AIM To analyze the viability of Ecological Momentary Assessment(EMA) for measuring the mental states associated with psychopathological problems in adolescents.METHODS In a sample of 110 adolescents,a sociodemographic...AIM To analyze the viability of Ecological Momentary Assessment(EMA) for measuring the mental states associated with psychopathological problems in adolescents.METHODS In a sample of 110 adolescents,a sociodemographic data survey and an EMA Smartphone application over a oneweek period(five times each day),was developed to explore symptom profiles,everyday problems,coping strategies,and the contexts in which the events take place.RESULTS The positive response was 68.6%.Over 2250 prompts about mental states were recorded.In 53% of situations the smartphone was answered at home,25.5% of casesthey were with their parents or with peers(20.3%).Associations were found with attention,affective and anxiety problems(P < 0.001) in the participants who took longer to respond to the EMA app.Anxious and depressive states were highly interrelated(rho = 0.51,P < 0.001),as well as oppositional defiant problems and conduct problems(rho = 0.56,P < 0.001).Only in 6.2% of the situations the subjects perceived they had problems,mainly associated with inter-relational aspects with family,peers,boyfriends or girlfriends(31.2%).We also found moderate-high reliability on scales of satisfaction level on the context,on positive emotionality,and on the discomfort index associated with mental health problems.CONCLUSION EMA methodology using smartphones is a useful tool for understanding adolescents' daily dynamics.It achieved moderate-high reliability and accurately identified psychopathological manifestations experienced by community adolescents in their natural context.展开更多
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...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.展开更多
目的分析基于生态瞬时评估法的症状管理研究现状。方法以Web of Seience数据库为基础，对基于生态瞬时评估法的症状管理研究进行文献检索，并在此基础上利用Refviz软件对检索的文献进行分析。结果共检索到484篇相关英文文献。通过文献...目的分析基于生态瞬时评估法的症状管理研究现状。方法以Web of Seience数据库为基础，对基于生态瞬时评估法的症状管理研究进行文献检索，并在此基础上利用Refviz软件对检索的文献进行分析。结果共检索到484篇相关英文文献。通过文献分析，发现近年来生态瞬时评估法在症状管理方面的研究呈现发展趋势，其中美国的文献量最多，其次是欧洲等发达国家。结论生态瞬时评估法已逐渐从最初戒烟、戒酒等研究引入临床心理、医学和护理等领域，并逐渐应用于症状管理研究。目前，随着移动设备智能化，基于生态瞬时评估法的症状管理方便易行，且获取数据更精确，利于指导临床实施精准的对症护理，值得进一步的临床应用和推广。展开更多
Depression is a serious medical condition and is a leading cause of disability worldwide.Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of...Depression is a serious medical condition and is a leading cause of disability worldwide.Current 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 recall.Digital phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes.DP includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural environment.This 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 devices.The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical status.Preliminary 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 status.These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients.Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines.The 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 presented.This 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 process.Benefits,challenges,and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.展开更多
基金Supported by Spain’s Ministry of Economy and Competitiveness,No.PSI 2013-46392-Pthe Agency for the Management of University and Research Grants from the Government of Catalonia,No.2014SGR1139
文摘AIM To analyze the viability of Ecological Momentary Assessment(EMA) for measuring the mental states associated with psychopathological problems in adolescents.METHODS In a sample of 110 adolescents,a sociodemographic data survey and an EMA Smartphone application over a oneweek period(five times each day),was developed to explore symptom profiles,everyday problems,coping strategies,and the contexts in which the events take place.RESULTS The positive response was 68.6%.Over 2250 prompts about mental states were recorded.In 53% of situations the smartphone was answered at home,25.5% of casesthey were with their parents or with peers(20.3%).Associations were found with attention,affective and anxiety problems(P < 0.001) in the participants who took longer to respond to the EMA app.Anxious and depressive states were highly interrelated(rho = 0.51,P < 0.001),as well as oppositional defiant problems and conduct problems(rho = 0.56,P < 0.001).Only in 6.2% of the situations the subjects perceived they had problems,mainly associated with inter-relational aspects with family,peers,boyfriends or girlfriends(31.2%).We also found moderate-high reliability on scales of satisfaction level on the context,on positive emotionality,and on the discomfort index associated with mental health problems.CONCLUSION EMA methodology using smartphones is a useful tool for understanding adolescents' daily dynamics.It achieved moderate-high reliability and accurately identified psychopathological manifestations experienced by community adolescents in their natural context.
基金thank the Swiss National Science Founda-tion for funding this study(grant number SNF 100014_126648/1).
文摘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.
文摘目的分析基于生态瞬时评估法的症状管理研究现状。方法以Web of Seience数据库为基础，对基于生态瞬时评估法的症状管理研究进行文献检索，并在此基础上利用Refviz软件对检索的文献进行分析。结果共检索到484篇相关英文文献。通过文献分析，发现近年来生态瞬时评估法在症状管理方面的研究呈现发展趋势，其中美国的文献量最多，其次是欧洲等发达国家。结论生态瞬时评估法已逐渐从最初戒烟、戒酒等研究引入临床心理、医学和护理等领域，并逐渐应用于症状管理研究。目前，随着移动设备智能化，基于生态瞬时评估法的症状管理方便易行，且获取数据更精确，利于指导临床实施精准的对症护理，值得进一步的临床应用和推广。
文摘Depression is a serious medical condition and is a leading cause of disability worldwide.Current 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 recall.Digital phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes.DP includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural environment.This 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 devices.The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical status.Preliminary 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 status.These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients.Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines.The 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 presented.This 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 process.Benefits,challenges,and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.
