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Jun 27, 2023

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Despite an exponentially growing number of digital or e-mental health services, methodological guidelines for research and practical implementation are scarce. Here we aim to promote the methodological quality, evidence and long-term implementation of technical innovations in the healthcare system. This expert consensus is based on an iterative Delphi adapted process and provides an overview of the current state-of-the-art guidelines and practical recommendations on the most relevant topics in e-mental health assessment and intervention. Covering three objectives, that is, development, study specifics and intervention evaluation, 11 topics were addressed and co-reviewed by 25 international experts and a think tank in the field of e-mental health. This expert consensus provides a comprehensive essence of scientific knowledge and practical recommendations for e-mental health researchers and clinicians. This way, we aim to enhance the promise of e-mental health: low-threshold access to mental health treatment worldwide.

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World Health Organization. Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact: Scientific Brief (2022).

Adair, C. E. et al. Continuity of care and health outcomes among persons with severe mental illness. Psychiatr. Serv. 56, 1061–1069 (2005).

Article PubMed Google Scholar

Schulte, J., Schulz, C., Wilhelm, S. & Buhlmann, U. Treatment utilization and treatment barriers in individuals with body dysmorphic disorder. BMC Psychiatry 20, 69 (2020).

Article PubMed PubMed Central Google Scholar

van Daele, T. et al. Online consultations in mental healthcare: modelling determinants of use and experience based on an international survey study at the onset of the pandemic. Internet Interv. 30, 100571 (2022).

Article PubMed PubMed Central Google Scholar

Paganini, S., Teigelkötter, W., Buntrock, C. & Baumeister, H. Economic evaluations of internet- and mobile-based interventions for the treatment and prevention of depression: a systematic review. J. Affect. Disord. 225, 733–755 (2018).

Article PubMed Google Scholar

Mendes-Santos, C., Nunes, F., Weiderpass, E., Santana, R. & Andersson, G. Understanding mental health professionals’ perspectives and practices regarding the implementation of digital mental health: qualitative study. JMIR Form. Res. 6, e32558 (2022).

Article PubMed PubMed Central Google Scholar

Atik, E., Schückes, M. & Apolinário-Hagen, J. Patient and therapist expectations for a blended cognitive behavioral therapy program for depression: qualitative exploratory study. JMIR Ment. Health 9, e36806 (2022).

Article PubMed PubMed Central Google Scholar

de Witte, N. A. J. et al. Online consultations in mental healthcare during the COVID-19 outbreak: an international survey study on professionals’ motivations and perceived barriers. Internet Interv. 25, 100405 (2021).

Article PubMed PubMed Central Google Scholar

Smoktunowicz, E. et al. Consensus statement on the problem of terminology in psychological interventions using the internet or digital components. Internet Interv. 21, 100331 (2020).

Article PubMed PubMed Central Google Scholar

O’Loughlin, K., Neary, M., Adkins, E. C. & Schueller, S. M. Reviewing the data security and privacy policies of mobile apps for depression. Internet Interv. 15, 110–115 (2019).

Article PubMed Google Scholar

Hennemann, S., Farnsteiner, S. & Sander, L. Internet- and mobile-based aftercare and relapse prevention in mental disorders: a systematic review and recommendations for future research. Internet Interv. 14, 1–17 (2018).

Article PubMed PubMed Central Google Scholar

Torous, J. et al. Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach. J. Technol. Behav. Sci. 4, 73–85 (2019).

Article Google Scholar

Lal, S. & Adair, C. E. E-mental health: a rapid review of the literature. Psychiatr. Serv. 65, 24–32 (2014).

Article PubMed Google Scholar

Pill, J. The Delphi method: substance, context, a critique and an annotated bibliography. Socio Econ. Plan. Sci. 5, 57–71 (1971).

Article Google Scholar

Bartlett Ellis, R. et al. Lessons learned: beta-testing the digital health checklist for researchers prompts a call to action by behavioral scientists. J. Med. Internet Res. 23, e25414 (2021).

Article PubMed PubMed Central Google Scholar

Shen, F. X. et al. An ethics checklist for digital health research in psychiatry: viewpoint. J. Med. Internet Res. 24, e31146 (2022).

Article PubMed PubMed Central Google Scholar

World Health Organization. Digital Implementation Investment Guide (DIIG): Integrating Digital Interventions into Health Programmes https://apps.who.int/iris/bitstream/handle/10665/334306/9789240010567-eng.pdf (2020)/

Unsworth, H. et al. The NICE Evidence Standards Framework for digital health and care technologies—developing and maintaining an innovative evidence framework with global impact. Digit. Health 7, 20552076211018617 (2021).

PubMed PubMed Central Google Scholar

Sundareswaran, V. & Sarkar, A. Chatbots RESET: A Framework for Governing Responsible Use of Conversational AI in Healthcare https://www3.weforum.org/docs/WEF_Governance_of_Chatbots_in_Healthcare_2020.pdf (World Economic Forum, 2020).

Doraiswamy, P. M. et al. Empowering 8 billion minds: enabling better mental health for all via the ethical adoption of technologies. NAM Perspect. https://doi.org/10.31478/201910b (2019).

Hekler, E. B. et al. Agile science: creating useful products for behavior change in the real world. Transl. Behav. Med. 6, 317–328 (2016).

Article PubMed PubMed Central Google Scholar

Fiedler, J., Seiferth, C., Eckert, T., Woll, A. & Wunsch, K. A just-in-time adaptive intervention to enhance physical activity in the SMARTFAMILY2.0 trial. Sport Exerc. Perform. Psychol. https://doi.org/10.1037/spy0000311 (2022).

Chan, A. H. Y. & Honey, M. L. L. User perceptions of mobile digital apps for mental health: acceptability and usability—an integrative review. J. Psychiatr. Ment. Health Nurs. 29, 147–168 (2022).

Article PubMed Google Scholar

Vial, S. & Boudhraâ, S. in Revolutions in Product Design for Healthcare (eds Subburaj, K. et al.) 21–34 (Springer, 2022).

Narayan, S., Mok, H., Ho, K. & Kealy, D. I don't think they're as culturally sensitive: a mixed-method study exploring e-mental health use among culturally diverse populations. J. Ment. Health 32, 241–247 (2022).

Article PubMed Google Scholar

Wright, M. T., Springett, J. & Kongats, K. in Participatory Health Research (eds Wright, M. T. & Kongats, K.) 3–15 (Springer, 2018).

Wright, M. T. Partizipative Gesundheitsforschung: Ursprünge und heutiger Stand. Bundesgesundheitsblatt Gesundheitsforsch. Gesundheitsschutz 64, 140–145 (2021).

Article Google Scholar

Carman, K. L. et al. Patient and family engagement: a framework for understanding the elements and developing interventions and policies. Health Affairs 32, 223–231 (2013).

Article PubMed Google Scholar

Mummah, S. A., Robinson, T. N., King, A. C., Gardner, C. D. & Sutton, S. IDEAS (Integrate, Design, Assess, and Share): a framework and toolkit of strategies for the development of more effective digital interventions to change health behavior. J. Med. Internet Res. 18, e317 (2016).

Article PubMed PubMed Central Google Scholar

McCurdie, T. et al. mHealth consumer apps: the case for user-centered design. Biomed. Instrum. Technol. https://doi.org/10.2345/0899-8205-46.s2.49 (2012).

Knowles, S. E. et al. Qualitative meta-synthesis of user experience of computerised therapy for depression and anxiety. PLoS ONE 9, e84323 (2014).

Article PubMed PubMed Central Google Scholar

Werner-Seidler, A. et al. A smartphone app for adolescents with sleep disturbance: development of the Sleep Ninja. JMIR Ment. Health 4, e28 (2017).

Article PubMed PubMed Central Google Scholar

Braun, V. & Clarke, V. in APA Handbook of Research Methods in Psychology, Vol 2: Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological (eds Cooper, H. et al.) 57–71 (American Psychological Association, 2012).

Dopp, A. R., Parisi, K. E., Munson, S. A. & Lyon, A. R. A glossary of user-centered design strategies for implementation experts. Transl. Behav. Med. 9, 1057–1064 (2019).

Article PubMed Google Scholar

Orlowski, S. K. et al. Participatory research as one piece of the puzzle: a systematic review of consumer involvement in design of technology-based youth mental health and well-being interventions. JMIR Hum. Factors 2, e12 (2015).

Article PubMed PubMed Central Google Scholar

Orji, R. & Moffatt, K. Persuasive technology for health and wellness: state-of-the-art and emerging trends. Health Inform. J. 24, 66–91 (2018).

Article Google Scholar

Esfandiari, N. et al. A specific internet-based cognitive behavior therapy for adolescents with social anxiety disorder: three-armed randomized control trial. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-2123795/v1 (2022).

Yardley, L., Bradbury, K. & Morrison, L. in Qualitative Research in Psychology: Expanding Perspectives in Methodology and Design 2nd edn (ed. Camic, P. M.) 263–282 (American Psychological Association, 2021).

Bailey, E. et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med. Ethics 21, 37 (2020).

Article PubMed PubMed Central Google Scholar

Sander, L. et al. Suicide risk management in research on internet-based interventions for depression: a synthesis of the current state and recommendations for future research. J. Affect. Disord. 263, 676–683 (2020).

Article PubMed Google Scholar

Kaurin, A., Dombrovski, A. Y., Hallquist, M. N. & Wright, A. G. C. Integrating a functional view on suicide risk into idiographic statistical models. Behav. Res. Ther. 150, 104012 (2022).

Article PubMed Google Scholar

Friedlander, A., Nazem, S., Fiske, A., Nadorff, M. R. & Smith, M. D. Self-concealment and suicidal behaviors. Suicide Life Threat Behav. 42, 332–340 (2012).

Article PubMed PubMed Central Google Scholar

Franklin, J. C. et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143, 187–232 (2017).

Article PubMed Google Scholar

Steeg, S. et al. Accuracy of risk scales for predicting repeat self-harm and suicide: a multicentre, population-level cohort study using routine clinical data. BMC Psychiatry 18, 113 (2018).

Article PubMed PubMed Central Google Scholar

Sisti, D. A. & Joffe, S. Implications of zero suicide for suicide prevention research. JAMA 320, 1633–1634 (2018).

Article PubMed Google Scholar

Büscher, R. et al. Digital cognitive-behavioural therapy to reduce suicidal ideation and behaviours: a systematic review and meta-analysis of individual participant data. Evid. Based Ment. Health 25, e8–e17 (2022).

Article PubMed PubMed Central Google Scholar

Holmes, E. A. et al. The Lancet Psychiatry Commission on psychological treatments research in tomorrow’s science. Lancet Psychiatry 5, 237–286 (2018).

Article PubMed Google Scholar

Torok, M. et al. Suicide prevention using self-guided digital interventions: a systematic review and meta-analysis of randomised controlled trials. Lancet Digit. Health 2, e25–e36 (2020).

Article PubMed Google Scholar

Ferreira, T. E-health applications and data protection: a comparison of selected European Union members’ national legal systems. Bioethica 8, 74–84 (2022).

Article Google Scholar

Wilkowska, W. & Ziefle, M. Privacy and data security in E-health: requirements from the user’s perspective. Health Inform. J. 18, 191–201 (2012).

Article Google Scholar

Albrecht, U.-V. Chances and Risks of Mobile Health Apps (CHARISHMA) (Medizinische Hochschule Hannover, 2016).

Lablans, M., Borg, A. & Ückert, F. A RESTful interface to pseudonymization services in modern web applications. BMC Med. Inf. Decis. Making 15, 2 (2015).

Article Google Scholar

Cummins, N., Schuller, B. W. & Baird, A. Speech analysis for health: current state-of-the-art and the increasing impact of deep learning. Methods 151, 41–54 (2018).

Article PubMed Google Scholar

Ghosh, S., Löchner, J., Mitra, B. & De, P. in Quantifying Quality of Life (eds Wac, K. & Wulfovich, S.) 209–267 (Springer, 2022).

Garriga, R. et al. Machine learning model to predict mental health crises from electronic health records. Nat. Med. 28, 1240–1248 (2022).

Article PubMed PubMed Central Google Scholar

Valstar, M. et al. AVEC 2013: the continuous audio/visual emotion and depression recognition challenge. In Proc. 3rd ACM International Workshop on Audio/Visual Emotion Challenge 3–10 (ACM, 2013).

Wang, R. et al. Tracking depression dynamics in college students using mobile phone and wearable sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1–26 (2018).

Google Scholar

Dhall, A., Goecke, R., Gedeon, T. & Sebe, N. Emotion recognition in the wild. J. Multimodal User Interf. 10, 95–97 (2016).

Article Google Scholar

Amin, M. M., Cambria, E. & Schuller, B. W. Will affective computing emerge from foundation models and general AI? A first evaluation on ChatGPT. IEEE Intelligent Systems 2, 15–23 (2023).

Article Google Scholar

Schuller, B. W. et al. Computational charisma—a brick by brick blueprint for building charismatic artificial intelligence. Preprint at https://doi.org/10.48550/arXiv.2301.00142 (2023).

Véliz, C. Chatbots shouldn’t use emojis. Nature 615, 375 (2023).

Article PubMed Google Scholar

Vazire, S. Who knows what about a person? The self-other knowledge asymmetry (SOKA) model. J. Person. Soc. Psychol. 98, 281–300 (2010).

Article Google Scholar

Garatva, P. et al. in Digital Phenotyping and Mobile Sensing (eds Montag, C. & Baumeister, H.) 395–411 (Springer, 2023).

Torous, J., Kiang, M. V., Lorme, J. & Onnela, J.-P. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment. Health 3, e16 (2016).

Article PubMed PubMed Central Google Scholar

Cornet, V. P. & Holden, R. J. Systematic review of smartphone-based passive sensing for health and wellbeing. J. Biomed. Inform. 77, 120–132 (2018).

Article PubMed Google Scholar

Moshe, I. et al. Predicting symptoms of depression and anxiety using smartphone and wearable data. Front. Psychiatry 12, 625247 (2021).

Article PubMed PubMed Central Google Scholar

Vasudevan, S., Saha, A., Tarver, M. E. & Patel, B. Digital biomarkers: convergence of digital health technologies and biomarkers. npj Digit. Med. 5, 36 (2022).

Article PubMed PubMed Central Google Scholar

Asare, O. K. et al. Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: exploratory study. JMIR mHealth uHealth 9, e26540 (2021).

Article Google Scholar

Fried, E. I., Rieble, C. & Proppert, R. K. K. Building an early warning system for depression: rationale, objectives, and methods of the WARN-D study. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/9qcvs (2022).

Lattie, E. G. et al. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review. J. Med. Internet Res. 21, e12869 (2019).

Article PubMed PubMed Central Google Scholar

Torous, J. et al. Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps. Curr. Psychiatry Rep. 20, 51 (2018).

Article PubMed Google Scholar

Kargl, F., van der Heijden, R. W., Erb, B. & Bösch, C. in Digital Phenotyping and Mobile Sensing (eds Montag. C. & Baumeister, H.) 13–23 (Springer, 2023).

Larsen, M. E. et al. Using science to sell apps: evaluation of mental health app store quality claims. npj Digit. Med. 2, 18 (2019).

Article PubMed PubMed Central Google Scholar

Klein, R. A. et al. Investigating variation in replicability. Soc. Psychol. 45, 142–152 (2014).

Article Google Scholar

Simblett, S., Birch, J., Matcham, F., Yaguez, L. & Morris, R. A systematic review and meta-analysis of e-mental health interventions to treat symptoms of posttraumatic stress. JMIR Ment. Health 4, e14 (2017).

Article PubMed PubMed Central Google Scholar

Magnusson, K., Andersson, G. & Carlbring, P. The consequences of ignoring therapist effects in trials with longitudinal data: a simulation study. J. Consult. Clin. Psychol. 86, 711–725 (2018).

Article PubMed Google Scholar

Johns, R. G., Barkham, M., Kellett, S. & Saxon, D. A systematic review of therapist effects: a critical narrative update and refinement to review. Clin. Psychol. Rev. 67, 78–93 (2019).

Article PubMed Google Scholar

Rozental, A., Andersson, G. & Carlbring, P. In the absence of effects: an individual patient data meta-analysis of non-response and its predictors in internet-based cognitive behavior therapy. Front. Psychol. 10, 589 (2019).

Article PubMed PubMed Central Google Scholar

Mander, J. et al. The therapeutic alliance in different mental disorders: a comparison of patients with depression, somatoform, and eating disorders. Psychol. Psychother. 90, 649–667 (2017).

Article PubMed Google Scholar

Mechler, J. et al. Sudden gains and large intersession improvements in internet-based psychodynamic treatment (IPDT) for depressed adolescents. Psychother. Res. 31, 455–467 (2021).

Article PubMed Google Scholar

von Klipstein, L., Riese, H., van der Veen, D. C., Servaas, M. N. & Schoevers, R. A. Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway. BMC Med. 18, 345 (2020).

Article Google Scholar

van den Bergh, R. et al. The content of personalised network-based case formulations. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/yan4k (2022).

Olthof, M. et al. Destabilization in self-ratings of the psychotherapeutic process is associated with better treatment outcome in patients with mood disorders. Psychother. Res. 30, 520–531 (2020).

Article PubMed Google Scholar

Schiepek, G. et al. Real-time monitoring of psychotherapeutic processes: concept and compliance. Front. Psychol. 7, 604 (2016).

Article PubMed PubMed Central Google Scholar

Hasselman, F. & Bosman, A. M. T. Studying complex adaptive systems with internal states: a recurrence network approach to the analysis of multivariate time-series data representing self-reports of human experience. Front. Appl. Math. Stat. https://doi.org/10.3389/fams.2020.00009 (2020).

Wallot, S. Recurrence quantification analysis of processes and products of discourse: a tutorial in R. Discourse Process. 54, 382–405 (2017).

Article Google Scholar

Myin-Germeys, I. & Kuppens, P. The Open Handbook of Sampling Methodology. A Step-by-Step Guide to Designing, Conducting, and Analyzing ESM studies (The Center for Research on Experience Sampling and Ambulatory Methods Leuven, 2021).

Trull, T. J. & Ebner-Priemer, U. Ambulatory assessment. Annu. Rev. Clin. Psychol. 9, 151–176 (2013).

Article PubMed Google Scholar

Insel, T. R. Digital phenotyping: a global tool for psychiatry. World Psychiatry 17, 276–277 (2018).

Article PubMed PubMed Central Google Scholar

Ebner-Priemer, U. W. et al. Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders. Int. J. Bipolar Disord. 8, 35 (2020).

Article PubMed PubMed Central Google Scholar

Myin-Germeys, I. et al. Experience sampling methodology in mental health research: new insights and technical developments. World Psychiatry 17, 123–132 (2018).

Article PubMed PubMed Central Google Scholar

Fortea, L. et al. Development and validation of a smartphone-based app for the longitudinal assessment of anxiety in daily life. Assessment https://doi.org/10.1177/10731911211065166 (2021).

Wrzus, C. & Neubauer, A. B. Ecological momentary assessment: a meta-analysis on designs, samples, and compliance across research fields. Assessment https://doi.org/10.1177/10731911211067538 (2022).

Geldhof, G. J., Preacher, K. J. & Zyphur, M. J. Reliability estimation in a multilevel confirmatory factor analysis framework. Psychol. Methods 19, 72–91 (2014).

Article PubMed Google Scholar

Kockler, T. D., Santangelo, P. S. & Ebner-Priemer, U. W. Investigating binge eating using ecological momentary assessment: the importance of an appropriate sampling frequency. Nutrients 10, 105 (2018).

Article PubMed PubMed Central Google Scholar

Ottenstein, C. & Werner, L. Compliance in ambulatory assessment studies: investigating study and sample characteristics as predictors. Assessment https://doi.org/10.1177/10731911211032718 (2021).

Lecomte, T. et al. Mobile apps for mental health issues: meta-review of meta-analyses. JMIR mHealth uHealth 8, e17458 (2020).

Article PubMed PubMed Central Google Scholar

Linardon, J., Cuijpers, P., Carlbring, P., Messer, M. & Fuller-Tyszkiewicz, M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 18, 325–336 (2019).

Article PubMed PubMed Central Google Scholar

Fleming, T. et al. Beyond the trial: systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety. J. Med. Internet Res. 20, e199 (2018).

Article PubMed PubMed Central Google Scholar

Torous, J. et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 20, 318–335 (2021).

Article PubMed PubMed Central Google Scholar

Chien, I. et al. A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions. JAMA Netw. Open 3, e2010791 (2020).

Article PubMed PubMed Central Google Scholar

Torous, J., Lipschitz, J., Ng, M. & Firth, J. Dropout rates in clinical trials of smartphone apps for depressive symptoms: a systematic review and meta-analysis. J. Affect. Disord. 263, 413–419 (2020).

Article PubMed Google Scholar

Richards, D. & Richardson, T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin. Psychol. Rev. 32, 329–342 (2012).

Article PubMed Google Scholar

Alon, N., Stern, A. D. & Torous, J. Assessing the Food and Drug Administration’s risk-based framework for software precertification with top health apps in the United States: quality improvement study. JMIR mHealth uHealth 8, e20482 (2020).

Article PubMed PubMed Central Google Scholar

Stern, A. D. et al. Advancing digital health applications: priorities for innovation in real-world evidence generation. Lancet Digit. Health 4, e200–e206 (2022).

Article PubMed Google Scholar

Lagan, S. et al. Mental health app evaluation: updating the American Psychiatric Association’s framework through a stakeholder-engaged workshop. Psychiatr. Serv. 72, 1095–1098 (2021).

Article PubMed Google Scholar

Stoyanov, S. R. et al. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth uHealth 3, e27 (2015).

Article PubMed PubMed Central Google Scholar

Ramos, G., Ponting, C., Labao, J. P. & Sobowale, K. Considerations of diversity, equity, and inclusion in mental health apps: a scoping review of evaluation frameworks. Behav. Res. Ther. 147, 103990 (2021).

Article PubMed Google Scholar

Carlo, A. D., Hosseini Ghomi, R., Renn, B. N. & Areán, P. A. By the numbers: ratings and utilization of behavioral health mobile applications. npj Digit. Med. 2, 54 (2019).

Article PubMed PubMed Central Google Scholar

Lagan, S. et al. Actionable health app evaluation: translating expert frameworks into objective metrics. npj Digit. Med. 3, 100 (2020).

Article PubMed PubMed Central Google Scholar

Szinay, D. et al. Influences on the uptake of health and well-being apps and curated app portals: think-aloud and interview study. JMIR mHealth uHealth 9, e27173 (2021).

Article PubMed PubMed Central Google Scholar

Roberts, A. E. et al. Evaluating the quality and safety of health-related apps and e-tools: adapting the Mobile App Rating Scale and developing a quality assurance protocol. Internet Interv. 24, 100379 (2021).

Article PubMed PubMed Central Google Scholar

Huber, M. et al. How should we define health? BMJ 343, d4163 (2011).

Article PubMed Google Scholar

Wasil, A. R., Gillespie, S., Shingleton, R., Wilks, C. R. & Weisz, J. R. Examining the reach of smartphone apps for depression and anxiety. Am. J. Psychiatry 177, 464–465 (2020).

Article PubMed Google Scholar

Gentili, A. et al. The cost-effectiveness of digital health interventions: a systematic review of the literature. Front. Public Health 10, 787135 (2022).

Article PubMed PubMed Central Google Scholar

Mitchell, L. M., Joshi, U., Patel, V., Lu, C. & Naslund, J. A. Economic evaluations of internet-based psychological interventions for anxiety disorders and depression: a systematic review. J. Affect. Disord. 284, 157–182 (2021).

Article PubMed PubMed Central Google Scholar

Wac, K. in Digital Health (eds Rivas, H. & Wac, K.) 83–108 (Springer, 2018).

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These authors contributed equally: Caroline Seiferth, Lea Vogel.

A full list of members and their affiliations appears in the Supplementary Information.

Institute of Psychology, University of Bamberg, Bamberg, Germany

Caroline Seiferth

National Centre for Early Prevention, German Youth Institute, Munich, Germany

Lea Vogel & Ansgar Opitz

Department of Psychology, LMU Munich, Munich, Germany

Lea Vogel & Benjamin Aas

sysTelios Think Tank, sysTelios Health Center, Siedelsbrunn, Germany

Benjamin Aas

Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Psychiatry and Psychotherapy, Tuebingen, Germany

Isabel Brandhorst, Annette Conzelmann, Marlene Finkbeiner, Karsten Hollmann, Heinrich Lautenbacher, Tobias J. Renner & Johanna Löchner

DZPG (German Center of Mental Health), Tuebingen, Germany

Isabel Brandhorst, Annette Conzelmann, Marlene Finkbeiner, Karsten Hollmann, Heinrich Lautenbacher, Tobias J. Renner & Johanna Löchner

Department of Psychology, Stockholm University, Stockholm, Sweden

Per Carlbring

PFH – Private University of Applied Sciences, Department of Psychology (Clinical Psychology II), Goettingen, Germany

Annette Conzelmann

Department of Psychology, Faculty of Education and Psychology, Shahid Beheshti University, Tehran, Iran

Narges Esfandiari

Medical Faculty, University of Tuebingen, Tuebingen, Germany

Heinrich Lautenbacher

Department of Education and Health Research, Institute of Sport Science, University of Tuebingen, Tuebingen, Germany

Edith Meinzinger & Sebastian Wolf

Sir Henry Wellcome Building for Mood Disorders Research, College of Life and Environmental Sciences, University of Exeter, Exeter, UK

Alexandra Newbold

Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

Lasse Bosse Sander

Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg

Philip S. Santangelo

LMU Munich, Munich, Germany

Ramona Schoedel

GLAM, Imperial College, London, UK

Björn Schuller

Institute of Behavioral Science and Technology, University of St. Gallen, St. Gallen, Switzerland

Clemens Stachl

Department of Clinical Psychology and Psychotherapy, University of Ulm, Ulm, Germany

Yannik Terhorst

Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA

John Torous

Quality of Life Lab, Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland

Katarzyna Wac

Black Dog Institute, UNSW, Sydney, New South Wales, Australia

Aliza Werner-Seidler

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J.L., C. Seiferth and L.V. conceptualized the study and supervised the writing process. J.L., L.V. and C. Seiferth wrote the first draft. The sections were written in the following writing groups: ‘Where to start’: K.H., I.B., J.L. and C. Seiferth; ‘Intervention content development’: C. Seiferth and J.L.; ‘UCD and participatory approaches’: L.V., L.B.S., A.W.-S. and J.L.; ‘Managing suicidality’: L.B.S. and K.H.; ‘Data protection and data security’: H.L., I.B. and A.C.; ‘AI in assessment and intervention’: B.S. and J.L.; ‘Sensing and wearables’: K.W., Y.T., R.S. and C. Stachl; ‘Efficacy evaluation, RCTs and other methods’: A.O., B.A., S.T.T. and A.C.; ‘EMA’: P.S.S. reand M.F.; ‘Transfer into (clinical) practice’: S.W., B.A., E.M. and SysTelios Think Tank; ‘AEFs’: J.T. All authors commented on the first and final draft. P.C., T.J.R., A.N. and N.E. reviewed the final version particularly. All authors share responsibility for the final version of the paper.

Correspondence to Johanna Löchner.

P.C. has received speaker fees from Angelini Pharma, Lundbeck and Koa Health within the past 3 years. J.T. is a scientific advisor for precision mental wellness. L.B.S. reported receiving personal fees from Psychotherapy Training Institutes, Health Insurances and Clinic Providers in the context of e-mental health but outside the submitted work. The other authors declare no competing interests.

Nature Mental Health thanks Ana Catarino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Seiferth, C., Vogel, L., Aas, B. et al. How to e-mental health: a guideline for researchers and practitioners using digital technology in the context of mental health. Nat. Mental Health 1, 542–554 (2023). https://doi.org/10.1038/s44220-023-00085-1

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Received: 15 January 2023

Accepted: 31 May 2023

Published: 07 August 2023

Issue Date: August 2023

DOI: https://doi.org/10.1038/s44220-023-00085-1

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