intercurrent illness intervening lifestyle in aging
TRANSCRIPT
122 December 2019 International Journal of Health and Rehabilitation Sciences volume 8
issue 4
INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
ORIGINAL RESEARCH Evaluation of inter-current illness intervening lifestyle in
stratified age groups: focus on diabetes and its
cardiovascular complication
Ezekiel U. Nwos, Christian Ojugbeli, Phillip T. Bwititi, Eunice O. Igumbor
Ezekiel U. Nwos 1Department of Public and
Community Health, Novena
University Ogume, Nigeria
2School of Community
Health, Charles Sturt
University, New South
Wales, Australia
Christian Ojugbeli
Department of Public and
Community Health, Novena
University, Ogume, Nigeria
Phillip T. Bwititi
School of Biomedical
Sciences, Charles Sturt
University, New South
Wales, Australia
Eunice O. Igumbor
Department of Public and
Community Health, Novena
University, Ogume, Nigeria
Corresponding Author:
Dr Uba Nwose.
School of Community Health,
Charles Sturt University.
Leeds Parade, Orange, NSW
2800 Australia. Email:
ABSTRACT
Background: We propound a lifestyle scores’ hypothesis on how changes in lifestyle
scores correlate with state of health including vulnerability of adults to diabetes and
CVD indices.
Objective: This study evaluates illnesses observed in a rural community and compares
stratified age groups in the prevalence of diabetes-related chronic diseases and inter-
current illness.
Methods and Materials: 203 participants were classified into five stratified
age-groups. Information on demography, diabetes, hypertension and other
illnesses were reviewed. Prevalence of inter-current illness in stratified age-
groups was cross-examined with percentage distribution of disease-
subpopulations into the groups. Percentage of each age-group whose lifestyles
were affected by ill-health was cross-checked with physical activities level.
Comparison of lifestyle between groups were performed.
Results: Age-groups differed in percentage of respondents whose daily routines
are interfered by ill-health (p < 0.0001). Good health decreased with age (p <
0.0001), but inter-current illness was not different across age-groups. Activities
of daily living and walking were similarly interfered by ill-health.
Conclusion: Inter-current illness was shown to be equally prevalent across age-groups,
though older adults had significantly greater interference on their lifestyle.
Keywords: activities of daily living, aging, diabetes, inter-current illness, lifestyle
scores
123 December 2019 International Journal of Health and Rehabilitation Sciences volume 8
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INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
INTRODUCTION
There are reports that antioxidants such as
ascorbic acid may alleviate oxidative stress in
older, but not in younger adults 1, and that some
exercise therapies are more effective in younger
than in older adults 2; 3. Thus, we propound a
lifestyle scores’ hypothesis on how changes in
lifestyle scores correlate with state of health
including vulnerability of adults to diabetes and
CVD indices. The concept of inter-current illness
is brought to the fore in this proposed lifestyle
scores’ hypothesis. This concept has been in
literature at least for some time 4, and research has
been done in this regard 5-9. In fact, a guide on
‘Management of Diabetes’ during inter-current
illness was endorsed in the United Kingdom 10.
Therefore, the effect of inter-current illness in
relation to chronic diseases such as diabetes and
its cardiovascular complications is acknowledged
as evident and necessary to be given
consideration.
Inter-current illness is a disease that intervenes
during the course of another disease condition 11.
In other words, a comorbidity or ill-health that
intervenes in or negatively interacts with another
physiological process such as aging 12. For
instance, age may be a non-modifiable risk factor
for diseases such as diabetes and lifestyle
intervention is an option for slowing down the
rate vis-à-vis physiological process 13. In the
context of lifestyle intervention, it is known that
duration is a factor in adherence to exercise
prescription 14. Age and inter-current illness have
been identified as related factors given that
exercise tolerance decreases with age and minor
illness incapacitates the elderly 15; 16.What has yet
to be investigated is cross-sectional population-
based prevalence of inter-current illness in
stratified age-groups.
Exercise and coronary artery disease are risk
factors in vulnerability of diabetes progression
and are also included in British United Provident
Association (BUPA’s) model of cardiovascular
risk assessment 17, beside the traditional
BMI/lipid factors 18; 19. However, most clients
may be unable to estimate their physical activity
level in the context of exercise 20 and there are
clients who have no diabetes, but have
comorbidities that impact on the hypothalamus-
pituitary-adrenalin axis to induce diabetes 21.
Such instances necessitate that lifestyle be re-
evaluated and probably re-defined for screening
purposes, especially for prediabetes.
In this brief review, the objective is to evaluate
illnesses observed in rural low to middle income
communities (LMIC) to identify how stratified
age groups compare in the (1) the extent that ill-
health interferes with routine activities of daily
living on one hand, and reported physical
activities on another hand; (2) prevalence of
diabetes-related chronic diseases and inter-
current illness and vice versa; and (3) responses
to the basic questions on International Physical
Activity Questionnaire (IPAQ) on one hand; and
another set of questions on routine activities of
daily living as per the World Health
Organization’s Stepwise instrument22.
The findings will help to articulate and delineate
if aging as a physiological condition intervenes
physical activities en-route interference of
lifestyle; or inter-current illness is significantly
more prevalent in the older adults, en-route
higher interference of lifestyle. Thus, we propose
the lifestyle scores’ hypothesis, with ability to
perform daily physical routine activities at the
core.
Methods and Materials
Ethics approval and study setting
This study was part of the prediabetes and
cardiovascular complications screening
(PACCS), an international research collaboration
involving the department of Public and
Community Health of Novena University;
anchored at the Catholic Hospital Abbi in
124 December 2019 International Journal of Health and Rehabilitation Sciences volume 8 issue 4
INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
Nigeria. Ethics approval were obtained from all
relevant authorities including Human Research
Ethics Committee approval for the
Cardiovascular risk assessment in prediabetes
and undiagnosed diabetes study (protocol
number 2014/158).
Study design
This was a descriptive cross-sectional
population-based study as defined in health
research methodology 23. The study was limited
to communities, visited the Catholic Hospital
Abbi for healthcare and included males and
females above 18 years old. Based on the
assumed population size of about 149 325 24,
sample size calculation determined using
Raosoft® online resource was 139 assuming 5%
margin of error, 90% response rate and 95
confidence level 25. Following public health
lectures and information regarding the nature and
general purpose of the study, consent was
indicated by the participants through one of two
ways. Firstly, participants who were not hospital
clients voluntarily registered with the Medical
Records department to own a file and secondly,
those already with a hospital file consented by
requesting their files from the Medical Records
Officer for consultation as part of the study.
Data Collection
Instruments of data collection included structured
questionnaire, clinical laboratory tests, and
medical history. Participants underwent vital
signs measurements, which included height,
weight, blood pressure, temperature, and pluses
pressure. Height and weight were used to
calculate body mass index (BMI), which was in
turn was employed to determine obesity (BMI >
30). The questionnaire was a adopted from the
IPAQ based on the World Health Organization’s
Stepwise instrument22; and used in ongoing
studies 26. The questionnaire comprised sections
on demographic information of participants,
general health including diabetes-related chronic
diseases as well as inter-current illnesses, daily
activities, physical activities, and others such as
dietary, laboratory and screening test result. In
this study, diabetes-related chronic disease was
limited to components of metabolic syndrome
(diabetes, dyslipidemias, hypertension, and
obesity). Inter-current illness included any other
ill-health reported by participants.
Lifestyle was defined as a combination of
activities including alcohol consumption levels,
cigarette smoking, and exercise. Further, exercise
was defined as a combination of daily and
physical activities. Dietary habit was
acknowledged as a lifestyle factor, but
discretionally excluded in analysis after
consideration of the respondents’ limitation as a
factor of affordances. Hence, sections D-Daily
activities, and section E-Physical activities were
used to determine lifestyle of participants. Based
on the assumed negative or positive impact of
each activity on diabetes and its CVD
complications, a ‘lifestyle score’ was determined
Table 1: The 12-questions on lifestyle activities
and how ‘lifestyle scores’ were determined
A: Lifestyle questions Questions
D
1
Have you been unable to go to work any day
because of ill health?
D
2
Has your health interfered with your normal daily
social activities?
D
3
Has your health interfered with your normal daily
hobbies & recreational activities?
D
4
Has your health interfered with your daily
household chores?
D
5
Has your health interfered with your errands and
shopping?
D
6* Smoking
D
7* Alcohol
E
1 Stretching or stretching exercise
E
2 Walk for exercise
E
3 Swimming
E
4 Bicycling
E
5 Other exercises or physical activities
Keys: D – daily activities; E – physical activities;
*negative effect on health
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Determination of ‘lifestyle scores’
The major focus of rigor in this study was
evaluation of responses to questions on physical
activities side-by-side with response to routine
activities of daily living. Afterwards, the
responses were combined to develop lifestyle
scores in view of developing a hypothesis.
Therefore, all responses to the daily and physical
activities’ questions were given numerical scores
between 0 and 4. A ‘lifestyle score’ for each
respondent was determined by adding the values
of all ten questions that positively impacts on
health and subtracting the values of the questions
with negative impact (Table 1). Negative effect
on health from 2/7 of the daily activities’
questions (alcohol and smoking), and the other
5/7 being due to interference of ill-health were
presumed.
Table 1: The 12-questions on lifestyle activities
and how ‘lifestyle scores’ were determined
A: Lifestyle questions
Questions
D1 Have you been unable to go to work any day because of ill health?
D2 Has your health interfered with your normal daily social activities?
D3 Has your health interfered with your normal daily hobbies & recreational activities?
D4 Has your health interfered with your daily household chores?
D5 Has your health interfered with your errands and shopping?
D6* Smoking
D7* Alcohol
E1 Stretching or stretching exercise
E2 Walk for exercise
E3 Swimming
E4 Bicycling
E5 Other exercises or physical activities
Keys: D – daily activities; E – physical activities; *negative effect on health
B: Scores awarded for responses to the lifestyle
questions
Score Daily activity Physical activity
0 None at all
1 Slightly <30 minutes
2 Moderately 30-60 minutes
3 Quite a bit 1 – 3 hours
4 Almost totally >3 hours
Physical activity potentially having positive
impact on diabetes and its CVD complications
was also presumed. Based on the presumptions,
the following formula was used to determine
lifestyle scores for each participant:
➢ Lifestyle score = (28-
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INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
(D1+D2+D3+D4+D5+D6+D7)) +
(E1+E2+E3+E4+E5)
The idea in the formula was to account for the
negative effect of ill-health on daily activities and
it was assumed that maximum points achievable
is 48. An absolutely ill person suffering absolute
effects of ill-health scores negative ‘7 x 4 = 28’,
given that ill-health conditions may be non-
modifiable factors, while an apparently healthy,
non-alcoholic and non-smoking individual scores
zero (0) on daily activities. In order to account for
variation in degrees of effect of ill-health, factor
28 is made a baseline from which points earned
due to ill-health are subtracted. Therefore, for the
apparently healthy, non-alcoholic and non-
smoking individual with a zero score on daily
activities there was nothing to subtract. On the
other hand, a point earned due to ill-health would
reduce the achievable lifestyle score.
Data analysis
All participants were classified into one of five
stratified age groups: group 1 (18 – 39 years),
group 2 (40 – 59 years), group 3 (60 – 69 years),
group 4 (70 – 79 years), and group 5 (≥80 years).
For the purpose of this review on inter-current
illness, only questionnaire sections A, B, D and E
were evaluated. Beside correlation analysis, two-
way assessments were performed to affirm
associations. Data generated were analyzed using
Micro Soft Excel Data Analysis Tool Pak 2010.
All responses to the daily and physical activities’
questions were given numerical scores between
zero (0) and four (4). A ‘lifestyle score’ for each
respondent was determined by adding the values
of all ten questions that positively impacts on
health and subtracting the values of the questions
that have negative impact (Table 1).
The statistical analyses included (1) descriptive
statistics – % of group members who responded
‘no’ to daily & physical activities’ questions as
well as prevalence of metabolic syndrome; (2)
prevalence of inter-current illness in stratified age
groups and percentage distribution of disease
subpopulations in the stratified age groups; and
(3) comparison of average lifestyle scores from
table 2
RESULTS
Descriptive statistics of group members who
responded ‘no’ to daily and physical activities’
questions as well as prevalence of metabolic
syndrome are presented in Table 2. The average
percentage of each group respondents whose
daily and physical activities were affected by ill-
health are presented in Figure 1.
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INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
Table: 2: Percentage of ‘no’ responses* and prevalence of metabolic syndrome components in age groups.
*‘No’ response refers to those whose daily and physical activities were un-interfered
**Based on calculated BMI >30
†Based on being clinically diagnosed as reported by client
‡Groups’ age stratification (years): 1 (18 – 39), 2 (40 – 59), 3 (60 – 69), 4 (70 – 79), and 5 (≥80)
Group 1 Group 2 Group 3 Group 4 Group 5
N 40 53 46 35 29
Hypertension† 2.50 22.64 30.43 34.29 31.03
Diabetes† 2.50 7.55 6.52 2.86 3.45
Dyslipidaemia† 0.00 1.89 0.00 0.00 0.00
Obesity** 7.50 15.09 10.87 2.86 6.90
Daily activity 1 65.00 62.26 34.78 28.57 31.03
Daily activity 2 70.00 60.38 47.83 48.57 31.03
Daily activity 3 72.50 60.38 36.96 40.00 27.59
Daily activity 4 75.00 66.04 36.96 34.29 20.69
Daily activity 5 82.50 66.04 41.30 37.14 27.59
Daily activity 6 92.50 88.68 93.48 100.00 93.10
Daily activity 7 80.00 64.15 86.96 68.57 79.31
Physical activity 1 70.00 62.26 67.39 80.00 82.76
Physical activity 2 10.00 5.66 10.87 5.71 31.03
Physical activity 3 77.50 92.45 91.30 94.29 100.00
Physical activity 4 42.50 56.60 50.00 42.86 68.97
Physical activity 5 97.50 94.34 100.00 100.00 100.00
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Figure 1. shows Percentage of each group whose
daily and physical activities were affected by ill-
health. On the 2nd statistical objective, the %
distribution of health subpopulations into age-
groups showed no significant difference, but
prevalence of various health conditions in age-
groups is statistically different (Figure 2).
Figure 1: Comparative % of age groups that constitute disease sub-populations and vice versa
Figure 2: Comparison of average scores from
responses on all 12 activities’ questions between
different age groups
Figure 2 shows no statistical difference overall
(P > 0.89 (D1 – E5)), except when limited to D1
– D5 variables (P < 0.00001). This affirms that
interference of ill-health on daily lifestyle
increases with age and this is corroborated by
response exercise.
Figure 3. Comparison of averages responses from all
12 questions on activities between age groups.
Analysis of correlation shows that age slightly
correlates positively with daily activity
responses, and negatively with exercise only.
Regression analysis shows that in the studied
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INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
population, age (p < 0.01), but not BMI
significantly impacts on lifestyle scores (Table 3)
Table 3: Regression analysis comparing impact of age and BMI on lifestyle scores
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 40.0266 2.4607 16.2661 0.0000 35.1741 44.8790
Age -0.0659 0.0239 -2.7625 0.0063 -0.1130 -0.0189
BMI -0.0319 0.0759 -0.4204 0.6747 -0.1816 0.1178
DISCUSSION
Analysis of variance of percentages of age-
groups who responded ‘none’ to all daily and
physical activities’ show no statistical difference
between age groups. Further analysis of ‘No’
responses to the questions on daily activities,
separate from physical activities shows linearity,
but only the former achieved statistical
significance (Fig. 1). Highest percentage of
group 1 (18 – 39 years) had daily work activities
unaffected by ill-health, relative to lowest
percentages in groups 4 & 5 (70 – 79 years) and
this implies that highest daily activities are in
group 1 and lowest in groups 4 & 5. In this study,
inter-current illness is considered in relation to
ability to perform daily physical routine
activities, as ill-health that intervenes with
individual’s normal abilities and interferes with
lifestyle. That is, physical activity can reduce the
physiological negative effects of aging and its
associated risk of disabilities 27. The implication
of this therapeutic effect is that physical activity
intervenes on disability arising from inter-current
illness 15, which is different from inter-current
illness intervening on lifestyle that includes daily
or routine physical activities. This may explain
why daily activities are compared to, or related
with health status (Fig. 1) and stratified age
groups (Figure 3) observed in our study cohort.
The significance of this report is that that inter-
current illness is a factor to be managed for
effective physical activity intervention 15,
especially considering that exercise tolerance
decreases with age and minor ill-health can
render the elderly to a zero level ‘independent’
adherence16. Indeed, potential barriers to physical
activity have long been highlighted for
considerations when prescribing exercise for the
elderly 28; 29. This study has evaluated age
differences and reports two contrasting findings
that need to be delineated – viz:
1. There are no significant differences between
stratified age-groups on physical activities or
in prevalence of inter-current illness. Figure
2 shows that young adults have highest
proportion of being apparently health
compared to the older adults and vice versa,
older age group has higher proportion of
individuals with various illnesses (Figure 2a).
For instance, Group 1 (18 – 39 years) makes
130 December 2019 International Journal of Health and Rehabilitation Sciences volume 8 issue 4
INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
up approximately 60% of the ‘healthy’ sub-
populations, but only 2% of those with
hypertension. Nevertheless, prevalence of
inter-current illness is neither lowest in
youngest group 1, nor highest in the oldest
group 5 (Figure 2b). This corroborates with a
previous report that prevalence of CVD risk
factors is neither lowest in the youngest, nor
highest in the oldest age groups 30.
2. Effect of age on daily activities is quite
significant, but not with itemized physical
activities (Figure 3). That is, considering
daily activities as a form of exercise that
improve health, the interference of ill-health
on going to work, social activities, recreation,
household and outside chores increases with
age (p < 0.00001). Other physical activities
show no unidirectional change with age,
except E2 (walking as a form of exercise) that
decreases with age. The implication is that
attempting to improve or quantify physical
activity level need to consider intervening
effects of inter-current illness
Aging as a factor of inter-current illness
Our results show that age impacts more on
lifestyle than obesity (Table 3). Aging comes
with the risk of disability, which in turn affects
lifestyle. Hence, the knowledge of physical
activity to reduce the impact of aging is difficult
to translate into practice and a new theory is now
bordering on ‘increasing physical activity in the
everyday lives’ 27, which is in tandem with
evaluation of daily activities in our lifestyle
scores hypothesis. What this report contributes is
that management of patients’ lifestyle should be
delimited to monitoring of conventional
‘structured’ physical activities to include daily
physical routines of a client, and how influence of
age and inter-current ill-health may be
intervening with the daily routines.
Perhaps, it is pertinent to note that current focus
of diabetes research includes vulnerability of
older adults to diabetes and its cardiovascular
complications. The relevance of this report is
arguably the diabetes and inter-current illness
interaction. We observed that the average age of
hypertension subpopulation is higher than that of
diabetes, which agrees with the pathogenesis
sequence of prediabetes to diabetes en-route
cardiovascular complications. Aging has been
identified as a risk factor for obesity, which has
been recognized as inter-current illness 31.
Therefore, considering aging as a risk factor in
diabetes cardiovascular disease, there is double-
implication of obesity that should be factored into
assessment of obese individuals. Hypothetically,
inter-current illness may induce diabetes in
adulthood through interference with lifestyle that
is pro-inactivity and leads to obesity. This is in
line with the position that low physical function
is a risk factor for DM 32.
No difference in obesity measured by BMI was
observed in the age groups in this study and we
noted how daily activities significantly decreased
with increasing age-groups (Figure 3), and
interference of ill-health being unidirectional
131 December 2019 International Journal of Health and Rehabilitation Sciences volume 8 issue 4
INTERCURRENT ILLNESS INTERVENING LIFESTYLE IN AGING
(Figure 1). This can be translated to emphasize a
potential for sedentary lifestyle change to
investigate, but in the context of age and inter-
current illness intervening daily activities.
Studies report that while concerted efforts to
improve physical activity are required, metabolic
syndrome may not be improved by being
physically active alone 33. What this report adds
or affirms is that aging, as a physiological
process, intervenes on ability for routine physical
activities en-route interference with lifestyle and
associated morbidities while inter-current illness
is equally prevalent across all age-groups and
may only constitute confounding interference on
lifestyle in older adults.
CONCLUSION
The report presents evidence that there is no
difference between stratified age-groups on
physical activities or in prevalence of intercurrent
illness. However, prevalence of cardiometabolic
conditions in different age-groups is significantly
different just as interference of ill-health in
regards to going to work, social activities,
recreation, household, and outside chores
increases with age. Given that inter-current
illness is a factor that can be managed and in this
context in part by effective physical activity, and
considering that exercise tolerance decreases
with age and minor ill-health can negate
exercise, it is suggested that evaluation of
lifestyle in relation to diabetes pathogenesis
should be delimited by conventional physical
activities. It is also import to consider the
influence of age and/or inter-current ill-health on
activities of daily living that possibly trigger
diabetes through obesity and sedentary lifestyle.
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