Clinical or gimmickal: The use and effectiveness of mobile mental health apps for treating anxiety and depression

Abstract

Objectives:

The increase in ownership of smartphones and tablet devices has seen a worldwide government push, championed by the World Health Organization, towards digital healthcare services generally. Mental health has been a strong presence in the digitisation of healthcare because of the potential to solve some of the difficulties in accessing face-to-face services. This review summarises the recent history of e-mental health services and illuminates two very different paths. The first is the considerable amount of research that has proven the effectiveness of many online mental health programmes for personal computers and laptops, resulting in widespread acceptance of their ability to make a contribution in an individual’s recovery from anxiety and depression. The second is associated with the more recent development of apps for smartphones and tablet devices and the contrasting paucity of research that has accompanied this burgeoning area of e-mental health. This review also outlines the current state of play for research into the effectiveness of mobile mental health apps for anxiety and depression, including issues associated with methodology, and offers sources of practical advice for clinicians wanting more information about these new digital tools.

Conclusion:

Research into the effectiveness of mental health apps is lacking, and the majority have no evidence of efficacy. Clinicians need to be aware of what apps have such evidence and should exercise caution when recommending apps to patients. Suggestions are offered on the direction of future research, including an appeal to further include clinicians in the development and efficacy testing of mental health apps.

Keywords

mHealth mental health apps e-mental health mobile mental health smartphones

Introduction

Smartphones and their applications (apps) are fast becoming devices that are involved in many aspects of our lives, including in the monitoring, assessment and treatment of physical and mental health. In healthcare, apps can do as little as remind us when to take medication (Santo et al., 2016) to more complex tasks such as monitoring blood glucose levels for people with diabetes (El-Gayar et al., 2013). Society, therefore, must have confidence that apps can accurately do what they say they can do, especially with regard to healthcare. In this respect, it is important to first look at the development of digital mental health resources as a whole and how these have emanated from the broader (ongoing) evolution in digital healthcare. In this context, mental health apps for smartphones and tablet devices will be examined to ascertain whether these have become a potential viable option for treating mental illness.

The rise of smartphones and tablet devices

A smartphone is a device that incorporates a mobile phone with computerised capabilities. A tablet device operates in a similar way to a smartphone, but is usually larger and may not necessarily be able to function as a traditional phone. It may, however, be able to communicate with other tablet devices and smartphones in alternative ways such as via short message service (SMS) text messaging, Facetime, Skype and other such programmes. Both devices are capable of connecting to the Internet, with accompanying capabilities accessible with a touchscreen. It is this combination of portability, accessibility and convenience that has led to a global explosion in mobile device usage and ownership. Worldwide, the number of mobile device users is expected to pass 5 billion people in 2019, and figures show smartphone ownership will continue to rise over the next decade as their reach penetrates less socioeconomically placed countries (Statista, 2019).

Close to 80% of Australians (including 80% of teenagers) own a smartphone, and 59% of households own a tablet device, with an estimated 5 million new smartphones purchased by Australian consumers in 2016 (Australian Communications and Media Authority, 2016; Deloitte, 2015). These statistics translate into over 15 million Australians owning a smartphone and over 11 million owning a tablet device (Nielsen, 2015). More than half the Australian population check their smartphone within 15 minutes of waking (for young people, it is within 5 minutes), and, collectively, Australians look at their smartphones more than 440 million times a day (Deloitte, 2015) and spend 33 hours using mobile apps each month (Nielsen, 2015).

In New Zealand, approximately 64% of the population owns a smartphone, but this figure is expected to rise to 90% by the end of 2019 (Concilio, 2019). Two-thirds of New Zealanders have access to three or more mobile devices, but most prefer to use their smartphone for Internet connection over any other device (Research New Zealand, 2015). Furthermore, while mobile Internet connection has steadily increased over the past 5 years, broadband connections (usually with devices of a less mobile nature such as personal computers and laptops) have started to decrease (Stats NZ, 2017). This is a further indication of how important mobile devices such as smartphones and tablets have become in daily life.

e-Health: healthcare goes digital

e-Health is the cost-effective and secure use of information and communications technologies in support of health and health-related fields (World Health Organization, 2016). At its core is the ability for patient information to be shared instantly and, potentially, more reliably. The World Health Organization is actively encouraging countries, in the form of a National eHealth Strategy Toolkit, to adopt an e-health policy in the hope that more efficient, equitable healthcare will be provided to individuals in the future (World Health Organization, 2012).

Both the Australian and New Zealand federal governments have embraced digital health. In a 2008 report, the Australian federal government noted that digital health is a ‘key enabler and driver of improved health outcomes for all Australians’ (Victorian Department of Human Services, 2008: 1). Australia’s National Digital Health Strategy was agreed on by all state and territory governments in 2017. It includes the My Health Record system, which gives people and their clinicians secure online access to key summary health information, with the desired end result being benefits such as improved patient safety, health outcomes and system efficiencies (Australian Digital Health Agency, 2017). The New Zealand Government has introduced a similar digital health strategy, NZ Vision for Health Technology (New Zealand Ministry of Health, 2019b), with the aim being for the population to have a personally controlled electronic health record, similar to that of Australia. The Digital Health Strategic Framework is being developed to guide the use of technology and data to improve the health system in New Zealand (New Zealand Ministry of Health, 2019a).

Mental health’s place within e-health

Mental health is embedded in Digital Health Strategies, but is yet to find its place. The sensitive nature of information in a mental health setting, and the stigma that can surround mental ill-health, contributes to mental health’s often uncomfortable position in the wider digital health sphere. Governments are still trying to define processes, delineations, methodologies and regulations that provide management and guidance over the use of e-mental health resources.

As a result, many mental health clinicians are confused and uncertain about e-mental health tools without such overarching guidance (Gun et al., 2011; Sinclair et al., 2013). While some mental health clinicians are becoming educated about digital options, many are not engaging (Sprenger et al., 2017). However, consumers are taking it upon themselves to embrace the new technology rather than acting on recommendations from their therapists (Schueller et al., 2018). Therefore, clinicians who ignore digital tools and resources do so at their own risk, as it is becoming increasingly important for them to have an understanding about these tools, and clinical informatics more broadly (Torous et al., 2018a).

Why do we need e-mental health services?

Anxiety and depression are the most prevalent mental illnesses worldwide (Vigo et al., 2016) with certain overlapping symptoms and causes for each. There are several evidence-based approaches to treating anxiety and depression symptoms. There are ‘talking’ and behavioural therapies such as cognitive behavioural therapy (CBT; Butler et al., 2006), interpersonal therapy (Barth et al., 2013), acceptance and commitment therapy (Ruiz, 2012), positive psychology interventions (Bolier et al., 2013) and others. There are other cognitive and ‘relaxation’ therapies such as mindfulness and mindfulness meditation (Hofmann et al., 2010). There are several classes of medication, including antidepressants, such as selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine reuptake inhibitors (SNRIs; Cipriani et al., 2009) and others. However, it has been estimated that only approximately 30% of people with a probable clinical diagnosis of anxiety and/or depression have received appropriate treatment for these common mental health conditions (Young et al., 2001), and they continue to pose a heavy burden on primary healthcare settings, and on modern society generally (e.g. by having days off work; Gunter and Whittal, 2010).

There are various reasons why many people do not receive evidence-based help for anxiety and depression via the methods listed in the preceding paragraph. The worldwide mental health system has limited resources in the first instance, with widespread calls for increased funding to help deal with the increase in mental illness across society (World Health Organization, 2018). People living in rural and other underserved areas may not be able to access scarce expert face-to-face services (Sakai et al., 2014). People from low socioeconomic backgrounds may not be able to afford mental health services (Jones et al., 2014). People in certain areas and from certain backgrounds (such as rural areas, cultural minorities and low socioeconomic backgrounds) may have low mental health literacy that hinders them from getting appropriate help (Gunter and Whittal, 2010; Memon et al., 2016). Still others place stigma on help-seeking for mental health problems (Bowers et al., 2013). It is therefore important to pursue other forms of treatment for anxiety and depression, and e-mental health services offer the potential to address many of these reasons.

Clinical or gimmickal: the use and effectiveness of e-mental health services

e-Mental health services are defined as those digital services such as mobile apps and online web-based programmes targeting mental health problems (Christensen et al., 2014). Online web-based programmes for anxiety and depression have a large evidence base (for a recent review, see Gratzer et al., 2018), but the evidence for mental health apps is less clear and still emerging (discussed in detail below). e-Mental health services and resources have been identified by the Australian Federal Government as a priority area for future prevention and treatment of mental illness (Department of Health and Ageing, 2012), especially in rural and regional areas where face-to-face mental health services are either unavailable or unaffordable (Hernan et al., 2010). It is widely believed that effective integration and use of e-mental health resources could significantly reduce the social and financial burden of mental illness in Australia (Australian Digital Health Agency, 2017; Black Dog Institute, 2016). Currently, frameworks and standards surrounding e-mental health are being researched and considered by both the Australian Commission on Safety and Quality in Health Care (https://www.safetyandquality.gov.au/our-work/safety-in-e-health/certification-framework-for-digital-mental-health-services/) and the Therapeutic Goods Administration (https://www.tga.gov.au/regulation-software-medical-device).

Caution needs to be taken when believing that digital interventions could be a panacea for the mental health sector’s difficulties. Socioeconomic status is already known to be correlated with risk of mental illness (Lorant et al., 2007). There may be a ‘digital divide’ between lower socioeconomic groups having the finances to own smartphones, where ownership is currently at 71%, compared to those in higher socioeconomic brackets where smartphone ownership is at 95% (Pew Research Center, 2019). For those from low socioeconomic backgrounds who do own smartphones, they have been found to download less apps (Tang, 2015), but there are also conflicting data on this (Rahmati et al., 2012), and to have lower mental health literacy (Kaneko and Motohashi, 2007; Von dem Knesebeck et al., 2013). While the gap in smartphone ownership is gradually closing between lower and higher socioeconomic groups (Pew Research Center, 2019), it cannot be assumed that those from lower backgrounds will benefit in the same way from the digital revolution in mental health care as those from higher backgrounds unless it is a cost-effective solution.

Previous research on web-based programmes designed for use on personal computers or laptops has found that individuals have more satisfactory ratings for e-mental health resources than clinicians do (Gun et al., 2011; Waller and Gilbody, 2009). There may also be a discrepancy between younger, early career mental health clinicians and older, more experienced clinicians in their knowledge and desire to incorporate digital tools into their practice (Kim et al., 2018). Furthermore, clinicians have indicated specific barriers to using online resources, including concerns about privacy and data security (Hendrikoff et al., 2019; Neary and Schueller, 2018; Stawarz et al., 2018; Terry and Gunter, 2018), lack of time to learn and understand how various online tools work, inability to access relevant training and concerns about client feedback (Sinclair et al., 2013). It has been recognised that the successful implementation of digital tools into a patient’s treatment is often determined by the clinician receiving recommendations from other clinicians (Hempel et al., 2018) and/or the clinician’s general attitude towards such tools (Hickie et al., 2010; Wangberg et al., 2007). Thus, the clinician’s perspective would appear to be vital in the successful uptake and wider dissemination of e-mental health resources (Hollis et al., 2018; Martinez-Martin and Kreitmair, 2018). Furthermore, there is a distinct lack of teaching about how to incorporate digital tools into practice in academic mental health programmes, including for those training to be psychiatrists (Kim et al., 2018; Torous et al., 2018a).

Over the last two decades, e-mental health programmes (particularly those with a CBT framework) have been found to be effective for both adults and children in reducing anxiety and depression symptoms (e.g. Ebert et al., 2015; Richards and Richardson, 2012). Some studies have compared the effectiveness of e-mental health programmes with face-to-face therapy and have found comparable (e.g. Andrews et al., 2010; Richardson et al., 2010) and, in some cases, even more favourable results (e.g. Merry et al., 2012). However, one of the main differences in results between e-mental health programmes and face-to-face therapy is that participant drop-out rates appear to be significantly higher for computerised conditions (Andrews et al., 2010; Richardson et al., 2010), and this issue requires further research. There is now an interest in how stand-alone e-mental health programmes (i.e. programmes that can be downloaded once and do not need ongoing Internet connection or regular therapist involvement) may further enhance the clinicians’ digital toolbox.

Mobile digital technology allows users to incorporate their smartphones and tablet devices into the diagnosis and treatment of their mental health conditions in various ways. This includes using physiological and other data automatically gathered by their device (Beiwinkel et al., 2016) to using specific programmes to enter data (Schwartz et al., 2016), or getting tips about how they might be able to improve their mood (Bakker et al., 2018). Researchers are also looking at how using incidental mobile phone data, such as phone calls made and amount of time the screen is ‘on’, can help predict an individual’s mental health diagnosis (Faurholt-Jepson et al., 2019). There may yet be further ways that smartphones can reveal important information about our mental health, and thus potentially lead to more effective treatments (Hidalgo-Mazzei and Young, 2019).

Enter the app

A mobile ‘app’ is a software programme that is either pre-loaded or downloaded from a website on to a mobile device such as a smartphone or tablet to fulfil a particular purpose. In a recent report (App Annie, 2019), global statistics on app usage revealed that there were 194 billion individual app downloads in 2018, of which China alone accounted for nearly 50%. These downloads represented over US$100 billion in revenue for the various app stores. The average user in the United States, South Korea, Japan and Australia has over 100 apps on their smartphone.

Health apps (including those related to mental health) are one of the fastest growing categories of apps (Khalaf, 2016). Such apps saw an increase of 300% between 2016 and 2018. Over two-thirds of adults would be willing to use their smartphone to help manage their health (Makovsky, 2015), and about 60% of people who own a smartphone have downloaded at least one health app (Carras et al., 2014). There are approximately 318,000 health apps currently available, with more than 200 new health apps on average appearing each day (IQVIA Institute, 2017). Of the overall number of health apps, more than 10,000 relate to mental health (Torous et al., 2018b), but this can include apps that are simply digital versions of existing validated inventories, such as the Kessler Psychological Distress Scale (K10; e.g. NovoPsych), to apps that teach users to breathe more effectively (e.g. Breathe2Relax) and apps that simply remind the user when to undertake a specific mental health-related task, such as when to take medication (e.g. Pill Alert Pro).

Individuals find health apps in a number of ways, including through social media and word of mouth, but less so through medical providers (Schueller et al., 2018). This may be because clinicians are reluctant to make such recommendations to their patients. Of the people who download health apps, approximately one-third stop using the app after a short time due to the app being ‘too confusing to use’, and nearly half stop because it either takes too much time to enter data or they simply lose interest (Krebs and Duncan, 2015). Finally, over half the people who have downloaded a health app value ease of use of the health app over trust of the app (Makovsky, 2016), possibly suggesting that research into a health app’s effectiveness is, alarmingly, not an important consideration for many consumers.

Increased desire for mobile apps designed for smartphones and tablet devices, as opposed to web-based programmes designed for use with laptops and personal computers, has not seen an equivalent increase in the evidence for their efficacy. That is, the vast majority of previous research into the effectiveness of e-mental health resources has examined programmes designed to be used on personal computers and laptops, with a paucity of research on the effectiveness of mental health apps for smartphones and tablets.

I bet there’s an app for that: research into mental health apps

Mental health apps offer a number of potential benefits, such as portability and accessibility (having immediate access to help any time); a convenient way of doing homework activities from face-to-face sessions; ability to set reminders for any number of things, for example, taking medication; people on waiting lists for face-to-face services may be able to get help via a mobile app; increased anonymity; improved access to treatment for people in rural and remote areas; access difficult-to-reach groups such as teenagers (Wang et al., 2018); and reduce the burden on primary care health services. There is, therefore, much to gain by pursuing research into the efficacy of mobile mental health apps.

Research has shown that people with mental illness increasingly own smartphones and other mobile and ‘wearable’ devices and are interested in using these to monitor their mental health (Carras et al., 2014; Pung et al., 2018; Torous and Powell, 2015). However, there appears to be a considerable gap between having an interest in such apps and actually using them (Torous et al., 2018c). Other research has shown that people living with mental illness have varying degrees of interest in and knowledge about mental health apps (Carpenter-Song et al., 2018). Still other research has shown that those with mental illness can have distinctly negative attitudes towards an app’s ability to manage the sensitive information often associated with mental health treatment (Hendrikoff et al., 2019). This all adds up to a confusing, uncertain picture about consumer interest in using mental health apps in the real world (as opposed to research settings). However, the potential advantages of having access to effective mental health apps are too great to ignore.

Currently, it appears that the main way that individuals decide which mental health app to download is by using the app ratings and reviews in the app stores (Huang and Bashir, 2017). Huang and Bashir (2017) reveal that the price of a mental health app has a significant negative correlation with downloads. Furthermore, lower priced mental health apps have significantly higher ratings than higher priced apps. Other ways that individuals decide on which mental health apps to download include advice via social media and word of mouth (Rubanovich et al., 2017).

Up until now, research on mental health apps has been poor (Alyami et al., 2017; Bakker et al., 2016; Orman and O’Dea, 2018). Consequently, there is a current push from governments, the mental health research community and clinicians to improve this situation. For example, the National Institute for Health Research in the United Kingdom has developed the mhabitat: Framework for the effectiveness evaluation of mobile (mental) health tools (Betton et al., 2017) as a way of guiding app developers towards best practice in producing mental health apps, including highlighting the importance of establishing an evidence base. While this framework provides information about outcome measures to consider in measuring the efficacy of an app, it is not prescriptive in suggesting a specific research design other than recognising that randomised controlled trials (RCTs) are the gold standard, but may not be appropriate for studying the efficacy of mental health apps.

The vast majority of the published research on the efficacy of mental health apps has been carried out by individuals and organisations involved in that app’s development (Firth et al., 2017a, 2017b). In other words, there appears to be little ‘independent’ replication being conducted (or at least published), thus raising the issue of bias and potential conflicts of interest. The research completed to date has found favourable outcomes for participants using the examined apps (e.g. Dillon et al., 2016; Firth et al., 2017a, 2017b; Miloff et al., 2015), but until increased independent replication takes place, there will continue to be questions about the effectiveness of the studied apps.

Research into the effectiveness of mental health apps is still in its infancy, and there are ongoing discussions in the scientific literature about how best to examine the efficacy of mental health apps (Pham et al., 2016). App development and the online selling of mental health apps through websites such as the Apple App Store and Google Play have increased without significant research to confirm the effectiveness of such apps. This lack of research is attributable to many factors, including limitations inherent in academic settings due to issues such as lack of funding and subsequent slow speeds in the research cycle of data-gathering, to write-up and to publication (Hidalgo-Mazzei and Young, 2019).

Mental health clinicians, who have been trained to follow evidence-based practice within the scientist–practitioner model, may therefore be reluctant to engage with and less willing to recommend mental health apps (Sinclair et al., 2013), especially as many mental health apps will not only have no evidence for their effectiveness but also may use interventions or include content that do not qualify as accepted evidence-based techniques (Huguet et al., 2016; Kertz et al., 2017). Given that mental health consumers will often listen to recommendations by their treatment providers (Pung et al., 2018), increasing the evidence base of such apps may increase the likelihood of clinicians recommending them to patients and therefore lead to an increase in the uptake of mental health apps (Neary and Schueller, 2018). Only when this occurs will it become more commonplace for clinicians to make a referral for a ‘digital antidepressant’ to their patients.

Although there are some published reviews on the effectiveness of apps (e.g. Ameringen et al., 2017; Bry et al., 2018; Donker et al., 2013; Firth et al., 2017a, 2017b; Wang et al., 2018), the ever changing landscape of this area means it is difficult for these reviews to remain current. By the time such reviews appear in peer-reviewed journals, new apps appear in the app stores, and existing apps have often been updated or are no longer available. Nevertheless, it is important to regularly review this space as the international mental health community continues to grapple with understanding the place of mental health apps in the overall assessment and treatment of mental disorders. There are still many questions relating to the efficacy of such apps. For example, there have been studies that have shown that the level of engagement with an app predicts improvements in anxiety and depression symptoms (Bakker and Rickard, 2018). However, other research has raised the possibility that simply getting participants to engage with the novel activity of interacting with their smartphone may be enough to activate decreases in symptomatology (Torous and Firth, 2016), a phenomenon known as the digital placebo effect. Furthermore, there are questions relating to the mechanisms of action of mental health apps, let alone whether they are efficacious. This includes needing to understand which individual techniques within an app are working more productively in achieving desired outcomes and targeting whether interventions within an app are evidence-based to begin with. With the research on mental health apps in such a fractured state, it is understandable why clinicians are ambivalent about recommending them.

Resources for clinicians wanting more information about apps

A description of the features and characteristics of individual mental health apps is outside the scope of this review, but for a summary, see Aryana et al. (2018) and Bakker et al. (2016). The following is a brief overview of some ways that clinicians can seek out information about appropriate digital mental health resources. It should be noted that the vast majority of mental health apps have been developed for individuals to use in isolation and therefore provide little opportunity for clinicians to integrate these tools into face-to-face therapy (Lan et al., 2018).

One recent effort to provide users of mental health apps with additional information over and above app store star ratings and comments is the development of PsyberGuide (https://psyberguide.org/), a United States–based website managed by One Mind, a research organisation dedicated to brain research under the umbrella of reputable departments of the University of California and Northwestern University (for further details, see Neary and Schueller, 2018). While PsyberGuide is a trusted source of new information to guide clinicians and consumers about choosing appropriate mental health apps, it does not advance the scientific evaluation of apps. That is, it contains reviews by certified mental health professionals and details of previous research (if any), but does not offer a new way to systematically evaluate an app’s efficacy that has been independently supervised by qualified researchers. Perhaps, unsurprisingly, the ratings found on PsyberGuide generally do not correlate with the ratings found in the various app stores (Neary and Schueller, 2018). The information on PsyberGuide is nonetheless useful and trustworthy, as it is managed by a not-for-profit organisation, and offers a new, additional source of information.

The American Psychiatric Association has formulated an app rating framework for clinicians to use when prescribing a mental health app to a patient (Torous et al., 2018b). It asks clinicians to evaluate four key areas when judging the appropriateness of an app in assessing for the following: (1) potential risk and harm, including privacy and data management; (2) research evidence for efficacy; (3) ease of use and (4) ability to share data with clinicians if necessary. The system does not include previous ratings by other individuals, and it is therefore up to the clinician to ‘rate’ the app from scratch. While this may have future benefits for researchers, it is difficult to see clinicians in primary care or private practice settings regularly going through this time-consuming process. Similar to PsyberGuide, it does not provide a measure of efficacy even though it considers the issue of efficacy to be important.

The Australian Federal Government recently launched the Head to Health website (https://headtohealth.gov.au/) that provides information about digital mental health options to both consumers and clinicians alike. Similarly, reachout.com (https://au.reachout.com/tools-and-apps), an Australian not-for-profit organisation, offers advice and ratings from both mental health professionals and consumers. The Australian National University also funded the development of the beacon website (https://beacon.anu.edu.au/), where digital mental health resources are rated by experts and research references are provided.

In New Zealand, the Health Navigator website (www.healthnavigator.org.nz/) was initially set up to assist general practitioners find information on a variety of health topics, including mental health. It can, however, be used by clinicians and consumers alike to find information on mental health apps, websites and digital mental health programmes. The website is supported by the Royal New Zealand College of General Practitioners and many of the District Health Boards. It includes links to different resources that the various District Health Boards may have created and makes these resources accessible to the wider population. The Health Navigator website also houses the New Zealand Health App Library that provides summary information on mental health apps, as well as health apps generally, including reviews by mental health specialists, general practitioners, consumers and technological/app development experts. While the Health Navigator, beacon, reachout and Head to Health websites acknowledge many of the difficulties involved in recommending mental health apps, and each uses a transparent process in deciding which apps to recommend, again they do not provide scientific evidence for the effectiveness of any app, but do recognise other research that may have been completed on the app.

The above sources of additional information on mental health apps are welcome tools for clinicians and consumers, but this type of information is not available at the point of download in the app stores. It would be preferable to have such additional information at this point, as this window may represent the moment of strongest motivation and likelihood to download an app for an individual (Huang and Bashir, 2017). It would also be easier for clinicians and consumers alike if the app stores had more involvement in the process of ‘certifying’ health apps, so that apps with research evidence to back-up their claims of effectiveness stand out from apps that lack such evidence.

Conclusion

There is wide scope for future research in the area of mental health apps. Clinicians have to be more involved in the app development process, as there is currently a lack of mental health apps with expert input (Alyami et al., 2017; Shen et al., 2015). The involvement of clinicians would improve and increase the number of apps developed for use in conjunction with face-to-face therapy, an area of huge potential. Clinicians’ participation in app research may also increase the number of independent and replication studies, boosting research and assisting mental health apps to gain greater legitimacy, while producing an evidence base from research that is proportionately less heterogeneous than it currently is.

The area of mental health apps, and health apps more broadly, is developing quickly. Technological innovations mean that it has been difficult for research to keep up with development. Studies involving large sample RCTs take time to organise and implement, to manage the large numbers of participants and amounts of incoming data and to synthesise that data into clinically meaningful results (Pham et al., 2016), and although the RCT is considered the gold standard for demonstrating efficacy in scientific research, there are considerable numbers of researchers who believe that RCTs are not the most appropriate experimental designs for health apps (Betton et al., 2017). If research in the area of mental health apps is to keep up with development, and to include the involvement of clinicians, perhaps newer, more novel ways of research/evaluation are required (Hollis et al., 2018; Menon et al., 2017; Neary and Schueller, 2018; Nicholas et al., 2016; Pham et al., 2016).

While this review has noted the potential of mental health apps to facilitate positive outcomes in conditions such as anxiety and depression, it still seems that more research is needed before these tools can confidently be prescribed by clinicians on a widespread basis.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by an Australian Government Research Training Program Stipend Scholarship.

ORCID iD

Jamie M Marshall

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