Eichler, Michael

Feiler, Stefanie

Friedriech, Hans-Christoph

Hartmann, Mechtild

Wild, Beate

English

Research Papers in Economics

Letters to the Editor 
 
250
therapy could produce an extra unit of outcomes at a cost of GBP 
53 (EUR 76) or it could be dominated by BTSteps    (i.e. the latter 
would have lower costs and better outcomes).
   Figure 1  shows the cost-effectiveness acceptability curves. The 
two active treatments become more cost-effective than relaxation 
at the points indicated by the ICERs, i.e. GBP 66 (EUR 95) for 
BTSteps and GBP 90 (EUR 130) for clinician-guided therapy. 
For     values between around GBP 40 (EUR 58) and GBP 133 
(EUR 192), BTSteps has more chance of being cost-effective com-
pared to relaxation than does clinician-guided therapy. For values 
above this range, clinician-guided therapy is more cost-effective 
than BTSteps.
  In  the  original  RCT,  BTSteps and clinician-guided therapy 
were more effective than relaxation, and BTSteps was less effec-
tive than clinician-guided ERP. However, BTSteps costs less, and 
there is a high probability that BTSteps is the most cost-effective 
option for modest values placed on a one-unit fall in the YBOCS. 
Clinician-guided therapy would be more cost-effective than 
 BTSteps   only if a unit reduction in the YBOCS was valued at 
around GBP 133 (EUR 192) or above. However, even if this were 
acceptable it ignores the fact that there is likely to be a limit on the 
number of therapists available. Therefore, the difference in cost-
effectiveness may be greater than presented here.
    The original trial did not collect detailed resource-use infor-
mation. We could only cost the therapies themselves and not oth-
er health services or lost employment. Therefore the perspective 
is limited. We also had to make an assumption about the propor-
tion of clinician-guided therapy sessions attended (75%) that may 
not be accurate. The main outcome measure is syndrome specific, 
making comparisons elsewhere problematic.
    Behaviour therapists for OCD are scarce and they have many 
competing demands on their time. This study suggests that com-
puter-aided therapy can be a cost-effective alternative for OCD.
  A c k n o w l e d g e m e n t s  
 ST  Solutions  Ltd.    funded these analyses. Drs Baer, Greist and 
Marks have intellectual property rights in BTSteps.
 
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BTSteps vs. relaxation control
Clinician vs. relaxation control
Clinician vs. BTSteps
20 40 60 80 100
Value of one-unit improvement in YBOCS (GBP)
120 140 160 180 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
   Fig.  1.  Cost-effectiveness acceptability curves. 
 References 
    1  Okasha A: Diagnosis of OCD: a review; in Maj M, Sartorius N, Okasha 
A, Zohar J (eds): Obsessive-Compulsive Disorder. Chichester, Wiley, 
2000, pp 1–19. 
    2  Knapp M, Henderson J, Patel A: Costs of obsessive-compulsive disor-
der; in Maj M, Sartorius N, Okasha A, Zohar J (eds): Obsessive-Com-
pulsive Disorder. Chichester, Wiley, 2000, pp 253–275. 
   3  Greist JH, Marks IM, Baer L, Kobak KA, Wenzel KW, Hirsch MJ, Man-
tle JM, Clary CM: Behaviour therapy for obsessive-compulsive disor-
der guided by a computer or by a clinician compared with relaxation as 
a control. J Clin Psychiatry    2002;    63:    138–145. 
    4  Goodman WK, Price LH, Rasmussen SA: The Yale-Brown Obsessive 
Compulsive Scale. Arch Gen Psychiatry 1989;    46:     1006–1016. 
    5  National Institute of Health and Clinical Excellence: Depression and 
Anxiety – Computerised Cognitive Behavioural Therapy (CCBT), 
2006 (http://www.nice.org.uk/page.aspx?o=ta097). 
   6  Curtis L, Netten A: Unit Costs of Health & Social Care. Canterbury, 
Personal Social Services Research Unit, University of Kent, 2004. 
   7  Briggs AH: A Bayesian approach to stochastic cost-effectiveness analy-
sis. Int J Technol Assess Health Care    2001;    17:     69–82. 
 Paul  McCrone
PO24 Health Services Research Department
Institute of Psychiatry, De Crespigny Park
London SE5 8AF (UK)
Tel. +44 20 7848 0874, Fax +44 20 7701 7600
E-Mail p.mccrone@iop.kcl.ac.uk 
 Binge eating disorder (BED) is characterized by the consump-
tion of large amounts of food over short periods of time, accom-
panied by a sense of loss of control. The episodes of binge eating 
are not compensated by inappropriate weight control behaviours 
such as purging, fasting or excessive exercise (DSM-IV)   [1]  . The 
association of BED with an extensive psychopathology, impaired 
quality of life and social functioning suggests that differentiating 
BED from obesity and other forms of psychopathology is a critical 
clinical issue   [2, 3]  .
    In this study, we investigated the electronic diary data of 35 
obese patients, both with and without a BED, who participated in 
a multimodal outpatient intervention program   [4, 5]  . The aim of 
  Psychother Psychosom 2007;76:250–252 
 DOI:  10.1159/000101505 
  Dynamic Analysis of Electronic Diary Data of Obese 
Patients with and without Binge Eating Disorder 
 Beate Wild a , Michael Eichler b , Stefanie Feiler b ,
Hans-Christoph Friederich a , Mechthild Hartmann a , 
Wolfgang Herzog a , Stephan Zipfel c  
   a 
    Department of General Internal and Psychosomatic Medicine, 
Medical University Hospital Heidelberg,  b 
    Institute of Applied 
Mathematics, University of Heidelberg,   Heidelberg  , and 
 c 
    Department of Psychosomatic Medicine and Psychotherapy, 
Medical University Hospital Tübingen,   Tübingen  , Germany
 Letters to the Editor 251
the study was to analyse the dynamic structure between eating 
behaviour and possible explanatory factors like depression, anxi-
ety and eating control throughout the course of the treatment for 
both subgroups. Based on the findings of Greeno et al.   [6]  , we ex-
pected, specifically, that the lagged associations between eating 
behaviour and depression would be different in both groups.
  At pretreatment, the Structured Clinical Interview for DSM-
IV   [7]   and a clinical interview based on the Structured Inventory 
for Anorexic and Bulimic Eating Disorders    [8]   were used to as-
sess general psychopathology and eating disorder psychopathol-
ogy. As continuous cross-sectional measurements, we used the 
depression sum score of the Patient Health Questionnaire  [9]  and 
the sum scores for physical and mental quality of life measured by 
the SF-12  [10] . Participants were 35 overweight patients: 28 wom-
en and 7 men. At the beginning of treatment, 16 patients were 
diagnosed as obese and met the DSM-IV diagnostic criteria for 
BED; the other 19 patients were diagnosed as obese without BED. 
The average age of the participants was 45 (SD = 10.2), the average 
BMI was 37.3 (SD = 5.0). Cross-sectional comparisons revealed 
that at the beginning of treatment the two subgroups did not dif-
fer in sex, age, BMI, frequency of axis I disorders, average depres-
sion level, and average quality of life level. Consequently, we could 
not confirm the finding that obese patients with BED experience 
considerably more negative affect and greater decrements in qual-
ity of life than obese patients without BED   [3, 6, 11]  . This may be 
attributable to the fact that the patients included in our study 
sought not only weight loss treatment, but presented for a multi-
modal intervention with a clear focus on psychotherapy.
  Throughout the course of the treatment period, participants 
recorded their eating behaviour, levels of depression, anxiety and 
self-perceived eating control on handheld computers (Palm m100) 
on a daily basis. The majority of the items were rated on a visual 
analogue scale with bipolar labels. Each time series had a length 
of 112 measurement points, equivalent to 112 days of monitoring. 
A daily assessment of the patients (regarding the number of meals 
eaten, the amount eaten at each meal and the occurrence of the 
binge episodes) was used to calculate the variable ‘eating behav-
iour’, which is a discontinuous measurement for the daily amount 
of food consumed   [12, 13]  .
  Recently,  Feiler  et  al.   [14]   have  presented  an  interesting  new 
methodology to analyse diary data at group level using interac-
tion graphs. This method allows the identification of dynamic 
relationships between therapy-relevant variables. However, a 
drawback of this method is that the direction of the influence can-
not be determined. Therefore, in the present approach, we used a 
dynamic modelling approach based on graphical vector autore-
gressive models for the analysis of diary data at group level   [15, 
16]  . This method allows the identification of the dynamic depen-
dence structure of a multivariate system, that is, how the variables 
in the systems at each point of time depend on their past instanc-
es.    As a dynamic model, we chose a vector autoregressive model 
of order 1   [15, 17]  . We expected, specifically, that in the subgroup 
with BED depression would be a positive predictor for the eating 
behaviour.
    For the dynamic analysis of the diary data, a likelihood ratio 
test revealed a significant difference in the dynamic structure be-
tween the two subgroups of obese patients with and without BED 
(T = 61.04, p = 0.006). The most obvious difference between the 
two subgroups lies in the partial directed correlations; that is, the 
partial correlations that reflect associations between variables 
across adjacent days (  table 1  b). The results indicate that for pa-
tients with BED, depression is predictive of eating behaviour; that 
is, after a day of high depression, a high level of eating becomes 
more probable. In contrast, obese patients without BED show the 
opposite dependency; in this group, a high level of eating on any 
one day predicts a higher depression level on the following day. 
Table 1. Analysis of optimal graphical VAR(1) model
a p values associated with partial contemporaneous correlations
Link eat-dep eat-ctl eat-anx dep-ctl dep-anx ctl-anx
Patients with BED <0.001 <0.001 – 0.063 <0.001 –
Patients without BED <0.001 <0.001 – 0.270 <0.001 0.053
b p values associated with autoregressive coefficients in the model
Patients with BED Patients without BED
To eat dep ctl anx eat dep ctl anx
From eat 0.944 – 0.119 – 0.392 0.001 – –
dep 0.010 <0.001 – <0.001 – <0.001 – 0.102
ctl 0.026 0.015 <0.001 – 0.053 – <0.001 –
anx – – 0.107 <0.001 0.097 – 0.003 <0.001
Variables included in the dynamic analysis: eat = eating behaviour; dep = depression; ctl = eating control; anx = anxiety.Letters to the Editor 
 
252
One could interpret this to mean that for obese patients with BED, 
eating serves the regularization of emotions; increased eating and 
binge episodes could be a reward or compensation for a bad previ-
ous day. This connection is missing with obese patients without 
BED. Regarding the contemporaneous correlations (  table 1  a), we 
did not find any differences between the two subgroups. In both 
groups, the strongest associations between the variables occurred 
on the same day. For instance, depression scores and level of eat-
ing are strongly related on the same day. These results correspond, 
in part, to the findings of previous studies which investigated the 
relationship between binge eating and mood in obese patients 
with and without BED, using ecologic momentary assessment 
(EMA) strategies   [6, 18, 19]  . Clearly, a direct comparison of our 
results with the findings of the EMA studies is not appropriate 
due to the fact that the EMA studies focused on the trajectory of 
mood immediately before and after a binge episode. For this pur-
pose, in each EMA study, the participants recorded both binge 
episodes and mood variables multiple times each day, for a period 
of 7–14 days. In contrast, the participants in the present study 
completed the data assessment once per day over a 4-month pe-
riod of treatment. The analysis of the diary data therefore focused 
on associations between eating behaviour and other variables 
across adjacent days.
    As the sample of our study is small, the results should be seen 
as preliminary. However, the differences revealed by the dynam-
ic analysis show that the subgroups of obese patients with and 
without BED seeking multimodal treatment are distinguishable 
by the patterns which influence their respective eating behav-
iours.
  
 References 
    1  American Psychiatric Association: Diagnostic and Statistical Manual 
of Mental Disorders, ed 4. Washington, American Psychiatric Associa-
tion, 1994. 
    2  Wilfley DE, Wilson GT, Agras WS: The clinical significance of binge 
eating disorder. Int J Eat Disord 2003;    34:S96–S106. 
    3  Rieger E, Wilfley DE, Stein RI, Marino V, Crow SJ: A comparison of 
quality of life in obese individuals with and without binge eating dis-
order. Int J Eat Disord 2005;    37:    234–240. 
   4  Zipfel S, Quenter A, Kielmann R, Herpertz S: Therapiekonzepte für die 
Behandlung von Patientinnen mit Adipositas und Essstörungen; in 
Herzog W, Munz D, Kächele H (eds): Essstörungen. Therapieführer 
und psychodynamische Behandlungskonzepte. Stuttgart, Schattauer, 
2004, pp 118–127. 
    5  Zipfel S: First results of a program for patients with binge eating disor-
der. J Psychosom Res 2004;    56:    627. 
    6  Greeno CG, Wing RR, Shiffman S: Binge antecedents in obese women 
with and without binge eating disorder. J Consult Clin Psychol 2000;  
 68:    95–102. 
   7  Wittchen HU, Zaudig M, Fydrich T: Strukturiertes klinisches Inter-
view für DSM-IV (SKID). Göttingen, Hogrefe, 1997. 
   8  Fichter M, Quadflieg N: SIAB-Ex. Strukturiertes Interview für anorek-
tische und bulimische Essstörungen nach DSM-IV und ICD-10. Göt-
tingen, Hogrefe, 1999.
 9  Löwe B, Spitzer RL, Zipfel S, Herzog W: Gesundheitsfragebogen für 
Patienten (PHQ-D). Karlsruhe, Pfizer GmbH, 2001. 
  10  Bullinger M, Kirchberger I: Der SF-36 Fragebogen zur Gesundheit
(SF-12, Kurzform). Göttingen, Hogrefe, 1998. 
 11  Bulik CM, Sullivan PF, Kendler KS: Medical and psychiatric morbidity in 
obese women and without binge eating. Int J Eat Disord 2002;    32:    72–78. 
  12  Wild B, Quenter A, Friederich H-C, Schild S, Herzog W, Zipfel S: A 
course of treatment of binge eating disorder: a time series approach. 
Eur Eat Disord Rev 2006;    14:     79–87. 
  Beate Wild, Dipl. Math., Dipl. Psych.
Department of Psychosomatic and General Internal Medicine
Medical University Hospital Heidelberg, Im Neuenheimer Feld 410
DE–69120 Heidelberg (Germany)
Tel. +49 6221 56 8663, Fax +49 6221 56 5749
E-Mail beate.wild@med.uni-heidelberg.de 
  13  Quenter A, Bludau HB, Friederich HC, Schild S, Riepe T, Zipfel S: Use 
of electronic diaries within treatment of obesity and binge eating dis-
order. Mob Comput Med 2002;    25–33. 
  14  Feiler S, Müller KG, Müller A, Dahlhaus R, Eich W: Using interaction 
graphs for analysing the therapy process. Psychother Psychosom 2005;  
 74:     93–99. 
  15  Eichler M: Granger causality and path diagrams for multivariate time 
series. J Econom, in press. 
  16  Eichler M: Graphical Modelling for Time Series. Preprint. Heidelberg, 
University of Heidelberg, 2001. 
 17  Eichler M: Maximum Likelihood Estimation in Gaussian Chain Graph 
Models under the Alternative Markov Property. Preprint. Heidelberg, 
University of Heidelberg, 2004. 
  18 Wegner KE, Smyth JM, Crosby RD, Wittrock D, Wonderlich SA, 
Mitchell JE: An evaluation of the relationship between mood and binge 
eating in the natural environment using ecological momentary assess-
ment. Int J Eat Disord 2002;    32:    352–361. 
  19  Le Grange D, Gorin A, Catley D, Stone AA: Does momentary assess-
ment detect binge eating in overweight women that is denied at inter-
view? Eur Eat Disord Rev 2001;    9:     309–324. 
  
 With great interest I read the article by Ryff et al.  [1] . Applying 
a cross-sectional design on 135 women, the authors used various 
biomarkers to investigate whether psychological well-being and 
ill-being comprise opposite ends of a bipolar continuum (i.e. mir-
rored hypothesis) or are best construed as separate, independent 
dimensions of mental health (i.e. distinct hypothesis). While 7 out 
of 10 biomarkers supported the distinct hypothesis (i.e. they sig-
nificantly correlated either with well-being or with ill-being), 2 
supported the mirrored hypothesis (i.e. well-being and ill-being 
correlated similarly with biomarkers, but showed opposite direc-
tional signs), and 1 biomarker conformed to neither the distinct 
nor the mirrored hypothesis.
 Our group approaches the psychobiology of emotions in a dif-
ferent way. We are primarily interested in understanding bio-
logical processes through careful analysis of a person’s emotion-
al adaptation to everyday psychosocial incidents. We empirically 
  Psychother Psychosom 2007;76:252–253 
 DOI:  10.1159/000101506 
  Psychological Well-Being and Ill-Being and Their 
Biological Correlates: Additional Findings from an 
Integrative Single-Case Study 
 Christian Schubert  
  Clinical Department of Medical Psychology and Psychotherapy, 
Innsbruck Medical University,   Innsbruck  , Austria

Agras WS: The clinical significance of binge eating disorder.

Association: Diagnostic and Statistical Manual of Mental Disorders, ed 4. Washington, American Psychiatric Association,

Eich W: Using interaction graphs for analysing the therapy process. Psychother Psychosom

First results of a program for patients with binge eating disorder.

Fydrich T: Strukturiertes klinisches Interview für DSM-IV (SKID).

Granger causality and path diagrams for multivariate time series.

Graphical Modelling for Time Series.

Herpertz S: Therapiekonzepte für die Behandlung von Patientinnen mit Adipositas und Essstörungen;

JE: An evaluation of the relationship between mood and binge eating in the natural environment using ecological momentary assessment.

Kirchberger I: Der

KS: Medical and psychiatric morbidity in obese women and without binge eating.

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property.

Quadflieg N: SIAB-Ex. Strukturiertes Interview für anorektische und bulimische

S: A course of treatment of binge eating disorder: a time series approach. Eur Eat Disord Rev

Shiffman S: Binge antecedents in obese women with and without binge eating disorder.

SJ: A comparison of quality of life in obese individuals with and without binge eating disorder.

Zipfel S: Use of electronic diaries within treatment of obesity and binge eating disorder. Mob Comput Med

Dynamic analysis of electronic diary data of obese patients with and without binge eating disorder.

http://arno.unimaas.nl/show.cgi?fid=10522

Dynamic analysis of electronic diary data of obese patients with and without binge eating disorder.

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