Liz Rolf-The Use of Struggle Language in Chronic Illness

Running head: USE OF STRUGGLE LANGUAGE IN CHRONIC ILLNESS 1
The Use Of Struggle Language When Discussing Chronic Illness:
Assessing The Effects On Patients
Liz Rolf
University of Missouri - St. Louis

Use of Struggle Language in Chronic Illness 2
Introduction
Problem Statement
With the rapid advances in medical technology, the incidence of patients with chronic
illnesses is quickly rising. Chronic illnesses that used to kill patients fairly quickly, such as
diabetes, lupus, or cystic fibrosis, can now be managed. With proper care, most patients with
illnesses such as these can live long lives, possibly even a full natural life span. Of course, as the
population has risen, so has the number of individuals with terminal illnesses, such as
Amyotrophic lateral sclerosis (ALS), that medical science cannot influence the course of.
As of 2005, it is estimated that the population of Americans with chronic illnesses stood
at 133 million, and that 63 million of those individuals had more than one chronic illness
(Bodenheimer, 2009). By 2020, it is estimated that the number of individuals with chronic
conditions will rise to 157 million, of which 81 million individuals will have more than one
chronic condition (Bodenheimer, 2009). As the number of individuals affected by disease grows,
and our culture becomes more open and informed about medical conditions in general, the
discourse on disease increases. In almost all discussion about disease, regardless of the speaker
or the audience, it is discussed using struggle language or combat metaphors. “The war on
cancer”, “cancer survivor”, “a virus invades human cells”, “your immune system defends your
body”; all are examples of discussing disease in terms of war and conquest. The use of such
language gives the impression of dividing patients into groups of “winners” (patients who
triumph over their disease, typically by being cured) and “losers” (those who succumb to illness,
or whose illness is incurable).

Study Objective
While research has been conducted on various aspects of chronic illness, including
population statistics, employment statistics on chronic illness patients, illness perception, and the
effects of specific diseases on patients’ lives, little research has been done on the effects of the
language commonly used when discussing illness, particularly the effects that might be found in
patients with chronic illnesses. Given the growing population of chronic illness patients,
determining the effects of such language on the perceptions of chronic illness and patients with
chronic illnesses seems prudent to this investigator. Awareness of the potential effects on
perception of patients, both how patients see themselves and how other see them, might
influence the language used when discussing illness, which in turn may affect patient perception.
This proposed study would seek to determine the potential impact of struggle language on
perceptions of chronic illness.
Literature Review
Much of the current research on chronic illness focuses on Health-Related Quality of Life
(HRQoL), particularly in patients with specific illnesses. A study conducted in 2014 by Steel, et
al., looked at HRQoL as a predictive factor in life spans of patients with advanced cancer. The
study sought to “investigate the prognostic value of HRQoL in patients with hepatocellular
carcinoma and cholangiocarcinoma after adjusting for sociodemographics, disease-related
factors, and treatment-related factors (Steel, et al., 2014).” The researchers administered the
Functional Assessment of Cancer Therapy-Hepatobiliary instrument to 321 patients. Using Cox

regression, the researchers determined that overall HRQoL was significantly associated with
longer survival rates, even after controlling for the factors listed above. Since HRQoL was found
to be predictive of survival rates of patients with hepatocellular and cholangiocarcinoma, the
researchers recommended that physicians stratify patients when testing new and novel
treatments.
A study conducted in Singapore in 2014 (Venkataraman, et al., 2014) looked at HRQoL
in 3514 individuals from the general community in Singapore. Subjects were tested on their
HRQoL (using the SF-36 health survey version) and checked for three common conditions:
diabetes mellitus, hypertension, and dyslipidemia. Each participant, for each condition, was
categorized as either: having no disease, undiagnosed, diagnosed but not taking medication, or
diagnosed and taking medication. Researchers used one-way ANOVA and multiple linear
regression to determine that disease awareness was associated with lower HRQoL, while
undiagnosed disease was associated with higher HRQoL. The researchers concluded that these
results indicate a reason why these individuals do not seek regular medical care.
A recent study conducted in the United Kingdom (Parker, et al., 2014) sought to study the
impact of chronic conditions and multimorbidity in the elderly, in order to determine which of 15
common chronic conditions impacted HRQoL the most, and what interventions might be
appropriate. Researchers studied a community-based population of individuals ages 65 and older.
The mean age of participants was 74.6 years, and of those subjects, 49.2% were male.
Multivariate modeling was used to determine that 13 of the 15 conditions studied significantly
impaired HRQoL. The three conditions that caused the greatest impact were osteoarthritis,
neurological disease, and depression. The researchers recommend that their results be used to aid

in clinical decision making, particularly when setting treatment priorities for patients with
multimorbidity.
In Norway, a study was recently conducted that analyzed illness perception (IP) in
patients with chronic obstructive pulmonary disorder (COPD) (Borge, et al., 2014). Illness
perception refers to how patients evaluate how they live with a disease. The researchers sought to
determine whether breathlessness, a common symptom of COPD, was a precursor of IP, and
whether IP was in turn linked to HRQoL. The researchers conducted a cross-sectional survey
using 154 COPD patients. The researchers used multiple regression analyses to determine that
patients with a high IP score (high IP indicates that patients consider their condition to be a
significant threat) experienced more breathlessness. Researchers suggest that their findings might
have implications for patient counseling, possibly by helping patients learn to cope with their
COPD by restructuring their personal models of illness, which could reduce their breathlessness.
The other main focus area in chronic illness research is employability of individuals with
chronic illnesses. A study that sought to analyze the long-term effects of cancer and cancer
treatment on employment was conducted in 2008 by Short, Vasey, and BeLue. The researchers
had two objectives with this study: “(1) to quantify the increase in work disability attributable to
cancer in a cohort of adult survivors who were an average of 46 months post-diagnosis and (2) to
compare disability rates in cancer survivors to individuals with other chronic conditions (Short,
Vasey, & BeLue, 2008). “ The study compared data from two groups: 647 cancer survivors, aged
55-65, in a sample taken from the Penn State Cancer Survivor Study, and 5988 similarly aged
subjects sampled from the Health and Retirement Study. The researchers used multivariate

logistic regression to develop estimated adjusted odds ratios for work disability for cancer
survivorship, heart disease, diabetes, stroke, lung disease, and arthritis. The results demonstrated
few significant differences in work disability for cancer survivors versus subjects with other
chronic conditions, and both subject groups had significantly higher work disability rates than
similarly aged individuals without any chronic medical conditions. The researchers concluded
that, given the elevated work disability rate, cancer survivorship should be considered a chronic
illness.
Researchers in the Netherlands recently studied the impact of illness duration and age at
diagnosis on labor participation chances (Rijken, et al., 2013). The researchers surveyed several
cohorts of individuals who have been diagnosed with a chronic illness since 1998, and studied
4634 subjects in total. Multi-level logistic regression analyses were used to determine that the
age at illness onset had a significant negative effect on labor participation chances, as did the
duration of illness. The duration of illness negative effect on employment had a stronger impact
on men that it did on women. The researchers suggest that further studies should be conducted to
study different diagnostic groups, and perhaps develop programs to guide young people with
chronic illnesses to help them develop suitable careers.
Research Questions and Hypotheses
Does the use of struggle language and combat metaphors in relation to disease affect
perceptions of disease or perceptions of individuals with a chronic illness? Does the frequency
with which the patient and/or others use struggle language in relation to illness have any
correlation to patients’ quality of life? Are there any demographic factors that correlate with

quality of life ratings? It is hypothesized that the use of struggle language and combat metaphors
has a negative effect on patients’ quality of life. It is also hypothesized that awareness of struggle
language usage will be connected to how frequently it is used.
Methods
Instruments
Questionnaire: The participants completed a 14 question self-reporting questionnaire.
Basic demographic information was collected, including age, gender, race/ethnicity, educational
background, and employment status. Given that the study is about the use of struggle language
and chronic illness, subjects were also asked whether they had worked in the health care field,
whether they or someone they knew had a chronic illness, whether they were aware of the use of
struggle language in regards to chronic illness, and how often they and others around them used
struggle language in regards to chronic illness. Subjects were also asked to rate their quality of
life, their satisfaction with their career path, and the quality of life of someone they knew who
had a chronic illness, using the following scale: 1= Very Low, 2= Low, 3= Neutral, 4= High, 5=
Very High.
Sampling and Data Collection
The sample was obtained through a convenience sampling. There were 33 participants in
this survey. To maintain each participant’s confidentiality, the consent form was signed and filed
separately from the completed surveys. Data from the surveys was then entered into SPSS 22 for
analysis by the researcher.
Variables

The independent variables of this study are awareness of struggle language, struggle
language use, and whether the participant has a chronic illness, or is close to someone who does.
The dependent variables of this study are quality of life of the participant and the participant’s
rating of the quality of life of someone they know with a chronic condition. These variables are
ranked on an ordinal scale, which is as follows: 1= Very Low, 2= Low, 3= Neutral, 4= High, 5=
Very High. These variables will help to determine whether struggle language use is related to
quality of life.
Analysis Strategy
For this study, there are many different ways that both the independent variables and the
dependent variables can be analyzed. Since most of the variables of interest are ordinal and
nominal variables, the tests used will be nonparametric. An Independent Samples Mann-Whitney
U test can be performed to present a significance value and an M place score to determine
whether there is a significant relationship between the respondent’s quality of life score and their
chronic illness status. An Independent Samples Mann-Whitney U test can also be used to
examine the relationship between the respondent’s chronic illness status and awareness of the
use of struggle language. An Independent Samples Kruskal-Wallis Test can be performed to
determine whether there is a relationship between the frequency with which the respondent uses
struggle language and what rating they give to the quality of life of someone with a chronic
illness. This will offer a significance value and an H score. A Spearman’s rho correlation test can
be performed to determine the relationship between an individuals’ awareness of the use of
struggle language and the frequency with which they use struggle language in relation to chronic
illness. This test will offer a significance value and a rho score.

Ethical Concerns
The potential ethical concerns were minimal; regardless, participants were informed of
the concerns in the informed consent form and in the cover letter. Participants were made aware,
both verbally and in writing, that their participation was voluntary, and that they could withdraw
from the survey at any time. Furthermore, participants were made aware of the separation of
signed consent form from completed survey, so as to protect their anonymity.
Results
The demographics are given in Table 1, below this paragraph. There were 33 participants
in the study. Ages ranged from 17 to 62 years old, with a mean age of 40.24 years old and a
median age of 42 years old. Most of the participants were female (n=25), while only 24.2% were
male (n=8). Most of the participants were White/Non-Hispanic (n=31), while one participant
was African American and one participant described themselves as Other. The largest proportion
of participants had Bachelor’s Degrees (n=12), followed by some college but no diploma (n=10),
high school diploma/GED (n=5), Master’s Degrees (n=2), some high school but no diploma
(n=2), Associate’s Degree (n=1), and one participant had a professional degree. Most
participants were employed for wages (n=27), while some were self-employed (n=3), students
(n=2), or homemakers (n=1).

Table 1
Demographic information and independent and dependent variables
N=33 Frequency Percent Mean Median Minimum Maximum
Demographics
Gender
Female 25 75.8% ---- ---- ---- ----
Male 8 24.2% ---- ---- ---- ----
Age, years ---- ---- 40.24 42.0 17 62
Ethnicity
African American 1 3.0% ---- ---- ---- ----
Other 1 3.0% ---- ---- ---- ….
White/Non-Hispanic 31 31.0% ---- ---- ---- ----
Education
Some High School, No Diploma 2 6.1% ---- ---- ---- ----
High School Diploma/GED 5 15.2% ---- ---- ---- ----
Some College, No Diploma 10 30.3% ---- ---- ---- ----
Associate’s Degree 1 3.0% ---- ---- ---- ----
Bachelor’s Degree 12 36.4% ---- ---- ---- ----
Master’s Degree 2 6.1% ---- ---- ---- ----
Professional Degree 1 3.0% ---- ---- ---- ----
Employment Status
Employed for Wages 27 81.8% ---- ---- ---- ----
Self-Employed 3 9.1% ---- ---- ---- ----
Homemaker 1 3.0% ---- ---- ---- ----
Student 2 6.1% ---- ---- ---- ----
Independent Variable
Current or Former Health Care Worker
Yes 7 21.2% ---- ---- ---- ----
No 26 78.8% ---- ---- ---- ----
Awareness of Struggle Language Use
Yes 17 51.5% ---- ---- ---- ----
Somewhat Aware 7 21.2% ---- ---- ---- ----
Not Sure 2 6.1% ---- ---- ---- ----
No 7 21.2% ---- ---- ---- ----
Respondent has a Chronic Illness
Yes 5 15.2% ---- ---- ---- ----
No 28 84.8% ---- ---- ---- ----
Respondent Knows Someone with a Chronic Illness
Yes 22 66.7% ---- ---- ---- ----
No 11 33.3% ---- ---- ---- ----

Table 1, Continued
Demographic information and independent and dependent variables
N=33 Frequency Percent Mean Median Minimum Maximum
How Frequently does Respondent use Struggle Language
Often 4 12.1% ---- ---- ---- ----
Sometimes 9 27.3% ---- ---- ---- ----
Rarely 9 27.3% ---- ---- ---- ----
Never 11 33.3% ---- ---- ---- ----
How Frequently do Others Use Struggle Language
Often 6 18.2% ---- ---- ---- ----
Sometimes 15 45.5% ---- ---- ---- ----
Rarely 6 18.2% ---- ---- ---- ----
Never 6 18.2% ---- ---- ---- ----
Dependent Variable
Respondent’s Quality of Life
Neutral 5 15.2% ---- ---- ---- ----
High 18 54.5% ---- ---- ---- ----
Very High 10 30.3% ---- ---- ---- ----
Respondent’s Satisfaction With Career Path
Low 3 9.1% ---- ---- ---- ----
Neutral 7 21.2% ---- ---- ---- ----
High 16 48.5% ---- ---- ---- ----
Very High 7 21.2% ---- ---- ---- ----
Respondent’s Rating of Quality of Life of Someone with a Chronic Illness
Very Low 1 3.0% ---- ---- ---- ----
Low 6 18.2% ---- ---- ---- ----
Neutral 16 48.5% ---- ---- ---- ----
High 8 24.2% ---- ---- ---- ----
Very High 2 6.1% ---- ---- ---- ----
Independent and Dependent Variable
Independent Variable: Of the participants, 7 had previously worked or volunteered in the health
care field, while most had not (n=26). The majority of participants were aware of the use of
struggle language in relation to chronic illnesses (n=17), while some were somewhat aware
(n=7), not aware (n=7), or not sure (n=2). Only a few participants had a chronic illness (n=5),
while most did not (n=28). The majority of participants did know someone with a chronic illness
(n=22), while the minority did not (n=11). A small majority of participants never use struggle

language in reference to chronic illness (n=11), while some participants rarely used it (n=9), or
used it sometimes (n=9); only 4 participants used struggle language often. More participants
were accustomed to hearing others use struggle language sometimes (n=15), while equal
numbers of participants heard other use struggle language often (n=6), rarely (n=6), or never
(n=6).
Dependent Variable: The majority of participants rated their quality of life as high (n=18), while
others rated their quality of life as very high (n=10), or neutral (n=5). Most participants rated
their career path satisfaction as high (n=16), while equal amounts rated their career path as either
very high (n=7) or neutral (n=7); only 3 participants rated their career path satisfaction as low.
When asked to rate the quality of life of someone they know/knew with a chronic illness, the
majority of participants rated the person’s quality of life as neutral (n=16), followed by high
(n=8), low (n=6), very high (n=2), or very low (n=1).
Statistical Tests
The statistical test results are given in Table 2, below this section. A Mann-Whitney U
test was used to examine the difference in quality of life scores between individuals who did and
did not have chronic illnesses. The significance level is 0.290 and the U-value is 91.50 with 33
degrees of freedom. These results do not appear to be significant. A Mann-Whitney U test was
used to examine the levels of awareness of struggle language use between individuals who did
and did not have chronic illnesses. The significance level is 0.045 and the U value is 110.0 with
33 degrees of freedom. This result is significant because the significance level is below 5%,
which indicates that there is a connection between struggle language awareness and chronic
illness status. Participants who have a chronic illness were significantly more likely to be aware

of the use of struggle language (M place = 9.00) than individuals who do not have a chronic
illness (M place = 18.43). A Kruskal-Wallis test was conducted to examine the relationship
between a respondent’s quality of life rating for someone with a chronic illness and the
frequency with which the respondent uses struggle language in relation to chronic illness. No
significant relationship was found (H(3)=.742, P=.863), indicating that the there were no
significant differences. A Spearman rho correlation coefficient was calculated for the
relationship between a respondent’s awareness of struggle language and the frequency with
which they use struggle language. A strong positive correlation was found (rho(31)=.458,
P=.007), indicating a significant relationship between awareness and usage. Participants who
were more aware of the use of struggle language in regards to chronic illness were more likely to
use struggle language themselves.
Table 2
Results from Statistical Tests
Independent Variable Dependent Variable Test Conducted Results
Respondent’s Chronic Illness Status Quality of Life Mann-Whitney U test U=91.50 P=0.290
Respondent’s Chronic Illness Status Awareness of Struggle Lang. Mann-Whitney U test U=110.0 P=.045
Respondent’s QoLRating for Person Frequency of Struggle Kruskal-Wallis test H(3)=.742 P=.863
With Chronic Illness Language Usage
Awareness of Struggle Language Frequency of Struggle Spearman Correlation rho(31)=.458 P=.007
Language Usage
Limitations
There are many limitations that may have affected the results of this survey. The sample
size was small, and it was heavily female and white, which is likely partly due to the distribution
process. There was also a very small population of respondents who had chronic illnesses, which
could certainly skew the results. And because the surveys were self-reported, respondents
falsifying their answers is always a possibility.

Conclusion
Implications
The results of this study might be useful in future practice if more work was done to
survey chronic illness patients to determine their feelings about the use of struggle language, and
if those feelings are negative, then perhaps society could work towards building a more person-
focused, less combat-focused vocabulary for chronic illness. Currently, this study is helpful in
proving that individuals, both with and without chronic illnesses, are aware of the use of struggle
language, and that awareness of such language is connected to how frequently it is used.
Conclusions
The results of this study have been inconclusive in proving my hypothesis that increased
use of struggle language would be connected to lower quality of life ratings in people with
chronic illnesses, as no such connection was found. My hypothesis that awareness of struggle
language usage and frequency of struggle language usage was not disproven, although I did
expect the opposite result (higher levels of awareness would result in less frequent usage).

References
Bodenheimer, T., Chen, E., & Bennett, H. (n.d.). Confronting The Growing Burden Of Chronic
Disease: Can The U.S. Health Care Workforce Do The Job? Health Affairs, 64-74.
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Borge, C. R., Moum, T., Puline Lein, M., Austegard, E. L., & Wahl, A. K. (2014). Illness
perception in people with chronic obstructive pulmonary disease. Scandinavian Journal
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Parker, L., Moran, G. M., Roberts, L. M., Calvert, M., & McCahon, D. (2014). The burden of
common chronic disease on health-related quality of life in an elderly community-
dwelling population in the UK. Family Practice, 31(5), 557-563.
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illness duration, age at diagnosis and the year of diagnosis for labour participation
chances of people with chronic illness: results of a nationwide panel-study in the
Netherlands. BMC Public Health, 13(1), 1-13. doi:10.1186/1471-2458-13-803
Short, P. F., Vasey, J. J., & BeLue, R. (2008). Work disability associated with cancer
survivorship and other chronic conditions. Psycho-Oncology, 17(1), 91-97.
doi:10.1002/pon.1194
Steel, J. L., Geller, D. A., Robinson, T. L., Savkova, A. Y., Brower, D. S., Marsh, J. W., &
Tsung, A. (2014). Health-related quality of life as a prognostic factor in patients with
advanced cancer. Cancer (0008543X), 120(23), 3717-3721. doi:10.1002/cncr.28902
Venkataraman, K., Khoo, C., Wee, H. L., Tan, C. S., Ma, S., Heng, D., & ... Thumboo, J. (2014).
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