Background: Several recent studies examined patient use and satisfaction with synchronous telemental health services in response to the widespread implementation during the COVID-19 pandemic. However, a systematic review of recent literature on the determinants of these outcomes is missing.
Objective: The aim of this systematic review was to give an extensive overview of the literature on and highlight the influential determinants of patient use and satisfaction with synchronous telemental health services during the COVID-19 pandemic.
Methods: This review satisfied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and was registered in PROSPERO. Peer-reviewed, quantitative studies that observed the determinants of patient use or satisfaction with synchronous telemental health services during the COVID-19 pandemic were included. PubMed, PsycInfo, and Web of Science database searches were conducted in August 2022 for English and German language studies published from 2020 onward. Key steps were performed by 2 reviewers. Determinants were synthesized into major categories informed by the dimensions of the widely used and established Unified Theory of Acceptance and Use of Technology.
Results: Of the 20 included studies, 10 studies examined determinants of patient use, 7 examined determinants of patient satisfaction, and 3 observed both outcomes. The quality of the studies was mainly good or fair. There was substantial heterogeneity in the study designs, methods, and findings. Sociodemographic characteristics and health-related determinants were mostly considered. Some of the major dimensions of the Unified Theory of Acceptance and Use of Technology were neglected in recent studies. Although most findings were mixed or nonsignificant, some indications for potential relationships were found (eg, for sex, age, and symptom severity).
Conclusions: The findings revealed potential target groups (eg, female and young patients with mild symptoms) for future postpandemic telemental health interventions. However, they also identified patient groups that were harder to reach (eg, older patients with severe symptoms); efforts may be beneficial to address such groups. Future quantitative and qualitative research is needed to secure and expand on recent findings, which could help improve services.
Trial Registration: PROSPERO CRD42022351576; https://tinyurl.com/yr6zrva5
Over the past 3 decades, health care services were usually delivered in person. Telemedicine is a promising, alternative service delivery model. The World Health Organization  summarized the four core characteristics of telemedicine as follows: (1) its purpose is to provide clinical support; (2) it is intended to overcome geographic barriers, connecting users who are not in the same physical location; (3) it involves the use of various types of information and communication technology; and (4) its goal is to improve health outcomes. Telemedicine benefits have been evaluated in the past and include, for example, reduced costs and improved access to services and information [ , ]. Evidence also suggests that telemedicine, in general, is a clinically and cost-effective tool with high satisfaction in patients and health care professionals [ ]. However, the implementation of telemedicine has often been hindered by multiple barriers regarding reimbursement and clinical, legal, sustainability, and social issues [ , ].
In the wake of the COVID-19 pandemic, rapid changes in the delivery of health care services had to be made to prevent further spread of the virus, to protect people at higher risk of severe illness from COVID-19 (eg, patients with cancer, cardiovascular disease, or chronic respiratory disease), and to relieve the strain on the health care system. Consequently, telemedicine has been used worldwide across multiple specialties [- ]. For instance, a large cohort study by Weiner et al [ ] reported an increase in telemedicine use from 0.3% of ambulatory contacts between March and June 2019 to 23.6% between March and June 2020 among privately insured working-age individuals in the United States. Most telemedicine services were delivered via synchronous video or telephone calls during those periods [ ].
The outbreak of the COVID-19 pandemic was also linked to stressors such as restrictions in everyday life, lifestyle changes, social isolation, and uncertainty and worries regarding health, finances, and work, which caused psychological burden . Consequently, multiple studies have observed an increase in public mental health problems [ , ]. Liu et al [ ] included 71 papers in their meta-analysis and detected an increased prevalence of anxiety (32.60%, 95% CI 29.10%-36.30%), depression (27.60%, 95% CI 24.00%-31.60%), insomnia (30.30%, 95% CI 24.60%-36.60%), and posttraumatic stress disorder (16.70%, 95% CI 8.90%-29.20%) during the pandemic. Moreover, preexisting mental health conditions were found to aggravate owing to the pandemic [ ]. Therefore, patients with mental health conditions represented an especially vulnerable group during that time.
Telemental health services played an essential role in managing the increased public mental health burden and preventing the worsening of psychological symptoms. Mental health services are well suited for the remote format, as they do not require physical examination and can be delivered in multiple ways (eg, via telephone and video calls or mobile apps) . In fact, telemental health services were found to be part of the medical specialty with the highest use rate during the pandemic [ ]. The National Institute of Mental Health defined telemental health services as the use of telecommunications or videoconferencing technology to provide mental health services [ ]. This can include synchronous (eg, videoconference and telephone) and asynchronous (eg, mobile apps and email) services. Regarding the effectiveness of telemental health services, an umbrella review of 19 systematic reviews on telemental health services before the pandemic suggested that remote mental health services produced at least moderate reductions in symptom severity and could be as effective as in-person formats [ ]. They also found that user acceptance and satisfaction of telemental health services were comparable with those of in-person interventions. Recent reviews have also reported the effectiveness of and high patient and provider satisfaction with telemental health services during the pandemic [ , ]. Therefore, telemental health services seem to be a valuable addition to the treatment of mental illnesses of which implementation should be supported in the postpandemic future [ , ].
A crucial factor in the successful implementation of telemental health services is patient acceptance. In previous research, no universal definition of technology or telemedicine acceptance was identified. However, past definitions can be sorted into four main categories, which refer to (1) the effectiveness or efficiency of the services, (2) the use or adoption of the services, (3) the intention or willingness to use the services, and (4) consumer or provider satisfaction with the services [- ]. To set a more precise focus, this systematic review concentrates only on patient use and satisfaction. In the course of this systematic review, patient use includes different measures of use behavior, such as the adoption of a new service, frequency of use, or attendance. Multiple definitions of patient satisfaction were introduced in the past and include various perspectives. For example, the expectancy-disconfirmation model defines consumer satisfaction as a function of expectation and expectancy disconfirmation, which can influence attitude change and purchase intention [ ]. Although this definition is widely used, there is a lack of consensus regarding the definition of satisfaction [ ]. The systematic review by Giese and Joseph [ ] summarized three essential components of consumer satisfaction: (1) a summary affective response, which varies in intensity; (2) satisfaction, which focuses on product choice, purchase, and consumption; and (3) time of determination, which varies by situation but is generally limited in duration.
Different theories have been introduced to explain why patients accept telemedicine services. The Unified Theory of Acceptance and Use of Technology (UTAUT)  was thereby one of the most frequently used theories to predict patient acceptance of telemedicine [ ]. In this theory, the key determinants of behavioral intention and technology use behavior are performance expectancy, effort expectancy, social influence, and facilitating conditions. In the context of telemedicine, performance expectancy is the degree to which an individual believes that using telemedicine could be helpful. Effort expectancy refers to the perceived ease of using the service, which also includes the effect of factors such as computer anxiety and computer self-efficacy. Furthermore, social influence means the degree to which an individual believes that others think that they should use telemedicine. Facilitating conditions include perceived organizational and technical infrastructure to support the use of telemedicine. Additional influential constructs in this theory include gender, age, experience, and voluntariness of use. User satisfaction was also found to be associated with major UTAUT constructs and to potentially contribute to the service reuse intentions [ , ].
In addition to theoretical models, only few systematic reviews have summarized the determinants of patient use or satisfaction with telemental health services from prepandemic studies [, ]. Potential determinants that were observed in these reviews were sex, age, education, socioeconomic status, living arrangement, cognitive function, experience with telehealth technology, comfort with using the internet, satisfaction with the health care provider, experience with the clinic, and cultural background [ , ]. Nevertheless, these reviews also highlighted the need for further research on this topic. The rapid, extensive implementation of synchronous telemental health services during the COVID-19 pandemic sparked international interest in the topic. Several studies examined the determinants of patient use and satisfaction with telemental health services since the pandemic. However, a systematic review of recent literature is missing.
Conducting such a systematic review may be helpful in identifying target groups, as well as groups that need further attention and support in relation to telemental health services. This could be of major importance to successfully implement postpandemic telemental health interventions and benefit from the remote format in the future, where it can be a valuable tool to deal with challenges, such as population aging (ie, shortage of health care professionals and increased demand for long-term care), stigma attached to visiting mental health facilities and undersupply in rural areas [, ]. Moreover, it could be useful to identify gaps in the literature and guide future research. Therefore, the objective of this systematic review was to give an extensive overview of the literature on and highlight the influential determinants of patient use and satisfaction with synchronous telemental health services during the COVID-19 pandemic. In other words, this systematic review examined the following research question: what are the determinants of patient use of and satisfaction with synchronous telemental health services in studies conducted during the COVID-19 pandemic?
The systematic review protocol is available in PROSPERO (registration number: CRD42022351576). This manuscript was written in accordance with the most recent version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines .
For this systematic review, peer-reviewed quantitative studies in German or English that observed determinants of patient use or satisfaction with synchronous telemental health services during the COVID-19 pandemic were included. Only peer-reviewed quantitative studies were considered to assure high quality of the included studies. As most of the telemedicine services were delivered via synchronous services during the pandemic  and to assure comparability among the studies, only synchronous telemental health services were included. Mental health patients of all age groups (ie, children, adolescents, and middle- and older-aged adults) were considered to obtain as much information as possible from recent studies. Therefore, studies were excluded if they referred to (1) asynchronous services or eHealth interventions, (2) exclusively individuals with physical illnesses (to assure comparability among the samples), (3) data that were collected before the COVID-19 pandemic, (4) qualitative data, (5) outcomes that were not related to the use or satisfaction with telemental health services, or (6) studies that did not examine determinants of use or satisfaction with the services.
We searched the PubMed, PsycInfo, and Web of Science databases for studies published from 2020 onward. The PubMed and Web of Science databases are well established and frequently used in medical and related research fields. Moreover, they have also been recommended for searching telemedicine-related studies . In addition, the PsycInfo database was included to account for the mental health context. A predefined search query was used to filter the databases (see for the PubMed search query). Moreover, reference lists of eligible studies were screened for additional relevant articles. A pretest including 100 titles and abstracts was conducted before the screening process started.
|Serial number||Search term||Limits (filter, limits, and refine)|
|1||telepsychiatry OR online therap* OR telepsychology OR teleconferenc* OR teleconsult* OR online consult* OR videoconferenc* OR video consult* OR phone consultation* OR telephone OR telemental* OR teletherapy OR video call OR televideo OR telehealth OR telemedicine|
|2||satisfaction OR utilization OR engagement OR usage OR adherence OR patient satisfaction OR patient engagement|
|3||predict* OR determin* OR associat* OR correlat*|
|4||#1 AND #2 AND #3|
In August 2022, all the results from the different databases were imported to EndNote (Clarivate), where duplicates were removed. For the next step, 2 reviewers (AN and JB) independently screened the titles and abstracts of the studies, followed by a full-text screening (Cohen κ=0.61). The Rayyan web application was used to support the double-screening process . Disagreements (15/144, 10.4% of studies) were resolved via discussion and consultation with a third reviewer (AH) when needed.
Data Collection Process
Relevant data from articles that passed the full-text screening were extracted by 1 reviewer (JB) and crosschecked by a second reviewer (AN) using an Excel spreadsheet (Microsoft Corp). The information that was extracted included study characteristics (author, year, study design, country, study period, and data source), population characteristics (sample size, sex, and age), setting (psychiatric care setting and telemental health service type), outcome definition, determinants, analytic approach, and key findings. For missing information or for reasons of clarification, the corresponding authors of the studies were contacted.
The risk of bias was assessed by 2 reviewers independently (AN and JB) using the assessment tool for observational cohort and cross-sectional studies by the National Heart, Lung and Blood Institute . Disagreements were resolved via discussion and consultation with a third reviewer (AH) when needed.
A formal narrative synthesis of the study results was conducted following the current reporting guidelines for syntheses without meta-analysis in systematic reviews . General study characteristics were summarized in a tabular format. Key findings concerning the determinants of patient use and satisfaction were grouped into categories based on the UTAUT constructs. The UTAUT constructs were adapted and extended depending on the focus of the different studies and the pandemic context. The final categories included performance expectancy, effort expectancy, facilitating conditions, and experience. Age and gender were included into a larger category that contained sociodemographic determinants. The social influence category was adapted to include psychosocial influence to account for the special pandemic situation. Owing to the pandemic circumstances, voluntariness of use was excluded as a category because there was often no option to choose between in-person and telemental health visits. In addition, health- and service-related factors were added as categories to account for potential satisfaction-specific determinants. A meta-analysis of the results was not conducted because of the high heterogeneity across the study designs, outcomes, and effect measures. However, regression coefficients, correlations, and odds ratios were reported when available. In addition, if available, related CIs were specified to assess the certainty of the findings.
The ratings for study quality are summarized inand . Most studies were rated as being of either good (n=12) or fair (n=6) quality. The quality criteria that were most commonly not met in the different studies were the reporting of participation rates (20% fulfilled) and sample size justification, power description or variance, and effect estimates (10% fulfilled).
|Ainslie et al ||Ceniti et al ||Chakawa et al ||Connolly et al ||Guinart et al ||Haxhihamza et al ||Hutchison et al ||Lewis et al ||Lohmiller et al ||Lynch et al |
|1. Was the research question or objective in this paper clearly stated?||Yes||Yes||Yes||Yes||No||No||Yes||Yes||Yes||No|
|2. Was the study population clearly specified and defined?||Yes||Yes||Yes||Yes||Yes||No||Yes||Yes||Yes||Yes|
|3. Was the participation rate of eligible persons at least 50%?||N/Aa||NRb||NR||N/A||No||CDc||No||Yes||NR||No|
|4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants?||Yes||No||Yes||No||Yes||Yes||Yes||Yes||Yes||No|
|5. Was a sample size justification, power description, or variance and effect estimates provided?||No||No||No||Yes||No||No||No||No||No||No|
|6. For the analyses in this paper, were the exposures of interest measured prior to the outcomes being measured?||N/A||N/A||N/A||N/A||N/A||N/A||Yes||N/A||N/A||N/A|
|7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed?||N/A||N/A||N/A||N/A||N/A||N/A||Yes||N/A||N/A||N/A|
|8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (eg, categories of exposure or exposure measured as continuous variable)?||Yes||Yes||Yes||Yes||Yes||CD||Yes||Yes||Yes||Yes|
|9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?||Yes||Yes||Yes||No (patients vs providers)||Yes||No||Yes||Yes||Yes||Yes|
|10. Was the exposures assessed more than once over time?||Yes (2 waves)||No||Yes (2 waves)||Yes (2 waves)||No||No||Yes (before and after)||No||No||Yes (3 waves)|
|11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|12. Were the outcome assessors blinded to the exposure status of participants?||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A|
|13. Was loss to follow-up after baseline 20% or less?||Yes/||N/A||Yes||Yes||N/A||N/A||Yes||N/A||N/A||Yes|
|14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposures and outcomes?||Yes||Yes||Yes||Yes||No||Yes||No||Yes||Yes||Yes|
aN/A: not applicable.
bNR: not reported.
cCD: cannot determine.
|Meininger et al ||Michaels et al ||Miu et al ||Morgan et al ||Nesset et al ||Severe et al ||Sizer et al ||Ter Heide et al ||Tobin et al ||Vakil et al |
|1. Was the research question or objective in this paper clearly stated?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|2. Was the study population clearly specified and defined?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|3. Was the participation rate of eligible persons at least 50%?||Yes||No||N/Aa||NRb||No||No||Yes||Yes||N/A||N/A|
|4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|5. Was a sample size justification, power description, or variance and effect estimates provided?||No||No||No||Yes||No||No||No||No||No||No|
|6. For the analyses in this paper, were the exposures of interest measured prior to the outcomes being measured?||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A|
|7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed?||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A|
|8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (eg, categories of exposure or exposure measured as continuous variable)?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|10. Was the exposures assessed more than once over time?||No||No||No||No||No||No||No||No||Yes (3 waves)||Yes (2 waves)|
|11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|12. Were the outcome assessors blinded to the exposure status of participants?||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A|
|13. Was loss to follow-up after baseline 20% or less?||N/A||N/A||N/A||N/A||N/A||N/A||N/A||N/A||Yes||Yes|
|14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposures and outcomes?||No||Yes||Yes||Yes||No||Yes||Yes||Yes||Yes||Yes|
aN/A: not applicable.
bNR: not reported.
Overview of Included Studies
After the study selection process, 20 studies remained for the final synthesis (; see [ - ] for the citations of all included studies). The main characteristics of these studies are summarized in and .
The study samples were predominantly from North America (n=14, with 12 from the United States and 2 from Canada). Furthermore, 5 study samples were from Europe (2 from Germany, 1 from the Netherlands, 1 from Norway, and 1 from North Macedonia), and 1 study sample was from Asia (Israel). Data sources consisted of electronic medical records in 7 studies as well as samples recruited from mental health clinics and community centers in 12 studies. One study used data from a sample that was recruited through targeted emails to mental health organizations nationwide, provincial psychiatric and family physician associations, hospital newsletters, existing participant networks within Canadian Biomarker Integration Network in Depression, and social media. A total of 4 studies were published in 2020, 6 in 2021, 8 in 2022, and 2 in 2023. Although most of the data were collected during the first months of the pandemic, starting from March 2020, some studies also included data from later periods until December 2021.
|Study design||Country||Study period||Data source||Population characteristics (sample size; sex: female; age [years], mean [SD])||Psychiatric care setting|
|Ainslie et al , 2022||Observational retrospective study||United States||Electronic medical record data||Community mental health centers|
|Ceniti et al , 2022||Cross-sectional, mixed methods study||Canada||Recruitment through targeted emails to mental health organizations nationwide, provincial psychiatric and family physician associations, hospital newsletters, existing participant networks within CAN-BINDb, and social media||General remote care experience|
|Chakawa et al , 2021||Comparative study||United States||Recruited from clinic||Large, inner-city pediatric primary care clinic within a large regional children’s hospital|
|Connolly et al , 2021||Cross-sectional study||United States||Electronic medical record||Department of Veterans Affairs|
|Guinart et al , 2020||Cross-sectional study||United States||Recruited from clinics and community centers||18 hospitals and community centers located in rural, suburban, small urban, and large urban areas|
|Haxhihamza et al , 2021||Cross-sectional study||North Macedonia||Recruited from clinic||Daily hospital as a part of the University Clinic in Skopje|
|Hutchison et al , 2022||Cross-sectional study||United States||Recruited from clinic||Community mental health clinic|
|Lewis et al , 2021||Cross-sectional study||Israel||Recruited from clinic||Hadarim Eating Disorders Treatment Center (part of the Shavata Mental Health Center)|
|Lohmiller et al , 2021||Cross-sectional study||Germany||Recruited from clinic||Psychosomatic outpatient clinic at the University Hospital in Tübingen|
|Lynch et al , 2021||Cross-sectional, mixed methods study||United States||Recruited from clinic||Private university-affiliated outpatient psychiatric treatment center|
|Meininger et al , 2022||Cross-sectional study||Germany||Recruited from clinic||University Hospital Cologne—School for Child and Adolescent Cognitive Behavior Therapy|
|Michaels et al , 2022||Cross-sectional study||United States||Recruited from clinic||Outpatient mental health clinic at a local psychiatric hospital that provides specialized postacute services to college students|
|Miu et al , 2021||Cross-sectional study||United States||Electronic medical record||Outpatient psychiatry clinic of an urban, academic medical center|
|Morgan et al , 2021||Cross-sectional study||United States||Electronic medical record||A total of 2 marriage and family training clinics|
|Nesset et al , 2023||Cross-sectional study||Norway||Recruited from clinic||Outpatient clinic at St Olav’s University Hospital, Center for Research and Education in Security, Prisons, and Forensic Psychiatry|
|Severe et al , 2020||Cross-sectional study||United States||Recruited from clinic||Outpatient Psychiatry Clinics at the University of Michigan|
|Sizer et al , 2022||Cross-sectional study||United States||Electronic medical record||A total of 6 Northeast Delta Human Services Authority outpatient behavioral health clinics|
|Ter Heide et al , 2021||Cross-sectional study||Netherlands||Recruited from clinic||ARQ Centrum \'45 (National institute for diagnostics and treatment of complex psychotrauma complaints)|
|Tobin et al , 2022||Retrospective cohort study||United States||Electronic medical record||Integrated psychology team within the general internal medicine primary care clinic at a large urban health system|
|Vakil et al , 2022||Retrospective cohort study||Canada||Electronic medical record||Crisis Response Center|
aSMI: serious mental illness.
bCAN-BIND: Canadian Biomarker Integration Network in Depression.
|Telemental health service type (telephone vs video)||Outcome (use vs satisfaction and assessment)||Determinants||Analytic approach||Quality rating|
|Ainslie et al , 2022||All forms of telemental health services||Use: use from pandemic identified by service claim codes; categorized based on percentage of total treatment services during the retention period (low: <25%; medium: 25%-75%; high: >75%)||Sex, age group, diagnosis, and zip code (rural vs urban)||Chi-square test and logistic regression||Good|
|Ceniti et al , 2022||All forms of telemental health services||Use: number of remote visits |
Satisfaction: 7-point Likert scale (from total dissatisfied to total satisfied) for overall satisfaction with remote care, security, user-friendliness, speed of access and provision of care, continuity of care, convenience, maintenance of therapeutic rapport
|Age, type of provider (psychiatrist or family physician vs other mental health care providers), level of connectedness with loved ones, living with others, province or territory, high-risk status for COVID-19, frequency of internet use, and number of people living at home||Chi-square test and Spearman correlation||Good|
|Chakawa et al , 2021||Video (or telephone or audio-only when there were technical problems)||Use: differences in service delivery modality use (in-person visit before COVID-19 vs telehealth use during COVID-19)||Sex, age, referral concern, health insurance type, race or ethnicity, language, controlling for primary care provider, visit control variable (assigned or familiar or not), and appointment type (first or follow-up visit)||Binominal logistic regression||Fair|
|Connolly et al , 2021||Telephone vs video vs in-person services||Use: having had any video experience (before COVID-19 vs during COVID-19); having had ≥50% of visits via phone vs video vs in person||Sex, age, socio economic status, race or ethnicity, rurality, marital status, ≥50% Department of Veterans Affairs disability rating, diagnosis, and history of mental health hospitalization||Binominal and multinomial logistic regression||Good|
|Guinart et al , 2020||Telephone vs video services||Satisfaction: overall experience (telephone or video), perceived helpfulness of remote sessions, challenges and advantages||Age and duration of care||Chi-square test||Fair|
|Haxhihamza et al , 2021||Not specified||Satisfaction: Patient Satisfaction Questionnaire (18 items with 7 dimensions of satisfaction with medical care measured by the Patient Satisfaction Questionnaire-III: general satisfaction, technical quality, interpersonal manner, communication, financial aspects, time spent with doctor, accessibility and convenience)||Age, gender, and place of living||Not specified||Poor|
|Hutchison et al , 2022||Video services||Use: attendance across sessions |
Satisfaction: Treatment Perception Questionnaire (10 items; general satisfaction and acceptability of mental health services); Internet Evaluation and Utility Questionnaire (15 items; ease of use, convenience, engagement, privacy, satisfaction and acceptability of an internet intervention)
|Risk status for adverse mental and behavioral outcomes, and symptom severity||Bivariate correlation and t test||Fair|
|Lewis et al , 2021||Web-based platforms, not specified||Satisfaction: Telemedicine Satisfaction Questionnaire (15 items, 5-point Likert scale, 3 factors: quality of care, similarity of remote meetings to face-to-face meetings, perception of the interaction); perspective toward the transition to web-based treatment (6 self-developed statements, 1-5 Likert scale, perception of care, preference of web-based treatment to face-to-face treatment, promotion of this mode of therapy toward others)||Age, gender, education, BMI, duration of treatment in days, past eating disorder, hospitalization, Eating Disorder Examination Questionnaire, Depression, Anxiety and Stress Scales-21, Working Alliance Inventory-S, fear of COVID-19 scale-19S||t test and Pearson correlation||Good|
|Lohmiller et al , 2021||Telephone vs video vs in-person services||Satisfaction: self-developed questionnaire with 4 subject areas: patient characterization (10 items), assessment of therapeutic contact (12 items), therapeutic relationship (11 items), hurdles (5 items), 5 additional free-text items||Age, gender, and type of contact||Chi-square test, ANOVA, and hierarchical regression||Good|
|Lynch et al , 2021||Video services||Use: no show or cancellation frequency||Age, gender, race or ethnicity, primary diagnosis, and time period||Model building approach using generalized linear modeling with a Poisson log link (multilevel approach because of nested data structure was used)||Fair|
|Meininger et al , 2022||Video services||Satisfaction: self-developed questionnaire, 11 items: stable internet connection, overall satisfaction, intention to use teletherapy after pandemic=mean satisfaction score; changes in treatment satisfaction and changes in the therapeutic relationship=mean satisfaction change score||Corona Child Stress Scale, psychosocial functioning (Children’s Global Assessment Scale, Child Behavior Checklist [6-18 R] and Youth Self Report [11-18 R]), Checklist for Screening Behavioral and Emotional Problems, and number of teletherapy sessions||Pearson correlation||Good|
|Michaels et al , 2022||Telephone vs video vs in-person services||Satisfaction: preferred telehealth method, overall experience (telephone or video), future telehealth use, perceived helpfulness of remote sessions||Sex, gender, race, and teletherapy format||Chi-square test, Mann-Whitney U test, and Kruskal-Wallis test||Fair|
|Miu et al , 2021||Video or telephone vs in-person services||Use: conversion rate to teletherapy for SMIa patients vs non-SMI patients, number of teletherapy sessions between SMI and non-SMI group, differences in new patients starting therapy via telehealth between SMI and non-SMI groups||Age, sex, ethnicity, previous engagement, and SMI vs non-SMI groups||Chi-square test and t test||Good|
|Morgan et al , 2021||Video and telephone services||Use: conversion to teletherapy (attendance of at least 1 teletherapy session vs opting out), engagement in teletherapy (number of teletherapy sessions)||Age, gender, race, ethnicity, relationship status, income, education, number of sessions before teletherapy, and case constellation (individual vs relational therapy)||t test, logistic regression, and multiple linear regression||Good|
|Nesset et al , 2023||Video services||Satisfaction: Client Satisfaction Questionnaire-8 (8 items measure respondents’ perception of treatment quality)||Gender||t test||Poor|
|Severe et al , 2020||Video and telephone services||Use: visit type||Age, sex, race, health insurance type, and number of previous clinic visits||Multiple logistic regression||Fair|
|Sizer et al , 2022||Video and telephone services||Use: number of visits||Age, gender, education (number of school years), race, referral source, monthly income, discharge, chronic condition, number of diagnoses, primary diagnosis type||Negative binomial regression||Good|
|Ter Heide et al , 2021||Video services||Use: 1 item: how did you stay in touch with your therapist during the past 2 mo? (Multiple answers could be given: face-to-face, via videoconferencing, via telephone, through email or chat, not at all) |
Satisfaction: one item: how satisfied were you with this form of contact, rated on a scale from 0 (not at all satisfied) to 10 (as satisfied as can be)?
|Age, gender, level of education, refugee status, Brief Symptom Inventory, Cantril Ladder (life satisfaction), COVID-19 stress level||Pearson product-moment correlation, MANCOVAb, ANCOVAc, chi-square test, binary logistic regression, and t test (2-tailed)||Good|
|Tobin et al , 2023||Telephone vs video vs in-person services||Use: visit type||Age, sex, race, and health insurance type||Logistic regression||Good|
|Vakil et al , 2022||Video or telephone vs in-person services||Use: visit type||Age, sex, distance to crisis response center, household income, prior visit to the center within 1 year, suicidal behavior, diagnosis, visit characteristics (day of the week, time of day, and period of pandemic)||Binary logistic regression||Good|
aSMI: serious mental illness.
bMANCOVA: multivariate analysis of covariance.
cANCOVA: analysis of covariance.
Patient use was examined in 10 studies [, , , , , , , , , ], patient satisfaction in 7 studies [ , , , , , , ], and both outcomes were observed in 3 studies [ , , ]. Patient use was mostly defined as having at least 1 telemental health visit during the pandemic [ , , , , , - ]. However, others have also considered the number of telemental health visits [ , , , ] and the percentage of telemental health services in overall mental health service use during the pandemic [ , ] or attendance [ , ]. For patient satisfaction, 6 studies used self-developed items and scales [ , , , , , ], whereas 4 studies used established instruments (ie, Telemedicine Satisfaction Questionnaire [ ], Client Satisfaction Questionnaire [ ], Patient Satisfaction Questionnaire [ ], Treatment Perception Questionnaire [ ], and Internet Evaluation and Utility Questionnaire [ ]) [ - , ]. The satisfaction questionnaires mainly focused on the overall satisfaction with the services. Nevertheless, specific satisfaction areas such as satisfaction with the therapeutic relationship and interaction, quality of care, technical aspects, or utility were also addressed.
Most samples included adult populations [, , , , , , , , ]. However, children or adolescents were also considered in other studies [ , , , , , ]. Moreover, some studies exclusively used data collected from children and adolescents [ , , ]. The sample sizes ranged from 28 to 1,054,670 individuals, with 5 studies including less than 100 individuals, 8 including more than 100 individuals, and 7 including more than 1000 individuals. The proportion of female participants ranged from 15.8% (Department of Veterans Affairs [ ]) to 90.5% (patients with an eating disorder [ ]). The mean percentage of female participants in the included studies was approximately 55%.
Although none of the included studies used a theoretical model as a background for their analysis, the following sections are based on the UTAUT dimensions to allow for some theoretical context. This may guide future research in this area.
Key findings for the determinants of patient use of telemental health services are summarized in(if reported, adjusted results are presented).
|Study, year||Sociodemographic factors (eg, sex, age, race, education, and area lived in)||Health factors (eg, diagnosis, symptoms, and symptom severity)||Service factors (eg, video or telephone and duration of treatment)||Experience (with mental health services)||Facilitating conditions (eg, electronic devices, internet connection, and insurance)|
|Ainslie et al , 2022||—d||—||—|
|Ceniti et al , 2022||—||—||—||—|
|Chakawa et al , 2021||—||—|
|Connolly et al , 2022||—||—||—|
|Hutchison et al , 2022||—||—||—||—|
|Lynch et al , 2021||—||—|
|Miu et al , 2021||—||—|
|Morgan et al , 2021||—||—|
|Severe et al , 2020||—||—|
|Sizer et al , 2022||—||—|
|Ter Heide et al , 2021||—||—||—|
|Tobin et al , 2023||—||—||—|
|Vakil et al , 2022||—|
aPsychosocial influence, effort, and performance expectancy were not included as categories in this table because none of the included studies observed the relationship of these determinants with patient use.
bOR: odds ratio.
cPTSD: posttraumatic stress disorder.
dNo information present in the study regarding this category of determinants.
eSMI: serious mental illness.
fRRR: relative risk reduction.
gIRR: incidence rate ratio.
hVCT: clinical videoconferencing.
iQ: income quintile (Q1: lowest and Q5: highest).
In total, 11 studies examined the relationship between sex and patient use of telemental health services. Approximately half of these studies (n=6) did not find significant sex differences in use [, , , , , ]. Nevertheless, 4 studies reported higher use rates in female participants [ , , , ]. In contrast, 1 study reported lower odds for female participants to go from low use rates (before the pandemic) to moderate or high use rates during the pandemic [ ].
A total of 13 studies examined the relationship between age and patient use of telemental health services. Nearly half of these studies (n=6) found a nonsignificant association of age with patient use [, , , , , ]. In contrast, 1 study found that older age was positively associated with telemental health service use [ ] and 3 studies found that older patients were more likely to use audio-only formats (eg, telephone services) compared with video formats [ , , ]. Nevertheless, 3 studies observed a negative association of age with telemental health service use [ , , ]. Ainslie et al [ ] reported mixed findings. In their sample, participants aged 0 to 17 years were more likely than those aged ≥55 years to go from having <25% of mental health services in a remote format (low use) to having 25% to 75% (moderate use) or >75% (high use) of use. However, participants aged 18 to 54 years were less likely than those aged ≥55 years to go from low to moderate or high use.
In total, 8 studies examined the relationship between race or ethnicity and patient use of telemental health services. Of these, 5 studies did not find a significant association [, , , , ]. However, Tobin et al [ ] reported that Black individuals were more likely to use audio-only services, which was also found in the study by Connolly et al [ ]. In addition, 2 studies found that Black patients were less likely to use telemental health services and used them less frequently compared with White patients [ , ]. Connolly et al [ ] found that other than Black races and Hispanic ethnicity compared with the White race, non-Hispanic race or ethnicity is positively associated with telemental health service use and frequency of video service use (but negatively associated with frequency of phone service use). Although being a person of color was a nonsignificant determinant for the conversion to teletherapy, a relationship between Hispanic ethnicity and the conversion was found in the sample of Morgan et al [ ]. However, when examining engagement with teletherapy, no significant association with ethnicity was observed in their sample.
A total of 3 studies examined the relationship of area lived in and patient use of telemental health services. Findings suggested a positive association with rurality: 1 study found that individuals from (highly) rural areas were more likely to use telemental health services  and 1 study stated that telehealth users lived further away from the clinic [ ]; however, 1 study found no significant association [ ].
Other sociodemographic determinants of patient use were considered in very few studies. A low socioeconomic and financial status was associated with lower use in 2 studies [, ] but failed to significantly predict telemental health service use in 2 other studies [ , ]. Years of schooling were positively associated with the number of visits in the sample by Sizer et al [ ]; however, Morgan et al [ ] did not find a significant association between educational attainment and opting out of teletherapy after clinical conversion from in-person therapy to teletherapy. In addition, being married was positively associated with telemental health service use and use frequency in 1 study [ ]. Language was not significantly associated with use, and refugee status was associated with lower odds of telemental health use in single studies [ , ].
A total of 9 studies examined the relationship of psychological symptom severity or diagnosis and patient use of telemental health services. Most of these studies (n=5) found that individuals with higher symptom severity (eg, patients with schizophrenia) had lower use rates [, , , , ]. However, the number of diagnoses, depression, anxiety or posttraumatic stress disorder diagnosis, past psychotic episodes, and serious mental illness status were each associated with a higher use frequency or fewer missed sessions in single studies [ , , , ]. Similarly, Ainslie et al [ ] reported that individuals with schizophrenia were more likely to go from low to moderate or high use than individuals with other diagnoses. Nevertheless, the risk status for adverse mental and behavioral outcomes and serious mental illness status were not significantly associated with use and visit intensity in single studies [ , ]. In addition, Chakawa et al [ ] found that children with internalizing problems were more likely to have a telemental health visit than children with externalizing problems.
Furthermore, the presence of chronic health conditions was associated with a higher number of visits in the sample studied by Sizer et al . A disability rating of ≥50% in US veterans was positively associated with telemental health service use and frequency of use in 1 study [ ].
A total of 3 studies examined the relationship between service factors and patient use of telemental health services. Morgan et al  found that patients undergoing individual therapy were more likely to convert to telemental health services. Referral source (self vs external sources) was not significantly associated with use rates [ ]. Regarding service times, Vakil et al [ ] stated that telehealth visits were significantly less likely during each pandemic period after the first lockdown, for nighttime visits (compared with daytime visits) and weekend visits (compared with weekday visits).
A total of 5 studies examined the relationship between experience with telemental health services and patient use of telemental health services. Previous engagement in mental health services was found to be negatively associated with telehealth visit use in the sample studied by Vakil et al  but failed to predict use in 2 other studies [ , ]. Although the number of sessions attended before teletherapy was not significantly associated with conversion to teletherapy in the analysis by Morgan et al [ ], it was found to significantly predict the number of telemental health visits in this sample. Moreover, Lynch et al [ ] reported that longer duration of participation in telemental health services was associated with fewer missed sessions.
A total of 3 studies examined the relationship between facilitating conditions and patient use of telemental health services. Health insurance type was not significantly associated with patient use in these studies [, , ]. Nevertheless, Tobin et al [ ] reported that Medicare- or Medicaid-insured individuals used audio-only formats more often than private payers.
Psychosocial Influence, Effort and Performance Expectancy
None of the included studies examined the relationship between psychosocial factors, effort or performance expectancy and patient use of telemental health services.
Key findings for the determinants of patient satisfaction with telemental health services are summarized in(if reported, adjusted results are presented).
|Study, year||Sociodemographic factors (eg, sex, age, race, education, and area lived in)||Health factors (eg, diagnosis, symptoms, and symptom severity)||Service factors (eg, video or telephone and duration of treatment)||Experience (with mental health services)||Psychosocial influence (what do families and peers think about program or psychosocial impact)||Facilitating conditions (eg, electronic devices, internet connection, and insurance)|
|Ceniti et al , 2022||—d|
|Guinart et al , 2020||—||—||—||—|
|Haxhihamza et al , 2021||—||—||—||—||—|
|Hutchison et al , 2022||—||—||—||—||—|
|Lewis et al , 2021||—||—|
|Lohmiller et al , 2021||—||—||—||—|
|Meininger et al , 2022||—||—||—||—|
|Michaels et al , 2022||—||—||—||—|
|Nesset et al , 2023||—||—||—||—||—|
|Ter Heide et al , 2021||—||—|
aEffort and performance expectancy were not included as categories in this table because none of the included studies observed a relationship between these determinants and patient satisfaction.
bUser-MD: mental health care users who saw an MD provider (psychiatrist or family physician).
cUser-HCP: mental health care users who saw another mental health care provider (eg, psychotherapist).
dNo information present in the study regarding this category of determinants.
eTSQ: telemedicine satisfaction questionnaire.
fEDE-Q: Eating Disorder Examination Questionnaire.
gVCT: clinical videoconferencing.
A total of 5 studies examined the relationship between sex and patient satisfaction with telemental health services, and all of them did not find a significant association of sex with the satisfaction scores [, , , , ].
A total of 6 studies examined the relationship between age and patient satisfaction with telemental health services. Most studies (n=4) did not find a significant association between age and satisfaction [, , , ]. Lohmiller et al [ ] also did not find a significant association between age and the overall satisfaction with therapeutic contact. However, older age was associated with lower satisfaction for some items, meaning that older individuals perceived the video intervention as less personal and more challenging and found it harder to fully concentrate on the content of the conversation. Guinart et al [ ] found lower satisfaction ratings for telephone services among older patients.
One study observed a nonsignificant relationship between race and patient satisfaction with telemental health services .
In total, 2 studies examined the relationship between area lived in and patient satisfaction with telemental health services. While Haxhihamza et al  did not find a significant association, Ceniti et al [ ] reported greater satisfaction ratings in users from Ontario compared with those in other Canadian provinces.
Other sociodemographic determinants of patient satisfaction were considered in some studies. Educational level was observed in 2 studies and was not significantly associated with satisfaction in these samples [, ]. In addition, Ter Heide et al [ ] reported that refugee status is not significantly associated with satisfaction. Moreover, Ceniti et al [ ] included living situation of participants as a potential determinant. While the number of people living in the household was not significantly associated with remote care satisfaction, living with others showed a significant association with this outcome.
A total of 4 studies examined the relationship between psychological symptom severity and patient satisfaction with telemental health services. Only 1 study found a significant association between symptom severity and satisfaction. In the sample studied by Hutchison et al , patients at moderate risk were more satisfied than patients who were at high risk for adverse mental and behavioral outcomes. However, the other 3 studies did not observe significant relationships [ , , ].
A total of 2 studies examined the relationship between physical health and patient satisfaction with telemental health services. Nonsignificant relationships were found between BMI and high-risk status for COVID-19, with satisfaction in single studies [, ].
A total of 7 studies examined the relationship between service factors and patient satisfaction with telemental health services. Of these, 3 studies reported that telemental health services delivered via video services were associated with higher patient satisfaction than those delivered via telephone services [, , ]. However, Ter Heide et al [ ] could not find this relationship. Furthermore, the therapeutic alliance bond was associated with higher satisfaction ratings in 1 study [ ]. The provider type (psychiatrists or family physicians vs other mental health care providers) was not significantly associated with patient satisfaction in the study by Ceniti et al [ ].
A total of 3 studies examined the relationship between experience with telemental health services and patient satisfaction with telemental health services. In the study by Lewis et al , longer treatment duration was associated with higher satisfaction, while Guinart et al [ ] observed that patients who were under care for less than a year perceived the transition to telemental health services as less negative (missed the clinic less and did not feel less connected). Moreover, the number of telemental health sessions was associated with higher satisfaction ratings in 1 study [ ].
A total of 3 studies examined the relationship between psychosocial factors and patient satisfaction with telemental health services. Level of connectedness with loved ones and life satisfaction were associated with greater patient satisfaction [, ]. Moreover, COVID-19–related aspects were considered in single studies. The COVID-19 stress level had a small negative correlation with satisfaction [ ], and fear of COVID-19 was associated with positive views toward the transition to teletherapy but was not significantly associated with overall satisfaction scores [ ].
One study examined the relationship between facilitating conditions and patient satisfaction with telemental health services. Ceniti et al  reported that the frequency of internet use was not significantly associated with patient satisfaction.
Effort and Performance Expectancy
None of the included studies examined the relationship between effort or performance expectancy and patient satisfaction with telemental health services.
This systematic review aimed to provide an extensive overview of the literature on and highlight the influential determinants of patient use and satisfaction with synchronous telemental health services during the COVID-19 pandemic. Various determinants of patient use and satisfaction were considered. Sociodemographic characteristics were most frequently examined. Nevertheless, health- and service-related determinants also received considerable attention. Major dimensions of the UTAUT, such as effort and performance expectancy, were neglected in recent studies. Although most associations were mixed or nonsignificant, some indications for potential relationships were found (eg, for sex, age, and symptom severity). This systematic review is the first to examine the determinants of patient use and satisfaction with synchronous telemental health services during the pandemic, thus markedly extending our current knowledge.
Regarding sociodemographic factors, a variety of determinants were observed in the included studies. Most studies found that sex was not significantly associated with patient use and satisfaction. However, some studies with large samples found that female participants were more likely to use telemental health services. This suggests that previous findings regarding greater use of mental health services among female participants may also apply to the field of telemental health [- ]. Moreover, this could explain the finding that women were less likely to go from low to either moderate or high telemedicine use [ ], as they already had higher use rates before the occurrence of the pandemic.
When looking at patient age, mostly nonsignificant associations with the outcomes were found. Nevertheless, some large-sample studies found that older age was negatively associated with the outcomes and that older patients were more likely to use audio-only services compared with video services. This could be not only because of the lower likelihood of older adults using mental health care services  but also because of the digital divide in mobile health [ ]. However, audio-only formats seem to be a promising alternative to video consultations for older adults, which was also found in other telemedicine areas during the pandemic (eg, academic medical center outpatient visits and oncological care) [ - ].
Race, ethnicity, area lived in (ie, rurality and province lived in), education, and other determinants (eg, refugee status, financial status, and living situation) were observed in only few studies and led to mainly nonsignificant or mixed associations with the outcomes. More research regarding these sociodemographic determinants is needed in the future. In summary, sociodemographic factors tend to play a role in patient use of telemental health services. In particular, sex and age appear to be potential determinants that were frequently observed. For patient satisfaction, mainly nonsignificant or mixed findings were reported.
Regarding health factors, symptom severity was observed in some studies and was mostly associated with lower use rates in patients with mental health conditions. This is in contrast to in-person mental health services research, where symptom severity was associated with an increased likelihood of seeking treatment . A potential reason for this could be that patients with very severe symptoms were preferably kept in an in-person setting despite the pandemic to assure appropriate treatment. However, findings on engagement or attendance were mixed, with some studies suggesting that more severe symptoms were associated with an increased frequency of telemental health visits. This could mean that individuals with more severe symptoms were less likely to start teletherapy, but once they were participating in telemental health services, they used it more frequently than patients with less severe symptoms. For satisfaction, most of the associations were nonsignificant. In conclusion, the associations with determinants were mostly observed for patient use. Although psychological symptom severity seemed to be negatively associated with the likelihood of telemental health service use, some indications for a positive association with use frequency were observed.
With regard to service factors, various determinants were observed in different studies. For patient use, there was great heterogeneity in the observed aspects. Therefore, it is challenging to compare the results of these studies. More research in this field is clearly needed. Nevertheless, services that were delivered in video format seemed to be associated with higher patient satisfaction than services delivered via telephone. A qualitative study in primary care highlighted potential reasons for the preference of video services, including nonverbal cues and reassurance, lower risk of miscommunication, more personal experience, and increased focus . A recent systematic review on using telephone and video services for mental health treatment also emphasized the strengths of the video format [ ]. However, they also stated that the telephone format can be superior to the video format in some cases (eg, fewer technological challenges [ ]).
With regard to the experience with telemental health services, previous engagement in mental health services was not significantly associated with patient use. This could potentially mean that telemental health use rather depends on need factors than on experience. Regarding patient satisfaction, findings for the treatment duration were mixed. However, the number of telehealth sessions attended seemed to be associated with fewer missed sessions and higher satisfaction ratings. Therefore, patients might have got used to the new situation over time and had adapted to the remote format.
With regard to psychosocial factors, no determinants of patient use were observed. For patient satisfaction, significant determinants were only observed in single studies. Further research, including on psychosocial determinants, is urgently required. Especially factors such as personality (eg, neuroticism or conscientiousness) and social determinants (eg, loneliness) could be of interest for the future of telemental health, considering their impact on health care use [, ].
With regard to facilitating conditions, the health insurance type was not significantly associated with patient use in some studies. The frequency of internet use was also not significantly associated with patient satisfaction in 1 single study. More research is needed in this area to identify potential facilitators of telemental health use and satisfaction.
Effort and Performance Expectancy
With regard to effort and performance expectancy, no study included determinants from these constructs. Considering that these 2 dimensions are key elements of the UTAUT, future research should urgently include determinants from this area.
Overall, the quality of the included studies was mainly good or fair and did not vary substantially between the different studies. Most studies included large samples and some included even very large electronic medical record data sets [, ]. However, the generalizability of our results is limited considering that the evidence mainly came from North America and Western countries and because of differences in psychiatric care and telemental health services. Most studies did not provide participation rates, sample size justification, power description, or variance and effect estimates, which are important information sources for the interpretation of the associations and the detection of potential biases (eg, selection bias).
Considering the findings of our systematic review, multiple research gaps were identified. In general, the inclusion of theoretical models is needed in future studies to set a more consistent focus on important determinants and to assure comparability of the studies. Future research should consider different types of use behavior (eg, frequency of use, adoption, and attendance) and satisfaction (different scales or areas). Established scales should be used to measure the outcomes rather than single items (especially for satisfaction) because single items are more prone to bias. Moreover, to improve the understanding of the relationships between the different determinants and their effects on patient use and satisfaction, future studies that examine the influencing chain and process behind the outcomes are needed. In addition, future studies should explore whether certain telemental health formats (eg, telephone, video, or asynchronous formats) are especially suited for the treatment of specific diagnoses (eg, depression, anxiety, or schizophrenia). Furthermore, longitudinal studies are needed to verify the findings and test for potential changes over time. Longitudinal studies are also of interest to see whether findings regarding use and satisfaction during the pandemic also apply to postpandemic circumstances. For instance, a recent qualitative study found that remote services were only seen as a good alternative to in-person mental health services during extreme circumstances . Additional qualitative research is needed, for example, to explore the barriers of users who do not indicate high use or satisfaction rates to make telemental health services more accessible and user friendly in the future.
With regard to the UTAUT dimensions, major research gaps were revealed. In particular, for the dimensions effort and performance expectancy, psychosocial influence and facilitating conditions research is missing in the respective literature. However, these dimensions could be valuable starting points for interventions, as they could potentially be influenced or adapted over time to improve use rates and satisfaction with telemental health services.
Strengths and Limitations
Our systematic review was registered in PROSPERO and conducted in accordance with PRISMA guidelines to ensure the quality and transparency of the manuscript. A double-screening approach was used to screen 3 databases, which generally was found to be advanced in comparison with single screening and lead to fewer missed studies in the screening process . In addition, data extraction and study quality assessment were performed by 2 reviewers. Furthermore, this review is the first to evaluate the existing literature on the determinants of use and satisfaction with synchronous telemental health services during the COVID-19 pandemic.
However, this study has some limitations. Only peer-reviewed quantitative studies were included. Therefore, potentially meaningful studies were not considered (eg, from the gray literature). Nevertheless, this step promoted the quality of the included studies and the comparability of the findings. In addition, only German and English language articles were screened, whereby relevant articles in other languages could have been missed. Finally, no meta-analysis was performed because of the high heterogeneity in study designs, outcomes, and effect measures.
The extensive implementation of synchronous telemental health services during the pandemic triggered new research in this field. This systematic review was the first to synthesize studies that observed the determinants of patient use and satisfaction with these services. Significant heterogeneity was observed among the included studies. The findings revealed potential target groups (eg, female and young patients with mild symptoms) for future postpandemic telemental health interventions. However, the findings also revealed that patient groups that were especially burdened during the pandemic (such as older patients with severe symptoms) were harder to reach, and efforts are required to address such groups. Finally, knowledge gaps in the recent literature were highlighted, which call for future quantitative and qualitative research to secure and expand the recent findings. This could help to better understand barriers as well as individual preferences and eventually improve telemental health services in the future.
The authors acknowledge financial support from the Open Access Publication Fund of UKE (Universitätsklinikum Hamburg-Eppendorf) and DFG (German Research Foundation).
AN, AH, and H-HK developed the concept and search strategy for this systematic review. AH supervised the study. Study selection, data extraction, and quality assessment were performed by AN and JB; AH was consulted in case of any disagreement in these processes. The manuscript was written by AN and critically revised by AH and H-HK. Text and tables were formatted by AN and JB. All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
List of all included studies in the narrative synthesis.DOCX File , 24 KB
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|PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses|
|UTAUT: Unified Theory of Acceptance and Use of Technology|
Edited by J Torous; submitted 01.02.23; peer-reviewed by J Severe, S Bidmon; comments to author 22.03.23; revised version received 03.05.23; accepted 31.05.23; published 18.08.23Copyright
©Ariana Neumann, Hans-Helmut König, Josephine Bokermann, André Hajek. Originally published in JMIR Mental Health (https://mental.jmir.org), 18.08.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.