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The link between stress and illness

Abstract

This research set out to investigate the links between stress and illness, by testing whether the number of life events that people face is associated with how often they become ill. The relationship between psychological stress and individual illness has been documented in a large number of studies and research pieces, often showing higher rates of illness in those who are deemed to be more stressed – through measurement most often in terms of life events (Holmes & Rahe, 1967) or daily hassles (Kanner et al,1981)
A study was designed to test the hypothesis that a greater number of life events would be associated with more illness and illness symptoms through number of illness days. 25 participants were involved in the research through opportunity sampling, which involved them completing a self-report style questionnaire. The questionnaire had two main parts; first to indicate which (if any) of the life events they had experienced in the last six months, and secondly to indicate how many days they had experienced feeling unwell within the last month. The data was then analysed to see whether statistically, stress and illness were related (correlated). It was found that a positive correlation existed between illness risk (from life event scores) and illness days, whereby increasing scores on the SRRS increased the number of days where illness was experienced. This means that the experimental hypothesis is accepted, and the null hypothesis can be rejected.

Introduction

It is now widely acknowledged within health fields such as medicine and psychology, that the experience of stress has the potential to make people ill. The literature surrounding this topic is vast – covering both the way in which physiologically stress can result in illness, and also into what types of stress and levels of stress that are most likely to lead to illness. One of the best recognised definitions of stress is that by Lazarus and Folkman (1974) who stated ‘stress is a particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her well-being.’ Also known as a person-environment mismatch, thought largely due to the ever increasing demands individuals feel they are put under at work and at home.
It is thought that there are two possible pathways by which stress can transmit from psychological to physiological changes. The direct effects approach in disease development (Cardwell, Clark & Meldrum, 2000) suggests that experiences are transmitted by the central nervous system that creates changes in other human organs in a way detrimental to health as our immune systems can become less effective making an individual more prone towards becoming ill. Indirectly stress can lead to increased participation in behaviours damaging to health – possibly such as smoking or drinking more.
Assessing the level of stress people are experiencing can be very difficult due to the wide variety in the types of pressures and stressors that people may be under. Some of the popular ways that this has been done is through using the measurements of life events. The Social Readjustment Rating Scale (SRRS) by Holmes & Rahe (1967) is a scale of life events such as bereavements or changes in circumstances that were deemed as the most highly stressful to adjust to. The events included within the scale were then rated by a subject group and scored according to their rank in comparison to the other events – the higher the score the greater the stress is assumed to be. ‘Death of a spouse’ was given the top score within the study at 100 points. Holmes and Rahe (1967) proposed that scores of 150 or more meant an individuals risk of becoming ill was increased by 30%, and a score of over 300 increased that risk to 50% (Cardwell, Clark & Meldrum, 2001). The Daily hassles (Kanner et al, 1981) method for measuring stress levels is another popular method, which assumes that it is more the struggles and annoyances that people face day to day (as opposed the rather extraordinary life events included within the SRRS) such as delayed trains, being late for appointments or worrying about your weight, are related to your chances of becoming ill from stress.

Hypothesis

The present study will look to test the assumptions by Holmes & Rahe (1967) that state those with a score of 150 on the SRRS are more likely to become ill (a high risk of illness group). This will be done by comparing the number of days that participants had felt unwell, or had experienced an illness symptom (to be called illness days), between those with low life event scores of less than 150 (a low risk of illness group) to those in the higher risk group (scores 150+).
The experimental hypothesis for the study thus follows that those in the high risk group will have significantly more days of illness (illness experiences and symptom experiences) than those in the low risk group. The null hypothesis is that there will be no difference in number of days of illness between high and low risk groups.

Method

Materials needed for the completion of the study was a questionnaire including the SRRS checklist (taken from Holmes & Rahe, 1967). The procedure involved random sampling in a local a shopping centre to recruit 25 participants to complete the study. Descriptive data of gender and age were noted at the top of each questionnaire, but names were not taken as to ensure anonymity. Consent was agreed before the questionnaire was completed, and participants were informed that they could withdraw their consent and data at any time during the study.
All participants were told that they would be completing a survey that was investigating possible effects of different types of life events on our health, and that in part 1 of the survey they would be required to tick any of the life events that applied to them in the last twelve months. (See the SRRS checklist in appendix 1) In part 2, participants would be asked to indicate how many days during the last month that they had experienced an illness or felt unwell (illness days). Illness days could include anything from having a headache one evening (counted as 1 day with an illness episode) to having the flu for a week (7 days of illness episode) for which the individuals total illness days score would be (1+7) 8.
The independent variable (IV) for the study is the risk of illness (high or low risk) and the dependent variable (DV) is the number of illness days. Participants grouping into high or low risk of illness groups depending on whether their life event score was less than 150 (low group) or 150+ (high group). Statistical analysis would be used to show the population type through descriptive statistics (such as proportion of males to females, mean and range of participant age etc) and then descriptive and correlational analysis (Spearman’s rho) completed to test the experimental hypothesis that those with high risk of illness will have more days of illness than those with low illness risk.
Reflection into the ethics of this study highlighted no foreseeable difficulties, as no deception is involved, consent is obtained and ability to withdraw from the study discussed with potential participants, and there are no long terms effects believed to result from the participation within this study. It was decided that marking of risk would be completed after all the questionnaires had been completed, and participants not told whether they were categorised as high or low risk of illness.

Results

Raw data from the survey of the participant sample is shown in Table 1. Descriptive statistics on the sample showed a female dominated sample (21:4 f/m) with the average age 34.96 years and the overall age range as 16 - 65
The mean life event score across the sample was 112.2 (=2805/25). 19 individuals gained less than 150 life event points and so formed the low risk illness group, with 6 individuals getting 150 or more and so classed as high risk illness group. The mean life event score for the high risk group was 205.5 (=1233/6) compared to 82.7 (=1572/19) for the low risk group.
The mean number of illness days across the sample was 4.52 (=113/25); with the average for the high risk group 8 (=48/6) compared to an average of 3 (=63/19) days of illness for the low risk group.

This initial stage of analysis based on descriptive statistics does therefore suggest that a difference in days of illness is evident between the two groups; whereby the high risk group have over double the average number of illness days than the low risk group. This finding will be strengthened through the use of more powerful statistical analysis, by performing a correlational analysis using spearman’s rho in order to check that the difference between the two groups is significant (i.e. not down to chance).

The correlation coefficient for the correlational analysis was r=0.5987 (for calculations see appendix 3). As this exceeds the critical value of 0.526, the correlation between the variables of risk and illness days is statistically significant to p<=0.005 as the test was 1-tailed. As the correlation coefficient is a positive value, this indicates the direction of the correlation to show that as risk increases, number if illness days will increase. The results of the correlational analysis mean that the null hypothesis (that there would be no difference in illness days between risk groups) can be rejected.

Discussion

The results of this study are therefore supportive of the original Holmes & Rahe (1967) study, in that they show people who have experienced more life events appear to show the effect of this increased stress through poorer health – in this particular case by having experienced more days of illness in the month prior to taking part in the study.

A much smaller number of participants fell into the high risk group (according to their life event scores) than into the low risk group, although this was expected to a degree, as one of the criticisms of the Life events method over other measurements of stress such as ‘daily hassles’ is that the events included in the SRRS checklist can be seen in many ways as extraordinary events, and so a smaller number of the population are likely to have experienced them within a set time frame, than daily hassles which people are obviously much more regularly exposed to. A more equal number of high and low risk participants, and ratio of males to females would be more ideal for future research, and this is likely to need much more time and consideration into sampling methods to achieve this. Observing the life events experienced through participants responses show that many items on the checklist are still culturally relevant, although it is noted that some items could do with being updated (such as cut off points for high and low mortgage responsibilities) and possibly re-scored according to more modern values.

The results of this study are important as there are many effects that increased illness can have on individuals and the population at large. Individuals can become resistant to medication that is needed to be taken repeatedly (like antibiotics), and so their health may suffer in the long run from repeated episodes of illness, as well as from suffering with the actual physical symptoms themselves. Economically the country may suffer as a result of increased population illness through both lost working days and increased cost to the NHS to treat and look after ill people. There are however a few points that should also be taken into consideration when analyzing the results of this study. Self report techniques were used to gather personal information about the participant for the study, alongside the retrospective nature of the questions have issues of reliability - on the accuracy that people recall the events they have experienced and the amount of times they had felt ill over the last month. Also, people have a tendency to overestimate certain events and therefore ways of assessing their actual number of illness days (such as a prospective diary to complete) would be useful in future studies.

The use of measurement tools to assess levels of stress in individuals can help in understanding why people may becoming ill, and can help in both treatment and long term outcomes. If people are suffering physically as a result of stress this would indicate that helping to deal with the stress (such as through counseling or cognitive behavioural therapy or practical help) could improve short term health, and teaching people coping skills could aid health long term, through developing strategies for dealing with future stressors.

References

Bartlett, D edited by Payne, S & Horn, S (1998) Health Psychology: Stress – Perspectives and processes Open University Press.

Cardwell, M (2002) AS Psychology Module 2: Physiological Paychology and Individual Differences (2nd Ed) Phillip Allan Updates

Cardwell, M. Clark, L. & Meldrum, C (2000) Psychology for AS Level Collins

Gross, R (1996) Psychology: The science of mind and behaviour (3rd Ed) Hodder & Stoughton

Holmes & Rahe (1967) cited in Cardwell, M. Clark, L. & Meldrum, C (2000) Psychology for AS Level Collins

Kanner et al, (1981) cited in Cardwell, M. Clark, L. & Meldrum, C (2000) Psychology for AS Level Collins

Lazarus and Folkman 1974, cited in Bartlett, D edited by Payne, S & Horn, S (1998) Health Psychology: Stress – Perspectives and processes Open University Press.

Wadeley, A & Cardwell, M (2002) AS Psychology Module 3: Social Psychology and Research Methods (2nd Ed) Philip Allan Updates

www.psychologystuff.com
Appendix

Appendix 1: SRRS checklist & point scores

Life event Score Life event Score
Death of a spouse 100 Son or daughter leaving home 29
Divorce 73 Trouble with in-laws 29
Marital separation 65 Outstanding personal achievement 28
Jail term 63 Wife begins or stops work 26
Death of a close family member 63 Begin or end school 26
Personal injury or illness 53 Change in living conditions 25
Marriage 50 Revision of personal habits 24
Fired at work 47 Trouble with boss 23
Marital reconciliation 45 Change in work hours / conditions 20
Retirement 45 Change in residence 20
Change in health of family member 44 Change in school 20
Pregnancy 40 Change in recreation 19
Sex difficulties 39 Change in church activities 19
Gain a new family member 39 Change in social activities 18
Business readjustment 39 Mortgage less than £10,000 17
Change in financial status 38 Change in sleeping habits 16
Death of a close friend 37 Change no.of family get-togethers 15
Change to a different line of work 36 Change in eating habits 15
Change in no. of arguments spouse 35 Holiday/vacation 13
Mortgage over £10,000 31 Christmas 12
Foreclosure of mortgage or loan 30 Minor violation of the law 11
Change in responsibilities at work 29

Appendix 2: Life event scores by participant

P-num Life event scores Total life event score
1 73 38 31 20 15 12 189
2 38 29 17 12 96
3 13 12 11 36
4 50 40 31 18 13 12 164
5 36 31 29 12 108
6 44 31 13 12 100
7 45 38 17 16 12 128
8 31 29 29 20 13 12 134
9 37 35 12 84
10 31 29 28 20 13 12 133
11 31 15 12 58
12 73 39 38 26 20 12 208
13 36 31 20 12 99
14 65 45 35 31 29 24 20 12 261
15 40 39 38 31 26 18 13 12 217
16 53 17 12 82
17 29 12 41
18 45 12 57
19 31 12 43
20 26 13 12 51
21 50 31 13 12 106
22 73 38 31 20 20 12 194
23 29 20 12 61
24 39 31 12 62
25 50 31 12 93

Appendix 3: Spearman’s Rho calculations

(statistics were completed using the online statistics tool from www.psychologystuff.com)

N = 25 N is obtained by counting the total number of cases
N3-1 = 15600 Cube N and then take away the value of N from the resultant number
∑ D2 = 1043.5 Add together all of the values in the D2 column above
(6 ( ∑ D2 )) / N3-1 = 0.4013 Times this number by six and divide by N3-1
r = 0.5987 Take this number away from 1 to obtain r (pronounced Rho)

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