Fuller-Tyszkiewicz et al. Journal of Eating Disorders 2014, 2:28
http://www.jeatdisord.com/content/2/1/28
METHODOLOGY ARTICLE
Open Access
Optimizing prediction of binge eating episodes: a
comparison approach to test alternative
conceptualizations of the affect regulation model
Matthew Fuller-Tyszkiewicz1*, Ben Richardson1, Helen Skouteris1, David Austin1, David Castle2,3, Lucy Busija4,
Britt Klein5,6, Millicent Holmes1 and Jaclyn Broadbent1
Abstract
Background: Although a wealth of studies have tested the link between negative mood states and likelihood of a
subsequent binge eating episode, the assumption that this relationship follows a typical linear dose–response pattern
(i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The
present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which
the strength of association between negative mood and probability of a binge episode increases above a threshold
value for the mood variable relative to the slope below this threshold value (threshold dose response model).
Methods: A sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day
for a period of one week. Participants self-reported their current mood state and whether they had recently engaged
in an eating episode symptomatic of a binge.
Results: As hypothesized, the threshold approach was a better predictor than the linear dose–response modeling of
likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood
(3 out of 10, with higher scores reflecting more negative mood). Additionally, severity of negative mood beyond this
threshold value appears to be useful for predicting time to onset of a binge episode.
Conclusions: Present findings suggest that simple dose–response formulations for the association between negative
mood and onset of binge episodes miss vital aspects of this relationship. Most notably, the impact of mood on binge
eating appears to depend on whether a threshold value of negative mood has been breached, and elevation in mood
beyond this point may be useful for clinicians and researchers to identify time to onset.
Keywords: Threshold modeling, Binge eating, Negative mood, Intensive longitudinal design, Experience sampling
Background
Binge eating, which is characterized by rapid overconsumption of food and a sense of loss of control over eating
[1], is a common feature of eating disorders, such as bulimia nervosa, anorexia nervosa-binge/purge subtype, and
eating disorder not otherwise specified. Furthermore, it is
the key symptom of Binge Eating Disorder, recently defined in the DSM-V [2]. Given its known links to a range
of health consequences, including obesity, gastrointestinal
problems, and Type II diabetes [3,4], understanding of the
antecedents (both distal and proximate) for binge eating is
crucial for developing effective treatment solutions. This
study proposes a novel modeling technique (threshold
modeling) that may be used to better understand the role
that negative mood states play in the onset of binge eating
episodes.
The affect regulation model [5,6] proposes that individuals binge eat in order to reduce severe and potentially
chronic states of negative mood (e.g., through distraction or
release of neurotransmitters, such as serotonin, that alleviate negative mood). One way of interpreting this model is
* Correspondence: matthewf@deakin.edu.au
1
School of Psychology, Deakin University, 221 Burwood Highway, Burwood,
VIC 3125, Australia
Full list of author information is available at the end of the article
© 2014 Fuller-Tyszkiewicz et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public
Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this
article, unless otherwise stated.
Fuller-Tyszkiewicz et al. Journal of Eating Disorders 2014, 2:28
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Figure 1 Alternative conceptualisations of the relationship between negative mood and likelihood of a binge eating episode.
as a linearized relationship, such that as the severity of
negative mood increases, so too will the likelihood of a
binge episode (see the top panel of Figure 1). Indeed, this is
the most common modeling approach in the literature, and
these studies have generally found negative mood severity
to be predictive of a binge event [5].
However, the relationship between negative mood and
risk of binge eating predicted in the affect regulation
model does not necessarily have to be linear. In particular, it could be that there is some level or ‘threshold’ of
negative affect that must be surpassed before a coping
response (i.e., binge eating) is triggered. That is, once a
‘comfortable’ level of negative mood is breached, the individual is highly likely to binge, whereas any negative
mood state below this threshold is highly unlikely to
produce a binge. Increases beyond this threshold may
have one of several consequences. First, subsequent increases beyond the threshold may have negligible impact
on likelihood of a binge as the act of crossing the threshold is decisive in predicting onset (we will call this the
threshold + stability model; see middle panel in Figure 1).
For instance, supposing that an individual’s threshold
value is 3 out of 10 on a negative mood scale, a mood
rating of 9 is just as likely (barring measurement error)
to result in a binge as a mood rating of 4, since both exceed the level of negative mood with which the individual is comfortable. An alternative, and more probable,
scenario is that the extent to which negative mood exceeds the threshold further increases the likelihood of a
binge episode as there is greater impetus to reduce negative feelings (the threshold + dose response model; see
bottom panel in Figure 1). To the authors’ knowledge,
neither of these threshold approaches has been tested
previously in relation to binge eating.
The nature of the mood-binge eating relationship is an
important issue that has both theoretical and practical
implications. For example, if either of the threshold
models is correct, severity of negative mood may be
more predictive of time to onset than simply likelihood
of a binge episode. That is, although the relationship
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between negative mood and time to onset of a binge
would be linear – negative mood steadily increases as time
to onset reduces – the actual point at which the binge becomes likely occurs earlier, at a lower level of negative
mood. This is important for timely intervention which
could be cued to occur once the threshold of negative
mood is surpassed rather than waiting until negative
mood has increased to a high level, at which point the
binge eating episode is imminent.
The present study used the experience sampling
method (ESM [7]) to explore the nature of the negative
mood-binge relationship by: (1) comparing the severity
and threshold approaches for modeling the relationship between negative mood and binge eating, and (2) evaluating
the extent to which severity of negative mood predicts time
to onset of a binge episode. ESM was used in preference for
experimental designs in order to monitor mood and binge
eating symptoms in a more ecologically valid manner [8],
and to also allow for multiple assessment points in order to
better ascertain the time course from trigger (elevated
negative mood) to event (binge episode) [8,9].
Superiority was operationalized as the model that exhibited the strongest effect of mood on binge eating. In
addition, we predicted that shape of the severity-time to
onset relationship would be negative and log-linear, such
that more extreme negative mood would be associated
with shorter times to onset for a binge.
Methods
Participants
Following ethics approval from the university’s Human
Research Ethics Committee, participants were recruited via
brief presentations at the beginning of tutorials and lectures
in numerous classes at a large metropolitan university in
Melbourne, Australia. The presentations emphasized that
the study aim was to evaluate mood and eating experiences
of women over a typical week. Participation was not limited
to those with a current diagnosis or prior history of an eating disorder. Recruitment was limited to women in order
to be consistent with the majority of prior studies investigating the affect regulation model from an experience sampling framework [5].
A total of 93 women agreed to participate, which is considerably greater than the average number of participants in
previous ESM studies (Mean number = 28.7 participants,
[5]). They had an age range of 18 to 40 years (M = 24.72,
SD = 4.15). The average self-reported body mass indices
(BMI = kg/m2) was 24.12 (SD = 4.39). Applying National Institute of Health [10] guidelines for classifying BMI categories, 3 (3.2%) participants were underweight (BMI < 18.5), 58
(62.4%) were in the normal weight category (BMI = 18.5 to
24.9), 27 (29%) were overweight (BMI = 25 – 29.9), and 5
(5.4%) were obese (BMI ≥30).
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Instruments
Trait measures (Phase 1: Pre-daily diary)
Demographics This questionnaire obtained information
concerning the participants’ age, height and weight.
Dietary restraint The restrained eating behavior subscale of the Dutch Eating Behaviour Questionnaire
(DEBQ [11]) was used to evaluate how often participants
employed different dietary restraint behaviours (e.g., “Do
you try to eat less at meal times than you would like to
eat?”). Items from the DEBQ were rated on a 5-point Likert
scale, ranging from 1 (never) to 5 (very often), and averaged
to form a single index of dietary restraint. The DEBQ has
been shown to have good internal consistency, concurrent
validity, and factorial validity [11-13]. Cronbach’s alpha for
the current sample was .92. The questionnaire was included
in order to evaluate the level of eating pathology in order to
provide context for the results.
State-based measures (Phase 2: Daily diary)
Negative mood The two negative mood items from the
Trait Affect Scale (TAS (Blore, J., Subjective wellbeing: an
assessment of competing theories, unpublished thesis))
[14] were used to measure negative state affect. The items
were modified so that participants were required to indicate how they felt “right now” instead of “in general”.
Items were rated on an 11-point Likert scale ranging from
0 (not at all) to 10 (extremely). Item scores were averaged
so that the possible score range was 0 to 10, with higher
scores reflecting greater negative mood. Previous research
has shown this measure to be sensitive to moment-bymoment fluctuations in mood [Blore, J., Subjective wellbeing: an assessment of competing theories, unpublished
thesis], and to correlate with other state-based constructs
such as body dissatisfaction (e.g., (Blore, J., Subjective wellbeing: an assessment of competing theories, unpublished
thesis)) [14]. Using Geldhof, Preacher, and Zyphur’s [15]
method for calculating internal consistency of state-based
scales in a multi-level framework, the maximal reliability
for the negative mood states scale in the present study
was estimated as .92.
Binge eating Participants were asked whether they had
consumed food since their previous assessment. An answer of ‘Yes’ was followed up with six items derived from
the Questionnaire for Eating and Weight Patterns –
Revised (QEWP-R [16,17]) to determine whether they had
engaged in a binge eating episode. Participants received
the following items reflecting DSM symptoms of a binge
episode (preceded by the stem ‘Did you experience…’):
(a) rapid consumption of food, (b) eating until uncomfortably full, (c) eating despite not being hungry, (d) eating
large quantities of food (relative to your usual meal size),
(e) eating alone, or (f) feeling guilt and shame after eating.
Fuller-Tyszkiewicz et al. Journal of Eating Disorders 2014, 2:28
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Responses for these items were assigned a value of 1 for
‘Yes’ and 0 for ‘No’, so that possible scores for binge eating
ranged from 0 to 6. An eating episode was classified as a
probable case of binge eating if the participant reported
three or more of the six symptoms listed above [1].
Procedure
Interested participants registered for the study by following a web link (presented in recruitment presentations)
that directed them to a Plain Language Statement (PLS).
Those who agreed to participate were directed to a subsequent webpage to complete the baseline questionnaire.
At the completion of this web-based survey, participants
provided mobile phone contact details to enable the researchers to text them notifications seven times per day
for a period of one week. This was automated using Red
Oxygen, a bulk SMS website.
To reduce the threat of response sets due to a predictable
notification schedule [18], notifications were scheduled at
random intervals each day between the hours of 10 am and
8 pm, with a minimum of 1 hour between notifications.
Each notification contained a web link prompting participants to complete the state-based measures. The websurvey was designed to rescale to an appropriate size for
smartphones so that participants could complete the survey
via either their phone or computer. Hour and date of survey completion were recorded for all samplings. At the end
of the 7-day testing period, participants were mailed a $20
gift voucher as an honorarium.
Data analytic strategy
Compliance to experience sampling protocol
Given the large number of assessment points used in a
typical experience sampling study, it is common for participants to fail to complete some of their allotted assessments. Although missing data may lower study power,
generalizability of results remain valid provided missingness is not meaningfully linked to individual difference
factors [7]. Consequently, number of assessments completed (out of a total of 49) was correlated against BMI
and dietary restraint, two variables that are highly relevant to the study of binge eating [19,20].
Threshold modeling
Hypothesis 1 was tested using logistic regression with
standard errors adjusted for clustering by individual. Occurrence of a probable binge episode (coded as ‘Yes’ for binge
symptoms scores greater than or equal to 3, and ‘No’ for
scores less than 3) was regressed onto negative mood states,
first as a continuous variable (the traditional approach), and
then as a categorical variable reflecting whether a negative
mood score met/exceeded a prescribed threshold value (i.e.,
the threshold + dose response approach). As the goal was
to demonstrate the value of the threshold approach rather
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than derive a singular threshold value, we trialed all values
of negative mood as thresholds, excluding the minimum
(0) and maximum (10) values. In all models, binge eating
was regressed onto the negative mood variable from the
preceding assessment point in order to establish temporal
precedence of the predictor variable. Moreover, time interval between assessment points was included as a covariate
to control for differences in time intervals produced by the
random assessment schedule. The superiority of the threshold + dose response model or the traditional model was
based on which version of the predictor variable had a
stronger effect size for predicting binge eating. Effect size
was reported in odds ratios given that the outcome measure was dichotomous.
Time to onset modeling
Survival analysis was used to evaluate the extent to
which level of negative mood predicted time to onset of
a binge episode. Initially, a Cox proportional hazard
model [21] was fitted, with negative mood as a continuous predictor; a variable to represent censoring due to
completion of testing period before the onset of a binge,
and participant ID as a grouping variable to allow for
multiple outcomes of the same type (i.e., a binge) within
the same person while correcting for non-independence
that this introduces [22]. After establishing the strength
and direction of linear association between severity of
negative mood and time to onset, a subsequent model
was run, with the same configurations as the first, with
the exception that the negative mood variable was
dummy coded so that more severe negative mood was
compared against a less severe reference group. These
dummy coded variables were introduced into the model
as predictors of time to onset instead of the continuous
mood variable in order to illustrate the extent to which
increasing negative mood beyond a threshold level hastened the onset of a binge.
Results
Data cleaning and descriptives
Data were screened for patterns of missingness, presence of
outliers and non-normality, and evidence of systematic bias
in compliance with the experience sampling component of
the study. Less than 2% missing data was found for Phase 1
(trait based measures), and dealt with using expectation
maximization. On average participants completed over 80%
of the experience sampling assessment points (M = 39.94
assessments, SD = 7.39, range 19–49). Individual differences
in the number of Phase 2 random assessments completed
were not associated reliably with BMI (r = −.05, p = .635) or
dietary restraint (r = −.07, p = .497). There were no outliers
or evidence of non-normality in any variable. Moreover,
levels of dietary restraint (M = 28.78, SD = 8.99, possible
range = 10–50) were moderate for the sample as a whole
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and showed considerable variability around this mean, ensuring coverage of the spectrum of severity for eating
disturbances.
As may be expected given that ESM samples randomly
many times per day, the prevalence of episodes of binge
eating was relatively infrequent compared to the total
number of assessments, occurring in 149 of 3714 assessments (4% overall across all participants). Thirty-one out
of 93 participants (33%) reported at least one binge episode and, of those who reported a binge episode, the
average number of binge episodes was 3.06 (SD = 2.46,
range = 1 to 9 binge episodes). The proportion of cases
that met the cut-off for the various thresholds for negative mood declined linearly, such that 94% of assessment
points included a negative mood score of 1 or more,
whereas only 3% of assessments included negative mood
scores of 10 (see the left-hand column in Table 1).
Threshold modelling
As shown in Table 1, the association between negative
mood and binge outcome was statistically significant,
although the effect size was negligible when using the
traditional approach of modeling negative mood as a
continuous variable (odds ratio, OR = 1.25, p = .001). In
contrast, the threshold approach more accurately predicted onset of a binge, with a linear trend in effect
sizes from threshold 3 (comparing negative mood
scores of 0–2 against 3–10) onwards. From threshold 6
(comparing negative mood scores of 0–5 against 6–10)
onwards, the threshold approach had an effect that was
at least three times as strong in predicting onset of a
binge episode than the traditional approach.
Time to onset modeling
Survival analysis established a negative log-linear association between level of negative mood and time to onset of
Table 1 Effect sizes (Odds Ratios) for the relationship
between negative mood and binge eating
Predictor
% above
Odds ratio
p value
Traditional
n/a
1.25
.001
Threshold 1
93.8%
2.91
.038
Threshold 2
82.9%
2.37
.023
Threshold 3
56.1%
1.68
.055
Threshold 4
40.1%
1.93
.022
Threshold 5
27.8%
2.38
.003
Threshold 6
15.5%
3.68
.000
Threshold 7
8.5%
3.97
.000
Threshold 8
4.7%
4.46
.002
Threshold 9
2.8%
5.58
.002
Notes: % above = proportion of assessment points (across all participants) that
met or exceeded the threshold cut-value. n/a = not applicable.
a binge episode (B = −.93, p = .035), indicating that extreme
negative mood states were temporally closer than low negative mood states to binge onset. In subsequent modeling,
negative mood was dummy coded with scores of 5 (representing the midpoint on the negative mood scale) used as a
reference category and values of 6 to 10 used as target
group. Results of this additional analysis showed that the
extent of negative mood generally heightened the onset
time for a binge, with the exception of negative mood
values of 9; negative mood 6 vs. 5: B = −.31, p = .020, negative mood 7 vs. 5: B = −.25, p = .039, negative mood 8 vs. 5:
B = −.31, p = .086, negative mood 9 vs. 5: B = .13, p = .273,
negative mood 10 vs. 5: B = −.16, p = .346.
Discussion
Overview of findings
Although a growing body of literature has documented
the influence of negative mood states on binge eating
[5], there has been limited discussion of how different
modeling approaches serve to optimize prediction of
binge eating. Therefore, the present study utilized the
experience sampling method [7] to test alternate models
of the nature of the negative mood-binge eating relationship. In particular, we tested whether the negative
mood-binge eating relationship is best characterized as
(i) linear or (ii) a threshold. If the latter is correct, surpassing this threshold will serve to increase both (i) risk
of binge eating and (ii) subsequent negative mood. Given
this, we also tested whether increased negative mood beyond the threshold predicts time to onset of the binge
episode; that is, that severity of negative mood can be
used to estimate time to onset of a binge.
Our findings offer support for both of our predictions.
Whether or not an individual exceeded a threshold value
proved to be a better predictor than the severity of negative mood for likelihood of a subsequent binge episode
for all thresholds. Once the cut-point reached 6 out of
10 on the negative mood scale, the threshold version of
negative mood was approximately three times stronger
in predicting binge occurrence than the traditional approach of modeling negative mood severity (i.e., using
mood as a continuous predictor). Model effect sizes
were further optimized as the cut-point was increased.
The fact that the odds ratios continued to increase
monotonically with the rising threshold cut-off rather
than stabilizing or reducing may be explained in several
ways. First, it may be that exceeding a threshold makes
the binge outcome likely, but that continued and intensifying negative mood enhances the likelihood of a binge
in much the same way that threshold dose–response
models in toxicology predict that whereas sub-threshold
doses of a carcinogen may have negligible and possibly
contrary effects on health, once exposure exceeds the
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threshold, health risk is proportional to level of exposure
[23,24]. Consistent with the affect regulation model
[5,6], an individual when faced with negative emotional
states may seek healthy solutions initially but, discouraged by continued or exacerbated symptoms, may then
escalate to more unhealthy, distracting solutions, such as
binge eating.
A second possible explanation for the increasing odds
of a binge as the cut-point increases has to do with the
timing between exposure to negative mood and onset of
binge. In the survival analysis, time to onset hastened as
severity of negative mood increased. Thus, whereas a
negative mood state of 8 out of 10 may indicate an
imminent binge episode, a moderate negative mood state
(such as 6 out of 10) may allow sufficient lead time for
intervening events to distract the individual, thus preventing a binge episode. Indeed, the odds of a binge episode at lower levels of negative mood may be attenuated
due to naturally occurring interventions, such as presence of others, work or family responsibilities, that may
take precedence and/or make it difficult or undesirable
to binge. This latter (timing-based) explanation is consistent with prior studies that have shown that binge eating is preceded by a period of escalating negative mood.
Crosby et al. [25] found that binge episodes were most
common on days in which individuals experienced either
stable elevated negative mood or consistently rising
negative mood throughout the day. Similarly, Smyth
et al. [26] demonstrated a steady rise in negative mood
in a 6 hour period that peaked immediately prior to a
binge episode.
The present findings show statistical superiority of the
threshold approach for modeling likelihood of a binge.
This approach may also have an advantage in interpretability over the severity approach. In the severity approach used in the present study, negative mood states
were modeled as group-mean centred predictors, meaning that the substantive research question is whether deviations in self-reported mood state above one’s own
usual mood state increase the likelihood of a binge eating episode. The interpretive adequacy of this approach
then depends on: (1) the average mood state of the individual over the testing period; (2) the extent to which
they fluctuate around this mean; and (3) how far they
deviate from their mean. In the present study, 52% of
participants had an average negative mood score of 3 or
below. Such a low average score would mean that many
instances in which the individual exceeds their average
negative mood would still be comfortable levels of negative mood that are unlikely to distress them or prompt
strategies such as overeating. In contrast, the threshold
value can be seen to be more meaningfully interpreted
as it is the level of negative mood beyond which a binge
episode becomes increasingly likely.
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Implications of findings for future research and clinical
practice
These findings have potential implications for treatment
of binge eating. While, on the one hand, extreme negative mood states (when modeled as a threshold) are the
best predictor of likelihood of a binge, it may be prudent
to intervene once a patient exceeds a moderate level of
negative mood for several related reasons. First, even at
a threshold of 6, the mood-binge eating relationship is
strong [27]. Second, as there appears to be a greater
temporal gap between experience of moderate negative
mood and a binge than there is between severe negative
mood and bingeing, there is more time to intervene.
Also, it is possible that reducing moderate negative
mood to a sub-threshold level is easier than attempting
to reduce severe negative mood. Third, with the advent
of self-guided e-health technologies, such as interactive
websites and smartphone apps that monitor risk and
provide early warning signals for target event [28], the
increase in false positives produced by using a less accurate threshold value may be justified if treatment is
not dependent upon consultation with a clinician. As an
app is more cost-effective than a therapy session and
does not burden the health system with unnecessary appointments [28], a conservative approach (erring on the
side of action instead of inaction) may have benefits that
outweigh costs. Furthermore, the alternative – setting an
extreme threshold – may increase specificity at the expense of sensitivity, as it may be that some episodes of
binge eating arise when the individual’s negative mood
state is only moderate.
Given that negative mood states are a common feature
of many psychological disorders, including depression
and bipolar disorder, this threshold model may have
broader applicability for predicting symptoms of psychological disorder other than binge eating. Future research
should evaluate the generalizability of our threshold approach for predicting onset of other events of clinical
importance.
Limitations
Our findings should be considered within the context of
several limitations of the study. First, as effect sizes were
used to compare models, it is possible that the difference
in model performance (linear vs threshold) is somewhat
overstated in the present study. After all, odds ratios in
logistic regression analyses will be larger for categorical
(threshold model) than continuous predictors (linear
model). It is encouraging then, that even with this potential bias, the strength of association increased substantially
as the threshold negative mood level was elevated.
Second, the method used in the present study to assess
binge episodes does not definitively diagnose a binge
episode. In particular, two key aspects of binge eating
Fuller-Tyszkiewicz et al. Journal of Eating Disorders 2014, 2:28
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(quantity consumed and perceived loss of control) may
not have been captured optimally. Quantity of food was
assessed by self-report rather than objectively, and the
five symptoms of binge eating used in the present study
correspond well with perceived loss of control during
eating [29], but do not explicitly ask about loss of control. Thus, it is possible that the present measure detects
related, yet distinct eating behaviors, such as dysregulated or emotional eating, instead of binge eating.
A related concern is that although the sample appeared to cover the spectrum of severity of dietary restraint, formal diagnoses of eating disorders were not
conducted, and given that a convenience sample of university students was recruited, it is likely that many of
the participants would not meet criteria for an eating
disorder diagnosis. Similarly, although we exclusively recruited women in order to be consistent with previous
literature [5], binge eating is also common among men
[30]. As such, it is possible that the frequency of binge
eating episodes and the strength of association between
negative mood and binge eating onset is weaker in the
present study than would be anticipated for a clinical
population that sampled both men and women. It is encouraging then, that this association yielded strong effects even in this non-clinical sample. Nevertheless,
future research is needed to validate present models in a
clinical sample, and this should include objective measures of binge eating occurrence.
Conclusion
The present findings clearly support a threshold approach for modeling the influence of negative mood on
likelihood of a binge episode. Even at low threshold
values, this approach clearly outperformed the traditional, severity approach for predicting negative mood
studies. However, the findings suggest that severity of
negative mood may be more usefully employed to predict time to onset of a binge episode. Although likelihood of a binge and time to onset were strongest and
shortest, respectively, at highest levels of negative mood,
we argue that there is need to intervene at even moderate levels of negative mood because moderate levels
were also strongly predictive of a binge outcome and,
importantly, this allows more time to prevent the binge
episode. This information could be used in the development of a smartphone app that seeks to disrupt the connection between negative mood and binge eating. A
useful next step for future research would be to evaluate
the efficacy of intervening at different threshold values
for reducing the frequency of binge episodes.
Competing interests
The authors declare that they have no competing interests.
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Authors’ contributions
MFT, BR, and LB conducted all analyses and wrote the Results section. MH,
HS, and JB contributed to conception of the project. DA, DC, and BK made
significant contributions to interpretation of findings in light of study
implications, as based on their clinical experience. All authors contributed to
drafting and revision of the manuscript. All authors read and approved the
final manuscript.
Acknowledgements
The authors would like to thank the reviewers for their valuable input in
shaping this manuscript. There were no funding sources for this project.
Author details
1
School of Psychology, Deakin University, 221 Burwood Highway, Burwood,
VIC 3125, Australia. 2Psychiatry Department, University of Melbourne,
Melbourne, VIC, Australia. 3St Vincent’s Hospital, Melbourne, VIC, Australia.
4
Faculty of Health, Deakin University, Burwood, Australia. 5DVC-Research &
Innovation Portfolio; the School of Health Sciences; and the Collaborative
Research Network, Federation University, Ballarat, Australia. 6National Institute
for Mental Health Research, The Australian National University, Canberra,
Australia.
Received: 1 July 2014 Accepted: 9 September 2014
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doi:10.1186/s40337-014-0028-9
Cite this article as: Fuller-Tyszkiewicz et al.: Optimizing prediction of
binge eating episodes: a comparison approach to test alternative
conceptualizations of the affect regulation model. Journal of Eating
Disorders 2014 2:28.
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