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Continuous prediction of user dropout in a mobile mental health intervention program: An exploratory machine learning approach
4 references·0 citing·23 second hop
Continuous prediction of user dropout in a mobile mental health intervention program: An exploratory machine learning approach (2025)Continuous prediction of user dropout in …Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions (2023)Finding the Best Match — a Ca…An emotion and cognitive based analysis of mental health disorders from social media data (2021)An emotion and cognitive base…Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach (2020)Developing a Process for the …Internet-Based Mental Health Interventions (2005)Internet-Based Mental Health …Overcoming Depression on the Internet (ODIN): A Randomized Controlled Trial of an Internet Depression Skills Intervention Program (2002)Web-based Cognitive Behavior Therapy: Analysis of Site Usage and Changes in Depression and Anxiety Scores (2002)Effectiveness of Internet-Based Interventions for the Prevention of Mental Disorders: A Systematic Review and Meta-Analysis (2016)Mental health monitoring with multimodal sensing and machine learning: A survey (2018)Priorities for addressing the impact of the COVID-19 pandemic on college student mental health (2020)A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies (2011)The Consensus Sleep Diary: Standardizing Prospective Sleep Self-Monitoring (2012)Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis (2017)Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials (2022)An interpretable machine learning approach to multimodal stress detection in a simulated office environment (2023)Web-based interpretation bias training to reduce anxiety: A sequential, multiple-assignment randomized trial. (2024)HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case (2019)A nationwide survey of psychological distress among Chinese people in the COVID‐19 epidemic: implications and policy recommendations (2020)Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed (2020)Deep learning techniques for suicide and depression detection from online social media: A scoping review (2022)Emotion fusion for mental illness detection from social media: A survey (2022)Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media (2023)A Global Measure of Perceived Stress (1983)Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. (1991)Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. (1991)Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions (2024)Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study (2024)Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT (2024)
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References

  • 2005Internet-Based Mental Health Interventions241 cited
  • 2021An emotion and cognitive based analysis of mental health disorders from social media data140 cited
  • 2020Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach29 cited
  • 2023Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions18 cited

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