select ad.sno,ad.journal,ad.title,ad.author_names,ad.abstract,ad.abstractlink,j.j_name,vi.* from articles_data ad left join journals j on j.journal=ad.journal left join vol_issues vi on vi.issue_id_en=ad.issue_id where ad.sno_en='17407' and ad.lang_id='9' and j.lang_id='9' and vi.lang_id='9' Clinical Network Analysis in a Bipolar Patient Using an Expe | 17407
బైపోలార్ డిజార్డర్: ఓపెన్ యాక్సెస్

బైపోలార్ డిజార్డర్: ఓపెన్ యాక్సెస్
అందరికి ప్రవేశం

ISSN: 2472-1077

నైరూప్య

Clinical Network Analysis in a Bipolar Patient Using an Experience Sampling Mobile Health Tool: An n=1 Study

ALA Voigt, DJ Kreiter, CJ Jacobs, EGM Revenich, N Serafras, M Wiersma, J van Os, MLFJ Bak and M Drukker

Objective: Analyses of longitudinal data pertaining to a single person can provide insight into the emotional dynamics of bipolar disorder at the symptom level. Aims were to examine (1) co-variation of a priori selected mental states in daily life in a patient with bipolar disorder; and (2) connections between these mental states during a hypomanic and a depressive period in bipolar disorder.
Methods: A single person diagnosed with bipolar disorder used the Experience Sampling Method (ESM) to collect data on experiences and mood ten times a day, for three months. Linear regression analyses were performed, stratified by hypomanic or depressive period. The a priori selected set of dependent variables included ‘anxiety’, ‘down’, ‘cheerful’, ‘satisfied’, ‘tired’ and ‘lonely’. Independent variables were the same symptoms as collected one random moment earlier during the same day (t-1, lagged). Regression coefficients were presented in network graphs.
Results: Mood fluctuated strongly over time. The variable ‘down’ was central in the networks of both the hypomanic and depressive period. ‘Satisfied’ was only central in the hypomanic network.
Conclusion: In this patient, depression was the central emotion during both hypomanic and depressive periods. The distinction between depression and hypomania may sometimes lie in respectively the absence and presence of certain positive mood states. Furthermore, the present paper showed that extreme mood shifts in bipolar disorder can be studied after generating mood networks to gain precise insights in dynamic relations and the degree of cohesion between symptoms. These insights may be useful in the clinical setting to support self-monitored and personalised feedback and interventions.

నిరాకరణ: ఈ సారాంశం కృత్రిమ మేధస్సు సాధనాలను ఉపయోగించి అనువదించబడింది మరియు ఇంకా సమీక్షించబడలేదు లేదా ధృవీకరించబడలేదు.
Top