Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Luke Balcombe
 -- 2627 2023-01-05 04:59:34 |
2 format
Jason Zhu
 -2 word(s) 2625 2023-01-06 04:01:33 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Balcombe, L.;  Leo, D.D. Use of Digital Mental Health Platforms and Interventions. Encyclopedia. Available online: https://encyclopedia.pub/entry/39773 (accessed on 20 May 2024).
Balcombe L,  Leo DD. Use of Digital Mental Health Platforms and Interventions. Encyclopedia. Available at: https://encyclopedia.pub/entry/39773. Accessed May 20, 2024.
Balcombe, Luke, Diego De Leo. "Use of Digital Mental Health Platforms and Interventions" Encyclopedia, https://encyclopedia.pub/entry/39773 (accessed May 20, 2024).
Balcombe, L., & Leo, D.D. (2023, January 05). Use of Digital Mental Health Platforms and Interventions. In Encyclopedia. https://encyclopedia.pub/entry/39773
Balcombe, Luke and Diego De Leo. "Use of Digital Mental Health Platforms and Interventions." Encyclopedia. Web. 05 January, 2023.
Use of Digital Mental Health Platforms and Interventions
Edit
This entry is adapted from the peer-reviewed paper 10.3390/ijerph20010362

The increasing use of digital mental health (DMH) platforms and digital mental health interventions (DMHIs) is hindered by uncertainty over effectiveness, quality and usability. There is a need to identify the types of available evidence in this domain. There was a small amount of significant evidence (1 in each 11), notably the (cost-)effectiveness of a DMHI with significant long-term impact on anxiety and depression in adults. Empirical research has demonstrated the feasibility of DMH platforms and DMHIs. 

mental health care suicide prevention digital mental health platforms digital mental health interventions

1. Introduction

1.1. Background

Mental illness and suicide are ongoing primary global health problems [1] that need accessible and scalable solutions. For example, digital mental health (DMH), which is a contemporary method of mental health care that is distinguished by the large-scale integration of telehealth [2], apps [3][4], and digital platforms [5] as well as the promise of big data, genomics and artificial intelligence (AI) [6]. DMH platforms are a key technology for the purpose of assessment, support, prevention, and treatment in mental health [7]. Generally, digital platforms are an online space to exchange products, services, and information. The DMH global market is predicted to grow from USD 2568.6 million in 2021 to USD 18,717.5 million by 2030, at a compound annual growth rate of 21.1% [8]. An overview of systematic reviews summarized the research on the effectiveness of technology in DMH and found an extensive amount of DMH interventions (DMHIs) to address gaps in mental health service provision, in addition to shifting focus and target populations [9]. A hindering issue for the advancement of DMH is the sustained engagement of service users [10]. Therefore, it is important to provide a systematic approach to discern which DMH platforms and DMHIs are effective, usable and of good quality. Furthermore, it is necessary to clarify what mental health indications and populations these digital solutions are suitable for.

1.2. Overview of Existing Work

1)
The use and functionality of DMH platforms
Digital platforms are used in various contexts in DMH. For example, DMH platforms are used in more than 100 services for adults with anxiety and depression [11][12]. There is a priority to establish evidence for use in servicing people with diagnosed mental disorders [5]. DMH platforms are also used to assist early intervention strategies for young people. For example, to assist practitioners to deliver quality, personalized and measurement-based care for young people’s overall health, mental health, everyday function, suicidal thoughts/behaviors and social connectedness [13]. The use of digital platforms for video chats, social networks, telephone calls, and emails as a means of communication are effective at the population level for anxiety and depression although screening and intervention, AI-driven technologies, social media and digital phenotyping are generally not effectively used in DMH [14].
Internet-delivered cognitive behavioral therapy (ICBT) is the most used DMHI. ICBT is widely accessible, efficient, (cost-)effective and adaptable [15][16]. Self-guided treatment (28.4%) and guided telehealth/peer-to-peer approaches (16.3%) are the most used DMH services followed by real-time AI diagnostic assessments in computational psychiatry (13.7%), consumer journaling and support signposting (10%), physical, augmented and virtual reality (6.8%), diagnostic support (6.3%), gamified digital treatments (5.3%), neurological interventions (4.7%), digital phenotyping (4.2%), and virtual assistants (4.2%) [17]. Suicide prevention standalone digital platforms are rare because they are usually combined with DMH platforms [18].
The different types and uses of DMH platforms means it is necessary to distinguish among them in terms of functionality, which is its usefulness, or how well it performs the designated job. For example, the futility of risk assessment in psychiatry means the functionality of AI based DMH platforms is dependent on it being combined with personalized mental health care [19].
2)
Effectiveness of and engagement with DMHIs
A systemic review found several efficacious, scalable and sustainable suicide prevention interventions providing the opportunity for population-level impact and strategies to enhance effectiveness and reach [20]. Psychiatric diseases contribute to 60–98% of suicides [21]. Suicide prevention DMHIs may help augment ongoing clinical care if practitioners exercise caution in recommending suitable interventions and are aware of the security of the data that is collected [18]. Although integrating DMHIs into psychiatric care shows promising results for real-time monitoring and feedback on changes in common symptoms (e.g., stress, anxiety, and depression) [22], caution needs to be exercised in making recommendations for interventions on distress and suicidality because of uncertainty about their effectiveness and evaluation [19].
Meta-analyses of randomized controlled trials (RCTs), an experimental form of impact evaluation with a randomly selected sample and control group from the same population, noted potential efficacy for DMHIs for anxiety [23][24] and depression [25] in general populations. It was suggested to focus studies on comparisons with face-to-face psychological care [23]. This focus may help extract which aspects of the technologies produce beneficial effects and for which populations [25]. It may also help focus more studies with routine care populations [24]. There is a good potential for DMH platforms to be used in applying affordable interventions and preventive treatments [26][27][28]. However, the consumer marketplace is currently inundated with apps that lack engagement and efficacy [5]. Systematic reviews found a lack of clear and comprehensive evidence-base although there is a growing consensus that the most effective DMHIs are used for anxiety and depression particularly with college students [29] and young people [30]. A systematic review reported DMHIs have higher sustained engagement than self-guided digital tools [10]. This finding was endorsed by meta-analyses centered on anxiety [31] and depression [32].
3)
Implementation barriers for DMH platforms
A range of barriers hinder effective and sustained implementation of DMH platforms. For example, the field is constrained by issues of affordability [33], accessibility, relevance, reliability, a lack of personalization and human capacity [12], technical and ethical considerations [34] as well as privacy and security, efficacy, engagement, and clinical integration [5]. There is rigorous evidence of efficacy in trials although a lack of real-world impact [35] means there is an inconsistent impact. This is because of difficulties in instructing patients and mental health care professionals in using DMH platforms as well as the regulatory context of health care delivery [5]. The promising results in support of DMH platforms may be hindered by the human factors of human–computer interaction (HCI) (e.g., organizational readiness and usability in the healthcare context) [14]. For example, there was a 500% increase in the use of tailored self-guided resources by healthcare workers during the COVID-19 pandemic, although most dropped out of treatment because of time constraints, privacy concerns, treatment relevancy and satisfaction with the digital health platform design and experience [36].
4)
Recommendations for overcoming implementation barriers
Different levels of DMH platform evaluation are required ranging from feasibility and pilot studies on user retention/acceptability, safety and satisfaction through to RCTs and implementation feasibility studies [37]. Apps need to be moved to an integrated digital platform, and digital tools need to be highly effective and engaging, address inequalities, and build trust in their authenticity [35]. There also needs to be better (cost-)effectiveness [38][39]. Furthermore, innovation is required to converge pattern-based and hypothesis-driven methods for evaluation of rigorous preventive strategies and interventions [5][19][40]. Codesign may help to strengthen the human-centered design process and instill an understanding of how an application achieves real-world effectiveness [14]. All the aspects surrounding innovation must be considered for the sustained use of DMH platforms. ‘Convergence mental health’ is recommended to facilitate access to and use of DMH services through integrating scientists, clinicians, bioinformaticists, global health experts, engineers, technology entrepreneurs, medical educators, caregivers, and patients as well as infusing synergy between government, academia, and industry for multidisciplinary applied and translational solutions [41].
5)
Evaluative research for the use of DMH platforms and DMHIs
There is a small amount of previous review and analysis on (1) evaluation of the use of DMH platforms and (2) evaluation of the use of DMHIs. As an example of 1, the DMH platform MOST was applied in evaluative research that highlighted the potential of novel multimodal approaches to help-seeking by connecting MOST with clinical services to provide support in real-time and to sustain mental health recovery for young people [42]. An earlier pilot study established the acceptability, safety and initial clinical benefits of the Horyzons DMH platform for peer-to-peer social networking, individually tailored interactive psychosocial interventions, and expert interdisciplinary and peer-moderation [43]. MOST was reported to be safe and effective for evidence-based mental health support for young people with psychoses, depression, social anxiety, mental illness and suicidal risk [44]. As an example of 2, an RCT study demonstrated the efficacy of an ICBT program—‘Space from Depression’—for adults with depressive symptoms [45].
6)
Convergence of empirical and theoretical literature to increase effectiveness of DMHIs
An integrated blueprint suggested eminent DMH platforms are needed to increase the effectiveness of DMHIs in self-guided and guided approaches [46]. The lack of highly effective, evaluated DMH platforms is entrenched in the struggle to sustainably innovate. There are underlying quality, safety and usability issues stemming from the difficulty converging theoretical, data-driven/technological and empirical research, as well as to satisfy mental health care professionals’ and users’ HCI demands [19][47][48]. The development of optimized patient-centric digital tools is not the problem. Rather, it is how long it takes mental health care professionals to adapt in using these tools. For example, DMHIs may assist the prevention of the sequalae of mental illness quickly and accurately through predictive systems that apply DMH platforms and AI-driven apps [19][39][49][50]. A trial-and-error approach may be necessary to overhaul how codesign, behavior theories, and clinical evaluation are applied [51]. There is also a need to confront the lagging human factors that limit the successful implementation of DMH platforms and effective industry standards.

2. Principal Findings of Empirical Literature

A slightly higher qualitative evidence base was found in comparison to quantitative studies although the difference was made up of mixed-methods studies. Overall, the studies mainly evaluated feasibility, usability, engagement, acceptability and effectiveness. Although feasibility was found for the use of DMH platforms and DMHIs in mental health care and suicide prevention, the results highlight the need to increase usability and engagement in addition to effectiveness and quality.
The main types of DMH platforms used in the 22 included empirical studies are categorized as integrated, guided, self-guided, integrated-multifunctional, multimodal, and direct to consumer tele-mental health. This contrasted with previous reviews which mostly reported off-the-shelf solutions through computers, mobile apps, text message, telephone, web, CD-ROM, and video for general population DMHIs for suicidal ideation and mental health co-morbidities [20]. Other previous reviews focused on general mental health support [10], in addition to self-guided digital tools for anxiety and depression in general populations [31][32]. In line with the previous reports of variability in the applications of use, the empirical evidence suggests DMH platforms and DMHIs are used for a range of purposes, e.g., to treat loneliness and to aid suicide prevention.
The high number and frequent use of DMH tools [9] is reflected in the evaluative evidence base on the use of DMH platforms and DMHIs. In line with the previous findings of Borghouts et al. [10], there was heterogeneity found in the mostly preliminary evidence. These findings mainly focused on feasibility, usability, engagement, and acceptability rather than the effectiveness of each DMH platform or DMHI.
The most significant finding overall arose from the RCT for SilverCloud’s ICBT for anxiety and depression [52]. This RCT proceeded a study that established efficacy with regards to ICBT for adults with depressive symptoms [45]. The general lack of study follow-up in the domain has hindered the evaluation when considering there are more than 100 DMH programs for depressed and anxious adults [11][12]. RCTs are considered the “gold standard” by which psychological interventions are evaluated and subsequently adopted into general clinical practice [53]. However, there are some limitations of RCTs in developing treatment guidelines in terms of the pragmatic application from a sample to the individual patient. For example, the baseline characteristics of the RCT by Richards et al. [52] reported that 70% of the sample were female, noting this is only slightly higher than program referral rate for females (65%). This incidental finding highlights the inherent difficulties in recruiting and engaging men in mental health research [48].
The significant and preliminary evidence found in the empirical literature do not tell the whole story regarding efficacy and effectiveness. For example, a previous review reported the DMH platform MOST is safe and effective [44]. However, it is not clearly stated what it is effective for. It appears from the qualitative study by Valentine et al. [54] that young people supported blended care through Horyzons (a derivative of MOST). Although, further evaluative research is needed on efficacy, e.g., on the therapeutic alliance, clinical and social outcomes, cost-effectiveness, and engagement. There was also no significant effect on social functioning compared with treatment as usual as a primary outcome of the RCT with Horyzons [55]. This RCT followed extensive design, implementation [42], and augmentation of social connectedness and empowerment in youth first-episode psychosis [43]. These examples highlight the need for the study which distinguished between evaluative research focused on the effectiveness of the DMH platform as well as the effectiveness of the DMHI applied on the DMH platform.
The previous body of knowledge noted the difference between rigorous evidence of efficacy in trials and outcomes that indicate a lack of real-world impact [35]. The study supports this finding. Although, it may help to also clarify about efficacy and effectiveness to generally assist in the evaluation of DMH platforms and DMHIs. For example, Craig et al. [56] evaluated the AFFIRM Online DMH platform and reported it brought about efficacy through working under ideal circumstances. However, the RCT for SilverCloud’s ICBT for anxiety and depression [52] was deemed to be more significant in evidence because it applied a waiting list to demonstrate pragmatic effectiveness by working in substandard circumstances. Evaluation of DMHIs may produce relevant, measurable, responsive, and resourced indications on safety or effectiveness for its intended mental health care and/or suicide prevention purpose. RCTs can bolster these claims by providing randomization which decreases bias and offers a rigorous tool to examine cause-effect relationships between an intervention and outcome. However, a successful RCT may not be required to demonstrate safety and effectiveness.

3. Secondary Findings of Empirical Literature

Robust stakeholder engagement is required to ensure there is responsiveness to needs and to gain support for DMH implementation. The previous review noted the existence of targeted strategies to serve young people in mental health care [13][42]. Although the evidence synthesis found more of a focus on adults, there was a slightly higher number of targeted strategies for young people. However, there is a need for more effective qualitative strategies such as in designing and implementing youth-oriented tailored solutions [57] and implementing a centralized DMH platform to improve stakeholder accessibility [58]. The previous review of Spadaro et al. [51] suggested overhauling the application of codesign, behavior theories, and clinical evaluation. In line, a qualitative study that evaluated DMHIs on the Innowell DMH platform articulated some implementation problems: restricted access, siloed services, interventions that are poorly matched to service users’ needs, underuse of personal outcome monitoring to track progress, exclusion of family and carers, and suboptimal experiences of care [59]. A consequential evaluation of the Innowell DMH platform led to the finding that national scalability is hindered by human factors—the main problem is not the technology but the humans that implement and use it [60]. This is in line with previous findings about the constraints in instructing the recipients of technologies [5] and transforming clinicians’ strong interest in using technology to actual use [4].
A previous review found human-centered design is important for the codesign process to instill an understanding of how DMH platforms can be used with engaging effectiveness [47]. However, human-centered design is often not implemented well in DMH services. The evidence for HCI issues was in line; for example, the relationship between the UI of a DMH platform and treatment effectiveness was unclear [61]. Furthermore, the results indicate that young people who perceived DMH platforms as useful in blended care were more willing to use the system in the future [54][62]. The results with the Innowell DMH platform suggested that codesign is not a foolproof method to increasing effectiveness with DMH platforms [60]. Previous findings on the need for key stakeholder and user input [3] were echoed in addition to the call for funding and resources to expand regional case studies to the state level and beyond.

References

  1. World Health Organization (WHO). Suicide Worldwide in 2019, Global Health Estimates. Geneva, World Health Organization. 2021. Available online: https://www.who.int/publications/i/item/9789240026643 (accessed on 22 February 2022).
  2. Gratzer, D.; Torous, J.; Lam, R.W.; Patten, S.B.; Kutcher, S.; Chan, S.; Yatham, L.N. Our Digital Moment: Innovations and Opportunities in Digital Mental Health Care. Can. J. Psychiatry 2020, 66, 5–8.
  3. Torous, J.; Jän Myrick, K.; Rauseo-Ricupero, N.; Firth, J. Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow. JMIR Ment. Health 2020, 7, e18848.
  4. Bell, I.H.; Thompson, A.; Valentine, L.; Adams, S.; Alvarez-Jimenez, M.; Nicholas, J. Ownership, Use of, and Interest in Digital Mental Health Technologies Among Clinicians and Young People Across a Spectrum of Clinical Care Needs: Cross-sectional Survey. JMIR Ment. Health 2022, 9, e30716.
  5. Torous, J.; Bucci, S.; Bell, I.H.; Kessing, L.V.; Faurholt-Jepsen, M.; Whelan, P.; Firth, J. The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021, 20, 318–335.
  6. World Health Organization. WHO Guideline: Recommendations on Digital Interventions for Health System Strengthening. Executive Summary. Geneva: World Health Organization. 2019. Available online: http://apps.who.int/iris/bitstream/handle/10665/311941/9789241550505-eng.pdf (accessed on 2 March 2022).
  7. Wies, B.; Landers, C.; Ienca, M. Digital Mental Health for Young People: A Scoping Review of Promises and Challenges. Front. Digit. Health 2021, 3, 697072.
  8. Research and Markets. Global Emerging Mental Health Devices and Platforms Market: Analysis and Forecast, 2021–2030. 2021. Available online: https://www.researchandmarkets.com/reports/5315021/global-emerging-mental-health-devices-and (accessed on 2 March 2022).
  9. De Witte NA, J.; Joris, S.; Van Assche, E.; Van Daele, T. Technological and Digital Interventions for Mental Health and Wellbeing: An Overview of Systematic Reviews. Front. Digit. Health 2021, 3, 754337.
  10. Borghouts, J.; Eikey, E.; Mark, G.; De Leon, C.; Schueller, S.M.; Schneider, M.; Stadnick, N.; Zheng, K.; Mukamel, D.; Sorkin, D.H. Barriers to and Facilitators of User Engagement with Digital Mental Health Interventions: Systematic Review. J. Med. Internet Res. 2021, 23, e24387.
  11. Andersson, G. Internet-Delivered Psychological Treatments. Annu. Rev. Clin. Psychol. 2016, 12, 157–179.
  12. Scholten, H.; Granic, I. Use of the Principles of Design Thinking to Address Limitations of Digital Mental Health Interventions for Youth: Viewpoint. J. Med. Internet Res. 2019, 21, e11528.
  13. Iorfino, F.; Cross, S.P.; Davenport, T.; Carpenter, J.S.; Scott, E.; Shiran, S.; Hickie, I.B. A Digital Platform Designed for Youth Mental Health Services to Deliver Personalized and Measurement-Based Care. Front. Psychiatry 2019, 10, 595.
  14. Balcombe, L.; De Leo, D. Digital Mental Health Amid COVID-19. Encyclopedia 2021, 1, 1047–1057.
  15. Titov, N.; Dear, B.F.; Staples, L.G.; Bennett-Levy, J.; Klein, B.; Rapee, R.M.; Shann, C.; Richards Nielssen, O.B. MindSpot Clinic: An Accessible, Efficient, and Effective Online Treatment Service for Anxiety and Depression. Psychiatr. Serv. 2015, 66, 1043–1050.
  16. Schueller, S.M.; Torous, J. Scaling evidence-based treatments through digital mental health. Am. Psychol. 2020, 75, 1093–1104.
  17. World Economic Forum. Global Governance Toolkit for Digital Mental Health: Building Trust in Disruptive Technology for Mental Health. 2021. Available online: https://www3.weforum.org/docs/WEF_Global_Governance_Toolkit_for_Digital_Mental_Health_2021.pdf (accessed on 15 March 2022).
  18. Braciszewski, J.M. Digital Technology for Suicide Prevention. Adv. Psychiatry Behav. Health 2021, 1, 53–65.
  19. Balcombe, L.; De Leo, D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Ment. Health 2021, 8, e26811.
  20. Kreuze, E.; Jenkins, C.; Gregoski, M.; York, J.; Mueller, M.; Lamis, D.A.; Ruggiero, K.J. Technology-enhanced suicide prevention interventions: A systematic review. J. Telemed. Telecare 2016, 23, 605–617.
  21. Bachmann, S. Epidemiology of Suicide and the Psychiatric Perspective. Int. J. Environ. Res. Public Health 2018, 15, 1425.
  22. Fowler, J.C.; Madan, A.; Bruce, C.R.; Frueh, B.C.; Kash, B.; Jones, S.L.; Sasangohar, F. Improving Psychiatric Care Through Integrated Digital Technologies. J. Psychiatr. Pract. 2021, 27, 92–100.
  23. Firth, J.; Torous, J.; Nicholas, J.; Carney, R.; Rosenbaum, S.; Sarris, J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J. Affect. Disord. 2017, 218, 15–22.
  24. Romijn, G.; Batelaan, N.; Kok, R.; Koning, J.; van Balkom, A.; Titov, N.; Riper, H. Internet-Delivered Cognitive Behavioral Therapy for Anxiety Disorders in Open Community Versus Clinical Service Recruitment: Meta-Analysis. J. Med. Internet Res. 2019, 21, e11706.
  25. Firth, J.; Torous, J.; Nicholas, J.; Carney, R.; Pratap, A.; Rosenbaum, S.; Sarris, J. The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry 2017, 16, 287–298.
  26. Bidargaddi, N.; Schrader, G.; Klasnja, P.; Licinio, J.; Murphy, S. Designing m-Health interventions for precision mental health support. Transl. Psychiatry 2020, 10, 1–8.
  27. Bergin, A.D.; Vallejos, E.P.; Davies, E.B.; Daley, D.; Ford, T.; Harold, G.; Hollis, C. Preventive digital mental health interventions for children and young people: A review of the design and reporting of research. NPJ Digit. Med. 2020, 3, 133.
  28. Davenport, T.A.; Cheng VW, S.; Iorfino, F.; Hamilton, B.; Castaldi, E.; Burton, A.; Hickie, I.B. Flip the Clinic: A Digital Health Approach to Youth Mental Health Service Delivery During the COVID-19 Pandemic and Beyond. JMIR Ment. Health 2020, 7, e24578.
  29. Lattie, E.G.; Adkins, E.C.; Winquist, N.; Stiles-Shields, C.; Wafford, Q.E.; Graham, A.K. Digital Mental Health Interventions for Depression, Anxiety, and Enhancement of Psychological Well-Being Among College Students: Systematic Review. J. Med. Internet Res. 2019, 21, e12869.
  30. Lehtimaki, S.; Martic, J.; Wahl, B.; Foster, K.T.; Schwalbe, N. Evidence on Digital Mental Health Interventions for Adolescents and Young People: Systematic Overview. JMIR Ment. Health 2021, 8, e25847.
  31. Pauley, D.; Cuijpers, P.; Papola, D.; Miguel, C.; Karyotaki, E. Two decades of digital interventions for anxiety disorders: A systematic review and meta-analysis of treatment effectiveness. Psychol. Med. 2021, 1–13.
  32. Moshe, I.; Terhorst, Y.; Philippi, P.; Domhardt, M.; Cuijpers, P.; Cristea, I.; Sander, L.B. Digital interventions for the treatment of depression: A meta-analytic review. Psychol. Bull. 2021, 147, 749–786.
  33. Webb, C.A.; Rosso, I.M.; Rauch, S.L. Internet-based cognitive-behavioral therapy for depression: Current progress and future directions. Harv. Rev. Psychiatry 2017, 25, 114–122.
  34. Nebeker, C.; Bartlett Ellis, R.J.; Torous, J. Development of a decision-making checklist tool to support technology selection in digital health research. Transl. Behav. Med. 2020, 10, 1004–1015.
  35. Roland, J.; Lawrance, E.; Insel, T.; Christensen, H. The Digital Mental Health Revolution: Transforming Care Through Innovation and Scale-Up. 2020. Available online: https://www.wish.org.qa/reports/the-digital-mental-health-revolution-transforming-care-through-innovation-and-scale-up/ (accessed on 22 February 2022).
  36. Baldwin, P.A.; Black, M.J.; Newby, J.M.; Brown, L.; Scott, N.; Shrestha, T.; Christensen, H. The Essential Network (TEN): Rapid development and implementation of a digital-first mental health solution for Australian healthcare workers during COVID-19. BMJ Innov. 2020, 8, 105–110.
  37. Maron, E.; Baldwin, D.S.; Balõtšev, R.; Fabbri, C.; Gaur, V.; Hidalgo-Mazzei, D.; Eberhard, J. Manifesto for an international digital mental health network. Digit. Psychiatry 2019, 2, 14–24.
  38. Himle, J.A.; Weaver, A.; Zhang, A.; Xiang, X. Digital Mental Health Interventions for Depression. Cogn. Behav. Pract. 2022, 29, 50–59.
  39. Teachman, B.A.; Silverman, A.L.; Werntz, A. Digital Mental Health Services: Moving from Promise to Results. Cogn. Behav. Pract. 2022, 29, 97–104.
  40. Torous, J.; Nicholas, J.; Larsen, M.E.; Firth, J.; Christensen, H. Clinical review of user engagement with mental health smartphone apps: Evidence, theory and improvements. Evid. Based Ment. Health 2018, 21, 116–119.
  41. Eyre, H.A.; Berk, M.; Lavretsky, H.; Reynolds, C. (Eds.) Convergence Mental Health: A Transdisciplinary Approach to Innovation; Oxford University Press: Oxford, UK, 2021.
  42. Alvarez-Jimenez, M.; Rice, S.; D’Alfonso, S.; Leicester, S.; Bendall, S.; Pryor, I.; Gleeson, J. A Novel Multimodal Digital Service (Moderated Online Social Therapy+) for Help-Seeking Young People Experiencing Mental Ill-Health: Pilot Evaluation Within a National Youth E-Mental Health Service. J. Med. Internet Res. 2020, 22, e17155.
  43. Alvarez-Jimenez, M.; Bendall, S.; Lederman, R.; Wadley, G.; Chinnery, G.; Vargas, S.; Gleeson, J.F. On the HORYZON: Moderated online social therapy for long-term recovery in first episode psychosis. Schizophr. Res. 2013, 143, 143–149.
  44. McGorry, P.D.; Mei, C.; Chanen, A.; Hodges, C.; Alvarez-Jimenez, M.; Killackey, E. Designing and scaling up integrated youth mental health care. World Psychiatry 2022, 21, 61–76.
  45. Richards, D.; Timulak, L.; O’Brien, E.; Hayes, C.; Vigano, N.; Sharry, J.; Doherty, G. A randomized controlled trial of an internet-delivered treatment: Its potential as a low-intensity community intervention for adults with symptoms of depression. Behav. Res. Ther. 2015, 75, 20–31.
  46. Balcombe, L.; De Leo, D. An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19. JMIR Ment. Health 2020, 7, e21718.
  47. Balcombe, L.; De Leo, D. Human-Computer Interaction in Digital Mental Health. Informatics 2022, 9, 14.
  48. Balcombe, L.; De Leo, D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Front. Psychol. 2022, 12, 796371.
  49. Muñoz, R.F.; Chavira, D.A.; Himle, J.A.; Koerner, K.; Muroff, J.; Reynolds, J.; Schueller, S.M. Digital apothecaries: A vision for making health care interventions accessible worldwide. mHealth 2018, 4, 18.
  50. Ćosić, K.; Popović, S.; Šarlija, M.; Kesedžić, I.; Gambiraža, M.; Dropuljić, B.; Jovanovic, T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Front. Psychol. 2021, 12, 782866.
  51. Spadaro, B.; Martin-Key, N.A.; Bahn, S. Building the Digital Mental Health Ecosystem: Opportunities and Challenges for Mobile Health Innovators. J. Med. Internet Res. 2021, 23, e27507.
  52. Richards, D.; Enrique, A.; Eilert, N.; Franklin, M.; Palacios, J.; Duffy, D.; Timulak, L. A pragmatic randomized waitlist-controlled effectiveness and cost-effectiveness trial of digital interventions for depression and anxiety. NPJ Digit. Med. 2020, 3, 85.
  53. Mulder, R.; Singh, A.B.; Hamilton, A.; Das, P.; Malhi, G.S. The limitations of using randomised controlled trials as a basis for developing treatment guidelines. Evid. Based Ment. Health 2018, 21, 4–6.
  54. Valentine, L.; McEnery, C.; Bell, I.; O’Sullivan, S.; Pryor, I.; Gleeson, J.; Bendall, S.; Alvarez-Jimenez, M. Blended Digital and Face-to-Face Care for First-Episode Psychosis Treatment in Young People: Qualitative Study. JMIR Ment. Health 2020, 7, e18990.
  55. Alvarez-Jimenez, M.; Koval, P.; Schmaal, L.; Bendall, S.; Gleeson, J. The Horyzons project: A randomized controlled trial of a novel online social therapy to maintain treatment effects from specialist first-episode psychosis services. World Psychiatry Off. J. World Psychiatr. Assoc. WPA 2021, 20, 233–243.
  56. Craig, S.L.; Leung VW, Y.; Pascoe, R.; Pang, N.; Iacono, G.; Austin, A.; Dillon, F. AFFIRM Online: Utilising an Affirmative Cognitive–Behavioural Digital Intervention to Improve Mental Health, Access, and Engagement among LGBTQA+ Youth and Young Adults. Int. J. Environ. Res. Public Health 2021, 18, 1541.
  57. Vichta, R.; Gwinner, K.; Collyer, B. What would we use and how would we use it? Can digital technology be used to both enhance and evaluate well-being outcomes with highly vulnerable and disadvantaged young people? Eval. J. Australas. 2018, 18, 222–233.
  58. Knapp, A.A.; Cohen, K.; Nicholas, J.; Mohr, D.C.; Carlo, A.D.; Skerl, J.J.; Lattie, E.G. Integration of Digital Tools Into Community Mental Health Care Settings That Serve Young People: Focus Group Study. JMIR Ment. Health 2021, 8, e27379.
  59. LaMonica, H.M.; Milton, A.; Braunstein, K.; Rowe, S.C.; Ottavio, A.; Jackson, T.; Easton, M.A.; Hambleton, A.; Hickie, I.B.; Davenport, T.A. Technology-Enabled Solutions for Australian Mental Health Services Reform: Impact Evaluation. JMIR Form. Res. 2020, 4, e18759.
  60. LaMonica, H.M.; Iorfino, F.; Lee, G.Y.; Piper, S.; Occhipinti, J.-A.; Davenport, T.A.; Hickie, I.B. Informing the Future of Integrated Digital and Clinical Mental Health Care: Synthesis of the Outcomes from Project Synergy. JMIR Ment. Health 2022, 9, e33060.
  61. Hentati, A.; Forsell, E.; Ljótsson, B.; Kaldo, V.; Lindefors, N.; Kraepelien, M. The effect of user interface on treatment engagement in a self-guided digital problem-solving intervention: A randomized controlled trial. Internet Interv. 2021, 26, 100448.
  62. Bucci, S.; Morris, R.; Berry, K.; Berry, N.; Haddock, G.; Barrowclough, C.; Edge, D. Early Psychosis Service User Views on Digital Technology: Qualitative Analysis. JMIR Ment. Health 2018, 5, e10091.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Health Care Sciences & Services
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Luke Balcombe
,
Diego De Leo
View Times: 533
Revisions: 2 times (View History)
Update Date: 06 Jan 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback