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Running Head: GRATITUDE, DEPRESSION, AND ANXIETY 1
Gratitude Interventions: Effective Self-Help? A Meta-Analysis of the Impact on Symptoms of
Depression and Anxiety
David R. Cregg and Jennifer S. Cheavens
Department of Psychology, The Ohio State University, Columbus, OH, USA, 43210
In press at Journal of Happiness Studies © 2020, Springer Nature B.V.
This paper is not the copy of record and may not exactly replicate the final, authoritative version
of the article. Please do not copy or cite without authors' permission. The final article will be
available upon publication via its DOI: 10.1007/s10902-020-00236-6. You may also view the
published typeset copy at: https://rdcu.be/b12vP
Jennifer S. Cheavens
147 Psychology Building
1835 Neil Avenue
Columbus, OH 43210
Conflict of Interest: The authors declare that they have no conflict of interest.
Acknowledgements: The authors would like to extend their sincerest thanks to Owen Morrish
for his assistance with data extraction and the risk of bias assessment.
GRATITUDE, DEPRESSION, AND ANXIETY 2
Research suggests gratitude interventions, designed to increase appreciation of positive qualities,
situations, and people in one’s life, may improve psychological well-being (e.g., Seligman et al.,
2005). Accordingly, mental health practitioners have promoted gratitude interventions as a
means of self-help. However, results from previous reviews (Davis et al., 2016; Wood, Froh, &
Geraghty, 2010) suggest that well-being improvements associated with gratitude interventions
may be attributable to placebo effects. With this meta-analysis, we examined the efficacy of
gratitude interventions (k = 27, N = 3,675) in reducing symptoms of depression and anxiety at
post-test and follow-up periods. Gratitude interventions had a small effect on symptoms of
depression and anxiety at both post-test (g = −0.29, SE = 0.06, p < .01) and follow-up (g = −0.23,
SE = 0.06, p < .01). Correcting for attenuation from unreliability did not change results.
Moderation analyses indicated effect sizes were larger for studies using waitlist, rather than
active, control conditions at post-test and follow-up. We did not find consistent evidence for
effects of other moderator variables (e.g., risk of bias, depressive symptom severity, or type of
intervention used). Our results suggest the effects of gratitude interventions on symptoms of
depression and anxiety are relatively modest. Therefore, we recommend individuals seeking to
reduce symptoms of depression and anxiety engage in interventions with stronger evidence of
efficacy for these symptoms.
Keywords: gratitude, thanks, intervention, positive psychology, depression, anxiety
GRATITUDE, DEPRESSION, AND ANXIETY 3
Gratitude is a state of affirming the goodness or good things in one’s life, accompanied
by a recognition that the sources of this goodness lie at least partially outside the self, such as
with the good intentions of another person (Emmons & Stern, 2013). Gratitude may be elicited
by another person when he or she provides some aid or benefit, but it may also stem from non-
social sources, such as a feeling of thanks for waking up in the morning (Wood, Froh, &
Geraghty, 2010). A number of studies have demonstrated that gratitude has strong associations
with measures of well-being, including positive correlations with positive affect, life satisfaction,
extraversion, and forgiveness, and negative associations with neuroticism and substance abuse
(see Watkins, 2014 and Wood et al., 2010 for reviews).
Additionally, the relationship between gratitude and symptoms of depression and anxiety
has been the focus of a number of empirical studies (see Petrocchi & Couyoumdjian, 2016 for a
review). For example, both Stoeckel, Weissbrod, and Ahrens (2014) and Watkins, Woodward,
Stone, and Kolts (2003) found an inverse correlation of moderate strength between gratitude and
symptoms of depression (r = −.48 and r = −.56, respectively). Likewise, Krumrei and Pargament
(2008) reported an inverse association of moderate strength between gratitude and symptoms of
anxiety (r = −.46). Additionally, Kendler et al. (2003) reported a reduced lifetime prevalence of
Generalized Anxiety Disorder among those scoring higher in thankfulness, OR = .82. Although
investigators are just beginning to uncover the specific mechanisms by which gratitude relates to
depression and anxiety, there are several possible explanations for the inverse association
between these variables. First, as Wood, Froh, and Geraghty (2010) noted, gratitude is associated
with interpreting various stimuli and life events in positive terms, which contrasts with the
selective attention to negative qualities of the self, the world, and the future that is characteristic
of depression and anxiety (Mogg & Bradley, 2005; Peckham, McHugh, & Otto, 2010).
GRATITUDE, DEPRESSION, AND ANXIETY 4
Consistent with Wood et al.’s idea, Petrocchi and Couyoumdjian (2016) found the inverse
relationship between gratitude and symptoms of depression and anxiety is accounted for by a less
critical, less punishing, and more compassionate view of oneself. Researchers have also found
gratitude is associated with greater relationship connection and satisfaction (Algoe, Gable, &
Maisel, 2010), which are well-established buffers against psychopathology (Seppala,
Rossomando, & Doty, 2013). Finally, another way gratitude may guard against anxiety, in
particular, is its relationship with uncertainty. A well-known characteristic of the worry observed
in anxiety disorders is an intolerance of uncertainty (Carleton et al., 2012). Practicing gratitude
may train one to be content in his or her present circumstances, whatever they may be, thus
attenuating a fear of uncertain outcomes.
Given that gratitude is associated with a number of positive qualities, researchers have
designed and tested several interventions to increase gratitude. For example, Emmons’ and
McCullough’s (2003) seminal study asked participants to record five things for which they felt
grateful each week for 10 weeks. They found significant increases in positive affect and hours
spent exercising among the gratitude group, as well as improved sleep quality and reduced
physical symptoms. Several researchers have also reported significant effects of gratitude
interventions on symptoms of depression and anxiety. For instance, Seligman and colleagues
(Seligman, Steen, Park, & Peterson, 2005) compared the effects of two gratitude interventions to
a control condition (journaling about early memories) for reducing depressive symptoms. In the
first gratitude condition, named “three good things,” participants were instructed to keep a daily
record of three good things and explain why they happened for one full week. In the second
gratitude condition, named the “gratitude visit,” participants were asked to write and personally
deliver a letter to someone they had never properly thanked. Seligman et al. found the three good
GRATITUDE, DEPRESSION, AND ANXIETY 5
things exercise led to reduced depressive symptoms for up to six months compared to the control
group, and the gratitude visit reduced depressive symptoms for up to one month (although it had
the largest positive improvements at post-test).
In a study on how gratitude interventions affect anxiety within a clinical sample, Kerr,
O’Donovan, and Pepping (2015) recruited individuals on a waitlist for an outpatient psychology
clinic. Client difficulties included depression, anxiety, and PTSD, as well as substance use and
eating disorders. Individuals were randomized to either record up to five things they felt grateful
for in the past day for 14 days, or to keep a daily mood diary in the control condition. The
researchers found the gratitude intervention – but not the control task – significantly reduced
anxiety over the 14-day period. Geraghty, Wood, and Hyland (2010) obtained similar results.
Using an online community sample, they randomized participants to a waitlist condition or to a
gratitude condition, in which participants listed six items for which they were grateful each day
for 14 days. The gratitude condition led to significant reductions in worry, whereas the waitlist
group experienced little change from baseline. Based on results such as these, researchers have
suggested gratitude interventions may be an effective, low-cost, and easily implementable
psychotherapeutic tool (Duckworth, Steen, & Seligman, 2005; Seligman, Rashid, & Parks,
However, in spite of these seemingly promising findings, a qualitative review by Wood,
Froh, and Geraghty (2010) questioned the efficacy of gratitude interventions for a variety of
outcomes, including depression and anxiety.
The authors argued many of the intervention
studies included control groups that made inferences about the efficacy of gratitude interventions
ambiguous. For example, they pointed out studies that, instead of including a neutral control
Additional outcomes reviewed included body dissatisfaction, changes in positive and negative affect, life
satisfaction, sleep quality, and physical pain.
GRATITUDE, DEPRESSION, AND ANXIETY 6
task, included comparisons that may actually have harmful effects, such as listing hassles
(Emmons & McCullough, 2003) or things one was unable to accomplish over the summer
(Watkins et al., 2003). Relying on control conditions that may have effects in the opposite
direction of the gratitude intervention (i.e., increasing symptoms of distress or reducing well-
being) may inflate the effect size estimate. Conversely, a study by Lyubomirsky and colleagues
with a more neutral control condition (writing about one’s weekly schedule) found the gratitude
intervention to have little effect on symptoms of depression (Lyubomirsky, Dickerhoof, Boehm,
& Sheldon, 2011). Given the heterogeneity of these control groups, Wood, Froh, and Geraghty
(2010) cautioned against a premature declaration of the success of gratitude interventions until
more rigorous investigations could be conducted. Indeed, they noted of the 12 interventions
included in their review, “only a very small number show that gratitude interventions are more
effective than genuine controls” (Wood et al., 2010, p. 898).
Responding to Wood et al.’s (2010) critique, Davis et al. (2016) recently conducted a
meta-analysis investigating the effects of gratitude interventions on gratitude, anxiety, and
psychological well-being. They found gratitude interventions had generally limited effects, with
effect sizes ranging from d = 0.31 for a measurement-only control (i.e., waitlist) to d = −0.03 for
a “psychologically active” comparison (i.e., one that might be reasonably expected to promote
psychological well-being, such as an automatic thought record). Based on these results, the
authors concluded the evidence for the efficacy of gratitude interventions on psychological well-
being, anxiety, and even gratitude itself is weak.
Although the conclusions reached by Wood et al. (2010), and later by Davis et al. (2016),
question the benefits of gratitude interventions, these exercises have grown quite prominent in
popular culture as a means of self-help. Paid-subscription smart-phone applications like Happify
GRATITUDE, DEPRESSION, AND ANXIETY 7
(2016), best-selling books like The Gratitude Diaries (Kaplan, 2015), and magazine editorials
(Graff, 2016) all claim that gratitude interventions will enhance one’s life. Additionally,
university wellness centers have begun to advocate for the use of gratitude interventions as
therapeutic tools (Emmons, 2013; “Heart Centered Practices”). With such widespread use, it is
important to determine whether gratitude interventions are indeed efficacious for specific
psychological symptoms, such as depression and anxiety, which are among the most common
mental health problems (Kendrick & Pilling, 2012).
Our meta-analysis extends the work of past reviews in several ways. First, new studies on
gratitude interventions have been published since the prior reviews in this area. Our meta-
analysis updates the literature for all studies published before May 17th, 2018 (see methods).
Second, past meta-analyses of gratitude interventions have focused on positive psychological
functioning, such as positive affect (Sin & Lyubomirsky, 2009) and life satisfaction (Davis et al.,
2016). To date, ours is the first to conduct focused analyses on effects for symptoms of
depression and anxiety. Sin and Lyubomirsky’s (2009) meta-analysis assessed the effects of
positive psychology interventions (PPIs) on symptoms of depression, but only four of the
included studies used a gratitude intervention; thus, it was not possible to disentangle the effect
of PPIs in general from the specific effect of gratitude interventions. Davis et al. (2016) also
included measures of depression and anxiety. However, depression and life satisfaction scores
were aggregated to create a “psychological well-being” outcome, and only 10 of the 21 studies
for this aggregate outcome included a depression measure; thus, the unique effect for symptoms
of depression could not be obtained. Their anxiety effect size also included a measure of marital
satisfaction (Snyder, 1998), which may capture marital distress rather than anxiety-specific
symptoms, such as worry (Meyer, Miller, Metzger, & Borkovec, 1990). Additionally, their
GRATITUDE, DEPRESSION, AND ANXIETY 8
inclusion of comparison groups with demonstrated therapeutic value makes conclusions drawn
from their anxiety analysis ambiguous. For example, evidence suggests that comparison
conditions such as progressive muscle relaxation (Cheung, Molassiotis, & Chang, 2003) and
automatic thought records (Persons & Burns, 1985) reduce symptoms of depression and anxiety.
Therefore, in Davis et al.’s meta-analysis, it is uncertain whether gratitude interventions are
ineffective for anxiety symptoms, or if they only improve symptoms to the same degree as other
therapeutic interventions. Similarly, they did not distinguish between neutral and therapeutic
controls in their moderator analyses of psychological well-being. As articulated by Chambless
and Hollon (1998), such comparisons are inherently difficult to interpret, as null results may
stem from a lack of power rather than equivalency between groups. Therefore, to eliminate this
ambiguity, we only included studies with no-treatment (waitlist) and neutral comparison groups,
i.e., active control tasks that were not intended to be therapeutic interventions and did not have
empirical evidence of benefit for depression and/or anxiety.
Most recently, Dickens (2017) published a meta-analysis that found gratitude
interventions have a small (d = 0.13) effect on depression when compared to waitlist or active
control tasks. However, she did not report specific effect sizes for each of these two control
types, but combined both under the label of “neutral conditions”. Additionally, she applied a
number of exclusion criteria that limit the generalizability of her results, i.e., excluding studies
involving daily gratitude journals, studies lasting three days or less, and studies that involved
multiple gratitude interventions. In the current meta-analysis, we did not apply such exclusions,
but instead included study duration and type of intervention as moderators of the effect size. For
studies with multiple gratitude interventions, we aggregated the effect sizes and ran our models
with and without these aggregate scores to include as much information as possible (see details
GRATITUDE, DEPRESSION, AND ANXIETY 9
under methods). With this broader inclusion criteria, we were able to include the largest number
of studies examining depressive symptoms of any published meta-analysis to date. Additionally,
the Dickens (2017) meta-analysis did not include symptoms of anxiety.
Additionally, neither Davis et al. (2016) nor Dickens (2017) performed a risk-of-bias
. Such an assessment can reveal how study features such as participants’ awareness
of condition, dropout, and baseline differences may influence effect size estimates. For example,
Bolier et al. (2013) found a larger effect size for PPIs with a greater risk of bias. Therefore, to
assess for influences of bias, we conducted a risk-of-bias assessment using guidelines developed
by the Cochrane Collaboration (Higgins, Altman, & Sterne, 2017).
Finally, for any variable assessed in a meta-analysis, there will be sources of error
affecting its accuracy, such as measurement error. The influence of measurement error is
particularly important for the outcome measure(s). For any given study in a meta-analysis, the
effect size estimate will be attenuated to the extent that there is measurement error (unreliability)
in the outcome measure(s) (Hedges & Olkin, 1985). If measurement error is present across
studies, then the overall effect size will be attenuated by the cumulative impact of the
unreliability across studies. Past meta-analyses of gratitude interventions have not controlled for
measurement error in the outcome(s). Therefore, we conducted a correction for attenuation to
minimize the influence of unreliability on our effect size estimates. The method for this
correction is described in the methods section below.
With this meta-analysis, we examined the effects of gratitude interventions on symptoms
of depression and anxiety at both immediate post-test and follow-up periods. Additionally, we
Following the recommendation of the Cochrane Collaboration (Higgins et al., 2017), we use the term “risk of bias”
here rather than “study quality,” as a study may be of the highest possible quality, yet still contain important sources
GRATITUDE, DEPRESSION, AND ANXIETY 10
assessed the influence of several moderator variables using meta-regression. Specifically, we
were interested in determining: 1) the effect size of gratitude interventions on symptoms of
depression and anxiety, considered separately; 2) the overall aggregate effect size on symptoms
of depression and anxiety, considered together; and 3) whether the type of control group and
other study characteristics (e.g., risk of bias, duration of intervention, type of intervention)
moderated the effects.
We searched four databases for studies investigating the effects of gratitude interventions
on symptoms of depression and anxiety (Cochrane Libraries, PsycINFO, PubMed, and Web of
Science). Additionally, to maximize the number of potential studies included, we manually
searched the reference sections of published review articles that discussed gratitude interventions
(Carl, Soskin, Kerns, & Barlow, 2013; Davis et al., 2016; Sin & Lyubomirsky, 2009; Wood et
al., 2010). Initial searches were conducted between June 1 – 2, 2016. A second search was
conducted between May 17 – 18, 2018 to update the literature. See Online Resource 1 for
detailed keyword profiles and filters applied to each database.
We used the following inclusion criteria:
1. Study was a scientific article in a relevant field and topic. Excluded studies were non-
scientific essays from the humanities or scientific studies not focused on gratitude, such as
miscellaneous biology or medical research.
2. Study used an experimental design with random assignment and a waitlist (measurement-
only control) or neutral control condition. Excluded studies involved correlational or
GRATITUDE, DEPRESSION, AND ANXIETY 11
qualitative research, or solely included comparisons with treatments of demonstrated
efficacy for anxiety and/or depression (i.e., solely including other active treatments
without a neutral control). A comparison activity was considered efficacious if it was
intended to be therapeutic by the study authors, and it had empirical evidence
demonstrating some benefit for depression and/or anxiety symptoms in past research (e.g.,
automatic thought records; Persons & Burns, 1985). If a study included both an active
treatment and a neutral/waitlist control, we used the neutral control as the comparison
3. Adequate statistical information was available to compute an effect size. If the study did
not contain adequate data, we contacted the corresponding or first author to retrieve the
necessary information. If he or she could or would not provide it, we excluded that study.
4. Study included at least one measure of symptoms of either anxiety or depression.
Excluded studies exclusively measured some other outcome(s), such as life satisfaction or
physical health. A full-text review was conducted for all studies excluded for this reason
to ensure that no depression or anxiety measures were reported in the manuscript.
5. Intervention was designed to induce or increase gratitude. Excluded studies used a non-
gratitude intervention (such as mindfulness) or combined gratitude with another technique
into a single group (such as a gratitude and best-possible-self exercise conducted
In total, we screened 1,277 abstracts for inclusion (953 unique studies across databases after
removing duplicates). See Figure 1 for a flowchart of the screening process.
GRATITUDE, DEPRESSION, AND ANXIETY 12
Data extraction and a risk-of-bias assessment were conducted by the first author (DC)
and independently checked by a second research assistant. If there was a discrepancy, each
reviewer returned to the original paper to double check the data extraction. Disagreements were
resolved by discussion. Data were collected on the study design, control group, intervention type,
study duration, participant characteristics, baseline depressive symptoms, outcomes measured,
post-test and follow-up raw data, sample size
, publication status and year, the presence of a
compliance or adherence check, and the risk-of-bias criteria outlined below. Raw data for each
study (means, SDs, and reliability coefficients) can be found in Online Resource 2. For studies
containing a depression measure with a published threshold score for clinically-relevant
symptoms of depression, we also coded whether the sample’s baseline depressive symptoms met
the recommended threshold. See Online Resource 4 for a list of these threshold scores.
Additionally, we extracted reliability (Cronbach’s alpha) coefficients at baseline for each
outcome within each study in order to correct effect sizes for attenuation. If the specific
timepoint was not reported (e.g., “reliabilities ranged from .91 to .93 across timepoints”), then
we used the median value. If the reliability was not reported for each subscale of an outcome
(e.g., the DASS-21), we used the reliability for the total scale. If the alpha value was not
reported, we used the reliability coefficient from the original publication of the scale. For one
study (Smullen, 2012), the reliability coefficient was not reported in either the study or the
original publication of the scale. Therefore, we used the mean reliability value for all depression
measures in our meta-analysis (0.86).
If authors did not report sample sizes for individual groups, we assumed equal sample sizes by dividing the
combined study sample by the number of groups in the study
GRATITUDE, DEPRESSION, AND ANXIETY 13
We used seven categories for the risk-of-bias assessment based on criteria from the
Cochrane Handbook for Systematic Reviews of Interventions (Higgins et al., 2017) and a
previous meta-analysis of PPIs by Bolier et al. (2013). Each criterion was coded as 1 (meets
criterion), 0 (fails to meet criterion), or N/A (insufficient information to determine criterion). A
composite summary score was calculated for each study by adding the number of criteria met.
We also created categorical groupings of bias risk. A study was categorized as low risk if 5 – 7
criteria were met, medium risk if 3 – 4 criteria were met, and high risk if 0 – 2 criteria were met.
Both the summary score and categorical groupings were included as moderators in the meta-
regression analyses. Additionally, because creating summary scores carries the limitation of
assigning equal weight to all criteria (Higgins et al., 2017), we also analyzed each bias criterion
as an independent categorical moderator in meta-regression, using the low-risk studies as the
The seven risk-of-bias criteria were: 1) Random sequence generation: did study authors
describe a method for ensuring random assignment to groups; 2) Randomization concealment:
were investigators unaware of assignment to groups and/or the randomization sequence; 3)
Participants kept unaware of study condition; 4) Baseline comparability: were baseline values of
depression and/or anxiety equivalent between groups, or were appropriate adjustments made to
correct for baseline differences (e.g., including baseline depression as a covariate); 5) Participant
attrition: if attrition occurred, was the attrition rate reported and analyzed, was attrition less than
50% of the initial randomized sample at post-test and follow-up, and was the attrition rate
comparable between groups (no more than a 10% difference); 6) Correcting for missing data: if
data were missing, did investigators make an attempt to correct for the missing data using an
intention-to-treat analysis or other means of imputing data; 7) Miscellaneous: any idiosyncratic
GRATITUDE, DEPRESSION, AND ANXIETY 14
risk of bias not captured in the other categories (e.g., excluding participants with psychiatric
conditions or reporting a deviation from the pre-specified study protocol).
We performed the primary calculations and analyses using the meta-analysis software
OpenMEE (Dietz et al., 2015). We computed a standardized mean difference for each study
using the formula for Hedges’ g (Hedges & Olkin, 1985). Hedges’ g is a form of Cohen’s d that
corrects for bias in sample size, with the same interpretation guidelines for small, medium, and
large effects of g = |0.2|, g = |0.5|, and g = |0.8+|, respectively. Values less than |0.2| can be
considered trivial (Cohen, 1988). The formula for Hedges’ g is:
, where “exp” denotes the experimental
group and “ctl” denotes the control group. The variance for Hedges’ g is calculated by the
formula: Vg = J2 Vd. In this equation, Vg is the variance of g, J is a correction factor defined by
, and Vd is the variance for Cohen’s d defined by
. For studies using a pre/post-test design, we calculated a change score for each
group by subtracting the pre-test mean from the post-test mean, thus obtaining a negative value if
a reduction in symptoms occurred. If pre-test measures were not reported, we calculated the
effect size using only post-test means. Only four studies did not include pre-test data (see Table
4). However, work by McKenzie, Herbison, and Deeks (2016) has demonstrated that mixing
change scores with post-test data in meta-analyses gives an unbiased estimate of the effect size
when heterogeneity is present and a random effects model is used, as was the case for our study.
GRATITUDE, DEPRESSION, AND ANXIETY 15
For follow-up data, we subtracted the pre-test mean from the follow-up mean
. If multiple
follow-up assessments were reported, we used the timepoint closest to one month, as this was the
most common design across studies. Following the suggestion of Becker (1988), where possible
we used the pre-test standard deviation to control for any intervention or practice effects that
might affect the post-test variance. For studies reporting post-test only data, we used the post-test
standard deviation. For studies reporting an intention-to-treat analysis, we used the means and
standard deviations from the intention-to-treat data. If the intention-to-treat data were not
reported for all groups (i.e., Southwell & Gould, 2017), we used the data from study completers.
All studies were weighted by the inverse of their variance, with studies of smaller variance
receiving a greater weight in analyses (see Table 2 for weights of all studies for the overall meta-
analysis at post-test).
We conducted separate meta-analyses for depression and anxiety, as well as an overall
aggregated analysis including both outcomes. For studies that assessed anxiety and depressive
symptoms within the same sample, we treated each outcome as independent for the separate
depression and anxiety analyses. However, for the overall analysis we aggregated their effect
sizes and variances into a single outcome using the method described by Borenstein, Hedges,
Higgins, and Rothstein (2009). This technique ensured we did not double-count these studies.
We assumed a correlation of .65 between depression and anxiety scales based on average
correlations reported in past research (Dobson, 1985). Finally, for studies that included multiple
independent groups of gratitude interventions compared to the same control group, we combined
One study included follow-up data without reporting pre-test data (Ozimkowski, 2007). Thus, we did not include
follow-up data for this study.
GRATITUDE, DEPRESSION, AND ANXIETY 16
the means and SDs across intervention groups into a single effect size for that study, again using
the method outlined in Borenstein et al. (2009)
Presumably, gratitude interventions have differing effect sizes based on unique study
characteristics such as the duration and type of intervention. Therefore, we decided to use the
more conservative random-effects model in our meta-analysis. The random effects model
accounts for heterogeneity in effect sizes across studies when calculating a pooled effect. We
used the DerSimonian and Laird (1986) estimator to adjust for this heterogeneity in our analyses.
Corrections for attenuation.
If the reliability coefficient of the outcome measure is known for each study, then the
individual effect sizes can be corrected for attenuation due to unreliability prior to conducting the
meta-analysis. These corrections will then provide an estimate of the disattenuated population
effect size. Following the procedure outlined in Hedges and Olkin (1985), we corrected
individual effect sizes by dividing Hedges’ g by the square root of the Cronbach’s alpha value.
We then corrected the corresponding variance for each study by dividing the variance by the
Cronbach’s alpha value. We report the corrected (disattenuated) effect size estimates in the
results section below. Additionally, corrected estimates for moderator analyses can be found in
Online Resource 5.
Description of Moderators
We included several moderator variables for meta-regression analyses. Reference groups
for categorical moderators are listed in Table 4. Consistent with the suggestion of Wood, Froh,
and Geraghty (2010), we coded control groups as either waitlist or active controls. Participants in
waitlist control groups completed no activities other than submitting symptom measures.
We ran our analyses with and without these studies (k = 2). Results changed by only two-hundredths of a decimal
point. Therefore, we retained the aggregated studies in all final analyses.
GRATITUDE, DEPRESSION, AND ANXIETY 17
Participants in active control groups completed non-gratitude tasks matched to the gratitude
interventions in terms of time. If a study contained a waitlist and an active control group, we
selected the active control as the comparison. If there were two active control groups, we
selected the more neutral of the two (e.g., using early childhood memories rather than early
positive childhood memories; Mongrain & Anselmo-Matthews, 2012). We predicted active
controls would have a smaller effect size than waitlist controls, as performing some structured
activity may confer a greater expectation of benefit (placebo effect) than just completing
measures in a waitlist group.
We coded the intervention type according to whether it was primarily interpersonal or
intrapersonal in nature, or a combination of the two, following the suggestion made by Davis et
al. (2016). Interpersonal interventions involved written and/or verbal expressions of gratitude to
another person, such as the gratitude visit. Intrapersonal interventions involved personal
reflections on things one has to be thankful for in life, but without instructions to direct the
expression of gratitude toward a particular individual (such as gratitude journals and guided
gratitude meditations). Combined interventions had participants complete both types of activities
in a single group. We expected combined and interpersonal interventions to have larger effects
than intrapersonal interventions at post-test (based on results from Seligman et al., 2005). If
studies included multiple gratitude intervention groups, such as a separate interpersonal and
intrapersonal condition, we excluded them from the moderator analysis for intervention type.
Additional planned moderators for the meta-regression were: 1) Online implementation:
whether the study was conducted online or offline (i.e., in-person); 2) Publication status: was the
study published or unpublished (i.e., a dissertation or thesis); 3) Depressive symptoms threshold:
for depression measures that have published interpretation guidelines, did the sample’s average
GRATITUDE, DEPRESSION, AND ANXIETY 18
baseline depressive symptoms meet the published thresholds for a clinically relevant level of
depressive symptomatology; 4) Baseline CES-D: the sample’s average baseline score on the
Center for Epidemiological Studies Depression scale (the most commonly used depression
instrument in our meta-analysis; Radloff, 1977); 5) Year of publication; 6) Percentage of female
participants; 7) Mean age of participants; 8) Duration of the intervention period, defined by both
number of weeks and the number of days on which an activity was actually performed; and 9)
whether the researchers included some form of an adherence or compliance check.
Based on the results of previous research (Harbaugh & Vasey, 2014; Sin & Lyubomirsky,
2009), we expected a larger effect size for more depressed samples, i.e., those meeting the
depressive symptoms threshold and with a higher baseline level of depressive symptoms. We
also expected a larger effect among samples with older adults, again based on past research by
Sin & Lyubomirsky (2009). Finally, we expected a larger effect for published articles, offline
studies, interventions of longer duration, and studies that included an adherence check. We had
no a priori hypotheses for year of publication or percentage of females. Furthermore, because
risk of bias in studies can influence effect sizes in either direction (underestimating or
overestimating effects; Higgins et al., 2017), we did not specify directional hypotheses for the
We assessed the influence of moderator variables using OpenMEE’s meta-regression
feature. For all moderator analyses, we used the overall aggregate effect size (k = 27) to
maximize power. The only exceptions were depressive symptoms threshold and baseline CES-D,
for which the depression-specific effect size was used.
GRATITUDE, DEPRESSION, AND ANXIETY 19
We identified potential outliers by examining studies for which their 95% confidence
intervals laid entirely outside of the pooled confidence interval for all studies for each outcome
(depression, anxiety, and the overall analysis). The statistical analyses were then repeated with
the outliers excluded. As a double check on the identification of outliers, we also used the
“influence” procedure contained within the R package “metafor” (Viechtbauer, 2010). The
influence procedure computes various diagnostic tests to identify outliers by multiple criteria
(e.g., Cook’s distance and the influence of each study on the heterogeneity of variance). The
influence procedure exactly replicated the results reported below, i.e., it identified the same
studies as outliers with no additional outliers identified.
Estimates of Publication Bias
In addition to the risk-of-bias assessment, we used Rosenthal’s (1979) fail-safe N to
assess the potential for publication bias. The fail-safe N calculates the number of studies with
null results that would be needed to inflate the observed p value above a specified alpha level in
which the effect is no longer statistically significant. It can be conceived of as an assessment of
the “file-drawer problem;” that is, the likelihood for unpublished studies with non-significant
results to be excluded from the meta-analysis. If a large number of such studies potentially exist,
the overall effect size is likely an overestimate of the true effect. A disadvantage of this method
is its exclusive focus on p values, which only indirectly account for the effect size that is of
primary interest. However, this method has clearly defined guidelines for interpretation and is
suitable for meta-analyses of any size. Rosenthal (1979) suggests a fail-safe N value above 5k +
10 reflects results that are tolerant to contradicting studies, where k is the number of studies
included in the meta-analysis. Rosenthal noted this is a conservative threshold, meaning that if
GRATITUDE, DEPRESSION, AND ANXIETY 20
the fail-safe N is well above this value, there is increased confidence that the observed effect size
estimate is trustworthy.
We did not include a funnel plot test in our study, as funnel plots do not provide valid
estimates of publication bias when fewer than 30 studies are included (Lau, Ioannidis, Terrin,
Schmid, & Olkin, 2006). Similarly, due to the highly subjective nature of interpreting these plots,
the Cochrane Collaboration has advised caution in their use (Sterne, Egger, Moher, & Boutron,
2017). However, interested readers may find the funnel plots for the overall meta-analysis at
post-test and follow-up in Online Resource 3. Instead of funnel plots, we conducted a cumulative
meta-analysis for the overall effect size at post-test and follow-up. For the cumulative meta-
analyses, we followed the procedure described in chapter 13 of Schmidt and Hunter (2015), in
which studies are sequentially added to the meta-analysis in order from largest to smallest
sample size (i.e., the most precise studies to the least precise). Publication bias is evident if
adding the smaller-sample studies causes an increase in the effect size’s magnitude. Forest plots
for the cumulative meta-analyses are also included in Online Resource 3.
Reliability of Data Extraction
We assessed inter-rater reliability for the extracted data (prior to any discussion of
discrepancies) using intra-class correlations (ICCs) for continuous variables and kappas for
categorical variables. Reliability for the continuous variables was excellent, with ICCs ranging
from .91 – 1.00 across post-test and follow-up periods, with a mean of .99. Reliability for the
categorical variables other than the risk-of-bias assessment was likewise good, with kappas
ranging from .70 – 1.00 with a mean of .91. Reliability for the risk-of-bias variables ranged from
moderate to good, with kappas ranging from .42 – .76 with a mean of .63.
GRATITUDE, DEPRESSION, AND ANXIETY 21
Full details of study characteristics are presented in Table 1. Twenty-seven studies were
included in the meta-analysis, with a grand total of 3,675 participants at post-test and 2,318
participants at follow-up. Of the total participants at post-test, 2,030 participants were in the
experimental (gratitude) group, and 1,645 were in the control group. The combined sample size
for individual studies ranged from 22 to 514. Eighteen studies included an active control group
(totaling 1,206 participants) and nine studies included a waitlist control (totaling 439
participants). Except for Ozimkowski (2007), all studies included a majority female sample, with
the total percentage of females ranging from 55 – 100%. Mean age ranged from 19 – 69, with an
average age of 32 years across studies. Thirteen studies (48% of the sample) included some form
of an adherence or compliance check.
Twenty-one studies included only a depression measure, two studies included only an
anxiety measure, and four studies included measures of both depression and anxiety. Only two
studies included a clinical sample (participants with a diagnosed disorder or seeking treatment
for a psychological condition; Kerr et al., 2015; Southwell & Gould, 2017). However, of the 18
studies for which published interpretation guidelines were available, 13 (over half of the studies
with depression data) met the recommended threshold for a clinically relevant level of depressive
symptoms, whereas only five did not meet this criteria. For the 10 studies that reported baseline
CES-D data, average scores ranged from 13.53 to 34.15. The average CES-D score across
studies was 20.31, which is 4.3 points above the recommended threshold of 16 for a clinically
relevant level of depressive symptoms (Lewinsohn, Seeley, Roberts, & Allen, 1997).
GRATITUDE, DEPRESSION, AND ANXIETY 22
The overall level of reliability (Cronbach’s alpha) for outcomes was high. Reliability
estimates for items on depression measures ranged from .60 – .94 with a mean of .86 (SD = .08).
Reliability estimates for items on anxiety measures ranged from .80 – .95 with a mean of .88 (SD
The majority of studies used a pre/post-test design, with only four studies reporting post-
test only data. Thirteen studies reported follow-up data, with the majority of studies (k = 9) using
a one-month follow-up. Assessing duration by days (on which an activity was performed), the
intervention period ranged in frequency from 1 day to 28 days across studies. Assessing duration
by total weeks, the intervention period ranged from less than one week to eight weeks. The
majority of studies (k = 19) used an intrapersonal gratitude intervention, four used an
interpersonal intervention, two combined both types of interventions into a single condition, and
two studies included both types of activities in separate groups and were thus not included in the
moderator analysis. Twenty studies were conducted online and seven were conducted offline.
The majority of studies (k = 22) were published articles, with only five unpublished theses or
Risk of bias characteristics.
See Online Resource 2 for the full risk-of-bias assessment. The summary score ranged
from 0 to 5, with an average of 3.07 (SD = 1.44). Six studies were categorized as low risk, 12 as
medium risk, and nine as high risk. No study met all seven criteria. The majority of studies
(21/27) met criteria for baseline comparability. About half of the studies met criteria for keeping
participants unaware of the condition (14/27 studies). Less than half of the studies passed the
criteria for attrition (10/27), sequence generation (9/27), or missing data (7/27). Only two studies
GRATITUDE, DEPRESSION, AND ANXIETY 23
reported sufficient information about randomization concealment, with the majority of studies
(19/27) categorized as N/A. Finally, only seven studies contained a miscellaneous risk of bias
(e.g., excluding participants older than 45; Jackowska, Brown, Ronaldson, & Steptoe, 2016).
Post-test Main Effects
Main effects for post-test outcomes are presented in Table 3, and effect sizes for each
study are listed in Table 2 (for the overall meta-analysis) and plotted in Figures 2 – 4. Gratitude
interventions had a small but statistically significant effect on depressive symptoms, k = 24, g =
−0.23, SE = 0.05, p < .01, τ2 = 0.02. The corrected (disattenuated) depression effect size was g =
−0.24, SE = 0.05, p < .01, τ2 = 0.03. For anxiety (k = 5), gratitude interventions had a medium
effect that approached statistical significance, g = −0.52, SE = 0.30, p = .09, τ2 = 0.41. The
corrected anxiety effect size was g = −0.55, SE = 0.32, p = .09, τ2 = 0.45. For the overall meta-
analysis (k = 27), gratitude interventions had a small, statistically significant effect on symptoms
of depression and anxiety, g = −0.29, SE = 0.06, p < .01, τ2 = 0.07. The corrected overall effect
size was g = −0.31, SE = 0.07, p < .01, τ2 = 0.08.
Tests for heterogeneity of effect sizes were significant for all outcomes (depression,
anxiety, and the overall effect), suggesting significant variation in effect sizes across studies. For
depression, effect sizes ranged from −0.96 to 0.07. For anxiety, effect sizes ranged from −1.64 to
−0.02. For the overall analysis, effect sizes ranged from −1.64 to 0.07. We identified two outliers
for post-test outcomes: Geraghty et al. (2010), with an anxiety effect size of g = −1.64, 95% CI
[−2.08, −1.20]; and Ki (2009), with a depression effect size of g = −0.96, 95% CI [−1.28, −0.63].
These two studies were outliers for their respective outcomes as well as the overall meta-analysis
(see Figures 2 – 4).
GRATITUDE, DEPRESSION, AND ANXIETY 24
After removing these outliers from the dataset, the depression effect size was reduced to a
trivial value, but remained statistically significant, g = −0.17, SE = 0.04, p < .01; this equates to a
26% reduction in magnitude of the effect size. The anxiety effect size was reduced by 69% to a
trivial value and was statistically non-significant, g = −0.16, SE = 0.11, p = .13. For the overall
meta-analysis, the effect size was trivial but remained statistically significant, g = −0.18, SE =
0.04, p < .01, equating to a 38% reduction in magnitude. Notably, after the removal of outliers,
all heterogeneity tests became non-significant, suggesting the outliers accounted for the
heterogeneity in effect sizes. Excluding these outliers for the corrected effect sizes resulted in the
same findings: the corrected depression g was −0.18, anxiety g = −0.17, and the overall g =
−0.19. Again, all heterogeneity tests became non-significant.
Follow-up Main Effects
Main effects for follow-up outcomes are presented in Table 3, and effect sizes for
individual studies are plotted in Figure 5. Because only two studies reported anxiety follow-up
data, we did not calculate an independent effect size for anxiety at follow-up. However, the
available anxiety follow-up data were incorporated into the overall meta-analysis. Gratitude
interventions had a small, statistically significant effect on depressive symptoms at follow-up, k
= 12, g = −0.24, SE = 0.06, p < .01, τ2 = 0.01. The corrected depression effect size was g = −0.25,
SE = 0.06, p <.01, τ2 = 0.02. For the overall meta-analysis (k = 13), gratitude interventions also
had a small, statistically significant effect, g = −0.23, SE = 0.06, p < .01, τ2 = 0.01. The corrected
overall effect size was g = −0.24, SE = 0.06, p < .01, τ2 = 0.02.
Heterogeneity tests were non-significant. However, a trend was observed for
heterogeneity for depression, τ2 = 0.01, Q (11) = 17.19, p = .10. For both depression and the
overall analysis, effect sizes ranged from −0.86 to 0.06. We identified one outlier for follow-up
GRATITUDE, DEPRESSION, AND ANXIETY 25
outcomes: Cheng, Tsui, and Lam (2015) reported a depression effect size of g = −0.86, 95% CI
After removing this outlier from the dataset, the depression effect size remained small
and statistically significant (p < .01), and it was reduced to a value of g = −0.20, SE = 0.05, a
17% reduction in magnitude. For the overall meta-analysis, the effect size also remained small
and statistically significant (p < .01), and it was reduced to a value of g = −0.19, SE = 0.05, a
17% reduction in magnitude. After removal of Cheng et al. (2015), the trend toward a significant
heterogeneity test for depression was eliminated. Excluding this outlier for the corrected effect
sizes resulted in the same findings: the corrected depression g was −0.21 and the overall g =
−0.20. Again, the trend toward a significant heterogeneity test was eliminated.
Results of all meta-regression analyses are presented in Table 4. For both the post-test
and follow-up time periods, the effect size was significantly moderated by type of control group.
The pooled effect size was larger for studies with waitlist control groups than for those using
active controls. No other moderators were significant for post-test or follow-up.
Due to the effect size differing based on the type of control group, we also tested
interactions between all the continuous moderators and the type of control group used at post-test
and follow-up. No significant interactions were found.
We repeated all moderator analyses with the corrected effect sizes. In all cases, the results
were identical to the uncorrected analyses (see Online Resource 5).
See Table 4 for results of the risk-of-bias analyses. Neither the summary bias score nor
the categorical risk groupings (low, medium, and high risk) significantly moderated the effect
GRATITUDE, DEPRESSION, AND ANXIETY 26
size at post-test or follow-up. When we examined individual risk categories, participants’
awareness of condition significantly moderated the effect size at follow-up only. Studies judged
to be at high risk of bias for threats to participants’ awareness of condition (k = 4) had a larger
pooled effect size (g = −0.51, SE = 0.15) than studies judged to be at low risk (k = 7; g = −0.17,
SE = 0.06) and studies with insufficient information (k = 2; g = −0.15, SE = 0.08). No other
individual risk categories had significant effects for either post-test or follow-up periods.
Repeating analyses with the corrected effect sizes did not change the results.
Results of Publication Bias Estimates
We computed the fail-safe N using an alpha of .05 for both the overall post-test and
follow-up effect sizes. The post-test fail-safe N was 560, and the follow-up fail-safe N was 122.
Both of these values are well above the 5k + 10 guidelines of 145 and 75 for post-test and
follow-up, respectively. Therefore, it is unlikely the overall effect sizes of −0.29 for post-test and
−0.23 for follow-up are overestimations resulting from the file drawer problem.
As can be observed in the forest plots in Online Resource 3, there was a trend for the
effect size to increase with smaller samples at post-test. This result suggests that there is a
possible bias toward publishing less precise studies (i.e., smaller samples) that report larger
effects of gratitude interventions at post-test. There was no discernible pattern of bias at follow-
up, though the fewer number of studies (13) limits the ability to detect visual patterns.
The primary aim of our meta-analysis was to determine the effectiveness of gratitude
interventions for symptoms of depression and anxiety. Considered altogether, our analyses
GRATITUDE, DEPRESSION, AND ANXIETY 27
suggest gratitude interventions are of limited efficacy for reducing these symptoms. The overall
effect sizes at post-test (g = −0.29) and follow-up (g = −0.23) suggest a small effect according to
Cohen’s (1988) guidelines.
The fail-safe N values also suggest these effect sizes are unlikely to be influenced by
potential unpublished studies with null results. However, if such studies exist, they would only
serve to diminish the effect sizes even further. This argument is supported by the cumulative
meta-analyses, which suggests that if publication bias was present in our results, it was likely
overestimating the effect size at post-test. Furthermore, excluding outliers reduced the effect
sizes to still-smaller values, particularly at post-test.
Additionally, and as predicted, the effect size was smaller when gratitude interventions
were compared to active control conditions. Consistent with past reviews (Davis et al., 2016;
Lyubomirsky et al, 2009), we found gratitude interventions had a medium effect when compared
with waitlist-only conditions, but only a trivial effect when compared with putatively inert
control conditions involving any kind of activity.
Finally, it should be noted that the reliability distribution of the dependent variables was
generally high. Effect-size estimates are only substantively attenuated when the level of
reliability is low (Schmidt & Hunter, 2015), and we confirmed this in our dataset. In all cases,
the analyses conducted with corrected effect sizes were consistent with the uncorrected
estimates, i.e., results changed by only a few hundredths of a decimal point. Therefore, there is
no evidence that the small effects obtained in this meta-analysis are a result of attenuation from
unreliability. Based on our results, we agree with Davis et al. (2016) that a parsimonious
explanation of gratitude interventions may be that they operate primarily through placebo effects,
at least for depression and anxiety symptoms.
GRATITUDE, DEPRESSION, AND ANXIETY 28
Excluding our prediction for the type of control group, we found little evidence to
support our other hypotheses about moderator variables. Indeed, with one exception, none of the
other tests of moderation were significant. The only exception was for participant awareness of
condition, which significantly moderated effects at follow-up (but not at post-test). It is possible
that among studies containing threats to participants’ awareness of condition, researchers may
have unwittingly influenced participants in ways that would favor gratitude interventions, such
as communicating an expectation of benefit to the gratitude but not to the control group.
However, given the lack of consistency between the post-test and follow-up effects for this
criterion, we interpret these results very cautiously. We also advise caution in interpreting the
null results of the other categorical moderators, particularly where there was an uneven number
of studies in each category. The differential representation of studies between categories may
create limited power to detect group differences. For example, only four studies included an
interpersonal intervention, whereas 19 studies included an intrapersonal intervention. The
substantially lower number of interpersonal interventions makes it difficult to draw conclusions
about whether interpersonal interventions are truly equivalent to intrapersonal interventions in
their effects. Indeed, evidence from Seligman et al. (2005) would suggest that they are not
equivalent, as the interpersonal intervention had a more substantial impact on depression at post-
test than the intrapersonal intervention in that study.
Our results should be interpreted in light of several important limitations. First, and most
crucially, the samples included in our meta-analysis were mostly comprised of unselected
participants, i.e., individuals who were not selected based on severity of depressive or anxiety
symptoms. Only two studies included clinical samples (Kerr et al., 2015; Southwell & Gould,
GRATITUDE, DEPRESSION, AND ANXIETY 29
2017). Prior research suggests treatment effects may increase with depressive symptom severity.
For example, Sin and Lyubomirsky (2009) found PPIs have a greater effect on reducing
depressive symptoms among those who meet diagnostic status for a depressive disorder.
Likewise, Harbaugh and Vasey (2014) found their gratitude intervention was effective only
among those high in baseline depressive symptoms. Therefore, one objection to the results of our
meta-analysis may be that the range of depressive symptoms was too restricted for gratitude
interventions to have an effect. We think this explanation is unlikely for several reasons. First,
over half of the studies in which symptoms of depression were assessed had a sample with
baseline depressive symptoms meeting the recommended thresholds for clinically-relevant
symptoms. We did not find evidence of a stronger effect for those samples in which participants,
on average, met the established thresholds. Second, for studies including baseline CES-D data,
the average scores ranged from 13.53 – 34.15. Six of the 10 studies reporting baseline CES-D
data exceeded the threshold for clinically-relevant symptoms of depression (i.e., 16), with an
average value across all studies of 20.31. However, we again did not find evidence that the
depression effect size was moderated by baseline CES-D severity. Third, the post-test
depression effect sizes for the two studies with clinical samples were among some of the smallest
in our meta-analysis (−0.12 and −0.06 for Kerr et al., 2015 and Southwell & Gould, 2017,
respectively). Based on these considerations, it appears that a sufficient range of depressive
symptoms was present in our meta-analysis, but the effect of gratitude interventions did not
increase with greater depressive symptomatology. Consequently, it seems unlikely that range
restriction in our study accounts for the small effect size of gratitude interventions.
These studies did not include follow-up data.
GRATITUDE, DEPRESSION, AND ANXIETY 30
That said, we certainly acknowledge that though depressive symptoms exist on a
continuum, meeting a threshold for clinically relevant symptoms on a depression measure does
not equate to a diagnosis of a depressive disorder made by a mental health professional
(American Psychiatric Association, 2013). Therefore, our meta-analysis should be interpreted
with this distinction between symptoms and diagnostic classes in mind. It is possible that future
researchers may find a greater benefit of gratitude interventions for depressive symptoms by
limiting recruitment to those meeting diagnostic status for a depressive disorder, which would
allow for testing gratitude interventions in a more severely impaired population. However, to
date, efforts along this line have yielded mixed results (Celano et al., 2017; Taylor,
Lyubomirsky, & Stein, 2017).
Likewise, there may be other moderators that could influence gratitude interventions’
effectiveness that we did not assess, such as one’s level of self-criticism or emotional neediness
(Sergeant & Mongrain, 2011). The instructions given to participants could also moderate effects.
For example, previous research by Sheldon, Boehm, and Lyubomirsky (2012) suggests that
variety is an important moderator of PPIs. Thus, if participants were instructed to list three new
things they are grateful for each day, it could reduce some of the hedonic adaptation that may
occur from repeating the same gratitude list daily. Additionally, it is possible that some
participants perceived the instructions differently or performed the activity differently within the
same condition of a study, i.e., there may be unreliability in the treatment variable (Schmidt &
Hunter, 2015). However, the studies in our meta-analysis did not report an interrater reliability
coefficient for the treatment variable, thus making it impossible to estimate the effect of
treatment reliability on outcomes. Understanding whether instructions, treatment reliability, or
other potential moderators increase the effectiveness of gratitude interventions is an important
GRATITUDE, DEPRESSION, AND ANXIETY 31
direction for future research. That said, if we base our conclusions on the currently available
data, there is little evidence to suggest gratitude interventions are efficacious for reducing
symptoms of depression or anxiety. Accordingly, suggestions by researchers to use gratitude
interventions as a psychotherapeutic tool (e.g., Emmons & Stern, 2013) should be taken
cautiously until more substantial benefits can be demonstrated. Consequently, we recommend
individuals seek more well-established treatments for difficulties with depression or anxiety
symptoms until stronger benefits of gratitude interventions are found. For example, meta-
analytic evidence suggests that computerized treatments for depression and anxiety, which are
relatively low-cost and easily accessible, have strong effect sizes across comparison groups
(Andrews, Cuijpers, Craske, McEvoy, & Titov, 2010).
A second limitation of our meta-analysis is the small number of studies (k = 5) for the
anxiety effect size, which leads us to interpret this effect size cautiously. Although the effect size
was of a medium magnitude (g = −0.52), it was statistically non-significant with a wide
confidence interval ranging from a strong, beneficial effect to a weak, harmful effect (−1.11 to
0.08). Indeed, removing the outlier of Geraghty et al. (2010) eliminated the heterogeneity in the
effect size and dropped its value to a trivial level of g = −0.16. Future investigations with
measures of anxiety are needed so that meta-analytic estimates will be better powered and less
influenced by outliers. Including more anxiety studies would also allow investigators to examine
if the anxiety effect is moderated by symptom severity, as we were unable to examine this
possibility with the small number of studies with anxiety measures currently available.
Nevertheless, based on the current data, it appears gratitude interventions have limited efficacy
for anxiety symptoms.
GRATITUDE, DEPRESSION, AND ANXIETY 32
Third, this meta-analysis applies only to gratitude interventions’ specific effects on
symptoms of depression and anxiety as standalone interventions. It is not our intention to dismiss
the value of gratitude interventions in general. It is entirely possible these interventions are more
powerful for anxiety or depressive symptoms when they are integrated into a larger treatment
package, as suggested by prior randomized trials with positive psychology exercises (Seligman,
Rashid, & Parks, 2006; Taylor, Lyubomirsky, & Stein, 2017). Additionally, gratitude
interventions may have stronger effects for constructs like relationship quality or general well-
being, as Dickens’ (2017) meta-analysis would suggest. However, it is important to understand
the outcomes for which gratitude interventions are the most efficacious, and then recommend
these interventions only when individuals seek to impact those particular outcomes. Indeed, our
meta-analysis suggests that whatever the merits gratitude interventions have for other outcomes,
they are not efficacious for symptoms of depression or anxiety as standalone interventions.
Fourth, there may be limitations for generalizability of our results based on other sample
characteristics such as age and sex. Though we did not find evidence that age and sex moderated
effects, all but one study included a majority female sample, and only five studies contained a
sample with a mean age of 40 or above. Prior research suggests women (Kashdan, Mishra,
Breen, & Froh, 2009) and older adults (Sin & Lyubomirsky, 2009) experience gratitude in
unique ways. Therefore, it is unclear if our results generalize to samples with fewer women and a
greater number of older adults.
Finally, our results only apply to gratitude interventions. They do not inform us about the
association of gratitude as a general disposition with depression and anxiety. As we mentioned in
the introduction, there is strong evidence that higher trait gratitude is associated with reduced
psychopathology and greater well-being (Wood et al., 2010). Therefore, gratitude as a general
GRATITUDE, DEPRESSION, AND ANXIETY 33
disposition may still be a vital factor in human flourishing, and the efficacy (or lack thereof) of
gratitude interventions should in no way be interpreted to mean gratitude is not an important
element of well-being and the good life.
Based on the currently available data, we find limited evidence for the efficacy of
gratitude interventions in reducing symptoms of depression and anxiety. They have a medium-
sized effect when compared to no intervention at all, but a small effect when compared with any
active control task. Nevertheless, future investigators may discover individual differences that
moderate the effectiveness of gratitude interventions, such as severity of psychopathology or
qualities like self-criticism and emotional neediness. Such distinctions will be crucial to uncover,
especially as exercises like gratitude journals have begun to permeate into popular culture as a
means of self-help. We believe that until gratitude interventions are shown to be more powerful,
the suggestions to use the existing approaches as tools for reducing symptoms of depression or
anxiety should be considered with caution.
GRATITUDE, DEPRESSION, AND ANXIETY 34
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GRATITUDE, DEPRESSION, AND ANXIETY 45
Characteristics of Included Studies
Cheng et al.
Gander et al.e
Kerr et al.
GRATITUDE, DEPRESSION, AND ANXIETY 46
Lambert et al.
Lambert et al.
Manthey et al.
in grades 3,
8, & 12)
Proyer et al.
Senf & Liau
GRATITUDE, DEPRESSION, AND ANXIETY 47
Toepfer et al.
Watkins et al.
GRATITUDE, DEPRESSION, AND ANXIETY 48
Watkins et al.
Note. Blank cells indicate data was either not reported in the study or it is not-applicable; Unpublished = unpublished thesis or
dissertation; N = combined sample size for experimental and control groups; D = Depression; A = Anxiety; WLC = waitlist control;
Intvn. = intervention; Intra = intrapersonal; Inter = interpersonal; Comb = combined inter & intrapersonal; Pop. = type of study
population; NC = non-clinical; C = clinical; Dep. Threshold = whether combined sample met recommended threshold for clinically
significant depressive symptoms; Baseline CES-D = average baseline depressive symptoms for combined sample, 20-item measure
only; d = days; w = weeks; m = months; Compliance = whether the authors reported a compliance or adherence check; DASS-21 =
Depression Anxiety Stress Scales; CES-D = Center for Epidemiological Studies Depression Scale; CES-D10 = 10 item version of the
Center for Epidemiological Studies Depression Scale; PSWQ = Penn State Worry Questionnaire; PHQ-9 = Patient Health
Questionnaire-9; IES-R = Impact of Event Scale Revised; HADS = Hospital Anxiety and Depression Scale; STADI = State-Trait-
a If the authors reported both an anxiety and a depression reliability for a scale (e.g., the DASS-21), the anxiety reliability is listed first.
b,c Values are rounded to the nearest whole integer.
d Study did not report overall mean age, so mean age was estimated based on sample sizes and typical ages for students in grades 3, 8, and
12 (9, 14, and 18 years old, respectively).
e Study had multiple intervention groups with different durations. Therefore, study duration was not included.
f The HADS assesses depression and anxiety simultaneously, therefore this study was only included in the overall meta-analysis.
GRATITUDE, DEPRESSION, AND ANXIETY 49
Weights and Effect Sizes of Included Studies for the Overall Meta-Analysis at Post-Test
Booker & Dunsmore
Cheng, Tsui, & Lam
Gander et al.
Geraghty Dissertation Study 4
Geraghty, Wood, & Hyland
Harbaugh & Vasey
Jackowska et al.
Kerr, O’Donovan, & Pepping
Lambert, Fincham, & Stillman Study 5
Lambert, Fincham, & Stillman Study 7
Lyubomirsky et al.
Manthey, Vehreschild, & Renner
Mongrain & Anselmo-Matthews
O’Leary & Dockray
Proyer et al.
Senf & Liau
Sergeant & Mongrain
Southwell & Gould
Timmons & Ekas
Toepfer, Cichy, & Peters
Watkins et al.
Watkins, Uhder, & Pichinevskiy
Wolfe & Patterson
GRATITUDE, DEPRESSION, AND ANXIETY 50
Main Effects of Meta-Analysis
Hedges’ g [95% CI]
−0.23 [−0.33, −0.13]
0.05 (p < .01)
τ2 = 0.02, Q (23) = 39.36, p = .02
−0.52 [−1.11, 0.08]
0.30 (p = .09)
τ2 = 0.41, Q (4) = 40.20, p < .01
−0.29 [−0.41, −0.16]
0.06 (p < .01)
τ2 = 0.07, Q (26) = 80.05, p < .01
−0.17 [−0.24, −0.10]
0.04 (p < .01)
τ2 = 0.00, Q (22) = 18.27, p = .69
−0.16 [−0.38, 0.05]
0.11 (p = .13)
τ2 = 0.01, Q (3) = 3.37, p = .34
−0.18 [−0.25, −0.11]
0.04 (p < .01)
τ2 = 0.00, Q (24) = 19.49, p = .73
−0.24 [−0.35, −0.12]
0.06 (p < .01)
τ2 = 0.01, Q (11) = 17.19, p = .10
−0.23 [−0.34, −0.12]
0.06 (p < .01)
τ2 = 0.01, Q (12) = 18.33, p = .11
−0.20 [−0.29, −0.11]
0.05 (p < .01)
τ2 = 0.00, Q (10) = 10.67, p = .38
−0.19 [−0.28, −0.10]
0.05 (p < .01)
τ2 = 0.00, Q (11) = 11.62, p = .39
Note. K = number of studies included in analysis. An independent follow-up analysis was not conducted for anxiety, as there were only 2 studies
assessing anxiety with follow-up data.
a Outliers were Geraghty, Wood, & Hyland (2010) for anxiety and Ki (2009) for depression.
b Outlier was Cheng, Tsui, & Lam (2015) for depression.
GRATITUDE, DEPRESSION, AND ANXIETY 51
K (with subgroups) &
Test of Moderation
Subgroup Hedges’ g (SE), p
27 (N = 3,675)
Active = 18
WLC = 9
b = −0.32, SE = 0.13, p = .01
†Active g = −0.18 (0.06), p < .01
WLC g = −0.51 (0.15), p < .01
Type of intervention
25 (N = 3,232)
Interpersonal = 4
Intrapersonal = 19
Combined = 2
Q (2) = 0.80, p = .67
†Interpersonal g = −0.15 (0.09), p =
Intrapersonal g = −0.34 (0.09), p <
Combined g = −0.27 (0.16), p = .09
27 (N = 3,675)
Online = 20
Offline = 7
b = 0.13, SE = 0.15, p = .41
†Online g = −0.32 (0.08), p < .01
Offline g = −0.20 (0.08), p = .02
27 (N = 3,675)
Published = 22
Not published = 5
b = −0.03, SE = 0.16, p = .85
†Published g = −0.28 (0.07), p < .01
Not published g = −0.30 (0.19), p =
18 (N = 2,695)
Met = 13
Not met = 5
b = −0.11, SE = 0.14, p = .46
Met g = −0.26 (0.08), p < .01
†Not met g = −0.14 (0.10), p = .16
10 (N = 1,798)
b = 0.00, SE = 0.01, p = .74
Year of publication
27 (N = 3,675)
b = 0.00, SE = 0.02, p = .86
26 (N = 3,589)
b = 0.00, SE = 0.01, p = .95
20 (N = 2,817)
b = 0.00, SE = 0.01, p = .96
GRATITUDE, DEPRESSION, AND ANXIETY 52
K (with subgroups) &
Test of Moderation
Subgroup Hedges’ g (SE), p
25 (N = 3,232)
b = −0.01, SE = 0.01, p = .50
26 (N = 3,563)
b = 0.00, SE = 0.03, p = .96
27 (N = 3,675)
No = 13
Yes = 14
b = 0.18, SE = 0.12, p = .15
†No g = −0.38 (0.13), p < .01
Yes g = −0.18 (0.05), p < .01
Risk of bias (summary
27 (N = 3,675)
b = −0.02, SE = 0.05, p = .71
Risk of bias (categorical
27 (N = 3,675)
Low = 7
Medium = 13
High = 7
Q (2) = 0.83, p = .66
†Low g = −0.23 (0.07), p < .01
Medium g = −0.35 (0.14), p < .01
High g = −0.20 (0.06), p < .01
13 (N = 2,318)
Active = 10
WLC = 3
b = −0.48, SE = .14, p < .01
†Active g = −0.16 (0.05), p < .01
WLC g = −0.63 (0.13), p < .01
Type of intervention
11 (N = 1,875)
Interpersonal = 2
Intrapersonal = 8
Combined = 1
Q (2) = 1.18, p = .55
†Interpersonal g = −0.25 (0.12), p =
Intrapersonal g = −0.24 (0.08), p <
Combined = N/A
13 (N = 2,318)
Online = 11
Offline = 2
b = −0.10, SE = .19, p = .62
†Online g = −0.22 (0.06), p < .01
Offline g = −0.32 (0.22), p = .14
13 (N = 2,318)
Published = 12
Not published = 1
GRATITUDE, DEPRESSION, AND ANXIETY 53
K (with subgroups) &
Test of Moderation
Subgroup Hedges’ g (SE), p
11 (N = 1,931)
Met = 7
Not met = 4
b = 0.10, SE = 0.15, p = .49
Met g = −0.21 (0.07), p < .01
†Not met g = −0.34 (0.15), p = .03
9 (N = 1,603)
b = −0.01, SE = 0.01, p = .40
Year of publication
13 (N = 2,318)
b = −0.04, SE = 0.02, p = .06
12 (N = 2,232)
b = 0.01, SE = 0.01, p = .07
9 (N = 1,903)
b = 0.01, SE = 0.01, p = .08
11 (N = 1,875)
b = 0.00, SE = 0.05, p = .94
12 (N = 1,983)
b = −0.01, SE = 0.02, p = .70
13 (N = 2,318)
No = 4
Yes = 9
b = −0.10, SE = 0.13, p = .44
†No g = −0.17 (0.09), p = .05
Yes g = −0.27 (0.08), p < .01
Risk of bias (summary
13 (N = 2,318)
b = −0.01, SE = 0.05, p = .87
Risk of bias (categorical
13 (N = 2,318)
Low = 4
Medium = 4
High = 5
Q (2) = 0.21, p = .90
†Low g = −0.26 (0.16), p = .11
Medium g = −0.23 (0.07), p < .01
High g = −0.21 (0.09), p = .02
Note. All analyses were performed using the overall aggregated effect size, except for depressive symptoms threshold and baseline CES-D, for which the
depression-specific effect size was used. N/A = test was not applicable. K = number of studies included in analysis, with subgroup if applicable; b =
unstandardized regression coefficient for meta-regression; WLC = waitlist control; Depression threshold = whether combined sample met recommended threshold
for clinically significant depressive symptoms; Baseline CES-D = average baseline depressive symptoms for combined sample, 20-item measure only; † =
reference group for categorical moderator in meta-regression. Subgroup Hedges’ g is listed only for categorical moderators.
GRATITUDE, DEPRESSION, AND ANXIETY 54
Fig 1 Flowchart of the study inclusion process. Template adapted from “The CONSORT
statement: revised recommendations for improving the quality of reports of parallel-group
randomised trials,” by D. Moher, K. Schulz, and D. Altman, 2001, Lancet, 357, p. 1193. Journals
publishing the original CONSORT flowchart have waived copyright protection
Total entries screened (N = 1,277)
Duplicates removed (N = 324)
Initial Exclusion (N = 759)
Irrelevant field/topic (N = 374)
Inadequate statistical information (N = 5)
Correlational/qualitative research, or no
control group (N = 302)
No gratitude intervention, or combined
with non-gratitude intervention (N = 78)
Excluded upon full review (N = 167)
Irrelevant field/topic (N = 50)
Inadequate statistical information (N = 1)
No anxiety/depression measure (N = 85)
Correlational/qualitative research, or no
control group (N = 17)
No gratitude intervention, or combined
with non-gratitude intervention (N = 14)
Full text reviewed (N = 194)
Final Inclusion (N = 27)
Unique abstracts screened for
inclusion (N = 953)
GRATITUDE, DEPRESSION, AND ANXIETY 55
Fig 2 Forest plot of included studies for overall post-test analysis. Squares are individual effect
sizes with their corresponding 95% CI indicated by the horizontal lines. The diamond is the overall
95% CI for all studies
GRATITUDE, DEPRESSION, AND ANXIETY 56
Fig 3 Forest plot of included studies for depression post-test analysis. Squares are individual effect
sizes with their corresponding 95% CI indicated by the horizontal lines. The diamond is the overall
95% CI for all studies
GRATITUDE, DEPRESSION, AND ANXIETY 57
Fig 4 Forest plot of included studies for anxiety post-test analysis. Squares are individual effect
sizes with their corresponding 95% CI indicated by the horizontal lines. The diamond is the
overall 95% CI for all studies
GRATITUDE, DEPRESSION, AND ANXIETY 58
Fig 5 Forest plot of included studies for overall follow-up analysis. Squares are individual effect
sizes with their corresponding 95% CI indicated by the horizontal lines. The diamond is the overall
95% CI for all studies
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