No matter where you look, technology has fundamentally changed the way we socialise, work, participate in leisure activities, and even view our world. Such changes have led to a more efficient way of living in many cases, but such efficiency often leads to the question “at what cost?”.
My wonderful 86 year old nan is very much the definition of a “silver surfer”. She communicates via WhatsApp with her friends at the flower club to share their creations of the week, can FaceTime friends and family on a regular basis and orders her groceries online. However, in a recent communication with her GP surgery she was informed she would have to use the NHS app to upload details about a health concern. Despite her technical prowess, she was unable to get the app to work and she became upset and frustrated as a result. Whilst this did provide her some form of a story to tell during the monotony of lockdown life, something that should have been an efficient and time-saving process ended up becoming distressing and time-consuming.
Technological innovations have also been implemented in the mental health domain to increase access, reduce waiting times, improve the accessibility of care and ultimately enhance client experience. Such innovations are particularly timely during a global pandemic where the opportunity for face-to-face therapy is limited (Torous et al., 2020). NHS Improving Access to Psychological Therapies (IAPT) services are one example where digital technology has been harnessed using a stepped care approach (National Collaborating Centre for Mental Health, 2020).
There is growing evidence for the efficacy of e-therapies for low mood (Karyotaki et al., 2017) and anxiety (Bell et al., 2012). However, there is a lack of evidence concerning the overall acceptability and efficacy of the specific e-therapies used in NHS services.
Therefore, the authors of this meta-analysis aimed to:
- Quantify the effect of 48 NHS-recommended therapies previously compiled in a survey of NHS app developers by Bennion and colleagues (2017)
- Compare the drop-out rates between experimental conditions and controls to understand acceptability
- Investigate the potential moderating factors (e.g. gender, age, symptom severity, treatment approach, treatment duration, settings, focus problem, and risk of bias) on e-therapy outcomes
E-therapies have been harnessed in UK IAPT services to increase access to evidence-based therapies.
Methods
Studies were selected for inclusion based on a 3-stage strategy:
- The names of each of the 48 e-therapies identified by Bennion and colleagues (2017) alongside their platform types were combined to develop a series of search terms (e.g. “Beating the Blues” AND “Website”). Searches were conducted using PsycINFO, Web of Science and PubMed (published up until April 2019)
- Reference lists of included studies and e-therapy reviews were searched to identify additional literature
- A survey was disseminated to the 48 app developers identified by Bennion et al, to identify any e-therapies that had not been developed under their commercial name, which were then included in the search terms.
Studies were eligible if they included an e-therapy that was:
- on the list of 48 NHS-recommended e-therapies (Bennion et al., 2017) for depression, anxiety and stress
- tested using a randomised controlled trial (RCT) design
- tested with participants from an adult population (aged >18 years)
- created by a developer who was locatable via a Google search when entering the e-therapy name as a search term
- explicit in referencing the targeted presenting problem
- assessed using a validated measure of anxiety and/or depression symptoms
- published in English.
Titles and abstracts were screened initially, with the full texts of identified studies then screened against the inclusion and exclusion criteria. Queries regarding eligibility were discussed within the research team. Studies were assessed using the Cochrane risk of bias tool (2011).
Results
A total of 24 studies were included that tested the efficacy of 7 of the 48 NHS-recommended e-therapies (Beating the Blues, FearFighter, MoodGYM, IESO, Headspace, Silver Cloud, and Work Guru). This means that only 15% of the NHS-recommended e-therapies had eligible RCT studies underpinning their clinical evaluation.
- Greater reductions in depression (small treatment effect) and anxiety (small-to-moderate treatment effect) followed e-therapy, in comparison with controls
- At follow-up, favourable outcomes for depression, anxiety and stress were also evident in comparison with controls (small treatment effect for both anxiety and depression)
- Variations in the effect of e-therapy could not be explained by gender, age, number of sessions or study quality at post-treatment or follow-up
- Outcomes were not significantly affected by the type of e-therapy, self-help type, recruitment setting, focus problem or analysis method
- There was a minimal-to-small influence of publication bias on the e-therapy treatment effect for depression, anxiety and stress outcomes.
- Poor and variable reporting of version numbers were evidenced across studies
- Significantly more participants dropped out during e-therapies compared with controls (31% versus 17%, respectively).
Only 15% of the NHS-recommended e-therapies had eligible RCT studies underpinning their clinical evaluation.
Conclusions
As noted by the authors, the lack of RCT evidence underpinning the use of e-therapies within IAPT settings does not align with the philosophy of evidence-based practice. However, there was some encouraging evidence that e-therapies do seem to benefit adults in managing depression, anxiety and stress in the short to medium term.
The poor reporting of version numbers across studies is worrying; the potential clinical impact of changes between versions are difficult to evaluate. Researchers should follow the CONSORT-EHEALTH (Eysenbach, 2011) checklist to ensure clarity and transparency in research outputs.
The lack of evidence underpinning NHS-recommended e-therapies contrasts with the philosophy of evidence-based practice.
Strengths and limitations
The authors highlighted that the higher drop-out rates for e-therapy in comparison with controls calls into question the overall acceptability of e-therapies. Indeed, this is not a new finding and several reviews of the studies included in the paper have also reported high drop-out rates being an indicator of poor acceptability (Karyotaki et al., 2017; Waller et al., 2009; Postel et al., 2010; Melville et al., 2010). However, assumptions regarding drop-out being an indicator of acceptability may be premature. Many of the included studies assessed acceptability using further measures such as treatment satisfaction and perceived helpfulness, which could have been considered and highlighted by the authors in this review. It is of note that the authors have identified the need for further qualitative work to further understand the acceptability of e-therapies, but I would add that qualitative methods are also required to understand how and why e-therapies are effective.
The authors highlighted that since the searches were completed (April 2019), trials of 3 NHS e-therapies have been published (and possibly more since the publication of the paper). Therefore, there will be further evidence regarding the efficacy of NHS-recommended e-therapies not included in this review. Given the fast-paced nature of research in the field of digital mental health, a living meta-analysis, which is continually updated, incorporating relevant new evidence as it becomes available could be considered in the future. More information about living systematic reviews can be found on the Cochrane Community website.
Qualitative methods are required to understand how and why e-therapies are effective, not just to understand their acceptability.
Implications for practice
This meta-analysis highlights the potential efficacy of e-therapies for individuals experiencing depression, anxiety or stress in comparison with controls. Therefore, e-therapies may be promising as an intervention for individuals on waiting lists for psychological support.
A lack of information regarding views towards acceptability highlights the need for practitioners to continually evaluate clients’ experiences when using e-therapies. Additionally, further evaluation of e-therapies must be prioritised to ensure that individuals accessing NHS services are not being short-changed. Ultimately, client preference should be key and individuals should be empowered through providing the evidence needed to allow them to make an informed choice.
Clients should be given informed choice over the intervention type and delivery method received.
Statement of interests
Natalie is currently on placement in an IAPT service where e-therapy is utilised. She also has a research background in digital mental health interventions.
Links
Primary paper
Simmonds-Buckley M, Bennion MR, Kellett S, Millings A, Hardy GE, Moore RK. Acceptability and effectiveness of NHS-recommended e-therapies for depression, anxiety, and stress: meta-analysis. Journal of Medical Internet Research 2020; doi: 10.2196/17049
Other references
Bell CJ, Colhoun HC, Carter FA, Frampton CM. Effectiveness of computerised cognitive behaviour therapy for anxiety disorders in secondary care. Aust N Z J Psychiatry 2012; doi: 10.1177/0004867412437345
Bennion MR, Hardy G, Moore RK, Millings A. E-therapies in England for stress, anxiety or depression: what is being used in the NHS? a survey of mental health services. BMJ Open 2017; doi: 10.1136/bmjopen-2016-014844
Cochrane. Cochrane Handbook for Systematic Reviews of Interventions. New York USA. 2011.
Eysenbach G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of web-based and mobile health interventions. J Med Internet Res 2011; doi: 10.2196/jmir.1923
Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, et al. Efficacy of self-guided internet-based cognitive behavioral therapy in the treatment of depressive symptoms: a meta-analysis of individual participant data. J Am Med Assoc Psychiatry 2017; doi: 10.1001/jamapsychiatry.2017.0044
Melville KM, Casey LM, Kavanagh DJ. Dropout from internet-based treatment for psychological disorders. British Journal of Clinical Psychology 2010; doi: 10.1348/014466509X472138
Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009; doi: 10.1136/bmj.b2535
National Collaborating Centre for Mental Health. The improving access to psychological therapies manual. 2020.
Postel MG, de Haan HA, ter Hururne ED, Becker ES, de Jong CA. Effectiveness of a web-based intervention for problem drinkers and reasons for dropout: randomised controlled trial. Journal of Medical Internet Research 2010; doi: 10.2196/jmir.1642
Torous J, Myrick KJ, Rauseo-Ricupero N, Firth J. Digital mental health and COVID-19: Using technology today to accelerate the curve on access and quality tomorrow. Journal of Medical Internet Research 2020; doi: 10.2196/18848
Waller R, Gilbody S. Barriers to the uptake of computerised cognitive behavioural therapy: a systematic review of the quantitative and qualitative evidence. Psychological Medicine 2009; doi: 10.1017/S0033291708004224
Photo credits
- Photo by Nicolas J Leclercq on Unsplash
- Photo by Christin Hume on Unsplash
- Photo by kaleb tapp on Unsplash
- Photo by Gabriella Clare Marino on Unsplash
- Photo by CoWomen on Unsplash
- Photo by Edgar Nunley on Unsplash
I am very concerned about increasing use of e-therapies for treatment of some mental health problems. Understandably their use has increased during the covid restrictions when face to face contacts were very limited. My concern is that they will continue to be used. Depression, anxiety and stress need face to face contact. Body language conveys information which can boost the spoken word. As a user of mental health services I would feel that my issues were insignificant, if my point of contact was a computer. E-therapies will be a cheaper form of treatment but in this situation people need to see people.