=Paper=
{{Paper
|id=Vol-1468/bd2015_meyer
|storemode=property
|title=Big data study for coping with stress
|pdfUrl=https://ceur-ws.org/Vol-1468/bd2015_meyer.pdf
|volume=Vol-1468
}}
==Big data study for coping with stress==
https://ceur-ws.org/Vol-1468/bd2015_meyer.pdf
Big data study for coping with stress
Denny MEYERabc, Jo-Anne M. ABBOTTabc and Maja NEDEJKOVICac
a
School of Health Sciences, Swinburne University of Technology
b
National eTherapy Centre, Swinburne University of Technology
c
Brain and Psychological Sciences Research Centre,
Swinburne University of Technology
Abstract. Coping strategies in response to perceived stress have been studied for
decades producing an interesting mix of arguments. A relatively large
psychological database of nearly 17000 responses, captured on Anxiety Online, an
online clinic for the diagnosis and treatment of anxiety disorders, provides a new
opportunity to address two of these arguments. Issues of particular interest include
the importance of multiple tactics for coping with stress, with claims that both
problem-focused and emotion focused coping strategies are generally applied
simultaneously, despite the clear separation of these two types of coping strategy
in terms of effectiveness and situation that is found in the literature. A second
argument concerns the relationship between coping behaviours and mental health
outcomes, sometimes with a reciprocal relationship acknowledged but more often
with mental health outcomes seen as the result of coping behaviours, allowing the
use of learnt coping behaviours as a means of improving mental health. In this
study we address these issues by using cluster analysis to define the common
groupings of coping behaviours found in the Anxiety Online database. The
relationship between these coping behavior clusters are explored in terms of
demographics, mental health diagnostics and support. However, instead of using
coping behaviours to predict mental health outcomes we consider the effect of
coping behaviours on the resolve and confidence of clients to make improvements
in their mental health. The results suggest that emotion/avoidant coping is less
effective than other coping strategies and that multiple coping strategies are more
likely for more severe stress.
Keywords. Mental Health Management, Coping with Stress, Online Mental
Health Diagnosis, Mental Health Support.
Introduction
General psychological models for perceived stress and coping commonly refer to either
problem- or emotion-focused coping. In problem-focused coping an individual engages
in behaviours to specifically address the sources of stress, such as visiting a doctor or
talking to a friend, while in emotion-focused coping an individual engages in
behaviours such as crying or eating to alleviate the emotional distress caused [1].
However, a third style for coping with perceived stress, namely avoidant-focused
coping, involving avoiding confrontation with the stress-factor, is also commonly
recognised [2,3]. More recently an additional style of coping, detached coping, has
been identified, in which the individual tries to temporarily remove themselves from
the problem in order to reduce their emotional response [4]. Both problem-focused
coping and detached coping styles are commonly regarded as efficient, while the
emotional and avoidant coping styles are usually regarded as inefficient [4].
However, other studies show more complexity in regard to the relationship
between choice of coping strategies and their efficacy. In particular it has been found
that individuals typically use multiple coping tactics with the use of both problem-
focused and emotion-focused strategies for 98 percent of 1300 stressful episodes in one
study [5]. A greater number of coping responses can be expected when the stress is
perceived as being more severe [6], with problem-solving coping more likely when
demands are appraised to be controllable, and emotion-focused coping more likely
when demands are appraised to be more uncontrollable [2,7]. Studies have consistently
shown that choices in regard to coping styles differ between men and women with
women favouring emotion-focused methods and men favouring problem-focused
methods. In addition it has been found that people with low education and income are
more likely to employ ineffective coping strategies [8].
However, there is argument about which coping strategies are most effective for
decreasing psychological distress [9,10], and in some situations it has been found that
emotion-focused coping strategies are more effective than problem-solving-focused
coping strategies. In particular, some emotion-focused strategies such as denial and
alcohol use have been found to be beneficial, but only in the short-term [10]. It has also
been found that the immediate and long-term effects of avoidance coping differ, being
more beneficial than other coping strategies, but only in the short-run [11]. The way in
which efficacy is measured is critical in all these studies. Studies of relationships
between perceived stress, coping behaviours and mental health outcomes have often
found reciprocal relationships, with coping choices sometimes being dictated by levels
of stress and sometimes increasing stress levels. So in the short-term it seems best to
consider other outcome measures, such as the usage of critical health-related services
[12].
1. Method
Anxiety Online (now Mental Health Online) is a system for the diagnosis and treatment
of anxiety disorders. Between October 2009 and June 2013 close to 17000 valid entries
were obtained by the Anxiety Online system, allowing the use of data mining methods
in an area where this is seldom possible. The data contained responses for 15899
distinct email addresses, with 1100 repeat logins from 862 clients. Respondent
confidentiality meant that no check of client legitimacy in terms of mental health
problems was possible. In addition to data relating to the diagnosis of 21 mental health
disorders, comprehensive demographic and contextual data was collected for each
client. In particular, data were collected for the methods used by clients to handle stress
with the following options; alcohol, substances, exercise, talk with friends and family,
medical doctor, hobby, meditate, other. The “other” category included mostly emotion-
focused strategies such as crying, sleeping, eating, self-harm or withdrawal. A Yes/No
response was elicited for each of these actions. In addition the number of diagnoses and
a K6 measure of psychological distress [13] were extracted, together with level of
support from family and community, using a Yes/No response, and the existence of
current or previous mental health assistance (now, in last month, previously, never).
These variables together with demographic data were considered as the drivers of
coping strategies, with outcome measures relating to resolve to make changes in regard
to mental health management, measured on a 4-point scale, and confidence in ability to
make these changes, measured on a 5-point scale. This model is illustrated in Figure 1.
Figure 1: Conceptual Model
The studies described previously have tended to use scales to measure the use of
strategies for coping with stress. Correlation and regression analyses have then been
applied to test hypotheses using relatively small sample sizes. In contrast the relatively
large data set available in the current study allows the grouping of the binary coping
responses using a two-stage cluster analysis [13]. Each grouping was named in
accordance with the most common coping actions. Nominal logistic regression was
then used to evaluate the strength of the relationship between these groupings and the
driver variables; support and engagement with family and community, mental health
assistance, mental health diagnoses (K6 and number of diagnoses) and demographic
data (gender, age, marital status, tertiary education and employment status). Finally
nominal logistic regression analyses were used to test for mediation effects by the
choice of coping strategies between these drivers and the two dependent variables;
resolve to improve mental health and confidence in ability to do so.
2. Results
As shown in Table 1, nine clusters emerged, each with a good representation in terms
of response numbers but not in terms of the number of activities used for coping. The
Emotion/Avoidant cluster comprised a mix of “other” coping strategies, so no activity
number was possible for this cluster. However, for the other clusters there was some
variation in terms of the number of activities, with two of the clusters (Logical GP and
Avoidant Substance) employing at least eight coping activities. However, clients in the
Logical F&F Talk cluster used only this single coping activity.
The characteristics of the coping clusters were analysed using univariate and then a
multivariate analysis, producing odds ratios relative to the first Emotion/Avoidance
cluster as shown in Table 2. These regressions showed highly significant differences
between the clusters for all the driver variables confirming the associations between
coping strategy choices and support/engagement with family and community, mental
health assistance, mental health diagnoses (K6 and number of diagnoses) and
demographic data (gender, age, marital status, education and employment status). Odds
ratios above one indicate higher probabilities of cluster membership compared to the
Emotion/Avoidant cluster, while odds ratios below one indicate lower probabilities of
cluster membership compared to this cluster.
Table 1: Clusters for Coping (* F&F Friends and Family)
% Responses per Coping Activity Number
Cluster Alco- Sub- Exer- Talk Family Hobby Medi- Other Responses Coping
hol stances cise F&F* Doctor tate (+) Activities
Emotion/Avoidant 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 2620 ?
Logical GP 24.9 2.8 38.4 65.9 100.0 27.4 22.0 16.2 1600 8+
Detached/Avoidant 26.4 0.0 35.6 53.3 0.0 29.5 14.5 100 1816 6+
DetachedMeditation 20.1 0.0 52.0 56.4 0.0 34.4 100.0 0.0 1407 5
Avoidant Substance 45.4 100 22.0 35.3 11.0 19.9 11.0 15.5 1210 8+
Detached Exercise 29.0 0.0 100 51.2 0.0 27.9 0.0 0.0 2674 4
Avoidant Alcohol 100 0.0 0.0 33.4 0.0 18.2 0.0 0.0 1537 3
Logical F&F Talk 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 2337 1
Detached Hobby 0.0 0.0 0.0 27.8 0.0 100 0.0 0.0 1798 2
Total 23.7 7.4 29.0 46.8 10.2 26.6 12.7 28.7 16999
Table 2: Univariate Nominal Logistic Regressions for Coping Clusters (* p<.001)
Odds Ratios for Coping Clusters Relative to Emotion/Avoidant Cluster (1)
Characteristics Emotion Logical Detached/ Detached Avoidant Detached Avoidant Logical Detached
of Coping /Avoidant GP Avoidant Meditation Substance Exercise Alcohol F&F Hobby
Clusters (1) (2) (3) (4) (5) (6) (7) Talk(8) (9)
No/Yes
Support 1.00 .58* .65* .38* 1.08 .40* .73* .41* .67*
Never/Now
Access MH 1.00 .14* .55* .85* .53* 1.56* 1.35* 1.34* 1.47*
K6 1.00 .96* .95* .86* 1.04* .88* .96* .90* .92*
# Disorders 1.00 1.00 .96 .79* 1.29* .79* 1.06* .83* .86*
Male/Female 1.00 1.07 .82 1.31* 1.28* 1.65* 1.76* .66* 1.69*
Married/Other 1.00 1.18 .86 1.22 .50* .95 1.03 1.00 .84
Single/Other 1.00 .85 1.01 .85 .92 .82 .86 .61* 1.34*
Cohab./Other 1.00 1.01 .79 .98 1.05 .98 1.07 .98 .87
Employ FT
/Other 1.00 1.14 1.00 1.40* .95 2.03* 1.90* 1.30* .94
Employ PT
/Other 1.00 1.16 1.13 1.39* 1.00 1.70* 1.32* 1.46* 1.02
No/Yes
Degree 1.00 .76 .66* .45* 1.18 .43* 1.00 .81 .99
Age<25/>45 1.00 .42* .99 .40* 1.49* .69* .59* 1.52* 2.12*
Age<35/>45 1.00 .87 1.09 .57* 1.61* 1.04 .99 1.70* 1.52*
Age<45/>45 1.00 .87 1.00 .70* 1.21 .86 .99 1.33* 1.08
Table 2 shows that in comparison to the first cluster (Emotion/Avoidant), the
members of seven clusters (2,3,4,6,7,8,9) are significantly better supported/engaged
with family and community, the members of 4 clusters have significantly better access
to mental health assistance (2,3,4,5) while the members of the other 4 clusters (6,7,8,9)
have significantly worse access to mental health assistance. In terms of the K6 the
members of all but the fifth cluster (Avoidant Substance) are identified by lower
distress scores than the first cluster. However, the members of 4 of the other clusters
(4,6,8,9) have lower numbers of diagnosed disorders while the members of two clusters
(5 and 7) have more diagnosed disorders than the first cluster. In terms of
demographics there are also important differences between the clusters.
Compared to the first cluster the members of five clusters (4,5,6,8,9) are more
likely to be male but the members of cluster 8 (Logical F&F Talk) are more likely to be
female. The members of the fifth cluster (Avoidant Substance) are less likely to be
married while the members of clusters 8 and 9 (Logical F&F Talk and Detached
Hobby) are more likely to be single than the members of cluster 1. Members of four
clusters (4,6,7,8) are more likely to be employed than the members of cluster 1. Also,
compared to cluster 1, members of three clusters (3,4,6) are less likely to have
completed a degree. Finally, compared to cluster 1, the members of 4 clusters (2,4,6,7)
tend to be older and the members of 3 clusters (5,8,9) tend to be younger.
Now considering the two dependent variables a 2-stage nominal logistic regression
analysis was conducted with the above cluster characteristics entered at stage 1 and the
cluster variable entered at stage 2. For confidence in ability to improve mental health
management there was a significant improvement in the model fit when the coping
clusters were added (Chi-Square = 172, df=24, p<.001) with a similar result for resolve
to improve mental health (Chi-Square = 365, df=24, p<.001). In all these models all the
characteristics were significant except for employment status, due to mediation by
education with fewer degreed people employed than people without degrees. Table 3
compares the resolve to improve mental health and the confidence to do so for the nine
clusters, again using the Emotion/Avoidant cluster as the reference cluster, while
controlling for the stage 1 characteristics.
Table 3 shows that in comparison with members of the first cluster
(Emotion/Avoidant), members of seven clusters (2,3,4,5,6,8,9) have significantly
higher odds of having good rather than poor confidence in their ability to improve their
mental health. In addition, in comparison with the first cluster, four clusters (2,3,4,6)
are significantly more likely to be currently engaged with improving their mental
health, with members in three of these clusters (2,3,4) more likely to be in need of help
have recently relapsed.
Table 3: Odds Ratios for Coping Clusters while Controlling for Stage 1 Characteristics (* p<.001)
Odds Ratios for Coping Clusters Relative to Emotion/Avoidant Cluster
Cluster Cluster Good versus Poor Making Improvements Relapsed Need Help to
Number Confidence to in Mental Health Now Improve Mental
Manage Mental Versus Health Versus No
Health No Interest Interest
1 Emotion/Avoidant 1.000 1.00 1.00
2 LogicalGP 1.60* 4.61* 4.19*
3 Detached/Avoidant 1.52* 2.24* 1.78*
4 Detached Meditation 2.69* 4.88* 2.79*
5 Avoidant Substance 1.67* 1.45 1.00
6 Detached Exercise 1.97* 2.16* 1.40
7 Avoidant Alcohol 1.18 1.31 .91
8 Logical F&F Talk 1.44* 1.47 1.40
9 Detached Hobby 1.60* 1.42 1.04
3. Discussion of Implications and Conclusions
The results show strong support for the model suggested in Figure 1 and some support
for the complexity of the relationship between choice of coping strategies and their
efficacy. There is support for the view that Emotion/Avoidant styles of coping tend to
be less effective than problem-solving approaches in that our first cluster is associated
with the lowest level of confidence regarding the management of one’s mental health.
There is also support for the view that individuals typically use multiple coping tactics
and that this tendency will be stronger when the stress is perceived to be more severe.
The Avoidant Substance cluster had the highest level of distress as measured by the K6
and had the highest number of disorders diagnosed, and more than eight coping
activities were utilised by clients in this cluster. The Logical GP cluster also used more
than 8 coping activities and this cluster also had a relatively high number of diagnoses.
The results confirm that women are more likely to favour Emotion/Avoidant (and
Detached/Avoidant) coping activities than males, but the results for education suggest
that detachment is a more common coping strategy for degreed clients. Also, despite no
evidence of age effects in the literature, this study shows that Substance and Alcohol
Avoidance and Talking with Friends and Family are more common in younger people.
Although the literature suggests no consensus about the optimum coping strategies
for decreasing psychological distress, this study suggests that Emotion/Avoidant
coping is indeed less effective than other strategies, if confidence and resolve regarding
mental health management are adequate indicators of mental health. However, although
this is a very large sample of data it represents only people who have chosen to seek
online help for mental health issues, and the results may therefore not reflect the reality
for a wider sample of clients. In addition only seven categories and an ‘Other’ category
were considered for coping activity, with only a ‘Yes/No’ response for each category.
A more sensitive response scale with more categories may have been more illuminating.
Finally, more robust outcome measures, such as a reduction in stress episodes should
be considered in future research. Despite these limitations this study shows the
advantages of a relatively large database in a study of this nature. A full picture of
coping with stress has been captured for the first time.
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