Abstract
BackgroundIn recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied.MethodsWe propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models.ResultsThe graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours.ConclusionThe use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research.
Highlights
In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time
This temporal ordering implies that the past and present values of a series X that influences another series Y should help to predict future values of this latter series Y. This improvement in the prediction of future values should persist after any other relevant information for the prediction has been exploited. This leads to the following definition of Granger-causality: For two time series X and Y let Z be the time series that comprises all variables that might affect the dependence between X and Y
We note that the definition depends on the set of variables Z included in the analysis
Summary
Electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. The use of electronic diaries in clinical research has become increasingly popular [1,2]. In many different medical areas--such as neurology [3], sleep medicine [4], paediatrics [5], dermatology [6], gynaecology [7], psychosomatic medicine [8], and rheumatology [9]--electronic diary data provide new insights into processes and temporal relationships. In clinical practice and research regarding chronic pain electronic diary assessment is frequently used to examine day-today variation in symptoms as well as to investigate the impact of constant self-monitoring on the patient's behaviour [10,11,12]. Electronic diaries can be used as a BioMed Central tribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
