directed use of app and mental health scores (adepressive symptoms,
banxiety, ccollege adjustment, dmindfulness) for control, Headspace,
and Smiling Mindapp users at time 2 (after 30 days) controlling for time 0
mental health scores. Frequency of app use was modelled around −1SD,
M,+1SD, for low, med, and high app frequency. Depressive symptom
scores can range between 0 and 60 with higher scores indicating higher
symptoms of depression; anxiety scores can range between 0 and 21 with
higher scores indicating greater symptoms of anxiety; college adjustment
scores can range between 19 and 133 with higher scores indicating greater
college adjustment; mindfulness scores can range between 12 and 48 with
higher scores indicating greater mindfulness.
†
p<.10, *p< .05,
**p<.01, ***p< .001
Mindfulness
influenced participant willingness and ability to use the apps.
Further, Smiling Mind has subsequently been redesigned with
substantial additional content added; therefore, additional re-
search is required to establish whether Smiling Mind in its
current design is equivalent to Headspace.
Limitations and Future Research
Although our study had high ecological validity, this came at
the cost of strict control over app adherence. While the drop-off
in self-reported app use during the 30-day discretionary period
was particularly high across conditions, this drop-off is typical
of naturalistic use of these sorts of tools (Aitken and Lyle 2015;
Farago 2012) and was in line with our hypotheses. Given the
high drop-off, the fact that some changes in mental health out-
comes held is reassuring in light of the short follow-up times in
previous research (Ahtinen et al. 2013; Carissoli et al. 2015;
Howells et al. 2016; Lim et al. 2015). Even so, continued prac-
tice of mindfulness may be necessary to fully reap the benefits
(Bergomi et al. 2015). In this respect, face-to-face delivery of
mindfulness instruction likely provides a superior social envi-
ronment for new mindfulness practitioners (Segal et al. 2013)
but it should not be forgotten that apps provide wide reach,
immediate access, superior scalability, and generally are avail-
able at lower cost than many alternatives (Price et al. 2014).
Further, when a health app is prescribed by a health provider
(e.g. doctor, counsellor), 30-day retention rates typically in-
crease by 10–30% (Aitken and Lyle 2015). Our sample was a
convenience sample of healthy undergraduate students, mean-
ing we cannot extrapolate the current findings to a clinical
sample. Nevertheless, mobile mindfulness apps could have po-
tential as an adjunct-to-treatment or may serve as a suitable
homework component in therapy to facilitate the treatment of
patients (Kladnitski et al. 2018; Price et al. 2014) with anxiety
and depressive symptoms, although this remains to be tested.
Further, while our sample size was higher than most previ-
ous mobile mindfulness research and we were adequately
powered to detect within group change over time, we were
still underpowered to detect between-group differences.
Future researchers should consider using more conservative
effect size estimates when conducting their power analyses.
Our strongest effect sizes in depressive symptoms (g=.23)
were in general, smaller than those found in previous research
on mobile, web-based, and face-to-face mindfulness medita-
tion programmes (Howells et al. 2016: single study of mobile
mindfulness, g= .35; Spijkerman et al. 2016: meta-analysis of
web-based mindfulness, g= .29; Goyal et al. 2014:meta-
analysis of face-to-face mindfulness, d= .30). Nevertheless,
given the brevity and ease of implementation, the reported
mental health improvements may still represent meaningful
change for those experiencing them (a topic we are exploring
in ongoing qualitative research) and clinically meaningful
change where app users move further from established cut-
off points (e.g. those used to suggest experience of clinically
significant experience of depression, Lewinsohn et al. 1997).
Our reliance on self-reported outcomes may also have led
participants to over- or under-estimate their app use during
the discretionary period and their responses may be subject
to a number of response biases (e.g. social-desirability bias).
Future researchers should design their protocol to collect ob-
jective measures of app usage. Attempts to measure trait
mindfulness via self-report are being challenged by re-
searchers (e.g. Van Dam et al. 2017); as such, future re-
searchers should consider collecting more objective behav-
ioural measures to support their self-reported measures (e.g.
breath counting; Levinson et al. 2014).
Finally, to establish compelling evidence for the effective-
ness of mobile mindfulness meditation, in the future, re-
searchers should investigate mindfulness-based apps as stand-
alone vs. adjunct-to-treatment as usual and should compare
mindfulness-based apps to not only established mindfulness
meditation programmes (e.g. MBSR, Kabat-Zinn 1982)but
also to active app-based controls. For instance, given our mod-
est findings of small improvements in depressive symptoms,
there would be merit in comparing mindfulness meditation
apps to an evidence-supported cognitive-behavioural therapy
(CBT) app. Doing so would allow us to establish whether there
is non-inferiority or superiority to an established treatment mo-
dality such as CBT, when delivered by mobile phone.
Acknowledgements This research was funded by the Office of the Vice-
Chancellor, University of Otago. The authors thank research assistants
Tayla Boock, Todd Johnston, and Samantha McDiarmid for their assis-
tance in collecting data.
Authors’Contributions JF: co-conceived the study idea and research de-
sign, co-conducted the statistical analyse, co-wrote the manuscript, and
managed the data collection and research team. HH co-conceived the
study idea and research design and co-wrote the manuscript. BR: contrib-
uted to the data collection, and co-wrote the manuscript. LT: contributed
to the data collection. TC: co-conceived the study idea and research de-
sign, co-conducted the statistical analyses, and co-wrote the manuscript.
Compliance with Ethical Standards
Ethical Approval All procedures performed in studies involving human
participants were in accordance with the ethical standards of the institu-
tional and/or national research committee and with the 1964 Helsinki
declaration and its later amendments or comparable ethical standards.
This article does not contain any studies with animals performed by any
of the authors.
Conflict of Interest The authors declare that they have no conflict of
interest.
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