The Efficacy of Synchronous Teletherapy Versus In-Person Therapy: A Meta-Analysis of Randomized Clinical Trials

Abstract

Despite the increasing use of teletherapy, it remains unclear if client outcomes differ between remote and in-person settings and, if they do differ, what factors might contribute to these differences. The cur- rent study synthesized findings on the comparison between teletherapy and in-person therapy using a meta-analytic approach. All known RCTs comparing teletherapy (telephone and videoconferencing therapy) to in-person therapy were identified via bibliographic database search (PsycINFO, Medline, and Cochrane database), manual searches of previously published meta-analyses, and expert contact. We identified 1,393 studies in the initial search, 20 of which satisfied study inclusion criteria. No significant difference was found between teletherapy and in-person therapy in treatment outcomes at posttreatment (g = -0.043) or follow-up (g = -0.045) or in attrition rates (RR = 1.006). Trainee therapists experienced greater client attrition rates in teletherapy than did licensed therapists. Videoconferencing therapy was at greater risk for client attrition than telephone therapy. Within-group findings showed that tele- therapy produced a symptom reduction of a large magnitude at posttreatment (g = 1.026) and follow-up (g = 1.021). These findings provide empirical support for the practice of teletherapy and that client out- comes in teletherapy do not differ from in-person versions of treatments.

Full text

Clinical Psychology: Science and Practice
The Efficacy of Synchronous Teletherapy Versus In-Person Therapy: A Meta-
Analysis of Randomized Clinical Trials
Tao Lin, Timothy G. Heckman, and Timothy Anderson
Online First Publication, December 30, 2021. http://dx.doi.org/10.1037/cps0000056
CITATION
Lin, T., Heckman, T. G., & Anderson, T. (2021, December 30). The Efficacy of Synchronous Teletherapy Versus In-Person
Therapy: A Meta-Analysis of Randomized Clinical Trials. Clinical Psychology: Science and Practice. Advance online
publication. http://dx.doi.org/10.1037/cps0000056

The Efficacy of Synchronous Teletherapy Versus In-Person Therapy:
A Meta-Analysis of Randomized Clinical Trials
Tao Lin
1
, Timothy G. Heckman
2
, and Timothy Anderson
1
1
Department of Psychology, Ohio University
2
College of Public Health, University of Georgia
Despite the increasing use of teletherapy, it remains unclear if client outcomes differ between remote
and in-person settings and, if they do differ, what factors might contribute to these differences. The cur-
rent study synthesized findings on the comparison between teletherapy and in-person therapy using a
meta-analytic approach. All known RCTs comparing teletherapy (telephone and videoconferencing ther-
apy) to in-person therapy were identified via bibliographic database search (PsycINFO, Medline, and
Cochrane database), manual searches of previously published meta-analyses, and expert contact. We
identified 1,393 studies in the initial search, 20 of which satisfied study inclusion criteria. No significant
difference was found between teletherapy and in-person therapy in treatment outcomes at posttreatment
(g = 0.043) or follow-up (g = 0.045) or in attrition rates (RR = 1.006). Trainee therapists experi-
enced greater client attrition rates in teletherapy than did licensed therapists. Videoconferencing therapy
was at greater risk for client attrition than telephone therapy. Within-group findings showed that tele-
therapy produced a symptom reduction of a large magnitude at posttreatment (g = 1.026) and follow-up
(g = 1.021). These findings provide empirical support for the practice of teletherapy and that client out-
comes in teletherapy do not differ from in-person versions of treatments.
Public Health Significance Statements
Teletherapy produces comparable outcome to in-person therapy. Trainee therapists are at greater
risk of client dropout in teletherapy than licensed therapists.
Keywords: teletherapy, meta-analysis, in-person therapy, attrition, telepsychology
Supplemental materials: https://doi.org/10.1037/cps0000056.supp
Teletherapy is defined as the administration of psychotherapy
using remote technologies (Telepsychology Task Force, 2013).
Teletherapy can be administered asynchronously and synchro-
nously. Asynchronous teletherapy, such as computerized therapy
and internet-administered therapy, involves clients accessing inter-
vention materials with varying levels of clinician support (Woot-
ton, 2016). The client-therapist interaction in asynchronous
teletherapy is not conducted in real-time (Varker et al., 2019). In
contrast, synchronous teletherapy is similar to traditional in-person
therapy vis-à-vis treatment intensity (Varker et al., 2019), in which
clients and therapists interact in real-time without being in the
same room. The most common synchronous teletherapy includes
videoconferencing and telephone therapy (Sammons et al., 2020).
Many individuals report barriers to accessing psychological
treatments, such as time constraints, cost-related concerns, trans-
portation inconveniences, and perceived stigma (Marques et al.,
2010;Mohr et al., 2006). Teletherapy can circumvent these bar-
riers and enable individuals to receive therapy regardless of geo-
graphic residence (Brenes et al., 2011;Kafali et al., 2014). In
addition to its convenience and accessibility, teletherapy is poten-
tially advantageous for patients with disorders that preclude them
from attending in-person treatments, such as social anxiety and
panic disorders (Chiauzzi et al., 2020).
The past decades have witnessed increased research on, and the
practice of, teletherapy (Brenes et al., 2011;Glueckauf et al.,
2018;Pierce et al., 2019;Varker et al., 2019). A nationwide sur-
vey across the US on the practice of teletherapy between January
2013 through December 2016 found that 43% of therapists admin-
istered at least “some hours”of remote therapy weekly (Glueckauf
et al., 2018). In early 2020, due in large part to the COVID-19
pandemic, teletherapy rapidly changed from an adjunct treatment
to standard practice (Markowitz et al., 2021;Pierce et al., 2021;
Sammons et al., 2020). The proportion of remotely administered
clinical services increased to 85.53% in 2020 and will likely
Tao Lin https://orcid.org/0000-0002-8883-870X
Timothy Anderson https://orcid.org/0000-0001-7224-2728
Research reported in this publication was supported, in part, by the
National Institute on Drug Abuse of the National Institutes of Health,
Award R21DA047893.
Correspondence concerning this article should be addressed to Timothy
Anderson, Department of Psychology, Ohio University, 22 Richland
Avenue, Athens, OH 45701, United States. Email: andersot@ohio.edu
1
Clinical Psychology: Science and Practice
©2021 American Psychological Association
ISSN: 0969-5893 https://doi.org/10.1037/cps0000056
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remain very high after the pandemic (Pierce et al., 2021). The
greater practice of teletherapy necessitates an evaluation of the ef-
ficacy and suitability of teletherapy compared to in-person therapy
and the identification of factors that facilitate or hinder its efficacy.
The Efficacy of Synchronous Teletherapy
Previous clinical trials have demonstrated the efficacy of syn-
chronous teletherapy for a variety of mental health disorders,
including depression (Egede et al., 2015), anxiety (Stubbings et
al., 2013), posttraumatic stress disorder (PTSD; Acierno et al.,
2016,2017), panic disorder (Bouchard et al., 2004), and eating
disorder (Mitchell et al., 2008). Several reviews of synchronous
teletherapy treatments have been published (Bee et al., 2008;Bol-
ton & Dorstyn, 2015;Mohr et al., 2008;Osenbach et al., 2013;
Varker et al., 2019;Wootton, 2016). For example, Mohr et al.
(2008) synthesized 12 telephone therapy trials and found signifi-
cant effects of telephone therapy for depression. Osenbach et al.
(2013) updated the findings by including 14 studies of videocon-
ferencing and telephone therapy for depression and found similar
results. In a meta-analysis of eighteen studies of remote cognitive
behavioral therapy (CBT), remote CBT resulted in significant
improvements for obsessive-compulsive symptoms (Wootton,
2016). Further, Hilty et al. (2013) reviewed the effectiveness of
telemental health and found that telemental health is effective for
diagnosis and assessment across different age and ethnic groups
and across various settings.
While previous meta-analyses suggest that teletherapy is effica-
cious, it is unclear if it is equally efficacious as in-person therapy.
Previous meta-analyses, however, included studies comparing tel-
epsychology to various types of control groups, including treat-
ment-as-usual (TAU), wait list controls, and in-person therapy
using the same or a different treatment manual (Mohr et al., 2008;
Osenbach et al., 2013;Wootton, 2016). For example, in Wootton’s
(2016) meta-analysis, only four of the eighteen included studies
compared remote therapy to in-person treatment. To date, no
meta-analyses have examined whether teletherapy and in-person
therapy produce different outcomes in head-to-head comparisons
while controlling for other relevant factors (i.e., with similar sam-
ples, clients, therapists, and treatment manuals).
The large number of teletherapy studies published since the
most recent meta-analysis, along with the rapid uptake of telether-
apy in response to COVID-19, warrant the conduct of a present
day meta-analysis (Pierce et al., 2021). Additionally, previous
meta-analyses have focused primarily on a single diagnositc cate-
gory (Wootton, 2016) and on a single telecommunication tool
(i.e., telephone-only; Mohr et al., 2008). Potential moderators of
the efficacy of teletherapy compared to in-person therapy, such as
diagnostic category and teletherapy format, remained unstudied
(Chiauzzi et al., 2020). It has yet to be determined for which
patients presenting with which conditions teletherapy is most effi-
cacious—perhaps even more efficacious than in-person therapy.
For example, Chiauzzi et al. (2020) suggested that teletherapy
may be preferable for female clients due to their responsibiltiy of
family care. Additionally, older clients may experience more chal-
lenges when trying to access and use teconologies and, therefore,
might prefer in-person therapy.
Young therapists report greater concerns in delivering teletherapy
and poorer common therapeutic skills in teletherapy than older
therapists (Lin et al., 2021,in press). This pattern may exist because
older therapists have sufficient clinical experience and competency to
adapt their skills to remote technologies. Furthermore, videoconfer-
encing therapy may be preferable to audio-only therapy given that it
can provides visual cues and is more similar to in-person communi-
cations. Treatment length is another potential factor when determine-
ing teletherapy appropriateness because it may more challenging,
thus requiring more time for therapists to build alliance with the
patients in the absence of interpersonal contact and physical presence.
Thus, it may be valuable to examine whether these factors regarding
patients, therapists, and treatments may moderate the efficacy of
teletherapy.
The present study (a) extends the literature and synthesizes the
research on the efficacy of synchronous telepsychology (telephone
and videoconferencing therapy) compared to in-person therapy at
posttreatment and follow-up, (b) identifies potential predictors of
the efficacy of synchronous telepsychology compared to in-person
therapy, and (c) examines differences in attrition rates of synchro-
nous telepsychology compared to in-person therapy.
Method
Protocol Registration and Search Strategy
Following the Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA; Moher et al., 2009), this meta-analy-
sis was preregistered with PROSPERO (CRD42020183998) with rel-
evant methods of review specified in advance. We conducted an
extensive systematic search of the literature to identify published and
unpublished studies from 1964 through May of 2020 for inclusion in
the meta-analysis. First, three bibliographical databases (PsycINFO,
Medline, and the Cochrane Library) were searched on 07/24/2021,
with a focus on titles and abstracts by combining terms indicative of
teletherapy, in-person therapy, and randomized clinical trials (RCTs):
Searched in PsycINFO and MEDLINE (all years): (tele-
phone or phone or audio or tele* or videoconferenc* or
video) AND (psychotherapy or counseling or therapy) AND
(trial or RCT or randomi*ed) AND (face to face OR face-to-
face OR traditional OR onsite)
Search in Cochrane Library (all years): (telephone or phone
or audio or tele* or videoconferenc* or video):ti AND (psy-
chotherapy or counseling or therapy):ab AND (“face to face”
OR face-to-face OR traditional OR onsite OR in-person OR
“in person”):ab
Second, a manual search was conducted of relevant articles by
reviewing the cited literature of earlier meta-analyses and systematic
reviews on teletherapy. Experts in the field were also contacted for
ongoing or recently completed trials that satisfied the inclusion crite-
ria of the meta-analysis (described below). Additionally, a forward-
referencing search was performed to identifies articles that cite
included articles. Finally, we cross-checked our search results against
previous meta-analyses and reviews related to teletherapy.
Inclusion and Exclusion Criteria
Each included study satisfied the following inclusion criteria:
(a) the study was a randomized clinical trial of telephone-adminis-
tered therapy or videoconferencing therapy compared to in-person
2LIN, HECKMAN, AND ANDERSON
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therapy, (b) the intervention included four or more individual ther-
apy sessions, (c) therapy was provided by health professionals,
such as licensed therapists, psychologists, counselors, social work-
ers, nurses, psychiatrists, and students who were receiving clinical
training, (d) the in-person treatment and teletherapy treatment fol-
lowed the same treatment manual, (e) the RCT’s design included
pretreatment and posttreatment evaluations of mental health symp-
toms using a validated measure, (f) patients were adults ($18
years of age) with mental health symptoms indicated by symp-
tom measures or clinicians’diagnoses, and (g) there were 10 or
more patients in each treatment condition.
Studies were excluded if (a) they included patients in inpatient
settings; (b) treatment was administered through a telephone hot-
line, crisis counseling service, or other short-term psychological
service; (c) the psychological intervention was adjunct to other
treatment (e.g. physical therapy, pharmacotherapy); (d) the treat-
ment was speech therapy, language, or occupational therapy or
limited to social support; or (e) the study was duplicated in one or
more included studies. Because researchers sometimes publish
multiple studies based on the same data, we followed recommen-
dations by Wood (2008) for strategies to detect duplicative studies
to avoid the problems of multiplicity. If duplicate studies were
identified, the study with most comprehensive data was included
and the others were excluded.
Risk of Bias Assessment and Data Extraction
Each study’s risk for bias was assessed using the Cochrane
Library risk of bias assessment tool and Cochrane Handbook (Hig-
gins et al., 2020). The following sources of bias in each study
were judged as high, low, or unclear risk: sequence generation
(whether the study used appropriate methods to generate compara-
ble groups); incomplete outcome data (the completeness of out-
come data for each main outcome); selective outcome reporting
(the possibility of selective outcome reporting); baseline difference
(did baseline differences exist across treatment conditions); treat-
ment compliance (whether patients’compliance to the treatment
was acceptable); attrition bias (whether the attrition rate in each
treatment condition is acceptable); intent-to-treat bias (whether all
patients were included in the analysis regardless of the number of
follow-up surveys they completed); and preregistered (whether the
study was preregistered).
Characteristics and data of each study were extracted using a pre-
developed codebook (see online supplemental materials). The fol-
lowing information was extracted from each study: paper descrip-
tion (study type; year of publication; titles; authors; country),
treatment description (format: videoconferencing, telephone; ses-
sion length; modality; provider characteristics; setting), patient
characteristics (age; gender; diagnoses; ethnicity; region: urban or
rural), primary outcomes, secondary outcomes, and attrition rates.
Means and standard deviations of pretreatment, posttreatment, and
follow-up evaluations of main outcome variables were extracted to
calculate effect sizes. If these data were not reported, other relevant
data were used (e.g., effect sizes of change and confidence intervals,
ratio of remission). Study authors were contacted if the data pre-
sented in the article were insufficient to calculate effect sizes. The
inclusion of studies, assessment of risk, and data extraction were
conducted by two trained investigators independently and checked
by a third investigator, with an initial agreement of 90.89%,
resolved to 100% agreement after discussion.
Data Analysis
Comprehensive Meta-Analysis (version 3.3070; CMA) was used
to calculate pooled between-group effect sizes of a teletherapy
group versus an in-person therapy group. A positive effect size indi-
cates that the teletherapy showed greater efficacy than the in-person
therapy whereas a negative effect size indicates that the teletherapy
showed lesser efficacy. Effect sizes at posttreatment and 3- to 6-
month follow-up were calculated. We also calculated the pooled
between-group risk ratio (RR) of attrition, which indicated the attri-
tion rate in the teletherapy group divided by the attrition rate in the
in-person therapy group. A risk ratio of 1 indicated that the attrition
rate in teletherapy was comparable to the attrition rate in in-person
therapy. A risk ratio greater than one indicated that the attrition rate
in teletherapy was larger than the attrition rate in in-person therapy
whereas a risk ratio less than one indicates that the attrition rate in
teletherapy was smaller than the attrition rate in in-person therapy.
Furthermore, we calculated the within-group effect sizes for tele-
therapy at posttreatment and follow-up. A random effects pooling
model was used in all analyses because heterogeneity is commonly
assumed across clinical studies. We also calculated the Qand I
2
to
test the homogeneity of effect sizes.
Additionally, subgroup analyses were performed for the follow-
ing characteristics of the study: treatment format (videoconferenc-
ing vs. telephone) and provider license status (licensed vs. trainee)
to examine whether any were associated the pooled effect sizes.
Sensitivity analyses were also conducted to assess risk of bias to
compare the pooled effect size of studies with low risk of bias
(low or unclear risk on all bias items) to studies with high risk on
at least one of the eight bias items. Furthermore, the “one-study
removed”method was used to examine whether systematically
removing each study impacted the overall effect size. Finally, for
continuous variables, metaregression analyses were conducted to
examine whether patients’average age, gender, and treatment
length were associated with the pooled effect sizes at posttreat-
ment and follow-up as well as attrition.
Results
Study Selection
A total of 1,751 studies (1,393 after removal of duplicates)
were identified via the procedures described above. After
abstract screening, 82 full-text articles were assessed for eligibil-
ity (see Figure 1). Twenty studies that directly compared tele-
therapy and in-person therapy and satisfied all inclusion criteria
were included in the meta-analysis. The PRISMA flowchart
described the inclusion process and reasons for exclusion (see
Figure 1).
Of the 20 studies, 3 did not provide sufficient data to calculate
effect sizes and 2 did not report attrition rates. Therefore, 17 stud-
ies with 18 comparisons were included in the meta-analysis of
effect sizes (including 35 unique samples and 2,004 participants),
and 18 studies with 19 comparisons were included in the meta-
analysis of attrition rate (including 37 unique samples and 2,159
participants). Additionally, 11 studies that included follow-up at 3
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and 6 months were included to calculate the longer-term effect of
synchronous teletherapy compared to in-person therapy.
Characteristics of Included Studies
The characteristics of the 20 included studies are outlined in Table
1. Of the 20 studies, 6 compared in-person therapy to telephone ther-
apy and 13 compared in-person therapy to videoconferencing ther-
apy; one study compared both videoconferencing and telephone
therapy to in-person therapy. The 20 studies were conducted in the
United States (n = 11), Canada (n = 3), the United Kingdom (n = 2),
New Zealand (n = 1), Spain (n = 1), China (n = 1), and Australia
(n = 1). The majority of studies tested CBT (n = 12); other specified
treatment modalities included cognitive processing therapy (CPT;
n = 2), behavioral activation (BA; n = 2), exposure therapy (n = 1),
and problem-solving therapy (n = 1). In terms of diagnostic category,
the included studies have primarily focused on PTSD (n = 5), depres-
sion (n = 4), generalized anxiety disorder (n = 1), eating disorder (n =
1), panic disorder with agoraphobia (n = 1), gambling (n = 1), and
dementia (n = 1). The others (n = 5) did not focus on any specific
diagnosis of mental disorder. Treatment lengths varied from 5 to 20
session, and each session ranged from 30 minutes to 90 minutes.
Risk of Bias Assessment
Among the 20 studies, eighteen studies were determined to have
adequate sequence generation, while two studies had high risk for
inadequate sequence generation. Fifteen studies had acceptable attri-
tion rates (,30% for treatments with less than 8 session; ,35% for
treatment with 8 or more sessions) whereas three studies had higher
attrition rates; two study did not report attrition. Eleven studies con-
ducted intent-to-treat analyses while seven did not. Ten studies had
been preregistered whereas ten had not. Seventeen studies were
determined to be at low risk for significant baseline differences
between groups while three had higher risk of baseline differences
between groups. Fourteen studies had lower risk for poor treatment
adherence or patient compliance; 6 did not provide any information
on treatment adherence. In terms of selective outcome reporting, 17
were at low risk. Sixteen studies had low risk for incomplete out-
come data. Overall, fourteen studies were at risk for some biases and
Figure 1
Selection and Exclusion of Studies
Records idenfied through
database searching
(n = 1094)
PsycINFO (n = 251), MEDLINE (n =
576), Cochrane Library (n = 509)
Screening
Included
Eligibility
Idenficaon
Addional records idenfied
through other sources
(n = 415)
Records aer duplicates removed
(n = 1393)
Records screened
(n = 1393)
Records excluded
(n = 1311)
Full-text arcles assessed
for eligibility
(n = 82)
Full-text arcles excluded (n =
62) with reasons:
-Duplicates of included
studies (n = 15)
-Not individual therapy
(n=15)
-Different treatment in
control group (n = 6)
-Not on mental health (n=6)
-No available data (n=5)
-Not synchronous therapy
(n=4)
-Not randomized (n=4)
-Not compared to in-person
treatment (n=3)
-Adjunct treatment (n=2)
-Less than 4 sessions (n=2)
Studies included in
qualitave synthesis
(n = 20)
Studies included in
quantave synthesis
(Posreatment: n = 17;
Follow-up: n = 11;
Arion: n = 18)
Note. See the online article for the color version of this figure.
4LIN, HECKMAN, AND ANDERSON
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Table 1
Characteristics of the Included Studies
Study Country Format Modality Therapist Expertise
Contain in-person
contact Diagnoses/problems Mage (SD)
%
female
Control
Group n
#of
session Follow-up
Acierno et al. (2016) USA VC BA Master’s level counselors No PTSD 45.6 (14.9) 5.60% F2F VC: 111F2F: 121 8 Pre- and posttreatment,
3 m and 12 m
Acierno et al. (2017) USA VC Exposure
therapy
Licensed master's level
counselors
No PTSD 41.8 (14.5) 3.80% F2F VC: 65F2F: 68 10 Pre- and posttreatment,
3 m and 6 m
Alegría et al. (2014) USA TP CBT Psychologists, licensed social
workers, and counselor
No Depression 44.9 81.71% F2F; TAU TP: 87F2F: 84TAU:
86
9 Pre- and posttreatment,
2 m and 4 m
Arnedt et al. (2021) USA VC CBT Psychologist No Insomnia 47.2 (16.3) 70.80% F2F VC: 31F2F: 31 6 Pre- and posttreatment,
3m
Bouchard et al. (2004) Canada VC CBT Psychologists, PhD candi-
dates, and psycho-
educators
No Panic disorder with
agoraphobia
37.99 71.40% F2F VC: 11F2F: 10 12 Intake, pre- and post-
treatment, 6 m
Burgess et al. (2012) UK TP CBT Trained nurse therapists Yes Chronic fatigue syndrome 37.4 (10.1) 78.75% F2F TP: 45F2F: 35 14 Pre- and posttreatment,
3 m, 6 m, and 12 m
Choi et al. (2014) USA VC PST Master's level social workers No Depression 64.8 (9.18) 78.48% F2F; CC VC: 40F2F: 45CC:
31
6 pretreatment, 12 w, 24
w, and 36 w
Cuevas et al. (2006) Spain VC CBT Psychiatrists No NS 40.21 66.43% F2F VC: 70F2F: 70 8 Pre- and posttreatment
Day and Schneider (2002) USA VC &
TP
CBT Doctoral students No NS 39.35 (15.88) 65.00% F2F F2F: 27VC: 26TP:
27
5 Pre- and posttreatment
Egede et al. (2015) USA VC BA Licensed master's level
counselors
No MDD 63.9 (5.1) 2.48% F2F VC: 120F2F: 121 8 pretreatment, 4 w, 8 w,
3 m and 12 m
Germain et al. (2009) Canada VC CBT Psychologists No PTSD 42.33 60.40% F2F VC: 16F2F: 32 20 Pre- and posttreatment
Maieritsch et al. (2016) USA VC CPT PhD-level psychologists and
social worker
Yes PTSD 30.9 (6.05) 6.70% F2F VC: 25F2F: 26 10 Pre- and posttreatment
Mitchell et al. (2008) USA TP CBT Psychologists No BN or unspecified eating
disorder
29.02 98.44% F2F TP: 62F2F: 66 14 Pre- and posttreatment,
3 m and 12 m
Mohr et al. (2012) USA TP CBT PhD-level psychologists No MDD 47.7(13.0) 77.54% F2F TP: 163F2F: 162 18 Pre- and posttreatment,
3 m and 6 m
Morland et al. (2015) USA VC CPT NS No PTSD 46.4 (11.9) 100% F2F VC: 63F2F: 63 12 pre- and posttreatment,
3 m and 6 m
Poon et al. (2005) China VC Cognitive
treatment
Social worker No Cognitive impairments Unclear Unclear F2F VC: 11F2F: 11 12 Pre- and posttreatment
Robillard et al. (2017) Canada VC CBT Doctoral students No GAD 41.15 82.05% F2F VC: 52F2F: 65 15 Pre- and posttreatment,
6 m and 12 m
Stubbings et al. (2013) Australia VC CBT Doctoral students Yes NS 30 (11) 57.69% F2F VC: 14F2F: 12 12 Pre- and posttreatment,
6w
Tse et al. (2013) New Zealand TP CPT Social workers and
counselors
No Pathological gambling 44.6 (12.3) 67.39% F2F TP: 46F2F: 46 6 Pre- and posttreatment
Watson et al. (2017) UK TP CBT NS No NS 50.42 72.03% F2F F2F: 58TP: 60 4 Pre- and posttreatment
Note. BA = Behavioral activation; BN = Bulimia nervosa; CBT = Cognitive-behavioral therapy; CC = Care call; CPT = Cognitive processing therapy; F2F = Face-to-face therapy; GAD =
Generalized anxiety disorder; MDD = Major depressive disorder; NS = nonspecified; PST = Problem-solving Therapy; PTSD = Posttraumatic stress disorder; TAU = Treatment as usual; TP =
Telephone therapy; VC = Videoconferencing therapy
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six showed low risk for all biases (see Supplemental Figure 1 for
details of individual study).
Between-Group Analyses
Posttreatment Effects of Teletherapy Compared to In-
Person Therapy
Table 2 shows the pooled effect sizes of teletherapy compared to
in-person therapy. The pooled between-group effect size of synchro-
nous teletherapy versus in-person therapy at posttreatment was
hedges’g=0.043 (k = 18; 95% CI [0.137, 0.051]; p= .367),
with zero heterogeneity (I
2
=0; Q = 12.634, p=.760). The pooled
between-group effect sizes at posttreatment ranged from 0.066 to
0.026 after removing each study, indicating no change to the effect
size. Specifically, compared to in-person therapy at posttreatment,
the pooled between-group effect sizes for video-conferenced therapy
was g=0.077 (k = 12; 95% CI [0.201, 0.046], p= .219) and g=
0.004 (k = 6; 95% CI [0.141, 0.148]; p= .960) for telephone-
administered therapy.
Longer-Term Effects of Teletherapy Compared to In-
Person Therapy
At 3- to 6-month follow-up, the pooled effect size of synchronous
teletherapy versus in-person therapy was g=0.045 (k = 11; 95%
CI [0.151, 0.082]; p= .411), with zero heterogeneity (I
2
=0; Q =
6.579; p=.765; see Table 2). Specifically, compared to in-person
therapy at follow-up, the pooled effect size of videoconferencing
therapy versus was g=0.02 (k = 7; 95% CI [0.160, 0.121]; p=
.785) and the pooled effect size of telephone-administered therapy
was g=0.055 (k = 4; 95% CI [0.263, 0.154]; p= .607). The
pooled effect sizes at follow-up ranged from 0.065 to 0.005 after
removing each study, indicating no change to the effect size.
Attrition Rate in Teletherapy Compared to In-Person
Therapy
Table 3 presents the risk ratio of attrition in teletherapy versus
in-person therapy. The pooled risk ratio of attrition in synchronous
teletherapy versus in-person therapy was 1.006 (k = 19; 95% CI
[0.850, 1.191]; p=.797),withnoheterogeneity(I
2
=0; Q =
16.149, p=.582). Specifically, the pooled risk ratio of attrition in
videoconferencing therapy versus in-person therapy was 1.249 (k =
12; 95% CI [0.971, 1.607]; p= .084). The pooled odds ratio of attri-
tion in telephone therapy versus in-person therapy was 0.852 (k =
7; 95% CI [0.669, 1.085]; p= .193). The pooled risk ratio ranged
from 0.970 to 1.092 after removing each study, indicating no
changes to the effect size.
Subgroup Analyses and Metaregressions
Table 4 shows differences in posttreatment and longer-term
effects and attrition rates of teletherapy compared to in-person
therapy when studies were grouped by treatment format, overall
risk of bias, and therapist license status. None of these factors
significantly moderated the effect sizes at posttreatment and
follow-up of teletherapy versus in-person therapy. Of note, the
risk ratio of attrition in teletherapy compared to in-person ther-
apy varied by treatment format (p= .032) and therapist license
status (p=.045). Compared to in-person therapy, videoconfer-
encing therapy had greater risk for client attrition compared to
telephone therapy. Compared to licensed therapists, trainee
therapists experienced greater client attrition when administer-
ing teletherapy.
Table 2
Between-Group Effect Sizes Comparing Telepsychology to In-Person Therapy at Postttreatment and Follow-Up
Study Treatment format
Pretreatment to
posttreatment Weight of included
study
Pretreatment to follow-up Weight of included
study
g 95% CI g 95% CI
Alegría et al. (2014) TP 0.169 [0.130, 0.468] 23.363 0.092 [0.207, 0.390] 12.724
Burgess et al. (2012) TP 0.210 [0.303, 0.723] 7.946 0.239 [0.306, 0.785] 3.819
Day and Schneider (2002) TP 0.053 [0.474, 0.580] 7.529 0.105 [0.563, 0.352] 5.417
Mitchell et al. (2008) TP 0.187 [0.622, 0.248] 11.033 —— —
Mohr et al. (2012) TP 0.064 [0.294, 0.166] 39.440 0.236 [0.470, 0.001] 20.608
Watson et al. (2017) TP 0.098 [0.540, 0.344] 10.689 —— —
TP 0.004 [0.141, 0.148] 0.055 [0.263, 0.154]
Acierno et al. (2016) VC 0.035 [0.311, 0.240] 20.027 0.006 [0.269, 0.282] 14.932
Acierno et al. (2017) VC 0.174 [0.514, 0.166] 13.151 0.162 [0.502, 0.178] 9.821
Arnedt et al. (2021) VC 0.094 [0.575, 0.387] 6.579 0.047 [0.528, 0.433] 4.915
Bouchard et al. (2004) VC 0.473 [0.370, 1.316] 2.139 0.200 [0.636, 1.037] 1.623
Choi et al. (2014) VC —— — 0.052 [
0.332, 0.435] 7.701
Cuevas et al. (2006) VC 0.067 [0.409, 0.275] 12.993 —— —
Egede et al. (2015) VC 0.242 [0.576, 0.092] 13.612 0.126 [0.481, 0.228] 9.036
Day and Schneider (2002) VC 0.030 [0.501, 0.561] 5.387 —— —
Germain et al. (2009) VC 0.706 [1.350, 0.063] 3.672 —— —
Maieritsch et al. (2016) VC 0.090 [0.633, 0.454] 5.148 —— —
Morland et al. (2015) VC 0.095 [0.253, 0.442] 12.606 0.109 [0.238, 0.457] 9.405
Poon et al. (2005) VC 0.041 [0.846, 0.764] 2.346 —— —
Stubbings et al. (2013) VC 0.348 [0.458, 1.154] 2.340 —— —
VC 0.077 [0.201, 0.046] 0.020 [0.160, 0.121]
Overall 0.043 [0.137, 0.051] 0.045 [0.151, 0.062]
Note. TP = Telephone therapy; VC = Videoconferencing therapy
6LIN, HECKMAN, AND ANDERSON
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Table 5 presents the results from metaregressions of the effects
of teletherapy and attrition rates in teletherapy compared to in-per-
son therapy. Patients’age and gender, and treatment length did not
moderate the effects at posttreatment and follow-up or attrition
rates in teletherapy compared to in-person therapy.
Within-Group Analyses
Table 6 shows the pooled within-group effect sizes of tele-
therapy of studies included in the meta-analysis. The pooled
within-group effect sizes of teletherapy were large (k = 16; g =
1.026; 95% CI [0.795, 1.256]; p,.001) from pretreatment to
posttreatment, with a high level of heterogeneity (I
2
=82.002;
Q = 83.343, p,.001). More specifically, the pooled within-
group effect sizes from pretreatment to posttreatment were g =
0.833 (k = 6; 95% CI [0.537, 1.130]; p,.001) for telephone
therapy and g = 1.196 (k = 10; 95% CI [0.830, 1.561]; p,.001)
for videoconferencing therapy.
The pooled within-group effect sizes of synchronous teletherapy
were maintained (k = 9; g = 1.021; 95% CI [0.773, 1.269]; p,
.001) from posttreatment to follow-up, with a high level of hetero-
geneity (I
2
=80.108; Q = 40.217, p,.001). More specifically,
the pooled within-group effect sizes from posttreatment to follow-
up were g = 0.946 (k = 4; 95% CI [0.689, 1.203]; p,.001) for tel-
ephone therapy and g = 1.118 (k = 5; 95% CI [0.658, 1.579]; p,
.001) for videoconferencing therapy.
The pooled attrition rates in teletherapy were 0.219 (k = 19;
95% CI [0.169, 0.269]; see Table 3), with a high level of heteroge-
neity (I
2
=74.066; Q = 69.407, p,.001). Attrition rates ranged
from 0.057 to 0.652.
Discussion
To the best of our knowledge, the present study was the first
meta-analysis to compare teletherapy to in-person therapy in
terms of posttreatment effects, longer-term effects, and attrition
rates. Overall, synchronous teletherapy demonstrated compara-
ble effects to in-person therapy at posttreatment (g=0.043)
and follow-up (g=0.045). Within-group analyses found that
teletherapy produced large pooled effect sizes at posttreatment
(g = 1.026) and that these effects were maintained at 3- to 6-
month follow-up (g = 1.021). Given the potential for lower asso-
ciated costs and easier access of teletherapy (Crow et al., 2009;
Egede et al., 2018;Kafali et al., 2014), the remote delivery of
psychotherapy appears to be particularly promising and equally
effective as in-person therapy.
These findings are consistent with previous comparisons of tele-
therapy to in-person therapy. In a meta-analysis of synchronous
teletherapy for depression (Osenbach et al., 2013), the pooled
effect size of six studies comparing remote therapy with in-person
therapy was -0.11 (i.e., a nonsignificant difference). Additionally,
Wootton (2016) synthesized four studies that compared remote
CBT to in-person CBT and found that the effect size differences
were not clinically meaningful but marginally statistically signifi-
cant at posttreatment (g = 0.21; 95% CI [0.43, 0.02]) and fol-
low-up (g = 0.28; 95% CI [0.58, 0.00]). This may be because
two of the four studies utilized asynchronous teletherapy treat-
ment, which produce smaller effects (Wootton, 2016). Compared
to previous findings, this studied identified a larger number of
studies and found smaller between-group effect sizes between two
treatment formats. Notably, the effect sizes differences in this and
previous studies, though not at traditionally significant levels, do
Table 3
Between-Group Risk Ratio Comparing Attrition Rate of Telepsychology to In-Person Therapy
Study Treatment format
Attrition rates of telepsy-
chology versus in-person
therapy Weight of included
study
Attrition rates of
telepsychology
Weight of included study
RR 95% CI AR 95% CI
Alegría et al. (2014) TP 0.841 [0.502, 1.409] 19.207 0.310 [0.193, 0.427] 15.995
Burgess et al. (2012) TP 1.667 [0.680, 4.088] 6.929 0.333 [0.165, 0.502] 12.082
Day and Schneider (2002) TP 2.266 [0.440, 11.678] 2.141 0.156 [0.019, 0.293] 14.398
Mitchell et al. (2008) TP 0.828 [0.468, 1.464] 16.085 0.339 [0.194, 0.484] 13.790
Mohr et al. (2012) TP 0.638 [0.414, 0.981] 26.150 0.209 [0.138, 0.279] 19.819
Tse et al. (2013) TP 1.071 [0.640, 1.793] 19.267 0.652 [0.419, 0.886] 8.442
Watson et al. (2017) TP 0.642 [0.309, 1.334] 10.220 0.218 [0.095, 0.342] 15.474
TP 0.852 [0.669, 1.085] 0.289 [0.204, 0.375]
Acierno et al. (2016) VC 1.452 [0.705, 2.992] 12.156 0.057 [0.001, 0.113] 12.231
Acierno et al. (2017) VC 1.770 [0.892, 3.514] 13.505 0.229 [0.117, 0.341] 8.720
Arnedt et al. (2021) VC 1.939 [0.176, 21.388] 1.102 0.246 [0.121, 0.370] 8.002
Choi et al. (2014) VC 0.875 [0.326, 2.349] 6.508 0.333 [0.165, 0.502] 5.871
Cuevas et al (2006) VC 0.667 [0.188, 2.362] 3.967 0.333 [0.102, 0.564] 3.884
Day and Schneider (2002) VC 1.933 [0.354, 10.555] 2.204 0.338 [0.197, 0.480] 7.104
Egede et al. (2015) VC 1.186 [0.621, 2.265] 15.188 0.061 [0.023, 0.145] 10.474
Germain et al. (2009) VC 1.222 [0.500, 2.990] 7.933 0.125 [0.032, 0.218] 9.922
Maieritsch et al. (2016) VC 0.882 [0.441, 1.767] 13.171 0.133 [0.003, 0.264] 7.662
Morland et al. (2015) VC 1.192 [0.567, 2.504] 11.510 0.157 [0.086, 0.228] 11.334
Robillard, et al. (2017) VC 1.621 [0.752, 3.492] 10.774 0.167 [0.094, 0.240] 11.179
Stubbings et al. (2013) VC 1.286 [0.215, 7.695] 1.983 0.214 [0.028, 0.457] 3.617
VC 1.249 [0.971, 1.607] 0.176 [0.123, 0.230]
Overall 1.006 [0.850, 1.191] 0.219 [0.169, 0.269]
Note. AR = attrition rate; RR = risk ratio; TP = Telephone therapy; VC = Videoconferencing therapy
TELETHERAPY VERSUS IN-PERSON THERAPY 7
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slightly favor of in-person therapy, suggesting that more studies
examining the effects of teletherapy are needed.
Treatment format and patient demographic variables did not signifi-
cantly moderate the between-group pooled effects sizes in teletherapy
versus in-person therapy. In particular, telephone therapy showed
equivalent efficacy to videoconferencing therapy, suggesting that key
therapeutic ingredients can be conveyed by therapists and perceived by
clients despite the lack of visual cues. It should be noted, however, the
included studies have merely focused on cognitive and/or behavioral
treatments and limited kinds of psychiatric disorders. Many potential
Table 4
Subgroup Analysis on Effects at Posttreatment and Follow-Up and Attrition of Telepsychology Versus In-Person Therapy
Moderator (k) Hedges’gRR 95% CI Qvalue pvalue
Effect at posttreatment
Format 0.700 .403
Videoconferencing (12) 0.077 [0.201, 0.046]
Telephone (6) 0.004 [0.141, 0.148]
Therapist License 1.080 .299
Licensed (11) 0.081 [0.195, 0.033]
Trainee (5) 0.044 [0.163, 0.252]
Overall risk of bias 0.789 .374
High (13) 0.005 [0.131, 0.120]
Low (5) 0.091 [0.232, 0.050]
Effect at follow-up
Format 0.075 .784
Videoconferencing (7) 0.020 [0.160, 0.121]
Telephone (4) 0.055 [0.263, 0.151]
Therapist License 0.508 .476
Licensed (8) 0.080 [0.204, 0.044]
Trainee (2) 0.025 [0.236, 0.287]
Overall risk of bias 2.251 .134
High (5) 0.063 [0.113, 0.239]
Low (6) 0.107 [0.240, 0.027]
Attrition
Format 4.611 .032*
Videoconferencing (12) 1.249 [0.971, 1.607]
Telephone (7) 0.852 [0.669, 1.085]
Therapist License 4.035 .045*
Licensed (12) 0.948 [0.782, 1.150]
Trainee (5) 1.587 [0.998, 2.525]
Overall risk of bias 0.076 .783
High (13) 1.063 [0.857, 1.318]
Low (6) 0.999 [0.682, 1.464]
Note. RR = risk ratio; TP = Telephone therapy; VC = Videoconferencing therapy.
*p,.05
Table 5
Metaregression on Effects at Posttreatment and Follow-Up and Attrition of Telepsychology Versusi In-Person Therapy
Moderator Coefficient 95% CI Z value pvalue
Effects at posttreatment
Intercept 0.114 [0.568, 0.795] 0.33 .744
Age 0.003 [0.016, 0.010] 0.46 .643
Gender: % of female 0.212 [0.090, 0.509] 1.40 .162
Treatment length 0.013 [0.036, 0.009] 1.16 .247
Effect at follow-up
Intercept 0.361 [0.443, 1.165] 0.88 .378
Age 0.004 [0.017, 0.009] 0.63 .526
Gender: % of female 0.187 [0.122, 0.497] 1.19 .236
Treatment length 0.028 [0.057, 0.001] 1.87 .062
Attrition
Intercept 0.732 [0.407, 1.872] 1.26 .208
Age 0.008 [0.030, 0.012] 0.76 .448
Gender: % of female 0.399 [0.934, 0.136] 1.46 .144
Treatment length 0.012 [0.052, 0.027] 0.60 .546
Note. TP = Telephone therapy; VC = Videoconferencing therapy.
*p,.05
8LIN, HECKMAN, AND ANDERSON
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moderators (e.g., clinical setting) for determining which venues and
factors are most amenable to teletherapy remained unknown.
It is premature to conclude that teletherapy is as efficacious as in-per-
son therapy across all conditions, symptoms, and patient populations.
Patients with severe psychiatric problems may require more therapist
engagement and therefore may show better outcome in in-person ther-
apy (Koblauch et al., 2018). For example, Mohr et al. (2011) found no
significant benefits for a 16-session telephone-delivered CBT over
TAU and attributed the null results to the unique nature of the sample
(veterans), who might have been more refractory to treatment in general
than other populations. In another RCT with specific treatment charac-
teristics (patients with bulimia nervosa), Mitchell et al. (2008) found
that in-person CBT produced showed significantly greater reductions in
certain symptoms compared to telephone CBT. Researchers and practi-
tioners may not provide teletherapy to patients who, in their opinion,
are unlikely to benefit from or not suitable for teletherapy, which may
have resulted in selection bias. Therefore, more research is needed to
examine potential moderators of efficacy in teletherapy.
Teletherapy did not significantly differ from in-person therapy in
terms of attrition rates. Attrition rates did, however, vary as a func-
tion of treatment format and therapist experience. Videoconferenc-
ing therapy evidenced higher attrition rates than in-person therapy
whereas telephone therapy showed lower attrition rates than in-per-
son therapy. Perhaps telephone therapy is easier to access and oper-
ate technically than videoconferencing therapy. Videoconferencing
has more disparities among patient use of technology than telephone
uses, including disparities in quality of personal device equipment,
internet access, clients’attitudes toward technology (e.g., Schuster et
al., 2020). For example, clients can attend telephone session from
venues without networks and equipment requisite for video calls.
In addition, whereas licensed therapists had comparable attrition
rates in teletherapy to in-person therapy, trainee therapists had
higher attrition rates during teletherapy than during in-person
therapy. A recent survey study evidenced that, compared to older
therapists, young therapists’skills in building alliance and repairing
ruptured alliance were more affected by telecommunication (Lin et
al., in press). Whereas experienced therapists may be competent in
building a strong alliance remotely and leverage advantages afforded
by teletherapy, trainee therapists may find it challenging to build a
strong alliance and sufficiently engage in teletherapy, which was fur-
ther associated with client dropout (Sharf et al., 2010). It may be
necessary to provide relevant and more real-world training for less
experienced therapists to enhance their teletherapy skills.
It is worth noting that, in spite of the documented successes of tel-
etherapy, many psychological professionals continue to doubt its ef-
ficacy and reported preferences for, and greater competency in, in-
person therapy (Perle et al., 2014;Perry et al., 2020). There may be
several explanations for this phenomenon. First, therapists’preferen-
ces for in-person therapy may be based more on what is traditional
and convenient and based less on empirical evidence. For example,
therapists may be unwilling to use teletherapy due to potential tech-
nical inconveniences and teletherapy-specific policies (e.g., obtain-
ing telepsychology consent). Second, teletherapy may be inferior to
in-person therapy in some aspects of therapy (e.g., expressing emo-
tions), even though these issues may not be necessary for positive
client outcomes. Additionally, whereas therapists in RCTs typically
receive trainings and follow intervention manuals, these same sup-
ports may not be available in community practice.
Limitations and Future Directions
Several limitations of this meta-analysis should be noted.
First, several potential moderators of teletherapy were not exam-
ined. For example, we were unable to examine race/ethnicity as a
moderator because some studies did not report race/ethnicity and
some studies were conducted in other countries with different
racial/ethnic distribution. Likewise, the included studies only
Table 6
With-Group Effect Sizes of Telepsychology at Posttreatment and Follow-Up
Study Treatment format
Pretreatment to post-
treatment
Weight of included study
Pretreatment to fol-
low-up
Weight of included study
g 95% CI g 95% CI
Alegría et al. (2014) TP 0.731 [0.489, 0.973] 18.878 0.852 [0.599, 1.105] 29.011
Burgess et al. (2012) TP 0.879 [0.455, 1.302] 14.985 0.885 [0.429, 1.342] 17.765
Day and Schneider (2002) TP 0.548 [0.153, 0.942] 15.619 —— —
Mitchell et al. (2008) TP 0.715 [0.376, 1.054] 16.837 0.713 [0.357, 1.070] 22.725
Mohr et al. (2012) TP 1.378 [1.156, 1.601] 19.244 1.244 [1.014, 1.474] 30.499
Watson et al. (2017) TP 0.641 [0.192, 1.090] 14.436 —— —
TP 0.833 [0.537, 1.130] 0.946 [0.689, 1.203]
Acierno et al. (2017) VC 1.225 [0.970, 1.480] 12.243 1.304 [1.043, 1.565] 22.542
Arnedt et al. (2021) VC 1.789 [1.244, 2.334] 10.099 1.861 [1.302, 2.420] 17.894
Bouchard et al. (2004) VC 1.231 [0.473, 1.989] 8.350 1.036 [0.323, 1.749] 15.362
Choi et al. (2014) VC —— — 1.062 [0.732, 1.392] 21.603
Cuevas et al. (2006) VC 1.783 [1.354, 2.212] 11.040
Day and Schneider (2002) VC 0.619 [0.209, 1.029 11.190 —— —
Germain et al. (2009) VC 3.094 [1.921, 4.268] 5.568 —— —
Maieritsch et al. (2016) VC 0.967 [0.498, 1.437] 10.718 —
Morland et al. (2015) VC 0.454 [0.197, 0.710] 12.236 0.454 [0.198, 0.711] 22.598
Poon et al. (2005) VC 0.893 [0.223, 1.563] 9.063
Stubbings et al. (2013) VC 0.879 [0.261, 1.497] 9.492 —— —
VC 1.196 [0.830, 1.561] 1.118 [0.658, 1.579]
Overall 1.026 [0.795, 1.256] 1.021 [0.773, 1.269]
Note. TP = Telephone therapy; VC = Videoconferencing therapy.
TELETHERAPY VERSUS IN-PERSON THERAPY 9
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covered limited types of psychiatric disorders and clinical settings.
The comparability of teletherapy to in-person therapy for other
disorders and in other settings should be further investigated.
Because most studies included in this meta-analysis used cogni-
tive and behavioral approaches, more research is needed to exam-
ine the efficacy of noncognitive and nonbehavioral therapy
modalities delivered remotely. Second, this study focused on indi-
vidual therapy for adults. Future research should examine whether
teletherapy can achieve equivalent effects for children and adoles-
cents and when delivered in group format.
Third, although this meta-analysis synthesized efficacy of tele-
therapy at 3- to 6-month follow-up, we were unable to examine lon-
ger-term effects because only a limited number of included studies
included long-term follow-up measurements. Furthermore, this
study synthesized findings from RCTs. Given the potential chal-
lenges in delivering remote treatment in community practice, future
meta-analyses may also include findings from naturalistic settings.
Finally, the current meta-analysis only included a limited number
of studies that were conducted prior to the pandemic. The results of
subgroup analyses and metaregressions should be interpreted with
caution. Future meta-analyses may update the current findings by
included more teletherapy studies in the postpandemic era.
Implication and Conclusion
This study found that teletherapy was comparable to in-person
therapy vis-à-vis symptom improvement and may achieve equally
large effects at posttreatment and follow-up. Telephone therapy
may be a reliable, alternative treatment for patients with limited
access to the internet or equipment required for videoconferencing
communication. Furthermore, the study examined moderators of
the effects and attrition rates of teletherapy versus in-person ther-
apy to determine which situations may be most optimal for tele-
therapy. Trainee therapists may be at greater risk for client
dropout in teletherapy compared to in-person therapy. These find-
ings may inform the optimal approach for delivering teletherapy
services. For example, considering their comparable efficacy, tele-
phone therapy be a good alternative treatment to videoconferenc-
ing therapy when required technology is unavailable. Also, clients
receiving telephone therapy may be more likely to stay in treat-
ment to experience the full “dose effect.”Our meta-analysis
underscores the comparability of teletherapy and in-person ther-
apy. Teletherapy has the potential to improve psychological health
and reach patients who might otherwise not be reached. Finally,
this meta-analysis may provide a roadmap for further research on
teletherapy for various disorders and in different settings.
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Received April 22, 2021
Revision received September 25, 2021
Accepted November 1, 2021 n
12 LIN, HECKMAN, AND ANDERSON
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