Researcher in digital health, addiction, and youth forensics.

Digital Health & Intervention Science

• Eysenbach, G. (2001). What is e-health?
• Eysenbach, G. (2005). The Law of Attrition.
• Mohr, D. C., et al. (2014). The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions.
• Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 1–17.
• Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33(5), 462–463.
• Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42.
• Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., … & Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46(1), 81–95.
• Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5), S112–S118.
• Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2015). Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychology, 34(S), 1220–1228.
• Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., … & Blandford, A. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51(5), 833–842.
• Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019). Objective user engagement with mental health apps: systematic search and panel-based usage analysis. Journal of Medical Internet Research, 21(9), e14567.
• Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., … & Shaw, S. (2017). Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e367.
• MARS (Mobile App Rating Scale): A tool for assessing the quality of mobile health apps. [Link]

Addiction Science & Clinical Psychopathology

• Koob, G. F. & Volkow, N. D. (2010). Neurocircuitry of Addiction.
• Marlatt, G. A. & Witkiewitz, K. (2002). Harm reduction approaches to alcohol use: Health promotion, prevention, and treatment.
• Logan, D. E., & Marlatt, G. A. (2010). Harm Reduction Therapy: A Practice-Friendly Review of Research.
• Caspi, A., et al. (2014). The p factor: one general psychopathology factor in the structure of psychiatric disorders?
• Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … & Wang, P. (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167(7), 748–751.
• Bickel, W. K., Johnson, M. W., Koffarnus, M. N., MacKillop, J., & Murphy, J. G. (2014). The behavioral economics of substance use disorders: reinforcement pathologies and their repair. Annual Review of Clinical Psychology, 10(1), 641–677.
• McLellan, A. T., Lewis, D. C., O'Brien, C. P., & Kleber, H. D. (2000). Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. JAMA, 284(13), 1689–1695.
• Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363–371.
• Hall, W., Carter, A., & Forlini, C. (2015). The brain disease model of addiction: is it supported by the evidence and has it delivered on its promises? The Lancet Psychiatry, 2(1), 105–110.
• Kwako, L. E., Momenan, R., Litten, R. Z., Koob, G. F., & Goldman, D. (2016). Addictions Neuroclinical Assessment: a neuroscience-based framework for addictive disorders. Biological Psychiatry, 80(3), 179–189.
• Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 1–10.
• Witkiewitz, K., Litten, R. Z., & Leggio, L. (2019). Advances in the science and treatment of alcohol use disorder. Science Advances, 5(9), eaax4043.

Youth Forensic & Adolescent Mental Health

• Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Psychological Review, 100(4), 674–701.
• Teplin, L. A., Abram, K. M., McClelland, G. M., Dulcan, M. K., & Mericle, A. A. (2002). Psychiatric disorders in youth in juvenile detention. Archives of General Psychiatry, 59(12), 1133–1143.
• Fazel, S., Doll, H., & Långström, N. (2008). Mental disorders among adolescents in juvenile detention and correctional facilities: a systematic review and metaregression analysis of 25 surveys. Journal of the American Academy of Child & Adolescent Psychiatry, 47(9), 1010–1019.
• Fazel, S., Yoon, I. A., & Hayes, A. J. (2017). Substance use disorders in prisoners: an updated systematic review and meta-regression analysis in recently incarcerated men and women. Addiction, 112(10), 1725–1739.
• Andrews, D. A., & Bonta, J. The Psychology of Criminal Conduct. The foundational statement of the Risk–Need–Responsivity (RNR) model that underpins most offender assessment and rehabilitation practice.
• SAVRY (Structured Assessment of Violence Risk in Youth): A structured professional judgement tool for assessing violence risk in adolescents. [Link]

Meta-Analysis Resources

Guides & Textbooks

• Doing Meta-Analysis in R: A Hands-on Guide. A comprehensive, accessible introduction to conducting meta-analyses using R. [Link]
• Meta-Analyses in Mental Health Research: A Practical Guide. Prof. Dr. Pim Cuijpers' free manual on conducting rigorous meta-analyses specifically in mental health contexts. [PDF Link]
• Cochrane Handbook for Systematic Reviews of Interventions: The reference standard for review methodology, free online and continuously updated. [Link]
• Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021). Introduction to Meta-Analysis (2nd ed.). Wiley.

Databases & Tools

• Metapsy: An open-access platform of living meta-analytic databases for psychological treatments. I am working on an addiction-focused counterpart, Metapsy-SUD. [Link]
• Open Science Framework (OSF): For preregistration of studies. [Link]
• PROSPERO: International register for systematic review protocols. [Link]
• The Cochrane Risk-of-Bias 2 (RoB 2) Tool. [Link]
• The GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Framework. [Link]
• AMSTAR 2: For appraising the methodological quality of systematic reviews. [Link] · [Paper]
• ROBINS-I: Cochrane's risk-of-bias tool for non-randomised studies of interventions. [Link]
• COMET Initiative: A searchable database of core outcome sets across health conditions (ORBITAL, below, is one such set). [Link]
• ASReview: Open-source active-learning software that substantially speeds up title and abstract screening. [Link] · [Paper]
• metafor & dmetar: Widely used R packages for meta-analysis; dmetar is the companion package to the hands-on guide above. [metafor] · [dmetar]
• robvis: For producing publication-ready risk-of-bias summary and traffic-light plots. [Link]

Important Papers

• Salanti, G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods, 3(2), 80–97.
• Assink, M., & Wibbelink, C. J. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154–174.
• Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011). Interpretation of random effects meta-analyses. BMJ, 342, d549.
• Borenstein, M., Higgins, J. P. T., Hedges, L. V., & Rothstein, H. R. (2017). Basics of meta-analysis: I² is not an absolute measure of heterogeneity. Research Synthesis Methods, 8(1), 5–18.
• IntHout, J., Ioannidis, J. P. A., Rovers, M. M., & Goeman, J. J. (2016). Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open, 6(7), e010247.
• Carter, E. C., Schönbrodt, F. D., Gervais, W. M., & Hilgard, J. (2019). Correcting for bias in psychology: a comparison of meta-analytic methods. Advances in Methods and Practices in Psychological Science, 2(2), 115–144.

Methodology & Reporting Standards

Finding the Right Guidance

• LIGHTS (Library of Guidance for Health Scientists): A searchable, continuously updated database of methods guidance for health research, from planning and design to analysis, reporting, and quality assessment. Entries are indexed with THEREMY, its taxonomy of study types and methodological concepts. Maintained by the Department of Clinical Research, University of Basel. [Link] · [Paper]
• EQUATOR Network: The master index of reporting guidelines in health research, cataloguing CONSORT, PRISMA, STROBE, TIDieR and several hundred others alongside guidance on choosing between them. [Link]

Clinical Trials & Interventions

• Chan, A. W., Boutron, I., Hopewell, S., Moher, D., Schulz, K. F., Collins, G. S., … & Hróbjartsson, A. (2025). SPIRIT 2025 statement: updated guideline for protocols of randomised trials. Nature Medicine, 31(6), 1784–1792. [Link]
• CONSORT 2025 (Consolidated Standards of Reporting Trials): The reporting guideline for randomised controlled trials. The 2025 update adds an open science section; CONSORT-EHEALTH (2011) covers web-based and mobile interventions. [CONSORT Home] · [CONSORT 2025] · [eHealth Extension]
• TIDieR (Template for Intervention Description and Replication): A comprehensive checklist for better reporting of interventions. [Link]
• Shorter, G. W., et al. (2021). The Outcome Reporting in Brief Intervention Trials: Alcohol (ORBITAL) Core Outcome Set: International Consensus on Outcomes to Measure in Efficacy and Effectiveness Trials of Alcohol Brief Interventions.
• Moons, K. G., Altman, D. G., Reitsma, J. B., Ioannidis, J. P., Macaskill, P., Steyerberg, E. W., … & Collins, G. S. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Annals of Internal Medicine, 162(1), W1–W73.
• CONSORT-SPI 2018: The CONSORT extension for randomised trials of social and psychological interventions. Montgomery, P., Grant, S., Mayo-Wilson, E., Macdonald, G., Michie, S., Hopewell, S., & Moher, D. (2018). Trials, 19, 407. [Link]
• mERA (mHealth Evidence Reporting and Assessment): The WHO-developed checklist for reporting health interventions delivered via mobile phones. Agarwal, S., LeFevre, A. E., Lee, J., L'Engle, K., Mehl, G., Sinha, C., & Labrique, A. (2016). BMJ, 352, i1174. [Link]
• StaRI (Standards for Reporting Implementation Studies): For reporting the implementation strategy alongside the intervention being implemented. Pinnock, H., et al. (2017). BMJ, 356, i6795. [Link]
• STROBE (Strengthening the Reporting of Observational Studies in Epidemiology): The counterpart to CONSORT for cohort, case-control and cross-sectional studies. [Link]
• COREQ (Consolidated Criteria for Reporting Qualitative Research). Tong, A., Sainsbury, P., & Craig, J. (2007). International Journal for Quality in Health Care, 19(6), 349–357.

Evidence Synthesis

• PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses): The gold standard for transparent reporting of systematic reviews and meta-analyses. [Link]
• PRISMA-IPD: Guidelines for transparent reporting of individual participant data meta-analyses. [Link]
• PRISMA-NMA: The PRISMA extension for systematic reviews incorporating network meta-analyses. Hutton, B., et al. (2015). Annals of Internal Medicine, 162(11), 777–784. [Link]
• SWiM (Synthesis Without Meta-analysis): Reporting guidance for reviews where pooling is not appropriate. Campbell, M., et al. (2020). BMJ, 368, l6890. [Link]
• GRADE-CERQual: For assessing confidence in findings from qualitative evidence synthesis; the qualitative counterpart to GRADE. [Link]

Open Science

• The Turing Way Community Handbook: A community handbook on reproducible, ethical, and collaborative data science. [Link]
• Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., … & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1, 0021.

Professional Organisations & Communities

• European Association for Forensic Child & Adolescent Psychiatry, Psychology & other involved Professions (EFCAP-EU) [Link]
• European Association for Forensic Child & Adolescent Psychiatry, Psychology & other involved Professions (EFCAP-CH) [Link]
• European Association of Substance Abuse Research (EASAR) [Link]
• European Society for Research on Internet Interventions (ESRII) [Link]
• International Network on Brief Interventions for Alcohol & Other Drugs (INEBRIA) [Link]
• International Society for Research on Internet Interventions (ISRII) [Link]
• Society of Digital Psychiatry (SODP) [Link]