intercurrent illness intervening lifestyle in aging

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


University, Ogume, Nigeria

Phillip T. Bwititi

School of Biomedical

Sciences, Charles Sturt

University, New South

Wales, Australia

Eunice O. Igumbor

Department of Public and


University, Ogume, Nigeria

Corresponding Author:

Dr Uba Nwose.

School of Community Health,

Charles Sturt University.

Leeds Parade, Orange, NSW

2800 Australia. Email:

[email protected]

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

mailto:[email protected]

123 December 2019 International Journal of Health and Rehabilitation Sciences volume 8

issue 4


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


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



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-



(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.



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







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



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



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



(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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