https://orcid.org/0000-0002-0031-964X
Figures
Abstract
Digital mental health interventions (DMHIs) are a potential scalable solution to improve access to psychological support and therapies. DMHIs vary in terms of their features such as delivery systems (Websites or Apps) and function (information, monitoring, decision support or therapy) that are sensitive to the needs and preferences of users. A decision aid is warranted to empower people to make an informed preference-based choice of DMHIs. We conducted a review of features of DMHIs to embed within a patient decision aid to support shared decision-making. DMHIs, with evidence of availability in the United Kingdom (UK) at the time of the review, were identified from interactive meetings with a multi-disciplinary steering group, an online survey and interviews with adults with lived experience of using DMHIs in the UK. Eligible DMHIs targeted users age ≥16 years with a mental health condition(s), delivered through a digital system. A previous classification system for DMHIs was extended to eight dimensions (Target population; System; Function; Time; Facilitation; Duration and Intensity; and Research Evidence) to guide data extraction and synthesis of findings. Twenty four DMHIs were included in the review. More than half (n = 13, 54%) targeted people living with low mood, anxiety or depression and were primarily delivered via systems such as Apps or websites (or both). Most DMHIs offered one-way transmission of information (n = 21, 88%). Ten (42%) also had two-way communication (e.g., with a healthcare provider). Eighteen (75%) had a function of therapy, with seven and five DMHIs providing monitoring and decision support functions respectively. Most DMHIs were capable of being self-guided (n = 18,75%). Cost and access were primarily free, with some free via referral from the UK NHS or through corporate subscription for employees (n = 11). Eight (33%) DMHIs had evidence of effectiveness from randomised controlled trials. Six statements were developed to elicit user preferences on features of DMHIs: Target Population; Function; Time and Facilitation; System; Cost and Access; and Research Evidence. Preference elicitation statements have been embedded into a prototype decision aid for DMHIs, which will be subjected to acceptability and usability testing.
Author summary
Digital mental health interventions (DMHIs) such as mobile applications (Apps), internet websites and video calls are growing in use globally. DMHIs potentially help with improving access to mental health support, and earlier access to therapy than in-person services. People can access DMHIs as part or single component of mental health therapy and support, or combined with traditional in-person interventions, but making decisions from amongst the available options is complicated for individuals and healthcare professionals who support them. In this review we aimed to develop a way of classifying DMHIs based on their features to inform the design of a decision aid. With support from a healthcare professional, the decision aid would help potential users of DMHIs to choose single or multiple DMHIs that best meets their needs. We identified DMHIs that people told us they had used in the United Kingdom. We then classified each DMHI based on their features to develop a classification system. We have added this classification system to a prototype decision aid that will be tested for its usability to support shared decision-making discussions between people with mental health support needs and healthcare professionals.
Citation: Bradley G, Rehackova L, Devereaux K, Bruce TA, Nunn V, Gilfellon L, et al. (2025) Classifying the features of digital mental health interventions to inform the development of a patient decision aid. PLOS Digit Health 4(3): e0000752. https://doi.org/10.1371/journal.pdig.0000752
Editor: Haleh Ayatollahi, Iran University of Medical Sciences, IRAN, ISLAMIC REPUBLIC OF
Published: March 26, 2025
Copyright: © 2025 Bradley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration (ARC) North East and North Cumbria (NIHR200173 to DF, LG, AC and RW). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The author has declared that no competing interests exist.
Background
In 2019, almost 1 billion people across the world were living with a mental health condition and it is estimated that depressive disorders increased by 28%, and anxiety disorders by 26% during the first year of the COVID-19 pandemic in 2020 [1]. Given this level of need, scaling up of quality and affordable mental health interventions and services is a key strategic action of the World Health Organisation special initiative for mental health [2].
Digital mental health interventions (DMHIs) are technology-based interventions that aim to prevent, educate, or treat mental health conditions, which are delivered via digital technologies such as mobile apps, internet websites, wearable devices, telephone, virtual reality and video games [3,4]. DMHIs can be self-guided, integrated with healthcare professional or peer support, in-person therapies, or offered through a combination of these approaches [5].
Given their low cost and resource constraints on in-person mental health services, DMHIs offer a scalable option for improving access to psychological support and therapies, potentially avoiding issues associated with stigma, as they can be personalised to individual needs [6,7]. Other potential benefits of DMHIs include averting delays or interruptions to support, expediting access to mental health advice and support as a complement to, or while waiting for, in-person services [8,9]. Furthermore, DMHIs have been suggested as a way of creating a tailored collection of different digital solutions—or a ‘poly-digital’ ecosystem—to address different aspects of an individual’s mental wellbeing [10].
Current estimates state that there are 227,500 health apps available in the United Kingdom (UK) [11]. There is evidence of DMHIs for impacting positively on outcomes for people living with depression, anxiety and stress [12–14] and specific populations such as children and young people [15,16], college students [12] and employed people engaging with work-based support [17]. A meta-analysis of 24 studies reporting on 7 of 48 DMHIs available via the National Health Service (NHS) in the UK for depression, anxiety or stress found small, but significant effects on outcomes, although attrition rates were high (31%) [18]. The authors concluded DMHIs are promising wait list interventions [18].
Given that only a small proportion of DMHIs have been subjected to an RCT evaluation [18], user preferences become critically important, and strategies are needed to empower them to make informed choices from the multitude of currently available DMHIs that are congruent with their preferences and values. Shared decision-making (SDM) is a person-centred approach whereby healthcare professionals and patients work collaboratively to identify and make an informed choice from the available options that is consistent with patient preferences and values [19]. There are few data on user preferences for features of DMHIs. Uptake of DMHIs will be highly dependent on whether they are perceived to be a good fit to people’s preferences, values and culture, and other preference and value sensitive features such as time commitment, anonymity, and cost [20,21].
SDM is frequently supported by a structured patient decision aid, which provides evidence-based information on the available options, benefits, risks/adverse events and likely outcomes. These tools impact positively on patients’ knowledge of available options, perception of risk, decisional conflict, and clarity about their preferences on available options, clinician–patient communication and active involvement of patients in decision making [22].
A decision aid for DMHIs does not currently exist. An important component of a decision aid in this context is clear information on features of DMHIs that are likely to be sensitive to individual preferences. An evidence based decision aid to support people experiencing mental health conditions would serve to engage them in SDM discussions with clinicians about whether to engage with a DMHI (indirectly addressing digital exclusion through better understanding of the digital offer), and facilitate identification of their preferred choice(s) of DMHI (as either the sole component of therapy, or as a precursor to prepare them to better engage with face-to-face delivery, leading to better outcomes for service users and their relatives).
Review methods
Search strategy
In order to develop a search strategy to identify DMHIs for inclusion in this review, a series of interactive meetings were held with a multi-disciplinary steering group of experts by experience (VN, SB, KD, KR), academics and practitioners from disciplines including mental health nursing (JB), health psychology (DF, LR), medical sociology (RW), occupational therapy (GB) and human computer interaction (TAB) and representatives from VCSE organisations providing mental health support and treatment (AC, LG). Electronic searching of bibliographic databases to identify DMHIs was ruled out in early discussions for being unable to address the aim and objectives of the review. A search of bibliographic databases would only identify DHMIs with published evaluations, and these DMHIs may no longer be available. Our aim was to classify the features of currently available DMHIs that have been used by adults or were available in the UK at the time of the review, as opposed to appraising the quality of the evidence-base for DMHIs.
The steering group identified DMHIs using the following methods:
- A systematic review of DMHIs for depression, anxiety and stress available from the NHS in the UK [18]
- Eight digitally enabled therapies to treat anxiety and depression conditionally recommended by the National Institute of Health and Care Excellence (NICE) in the UK [14].
- DMHIs that people had used (or had been offered, but decided not to use) identified from responses to an online-survey (n = 46 respondents) and semi-structured interviews (n = 13 participants), which were conducted as part of the wider study to co-produce a decision aid for DMHIs [23].
- The steering group compiled a list of DMHIs that were currently available based on their personal experience.
The first two methods helped to identify DMHIs which have been recommended for use within the NHS in the UK and by NICE (the guideline producing organisation in the UK). Methods three and four aimed to identify additional DMHIs that were known to be available or used by mental health service users and direct care providers.
Review criteria
Eligible DMHIs had to meet the following criteria:
- Target population age ≥16 years
- Targeted a specific mental health condition(s) (e.g., depression, anxiety) or symptom (e.g., sleep)
- Delivered through any digital system such as internet websites, mobile apps, electronic messaging, video-conferencing (including virtual reality-based and wearable-integrated DMHIs) or through blended approaches of these systems”.
- Evidence of availability in the UK at the time of the review
Data extraction
The data extraction form consisted of the four dimensional classification system developed by Gagnon and colleagues [24] consisting of System (for example, website, software, mobile app, electronic messaging); Function (decision support [screening or prompts/alerts], communication [one-way transmission, communication with healthcare providers or peer to peer], therapy [CBT, other psychotherapy, gamification] and monitoring [provider or self-monitoring]); Timing of Communication (DMHIs with synchronous or asynchronous communication with healthcare providers); and Facilitation (entirely or partially support by healthcare providers, self-guided).
In addition, from discussions with the steering group, and findings from the online survey and interview study, we added further data fields: Target population (e.g., people living with depression); Duration and recommendations for intensity [frequency of engagement]; Cost and access [free to access, free to access via NHS referral or other referral, fee for a personal subscription); Research evidence (DMHI has evidence of effectiveness from at least one positive randomised controlled trial). The therapy sub-component of the classification system developed by Gagnon and colleagues [24] was amended to CBT, other psychological therapy, other psychotherapy or counselling approach, gamification.
The data extraction form was piloted with five DMHIs. Three reviewers (GB, DF, LR) independently extracted data on each DMHI using information on developer websites or other information available in the public domain. Published research for DMHIs included in the review was identified from searches of electronic databases (Medline, PsycINFO, and Google Scholar). A fourth reviewer (TAB) independently reviewed the data extraction for each DMHI, with any amendments resolved through discussion with the review team.
Data synthesis
The resultant classification system was reviewed by the study authors (including people with lived experience of mental health conditions) to develop a series of questions to embed within a decision aid to facilitate the elicitation of user preferences and values on the features of DMHIs. This process was guided by guidelines on the development of decision aids [25–27], and a readability assessment available in MS Word (Flesch–Kincaid Grade Level [28]) to maximise accessibility to users with a range of reading abilities.
Findings
A total of 64 DMHIs were identified after removal of duplicates. Four DMHIs were excluded as their existence could not be verified. Out of the remaining 60 DMHIs, 36 were excluded with reasons, with 24 included in the review (Fig 1).
A summary of the features of the 24 DMHIs [29–52] included in the review is presented in Table 1.
Target population
Thirteen (54%) DMHIs made specific reference to people living with low mood [45,50], depression [29–31,33,35,39,42] or anxiety [29–31,33,35,41–45,50,51]. People experiencing ‘stress’ was mentioned by four DMHIs [32,36,37,41]. DMHIs also targeted people living post-traumatic stress disorder [31,40], obsessive-compulsive disorder [31], anorexia nervosa [35], bi-polar disorder [35], phobias [31,35] and suicidality [49].
Eight DMHIs also referred to anyone who wanted support with their mental health [32,34,38,42,45–48].
System
Eight DMHIs were delivered entirely [32,36,41,44,47,49,51,52] through a mobile app, with a further eight using Apps in combination with websites [33,37–40,43,46,50] and four used Apps in combination with electronic messaging [38,43,45,46]. Five were exclusively delivered through websites [29,30,35,42,48], with one using a combination of website and electronic messaging [34]. One DMHI was exclusively delivered through electronic messaging [31]. Three were delivered using a combination of apps, websites and electronic messaging [38,43,46]. Two DMHIs also used podcasts [37,38].
Function
Eighteen (75%) DMHIs had a function of therapy [29–33,36–41,44–47,50–52]. The most frequently cited therapeutic model in these DMHIs was Cognitive Behavioural Therapy (CBT), which was offered by nine DMHIs [29–31,33,39–41,45,50]. Two DMHIs offered Dialectical Behavioural Therapy (DBT) [41,45]. One offered Acceptance and Commitment Therapy (ACT) as well as CBT and DBT [41]. Three DMHIs offered mindfulness [32,36,51] and four others offered meditation [37,44,45,51]. Counselling was provided by two DMHIs [38,46]. Three DHMIs offered additional therapeutic methods - gamification in the form of encouraging people to attend to own self care through taking care of a virtual pet [47], music-based relaxation [52] and hypnosis [44].
Twenty one (88%) DMHIs had one-way transmission of information [29,30,32–42,44,45,47–52]. Ten also had two-way communication with a healthcare provider [31,33,38,45,46], peer to peer communication [34,38,42,43] or with an AI-coach [45].
Seven DMHIs had a function of monitoring [32,33,39,40,43,47,51], for example mood or time spent meditating. Five DMHIs had decision-support functions [33,36,39,40,49] with features such as prompts and alerts to build or maintain meditation routine [36] or prompts to seek help when experiencing suicidality [49]. One DMHI enabled a personalised storage function of items such as photos to support emotional regulation [49].
Facilitation
Eighteen (75%) DMHIs were capable of being self-guided [20,29,32,34,35,37,39,41–44,47–52]. Seven were partially or entirely supported by healthcare professionals [31,33,36,38,40,45,46]. One was also guided by an AI coach [45].
Timing of communication, duration and intensity
Only four DMHIs provided synchronous communication [33,36,40,46] and six provided asynchronous communication [31,33,34,38,42,45]. One offered both asynchronous and synchronous communication [33]. The remaining DMHIs were completely self-guided with no communication feature.
Six (25%) DMHIs stated or provided recommendations on duration and intensity of use [29,31–33,39,41]. One provided information on only duration of sessions [45]. One relaxation-based App [52] stated that users could personalise the duration and intensity.
Cost and access
Eleven DMHIs could be accessed at no cost via a referral from the NHS in the UK [29–31,33,38–40], which varied based on geographical location/local commissioning arrangements, or access via a corporate subscription [30–33,41,46,51]. Six could be accessed outwith the NHS or corporate setting at no cost [34,35,42,48,49,51], although one was restricted to residents of England and Wales [34]. Ten were also available at no cost, but with access only to basic features, with a fee to gain access to premium content [32,36,37,41,43–45,47,50,52], with two of these offering a free trial [32,36]. Five offered access via personal subscriptions for a fee [29–31,39,46].
Research evidence
Eight [29,30,32,33,36,39,41,51] of the 24 DMHIs included in the review had evidence of effectiveness from randomised controlled trials (Table 2)
Preference-elicitation statements for inclusion in a decision aid
Discussions with the multi-disciplinary steering group, with reference to the classification system and summary of research evidence for DMHIs was used to develop statements to elicit user preferences on features of DMHIs (Table 3). Timing of Communication (DMHIs with synchronous or asynchronous communication with healthcare providers) and Facilitation (entirely or partially support by healthcare providers, self-guided) were combined into one category. The steering group considered that people will have specific preferences for self-completion [not involving communication with others] and communication with a healthcare professional either in real-time [via phone] or not [via email], with the latter, by definition, facilitated entirely or partially supported by healthcare providers. Therefore, a separate category of facilitation might potentially confuse potential users. Peer support was also added as a sub-category to Function.
The statements to elicit preferences had a Flesch-Kincaid Grade Level of 8.
The decision aid will help people to identify potential options of DMHIs based on their preferences and discuss these options with a supporter or health professional. We provide an illustrative example in Fig 2.
Discussion
We used a pragmatic approach to identify 24 DMHIs with evidence of current use or availability in the UK, which varied across seven key features that are likely to be preference-sensitive (target population, function, time and facilitation, system, cost and access, and research evidence). The heterogeneity of features of DMHIs in this review (and available more widely) represents a considerable challenge for users to identify a DMHI that meets their needs and is consistent with their preferences. It is likely that more than one DMHI is needed to address the full range of user needs, which reflects the concept of ‘poly-digital’, where many different features across multiple DMHIs (e.g., one for therapy, and others for mood monitoring and mindfulness) can each address different facets of wellbeing needs, potentially resulting in an aggregation of marginal gains [10].
Therefore, accessible summaries of features of DMHIs are imperative for users and healthcare providers to engage in SDM discussions to identify the best option(s) for DHMIs to optimising uptake and subsequent outcomes. Engaging people in SDM discussions about DMHIs may help to reduce dropout rates, which are high compared with in person therapy [63], and address ‘App Fatigue’ arising from difficulty in navigating the huge number of available options [11]. A traditional decision aid, where features for all the available options are presented, along with probabilities for the range of outcomes is not feasible for DHMIs. Furthermore, relatively few DMHIs are supported by evidence from randomised controlled trials, and where such evidence does exist, there are issues associated with conflicts of interest. For example, a review of the evidence for Headspace identified that 50% (7/14) of RCTs reported a conflict of interest that involved the owner of the DMHI [55].
Therefore, a decision aid for DMHIs would benefit from focussing on features of DMHIs using the classification system developed in this review. Identified user preferences for features of DMHIs arising from a SDM interaction would then inform a discussion about available options that are linked to an electronic repository of DMHIs (for example, Beacon: https://beacon.anu.edu.au/) [64] using our classification system.
Seven DMHIs in our review were facilitated (at least in part) by health professionals and for others, health professionals will have variable involvement in supporting people to consider using DMHIs. Therefore, further proliferation of DMHIs would necessitate rapid workforce development across sectors involved in providing mental health support [65]. Many healthcare providers (within and outwith mental health contexts) may not have received training on the use of DMHIs in their practice and core competencies such as evidence, integration, security and privacy, ethics, and cultural considerations are recommended [66]. It is essential that SDM discussions about DMHIs, including within a decision aid to support such discussions includes an overview on data privacy, security, and ethical considerations to maximise both user safety and trust in these types of interventions.
Limitations
The DMHIs included in our review do not represent the entirety of currently available DMHIs in the UK, or globally in particular emerging systems such as VR-based and wearables. In a paper published in 2020, it was estimated that there are between 10,000 and 22,750 mental health apps [66]. The field of digital mental health is constantly in flux, with new and updated versions of DHMIs being released. Given this ‘temporal validity’, it is likely that several DMHIs in this review will no longer be available or have since been updated with additional features. We did not identify any DMHIs based on other emerging technologies, such as VR-based or wearable-integrated DHMIs, which are increasingly being applied in mental health contexts [67]. It is also possible that information about features of DMHIs may exist that is not explicitly stated on developer websites or other information available in the public domain.
In order to identify DMHIs for inclusion in this review, we drew on a multi-disciplinary steering group (experts by experience, academics, and representatives from VCSE organisations providing mental health support and treatment), data from an online survey and semi-structured interviews, plus relevant literature. This represented a range of perspectives and experiences to identify DMHIs, although we acknowledge that members of the steering group, and respondents to the survey and interviews, were primarily located around one region of the UK (north east). Future research on the classification system would benefit from wider engagement with service users and direct care providers across the UK and other countries to balance these perspectives.
A precursor to making preference-based choice of DMHIs, is establishing a person’s preference for digital technology, and addressing digital literacy and access to digital resources. Therefore, a decision aid would also benefit from initially establishing a person’s preference for use of DMHIs with clear and unbiased information on their potential benefits and challenges. If this supported discussion yields a positive preference, options to support digital literacy and access to an individual’s preferred DMHIs could then be provided.
Conclusions
Our review has identified DMHIs with evidence of use or availability in the UK at the time of the review. The statements designed to elicit user preferences on features of DHMIs have been embedded into a prototype decision aid. Future work will involve subjecting our decision aid for DMHIs to acceptability and usability testing, alongside the development of a digital explainer to convey clear information on DMHIs and the importance of engaging in a SDM process to identify the best option(s) for the individual that matches their needs and preferences for features of DMHIs.
Acknowledgments
The authors would like to thank the following people for their assistance with this review: Jill Barker (formerly at Teesside University), Dave Belshaw (Health Innovation North East and North Cumbria) and Julie Taylor (Cumbria University).
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