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Digital mental health interventions (DMHIs), which deliver mental health support via technologies such as mobile apps, can increase access to mental health support, and many studies have demonstrated their effectiveness in improving symptoms. However, user engagement varies, with regard to a user’s uptake and sustained interactions with these interventions.
This systematic review aims to identify common barriers and facilitators that influence user engagement with DMHIs.
A systematic search was conducted in the SCOPUS, PubMed, PsycINFO, Web of Science, and Cochrane Library databases. Empirical studies that report qualitative and/or quantitative data were included.
A total of 208 articles met the inclusion criteria. The included articles used a variety of methodologies, including interviews, surveys, focus groups, workshops, field studies, and analysis of user reviews. Factors extracted for coding were related to the end user, the program or content offered by the intervention, and the technology and implementation environment. Common barriers included severe mental health issues that hampered engagement, technical issues, and a lack of personalization. Common facilitators were social connectedness facilitated by the intervention, increased insight into health, and a feeling of being in control of one’s own health.
Although previous research suggests that DMHIs can be useful in supporting mental health, contextual factors are important determinants of whether users actually engage with these interventions. The factors identified in this review can provide guidance when evaluating DMHIs to help explain and understand user engagement and can inform the design and development of new digital interventions.
Nearly 1 in 5 adults in the United States experience a mental illness at some moment in their life [
DMHIs can be delivered via smartphone apps, internet websites, wearable devices, virtual reality, or video games [
This paper aims to systematically review the literature on DMHIs to identify common barriers and facilitators that may influence user engagement with these interventions. There are different ways to define user engagement. For example, engagement can be referred to as the time a user spends on an intervention. However, the time spent on an intervention varies between different types of interventions, and little time spent using a DMHI does not have to be a negative feature per se. To get a comprehensive understanding of people’s use of DMHIs, we use a broader definition of user engagement. In this review,
A range of factors can influence engagement with DMHIs, such as the relevance of information to the user provided by a digital intervention [
Although analyzing engagement metrics of commercial apps can be used to examine variability in engagement, user studies are valuable to understand the underlying reasons why people may engage with some interventions more than others. For the purpose of this review, we focus on reviewing the academic literature.
Researchers and developers of DMHIs can use this knowledge to inform evaluations of engagement and the development of new digital interventions. In addition, it may provide insights into what services and facilitating conditions need to surround DMHIs to promote technology-enabled services and may help mental health service providers in selecting suitable interventions for their clients.
This review focuses on common mental health issues, such as depression, anxiety, psychological well-being and distress, and stress. There may be different barriers or facilitators for user engagement with other specific, serious mental illness interventions (eg, psychosis intervention) that are beyond the scope of this paper.
The inclusion and exclusion criteria of articles for this review are presented in
Report on an intervention aimed to improve mental health, psychological well-being, anxiety, depression, stress, and/or mood
Report on an intervention delivered in a digital format, such as a smartphone app or website
Report on some aspects of user experience (eg, usability, user satisfaction, and user feedback)
Report on factors that affected user experience
Include participants aged ≥16 years (eg, child and adolescent samples were excluded)
Report on an empirical study (eg, literature reviews that synthesized findings from other articles, columns, opinion pieces, comments or replies, and editorials were excluded)
Be a peer-reviewed article (eg, dissertations were excluded)
Be written in English
Report on interventions that have a mental health component but do not have mental health as a primary intervention target (eg, an app that is primarily focused on physical pain symptoms, with a mental health component)
Report on interventions that only serve as an appointment booking system for in-person therapy
Report on interventions that are used as a component during an in-person session but cannot be used remotely outside of these sessions
Articles published before January 1, 2010
The first exclusion criterion was added to identify barriers and facilitators that would be applicable to DMHIs. For example, a study that tests an app primarily focused on physical pain symptoms, with a mental health component, may find physical pain issues as a barrier to engaging with the app. It may not be clear from the study whether this is a common barrier related to DMHIs or interventions addressing physical pain.
The second and third exclusion criteria were added, as these types of interventions were designed to be a part of in-person sessions. It may not be clear from these studies whether users would be willing or able to engage with DMHIs apart from existing and traditional in-person sessions.
Finally, digital health interventions evolve rapidly [
A literature search was conducted in multiple databases, including SCOPUS, PubMed, PsycINFO, Web of Science, and the Cochrane Library. On the basis of the inclusion criteria, a search query was developed to include an article if its title or abstract contained at least one keyword related to mental health, at least one keyword related to digital interventions, and at least one keyword related to user experience (
The search query was built on keywords used in previous reviews on the uptake of mental health technologies [
TITLE-ABS-KEY ( depress* OR anxiet* OR anxious OR mood OR “mental health”
OR “psychological wellbeing” OR “mental wellbeing” OR “behavioral health” OR
“mental illness” )
AND TITL E-ABS-KEY ( ( online PRE/5 intervention* ) OR ( online PRE/5
treatment ) OR ( digital PRE/5 intervention* ) OR ( digital PRE/5 treatment )
OR ( mobile PRE/5 intervention* ) OR ( mobile PRE/5 treatment ) OR ( smartphone PRE/5 intervention* ) OR ( smartphone PRE/5 treatment ) OR ( web-based
PRE/5 intervention* ) OR ( web-based PRE/5 treatment ) OR ( internet PRE/5
intervention* ) OR ( internet PRE/5 treatment ) OR ( computer PRE/5 intervention* ) OR ( computer PRE/5 treatment ) OR ( cyber PRE/5 intervention* ) OR ( cyber PRE/5 treatment ) OR ( electronic PRE/5 intervention* ) OR ( electronic PRE/5 treatment ) OR ( mobile AND program* ) OR mhealth OR ehealth OR mtherap* OR etherap* OR telehealth OR telemedicine OR “mobile app*” )
AND TITLE-ABS-KEY ( usability OR “user experience” OR evaluation* OR
engagement OR interface OR satisfaction OR usage OR adoption OR acceptability OR qualitative OR user perspective* OR barrier* OR interview* OR focus group* )
The search results were uploaded to Rayyan [
The first author reviewed all titles and abstracts. Explicit inclusion criteria were determined between the first 3 authors a priori article selection to reduce coder bias. The coder (JB) was a PhD researcher with years of research expertise in user experience and thematic analysis.
A total of 6146 papers were extracted for the review. After the removal of 77 duplicates, 6069 article titles and abstracts were screened by the first author and discussed with the second and third authors. Uncertainties about inclusion were resolved by discussion among the first 3 authors, and reasons for exclusion or inclusion of these articles were discussed.
Furthermore, 480 full-text articles were reviewed, of which 208 met the inclusion criteria.
Articles that were not available were either not available on the web or were behind a paid firewall. Article types that were out of scope did not report on an empirical study.
Although there is a risk of bias in studies, the review considered all studies that met the inclusion criteria and included a large variety of different study methodologies, including qualitative studies with no reported quantitative outcomes. The primary focus of this review was to establish themes across the literature rather than extract the outcomes of quantitative studies. Therefore, the risk of publication bias with significant results is small compared with a meta-analysis of outcomes [
Flow diagram of article screening and inclusion.
A data extraction template (
Other extracted data were intended to document study and intervention characteristics, such as the type of technology and whether the intervention was publicly available, the target population, and the length of time that participants were able to engage with the intervention during the study.
To account for the methodological variety of studies, the quality of reporting tool by Carroll et al [
An inductive thematic analysis [
We used a single coder approach, in which the first author iteratively identified codes from the data and refined themes throughout the analysis. Single coder approaches are methodologically sound when they include checks on validity and reliability [
The first author began the analysis by systematically reviewing each paper. For each paper, the following sections were analyzed: abstract, results or findings, and discussion. Individual codes were created each time a factor was described that affected engagement with DMHIs.
Factors were considered a barrier or facilitator if it was explicitly defined as a facilitator or barrier by the authors of the paper and/or the description in the paper pointed to it being a barrier or facilitator. For example, “participants reported they did not use mental health apps because they had privacy concerns on what would happen with their information.” In this instance, privacy concerns are identified as barriers.
A spreadsheet was used to keep track of the emerging codes. Each spreadsheet row corresponds to a single paper. The row contains the raw text of the paper that includes the identified factors and the initial codes. These codes were iteratively reviewed and compared with the raw text they were extracted from. Codes that referred to similar concepts, such as the ability to
As seen in
Type of technology studied in included articles.
Type of technology | Values, n (%)a |
Web based | 80 (38.5) |
Smartphone based | 57 (27.4) |
Computer based, but not web based | 9 (4.3) |
Mobile phone (but not a smartphone) | 5 (2.4) |
Wearable technology | 2 (1.0) |
Tablet based | 2 (1.0) |
Combination of technologies | 18 (8.7) |
aNot all studies mentioned a particular treatment; hence, the percentages do not add up to 100%.
Measures related to user engagement included time spent using an intervention, number of log-ins, usability, acceptability, and feasibility. The usability and acceptability of the technology were assessed using qualitative methods and standard measures, such as the survey based on the Unified Theory of Acceptance and Use of Technology [
Factors that influenced user engagement were assessed through surveys (72/208, 34.6%), interviews (42/208, 20.2%), focus groups (34/208, 16.3%), randomized controlled trials (23/208, 11.1%), field studies (8/208, 3.8%), workshops (3/208, 1.4%), analysis of app usage data (7/208, 3.4%), and analysis of user reviews (2/208, 1.0%), using both qualitative and quantitative methods. For example, qualitative methods gathered subjective user perceptions of what formed barriers and facilitators for them to engage with interventions. A quantitative approach explored associations between variables, such as sociodemographic factors and intervention usage data, user satisfaction, and/or interest in using DMHIs.
The number of participants involved in these studies ranged from 6 to more than 2 million. In total, 6 studies conducted a secondary analysis of the usage data of an existing intervention or health database. For these 6 studies, the sample size was relatively large, ranging from 3158 to 2,171,325 users. Among the remaining 202 studies, the sample size ranged between 6 and 1558 users. For instance, 25% (52/208) of the studies had <18 participants, 49.5% (103/208) had <40 participants, and 75% (156/208) had <177 participants. The extent to which participants were exposed to an intervention ranged from a short demonstration before a focus group or survey to up to 1 year of usage.
All studies were assessed as
The target population included students, transitional age youth (aged 16-24 years), refugees, people who were homeless, veterans diagnosed with post-traumatic stress disorder, mothers with postpartum depression, patients being treated for a mental illness or another health concern, older adults, and caregivers and workers experiencing stress. Not all interventions specified the target population.
Type of technology studied in included articles.
Type of technology | Values, n (%)a |
Web-based | 80 (38.5) |
Smartphone-based | 57 (27.4) |
Computer-based, but not web-based | 9 (4.3) |
Mobile phone (but not a smartphone) | 5 (2.4) |
Wearable technology | 2 (1.0) |
Tablet-based | 2 (1.0) |
Combination of technologies | 18 (8.7) |
aNot all studies mentioned a particular treatment; hence, the percentages do not add up to 100%.
Type of treatment and resources offered.
Type of treatment or resources | Values, n (%)a |
Cognitive behavioral therapy | 30 (14.4) |
Informational or educational resources | 23 (11.1) |
Counseling | 17 (8.2) |
Self-tracking tools (eg, journaling, monitoring symptoms) | 12 (5.8) |
Mindfulness | 9 (4.3) |
Acceptance and commitment therapy | 8 (2.9) |
Peer support (eg, peer chat) | 7 (3.4) |
Text messaging (eg, reminders) | 4 (1.9) |
Positive psychology interventions | 3 (1.4) |
Prolonged exposure therapy | 1 (0.5) |
Passive data collection | 1 (0.5) |
Combination of treatments and/or resources | 40 (19.2) |
aNot all studies mentioned a particular treatment; hence, the percentages do not add up to 100%.
User
Demographic variables (31)
Personal traits (5)
Mental health status (59)
Beliefs (55)
Mental Health and Technology Experience and Skills (33)
Integration into life (42)
Program
Type of content (54)
Perceived fit (61)
Perceived usefulness (35)
Level of guidance (40)
Social connectedness (53)
Impact of intervention (62)
Technology and environment
Technology factors (100)
Privacy and confidentiality (47)
Social influence (16)
Implementation (39)
Summary of findings for each construct.
Construct | Summary of main findings | |
|
||
|
Demographic variables (sociodemographic factors, such as age, gender, and education) | Overall, women were more likely to engage with DMHIsa than men |
|
Personal traits (factors related to personality traits, such as neuroticism and extraversion) | The personality traits neuroticism, agreeableness, openness, and resistance to change were associated with higher engagement, whereas extraversion was associated with lower engagement |
|
Mental health status (factors related to the current mental health status of the user, such as the type and severity of symptoms) | Severity of mental health symptoms increased the interest in DMHIs, but symptoms related to depression, mood, and fatigue were a barrier to actual engagement |
|
Beliefs (beliefs held by the user with regard to technology, mental health, and mental health services) | People’s positive beliefs about mental health help-seeking and technology-facilitated engagement |
|
Mental health and technology experience and skills (previous experience the user has had with technology, mental health technology, and mental health services and skills related to their digital or mental health or digital health literacy) | Digital health literacy and positive experiences with mental health services and technology were facilitators to engagement |
|
Integration into life (the extent to which the user is able to find time and space to use the intervention and make the intervention part of their routine or life) | Engagement was facilitated if people were able to integrate DMHI use into their daily lives |
|
||
|
Type of content (the type of content and features offered by the intervention) | Engagement was facilitated if content was credible and if activities offered by the DMHI were of an appropriate length (ie, not too short or too long) |
|
Perceived fit (factors related to how well the intervention is appropriate to the user’s culture and values and is adaptable to the user’s needs rather than a one-size-fits-all solution) | Engagement was facilitated if information offered by a DMHI was customizable and relevant to the user |
|
Perceived usefulness (factors related to expected benefits of using the digital intervention over existing resources) | Participants were more likely to engage with DMHIs if they understood the data and knew how to use it |
|
Level of guidance (the level of guidance offered by the intervention on how [eg, when, how often] to use it, for example, through notifications or a coach) | Guided interventions, either through a human therapist or automated reminders to use a DMHI, had higher engagement than unguided interventions |
|
Social connectedness (the extent to which the intervention connects or isolates the user with or from others) | Being able to connect with other people through a DMHI facilitated engagement |
|
Impact of intervention (the impact that intervention usage had on the user, such as an improvement or exacerbation of mental health symptoms [as measured by a validated survey scale]) | DMHI engagement was facilitated if participants experienced a positive impact as a result of using a DMHI, such as the improvement of symptoms |
|
||
|
Technology-related factors (factors related to the technology through which the intervention is offered, such as the resources and costs required to use it, usability, and technical issues experienced by the user) | Technical issues were a common barrier to engagement |
|
Privacy and confidentiality (factors related to data security, storage, confidentiality, and privacy of the digital intervention) | Engagement was facilitated if participants had a sense that the digital platform was private and anonymous, and they could safely disclose information |
|
Social influence (factors from the users’ social environment, such as perceptions held by their peers, family, and health care provider, that influence their intention to use an intervention) | Participants were more likely to use DMHIs if people close to them, such as family and friends, thought they should use DMHIs |
|
Implementation (factors related to the implementation of the intervention that affects use, such as the availability of user training, the phase of the user’s mental health care–seeking process during which the intervention is introduced or accessed and characteristics of the health care organization supporting the DMHI) | DMHI engagement was facilitated if people were trained on how to use it |
aDMHI: digital mental health intervention.
User-related factors refer to factors related to the user, such as personal beliefs, skills, and experiences.
Some demographic variables were found to be associated with DMHI engagement. Studies that found an effect of gender showed that women were more likely to adopt and engage with interventions [
Chudy-Onwugaje et al [
Other demographic variables associated with user engagement were as follows: (1) employment status, with people who worked full time more likely to use the intervention than people who were retired [
Certain personality traits were associated with willingness and interest in using DMHIs. People who scored high on neuroticism and agreeableness of the Big 5 personality traits were more interested in using smartphone apps to reduce stress [
In addition, extraversion was a predictor of lower likelihood to prefer web-based mental health services over in-person services [
A total of 59 studies reported that people’s mental health status plays a role in participants’ interest in and use of a digital intervention. First, certain mental health symptoms appeared to inhibit people’s motivation and/or ability to interact with an intervention. Depressive symptoms [
Beliefs refer to preexisting beliefs the user has about mental health help-seeking [
A positive prior experience with technology [
Mental health literacy refers to knowledge about mental health symptoms and appropriate treatment options [
Users reported that their engagement was affected by the extent to which they were able to integrate an intervention into their daily lives. Barriers that limited use included that participants felt they lacked time [
Access to a private space to access mental health resources also affected the extent to which participants could integrate an intervention into their lives. In 3 studies, participants mentioned that as opposed to going to a health care provider office, it was challenging to find a private space at home or work to use an intervention, which formed a barrier to engaging with it [
Studies have also found difficulties among users in integrating the information and tips offered by the intervention into their lives. For example, Jonathan et al [
In summary, user engagement with DMHIs is partly influenced by factors related to the users themselves. Demographic variables such as age, gender, employment, education, and housing situation can affect user engagement. The personality traits neuroticism, agreeableness, openness, and resistance to change facilitated engagement, whereas extraversion was a barrier.
If mental health symptoms were more severe, participants were more interested in using DMHIs, but symptoms related to depression, low mood, and tiredness prevented engagement. People’s beliefs about and past experiences with mental health services and technology were facilitators if these beliefs and experiences were positive, and they formed a barrier if these beliefs and experiences were negative. Participants’ literacy in understanding mental health and using technology facilitated their ability to use DMHIs, and any further engagement depended on the extent to which people were able to integrate it into their daily lives.
The second group of constructs is related to the type of therapy or content offered through the DMHI.
Higher satisfaction with the type of content and features offered increased user engagement. Uncertainty about the credibility of the information, which related to the evidence base of the intervention and the source of information, was a barrier [
Some interventions offered programs of a fixed length or time commitment, such as a CBT program consisting of 8 weekly sessions. The length of the program as well as the length of individual sessions played a role in participants’ satisfaction and their motivation to continue with the program [
Perceived fit refers to the extent to which users felt the intervention was appropriate and relevant to their culture and values and/or targeted to people similar to them, rather than a one-size-fits-all solution. This fit was, for example, facilitated by relevance of information to their current situation [
Perceived usefulness refers to the user’s experience with an intervention and their perceptions of whether the intervention would be useful to them. This perception was facilitated by whether users were able to understand the data presented to them [
The level of guidance refers to the extent to which users were guided to use an intervention, for example, through reminders or a web-based supporter, holding them accountable to regularly engage with the content. A facilitating factor in using DMHIs was whether the use of the intervention increased locus of control, meaning that users felt more ownership over their own health [
The effect that an intervention had on participants’ sense of social connectedness was found to facilitate user engagement. For example, being able to connect to peers or have regular contact with a personal therapist through DMHIs facilitated engagement in 18 studies [
Participants reported that the perceived changes they experienced in their mental health as a result of using an intervention affected their further engagement. Perceived symptom improvement facilitated further engagement [
Another facilitating factor was whether the intervention normalized people’s experiences [
In summary, the content offered by a DMHI had to be credible and ideally offered in more than one modality. Participants engaged with DMHIs if they felt the intervention was a good fit, which could be facilitated if content was relevant, and the DMHI was customizable, culturally appropriate, and used a language that was understandable to the participant. Engagement was facilitated by participants’ perception of whether a DMHI was useful, which included whether they were able to understand the data and how to use it, and whether a DMHI provided a clear advantage over resources they already had access to.
Guided DMHIs had higher engagement than unguided interventions, and participants liked being able to connect with other people, although some studies identified concerns that DMHIs could be used to avoid in-person contact. The negative and positive impacts of DMHI use could form barriers and facilitators, respectively, to further engagement.
The third group of constructs refers to factors related to the technology itself or the implementation of the technology.
Technology-related factors refer to factors related to the technology through which the intervention was offered. The primary barrier to engagement noted in 25 studies was users’ experience of technical issues [
In addition to technical issues that formed barriers to engagement, there were also factors related to technology that facilitated the use of mental health resources and support. Facilitating factors made possible by the technology used were the flexibility of being able to access resources at any location [
Privacy and confidentiality relate to how data were stored and shared and whether users felt safe and comfortable to disclose confidential information through an intervention. In 2 studies, participants were uncomfortable about their physical location being recorded [
Accessing mental health resources via a digital platform raised concerns regarding privacy. Facilitators of user engagement and feeling safe to disclose information included assurance that the digital platform was private and participants’ information could not be easily accessed by third parties [
Participants in 5 studies expressed that concerns about confidentiality formed a barrier to engagement [
Anonymity was found to be both a facilitator and a barrier to engagement. Overall, 7 studies listed anonymity, meaning that users could share and receive information anonymously, as a facilitating factor to engage and encourage disclosure of information [
Users’ engagement was facilitated by whether the intervention was endorsed by other users [
Although most studies in this review (93%, 194/208) primarily focused on factors related to the user and the intervention itself, 14 studies also described factors related to the implementation of the intervention. Examples included whether users received training on how to use the intervention [
In summary, although DMHIs introduced technical and usability issues that could form a barrier for participants to engage, the digital format also provided flexibility to access resources anywhere at any time and to have a record of health data. It was important that information was private and that participants could safely disclose information anonymously, although complete anonymity also made it more difficult to trust other people on the platform. Negative and positive opinions held by other people about DMHIs could form a barrier and facilitator, respectively, to engagement, and if DMHIs were to be used as part of ongoing therapy, the therapists’ past experience with DMHIs and the ability to integrate it into their practice played a role in user engagement. Finally, successful implementation facilitated user engagement. Providing training on how to use DMHIs and labeling an intervention for well-being or mental fitness (as opposed to mental health) can help users engage with DMHIs more. Participants may be more engaged with DMHIs if they are just starting treatment, but the identified benefit of introducing DMHIs at a later stage is that users may be more knowledgeable about their health and better able to make sense of their health information.
This study aims to synthesize the literature on DMHIs and summarize the identified factors affecting user engagement with DMHIs. This review identifies 3 key areas that all contribute to DMHI engagement: (1) user characteristics, such as severe mental health symptoms, can form a barrier to engagement; (2) users’ experience of the program or content, with participants more likely to engage if they perceive the program to be useful and a good fit to them; and (3) the technology and implementation environment, such as technical issues being a common barrier to engaging with DMHIs. Providing content that is relevant and customizable according to personal preferences and offering technical assistance and/or training are important to achieve engagement. However, although these considerations may increase interest and uptake of DMHIs, it is important to understand whether characteristics specific to the user, such as their symptoms, will affect motivation to engage with these interventions. We first discuss the 3 key areas in more detail in the following three subsections; compare our constructs with other models on user engagement; and then discuss implications for researchers, developers, and health service providers.
Individual differences among users can affect engagement, including demographic variables such as age and gender, personality traits, mental health status, beliefs about mental health and DMHIs, experience with technology and mental health, and people’s ability to integrate DMHI use into their lives. Although the severity of symptoms may increase interest in engaging with health interventions [
The contrasting role of symptom severity between studies highlights the importance of understanding how people who would be more interested in DMHIs and may benefit more from its use are not limited by their symptoms to actually engage with these interventions. The contrast also illustrates the importance of including users at various stages of the design process, as people may be interested in the concept of a DMHI but may not be able to actually engage with it because of the nature of their symptoms.
Although studies looking at DMHI usage over 1 year found that younger people were more engaged with DMHIs, shorter research studies (ie, up to 12 weeks) found that older people were more engaged. Potentially, older adults perform better on study adherence, and younger people continue to engage more with an intervention long term, although the different interventions and settings make it difficult to make a direct comparison between these studies.
Engagement with DMHIs was facilitated if participants liked the type of content; they perceived a DMHI to be a good fit for them and perceived it to be useful; there was a level of guidance on how to use it, it facilitated social connectedness, and it had a positive impact, such as improvement of symptoms.
Guided interventions typically have higher engagement than unguided interventions. However, human guidance can be resource intensive, and it may not always be possible or feasible to provide the desired level of guidance. Although human support enhances engagement more than automated means such as email reminders [
Furthermore, appropriate time commitments differ for self-guided exercises versus guided sessions. Participants across studies preferred shorter self-guided modules but longer guided therapy sessions. Finally, personalization may also meet different preferences. People who find videos or text-based material time consuming may be more engaged with shorter actionable exercises, whereas people with a preference for synchronous communication may engage more when they get dedicated time on one-on-one sessions. It would be worthwhile to further explore how engagement can be encouraged in self-guided interventions.
An important facilitator was whether a DMHI facilitated social connectedness and enabled the user to interact with other people. Previous work has shown that social support through social networks not only increases engagement but may also have a positive effect on depression symptoms [
Offering mental health resources through technology offers both barriers and facilitators. Technical issues and concerns about privacy were common barriers, but technology also offered flexibility and could facilitate anonymity. Furthermore, the environmental context in which DMHIs are to be used are important to consider. Participants were more likely to use DMHIs if people close to them thought they should use it and if they received training on how to use it.
Anonymity was a prominent topic among studies but engaging with an intervention anonymously was seen as both a barrier to and facilitator of engagement, sometimes within the same study. This difference can be explained by factors related to the user, the type of implementation setting, and the type of intervention features that were anonymous, as outlined in the following paragraph.
First, a facilitating aspect of an anonymous intervention was that study participants found it less stigmatizing than seeing a live or in-person therapist. Anonymity may be an important facilitator for people who have experienced stigma and embarrassment, which is known to be a barrier to help-seeking for mental health concerns [
These differing perceptions shed light on an important trade-off. Should an intervention strive to be anonymous to address stigma and potential embarrassment or focus on allowing people to establish a trusted relationship with someone? This decision may depend on the objective of the intervention and whether anonymity is possible in the context in which it is to be used. Alternatively, a hybrid form or multiple options can be considered and offered. For example, forums with a larger number of users can be anonymous, whereas a private one-on-one session with a therapist can include telehealth options to allow for therapeutic alliance building between the user and therapist. The Supportive Accountability Model [
A previous review of user engagement with mental health apps theorized that one reason for low engagement is that these apps do not consider user privacy [
These differing experiences highlight that technology can overcome existing privacy barriers of seeking mental health care but can also introduce other privacy issues, and users’ situational context (ie, where they are physically accessing the digital intervention) should be taken into account.
Some of the themes identified in this review overlap with previous models conceptualizing engagement with digital health interventions, as well as general technology acceptance and health behavior, such as the Efficiency Model of Support [
For instance, the Efficiency Model of Support [
Our findings are in line with the Technology Acceptance Model, which explains that users’ decisions to accept and use a technology are influenced by perceived usefulness, ease of use, and social influence of others. The Health Belief Model explains that adoption of health interventions is, among things, influenced by a person’s belief in the severity of their illness or health symptoms and the perceived benefits of seeking treatment for these symptoms, which map onto our constructs of beliefs and impact of the intervention. Themes revealed in this review, which have not been highlighted in these previous models, are the level of guidance, integration into life, and social connectedness. This gap may be explained by the way in which mental health interventions were intended to be used. To be effective, most DMHIs were intended to be used regularly by users on their own. This characteristic introduces the challenge for people to integrate it into their routine and have the discipline to use it regularly; therefore, the level of guidance provided within the intervention may have a particularly salient effect on engagement. Social connectedness may be especially important for mental health interventions, as it can improve mood [
In this review, we have synthesized the literature on DMHIs to identify common factors influencing user engagement. This synthesis can be described as follows.
Researchers can use these factors to develop constructs that are important to measure when evaluating DMHIs. More concretely, it is important to capture user characteristics, users’ experience of the program and content, and details regarding the implementation setting. These constructs may help explain why someone would use one DMHI over another and may help evaluate how engaging a DMHI will be.
Developers can use these factors to facilitate engagement with DMHIs. Specifically, when developing a DMHI, it is important to understand the specific characteristics of the target audience, for example, if the severity of the audience’s symptoms can form a barrier to engagement; to tailor the program to the audience, such as offering the option to customize content; and to address issues related to the technology and environment, for example, by mitigating technical issues and providing technical assistance.
Mental health service providers, such as clinicians, can use this overview as guidance to select interventions that are appropriate for their clients or help guide their clients in selecting suitable interventions. For example, it is important to consider whether an intervention can be easily integrated into clients' lives and routines. In addition,
The themes highlighted in this review identify factors that can facilitate engagement and barriers that should be considered to facilitate the successful implementation of a digitally mediated mental health intervention.
We did not limit this review to particular study designs. As such, this review takes a much broader look at what factors influence engagement with digital mental health technologies rather than focusing on a single research method or technology. However, because of the heterogeneity of the included studies, we were unable to conduct a meta-analysis. In addition, there was inconsistency across studies in measures used to assess user engagement, such as the number of log-ins to an intervention, the length of continuing to engage with it, the total time spent using an intervention, or a self-reported measure of engagement by participants. This inconsistency has been found to be an issue in previous reviews on the user engagement of DMHIs [
Finally, this review was conducted before the global COVID-19 pandemic. There may be unique factors that are pandemic related that make DMHI engagement more or less likely. For example, stay-at-home orders may exacerbate feelings of social isolation and make people more likely to engage with apps that increase social connectedness. On the other hand, it may also introduce additional barriers to finding a private space to use DMHIs if sheltering in place with others. The results presented in this review should be interpreted and used to understand DMHI engagement before and after the pandemic. A future review could be conducted solely during the pandemic period, and it could be compared with this review to understand DMHI use outside versus during a pandemic.
Previous studies have shown the potential of DMHIs to improve mental health. However, for these interventions to be clinically effective, they require engagement by users in real-world settings. Across the studies reviewed, we identified 16 common factors that affect user engagement. Further research on DMHIs can use these factors as guidelines when evaluating interventions with users, and future interventions can be developed with these factors in mind. By understanding the factors that affect engagement, targeted strategies can be developed to overcome addressable barriers and work toward the successful implementation of these interventions.
Overview of search queries or terms used for each database.
Data extraction template with metadata of the reviewed articles.
Quality assessment of included studies.
Overview of barriers and facilitators for each theme.
cognitive behavioral therapy
digital mental health intervention
The authors thank Vicky Yu who helped with data entry for this manuscript.
SMS has received consulting payments from Otsuka Pharmaceuticals. All other authors declared no conflicts of interest.