Importance of stressors and their impact on human life have attracted many researchers’ interest in the recent years. When investigating stressors, their frequency and intensity are the most important characteristics that must be considered [
To measure stressors, different scales and tools have been developed in developed countries [
The use of self-administered questionnaires is demonstrated to be a great tool to obtain useful information about the health status in epidemiologic studies and health surveys [
Being user-friendly for questionnaire provides a key characteristic and a better chance for success in questionnaire-based researches. One of the advantages of the user-friendly questionnaire is that individuals could become aware of their health status while using them.
Questionnaires should be designed in a way to be an indicator of health status or other under-research conditions. In other words, a user friendly questionnaire could easily provide information about the individual health status by comparing the scores that are calculated with its cut point.
By using machine learning algorithms such as artificial neural networks (ANNs) and determining a cut point we aimed to provide a weight for stressful life events that are introduced in the stressful life event (SLE) questionnaire. This way, a revised SLE questionnaire could be used as a screening tool to differentiate healthy individuals from those who are at risk of a disease.
The data we used in this study is a part of the data collected for the Isfahan Healthy Heart Program (IHHP) research. IHHP is a community-based intervention program to prevent and control noncommunicable diseases. Details of the methodology used in IHHP including sampling strategies, survey instruments, data entry, and analysis in addition to the evaluation and followup of the subjects are described by Sarraf–Zadegan et al. elsewhere [
In summary, IHHP includes a baseline survey, four follow-ups and eventually a final phase. The baseline survey was conducted in 2001 in 2 interventional and 1 referral regions. The interventional regions include Isfahan and Najafabad. We selected Arak as the referral region. Four phases of annual followup and evaluation were performed on independent samples from 2002 to 2005. The Final phase was conducted in 2007. According to the regional population distribution based on the CINDI protocol, a multistage cluster random sampling was applied in order to differentiate urban versus rural areas [
Data used for the current study include the information from 4569 adults aged over 18 years who have completed the final phase and have all their related data available. The related data include demographic information such as age, sex, and educational years. We asked all participants to complete the SLE questionnaire and the general health questionnaire-12 (GHQ-12). An informed consent was obtained from all subjects.
We evaluated the frequency and intensity of the stressful life events for participants by the SLE questionnaire which was developed and validated by Roohafza et al. [
The GHQ-12 was used for the validation and evaluation of the SLE questionnaire. The GHQ-12 is a well-established screening tool to detect psychological morbidity in community and clinical settings such as in primary care or general practice [
Inspired from the biology of human brain, an artificial neural network (ANN) is a parallel processing system which has an executive performance [
An example of an ANN with an input layer, 2 hidden layers, and an output layer.
In Figure
Each layer is determined by its weight matrix. The matrix multiplication operation of the various layers along with applying transfer functions is formed network structure and map input signals transfer to output data. If the network consists of
The number of neurons in input and output layers depends on the structure of the subject of interest.
An overall method for determining the number of hidden layers and neurons is not found in the literatures. Applying an evolutionary algorithm such the genetic algorithm (GA) and the particle swarm optimization (PSO) is one of the most widely used methods in this field.
GA is a part of evolutionary computing theory that is growing rapidly. The main idea of this algorithm lies in Darwin’s theory of evolution. In every step of this method, an initial population of chromosomes (initial responses) produces a new population of chromosomes (secondary responses). Repeating this process and generating a new population from a previous one in each step result in population growth and optimum response.
The process of producing a new population from a previous one uses four operators as follows; Selection: This operator selects a number of chromosomes to produce the next generation in each population. The selected chromosomes are parents. The probability of selection of chromosomes with potential to reach more optimal results is higher than others. Crossover: This operator takes more than one parent solution and produces one or more child solutions from them. Mutation: Mutation is a genetic operator that alters one or more gene values in a chromosome from its initial state and will produce a new chromosome. Mutation helps to prevent the population from stagnating at any local optima. Elite: It is possible that some relatively optimal chromosomes are eliminated during the selection process so by this operator a number of elite chromosomes are transferred directly to the next population.
Figure
Genetic algorithm cycle.
In this paper, the objective is to obtain a linear relation between the stressful life events introduced in the SLE questionnaire and the person’s demographic characteristics. Demographic characteristics are considered as inputs and the stress level as an output variable. Stress level was measured by GHQ-12. In other words, we aim to determine the coefficients
In the above equation,
If the GHQ score is less than 4, the individual is classified in the low stress level group and otherwise in the high stress level group. The cut-point score of 100 in GHO is well-defined. That means if
The purpose of this study is to measure the agreement between SLE questionnaire data and variable
In this study, we have used a hybrid model of GA and ANN to obtain coefficients of variables (
After determining
In the above equation,
We established an expert panel consisting of three psychiatrists, two psychologists, two epidemiologists, and one statistician to evaluate
Once the coefficients in the equation were defined, the stress level of individuals was calculated (
In our study,
We studied 4569 individuals including a female population of 2252 (49.2%). The mean age of female and male participants was 38.6 ± 15.1 and 38.5 ± 15.4, respectively. The mean number of educational years in the female population was 8.9 ± 4.8 and in the male population was 7.2 ± 4.9 (mean ± standard deviation).
The SLE questionnaire included 46 stressors which were scored by a 6-point Likert scale (0 = never, 1 = very mild, 2 = mild, 3 = moderate, 4 = severe, and 5 = very severe). Mean scores for stressful life events in the study population are shown in Table
Mean of stressful life events in studied population.
Domain | Stressful life events | Mean |
---|---|---|
Addiction (self or family member) | 0.18 | |
Divorce or separation | 0.05 | |
Concern about addiction of a family members | 0.45 | |
Home life | Quarrels with spouse | 0.31 |
Being accused | 0.29 | |
Legal problems | 0.24 | |
Troubles with children | 0.50 | |
| ||
Financial inflation | 2.46 | |
Low income | 2.06 | |
Financial problems | Get into debt | 1.69 |
Taking on a mortgage | 0.96 | |
Major financial problems | 1.08 | |
| ||
Concern about your future | 1.34 | |
Social relations | Major social changes | 0.77 |
Social discrimination | 1.15 | |
Social insecurity | 1.01 | |
| ||
Loneliness | 0.86 | |
Failure in achieving the life goals | 1.04 | |
Personal conflicts | Lack of social support | 0.79 |
Cultural alienation | 0.65 | |
Not having an intimate friend | 0.50 | |
| ||
Dealing with customers | 0.50 | |
Job conflicts | Quarrel with colleagues/boss | 0.27 |
Improper working place and environment | 0.39 | |
Increased working hours | 0.44 | |
| ||
Educational problems of children | 0.43 | |
Educational concerns | Participation major examinations | 0.31 |
Failure in major examinations | 0.18 | |
High educational expenses | 0.50 | |
| ||
Low salary | 1.08 | |
High responsibility job | 1.04 | |
Job security | Concern about job future | 1.00 |
Job layoff | 0.53 | |
Long-lasting unemployment | 0.34 | |
| ||
Death of a close family member | 0.81 | |
Loss and separation | Major disease of family members leading to hospitalization | 0.68 |
Death of parents, spouse, or siblings | 0.21 | |
Children’s separation from family | 0.29 | |
| ||
Birth of a child | 0.21 | |
Sexual life | Sexual relationship problems | 0.12 |
Pregnancy | 0.08 | |
Unwanted pregnancy | 0.05 | |
| ||
Daily life | Major changes in sleeping and eating habits | 0.71 |
Air pollution and traffic | 0.62 | |
| ||
Health concerns | Mild illness | 1.00 |
Major physical disease leading to hospitalization | 0.32 |
As shown in Table
If zero was selected for specific stressors in the SLE questionnaire, the total score remains unchanged, and if any of the values
After determining
Stressful life event score.
Stressful life events | Score |
---|---|
Death of parents, spouse, or siblings | 46 |
Children’s separation from family | 32 |
Participation of major examinations | 31 |
Low income | 30 |
Long-lasting unemployment | 29 |
Being accused | 28 |
Educational problems of children | 27 |
Major disease of family members leading to hospitalization | 27 |
Concern about job future | 26 |
Major financial problems | 24 |
Unwanted pregnancy | 22 |
Lack of social support | 21 |
Failure in major examinations | 21 |
Failure in achieving the life goals | 19 |
Low salary | 19 |
Major physical disease leading to hospitalization | 19 |
High responsibility job | 18 |
Concern about your future | 17 |
Not having an intimate friend | 17 |
Job layoff | 16 |
Birth of a child | 16 |
Addiction (self or family member) | 15 |
Legal problems | 14 |
Sexual relationship problems | 14 |
Troubles with children | 13 |
Quarrels with spouse | 12 |
Mild illness | 12 |
Divorce or separation | 11 |
Financial inflation | 11 |
Dealing with customers | 11 |
Pregnancy | 11 |
Major social changes | 10 |
Death of close family member | 10 |
Major changes in sleeping and eating habits | 10 |
Social insecurity | 9 |
Air pollution and traffic | 7 |
Cultural alienation | 6 |
Quarrel with colleagues/boss | 6 |
Improper working place and environment | 5 |
Get into debt | 4 |
High educational expenses | 4 |
Taking on a mortgage | 3 |
Social discrimination | 3 |
Loneliness | 3 |
Concern about addiction of a family members | 1 |
Increased working hours | 1 |
Among all stressful life events, death of parents, spouse, or siblings was the most important and influential in our studied population. In contrast, increased working hours and concern about addiction of a family member did not gain much scores and was not as important.
The results for the coefficients of demographic characteristics (
In this study, sensitivity was 83% and specificity was 81%. Our result shows that the SLE-R questionnaire is a screening tool with acceptable accuracy.
In this paper, a revised SLE questionnaire is presented. We added weights to the SLE questionnaire’s stressful life events using a hybrid model of GA and ANN. The importance of this promoted questionnaire is because of added weights to stressful life events. The weights calculated based on the population response. The SLE-R questionnaire is a well-established tool that differentiates individuals who are at risk of a health problem or disease from those who are not at risk. In this study sensitivity of 83% and specificity of 81% were obtained for a cut-point of 100.
Agreement between questionnaire data and criterion standards is the subject of many questionnaire-based studies. The objective of these studies is developing a diagnosis tool by determining a pattern between questionnaire data and a criterion standard. Many of these studies are dealt with multi-variables and it is highly likely that there are some interactions between them; ANNs are powerful tools that are used for correlations between known inputs and outputs and could consider the interaction between inputs in identified patterns.
Many studies have showed high efficiency of ANNs in various applications. DeGroff et al. used ANNs as a diagnostic tool and thereby classified heart sound data to innocent and pathological classes [
The Holmes and Rahe stress scale (HRSS) is one of the most commonly and widely used quantitative measurements of psychosocial stress. It is a self-reported list of 43 common events associated with some degree of disruption of an individual’s life. HRSS assists people to discern their internal stress and understand their cumulative stress over a one-year period. It assigns a number to the amount of stress being felt by a person with no margin for differential of how a person actually internalizes the stress. Individual’s stress level is determined based on the calculated value in HRSS. User-friendliness is one of the prominent characteristics of HRSS that makes it to be commonly used. One of the other widely used stress scales named Life Experiences Survey (LES) that developed by Sarason et al. is a self-reported structured interview that assesses major life events in the past year. This user-friendly questionnaire includes 57 items divided into two sections. Each participants can record positive, negative, or no effect for each event on their life.
The SLE-R questionnaire that is proposed in this paper is a screening tool similar to HRSS and LES. Despite simplicity, these questionnaires are high-performance screening tools for investigating the stress levels. In many cases, clinical tests are costly and time consuming and associated with uncertainty. In this situation, clinical screening tools can help experts to better identify a diagnosis. Because these instruments are self-reported, individuals could gain some information about their own health status while filling them up.
In the SLE-R questionnaire, a global number is determined for each stressful life event, and differences between individuals are not considered. This questionnaire being self-reported is another limitation for our study because the participant could only remember that stressor that is subject of a question in the questionnaire. The SLE-R questionnaire has not been tested for normalization. The questionnaire should be compared with other questionnaires and tested in different communities.
In conclusion, the SLE-R questionnaire is introduced as a screening tool with high sensitivity and specificity. The features of this questionnaire make it a useful research tool that could be used in clinical and primary care settings. Offering clinical diagnostics to communities is very costly and not practical. Screening tools such as the SLE-R questionnaire are required to consider a smaller target group. The SLE-R questionnaire can be completed by individuals with low literacy.
This study was supported by the Isfahan Cardiovascular Research Institute. The authers would like to thank their expert panel for the invaluable cooperation: Ali Abbasalizadeh, Abbas Attari, Hamid Afshar, and Mahjoobeh Kaviani.