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The efficacy of smartphone‐based mental health interventions for depressive symptoms: a meta‐analysis of randomized controlled trials

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The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g=0.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g=0.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g=0.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p=0.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.
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The efficacy of smartphone-based mental health interventions for
depressive symptoms: a meta-analysis of randomized controlled trials
Joseph Firth
1,2
, John Torous
3,4
, Jennifer Nicholas
5,6
, Rebekah Carney
1
, Abhishek Pratap
7,8
, Simon Rosenbaum
5,6
, Jerome Sarris
1,9
1
NICM, School of Science and Health, Western Sydney University, Campbelltown, Australia;
2
Faculty of Biology, Medicine and Health, Division of Psychology and Mental
Health, University of Manchester, Manchester, UK;
3
Department of Psychiatry and Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, MA, USA;
4
Harvard Medical School, Boston, MA, USA;
5
Black Dog Institute, University of New South Wales, Sydney, Australia;
6
Faculty of Medicine, School of Psychiatry, University of
New South Wales, Sydney, Australia;
7
Sage Bionetworks, Seattle, WA, USA;
8
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle,
WA, USA;
9
Department of Psychiatry, University of Melbourne, Professorial Unit, The Melbourne Clinic, Melbourne, Australia
The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a popu-
lation scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence support-
ing the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of
smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22
smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps
than control conditions (g50.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate
positive effect in comparison to inactive controls (g50.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions
(g50.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/
computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training
apps had a significantly smaller effect size on depression outcomes (p50.004) than those of apps focusing on mental health. The use of mood
monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not
affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression.
Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.
Key words: Smartphone technology, mental health interventions, depression, e-health, mhealth, apps, cognitive training, mood monitoring,
cognitive behavioral therapy, mindfulness training
(World Psychiatry 2017;16:287–298)
Depression is now recognized as a leading cause of global
disability, impacting over 300 million people around the
world
1
. In countries like the US, 9% of the population may
have depression at any one time
2
. Beyond the personal suffer-
ing, depression is associated with unemployment, poor physi-
cal health, impaired social functioning and, in its most severe
forms, suicide
3
. Thus, the disorder carries a high cost for both
the individual and the society, particularly when considering
the economic burden incurred through clinical care and lost
productivity
4
.
Depression is a potentially treatable condition, with a range
of available medications and psychological interventions that
are supported by robust clinical evidence. While the choice of
pharmacotherapy or psychotherapy depends on many factors,
for most individuals with mild or moderate depression they
may be nearly equivalent
5
.
However, there are many barriers towards both of these
treatment methods. For instance, access to mental health care
remains limited, as almost half of the world’s population lives
in countries where there is less than one psychiatrist per
100,000 people
6
, and continued shortage in mental health care
staff is expected for both the near and long term future
7,8
.
Additionally, medications and psychotherapies may carry
some level of stigma (particularly among younger people),
which further limits their effectiveness
9,10
.
Furthermore, although these therapies demonstrate high
clinical efficacy for reducing symptoms, they may not always
bring about full and sustained remission in those treated. Finally,
many people experience either subclinical depression or resid-
ual depressive symptoms even after achieving clinical response
to treatment. Therefore, novel primary and/or adjunctive meth-
ods for reducing depression on a population scale are urgently
needed.
Digital technologies may represent a novel and viable solu-
tion. Mobile phones are among the most rapidly adopted in-
novations in recent history, and smartphone ownership con-
tinues to increase in both developed and developing coun-
tries
11
. Through providing ubiquitous Internet connectivity,
along with the capacity to download and run externally cre-
ated applications (“apps”), smartphone technology presents
an opportunity to transform mobile phones into devices which
could provide global, cost-effective and evidence-based men-
tal health services on demand and in real time
12
.
This clear therapeutic potential has triggered a wave of in-
terest and investment in mental health apps from govern-
ments, technology companies, advocacy groups, and research
groups internationally
13,14
. But in the enthusiasm to realize the
potential of apps for depression, it has become difficult to sep-
arate actual efficacy from overzealous aspirational claims
15
.
With thousands of mental health apps readily available through
Apple or Google marketplaces, finding a useful tool supported
by robust evidence to manage ones depression is clearly a chal-
lenge for a lay person
16,17
. The increasing media promotion and
accessibility of apps for mental health now presents a “duty of
World Psychiatry 16:3 - October 2017 287
RESEARCH REPORT
care” issue towards ensuring that people have information and
understanding of evidence-based digital treatments for depres-
sion.
Recent meta-analyses have documented that various smart-
phone interventions can have positive effects on physical dis-
eases, such as diabetes
18
, and mental health conditions, such
as anxiety
19
. However, the clinical effect of smartphone inter-
ventions on symptoms of depression has yet to be established.
Thus, our aim was to examine the efficacy of delivering mental
health interventions via smartphones for reducing depressive
symptoms in both clinical and non-clinical populations. We
also sought to use subgroup and meta-regression analyses in
order to explore which aspects of smartphone interventions
are associated with greater or lesser efficacy for depressive
symptoms. The results of these meta-analyses provide the first
overall estimate of effects from such interventions, along with
informing treatment choices and future research in this area.
METHODS
This systematic review and meta-analysis followed the
PRISMA statement for transparent and comprehensive report-
ing of methodology and results
20
. In order to eliminate re-
searcher bias, the search strategy, inclusion criteria and data
extraction, as well as the overall and pre-planned subgroup
analyses, strictly adhered to those adopted in a previous sys-
tematic review of smartphone interventions for anxiety
19
, as
specified in a registered online protocol (CRD42017064882).
Search strategy
We conducted an electronic search of the following data-
bases: Cochrane Central Register of Controlled Trials, Health
Technology Assessment Database, Allied and Complementary
Medicine (AMED), Health Management Information Consor-
tium (HMIC), Ovid MEDLINE, Embase, and PsycINFO, from
inception to May 1, 2017. The search applied the PICO frame-
work
21
, using a range of relevant terms to capture all poten-
tially eligible results relating to smartphone mental health
interventions for depressive symptoms. An additional search
of Google Scholar was implemented, and reference lists of
retrieved articles were checked to identify any further eligible
studies.
Eligibility criteria
Only English-language articles were included. Eligible stud-
ies were all randomized controlled trials (RCTs) examining the
effects of mental health interventions delivered via smartphone
devices with at least one outcome measure for depressive
symptoms. We aimed to examine the effects of smartphone
interventions on primary depression, comorbid depression and
subclinical depressive symptoms. No restrictions were placed
on diagnosis or any other clinical or demographic characteris-
tics of eligible samples.
Three independent investigators judged article eligibility (JF,
JN and JT), with any disagreements resolved through discus-
sion. “Smartphones” were defined as mobile phones with 3G or
4G Internet connectivity, along with the ability to download,
install and run external applications (“apps”). RCTs of interven-
tions delivered solely or in part via smartphone devices match-
ing this definition, aimed at improving mental health or well-
being (with depression as a primary or secondary outcome),
were included in the review.
Studies using either “inactive” or “active” control groups
were eligible for inclusion. “Inactive” control groups were clas-
sified as those in which participants received no intervention
during the trial period (or were put into a waitlist until pre-and-
post measures had been collected from both groups). “Active”
control groups were categorized as those which attempted to
control for the time and attention given to people in the smart-
phone intervention condition, by using apps not aimed at treat-
ing depression, in-person interventions, or other forms of ac-
tivities or patient contact. RCTs comparing smartphone inter-
ventions to antidepressant medications were also eligible for
inclusion. All eligible studies had a duration of at least one week
(thus excluding studies measuring changes in mood following a
single use of smartphone apps).
Data extraction
A systematic extraction form was used for each article to
collect the following data: a) study information (sample size,
mean age of participants, diagnostic information or relevant
inclusion criteria, study length and trial quality); b) interven-
tion features (app/program name, regularity of instructed use,
smartphone program summary, any additional intervention
components, details of the control condition); c) effects on
depressive symptoms (changes in total depressive symptoms
scored before and after smartphone and control interventions
using any clinically validated rating scale). For studies which
used more than one measure of depression, a mean total
change was calculated by pooling outcomes from each mea-
sure.
Statistical analyses
All analyses were conducted by Comprehensive Meta-
Analysis 2.0
22
, using a random-effects model
23
to account for
between-study heterogeneity. The total difference in changes
in depressive symptoms between smartphone interventions
and control conditions were pooled to compute the overall
effect size of the former (as Hedges’ g), with 95% confidence
intervals (CI). For RCTs comparing smartphone interventions
to both inactive and active control conditions, the compara-
tive effects with active control groups were used in the primary
288 World Psychiatry 16:3 - October 2017
analysis. After computing main effects, a sensitivity analysis
was applied to investigate effects of smartphone interventions
in RCTs which used intention-to-treat analyses or had com-
plete outcome data.
To quantify the degree to which statistical heterogeneity in
the meta-analyses arose due to between-study differences,
rather than due to chance, Cochrans Q (with p value) and I
2
were used. Included studies were also assessed using the
Cochrane Collaboration’s Risk of Bias tool. This examined
study quality in six areas of trial design (sequence generation,
allocation sequence concealment, blinding of participants and
personnel, blinding of outcome assessment, incomplete out-
come data, selective outcome reporting), ranking each area as
high, low or unknown for risk of bias
24
.
Risk of publication bias was examined using a funnel plot of
study effect sizes, and Egger’s regression test was applied to all
aforementioned analyses. Furthermore, a Duval and Tweedie’s
trim-and-fill analysis was conducted to re-calculate the pooled
effect size after removing any studies which may introduce
publication bias (i.e., small studies with large effect sizes from
the positive side of the funnel plot). Additionally, a “fail-safe
N” was used to account for the file draw problem
25
, estimating
the number of non-significant unpublished trials which would
be needed to cause the observed p value to exceed 0.05.
Pre-planned subgroup analyses were conducted to examine
whether effects of smartphone interventions differed when
comparing them to inactive or active control conditions. Addi-
tionally, we carried out a range of exploratory post-hoc sub-
group and meta-regression analyses in order to examine which
factors may impact the effectiveness of smartphone interven-
tions, particularly with regards to sample details (i.e., clinical
population, age, gender) and treatment characteristics (i.e.,
psychological basis, technological features and length of
smartphone interventions).
RESULTS
The search returned a total of 1,517 records; 981 after dupli-
cates were excluded. Title and abstract screening removed a
further 913 articles. Full versions were retrieved for 68 papers,
of which 16 met eligibility criteria. Two further articles were
retrieved following an additional search of Google Scholar.
Thus, 18 unique RCTs were included in the meta-analysis,
assessing the effects of 22 different smartphone-delivered
mental health interventions. The article inclusion/exclusion
process is shown in Figure 1.
Characteristics of included studies
Full details of each study are displayed in Table 1. Outcome
data were available from 18 RCTs. Two papers reported outcome
data in a format not suited for meta-analysis, but the corre-
sponding authors provided the raw data to enable inclusion
26,30
.
Mean sample ages ranged from 18 to 59 years (median 39 years).
All but two studies
32,34
used some indication of mental health
issues as inclusion criteria. For clinical populations, two studies
Figure 1 PRISMA flow chart of study selection
World Psychiatry 16:3 - October 2017 289
Table 1 Details of included studies
Study Sample type
N (each
condition)
Age
(years,
mean) Design Other intervention aspects
Outcome
measure
Arean et al
26
Self-reported
mild-to-moderate
depression
211,209,206 33.9 12 weeks of Project EVO
(cognitive training app) vs.
iPST (problem-solving ther-
apy app) vs. Health Tips
control app
None
PHQ-9
Birney et al
27
Self-reported
mild-to-moderate
depression
150,150 40.7 6 weeks of MoodHacker
(CBT-based depression
app) vs. links to approved
depression websites
Daily e-mails to provide addi-
tional digital content and
prompt engagement
PHQ-9
Depp et al
28
DSM-IV bipolar
disorder
41,41 47.5 10 weeks of PRISM (mood
monitoring and self-
management app) vs. paper
and pencil equivalent
Both groups received four ses-
sions of individual therapy MADRS
Enock et al
29
Self-reported high
social anxiety
158,141 34.8 4 weeks of CBM Active (cog-
nitive bias modification
training app) vs. inactive
training or waitlist control
None DASS
Faurholt-Jepsen
et al
30
ICD-10 bipolar
disorder
33,34 29.3 6 months of MONARCA
(self-monitoring app) vs.
regular smartphone use
Patients could also contact
their clinicians directly
using the smartphone, in
case of deterioration
HAM-D
Horsch et al
31
Self-reported
mild insomnia
74,77 39.7 6 to 7 weeks of Sleepcare
(CBT-based insomnia app)
vs. waitlist control
None CES-D
Howells et al
32
General population 57,64 40.3 10 days of Headspace (mind-
fulness app) vs. list-making
app control
None
CES-D
Ivanova et al
33
Self-reported
social anxiety
50,51,51 35.3 10 weeks of guided ACTsmart
(acceptance and commit-
ment therapy app) vs.
unguided ACTsmart vs.
waitlist control
Participants also provided
with pen-and-paper book-
let for completing written
assignments and a CD with
ACT exercises
PHQ-9
Kahn et al
34
US veterans 44, 41,42, 46 NA 16 weeks of Mission Recon-
nect program (using mind-
fulness and awareness
techniques) vs. Prevention
and Relationship Enhance-
ment program vs. both pro-
grams together vs. waitlist
control
Strategies for applying learnt
techniques in challenging
situations, and additional
audio exercises
BDI-II
Kuhn et al
35
Self-reported
traumatic
event 1PTSD
symptoms
62,58 39 3 months of PTSD Coach
(app providing psychoedu-
cation, symptom tracking
and self-management strat-
egies) vs. waitlist control
None PHQ-8
Ly et al
36
DSM-IV major
depression
46,47 30.6 10 weeks of Behavioral Acti-
vation app plus 4 face-to-
face behavioral activation
sessions vs. 10 face-to-face
behavioral activation
sessions
None
BDI-II
Moell et al
37
Self-reported
data to
diagnose
ADHD
26,27 36.8 6 weeks of LivingSMART
(app facilitating life organi-
zation and improving
attentional control) vs.
waitlist control
Computer-aided training on
how to use the apps; partic-
ipants were also allocated a
coach to help with app
usage
HADS
290 World Psychiatry 16:3 - October 2017
recruited people with major depression
36,43
, two individuals
with bipolar disorder
28,30
, one young people in primary care
with any mental health condition
40
. Others recruited individuals
from the general population with self-reported mild-to-moder-
ate depression
26,27,39,41
, suicidal thoughts/tendencies
42
, proba-
ble attention-deficit/hyperactivity disorder (ADHD)
37
, anxiety
disorders
29,33
, insomnia
31
, or symptoms of post-traumatic stress
disorder (PTSD)
35
. One further study examined older adults
with memory complaints
38
.
Smartphone interventions lasted between 4 and 24 weeks.
Depressive symptoms were measured as a primary outcome in
12 studies, and as a secondary outcome in six. The following
tools were used: the Depression Anxiety Stress Scale
44
depression
subscale in three studies
29,39,40
; the Center for Epidemiological
Studies Depression scale
45
in four
31,32,38,41
;theBeckDepression
Inventory II
46
in three
34,36,43
; the Patient Health Questionnaire
47
in six
26,27,33,35,42,43
; the Hamilton Rating Scale for Depression
48
in
one
30
; the Hospital Anxiety Depression Scale
49
in one
37
; and the
Montgomery-A
˚sberg Depression Rating Scale
50
in one
28
.
The results from the Cochrane Risk of Bias assessments are
displayed in Table 2. This shows that the most frequent risk fac-
tor for bias was inadequate blinding of participants, with only
five of 18 studies using intervention-matched comparators for
which the participants would not be aware of their treatment/
control status or of the hypothesized outcomes of the trial.
Overall effects of smartphone interventions on
depressive symptoms
Figure 2 displays the pooled effect size from smartphone
interventions on depressive symptoms, along with individual
Table 1 Details of included studies (continued)
Study Sample type
N (each
condition)
Age
(years,
mean) Design Other intervention aspects
Outcome
measure
Oh et al
38
Older adults with
self-reported
memory
complaints
18,19,16 59.3 8 weeks of SMART vs. Fit
Brains (two cognitive train-
ing apps) vs. waitlist
control
None CES-D
Proudfoot et al
39
Self-reported
mild-to-moderate
depression
126,195,
198
39 7 weeks of MyCompass (app
enabling self-monitoring of
problematic moods,
thoughts and behaviors,
tracking their severity, and
receiving feedback advice
and mental health manage-
ment tips by SMS) vs.
attention-matched and
waitlist control
Computer modules provided
to deliver evidence-based
interventions
DASS
Reid et al
40
Youth mental
health patients
68,46 18 2 to 4 weeks of MobileType
(app tracking mental health
relevant thoughts and
behaviors) vs. using a con-
trol app which tracks irrele-
vant behaviors
Participants reviewed infor-
mation gathered by Mobile-
Type with their general
practitioner, and were
given guides for managing
mental health
DASS
Roepke et al
41
Clinically significant
depression
93,97,93 40.2 1 month of SuperBetter (app
supporting self-esteem and
self-acceptance) vs. Super-
Better Plus (app adopting
principles of CBT and posi-
tive psychology) vs. waitlist
control
None CES-D
Tighe et al
42
Recent suicidal
thoughts
31,30 26.3 6 weeks of ibobbly (app based
on acceptance and commit-
ment therapy principles) vs.
waitlist control
24-hour helpline details avail-
able through the app in
case of suicidality
PHQ-9
Watts et al
43
DSM-IV major
depression
10,15 41 8 weeks of Get Happy (CBT-
based depression app) vs.
computerized CBT
program
Clinician contact during first
two weeks to check and
promote adherence
BDI-II
PHQ-9
CBT – cognitive behavioral therapy, PTSD – post-traumatic stress disorder, ADHD – attention-deficit/hyperactivity disorder, PHQ – Patient Health Question-
naire, MADRS – Montgomery-A
˚sberg Depression Rating Scale, DASS – Depression Anxiety Stress Scale, HAM-D – Hamilton Rating Scale for Depression, CES-
D – Center for Epidemiological Studies – Depression, BDI-II – Beck Depression Inventory II, HADS – Hospital Anxiety Depression Scale, NA – not available
World Psychiatry 16:3 - October 2017 291
effects from each app trialled. A random-effects meta-analysis
revealed a small-to-moderate positive effect size of smart-
phone mental health interventions for reducing depressive
symptoms in comparison to control conditions (18 studies,
N53,414, g50.383, 95% CI: 0.24-0.52, p<0.001).
Although there was heterogeneity across the study data
(Q580.8, p<0.01, I
2
574.0%), there was no evidence of publica-
tion bias (p50.255 in Egger’s regression test), and the fail-safe
N was 567 (estimating that 567 unpublished “null” studies
would need to exist for the actual p value to exceed 0.05). A
trim-and-fill analysis identified no outlier studies, and thus
did not change the observed effect size.
When considering only the studies which used intention-
to-treat analyses and/or reported complete outcome data, we
found a similar effect of smartphone interventions on depres-
sive symptoms (16 studies, N53,320, g50.399, 95% CI: 0.25-0.55,
p<0.001; Q580.0, I
2
577.5%).
In our pre-planned subgroup analyses, we found that effect
sizes were significantly greater when comparing smartphone
interventions to inactive conditions than when using active
control conditions (Q59.76, p50.002; Figure 3). Compared to
inactive control conditions, the pooled effect size across 13
smartphone interventions (N51,674) was g50.558 (95% CI: 0.38-
0.74), indicating a moderate effect on depressive symptoms.
However, when compared to active control conditions, smart-
phone interventions had only a small effect size on depressive
symptoms (12 studies, N52,381, g50.216, 95% CI: 0.10-0.33).
Both studies with active and inactive controls had significant het-
erogeneity, but no evidence of publication bias (Table 3).
Population characteristics and effects on depressive
symptoms
We also applied post-hoc subgroup analyses to studies that
had used mood disorder inclusion criteria, in order to explore
which populations smartphone interventions may be most
effective for. As shown in Table 4, the only populations in
which smartphone interventions significantly reduced depres-
sive symptoms were those with self-reported mild-to-moder-
ate depression (5 studies, N51,890, g50.518, 95% CI: 0.28-
0.75, p<0.001; Q536.6, I
2
583.6). There was no significant
effect among the smaller samples with major depressive disor-
der, bipolar disorder and anxiety disorders (two studies each).
Mixed-effects meta-regressions were applied to explore wheth-
er continuous moderators of average age, gender distribution
and sample size affected study findings, but found no indica-
tion that these factors influenced observed effect sizes (all
p>0.2).
Intervention characteristics and effects on depressive
symptoms
In order to gain insight into which aspects of smartphone
interventions make them effective for depressive symptoms, we
performed further comparative subgroup analyses after separat-
ing studies on the basis of common characteristics, such as
intervention components, feedback types, and therapeutic ap-
proaches applied. The common features examined, and the
results of all subgroup comparisons, are detailed in full in Table 5.
These analyses showed that smartphone interventions which
involved “in-person” (i.e., human) feedback had small, non-
significant effects on depressive symptoms (g50.137, 95% CI:
20.08 to 0.35, p50.214), whereas those which did not use in-
person feedback had moderate positive effects (g50.465, 95%
CI: 0.30-0.63, p<0.001). The difference between these subgroups
was statistically significant (p50.017).
Additionally, the effects of smartphone interventions which
were delivered entirely via the smartphone device (10 studies,
N52,178, g50.479, 95% CI: 0.27-0.69, p<0.001) appeared
larger than those which were not self-contained smartphone-
only interventions (8 studies, N51,236, g50.241, 95% CI: 0.09-
0.39, p50.002), although the difference between these sub-
groups fell short of significance (p50.07).
Similarly, interventions which provided “in-app feedback”,
such as summary statistics and progress scores, had greater
effect sizes (g50.534, 95% CI: 0.26-0.81, p<0.001) than those
which did not have in-app feedback (g50.266, 95% CI: 0.14-
0.39, p<0.001), although again the difference between sub-
groups was non-significant (p50.082).
The only other notable finding was that the studies of cog-
nitive training apps had a significantly (p50.004) smaller effect
size on depression outcomes (four studies, N5836, g50.123,
Table 2 Quality assessment in included studies
Study 1 2 3 4 5 6 7
Arean et al
26
111111
Birney et al
27
11111
Depp et al
28
11 1111
Enock et al
29
11111
Faurholt-Jepsen et al
30
111111
Horsch et al
31
11––11
Howells et al
32
11111
Ivanova et al
33
11 11
Kahn et al
34
1111
Kuhn et al
35
1–– 11
Ly et al
36
111111
Moell et al
37
111
Oh et al
38
––11
Proudfoot et al
39
11 1111
Reid et al
40
1111111
Roepke et al
41
11111
Tighe et al
42
11––111
Watts et al
43
11 1
1 – random sequence generation, 2 – allocation concealment, 3 – blinding of
participants and personnel, 4 – blinding of outcome assessment, 5 – incomplete
outcome data, 6 – selective outcome reporting, 7 – other bias
292 World Psychiatry 16:3 - October 2017
Figure 2 Meta-analysis of the effects of smartphone interventions on depressive symptoms. Box size represents study weighting. Diamond represents overall effect size and 95% CI.
World Psychiatry 16:3 - October 2017 293
Figure 3 Meta-analysis showing effects of smartphone interventions on depressive symptoms in comparison to active and inactive controls. Box size represents study weighting. Dia-
monds represent overall effect size and 95% CI.
294 World Psychiatry 16:3 - October 2017
Table 3 Effects of smartphone-delivered mental health interventions on depressive symptoms: pre-planned subgroup analyses
Studies
Sample size
(smartphone/control)
Meta-analysis Heterogeneity
Publication bias
(Egger’s
regression)
Hedges’ g 95% CI p Q p I
2
Intercept p
Main analysis 18 1,716/1,698 0.383 0.242 0.524 <0.001 80.8 <0.01 74.0 0.80 0.26
Intent-to-treat or complete
outcome data
16 1,669/1,651 0.399 0.248 0.550 <0.001 80.0 <0.01 77.5 1.68 0.15
Smartphone vs. active control 12 1,195/1,186 0.216 0.098 0.334 <0.001 20.8 0.03 47.2 20.49 0.34
Smartphone vs. inactive control 13 891/783 0.558 0.379 0.736 <0.001 34.9 <0.01 65.6 0.25 0.25
Significant values are highlighted in bold prints
Table 4 Post-hoc analyses: mood disorder samples
Studies
Sample size
(smartphone/control)
Meta-analysis Heterogeneity
Hedges’ g 95% CI p Q p I
2
Self-reported mild-to-moderate
depression
5 917/973 0.518 0.282 0.754 <0.001 36.6 <0.001 83.6
Major depressive disorder 2 56/62 0.085 20.273 0.443 0.642 0.49 0.484 0.00
Bipolar disorder 2 74/75 0.314 20.198 0.827 0.229 2.53 0.112 60.4
Anxiety disorders 2 259/242 0.250 20.023 0.523 0.073 4.13 0.127 51.6
Significant values are highlighted in bold prints
Table 5 Post-hoc analyses: intervention features
Studies
Sample size
(smartphone/control)
Meta-analysis Heterogeneity
Between
groups tests
Hedges’ g 95% CI p Q p I
2
Qp
Delivered solely via smartphone 10 1,103/1,075 0.479 0.271 0.687 <0.001 62.05 <0.01 80.66
Not delivered solely via
smartphone
8 613/623 0.241 0.088 0.394 0.002 13.38 <0.01 40.22 3.277 0.070
In-app feedback 8 750/816 0.534 0.258 0.810 <0.001 54.41 <0.01 85.02
No in-app feedback 11 966/882 0.266 0.143 0.389 <0.001 18.95 <0.01 36.68 3.02 0.082
In-person feedback 6 309/246 0.137 20.079 0.353 0.214 8.66 0.12 42.25
No in-person feedback 13 1,407/1,452 0.465 0.302 0.627 <0.001 61.6 <0.01 75.645 5.654 0.017
Mental health focused apps 15 1,286/1,292 0.438 0.276 0.601 <0.001 2.09 0.72 0.00
Cognitive training apps 4 430/406 0.123 20.012 0.258 0.074 63.6 <0.01 74.83 8.517 0.004
Mood monitoring features 9 653/709 0.336 0.182 0.489 <0.001 16.6 0.06 82.81
No mood monitoring 9 1,063/989 0.418 0.191 0.645 <0.001 64.0 <0.01 45.71 0.348 0.555
CBT-based intervention 7 541/615 0.531 0.339 0.722 <0.001 13.5 0.04 55.58
Not CBT-based 12 1,175/1,083 0.311 0.130 0.493 0.001 59.0 <0.01 76.26 2.661 0.103
Mindfulness aspects 6 615/573 0.487 0.214 0.760 <0.001 38.3 <0.01 81.716
No mindfulness aspects 12 1,101/1,125 0.321 0.160 0.482 <0.001 38.9 <0.01 66.549 1.049 0.306
CBT – cognitive behavioral therapy
Significant values are highlighted in bold prints
World Psychiatry 16:3 - October 2017 295
95% CI: 20.012 to 0.26, p50.074) than those which focused on
mental health (15 studies, N52,578, g50.438, 95% CI: 0.28-
0.60, p<0.001).
The use of mood-monitoring softwares, cognitive behavioral
therapy (CBT)-based interventions and mindfulness training
did not appear to influence study effect sizes (all p>0.1 between
subgroups with vs. without these features).
A mixed-effects meta-regression of study effect size with
intervention length (in weeks) found indication of a slight neg-
ative relationship between the two, with smaller effects observ-
ed from longer interventions, although this correlation fell short
of statistical significance (B5–0.025, SE50.014, Z521.72, p5
0.086).
DISCUSSION
To our knowledge, this is the first meta-analysis to examine
the efficacy of smartphone interventions for depressive symp-
toms. Our systematic search identified 18 RCTs, examining 22
mental health interventions delivered via smartphone devices,
across a total of 3,414 participants. Thus, the literature base
for this particular area has evolved swiftly, and is considerably
larger than that found for smartphone interventions in other
conditions. Around twice the number of eligible interventions
and participants were identified compared to recent meta-
analyses of smartphone interventions for diabetes and anxi-
ety
18,19
. Furthermore, 14 of the 18 eligible studies were pub-
lished within the last two years, which may reflect both the
increased research interest in using apps for mental health
13
and the increased ownership, access and use of mental health
apps by patients and health care organizations.
The main analysis found that smartphone interventions had
a moderate positive effect on depressive symptoms, with no
indication of publication bias affecting these findings. How-
ever, our subgroup analyses found that the effects of smart-
phone interventions were substantially larger when compared
to inactive (g50.56) than active (g50.22) control conditions.
The same pattern of effect sizes was observed in our meta-
analysis of smartphone interventions for anxiety
19
. Previous
reviews of other technological interventions for mental health
conditions have reported similar findings, as a meta-analysis
of virtual reality interventions for treating anxiety found signif-
icant effects in comparison to inactive controls, but no differ-
ence from traditional psychological treatments
51
. The extent
to which the observed effects on depressive symptoms arise
from using the device itself, rather than the psychotherapeutic
components of the intervention, should be examined and
quantified in future research, to further explore the notion of a
“digital placebo” influencing findings
52
.
We also explored other factors which may drive the effects of
smartphone interventions for depressive symptoms, using a
range of post-hoc subgroup analyses. With regards to popula-
tion type, significant benefits of smartphone apps were only
found for those with self-reported mild-to-moderate depres-
sion. This may be due to variations in subgroup sample sizes, as
the majority of studies were conducted in non-clinical popula-
tions, thus leaving the analyses for major depression and bipolar
disorder underpowered to detect significant effects. Nonethe-
less, the nature of smartphone interventions does appear to
position them as an ideal self-management tool for those with
less severe levels of depression. The observed effects indicate
that these interventions are well-placed for delivering low-
intensity treatment within a stepped-care approach
53
, or even
prevention of mild-to-moderate depression among the millions
of people affected by subclinical symptoms
54
. The findings that
neither age nor gender had any relationship with study effect
size indicate that smartphone interventions may be applicable
to a broad range of individuals.
With regards to intervention features, we found that those
delivered entirely via smartphone devices had significantly great-
er effects than those which also involved other human/comput-
erized aspects. Similarly, those using “in-person feedback” com-
ponents had significantly smaller effects than those which did
not. It seems counterintuitive that additional features/human
feedback would decrease smartphone effectiveness. However,
this relationship is likely due to the fact that apps not relying on
external components have been designed as more comprehen-
sive and self-contained tools. Indeed, we found some indication
that studies which provided in-app feedback were more effective
than those without. It should also be noted that the single study
which compared a therapist-guided smartphone intervention to
the same intervention without therapist support found equal
effects across the two groups
33
.
Smartphone interventions based on CBT significantly reduc-
ed depressive symptoms, as did those which incorporated as-
pects of mindfulness training or mood monitoring. However, we
were not able to elucidate which of the features were most ef-
fective. A previous study which directly compared smartphone
apps based on principles of either behavioral activation or
mindfulness also found no overall difference between the two
approaches
55
. Nonetheless, results showed that those with more
severe depression experienced greater benefits from the be-
havioral activation app, whereas those with mild depression
benefitted more from the mindfulness app. Understanding
both which psychological interventions are best delivered via
a smartphone and which patient populations will most bene-
fit from smartphone-based interventions will require further
research. As smartphone apps for mental health are becom-
ing easier to create, focusing research on specific populations
will enable more personalized and likely effective uses.
The trend-level negative correlation between effectiveness
and length of intervention indicates that another factor to con-
sider when designing optimal apps is user engagement
56
. Lower
rates of user engagement over time have been found in numer-
ous other mental health app studies
57-59
. Higher rates of engage-
ment have also been associated with those apps designed for
brief interactions
60
, suggesting the need to customize interven-
tions to the ways people use smartphones. While there is early
296 World Psychiatry 16:3 - October 2017
research on the optimal design and presentation of telehealth
platforms
61,62
, the impact on patient engagement and outcomes
remains an area of nascent exploration. Understanding other
factors related to app use, such as socioeconomic status, health
literacy
63
, technology literacy and health status
64,65
, also remain
important targets for further research.
A major strength of this meta-analysis is the strict adher-
ence to a registered protocol which exactly described the
search strategy, inclusion criteria, data extraction and analytic
procedures. However, one drawback is that we only included
smartphone interventions which have been evaluated in RCTs.
Given the wide availability of mental health apps, ensuring
that consumers and clinicians have access to evidence-based
interventions is vital for informed decision making. While the
sheer number of apps available, and their frequent updat-
ing
14,66
, makes rating each impossible, research elucidating
the components of effective apps and highlighting best practi-
ces may offer information immediately useful for clinical care.
Of note, future studies must identify and report safety con-
cerns regarding the use of smartphone interventions
67
. The
ability of smartphones to immediately register entered mood
data, compute if responses exceed a certain threshold, and if
so activate emergency response systems, offer real time safety
monitoring absent from traditional depression treatment.
Another limitation is the significant heterogeneity found
across the analyses. Although this heterogeneity was statisti-
cally accounted for by the random-effects models when com-
puting the effect size and respective p values, this still does
indicate that significant between-study differences existed,
even when subgrouping by sample/intervention type. Due to
the extent of differences between studies, it was difficult to
establish the single most effective components of smartphone
interventions, or determine which populations these interven-
tions are best suited for. Future studies which directly test
alternative approaches against each other in non-inferiority
controlled trials, while assessing outcome variation between
subsamples of participants
55
, would add great value to our
understanding of what would constitute the optimal smart-
phone app for depressive symptoms, and in which popula-
tions these methods may be most effective.
In conclusion, the evidence to date indicates that mental
health interventions delivered via smartphone devices can
reduce depressive symptoms. However, delivering treatments
via a smartphone introduces several new aspects which need
to be considered, beyond the platform change alone. Specifi-
cally, we have yet to establish the ways in which user engage-
ment, feedback loops, expectancy effects, and individual pa-
tient characteristics influence intervention outcomes. Rather
than a barrier, these variables represent new opportunities for
further research to optimize and personalize smartphone-
based interventions.
Given the early indication of efficacy, and rapidly growing
empirical research base, it is possible to envisage that contin-
ued technological advances will ultimately lead to scalable
and cost-effective digital treatments for depressive symp-
toms
56,68
. Thus, along with continuing to design and evaluate
optimal apps, further research should also be dedicated to-
wards establishing feasible methods for implementing smart-
phone-based interventions within health care systems.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the kind assistance of J. Anguera
(Neuroscape, University of California San Francisco), K. Hallgren (Behavioral
Research in Technology & Engineering Center, University of Washington) and
M. Faurholt-Jepsen (Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen)
who agreed to share study data necessary for the meta-analysis. J. Firth is funded
by a Blackmores Institute Fellowship and a Medical Research Council doctoral
training grant; J. Torous by a National Library of Medicine T15 training grant
(4T15LM007092-25) and the Natalia Mental Health Foundation; S. Rosenbaum by
a University of New South Wales Scientia & National Health and Medical Research
Council (NHMRC) Early Career Fellowship (APP1123336); J. Nicholas by an Aus-
tralian Postgraduate Award, and the NHMRC Centre for Research Excellence in
Suicide Prevention (APP1042580); R. Carney by an Economic and Social Research
Council grant (E SJ5000991); J. Sarris by an NHMRC Research Fellowship
(APP1125000). The first two authors contributed equally to this work.
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DOI:10.1002/wps.20472
298 World Psychiatry 16:3 - October 2017
... Such a high prevalence rate is associated with cancer, which is characterized by a high mortality rate, painful and invasive treatment, and severe aftereffects . Such sleep-related characteristics of patients with cancer reduce immune functioning (Firth et al., 2017a) and elicit changes in carbohydrate metabolism and endocrine function (Spiegel et al., 1999). Further, they slow their recovery and exacerbate the disorder (Blask, 2009). ...
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Objective: This pilot study aimed to evaluate the efficacy of a digital cognitive behavioral therapy (dCBT) in patients with cancer experiencing sleep problems. Methods: A total of 57 participants aged 25-65 years (6M/51F with a mean of 42.80 years and a standard deviation of 14.15 years) were randomly assigned to three groups-21 participants to a dCBT program (HARUToday Sleep), 20 participants to an app-based attentional control program (HARUCard Sleep), and 16 participants to a waitlist control group-and evaluated offline before and after the program completion. Of the 57 participants, there were a total of 45 study completers, 15 participants in each group. The dependent variables were sleep quality scores, measured by the Pittsburgh Sleep Quality Index (PSQI) and health-related quality of life scores, measured using the Short-Form 36 (SF-36), and attentional bias scores from a dot-probe computer task. Results: For both the intention-to-treat (N = 57) and study-completers analyses (N = 45, 15 for each group), a significant increase supported by a large effect size was found in the quality of sleep score of the HARUToday Sleep group compared to both the app-based attentional control and the waitlist control group. However, no significant changes were found in the quality of life and attentional bias scores. Conclusion: Our results suggest that the HARUToday Sleep app has the potential to serve as an intervention module to enhance the sleep quality of patients with cancer experiencing sleep problems.
... A possible future direction to address these problems is to develop a smartphone app. An 18-study meta-analysis [57] found apps for depression showed a significant symptom reduction compared to control, and it is possible that there may be benefit for managing distress in people with IBD. Using an app to run an expressive writing intervention could enable access to the intervention at the most suitable time for the participant with built-in weekly scheduling, reminders and information. ...
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Background We explored feasibility, acceptability and preliminary efficacy of an online writing intervention (WriteforIBD) against an active control condition for distress in people with inflammatory bowel disease (IBD) at the time of the COVID-19 pandemic. Methods A feasibility RCT was conducted in 19 adults (89.5% female, aged 20–69 years) with IBD and mild-moderate distress. Participants allocated to the WriteForIBD group completed a 4-day 30-min writing program adapted for IBD. The active control group wrote about trivial topics provided by researchers. Feasibility was established based on the recruitment and retention while acceptability based on completion rates and a numeric rating scale. All participants completed measures of mental health and disease activity before and after the intervention (one week) and at follow-up three months after the study commencement. Results The retention rate in the study was high (100% WriteForIBD; 82% control). All participants attended every session. 84.2% of participants were satisfied with the intervention. All participants reported a significant improvement in IBD-Control immediately after the intervention; F (2, 33.7) = 7.641, p = .002. A significant interaction of group*time for resilience was noted, R² = 0.19, p < .001, with the active control group reporting a significant decline in resilience from the first follow-up to three months while no significant change in resilience for the WriteForIBD group was recorded. Conclusions Online expressive writing is potentially feasible and highly acceptable to people with IBD who report distress. Future large-scale trials should explore the intervention that is adapted from this feasibility study. Registration: id ACTRN12620000448943p.
... In both studies, these interventions were unable to demonstrate superior effects for suicidal ideation [16,17], although one study did show an intervention effect for suicidal plans and suicidal behavior [16]. This latter finding, together with meta-analytic evidence that smartphone applications have a moderate effect on depressive symptoms [18] and a small effect of anxiety symptoms [19], shows promise for these interventions. To advance the field and realize the potential of smartphone applications as effective components of suicide prevention efforts, more trials of interventions that directly target distress processes specific to suicide are needed, rather than more trials of mental health interventions that incidentally measure suicidal outcomes. ...
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Background Suicidal ideation is a major risk for a suicide attempt in younger people, such that reducing severity of ideation is an important target for suicide prevention. Smartphone applications present a new opportunity for managing ideation in young adults; however, confirmatory evidence for efficacy from randomized trials is lacking. The objective of this study was to assess whether a therapeutic smartphone application (“LifeBuoy”) was superior to an attention-matched control application at reducing the severity of suicidal ideation. Methods and findings In this 2-arm parallel, double-blind, randomized controlled trial, 455 young adults from Australia experiencing recent suicidal ideation and aged 18 to 25 years were randomly assigned in a 2:2 ratio to use a smartphone application for 6 weeks in May 2020, with the final follow-up in October 2020. The primary outcome was change in suicidal ideation symptom severity scores from baseline (T0) to postintervention (T1) and 3-month postintervention follow-up (T2), measured using the Suicidal Ideation Attributes Scale (SIDAS). Secondary outcomes were symptom changes in depression (Patient Health Questionnaire-9, PHQ-9), generalized anxiety (Generalized Anxiety Disorder-7, GAD-7), distress (Distress Questionnaire-5, DQ5), and well-being (Short Warwick–Edinburgh Mental Well-Being Scale, SWEMWBS). This trial was conducted online, using a targeted social media recruitment strategy. The intervention groups were provided with a self-guided smartphone application based on dialectical behavior therapy (DBT; “LifeBuoy”) to improve emotion regulation and distress tolerance. The control group were provided a smartphone application that looked like LifeBuoy (“LifeBuoy-C”), but delivered general (nontherapeutic) information on a range of health and lifestyle topics. Among 228 participants randomized to LifeBuoy, 110 did not complete the final survey; among 227 participants randomized to the control condition, 91 did not complete the final survey. All randomized participants were included in the intent-to-treat analysis for the primary and secondary outcomes. There was a significant time × condition effect for suicidal ideation scores in favor of LifeBuoy at T1 ( p < 0.001, d = 0.45) and T2 ( p = 0.007, d = 0.34). There were no superior intervention effects for LifeBuoy on any secondary mental health outcomes from baseline to T1 or T2 [ p -values: 0.069 to 0.896]. No serious adverse events (suicide attempts requiring medical care) were reported. The main limitations of the study are the lack of sample size calculations supporting the study to be powered to detect changes in secondary outcomes and a high attrition rate at T2, which may lead efficacy to be overestimated. Conclusions LifeBuoy was associated with superior improvements in suicidal ideation severity, but not secondary mental health outcomes, compared to the control application, LifeBuoy-C. Digital therapeutics may need to be purposefully designed to target a specific health outcome to have efficacy. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12619001671156
Article
This rapid review of reviews aimed to determine the extent of research undertaken on the effectiveness of dietary interventions for individuals with a mental disorder. Three databases (MEDLINE, Embase, Cochrane Reviews and Cochrane Trials) were searched to February 2021 for systematic reviews including experimental studies assessing the effectiveness of dietary interventions with physical or mental health related outcomes in adults or children with one or more of: severe mental illness, depression or anxiety, eating disorders, or substance use disorder. Results are presented descriptively. The number of included reviews was 46 (67% in severe mental illness, 20% in depression and anxiety, 7% in eating disorders, and 7% in substance use disorders). Most reviews were published since 2016 (59%), and included studies conducted in adults (63%). Interventions in the eating disorders and severe mental illness reviews were predominantly education and behaviour change, whereas interventions in the substance use disorders, and depression and anxiety reviews were predominantly supplementation (e.g. omega‐3). Twenty‐eight and twelve of the reviews respectively reported mental health and dietary outcomes for one or more included studies. Most reviews in severe mental illness, and depression and anxiety reported conclusions supporting the positive effects of dietary intervention, including positive effects on weight‐related or mental health outcomes, and on mental health outcomes, respectively. A larger number of systematic reviews were identified which evaluated dietary interventions in individuals with severe mental illness, and depression and anxiety, compared with substance use disorders, and eating disorders. Dietary intervention is an important component of the treatment that should be available to individuals living with mental disorders, to support their physical and mental health.
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Resilience, a person's mental ability to deal with challenging situations adaptively, is an important life skill. Supporting students in building psychological resilience and coping during crises (with the COVID-19 pandemic being a prime example) is crucial. Very few mobile applications (apps) for mental health explicitly report behavioral change techniques. Moreover, only a handful of the apps that support resilience are gamified, or use smartphone sensors readily available in modern smartphones for health self-management, or were designed for use by a nonclinical population. This study describes the design of a prototype for a gamified, theory-based mobile app that utilizes the Internet of Things to provide personalized data and enhance undergraduate students' resilience. A total of 74 participants evaluated the prototype and completed an online questionnaire during the COVID-19 lockdowns. The questionnaire included questions examining the design's feasibility for supporting resilience and questions on the System Usability Scale evaluating its usability. Regarding the evaluation of the prototype on improving psychological resilience, positive responses (M = 3.76 out of 5, SD = 0.82) were received for all functions (goal setting for studying, socializing and physical exercise, progress monitoring using sensors or self-reporting, reflection, motivational badges). The System Usability Scale returned an evaluation score of 72.9, indicating a satisfactory degree of usability. The resilience app is a promising proof of concept. Combining Internet of Things capabilities with active user interaction while incorporating behavior change techniques in a gamified environment was well accepted by students. Implications for the design of gamified environments for well-being are drawn. Future research will empirically validate its design using quasi-experimental methods.
Article
Background: Transcranial electrical stimulation (tES) is considered effective and safe for depression, albeit modestly, and prone to logistical burdens when performed in external facilities. Investigation of portable tES (ptES), and potentiation of ptES with remote psychological interventions have shown positive, but preliminary, results. Research design: We report the rationale and design of an ongoing multi-arm, randomized, double-blind, sham-controlled clinical trial with digital features, using ptES and internet-based behavioral therapy (iBT) for major depressive disorder (MDD) (NCT04889976). Methods: We will evaluate the efficacy, safety, tolerability and usability of (1) active ptES + active iBT ("double-active"), (2) active ptES + sham iBT ("ptES-only"), and (3) sham ptES + sham iBT ("double-sham"), in adults with MDD, with a Hamilton Depression Rating Scale - 17 item version (HDRS-17) score ≥ 17 at baseline, during 6 weeks. Antidepressants are allowed in stable doses during the trial. Results: We primarily co-hypothesize changes in HDRS-17 will be greater in (1) "double-active" compared to "ptES-only", (2) "double-active" compared to "double-sham", and (3) "ptES-only" compared to "double-sham". We aim to enroll 210 patients (70 per arm). Conclusions: Our results should offer new insights regarding the efficacy and scalability of combined ptES and iBT for MDD, in digital mental health. Clinical trial registration: NCT04889976.
Article
Background The COVID-19 pandemic has been suggested to constitute a broad base stressor with severe mental health consequences. mHealth applications are accessible self-help tools that can be used to reduce psychological distress during the pandemic. This randomized controlled trial evaluated the effects of mobile-based cognitive training exercises on COVID-19 related distress and maladaptive cognitions. Methods Following initial screening (n = 924), participants scoring 1 standard deviations above the mean of the COVID-19 Distress Scale were randomized into two groups. Participants in the immediate-app group (iApp; n = 25) started using the application at baseline (T0) for 12 days (from T0 to T1). Participants in the delayed-app group (dApp; n = 22) started using the mobile application at T1 (crossover) and used it for the following 12 days (T1 to T2). Results Intention to treat analyses indicated that the iApp group exhibited lower COVID-19 distress, lower depression, fewer intolerance of uncertainty and obsessive beliefs than the dApp group at T1. In addition, using the app for 12 consecutive days was associated with large effect-size reductions (Cohen's d ranging from 0.81 to 2.35) in COVID-19 distress and related maladaptive cognitions in the iApp group (from T0 to T1) and the dApp group (from T1 to T2). Moreover, these reductions were maintained at the follow-up. Limitations This study was a crossover trial with a relatively limited sample size and mainly female participants. Conclusion Our findings underscore the usefulness of brief, low-intensity, portable interventions in alleviating the negative effects of the pandemic on mental health.
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Background This study is one of the first randomized controlled trials investigating cognitive behavioral therapy for insomnia (CBT-I) delivered by a fully automated mobile phone app. Such an app can potentially increase the accessibility of insomnia treatment for the 10% of people who have insomnia. Objective The objective of our study was to investigate the efficacy of CBT-I delivered via the Sleepcare mobile phone app, compared with a waitlist control group, in a randomized controlled trial. Methods We recruited participants in the Netherlands with relatively mild insomnia disorder. After answering an online pretest questionnaire, they were randomly assigned to the app (n=74) or the waitlist condition (n=77). The app packaged a sleep diary, a relaxation exercise, sleep restriction exercise, and sleep hygiene and education. The app was fully automated and adjusted itself to a participant’s progress. Program duration was 6 to 7 weeks, after which participants received posttest measurements and a 3-month follow-up. The participants in the waitlist condition received the app after they completed the posttest questionnaire. The measurements consisted of questionnaires and 7-day online diaries. The questionnaires measured insomnia severity, dysfunctional beliefs about sleep, and anxiety and depression symptoms. The diary measured sleep variables such as sleep efficiency. We performed multilevel analyses to study the interaction effects between time and condition. Results The results showed significant interaction effects (P<.01) favoring the app condition on the primary outcome measures of insomnia severity (d=–0.66) and sleep efficiency (d=0.71). Overall, these improvements were also retained in a 3-month follow-up. Conclusions This study demonstrated the efficacy of a fully automated mobile phone app in the treatment of relatively mild insomnia. The effects were in the range of what is found for Web-based treatment in general. This supports the applicability of such technical tools in the treatment of insomnia. Future work should examine the generalizability to a more diverse population. Furthermore, the separate components of such an app should be investigated. It remains to be seen how this app can best be integrated into the current health regimens. Trial Registration Netherlands Trial Register: NTR5560; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5560 (Archived by WebCite at http://www.webcitation.org/6noLaUdJ4)
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After this article [1] was published the authors noticed that the wrong version of Fig. 1 had been uploaded. The Correct figure, is shown below.
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Objectives Rates of youth suicide in Australian Indigenous communities are 4 times the national youth average and demand innovative interventions. Historical and persistent disadvantage is coupled with multiple barriers to help seeking. Mobile phone applications offer the opportunity to deliver therapeutic interventions directly to individuals in remote communities. The pilot study aimed to evaluate the effectiveness of a self-help mobile app (ibobbly) targeting suicidal ideation, depression, psychological distress and impulsivity among Indigenous youth in remote Australia. Setting Remote and very remote communities in the Kimberley region of North Western Australia. Participants Indigenous Australians aged 18–35 years. Interventions 61 participants were recruited and randomised to receive either an app (ibobbly) which delivered acceptance-based therapy over 6 weeks or were waitlisted for 6 weeks and then received the app for the following 6 weeks. Primary and secondary outcome measures The primary outcome was the Depressive Symptom Inventory—Suicidality Subscale (DSI-SS) to identify the frequency and intensity of suicidal ideation in the previous weeks. Secondary outcomes were the Patient Health Questionnaire 9 (PHQ-9), The Kessler Psychological Distress Scale (K10) and the Barratt Impulsivity Scale (BIS-11). Results Although preintervention and postintervention changes on the (DSI-SS) were significant in the ibobbly arm (t=2.40; df=58.1; p=0.0195), these differences were not significant compared with the waitlist arm (t=1.05; df=57.8; p=0.2962). However, participants in the ibobbly group showed substantial and statistically significant reductions in PHQ-9 and K10 scores compared with waitlist. No differences were observed in impulsivity. Waitlist participants improved after 6 weeks of app use. Conclusions Apps for suicide prevention reduce distress and depression but do not show significant reductions on suicide ideation or impulsivity. A feasible and acceptable means of lowering symptoms for mental health disorders in remote communities is via appropriately designed self-help apps. Trial registration number ACTRN12613000104752.
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Background Mobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the impact they have on mental health outcomes. Objective The objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self-guided mobile apps for depression. Methods Participants were recruited through Web-based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant’s smartphone or tablet with minimal contact with study staff. We enrolled 626 English-speaking adults (≥18 years old) with mild to moderate depression as determined by a 9-item Patient Health Questionnaire (PHQ-9) score ≥5, or if their score on item 10 was ≥2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence-based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ-9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed. Results We randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ-9 score >10 (moderately depressed). Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ-9 score >10, with the cognitive training and problem-solving apps resulting in greater effects on mood than the information control app (χ22=6.46, P=.04). Conclusions Mobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression. ClinicalTrial Clinicaltrials.gov NCT00540865; https://www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http://www.webcitation.org/6mj8IPqQr)
Article
Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.
Article
Background: Various psychological interventions are effective for reducing symptoms of anxiety when used alone, or as an adjunct to anti-anxiety medications. Recent studies have further indicated that smartphone-supported psychological interventions may also reduce anxiety, although the role of mobile devices in the treatment and management of anxiety disorders has yet to be established. Methods: We conducted a systematic review and meta-analysis of all randomized clinical trials (RCTs) reporting the effects of psychological interventions delivered via smartphone on symptoms of anxiety (sub-clinical or diagnosed anxiety disorders). A systematic search of major electronic databases conducted in November 2016 identified 9 eligible RCTs, with 1837 participants. Random-effects meta-analyses were used to calculate the standardized mean difference (as Hedges' g) between smartphone interventions and control conditions. Results: Significantly greater reductions in total anxiety scores were observed from smartphone interventions than control conditions (g=0.325, 95% C.I.=0.17-0.48, p<0.01), with no evidence of publication bias. Effect sizes from smartphone interventions were significantly greater when compared to waitlist/inactive controls (g=0.45, 95% C.I.=0.30-0.61, p<0.01) than active control conditions (g=0.19, 95% C.I.=0.07-0.31, p=0.003). Limitations: The extent to which smartphone interventions can match (or exceed) the efficacy of recognised treatments for anxiety has yet to established. Conclusions: This meta-analysis shows that psychological interventions delivered via smartphone devices can reduce anxiety. Future research should aim to develop pragmatic methods for implementing smartphone-based support for people with anxiety, while also comparing the efficacy of these interventions to standard face-to-face psychological care.
Article
Objective: Posttraumatic stress disorder (PTSD) is highly prevalent in the population, but relatively few affected individuals receive treatment for it. Smartphone applications (apps) could help address this unmet need by offering sound psychoeducational information and evidence-based cognitive behavioral coping tools. We conducted a randomized controlled trial to assess the efficacy of a free, publicly available smartphone app (PTSD Coach) for self-management of PTSD symptoms. Method: One hundred 20 participants who were an average of 39 years old, mostly women (69.2%) and White (66.7%), recruited primarily through online advertisements, were randomized to either a PTSD Coach (n = 62) or a waitlist condition (n = 58) for 3 months. Web-administered self-report measures of PTSD, PTSD symptom coping self-efficacy, depression, and psychosocial functioning were conducted at baseline, posttreatment, and 3 months following treatment. Results: Following the intent-to-treat principle, repeated-measures analyses of variance (ANOVAs) revealed that at posttreatment, PTSD Coach participants had significantly greater improvements in PTSD symptoms (p = .035), depression symptoms (p = .005), and psychosocial functioning (p = .007) than did waitlist participants; however, at posttreatment, there were no significant mean differences in outcomes between conditions. A greater proportion of PTSD Coach participants achieved clinically significant PTSD symptom improvement (p = .018) than waitlist participants. Conclusion: PTSD Coach use resulted in significantly greater improvements in PTSD symptoms and other outcomes relative to a waitlist condition. Given the ubiquity of smartphones, PTSD Coach may provide a wide-reaching, convenient public health intervention for individuals with PTSD symptoms who are not receiving care. (PsycINFO Database Record
Article
Objectives: We explored whether newly developed application (Smartphone-based brain Anti-aging and memory Reinforcement Training, SMART) improved memory performance in older adults with subjective memory complaints (SMC). Method: A total of 53 adults (range: 50-68 years; 52.8% female) were randomized into either one of two intervention groups [SMART (n = 18) vs. Fit Brains® (n = 19)] or a wait-list group (n = 16). Participants in the intervention groups underwent 15-20 minutes of training per day, five days per week for 8 weeks. We used objective cognitive measures to evaluate changes with respect to four domains: attention, memory, working memory (WM), and response inhibition. In addition, we included self-report questionnaires to assess levels of SMC, depression, and anxiety. Results: Total WM quotient [t(17) = 6.27, p < .001] as well as auditory-verbal WM score [t(17) = 4.45, p < .001] increased significantly in the SMART group but not in the control groups. Self-reports of memory contentment, however, increased in the Fit Brains® group only [t(18) = 2.12, p < .05). Conclusion: Use of an 8-week smartphone-based memory training program may improve WM function in older adults. However, objective improvement in performance does not necessarily lead to decreased SMC.