Digital mental health interventions have the potential to be beneficial for adolescents and young adults. These groups seem particularly vulnerable to mental health problems, with an estimated 75% of mental health issues beginning by the age of 24 (WHO, 2019) and 1 in 5 adolescents experiencing a mental health problem each year (UNICEF, 2019). Young people are familiar with digital platforms, with >70% of young people aged 15-24 being ‘online’. However, there are substantial income and geographically based disparities, with only 43% of people in low and middle-income countries using the internet (International TU, 2018).

There are over 2 million apps with a mental health orientation already available; however, there is concern that these may not be substantiated by a sufficiently large evidence base.

This systematic overview sought to provide a high-level synthesis of existing meta-analyses around the topic and identify converging evidence or issues within the research. It had four main research questions (directly quoted from paper):

1. “In adolescents and young people aged between 10 and 24 years, to what extent are digital health interventions effective in addressing mental health conditions, compared with standard face-to-face treatment, placebo, or no treatment?
2. What factors contribute to effectiveness (ie, what makes effective interventions effective)?
3. To what extent is there evidence on cost-effectiveness?
4. To what extent are the findings generalizable to adolescents and young people from a range of settings, including low and middle-income countries?”

Methods

The authors searched papers published in English between January 2011 and July 2020.

Inclusion criteria for further analysis followed the PICOS (Population, Intervention, Comparator, Outcome, Setting) framework (directly quoted from paper):

• “Population: Adolescents and young people, defined as primarily aged between 10 and 24 years (or if older participants were included, the mean age was <25 years), with a mental health condition, including anxiety, affective, and behavioral conditions (diagnosed and self-reported)
• Intervention: Consumer-facing, partially or fully self-administered, mental health intervention delivered through a digital platform (eg, web-based, computer, or mobile phone)
• Comparator: Active (ie, standard nondigital care and alternative materials) or passive control (ie, placebo and no treatment)
• Outcome: Mental health improvement as reported by studies (ie, diagnosed or self-reported mental health conditions, including affective, behavioral, and trauma-related conditions)
• Setting: Nonclinical, nonfacility-based setting in any country”

Data from studies was extracted independently by two reviewers and quality was assessed using AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews) (Shea et al., 2017). No quantitative analysis was conducted given the high heterogeneity of the studies.

Results

18 systematic reviews and meta-analyses were included for synthesis. Results were categorised in line with the main objectives of the paper. In terms of quality appraisal, research in this area was analysed as having a consistently low quality.

To what extent are digital health interventions effective in addressing mental health conditions in adolescents?

• Evidence suggests that digital mental health interventions have small to medium positive effects over nonactive controls (e.g. waitlist or placebo) but are similar when compared with active controls (e.g. face-to-face therapy).
• However, the effectiveness varied across different sets of symptoms. It was most effective for depression and anxiety and to a lesser extent for stress.
• There was less research regarding ADHD, autism spectrum disorders, eating disorders, psychosis and posttraumatic stress and it was limited by high variability and differences in use of evidence-based interventions, making the results inconclusive
• Computerised cognitive behavioural therapy (cCBT) was the most common clinical intervention used across the studies. Results suggested no difference in effectiveness of cCBT over active controls, i.e. in-person CBT, but there were benefits of cCBT over nonactive controls.
• Similarly, most of this evidence was based on studies addressing depression and anxiety. Although there was less research, small positive effects were found for attention bias modification interventions for depression and anxiety over nonactive controls, whereas no benefit was found for cognitive bias modification over controls.

What factors contribute to effectiveness?

• Research generally suggests that guided digital interventions supported with an in-person element (e.g. therapist, parent or peer) were more effective and associated with greater adherence than those that were fully digital (i.e. unguided).
• Pharmacological treatment alongside digital interventions may also increase effectiveness.
• Other factors such as setting (interventions delivered in a school setting may deliver greater adherence) and gender (females were more likely to complete interventions than males) also seem relevant.
• Evidence on the relationship between mental health status and adherence was mixed, with higher depression, longer history of mood disorders and lower anxiety associated with greater adherence.

To what extent is there evidence on cost-effectiveness?

• Data on cost-effectiveness was not reported in any of the studies. The authors note that a number of included studies acknowledged that “despite being widely considered low cost, for example, because of reduced time and personnel expenses, there is still a lack of data on the cost-effectiveness and economic benefits of digital mental health interventions.”

To what extent are the findings generalisable to adolescents and young people from a range of settings?

• Most studies were conducted in high income countries with no additional data reported on the sociodemographic characteristics of participants. Studies were from Europe (n=71), United States (n=21), Australia (n=21), Canada (n=13), and New Zealand (n=9), China (n=9), including Hong Kong, Chile (n=2), Egypt (n=1), and Thailand (n=1).
• As a result, the generalisability of findings is limited and there is an urgent need to carry out research in low- and middle-income countries.

Conclusions

This overview of meta-analyses and systematic reviews suggests that digital mental health interventions for adolescents and young people have modest positive effects, especially when relying on evidence-based treatment content or in-person elements that boost engagement. Their potential for settings with limited resources for health and cost savings compared with traditional treatment remains understudied.

Positive effects were found particularly for symptoms of depression and anxiety, mainly using computerised versions of CBT. Other digital platforms, such as social media sites and therapeutic gaming, showed promise in promoting health-related behaviours, but were mixed in their outcomes (Laranjo et al., 2015; Korda et al 2013).

Strengths and limitations

The synthesis approach of this systematic overview allowed analysis across a heterogeneous set of studies and provides a useful broad sense of the direction and issues within the current research area. However, the variation in study settings, methods and comparators has to be acknowledged and caution should be taken when interpreting the results.

The authors called for more systematic approaches to assessing digital mental health interventions to facilitate meta-analyses, and noted that much of the research was of a “consistently low quality” using the AMSTAR criteria, citing heterogeneity in primary studies within the systematic reviews, self-selection of participants and limited blinding among other issues.

Implications for practice

This paper supports the general sense of promise and increased research around digital mental health interventions, but highlights the need for further study and nuance.

Digital interventions may help manage the increasing demand on mental health services (Canaway, 2021). Potentially they could also help improve accessibility of services, especially for groups which are historically deemed ‘harder to reach’, eg minority ethnicities, LGBTQ+, traveller communities and others, who are also digitally connected. However, notably where there is not digital access, it could serve to only widen disparities. Both of these points belie a concern that digital interventions will be used as a ‘sticking plaster’ when broader service improvement is needed, in terms of accessibility and resources. Furthermore, it does little to address the root causes of high rates of mental health problems in young people. Notably, this may partly relate to digital use which, as the authors comment, can also carry harms and so services need to be mindful of this when implementing interventions digitally.

Ongoing cross-cultural critical analysis is required to ensure, especially given the understudied generalisability, that digital interventions do not become a lower cost model by which to problematically export predominantly Western mental health concepts and interventions to other settings.

Digital interventions may be particularly valuable for those who prefer this method of delivery over ‘standard’ in-person interventions, or as a low threshold form of early intervention, e.g. in schools. However, related to what is highlighted by the authors, more research is needed around types of digital intervention, symptom status, background characteristics of young people and their preferences to identify nuance within this promise. A key question remains: will the research evidence ever be able to catch up with what is happening out there in the real world?

Statement of interests

No conflicts of interest to report.

Links

Primary paper

Lehtimaki S, Martic J, Wahl B, Foster KT, Schwalbe N. Evidence on Digital Mental Health Interventions for Adolescents and Young People: Systematic Overview. JMIR Ment Health. 2021 Apr 29;8(4):e25847. doi: 10.2196/25847. PMID: 33913817; PMCID: PMC8120421.

Other references

Canaway, A. Internet-based psychotherapy may be cost-effective for anxiety and depression. The Mental Elf, Dec 2021.

Korda H, Itani Z. Harnessing social media for health promotion and behavior change. Health Promot Pract 2013 Jan;14(1):15-23. [doi: 10.1177/1524839911405850] [Medline: 21558472]

Laranjo L, Arguel A, Neves AL, Gallagher AM, Kaplan R, Mortimer N, et al. The influence of social networking sites on health behavior change: a systematic review and meta-analysis. J Am Med Inform Assoc 2015 Jan;22(1):243-256. [doi: 10.1136/amiajnl-2014-002841] [Medline: 25005606]

International TU, World TDR. Mobile cellular subscriptions; individuals using the Internet. Published. 2018. URL:

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 2017 Dec 21;358:j4008

UNICEF. Adolescent mental health. Published August. 2019.

WHO. Adolescent mental health. Published October 23. 2019.

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