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The Effect of Mindfulness-Based Therapy on Anxiety and Depression: A Meta-Analytic Review

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Although mindfulness-based therapy has become a popular treatment, little is known about its efficacy. Therefore, our objective was to conduct an effect size analysis of this popular intervention for anxiety and mood symptoms in clinical samples. We conducted a literature search using PubMed, PsycINFO, the Cochrane Library, and manual searches. Our meta-analysis was based on 39 studies totaling 1,140 participants receiving mindfulness-based therapy for a range of conditions, including cancer, generalized anxiety disorder, depression, and other psychiatric or medical conditions. Effect size estimates suggest that mindfulness-based therapy was moderately effective for improving anxiety (Hedges's g = 0.63) and mood symptoms (Hedges's g = 0.59) from pre- to posttreatment in the overall sample. In patients with anxiety and mood disorders, this intervention was associated with effect sizes (Hedges's g) of 0.97 and 0.95 for improving anxiety and mood symptoms, respectively. These effect sizes were robust, were unrelated to publication year or number of treatment sessions, and were maintained over follow-up. These results suggest that mindfulness-based therapy is a promising intervention for treating anxiety and mood problems in clinical populations.
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The Effect of Mindfulness-Based Therapy on Anxiety and
Depression: A Meta-Analytic Review
Stefan G. Hofmann, Alice T. Sawyer, Ashley A. Witt, and Diana Oh
Boston University
Abstract
BACKGROUND—Although mindfulness-based therapy has become a popular treatment, little is
known about its efficacy.
OBJECTIVES—To conduct an effect size analysis of this popular intervention for anxiety and mood
symptoms in clinical samples.
DATA SOURCES—A literature search was conducted using PubMed, PsycInfo, the Cochrane
Library, and manual searches.
REVIEW METHODS—The search identified 39 studies totaling 1,140 participants receiving
mindfulness-based therapy for a range of conditions, including cancer, generalized anxiety disorder,
depression, and other psychiatric or medical conditions.
RESULTS—Effect size estimates suggest that mindfulness-based therapy was moderately effective
for improving anxiety (Hedges’ g = 0.63) and mood symptoms (Hedges’ g = 0.59) from pre to post-
treatment in the overall sample. In patients with anxiety and mood disorders, this intervention was
associated with effect sizes (Hedges’ g) of 0.97 and 0.95 for improving anxiety and mood symptoms,
respectively. These effect sizes were robust, unrelated to publication year or number of treatment
sessions, and were maintained over follow-up.
CONCLUSION—These results suggest that mindfulness-based therapy is a promising intervention
for treating anxiety and mood problems in clinical populations.
Keywords
Mindfulness; Therapy; Anxiety Disorders; Depression; Efficacy
The Effect of Mindfulness-Based Therapy on Anxiety and Depression: A
Meta-Analytic Review
Derived from ancient Buddhist and Yoga practices, mindfulness-based therapy (MBT), which
includes mindfulness-based cognitive therapy (MBCT; e.g., Segal, Williams, & Teasdale,
2002) and mindfulness-based stress reduction (MBSR; e.g., Kabat-Zinn, 1982), has become a
very popular form of treatment in contemporary psychotherapy (e.g., Baer, 2003; Bishop,
2002; Hayes, 2004; Kabat-Zinn, 1994; Salmon, Lush, Jablonski, & Sephton, 2009). Several
of the applications of MBT (such as MBCT) have been designed as relapse prevention strategies
rather than to reduce acute symptoms. Other studies have examined MBT as a symptom-
Corresponding Author: Stefan G. Hofmann, Ph.D. Professor Department of Psychology, Boston University 648 Beacon Street, 6th Floor
Boston, MA 02215-2002 Fax: (617) 353-9609 Tel: (617) 353-9610 shofmann@bu.edu.
Dr. Hofmann is a paid consultant by Merck/Schering-Plough and supported by NIMH grant 1R01MH078308 for studies unrelated to the
present investigation.
NIH Public Access
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Published in final edited form as:
J Consult Clin Psychol. 2010 April ; 78(2): 169–183. doi:10.1037/a0018555.
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focused treatment. The present study is a review of MBT as a therapy to reduce acute symptoms
of anxiety and depression.
Mindfulness refers to a process that leads to a mental state characterized by nonjudgmental
awareness of the present moment experience, including one's sensations, thoughts, bodily
states, consciousness, and the environment, while encouraging openness, curiosity, and
acceptance (Bishop et al., 2004; Kabat-Zinn, 2003; Melbourne Academic Mindfulness Interest
Group, 2006). Bishop and colleagues (2004) distinguished two components of mindfulness,
one that involves self-regulation of attention and one that involves an orientation toward the
present moment characterized by curiosity, openness, and acceptance. The basic premise
underlying mindfulness practices is that experiencing the present moment nonjudgmentally
and openly can effectively counter the effects of stressors, because excessive orientation toward
the past or future when dealing with stressors can be related to feelings of depression and
anxiety (e.g., Kabat-Zinn, 2003). It is further believed that, by teaching people to respond to
stressful situations more reflectively rather than reflexively, MBT can effectively counter
experiential avoidance strategies, which are attempts to alter the intensity or frequency of
unwanted internal experiences (Hayes, Luoma, Bond, Masuda, & Lillis, 2006). These
maladaptive strategies are believed to contribute to the maintenance of many, if not all
emotional disorders (Bishop et al., 2004; Hayes, 2004). In addition, the slow and deep breathing
involved in mindfulness meditation may alleviate bodily symptoms of distress by balancing
sympathetic and parasympathetic responses (Kabat-Zinn, 2003). For example, in the case of
MBSR (Kabat-Zinn, 1982), the three key components are sitting meditation, Hatha Yoga, and
body scan, which is a sustained mindfulness practice in which attention is sequentially directed
throughout the body (Kabat-Zinn, 2003).
A number of reviews have recently been conducted to examine the efficacy of MBT (Baer,
2003; Carmody & Baer, 2009; Grossman, Niemann, Schmidt, & Walach, 2004; Ledesma &
Kumano, 2008; Mackenzie, Carlson, & Speca, 2005; Matchim & Armer, 2007; Ott, Norris, &
Bauer-Wu, 2006; Praissman, 2008; Smith, Richardson, Hoffman, & Pilkington, 2005; Teixeira,
2008; Toneatto & Nguyen, 2007; Winbush, Gross, & Kreitzer, 2007). In fact, it could be argued
that the field has become saturated with qualitative reviews on MBT. These reviews generally
suggest that MBT may be beneficial to reduce stress, anxiety, and depression. However, the
vast majority of these reviews are qualitative in nature and do not quantify the size of the
treatment effect. In contrast, only a few reviews applied meta-analytic methods to quantify the
efficacy of this treatment (Baer, 2003; Grossman, Niemann, Schmidt, & Walach, 2004;
Ledesma & Kumano, 2008). 1 One of these reviews focused on MBT for stress reduction in
cancer patients (Ledesma & Kumano, 2008), whereas another study examined the efficacy of
mindfulness for treating distress associated with general physical or psychosomatic problems,
such as chronic pain, coronary artery disease, and fibromyalgia (Grossman et al., 2004). The
results of these reviews were encouraging, suggesting that MBSR is moderately effective for
reducing distress associated with physical or psychosomatic illnesses. However, both reviews
were based on a small number of studies with relatively small sample sizes per study. The two
reviews that specifically examined the effects of MBT on mood and anxiety symptoms came
to divergent conclusions (Baer, 2003; Toneatto & Nguyen, 2007). Whereas Baer (2003)
interpreted the literature as suggesting that MBT may be helpful in treating anxiety and mood
disorders, Toneatto and Nguyen (2007) concluded that MBT has no reliable effect for these
problems.
1Two additional meta-analyses have examined the efficacy of Acceptance and Commitment Therapy (ACT), which includes mindfulness
techniques (Powers, Zum Vörde Sive Vörding, & Emmelkamp, 2009; Öst, 2008). Mindfulness exercises in ACT are firmly rooted in the
behavioral analytic model of ACT, which is different from mindfulness-based cognitive-behavioral therapy. Furthermore, mindfulness
is a relatively small aspect of ACT when compared to the other treatment components, and the two recently published meta-analyses on
ACT are comprehensive and still up to date. Therefore, we did not include ACT in our discussion and analyses and instead followed
more closely the general approach by Baer (2003) and Toneatto & Nguyen (2007).
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In sum, although a very popular treatment, it remains unclear whether MBT is effective for
reducing mood and anxiety symptoms. Therefore, the goal of the present study was to provide
a quantitative, meta-analytic review of the efficacy of MBT for improving anxiety and mood
symptoms in clinical populations. For this purpose, we reviewed treatment studies examining
the effects of MBT on anxiety and depression in psychiatric and medical populations.
We tested the hypothesis that MBT is an effective treatment for reducing symptoms of anxiety
and depression, especially among patients with anxiety disorders and depression. Furthermore,
we expected that MBT reduces symptoms of anxiety and depression in chronic medical
conditions, such as cancer, which may be experienced by patients as an effect of their physical
condition and potential side-effects of treatments.
Methods
Searching
Studies were identified by searching PubMed, PsycInfo, and the Cochrane Library. Searches
were conducted for studies published between the first available year and April 1, 2009 using
the search term mindfulness combined with the terms meditation, program, therapy, or
intervention and anxi*, depress*, mood, or stress. Additionally, an extensive manual review
was conducted of reference lists of relevant studies and review papers extracted from the
database searches. Articles determined to be related to the topic of mindfulness were selected
for further examination.
Selection
Studies were selected if: (1) they included a mindfulness-based intervention, (2) they included
a clinical sample (i.e., participants had a diagnosable psychological or physical/medical
disorder); (3) they included adult samples (ages 18-65); (4) the mindfulness program was not
coupled with Acceptance and Commitment Therapy or Dialectic Behavior Therapy; (5)
included a measure of anxiety and/or mood symptoms at both pre- and post-intervention; and
(6) provided sufficient data to perform effect size analyses (i.e., means and standard deviations,
t or F values, change scores, frequencies, or probability levels). Studies were excluded if the
sample overlapped either partially or completely with the sample of another study meeting
inclusion criteria for the meta-analysis. In these cases, we selected for inclusion the study with
the larger sample size or more complete data for measures of anxiety and depression symptoms.
For studies that provided insufficient data but were otherwise appropriate for the analyses,
authors were contacted for supplementary data.
Because the vast majority of studies meeting our criteria employed MBSR, MBCT (Segal et
al., 2002), or interventions modeled upon MBSR or MBCT, we excluded studies in which the
intervention differed substantially from MBSR and MBCT in length (i.e., two sessions as
opposed to the typical eight). Furthermore, we excluded studies in which the MBT was not
delivered in person (i.e., audio-taped or internet-delivered interventions).
Validity Assessment
In order to address publication bias, we computed the fail-safe N (Rosenthal, 1991; Rosenthal
& Rubin, 1988) using the following formula: . In this formula, K is the
number of studies in the meta-analysis and Z
̄
is the mean Z obtained from the K studies. The
effect size can be considered robust if the required number of studies (X) to reduce the overall
effect size to a non-significant level exceeds 5K + 10 (Rosenthal, 1991). In addition, we
constructed a funnel plot to examine the publication bias. No publication bias results in a funnel
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plot that is symmetrical around the mean effect size. The Trim and Fill method examines
whether negative or positive trials are over or under-represented, accounting for the sample
size (i.e., where the missing studies would need to fall to make the plot symmetrical). This
information can then be used to re-calculate the effect size estimate.
Data Abstraction
For each study, two of the authors (AAW, ATS) selected psychometrically validated measures
of depression and anxiety symptoms. In cases where data from only select subscales of a
measure were reported, authors were contacted for anxiety and depression subscale data. Three
of the authors (AAW, ATS, DO) extracted numerical data from the studies. Data were extracted
to analyze changes from pre to post treatment, pre treatment to follow-up, and intent-to-treat
(ITT) with last observation carried forward method.
Study Characteristics
We examined whether the effect sizes varied as a function of study characteristics (type of
mindfulness-based therapy, study year, number of treatment sessions, quality of study) and
clinical characteristics (disorder targeted by the intervention) by using meta-regression
analyses. To investigate the effects of categorical moderator variables, we examined 95%
confidence intervals. All analyses were completed manually or by using the software program
Comprehensive Meta-Analysis, Version 2 (Borenstein, Hedges, Higgins, & Rothstein, 2005).
Quantitative Data Synthesis
Effect sizes for continuous measures of anxiety and depression were calculated using pre-post
treatment differences (within-group) for uncontrolled studies and also for controlled studies
using Hedges’g and its 95% confidence interval.2 The magnitude of Hedges’ g may be
interpreted using Cohen's (1988) convention as small (0.2), medium (0.5), and large (0.8).
The correlation between pre-and post-treatment measures is needed in order to calculate the
pre-post effect sizes. This correlation could not be determined from the study reports.
Therefore, we followed the recommendation by Rosenthal (1993) and assumed a conservative
estimation of r = 0.7. We calculated an average Hedges’ g effect size for studies that included
measures of severity of anxiety symptoms and a separate Hedges’ g effect size for measures
of depressive symptom severity.
Effect size estimates were pooled across studies in order to obtain a summary statistic. The
effect size estimates were calculated using the random-effects model rather than the fixed-
2Hedges’ g is a variation of Cohen's d that corrects for biases due to small sample sizes (Hedges & Olkin, 1985). Within-group effect
size were calculated using the following formula: , where is the pretreatment sample mean, is the
posttreatment sample mean, SDifference is the standard deviation of the difference, and r is the correlation between pretreatment and
posttreatment scores. Hedges’ g can be computed by multiplying d by correction factor , where df is the degrees
of freedom to estimate the within-group standard deviation.
The controlled effect sizes were computed using the following formula: , where is the
mean pre- to posttreatment change, SD is the standard deviation of posttreatment scores, n is the sample size, MBT refers to the
mindfulness-based therapy condition, and CONT refers to the control condition.
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effects model because the studies included were not functionally identical (Hedges & Vevea,
1998; Moses, Mosteller, & Buehler, 2002). Effect size estimates for ITT and follow-up data
were also calculated in the manner described above.
Assessment of Pre-Treatment Symptom Severity
If symptoms of anxiety or depression are not elevated at baseline, there may be little room for
improvement over the course of treatment. In order to assess whether the symptoms of anxiety
and depression at pre-treatment were elevated in samples not diagnosed with anxiety or mood
disorders (e.g., individuals with cancer, pain or other medical problems), we compared scores
on the measures of anxiety and depression used in the relevant studies with cutoff scores that
mark an elevated level. Specifically, we calculated 95% confidence intervals for the pre-
treatment means on all anxiety and depression measures for which established or suggested
clinical cutoff scores are available. If the lower bound of the 95% confidence interval was
greater than or equal to the cutoff score, we considered the sample to have an elevated level
of anxiety or depression at pre-treatment.
In cases where different cutoff scores were recommended for males and females (e.g., the State-
Trait Anxiety Inventory), we chose the higher cutoff score in order to be more conservative.
The cutoff scores utilized were as follows: Beck Anxiety Inventory: 10 (Beck & Steer,
1990); Beck Depression Inventory: 10 (Beck, Steer, & Garbin, 1988; Kendall, Hollon, Beck,
Hammen, & Ingram, 1987); Beck Depression Inventory-II: 14 (Beck, Steer, & Brown, 1996);
Beck Depression Inventory- Short Form: 5 (Beck & Beck, 1972); Center for Epidemiologic
Studies Depression Scale: 16 (Boyd, Weissman, Thompson, & Meyer, 1977; Radloff, 1991);
Hospital Anxiety and Depression Scale: 8 for each subscale (Zigmond & Snaith, 1983); Profile
of Mood States- Anxiety subscale: 16 (Higginson, Fields, Koller, & Tröster, 2001); Profile of
Mood States- Depression subscale: 14 (Griffith et al., 2005); Symptom Checklist 90- Revised-
Anxiety subscale: 0.75 (Schmitz, Hartkamp, & Frake, 2000); Symptom Checklist 90- Revised-
Depression subscale: 0.73 (Schmitz et al., 2000); State-Trait Anxiety Inventory: 40 for each
subscale (Leong, Farrell, Helme, & Gibson, 2007).
Results
Trial Flow
Our study selection process is illustrated in Figure 1. Of the 727 articles identified in our initial
searches as potentially relevant, 39 studies met our selection criteria and were included in the
meta-analysis. The characteristics of the included studies are shown in Table 1. These studies
included a total of 1,140 patients who received MBT. The most common disorder studied was
cancer (n = 9), followed by generalized anxiety disorder (n = 5), depression (n = 4), chronic
fatigue syndrome (n = 3), panic disorder (n = 3), fibromyalgia (n = 3), chronic pain (n = 2),
social anxiety disorder (n = 2), attention-deficit hyperactivity disorder (n = 1), arthritis (n = 1),
binge eating disorder (n = 1), bipolar disorder (n = 1), diabetes (n = 1), heart disease (n = 1),
hypothyroidism (n = 1), insomnia (n = 1), organ transplant (n = 1), stroke (n = 1), and traumatic
brain injury (n = 1). Many studies targeted more than one disorder, and thus the sum of the
above numbers exceeds the total number of studies included. In addition, one study used a
sample of patients meeting criteria for any mood disorder (either current or lifetime), one study
included patients with heterogeneous anxiety and mood disorders, and one study used a sample
of patients with heterogeneous medical diagnoses. All included studies provided data for
continuous measures of anxiety and/or depressive symptom severity at pre and post-treatment.
Study Characteristics
Using the following modified Jadad criteria (Jadad et al., 1996) to provide a relative index of
the quality of included studies, the design of each study was evaluated as follows: (1) the study
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was described as randomized; (2) participants were adequately randomized; (3) the study was
described as double blind; (4) the method of double blinding was appropriate; and (5) a
description of drop-outs and withdrawals was provided. One point was assigned for each
criterion met for a maximum of 5 points. As shown in Table 1, total Jadad scores for included
studies ranged from 0 to 3, with a median of 1 (M = 1.23; SD = 0.77). Two independent ratings
of Jadad criteria were performed; inter-rater reliability was r = 0.96. Disagreements were
resolved through discussion.
Quantitative Data Synthesis
Pre-post effect size—The average pre-post effect size estimate (Hedges’ g) based on the
39 studies was 0.63 (95% CI: 0.53-0.73, p < .01) for reducing anxiety, and 0.59 (95% CI:
0.51-0.66, p < .01) for reducing depression. The details of these analyses are depicted in Tables
2 and 3.
Publication bias—The effect size observed for measures of depressive symptom severity
for uncontrolled trials and MBT of controlled trials corresponded to a z-value of 21.82,
indicating that 4,302 studies with an effect size of zero would be necessary to nullify this result
(i.e., for the combined 2-tailed p-value to exceed .05). The fail-safe N for measures of anxiety
disorder severity was 4,150 (z-value = 21.74). We also constructed funnel plots, which are
depicted in Figures 2 and 3. Using the Trim and Fill method, the number of missing studies
that would need to fall to the left of the mean effect size in order to make the plot symmetric
was n = 7 studies for the analysis of anxiety measures and n = 10 for the analysis of depression
measures. Assuming a random-effects model, the new imputed mean effect size was Hedges’
g = 0.51 (95% CI: .39-.63) for anxiety and Hedges’ g = 0.50 (95% CI: 0.42-.58) for depression.
In sum, these analyses suggest that the effect size estimates of the pre-post analyses are
unbiased.
Effect sizes of studies with participants showing elevated levels of anxiety or
depression—A total of 10 studies used MBT in patients without a clinically defined anxiety
or mood disorder, but met our criteria for elevated levels of anxiety at pre-treatment: two studies
in cancer populations (Tacon, Caldera, & Ronaghan, 2004; Tacon, Caldera, & Ronaghan,
2005), four studies in populations with pain (Grossman, Tiefenthaler-Gilmer, Raysz, & Kesper,
2007; Lush et al., 2009; Sagula & Rice, 2004; Rosenzweig et al., 2009), three studies in
populations with other medical problems (Schulte, 2007; Surawy, Roberts, and Silver, 2005
Studies 1 and 2), and one study using a sample with Binge Eating Disorder (Kristeller & Hallett,
1999). The average pre-post effect size estimate (Hedges’ g) based on these studies was 0.67
(95% CI: 0.47-0.87, p < .01). The fail-safe N was robust at 401 (z-value = 12.55). The average
pre-post effect size estimate (Hedges’ g) for the 15 studies that did not have elevated levels of
anxiety symptoms at pre-treatment was 0.53 (95% CI: 0.42-0.64, p < .01). This result was also
robust (fail-safe N = 774; z-value = 14.21).
A total of 8 studies met our criteria for elevated levels of depressive symptoms at pre-treatment:
four studies in populations with pain (Lush et al., 2009; Sagula & Rice, 2004; Sephton et al.,
2007; Rosenzweig et al., 2009), two studies in populations with other medical problems
(Bedard et al., 2003; Reibel, Greeson, Brainard, & Rosenzweig, 2001), one study using a
sample with Binge Eating Disorder (Kristeller & Hallett, 1999), and one study using a sample
with ADHD (Zylowska et al., 2008). The average pre-post effect size estimate (Hedges’ g)
based on these studies was 0.53 (95% CI: 0.44-0.61, p < .01). The fail-safe N was 296 (z-value
= 12.08), indicating that these results are also robust. The average pre-post effect size estimate
(Hedges’ g) for the 16 studies that did not have elevated levels of depressive symptoms at pre-
treatment was 0.50 (95% CI: 0.39-0.61, p < .01). This result was also robust (fail-safe N = 667;
z-value = 12.80).
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Controlled effect sizes—Sixteen of the identified studies included a control or comparison
group. Eight of these studies compared a MBT to a waitlist control, 3 to treatment-as-usual
(TAU), and 5 to an active treatment comparison. Because patients in the waitlist control
conditions typically received treatment-as-usual, we pooled together studies employing a
waitlist control condition with those employing a TAU control condition. The random-effects
analysis of the controlled studies employing a waitlist or TAU comparison condition yielded
a mean Hedges’ g effect size of 0.41 (95% CI: 0.23-0.59, z = 4.35, p < .01) for continuous
measures of depressive symptom severity, and 0.33 (95% CI: 0.11-0.54, z = 2.97, p < .01) for
anxiety symptom severity. The random-effects analysis of the controlled studies employing an
active treatment comparison condition yielded a mean Hedges’ g effect size of 0.50 (95%
CI: 0.26-0.74, z = 4.06, p < .01) for continuous measures of depressive symptom severity, and
0.81 (95% CI: 0.35-1.27, z = 3.47, p < .01) for anxiety symptom severity. However, the fail-
safe Ns for controlled studies for measures of depression and anxiety disorder severity were
n = 35 studies (z = 4.31) and n = 11 (z = 3.08) for waitlist controlled and TAU studies, and n
= 19 studies (z = 4.21) and n = 42 (z = 5.97) for active treatment controlled studies, respectively.
These results suggest that the effect size for anxiety disorder severity for active treatment
controlled studies is robust. However, the effect sizes for the controlled studies are unreliable
and should be considered preliminary.
Intent-to-Treat Analyses—For the six studies that reported ITT data for continuous
measures of anxiety or depression symptom severity, we examined effect sizes for MBT from
pre- to post-treatment. Three studies reported ITT data for anxiety measures. The effect size
for the pooled data was Hedges’ g = 1.06 (95% CI: 0.29-1.84, p = .007). Six studies reported
ITT data for depression measures. The effect size for this pooled data was Hedges’ g = 0.55
(95% CI: 0.43-0.67, p < .001). The fail-safe N for measures of anxiety severity was 42 (z-value
= 7.55), indicating that 42 studies with an effect size of zero would be necessary to nullify this
result. The fail-safe N for measures of depression severity was 123 (z-value = 9.07). Given the
small number of studies for these analyses, these results should be interpreted with caution.
Effects at Follow-Up—To examine long-term outcome, we further conducted an effect size
analysis for MBT from pre-treatment to the last available follow-up point. A total of nineteen
studies reported follow-up data for measures of anxiety or depression symptoms. The mean
length of follow-up was 27 weeks (SD = 32), with a median of 12 weeks. Seventeen studies
reported follow-up data for anxiety measures. The effect size for the pooled data was Hedges’
g = 0.60 (95% CI: 0.48-0.71, p < .001). Eighteen studies reported follow-up data for depression
measures. The effect size for this pooled data was Hedges’ g = 0.60 (95% CI: 0.48-0.72, p < .
001). The fail-safe N for measures of anxiety symptoms at follow-up was 806 (z-value = 13.63),
and of depression symptoms at follow-up was 952 (z-value = 14.38), suggesting that both effect
size estimates can be considered robust.
Moderator Analyses
In order to explore possible predictors of treatment outcome, we conducted moderator analyses
only for the within-subject data from participants receiving a MBT.
Treatment Target—In order to examine whether MBT for patients with anxiety disorders
and depression results in greater reductions of symptoms of anxiety and depression than MBT
for other patients, we compared effect sizes for continuous measures of anxiety and depression
symptoms across the following 4 diagnostic categories: anxiety disorders, mood disorders,
cancer, and pain.
MBT showed significant effects for reducing anxiety symptoms in individuals with anxiety
disorders (n = 7 studies; Hedges’ g = 0.97, 95% CI: 0.72-1.22, p < .01), followed by individuals
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with cancer (n = 8 studies; Hedges’ g = 0.64, 95% CI: 0.45-0.82, p < .01), and pain disorders
(n = 5 studies; Hedges’ g = 0.44, 95% CI: 0.21-0.68, p < .01). However, the intervention had
no significant effect on anxiety symptoms in individuals with depression (n = 1 study; Hedges’
g = 0.12, 95% CI: 0.50-0.74, p = 0.70).
Similarly, MBT was effective for reducing depressive symptoms in individuals with a
diagnosis of depression (n = 4 studies; Hedges’ g = 0.95, 95% CI: 0.71-1.18, p < .01), followed
by individuals with an anxiety disorder (n = 6 studies; Hedges’ g = 0.75, 95% CI: 0.58-0.92,
p < .01), pain (n = 6 studies; Hedges’ g = 0.51, 95% CI: 0.39-0.63, p < .01), and cancer (n = 7
studies; Hedges’ g = 0.45, 95% CI: .34-0.56, p < .01).
Type of mindfulness-based intervention—We compared pre-post effect sizes for
MBCT and MBSR on both depression and anxiety symptom severity. Nine studies employing
MBCT reported data from measures of depressive symptom severity. The mean effect size for
this pooled data was Hedges’ g = 0.85 (95% CI: 0.71-1.00, p < .01). Nineteen studies employing
MBSR reported data from measures of depressive symptom severity, and the effect size for
the pooled data was Hedges’ g = 0.49 (95% CI: 0.42-0.56, p < .01). Six studies employing
MBCT reported data from measures of anxiety symptom severity, and the mean effect size for
this pooled data was Hedges’ g = 0.79 (95% CI: 0.45-1.13, p < .001). Twenty studies employing
MBSR reported data from measures of anxiety symptom severity, and the effect size for the
pooled data was Hedges’ g = 0.55 (95% CI: 0.44-0.66, p < .001). These results suggest that
MBCT and MBSR are both effective for reducing anxiety and depression from pre to post-
treatment.
Publication year—Hedges’ g was not moderated by publication year for either depression
(B = 0.002, SE = 0.011, p = 0.86) or anxiety symptoms (B = 0.00007, SE = 0.015, p = 0.99).
Treatment length—Hedges’ g was not moderated by number of treatment sessions for either
depression (B = 0.051, SE = 0.041, p = 0.21) or anxiety symptom severity (B = 0.074, SE =
0.055, p = 0.18).
Study Quality—Jadad score did not moderate Hedges’ g for either depression (B = 0.0017,
SE = 0.048, p = 0.96) or anxiety symptoms (B = 0.013, SE = 0.042, p = 0.85).
Discussion
MBT is an increasingly popular form of therapy for anxiety and mood problems. Two earlier
reviews on the effects of MBT on symptoms of anxiety and depression came to contradictory
conclusions with regards to the efficacy of these interventions (Baer, 2003; Toneatto &
Nguyen, 2007). Since the publication of these reviews, a sufficient number of clinical trials
have been published that justifies a comprehensive effect size analysis of this promising
treatment.
Our review of the literature identified 727 articles, of which we analyzed 39 studies to derive
effect size estimates. The results showed that the uncontrolled pre-post effect size estimates
were in the moderate range for reducing anxiety symptoms (Hedges’ g = 0.63) and depressive
symptoms (Hedges’ g = 0.59). MBT in patients with anxiety disorders and depression was
associated with large effect sizes (Hedges’ g) of 0.97 (95% CI: 0.72-1.22) and 0.95 (95% CI:
0.71-1.18) for improving anxiety and depression, respectively.
Among individuals with disorders other than anxiety disorders or depression, but who had
elevated levels of symptoms of anxiety and depression, MBT was moderately strong (effect
sizes of 0.67 and 0.53, respectively), but not significantly greater than among those with
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relatively lower pre-treatment levels of anxiety and depression (0.53 and 0.50). These results
suggest that MBT improves symptoms of anxiety and depression across a relatively wide range
of severity and even when these symptoms are associated with other disorders, such as medical
problems. It is possible that MBT is associated with a general reduction in stress, perhaps by
encouraging patients to relate differently to their physical symptoms so that when they occur
their consequences are less disturbing.
It should be noted that two of the four studies investigating depression focused on patients with
chronic or treatment-resistant depression (Barnhofer et al., 2009; Kenny & Williams, 2007),
and therefore the effect sizes for these studies might be lower than would otherwise be expected.
It should also be noted that the effects of MBT on depression and anxiety in chronic conditions,
such as cancer, might be smaller because patients may experience physical symptoms listed
on depression or anxiety scales as a result of their physical condition or as potential side-effects
of medical treatments. In addition, effect sizes for depression and anxiety symptoms in
populations with cancer, pain, or other medical conditions may be smaller than effect sizes in
populations with anxiety or mood disorders due to a floor effect: that is, patients with a low
level of anxiety or depression at pre-treatment may show a relatively smaller degree of
improvement after treatment than those with a high level at pre-treatment.
Earlier quantitative and qualitative reviews that were most closely related to our study include
the studies by Baer (2003) and Toneatto and Nguyen (2007). Baer (2003) reported an average
pre-post effect size of d = 0.59 based on 15 studies that were weighed by sample size. However,
the dependent variables were not restricted to anxiety and depression measures but were based
on a range of symptom measures, including measures of stress, pain, memory, and binge eating.
Therefore, it is difficult to directly compare the effect size estimates found in our study with
those reported by Baer.
In contrast to Baer (2003), Toneatto and Nguyen (2007) focused only on anxiety and depression
measures. Although published very recently, this review identified only 15 studies that
measured anxiety and depression in patients treated with MBT for a variety of problems,
including medical conditions (pain, cancer and heart disease). The study also examined non-
clinical populations (i.e., community samples). The authors concluded that MBT does not have
reliable effects on anxiety and depression. Our study suggests that this conclusion was
premature and unsubstantiated. The authors included only controlled studies, thereby
excluding a substantial portion of the MBT research. In addition, it is unclear how many studies
were identified, how many were excluded, and for what reasons, because this information was
not provided. Furthermore, the authors did not conduct an effect size analysis or apply any
other standard meta-analytic procedures. Instead, the conclusion was based solely on a
qualitative review of a very small number of studies. Finally, their findings were largely based
on patients without anxiety disorders or depression. As our review demonstrated, MBT is most
efficacious for reducing symptoms of anxiety and depression in populations with mood or
anxiety disorders.
In addition to changes from pre to post, we also examined controlled effect sizes. These effect
sizes were smaller but still significant (Hedges’ g = 0.50 and 0.81 for reducing symptoms of
depression and anxiety in active treatment controlled studies, and Hedges’ g = 0.41 and 0.32
in waitlist and TAU controlled studies). However, the fail-safe N analysis suggested that, except
for measures of anxiety symptom severity in active controlled studies, the results of the
controlled effect size analyses were unreliable due to the small number of studies. Similarly,
although significant, the ITT effect sizes (Hedges’ g = 1.06 and 0.55 for reducing symptoms
of depression and anxiety, respectively) should only be considered preliminary. In contrast,
the pre-post effect sizes were robust. A meta-analysis of the effects of psychological placebo
conditions in anxiety disorder trials (Smits & Hofmann, 2009) yielded a pre- to post-treatment
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effect size (Hedges’ g) of 0.45 (95% CI: 0.35-0.46), suggesting that the effect sizes associated
with MBT are significantly greater than the placebo effect size.
In general, the observed effect sizes were unrelated to publication year, treatment length, or
study quality. Finally, the follow-up data suggested that the effects were maintained at follow-
up (with a median follow-up period of 12 weeks). It should be noted that conventional CBT
(i.e., without mindfulness procedures) is also quite effective for depression and anxiety
disorders (e.g., Butler, Chapman, Forman, & Beck, 2006; Hofmann & Smits, 2008a). In their
review of meta-analyses examining the efficacy of conventional CBT for unipolar depression,
generalized anxiety disorder, panic disorder with or without agoraphobia, social anxiety
disorder, and PTSD, Butler et al. (2006) estimated the effect size to be 0.95 (SD: 0.08). Future
studies should directly compare the efficacy, cost-effectiveness, patient (and therapist)
preference, treatment acceptability, and attrition of conventional CBT and MBT.
In sum, our findings are encouraging and support the use of MBT for anxiety and depression
in clinical populations. This pattern of results suggests that MBT may not be diagnosis-specific,
but, instead, may address processes that occur in multiple disorders by changing a range of
emotional and evaluative dimensions that underlie general aspects of wellbeing. Therefore,
MBT may have general applicability. At the same time, a number of limitations should be
noted. Most importantly, the results of this study are limited to the meta-analytic technique
and, therefore, are dependent on the study selection criteria, the quality of the included studies,
expectancy effects, and statistical assumptions about the true values of the included studies
(Henggeler, Schoenwald, Swenson, & Borduin, 2006; Hofmann & Smits, 2008b; Moses et al.,
2002; Rief & Hofmann, 2008). In order to limit any possible biases, we adopted a relatively
conservative approach. Following the recommendations by Moses et al. (2002) and Hedges
and Vevea (1998), we analyzed the effect sizes using a random effect model and quantified
the quality of the included studies using modified Jadad criteria, which we considered in our
analyses as a possible moderator variable. Because we used modified Jadad criteria, the Jadad
scores cannot be directly compared with other meta-analytic studies.
Despite the popularity of MBT, relatively few clinical trials have specifically examined this
treatment in anxiety disorders and depression. However, a relatively large number of studies
have examined changes in anxiety and depressive symptoms in a range of psychiatric and
medical disorders. We decided to examine all available studies that reported changes in anxiety
and depressive symptoms during the course of MBT. As a result, the included studies differ in
the disorders targeted and also in their methodological quality. However, the Jadad scores did
not moderate the effect size estimate. Furthermore, it should be noted that the quality and
homogeneity of the studies included in the meta-analysis was considerably better than that of
studies used for other recently published meta-analytic reviews of established but poorly
validated psychodynamic interventions (Leichsenring & Rabung, 2008; Leichsenring, Rabung,
& Leibing, 2004). Moreover, the fail-safe N and funnel plot analyses suggest that the results
for uncontrolled pre-post effect sizes are robust and unlikely to be the effect of a publication
bias or number of treatment sessions and were maintained over an average 27 week follow-up
period (median: 12 weeks).
Perhaps the most important bias of meta-analyses is the expectancy effect. Cotton and Cook
(1982) recommended early on that the investigators of meta-analyses explicitly state their
personal view with regards to the outcome in order to acknowledge and possibly avoid the
expectancy effect. At the outset of our review, we were rather critical toward the efficacy of
MBT. We expressed our personal view in an earlier theoretical article (Hofmann &
Asmundson, 2008) and were fully prepared to report non-significant or only small effects of
MBT. We were surprised to find these effects to be rather robust and strong. Therefore, we
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believe that the expectancy bias was unlikely to be a significant contributor to the results, which
generally support the efficacy of MBT.
In order to avoid other common methodological pitfalls of meta-analyses (e.g., Hofmann &
Smits, 2008b), we decided to apply relatively liberal selection criteria by including any studies
that used MBT while examining treatment related changes in anxiety and depression.
Nevertheless, it is important to interpret the findings in the context of the study criteria, because
the average effect size estimate is a direct function of these criteria.
Another limitation was the fact that it was possible to calculate a controlled effect size for only
16 of the 39 trials, and except for measures of anxiety symptom severity in active treatment
controlled studies, the effect size estimates were not reliable due to a considerable publication
bias. However, the pre-post treatment effects were robust and unlikely to be the result of a
psychological placebo because the observed effect size is greater than what would be expected
from a psychological placebo (Smits & Hofmann, 2009). Nevertheless, future studies are
needed to clearly establish the efficacy of MBT in randomized controlled trials.
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Figure 1.
Flow diagram of study selection process.
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Figure 2.
Funnel plot of precision by Hedges’ g for anxiety measures. Note that in the absence of a
publication bias, the studies should be distributed symmetrically with larger studies appearing
toward the top of the graph and clustered around the mean effect size and smaller studies toward
the bottom.
Hofmann et al. Page 18
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Figure 3.
Funnel plot of precision by Hedges’ g for depression measures.
Hofmann et al. Page 19
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Table 1
Description of Studies
Study Year Primary Disorder Targeted by
Intervention Number of
Tx Sessions Mindfulness Intervention (N) Comparison Condition (N) Total Sample Size Anxiety Measures Depression Measures Jadad Score
Barnhofer et al. 2009 Depression 8 MBCT (14) TAU, excluding individual
psychotherapy (14) 28 BDI-II 3
BSS
Bedard et al. & *Bedard et
al. 2003 Traumatic 12 MBSR approach (10) Dropouts used as controls (3) 13 SCL-90-R anxiety subscale BDI-II 1
2005 brain injury SCL-90-R depression subscale
Bogels et al. 2006 SAD 9 Mindfulness and Task
Concentration Training (9) None 9 FNE 1
SCS
SFA
SPAI social phobia subscale
SPB
Carlson et al. & *Carlson
et al. 2003 Cancer 8 + 3-hr
retreat MBSR (42) None 42 POMS anxiety subscale POMS depression subscale 1
2007
SOSI anxiety/fear subscale SOSI depression subscale
Carlson & Garland 2005 Cancer 8 + 3-hr
retreat MBSR (63) None 63 POMS anxiety subscale POMS depression subscale 0
SOSI anxiety/fear subscale SOSI depression subscale
Craigie et al. 2008 GAD 9 MBCT (20) None 20 BAI BDI-II 1
DASS21 anxiety subscale DASS21 depression subscale
PSWQ
Dobkin 2008 Breast cancer 8 MBSR (13) None 13 CES-D 0
Evans et al. 2008 GAD 8 MBCT (11) None 11 BAI BDI-II 1
POMS anxiety subscale
PSWQ
*Finucane & Mercer 2006 Depression Anxiety 8 MBCT (11) None 11 BAI BDI-II 1
Garland et al. 2007 Cancer 8 + 3-hr
retreat MBSR (60) Healing though the Creative Arts
(44) 104 POMS anxiety subscale POMS depression subscale 1
SOSI anxiety/fear subscale SOSI depression subscale
Grossman 2007 Fibromyalgia 8 + 1-day
retreat MBSR (39) Educational social support group
with relaxation training (13) 52 HADS anxiety subscale HADS depression subscale 1
IPR anxiety subscale IPR depression subscale
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Study Year Primary Disorder Targeted by
Intervention Number of
Tx Sessions Mindfulness Intervention (N) Comparison Condition (N) Total Sample Size Anxiety Measures Depression Measures Jadad Score
Kabat-Zinn et al. 1992 GAD 8 + 1-day
retreat MBSR (22) None 22 BAI BDI 1
PD HAM-A HAM-D
MSCL anxiety subscale
SCL-90-R anxiety subscale
Kenny & Williams 2007 MDD 8 MBCT (46) None 46 BDI 1
BPAD (depressed phase)
Kievet-Stijnen et al. 2008 Cancer 8 + 1-day
retreat MBSR (47) None 47 POMS anxiety subscale POMS depression subscale 1
Kim et al. 2009 GAD 8 MBCT (24) Anxiety disorder education
program (22) 46 BAI BDI 1
PD HAM-A HAM-D
SCL-90-R anxiety subscale SCL-90-R depression subscale
Kingston et al. 2007 MDD 8 MBCT (6) TAU (11) 17 BDI 1
RS
Koszycki et al. 2007 SAD 8 + 1-day
retreat MBSR (22) CBGT (18) 40 IPSM BDI-II 2
LSAS
SIAS
SPS
Kreitzer et al. 2005 Organ transplant 8 MBSR (19) None 19 STAI state anxiety subscale CES-D 1
Kristeller & Hallett 1999 BED 7 Mindfulness meditation training
(18) None 18 BAI BDI 1
Lee et al. 2007 GAD 8 Meditation-based stress
management (21) Educational program (20) 41 HAM-A BDI 2
PD SCL-90-R anxiety subscale HAM-D
SCL-90-R depression subscale
STAI
Lengacher et al. 2009 Breast cancer 6 MBSR (40) Usual care (42) 82 STAI CES-D 2
Lush et al. 2009 Fibromyalgia 8 MBSR (24) None 24 BAI BDI 1
Moustgaard 2005 Stroke 9 Adapted MBCT (23) None 23 BAI BDI-II 1
HADS anxiety subscale HADS depression subscale
Pradhan et al. 2007 Arthritis 8 MBSR (31) Waitlist (32) 63 SCL-90-R anxiety subscale SCL-90-R depression subscale 3
Ramel et al. 2004 Mood disorders (current or lifetime) 8 + half-day
retreat MBSR (11) Waitlist (11) 22 STAI BDI 1
DAS
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Study Year Primary Disorder Targeted by
Intervention Number of
Tx Sessions Mindfulness Intervention (N) Comparison Condition (N) Total Sample Size Anxiety Measures Depression Measures Jadad Score
RSQ rumination subscale
Ree & Craigie 2007 Anxiety, Mood (heterogeneous
sample) 8 MBCT (23) None 23 DASS anxiety subscale BDI 1
DASS depression subscale
Reibel et al. 2001 Heterogeneous medical diagnoses 8 + 1-day
retreat MBSR (103) None 103 SCL-90-R anxiety subscale SCL-90-R depression subscale 1
Rosenzweig et al. 2007 Diabetes 8 + 1-day
retreat MBSR (11) None 11 SCL-90-R anxiety subscale SCL-90-R depression subscale 1
Rosenzweig et al. 2009 Chronic pain 8 + 1-day
retreat MBSR (99) None 99 SCL-90-R anxiety subscale SCL-90-R depression subscale 1
Sagula & Rice 2004 Chronic pain 8 Mindfulness meditation program
(39) Waitlist or medical assistance
(18) 57 STAI BDI- Short Form 1
Schulte 2007 Hypothyroidism 8 MBCT (8) None 8 STAI BDI-II 1
Sephton et al. 2007 Fibromyalgia 8 + 1-day
retreat MBSR (51) Waitlist (39) 90 BDI 3
Speca et al. & *Carlson et
al. 2000 Cancer 7 MBSR (53) Waitlist (37) 90 POMS anxiety subscale POMS depression subscale 3
2001
SOSI anxiety/fear subscale SOSI depression subscale
Surawy et al. 2005 Chronic Fatigue Syndrome
Study 1 8 Mindfulness training based on
MBSR and MBCT (9) Waitlist (8) 17 HADS anxiety subscale HADS depression subscale 2
Study 2 8 Mindfulness training based on
MBSR and MBCT (10) None 10 HADS anxiety subscale HADS depression subscale 1
Study 3 8 Mindfulness training based on
MBSR and MBCT (9) None 9 HADS anxiety subscale HADS depression subscale 1
Tacon et al. 2003 Heart disease 8 MBSR (9) Waitlist (9) 18 STAI state anxiety subscale 2
Tacon et al. 2004 Breast cancer 8 MBSR (27) None 27 STAI state anxiety subscale 0
Tacon et al. 2005 Breast cancer 8 MBSR (30) None 30 STAI state anxiety subscale 0
Zylowska et al. 2008 ADHD 8 Mindful Awareness Practices for
ADHD (24) None 24 BAI BDI 1
Note: ADHD = Attention Deficit Hyperactivity Disorder; BED = Binge Eating Disorder; BPAD = Bipolar Affective Disorder; GAD = Generalized Anxiety Disorder; MDD = Major Depressive Disorder; OCD = Obsessive Compulsive Disorder; PD = Panic Disorder; SAD = Social
Anxiety Disorder; MBCT = Mindfulness-Based Cognitive Therapy (Segal et al., 2002); MBSR = Mindfulness-Based Stress Reduction (Kabat-Zinn, 1982); TAU= Treatment as usual; BAI = Beck Anxiety Inventory (Beck & Steer, 1990); BDI = Beck Depression Inventory (Beck,
Ward, Mendelson, Mock, & Erbaugh, 1961); BDI-II = Beck Depression Inventory II (Beck, Steer, & Brown, 1996); BDI- Short Form = Beck Depression Inventory- Short Form (Beck & Beck, 1972); BSS = Beck Scale for Suicidal Ideation (Beck & Steer, 1991); CES-D = Center
for Epidemiologic Studies Depression Scale (Radloff, 1977); DAS = Dysfunctional Attitudes Scale (Weissman & Beck, 1978); DASS = Depression Anxiety Stress Scales (Lovibond & Lovibond, 1996); DASS21 = Depression Anxiety Stress Scales- Short Form (Lovibond &
Lovibond, 1996); FNE = Fear of Negative Evaluation Scale (Leary, 1983); HADS = Hospital Anxiety and Depression Scale (Zigmond & Snaith, 1983); HAM-A = Hamilton Anxiety Rating Scale (Hamilton, 1959); HAM-D = Hamilton Depression Rating Scale (Hamilton,
1960); IPR = Inventory of Pain Regulation (Schermelleh-Engel, 1995); IPSM = Interpersonal Sensitivity Measure (Boyce & Parker, 1989); LSAS = Liebowitz Social Anxiety Scale (Liebowitz, 1987); MSCL = Medical Symptom Checklist (Kabat-Zinn, 1982); POMS = Profile of
Mood States (McNair et al., 1971); PSWQ = Penn State Worry Questionnaire (Meyer, Miller, Metzger, & Borkovec, 1990); RS = Rumination Scale (Nolen-Hoeksema & Morrow, 1991); RSQ = Response Style Questionnaire (Nolen-Hoeksema & Morrow, 1991); SCL-90-R =
Hopkins Symptom Checklist- Revised (Derogatis, 1983); SCS = Self Consciousness Scale (Fenigstein, Scheier, & Buss, 1975); SFA = Self-Focused Attention Scale (Bögels, Alberts, & de Jong, 1996); SIAS = Social Interaction Scale (Mattick & Clarke, 1988); SOSI = Symptoms
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of Stress Inventory (Leckie & Thompson, 1979); SPAI = Social Phobia and Anxiety Inventory (Turner, Beidel, Dancu, & Stanley, 1989); SPB = Social Phobic Belief Scale (Voncken, Bögels, & De Vries, 2003); SPS = Social Phobia Scale (Mattick & Clarke, 1988); STAI = State-
Trait Anxiety Inventory (Speilberger, Gorsuch, & Lushene, 1970).
*Denotes studies providing follow-up data not included in initial study
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Table 2
Effect size analysis of studies examining the efficacy of mindfulness-based therapy on anxiety symptoms in
various disorders.
Category Study Hedges'g95% Confidence Interval p-value
Targeted Disorder
Anxiety Disorders
GAD Craigie et al., 2008 0.69 0.32 – 1.06 <0.01
Evans et al., 2008 0.89 0.38 – 1.41 0.02
GAD/Panic Disorder Kabat-Zinn et al., 1992 0.84 0.46 – 1.22 <0.01
Kim et al., 2009 1.61 1.08 – 2.14 <0.01
Lee et al., 2007 2.13 1.29 – 2.97 <0.01
SAD Bogels et al., 2006 0.48 0.01 – 0.98 0.06
Koszycki et al., 2007 0.93 0.54 – 1.32 <0.01
Subtotal Anxiety Disorders 0.97 0.73 – 1.22 <0.01
Depression Ramel et al., 2004 0.12 0.30 – 0.55 0.70
Pain Disorders
Arthritis Pradhan et al., 2007 0.21 0.08 – 0.50 0.15
Chronic Pain Rosenzweig et al., 2009 0.54 0.37 – 0.70 <0.01
Sagula & Rice, 2004 0.64 0.38 – 0.91 <0.01
Fibromyalgia Grossman, 2007 0.55 0.29 – 0.80 <0.01
Lush et al., 2009 0.24 0.06 – 0.55 0.12
Subtotal Pain Disorders 0.44 0.22 – 0.67 <0.01
Cancer
Breast Cancer Lengacher et al., 2009 0.75 0.48 – 1.02 <0.01
Tacon et al., 2004 1.25 0.87 – 1.64 <0.01
Tacon et al., 2005 1.19 0.84 – 1.55 <0.01
Breast/ Prostate Cancer Carlson et al., 2003 0.21 0.03 – 0.44 0.08
Heterogeneous Carlson & Garland, 2005 0.51 0.31 – 0.71 <0.01
Garland et al., 2007 0.50 0.29 – 0.70 <0.01
Kieviet-Stijnen et al.,
2008 0.36 0.13 – 0.58 <0.01
Speca et al., 2000 0.63 0.41 – 0.86 <0.01
Subtotal Cancer 0.63 0.45 – 0.81 <0.01
Medical Problems
Chronic Fatigue Surawy et al., 2005 (1) 0.69 0.17 – 1.21 0.01
Surawy et al., 2005 (2) 1.07 0.50 – 1.64 <0.01
Surawy et al., 2005 (3) 0.73 0.20 – 1.25 0.01
Diabetes Rosenzweig et al., 2007 0.28 0.15 – 0.71 0.21
Heart Disease Tacon et al., 2003 0.79 0.25 – 1.32 <0.01
Heterogeneous Reibel et al., 2001 0.53 0.37 – 0.69 <0.01
Hypothyroidism Schulte, 2007 0.30 0.20 – 0.80 0.23
Organ Transplant Kreitzer et al., 2005 0.41 0.06 – 0.76 0.02
Stroke Moustgaard, 2005 0.98 0.59 – 1.36 <0.01
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Category Study Hedges'g95% Confidence Interval p-value
Targeted Disorder
TBI Bedard et al., 2003 0.47 0.01 – 0.94 0.05
Subtotal Medical Problems 0.61 0.41 – 0.80 <0.01
Other
ADHD Zylowska et al., 2008 0.68 0.35 – 1.02 <0.01
Anxiety/ Mood Ree & Craigie, 2007 0.62 0.28 – 0.95 <0.01
BED Kristeller & Hallett,
1999 0.63 0.25 – 1.00 <0.01
Overall Total 0.63 0.53 – 0.73 <0.01
Note. The Table shows effect size estimates (Hedges’ g), the 95% confidence intervals, and the significance test of changes in anxiety symptoms from
before to after a mindfulness-based intervention in various psychiatric and medical disorders.
ADHD = Attention Deficit Hyperactivity Disorder; BED = Binge Eating Disorder; GAD = Generalized Anxiety Disorder; SAD = Social Anxiety
Disorder; TBI = Traumatic Brain Injury.
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Table 3
Effect size analysis of studies examining the efficacy of mindfulness-based therapy on depressive symptoms in
various disorders.
Category Study Hedges'g95% Confidence Interval p-value
Targeted Disorder
Anxiety Disorders
GAD Craigie et al., 2008 0.75 0.37 – 1.13 <0.01
Evans et al., 2008 0.56 0.10 – 1.02 0.02
GAD/Panic Disorder Kabat-Zinn et al., 1992 0.81 0.44 – 1.18 <0.01
Kim et al., 2009 0.92 0.56 – 1.29 <0.01
Lee et al., 2007 0.78 0.41 – 1.15 <0.01
SAD Koszycki et al., 2007 0.62 0.28 – 0.96 <0.01
Subtotal Anxiety Disorders 0.75 0.58 – 0.91 <0.01
Depression Barnhofer et al., 2009 0.80 0.35 – 1.26 <0.01
Kingston et al., 2007 1.52 0.67 – 2.36 <0.01
Kenny & Williams, 2007 1.05 0.77 – 1.32 <0.01
Ramel et al., 2004 0.63 0.14 – 1.13 0.01
Subtotal Depression 0.95 0.71 – 1.18 <0.01
Pain Disorders
Arthritis Pradhan et al., 2007 0.48 0.18 – 0.78 <0.01
Chronic Pain Rosenzweig et al., 2009 0.49 0.33 – 0.65 <0.01
Sagula & Rice, 2004 0.71 0.45 – 0.98 <0.01
Fibromyalgia Grossman, 2007 0.50 0.24 – 0.75 <0.01
Lush et al., 2009 0.47 0.16 – 0.79 <0.01
Sephton, 2007 0.45 0.23 – 0.67 <0.01
Subtotal Pain Disorders 0.51 0.39 – 0.63 <0.01
Cancer
Breast Cancer Dobkin et al., 2008 0.58 0.15 – 1.01 0.01
Lengacher et al., 2009 0.66 0.40 – 0.92 <0.01
Breast/ Prostate Cancer Carlson et al., 2003 0.15 0.09 – 0.38 0.22
Heterogeneous Carlson & Garland, 2005 0.44 0.24 – 0.64 <0.01
Garland et al., 2007 0.45 0.24 – 0.65 <0.01
Kieviet-Stijnen et al.,
2008 0.30 0.07 – 0.52 0.01
Speca et al., 2000 0.67 0.44 – 0.90 <0.01
Subtotal Cancer 0.45 0.34 – 0.55 <0.01
Medical Problems
Chronic Fatigue Surawy et al., 2005 (1) 0.13 0.33 – 0.59 0.58
Surawy et al., 2005 (2) 0.25 0.19 – 0.70 0.26
Surawy et al., 2005 (3) 0.80 0.26 – 1.35 <0.01
Diabetes Rosenzweig et al., 2007 0.79 0.30 – 1.29 <0.01
Heterogeneous Reibel et al., 2001 0.48 0.32 – 0.63 <0.01
Hypothyroidism Schulte, 2007 0.73 0.18 – 1.28 0.01
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Category Study Hedges'g95% Confidence Interval p-value
Targeted Disorder
Organ Transplant Kreitzer et al., 2005 0.51 0.15 – 0.87 0.01
Stroke Moustgaard, 2005 1.01 0.63 – 1.40 <0.01
TBI Bedard et al., 2003 0.73 0.22 – 1.23 <0.01
Subtotal Medical Problems 0.58 0.47 – 0.70 <0.01
Other
ADHD Zylowska et al., 2008 0.68 0.35 – 1.02 <0.01
Anxiety/ Mood Ree & Craigie, 2007 0.62 0.28 – 0.95 <0.01
BED Kristeller & Hallett,
1999 0.63 0.25 – 1.00 <0.01
Overall Total 0.59 0.51 – 0.66 <0.01
Note. The Table shows effect size estimates (Hedges’ g), the 95% confidence intervals, and the significance test of changes in depressive symptoms
from before to after a mindfulness-based intervention in various psychiatric and medical disorders.
ADHD = Attention Deficit Hyperactivity Disorder; BED = Binge Eating Disorder; GAD = Generalized Anxiety Disorder; SAD = Social Anxiety
Disorder; TBI = Traumatic Brain Injury.
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Improving the quality of reports of meta-analyses of randomized controlled trails: the QUOROM statement
checklist
Heading Subheading Descriptor Reported? (Y/N) Page number
Title Identify the report as a
meta-analysis [or
systematic review] of
RCTs
Yes 1
Abstract Use a structured format27 Yes 2
Describe
Objectives The clinical question
explicitly Yes 2
Data Sources The databases (ie, list)
and other information
sources
Yes 2
Review Methods The selection criteria (ie,
population, intervention,
outcome, and study
design); methods for
validity assessment, data
abstraction, and study
characteristics, and
quantitative data
synthesis in sufficient
detail to permit
replication
Yes 2
Results Characteristics of the
RCTs included and
excluded; qualitative and
quantitative findings (ie,
point estimates and
confidence intervals); and
subgroup analyses
Yes 2
Conclusion The main results Yes 2
    Describe    
Introduction The explicit clinical
problem, biological
rationale for the
intervention, and
rationale for review
Yes 5
Methods Searching The information sources,
in detail28 (eg, databases,
registers, personal files,
expert informants,
agencies, hand-
searching), and any
restrictions (years
considered, publication
status,29 language of
publication30,31)
Yes 6
Selection The inclusion and
exclusion criteria
(defining population,
intervention, principal
outcomes, and study
design32)
Yes 6-7
Validity assessment The criteria and process
used (eg, masked
conditions, quality
Yes 7
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Hofmann et al. Page 29
Heading Subheading Descriptor Reported? (Y/N) Page number
assessment, and their
findings33–36)
Data abstraction The process or processes
used (eg, completed
independently, in
duplicate)35,36
Yes 7-8
Study characteristics The type of study design,
participants’
characteristics, details of
intervention, outcome
definitions, &c,37 and
how clinical
heterogeneity was
assessed
Yes 8
Quantitative data synthesis The principal measures of
effect (eg, relative risk),
method of combining
results (statistical testing
and confidence intervals),
handling of missing data;
how statistical
heterogeneity was
assessed;38 a rationale for
any a-priori sensitivity
and subgroup analyses;
and any assessment of
publication bias39
Yes 8-10
Results Trial flow Provide a meta-analysis
profile summarising trial
flow (see figure)
Yes 10-11; 55
Study characteristics Present descriptive data
for each trial (eg, age,
sample size, intervention,
dose, duration, follow-up
period)
Yes 11
Quantitative data synthesis Report agreement on the
selection and validity
assessment; present
simple summary results
(for each treatment group
in each trial, for each
primary outcome);
present data needed to
calculate effect sizes and
confidence intervals in
intention-to-treat
analyses (eg 2×2 tables of
counts, means and SDs,
proportions)
Yes 11-17
Discussion Summarise key findings;
discuss clinical
inferences based on
internal and external
validity; interpret the
results in light of the
totality of available
evidence; describe
potential biases in the
review process (eg,
publication bias); and
suggest a future research
agenda
Yes 17-24
Quality of reporting of meta-analyses
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This research explores the intervention effect of the mindfulness acceptance insight commitment (MAIC) training program on the mindfulness level, flow state, and mental health of college swimmers. A sample of 47 college swimmers from a regular university was recruited and randomly divided into two groups before the intervention. Independent variables between groups are psychological training mode (MAIC training/no training), and the independent variable within group was time (pre-test, post-test, and continuity test). The dependent variables are mindfulness level, flow state, and mental health (anxiety, depression, training, and competition satisfaction). Results show that after the intervention of MAIC training, the mindfulness level of athletes’ flow state has been significantly improved, whereas anxiety and depression significantly decreased. In addition, the satisfaction with training and competition significantly improved. In the continuous stage after the intervention, the mindfulness level, flow state, and mental health of athletes are still significantly higher than those in the pre-test. The comparison of the post-test and continuity test show no significant differences in the mindfulness level, flow state, depression, and training and competition satisfaction of athletes. Still, the anxiety level shows an upward trend with a significant difference. This study demonstrates that the MAIC mindfulness training program can significantly improve the mindfulness level, flow state, anxiety, depression, and training and competition satisfaction of college swimmers with a good continuity effect. Thus, the athletes’ sports experience can be improved, and good psychological benefits can be attained.
... Mindfulness interventions focus on modifying habitual patterns of thinking as a means of preventing responses likely to maintain depression or contribute to relapse. (Ma and Teasdale 2004;Teasdale et al. 2000;Hofmann et al. 2010;Khoury et al. 2013) While the emphasis on internal self-control fits well with cultures sharing Buddhist values of equanimity and containment of affect, the approach has been repackaged to appeal to Euro-Americans intent on self-improvement (Kirmayer, 2015). Other Buddhism-inspired interventions include Acceptance and Commitment Therapy (ACT), which has also been further adapted to fit particular cultural contexts and communities (Fuchs et al. 2013;Perry et al. 2019;Shehadeh et al. 2016). ...
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Depression is now recognized as a global health problem, with estimates that it accounts for up to 10% of years lost to disability in developing countries (Desjarlais et al. 1995; Murray and Lopez 1997). Global disability due to depression has increased over the last two decades, especially for women (GBD 2017 DALYs and HALE Collaborators 2018). Given this widespread prevalence and social impact, there is a pressing need to examine cross-cultural differences in the causes, course and treatment of depression (Scott and Dickey 2003). At the same time, responding to the health disparities associated with cultural diversity is increasingly recognized as an important issue in high income countries, creating new challenges for mental health care (US Surgeon General 2002; Napier et al. 2014). Cultural psychiatry is concerned with the impact of variations in ways of life on psychiatric disorders and their treatment (Kleinman 1988). Among the questions it seeks to address are: How does culture shape the experience and expression of human suffering? What are the most effective methods of interpreting and responding to suffering in a given social or clinical context? Are the forms of distress identified in current psychiatric nosology similar around the globe? More specifically, can mood disorders be recognized across diverse cultures and do they have similar symptoms, course, treatment response and outcome? These questions raise complex epistemological and methodological problems for psychiatric research and practice (Kirmayer and Ban 2013). The answers are crucial to the advance of research, the design of health care systems and the delivery of clinical care in societies facing increasing cultural diversity (Kirmayer and Jarvis 2019). Much of what we know about the role of culture in psychopathology comes from qualitative ethnographic research in clinical settings and in the community (Kleinman 1988; Kirmayer 1989). Conventional psychiatric research is ill suited to explore the cultural meaning of distress because it tends to reduce the complexity of illness narratives to checklists of symptoms and signs of disorder. However, there is a growing body of epidemiological research informed by ethnography that goes beyond parochial assumptions to identify clinically important cultural variation. In this chapter, we review some of what is known about cultural variations in the prevalence, clinical presentation, mechanisms and treatment of depressive disorders and address some of the broader conceptual issues that are central to meaningful research and effective clinical intervention.
... Yet, our findings suggest that different interventions (not only those confined to positive psychology) can improve well-being, including ACT and mindfulness-based interventions. This is relevant, since it extends on previous meta-analytic findings showing that ACT (A-tjak et al., 2015; Öst, 2014) and mindfulness (Hofmann et al., 2010;Khoury et al., 2013) are effective in alleviating psychological symptoms. Our findings suggest that these interventions, in addition to alleviating symptoms, also have the potential to enhance overall well-being to varying degrees. ...
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... Mindfulness-based interventions (MBIs) refer to a set of interventions such as: (1) mindfulness-based stress reduction (MBSR), (2) mindfulness-based cognitive therapy (MBCT), (3) acceptance and commitment therapy (ACT), and (4) acceptance-based behavioral therapy (ABBT). The MBSR method is believed to improve current awareness of thoughts, feelings, and sensations through focused attention and open observation, and generate acceptance and non-judgmental attitudes towards current experience (Hofmann et al., 2010), and these interventions are aimed at reducing the relationship between negative thoughts and stressful emotions through awareness and acceptance (Greco et al., 2011). Many of these interventions have been shown to be effective in reducing depressive symptoms and promoting mental health (Raphiphatthana et al., 2016). ...
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This study aimed to explore emerging publication trends on mindfulness in children, using a bibliometric method. Articles or reviews from the Scopus database with the search terms “Mindfulness” and “Children” in the title, abstract, or keywords were included as the data for analysis. Results showed that there were 1,662 publications from 1985 to 2022. There were more than 100 publications in 2016, and the number of publications has been continuously increasing afterward. The top five cited publications explored the effects of various mindfulness-based activities or programs on children and youth. The most prolific journal was Mindfulness. The United States of America was the country that has had the largest number of corresponding authors and cited publications. The most prolific author was Nirbhay N. Singh. Moreover, from additional search terms “Thai” and “Thailand”, four publications that studied the effect of mindfulness-based activities and programs in Thailand were identified. However, results showed very few publications focusing on mindfulness scale development, especially for children, suggesting a research gap in the study of mindfulness in children. For future research, using the bibliometric method comparing studies in clinical-based settings with those in school-based settings might help classify emerging trends in mindfulness for children as an intervention and prevention approach.
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It is likely safe to assume that nearly all students are coming to, and engaging in, school settings with multiple stressors and personal challenges. Moreover, the global pandemic has likely exacerbated these mental health issues. As such, the general problem is that many students are not adequately prepared to handle stress and emotional challenges in conjunction with everyday life and in school. This has the potential to derail both their personal well-being and their academic success. Mindfulness is a viable resource in PK-12 school systems to guide the acquisition and development of emotion regulation. This chapter describes the conceptual underpinnings that make up mindfulness. More specifically, the purpose of this chapter is to delineate how mindfulness in PK-12 classrooms may be used to promote students' emotion regulation, and to provide clear and specific examples and strategies of mindfulness practices that facilitate the development of empathy and sound emotion regulation.
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