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Identifying Distinct Quitting Trajectories after an Unassisted Smoking Cessation Attempt: An ecological momentary assessment Study

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Open Journal of Medical Psychology 2012年第3期1卷 44-50页

作者： Monica S. Bachmann Hansjorg Znoj Jeannette Brodbeck Department of Clinical Psychology and Psychotherapy Institute of Psychology University of Berne Berne Switzerland

Objectives: This study aimed at identifying distinct quitting trajectories over 29 days after an unassisted smoking ces- sation attempt by ecological momentary assessment (EMA). In order to validate these trajectories we tested if they predict smoking frequency up to six months later. Methods: EMA via mobile phones was used to collect real time data on smoking (yes/no) after an unassisted quit attempt over 29 days. Smoking frequency one, three and six months after the quit attempt was assessed with online questionnaires. Latent class growth modeling was used to analyze the data of 230 self-quitters. Results: Four different quitting trajectories emerged: quitter (43.9%), late quitter (11.3%), returner (17%) and persistent smoker (27.8%). The quitting trajectories predicted smoking frequency one, three and six months after the quit attempt (all p < 0.001). Conclusions: Outcome after a smoking cessation attempt is better described by four distinct trajectories instead of a binary variable for abstinence or relapse. In line with the relapse model by Marlatt and Gordon, late quitter may have learned how to cope with lapses during one month after the quitting attempt. This group would have been allocated to the relapse group in traditional outcome studies.

关键词： Smoking Cessation Lapse Relapse Process ecological momentary assessment Self-Quitter

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Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

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World Journal of Psychiatry 2022年第3期12卷 393-409页

作者： Jayesh Kamath Roberto Leon Barriera Neha Jain Efraim Keisari Bing Wang Department of Psychiatry and Immunology University of Connecticut School of MedicineUniversity of Connecticut Health CenterFarmingtonCT 06030United States Department of Psychiatry University of Connecticut School of MedicineUniversity of Connecticut Health CenterFarmingtonCT 06032United States Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269United States

Depression is a serious medical condition and is a leading cause of disability *** depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of objective assessments,and assessments that rely on patients'perceptions,memory,and *** phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable *** includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural *** includes active EMA,data that require active input by the subject,and passive EMA or passive sensing,data passively and automatically collected from subjects'personal digital *** raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical *** investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression *** other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed *** of DP endeavors depends on critical contributions from both psychiatric and engineering *** current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations.A clinically-relevant model for incorporating DP in clinical setting is *** model,based on investigations conducted by our group,delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making ***,challenges,and opportunities pertaining to clinical integration of DP of depressio

关键词： Digital phenotyping Depression ecological momentary assessment Telepsychiatry Passive sensing Smart phone

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