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Momentary Assessment (momentary + assessment)

Distribution by Scientific Domains
Medical Sciences55%
Psychology44%

Kinds of Momentary Assessment

  • ecological momentary assessment
    		•

    Selected Abstracts

    An in vivo Study of the Relationship between Craving and Reaction Time during Alcohol Detoxification Using the Ecological Momentary Assessment

    ALCOHOLISM, Issue 12 2005
    M Lukasiewicz
    Abstract: Background: To study cognitive interference associated with craving for alcohol, the Ecological Momentary Assessment (EMA) method was used to measure the relationship between craving and reaction time. A secondary aim was the study of the predictive factors for craving during alcohol detoxification. The EMA enables both repeated measures of craving in a natural setting and the recording of reaction time without the patient being aware of this. Methods: Craving for alcohol, reaction time, sadness and anxiety were recorded 8 to 12 times a day, over three weeks of detoxification in 14 alcoholics (n= 1767 measures), on an electronic diary issuing random prompts. Mixed models were used for statistical analysis (,= 5%, 1-,= 88%). Results: Reaction time was significantly increased in univariate analysis when a craving episode occurred but this difference did not persist after multivariate analysis. Craving episodes were more frequent and intense than previously reported. Predictive factors of craving during detoxification were: age, gender, sadness, anxiety and the number of previous detoxifications. Antidepressants, anticraving medications but not benzodiazepines were negatively associated to craving. [source]

    Automation in an addiction treatment research clinic: Computerised contingency management, ecological momentary assessment and a protocol workflow system

    DRUG AND ALCOHOL REVIEW, Issue 1 2009
    MASSOUD VAHABZADEH
    Abstract Introduction and Aims. A challenge in treatment research is the necessity of adhering to protocol and regulatory strictures while maintaining flexibility to meet patients' treatment needs and to accommodate variations among protocols. Another challenge is the acquisition of large amounts of data in an occasionally hectic environment, along with the provision of seamless methods for exporting, mining and querying the data. Design and Methods. We have automated several major functions of our outpatient treatment research clinic for studies in drug abuse and dependence. Here we describe three such specialised applications: the Automated Contingency Management (ACM) system for the delivery of behavioural interventions, the transactional electronic diary (TED) system for the management of behavioural assessments and the Protocol Workflow System (PWS) for computerised workflow automation and guidance of each participant's daily clinic activities. These modules are integrated into our larger information system to enable data sharing in real time among authorised staff. Results. ACM and the TED have each permitted us to conduct research that was not previously possible. In addition, the time to data analysis at the end of each study is substantially shorter. With the implementation of the PWS, we have been able to manage a research clinic with an 80 patient capacity, having an annual average of 18 000 patient visits and 7300 urine collections with a research staff of five. Finally, automated data management has considerably enhanced our ability to monitor and summarise participant safety data for research oversight. Discussion and Conclusions. When developed in consultation with end users, automation in treatment research clinics can enable more efficient operations, better communication among staff and expansions in research methods. [Vahabzadeh M, Lin J-L, Mezghanni M, Epstein DH, Preston KL. Automation in an addiction treatment research clinic: Computerised contingency management, ecological momentary assessment and a protocol workflow system. Drug Alcohol Rev 2009;28:3,11] [source]

    Modeling mood variation associated with smoking: an application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data

    ADDICTION, Issue 2 2009
    Donald Hedeker
    ABSTRACT Aims Mixed models are used increasingly for analysis of ecological momentary assessment (EMA) data. The variance parameters of the random effects, which indicate the degree of heterogeneity in the population of subjects, are considered usually to be homogeneous across subjects. Modeling these variances can shed light on interesting hypotheses in substance abuse research. Design We describe how these variances can be modeled in terms of covariates to examine the covariate effects on between-subjects variation, focusing on positive and negative mood and the degree to which these moods change as a function of smoking. Setting The data are drawn from an EMA study of adolescent smoking. Participants Participants were 234 adolescents, either in 9th or 10th grades, who provided EMA mood reports from both random prompts and following smoking events. Measurements We focused on two mood outcomes: measures of the subject's negative and positive affect and several covariates: gender, grade, negative mood regulation and smoking level. Findings and conclusions Following smoking, adolescents experienced higher positive affect and lower negative affect than they did at random, non-smoking times. Our analyses also indicated an increased consistency of subjective mood responses as smoking experience increased and a diminishing of mood change. [source]

    Does ecological momentary assessment improve cognitive behavioural therapy for binge eating disorder?

    EUROPEAN EATING DISORDERS REVIEW, Issue 5 2002
    A pilot study
    Abstract The purpose of this pilot study was to test whether self-monitoring in CBT could be enhanced in order to improve the identification of proximal antecedents of binge eating in binge eating disorder (BED). CBT was modified by asking participants to monitor all eating intensively through ecological momentary assessment (EMA). A total of 41 females (mean BMI,=,37.9; SD,=,8.2) meeting DSM-IV criteria for BED were randomly assigned to one of two group treatments; CBT (n,=,22) or CBT with EMA (n,=,19). CBT with EMA differed from CBT in that for the first 2 weeks of treatment, participants completed detailed pocket diaries about mood, events, etc., when signalled at random by programmable wristwatches, as well as at all times when eating. All participants completed measures of eating (EDE-Q, TFEQ, EES) and general psychopathology (BDI, RSE) before treatment, at the end of treatment, and at 1-year follow-up. While both treatment groups showed improvement on the outcome variables of interest, the individual data gained via EMA did not significantly enhance standard CBT. Therefore, it is unlikely that further research incorporating EMA as a therapeutic technique within CBT for BED will be compelling. Copyright © 2002 John Wiley & Sons, Ltd and Eating Disorders Association. [source]

    Real-Time Data Collection for Pain: Appraisal and Current Status

    PAIN MEDICINE, Issue 2007
    Arthur A. Stone PhD
    ABSTRACT Objective., Real-time data capture (RTDC) techniques have rapidly developed with the advent of computer and information technology. We plan to discuss the use of RTDC in the assessment of pain, including issues pertaining to its rationale, sampling protocols, and our opinion on the current status of the methodology. Design., This is "thought" piece involving no systematic data collection methods. Results., We described the rationale for using RTDC, including issues in recall bias, the desire for detailed information about pain, and the ability to examine within,person associations between pain and other variables. The mechanics of RTDC implementations were discussed with a focus on sampling protocols and data collection methods. The final section concerned the status of RTDC. Current acceptance of RTDC is evaluated and three issues in the science of RTDC were discussed: the interpretation of differences between recall and the average of momentary assessments for the same period; if RTDC is advancing our understanding of pain; and, the issue of what consumers of pain assessments actually desire. RTDC extensions to feedback based on momentary assessments are also discussed. Conclusion., Real-time data collection can be a useful methodology for improving our understanding of pain and especially of its dynamic nature in real-world settings. [source]