Development and evaluation of a self-care assessment inventory for workers

Authors


Eiko Ogasawara, School of Nursing, Faculty of Health Science, Gumma Paz College, 1-7-1 Tonya-machi, Takasaki, Gunma 370-0006, Japan. Email: ogasawara@paz.ac.jp

Abstract

Aim:  To develop and evaluate a self-care assessment inventory for workers (SCAI-W).

Methods:  A study using a self-care assessment inventory for workers consisting of 27 self-care items, the Japanese version of the Beck Depression Inventory (BDI), and the Japanese version of the University of Wales Institute of Science and Technology Mood Adjective Checklist (JUMACL) was conducted. These questionnaires were distributed to 2297 workers. There were 893 valid responses (39.9%, 584 men and 309 women, mean age 37.2 ± 10.2 years).

Results:  Three primary and eight secondary factors were established for the conceptual structure of self-care and validated by structural equation modeling. “Positive attitude” comprised the secondary factors, “hope” and “sense of fulfillment”, and was influenced by another secondary factor, “social support”. “Positive attitude” contributed to “attitude toward health”. “Attitude toward health” comprised the secondary factors, “care about one's health” and “correction of bad habits”. “Attitude toward health” influenced a primary factor, “everyday behavior”, comprised of “wakefulness”, “eating in moderation”, and “lack of self-control”. The primary factors “positive attitude” and “everyday behavior” influenced the BDI scores. A multiple regression analysis indicated that JUMACL subscale scores (energetic arousal and tense arousal), demographic data (living alone, sex, and age) and health-related data (exercise, smoking, body mass index, drinking more than three alcoholic drinks/day, and gambling) predicted the scores of the self-care assessment inventory for workers.

Conclusion:  This assessment inventory could be a useful measure of workers' self-care because it establishes a relationship between psychological and behavioral concepts that are important for health promotion.

INTRODUCTION

Self-care in the workplace has become a common strategy to prevent mental illness and lifestyle-related disorders. The death rate from suicide in Japan was 25.8/100,000 in 2009 and has maintained that level since 1998. Cases of mood disorders, including depression, have increased 2.4 times over the last 12 years (Japanese Ministry of Health, Labour and Welfare, 2010). The “Guidelines for Promoting Mental Health in the Workplace” (Japanese Ministry of Labour, 2000, Japanese Ministry of Health, Labour and Welfare, 2006) defined “self-care” as a worker's awareness of his or her own stress or stressors as well as the knowledge and strategies for coping with stressors.

A “specific medical examination and specific health consultation” was planned with a focus on metabolic syndromes in the field of lifestyle-related disorders (Japanese Ministry of Health, Labour and Welfare, 2008). Cardiovascular disease and cerebral apoplexy were the second and third leading causes of death. The common risk factors of these causes are hypertension, hyperglycemia, and dyslipidemia, which relate to lifestyle. The aim of specific health consultation was to promote health by being aware of and caring for lifestyle problems, namely by administering “self-care” for one's own life and health.

In addition to mental illness and lifestyle-related disorders, addictive behaviors such as smoking, drinking, and drug use are important issues in the workplace. In recent years, overeating, gambling, Internet surfing, and playing computer games have also been classified as addictive behaviors (Niiro, 2008). Chronic use of alcohol is particularly problematic because it can lead to accidents or decrease worker efficiency (Criqui, 1997; Tamura et al., 2008). Alcohol consumption, which is used for the relief of insomnia, makes depression worse. An investigation of suicides also indicates that alcohol problems are an important contributor to suicides (Japanese Ministry of Health, Labour and Welfare, 2010).

As for prevention strategies or recovery programs for addictive behavior, classic criminological theory (Roche, Bywood, Pidd, Freeman, & Steenson et al., 2009) has been the basis for educating young people about drug use. Alternative approaches include health-promoting strategies such as developing skills for coping with stress and urges to engage in risky behavior, and getting emotional support from family and peers (Granfield & Cloud, 2001; Laudet & White, 2008). These strategies are effective in maintaining quality of life, imbuing existence with meaning, and ensuring social support and stress coping strategies (Laudet, 2008). However, few reports have focused on health-promoting strategies or “self-care” in the addiction prevention published work.

In this study, “self-care” is defined as one of the health-promoting factors containing psychological and behavioral concepts (Antonovsky, 1987, 1993; Belloc & Breslow, 1972; Orem, 1984, 2001; Rosenbaum, 1980; Smith, Wallston, & Smith, 1995; Walker, Sechrist, & Pender, 1987). Further, “self-care” is expected to promote health and prevent lifestyle-related disorders, mental illness, and addiction, through awareness and regulation of one's lifestyle.

The conceptual structure of “self-care” is comprised of three primary factors and eight secondary factors (Fig. 1). The arrows from primary factors to secondary factors indicate that the latter are components of the former. The arrows pointing towards primary factors indicate influences from secondary or other primary factors. Primary factors were “positive attitude”, “attitude toward health”, and “everyday behavior”. The authors believe that realizing one's own “positive attitude” led to an “attitude toward health” and then to a healthy “everyday behavior”. “Positive attitude” is defined as a state of mind in which the person perceives benefits while experiencing stressful life events. “Attitude toward health” refers to one's willingness to take care of one's health and to recognize this as a necessary resource in managing stressors. This leads to the awareness and regulation of lifestyle and helps to maintain healthy behavior every day.

Figure 1.

Conceptual structure of the self-care assessment inventory for workers (SCAI-W) instrument. The conceptual structure of “self-care” comprised three primary (gray) and eight secondary factors (white).

“Positive attitude” consisted of the secondary factors: “hope” (Herth, 1991; Snyder et al., 1991) and “sense of fulfillment” (Nomura, 2005; Shimai, Otake, Utsuki, Ikemi & Lyubomirsky et al., 2004; Shirai, 1994). “Positive attitude” was influenced by “social support” (Cohen & Will, 1985; Hurrell & McLaney, 1988). “Social support” was set as a secondary factor, which promotes a “positive attitude” through the fostering of a feeling of support from others. “Attitude toward health” consisted of the secondary factors: “care about one's health” (Rosenbaum, 1980; Smith et al., 1995) and “correction of bad habits” (Saito & Ikegami, 1978). “Everyday behavior” consisted of the secondary factors: “wakefulness” (Gustafsson, Lindfors, Aronsson, & Lundberg et al., 2008; Jankowski & Ciarkowska, 2008), “eating in moderation” (Toyoshima et al., 2009), and “lack of self-control” (Saito & Ikegami, 1978).

The assessment instruments in previous studies do not clarify the relationship between the psychological and behavioral concepts that inform self-care. The authors believe that workers require comprehensive assessment instruments that have the aim of promoting health and awareness of self-care.

This study aimed to develop and evaluate a self-care assessment inventory for workers (SCAI-W). The items were prepared on the basis of previous studies and the questionnaires were distributed to workers in order to examine the validity and reliability of the SCAI-W.

METHODS

Participants and procedure

The SCAI-W were distributed to 2297 workers at six large enterprises and one local government in Gunma Prefecture, Japan. These enterprises consented to the study after the authors' recruitment through the Gunma Occupational Mental Health Network and the Gunma Occupational Nursing Network. The percentage of effective responses that did not contain missing values was 38.9% (893/2297; 584 men and 309 women). Participants' mean age was 37.2 ± 10.2 years (range, 18–66 years). Table 1 shows the participants' demographic and health-related data.

Table 1. Demographic and health-related data (n = 893)
CharacteristicN%
  • Three alcoholic drinks (540 mL, 15% alcohol) are equivalent to 66 g of alcohol.

Sex  
 Male58465.4
 Female30934.6
Age  
 Under 29 years24627.5
 30–39 years29733.3
 40–49 years22325.0
 ≥50 years12714.2
Company content  
 Manufacturing38643.2
 Local government20523.0
 Research and development15917.8
 Information services10411.6
 Medical services394.4
Job position  
 Manager535.9
 Middle manager11012.3
 General duties60167.3
 Irregular employment12914.5
Household  
 Live alone18921.2
 Live with family70478.8
Body mass index  
 Underweight768.5
 Normal64672.3
 Overweight15016.8
 Obese212.4
Exercise  
 Yes31034.7
 No58365.3
Smoking  
 Yes23526.3
 No65873.7
Drinking more than three alcoholic drinks (or equivalent)
 Yes36440.8
 No52959.2
Gambling  
 Yes12313.8
 No77086.2

Managers and occupational public-health nurses at each company distributed four questionnaires (see detailed description next) to all individuals under their management from May to December 2008. An attached letter included the following: (i) the purpose of the study; (ii) a statement that participation was voluntary; (iii) a request for the respondent's consent in submitting the questionnaires; (iv) a note stating that it would take approximately 10 min to complete the questionnaires; (v) an assurance of the protection of privacy; and (vi) a statement that the outcome of the study would be published in academic journals and presented at conferences. Managers and occupational public-health nurses collected the questionnaires by setting up a post box into which participants dropped them. The study was approved by the Epidemiologic Research Ethics Committee of Gunma University Faculty of Medicine in May 2008.

Questionnaires

SCAI-W

The preparatory study was carried out to explore the conceptual structure of self-care through a factor analysis. First, primary factors were set but not secondary factors, which were to be determined by the factor analysis in the preparatory studies.

In the first preparatory study, items taken from previous studies concerning the three primary factors –“positive attitude”, “attitude toward health”, and “everyday behavior”– were prepared, collecting data on 110 items from 917 university students, among whom there were 576 responses (62.8%; mean age, 20.3 ± 1.8 years). An exploratory factor analysis (weighted least squares and promax rotation) was performed, and factors with an eigenvalue of 1 or more were extracted. Subsequently, items with a factor loading of less than 0.4 were excluded. Factor analysis was performed repeatedly and 12 factors (35 items) were extracted.

A second preparatory study of 500 university students, which yielded 349 responses (69.8%; mean age 21.0 ± 2.3 years), was then conducted. An exploratory factor analysis (as above) of the 35 items was performed repeatedly. Eight factors (27 items) were extracted. As a result of these preparatory studies, eight secondary factors associated with the three primary factors were set (Fig. 1).

However, workers proved to be different from students on points of age and lifestyle. In order to apply these 27 items to workers, the content and phrasing of items were checked by a psychiatrist, occupational public-health nurses, and a psychologist. Finally, 27 items for the workers were prepared (see Table 2). Each item was written in Japanese. Answers to behavioral items were rated on a 5 point Likert scale ranging from 1 (almost daily) to 5 (almost never). Answers to other items were also rated from 1 (strongly agree) to 5 (strongly disagree). Each positive response was given a value of 5 and each negative response was given a value of 1. The SCAI-W score was calculated as the total score of the 27 items. The total SCAI-W score ranged 27–135. Higher scores indicate better states of self-care. The scores of secondary factors on the SCAI-W were the sums of the scores of items contained within each secondary factor.

Table 2. Factor loadings of the self-care assessment inventory for workers) by factor analysis (n = 893)
ItemFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8
  1. Bold type shows factors loadings over 0.40. (r), Reversed items.

Factor 1: Social Support (Cronbach's alpha = 0.872)
 Q20. I have someone who can comfort me. 0.929 −0.0350.001−0.027−0.0310.0470.032−0.027
 Q25. I have someone who can support me. 0.865 −0.0460.009−0.0600.0340.013−0.0550.026
 Q27. I have someone who will praise me for improving my daily habits. 0.709 0.052−0.0440.0560.033−0.0800.070−0.044
 Q18. I have someone who can provide me with a sense of security. 0.680 −0.0270.0900.111−0.0380.037−0.0160.021
Factor 2: Care about one's health (Cronbach's alpha = 0.769)
 Q16. I am careful about my diet and nutritional balance.0.026 0.776 0.061−0.027−0.0310.039−0.088−0.074
 Q12. I take care of my health.−0.042 0.761 −0.023−0.0120.0050.0280.0210.031
 Q14. I am careful about my daily habits.0.025 0.678 −0.004−0.0340.069−0.0300.0330.086
 Q5. I take care to eat vegetables at each meal.0.052 0.570 0.024−0.002−0.0470.041−0.0820.003
 Q4. I am careful about getting enough exercise in my daily life.−0.173 0.451 0.0260.0780.054−0.0250.0780.067
Factor 3: Hope (Cronbach's alpha = 0.780)
 Q24. I have a 5 year goal.0.0700.057 0.789 −0.095−0.011−0.0300.007−0.038
 Q26. I have a dream for the future.0.0020.059 0.781 −0.040−0.008−0.0250.003−0.001
 Q22. I am working to achieve my dream.−0.0760.002 0.613 0.208−0.0080.0210.075−0.096
 Q19. I prefer not to think much about my future (r).0.039−0.062 0.526 0.0040.0310.045−0.0720.132
Factor 4: Sense of fulfillment (Cronbach's alpha = 0.800)
 Q23. I find fulfillment in my daily life.0.060−0.0270.057 0.792 0.0370.014−0.0260.011
 Q17. I am satisfied with my present life.0.0730.046−0.137 0.740 −0.0010.015−0.0090.016
 Q21. I am able to do what I want to do.−0.075−0.0440.170 0.690 −0.005−0.0220.0010.004
Factor 5: Wakefulness (Cronbach's alpha = 0.814)
 Q2. I do not feel like getting up in the morning (r).−0.005−0.0420.021−0.015 0.920 0.015−0.002−0.018
 Q1. I wake up easily in the morning.0.0110.072−0.0250.046 0.743 −0.001−0.013−0.007
Factor 6: Lack of self-control (Cronbach's alpha = 0.645)
 Q13. Others sometimes point out my poor health habits (r).−0.0280.028−0.003−0.008−0.002 0.996 0.0080.001
 Q11. I have a bad habit that I cannot break, even though it is not good for my health (r).0.0620.033−0.0030.0220.023 0.447 0.0370.032
Factor 7: Eating in moderation (Cronbach's alpha = 0.604)
 Q3. I eat in moderation.0.0590.013−0.0470.016−0.0060.014 0.845 −0.052
 Q8. Even when I try to eat in moderation, I still overeat (r).−0.085−0.1900.090−0.057−0.0050.037 0.539 0.218
 Q10. When I eat late, I eat less.0.0570.248−0.019−0.006−0.008−0.005 0.445 −0.138
Factor 8: Correction of bad habits (Cronbach's alpha = 0.573)
 Q7. I do not know how to change my daily habits (r).0.0150.1770.0090.024−0.030−0.0650.070 0.535
 Q15. I do not have any good opportunity to change my bad habits (r).−0.0890.024−0.0490.111−0.0990.042−0.018 0.492
 Q6. I sometimes try to hide bad habits from others (r).−0.021−0.0990.035−0.1100.0880.0440.022 0.492
 Q9. I do not have anyone around me who can help me improve my daily habits (r).0.2150.062−0.0170.0280.011−0.019−0.018 0.476
Eigenvalue6.4872.2981.8051.7481.4671.3281.2611.017
Proportion of variance explained (%)6.05120.7486.4455.0005.0473.3792.3293.264
Cumulative proportion (%)6.05126.79933.24538.24543.29246.67149.00052.264

Mood adjective checklist

To assess moods, the Japanese version of the University of Wales Institute of Science and Technology Mood Adjective Check List (JUMACL) was used (Shirasawa, Ishida, Hakoda, & Haraguchi, 1999). The JUMACL consists of two subscales: “energetic arousal” (10 items) and “tense arousal” (10 items). Respondents were asked to answer on a 4 point Likert scale. High energetic arousal represents being active and happy, while low tense arousal represents being calm and quiet (Thayer, 1986).

Depression inventory

To assess depression, we used the Japanese version of the Beck Depression Inventory (BDI) (Hayashi, 1988). The BDI was used to measure the severity of depression and consisted of 21 items rated on a 4 point Likert scale. The total sum of these BDI scores was calculated and the subjects were categorized as “non-depressive” (BDI score <10) or “depressive” (BDI score ≥11).

Demographic and health-related data

Participants recorded demographic data such as sex, age, job position, and household information. They also answered questions about height, weight, exercise, smoking, gambling, and drinking as health-related data (see Table 1).

Statistical analysis

First, the validity of the SCAI-W was evaluated through an exploratory factor analysis. Second, as a confirmatory factor analysis, the conceptual structure of the SCAI-W was assessed by structural equation modeling (SEM), which compares a proposed hypothetical model elucidating a relationship with a set of actual data. The model was assessed using the following fit indices: χ2-test, goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR).

Third, to examine whether the SCAI-W was related to other outer variables, a multiple linear-regression analysis was performed with the total SCAI-W score as the dependent variable and the JUMACL, demographic data, and health-related data as the independent variables. To examine the characteristics of the SCAI-W's secondary factors and their relationship with health-related data, an anova was carried out, followed by multiple comparisons using Tukey's test. Further, to examine the relationship between the SCAI-W and depression, SEM was performed with the model, which added the BDI to the original SCAI-W model.

Amos ver. 16.0 (Tokyo, Japan) was used for structural equation modeling. SPSS ver. 16.0 (SPSS, Tokyo, Japan) was used for other statistical analyses. Values of P < 0.05 were considered statistically significant.

RESULTS

Validity and reliability of the SCAI-W

Factor analysis of the SCAI-W

Cases with one or more missing values and outliers were excluded. A factor analysis by weighted least squares and promax rotation was performed on the 27 items of the SCAI-W. The number of factors was determined by the Kaiser–Guttman criterion (Ichikawa, 2010). Factors with an eigenvalue of 1 or more and items with a factor loading of more than 0.4 were selected. Table 2 shows eight factors identified by this factor analysis. The cumulative proportion was not large because factors 6–8 showed a low proportion of variance explained.

The first factor was “social support”, consisting of four items such as “I have someone who can comfort me”. The second factor was “care about one's health”, consisting of five items such as “I am careful about my diet and nutritional balance”. The third factor was “hope”, which consisted of four items such as “I have a 5 year goal.” The fourth factor was “sense of fulfillment”, consisting of three items such as “I find fulfillment in my daily life”. The fifth factor was “wakefulness”, consisting of two items such as “I don't feel like getting up in the morning”. The sixth factor was “lack of self-control”, consisting of two items such as “Others sometimes point out my poor health habits”. The seventh factor was “eating in moderation”, consisting of three items such as “I eat in moderation”. The eighth factor was “correction of bad habits”, which consisted of four items such as “I do not know how to change my daily habits”.

Cronbach's alpha of the SCAI-W

To assure the internal consistency of this inventory, Cronbach's alpha was calculated for each of the eight secondary factors (see Table 2). The Cronbach's alphas for factors 1–5 were sufficient, with values above 0.7, but factors 6–8 had relatively low values.

SEM of the SCAI-W

Structural equation modeling was performed for the conceptual structure of the SCAI-W, composed of the eight secondary factors from the factor analysis (Table 2) and the three primary factors (Fig. 2). In this model, only “lack of self-control” indicated a negative condition. For a clear understanding of the negative relation with “everyday behavior”, the scores of items comprising “lack of self-control” were reversed again. Tables 3 and 4 show correlations and covariances of the model, respectively.

Figure 2.

Structural equation model of the self-care assessment inventory for workers (SCAI-W). Grey, primary factor; white, secondary factor; Q, an item; e, an error variable; AGFI, adjusted goodness of fit index; CFI, comparative fit index; GFI, goodness of fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual.

Table 3. Correlations of structural equation model of the self-care assessment inventory for workers (n = 893)
  Q11Q13Q8Q10Q3Q2Q1Q7Q15Q6Q9Q12Q14Q16Q5Q4Q27Q20Q18Q25Q23Q21Q17Q24Q26Q22Q19
Lack of self-controlQ111.00                          
Q130.481.00                         
Eating in moderationQ80.110.141.00                        
Q100.120.080.171.00                       
Q30.100.140.400.451.00                      
WakefulnessQ20.110.120.100.130.151.00                     
Q10.110.110.030.120.160.691.00                    
Correction of bad habitQ70.130.120.140.130.120.160.171.00                   
Q150.090.170.07−0.040.000.050.030.281.00                  
Q60.100.150.20−0.060.010.130.130.220.201.00                 
Q90.090.130.040.030.060.160.180.350.250.191.00                
Care about one's healthQ120.120.180.070.300.250.210.260.220.090.040.181.00               
Q140.170.140.050.270.260.270.290.290.110.050.230.571.00              
Q160.140.160.010.280.190.170.210.230.05−0.010.150.510.501.00             
Q50.110.14−0.010.160.150.120.170.170.060.040.200.340.350.511.00            
Q40.040.110.070.200.210.190.240.190.060.050.130.450.360.270.261.00           
Social supportQ270.09−0.03−0.030.140.140.170.180.190.06−0.030.290.230.270.280.210.091.00          
Q200.110.06−0.030.120.100.110.160.190.060.080.300.220.250.250.230.060.661.00         
Q180.150.09−0.020.100.060.130.150.250.090.040.270.200.280.270.200.110.530.661.00        
Q250.090.03−0.080.080.030.140.150.180.040.060.360.190.230.200.200.030.570.740.631.00       
Sense of fulfillmentQ230.170.180.000.120.080.300.310.230.170.090.260.300.300.320.260.230.360.380.430.321.00      
Q210.090.130.020.090.080.220.240.220.100.040.200.240.240.270.190.200.200.230.320.220.611.00     
Q170.180.16−0.010.100.090.230.270.190.130.020.260.260.290.280.240.180.310.320.360.300.620.501.00    
HopeQ240.100.060.070.140.130.130.130.160.060.050.160.270.290.290.210.180.240.250.300.220.380.320.261.00   
Q260.090.080.070.170.120.140.170.190.090.060.180.260.270.320.210.200.210.220.290.180.390.420.250.601.00  
Q220.090.130.110.130.160.160.160.170.04−0.030.120.250.270.240.240.270.160.170.240.150.450.450.290.530.521.00 
Q190.120.130.06−0.010.030.140.140.180.120.110.200.160.140.180.180.110.120.200.250.200.340.290.210.410.420.351.00
Table 4. Covariances of structural equation model of the self-care assessment inventory for workers (n = 893)
  Q11Q13Q8Q10Q3Q2Q1Q7Q15Q6Q9Q12Q14Q16Q5Q4Q27Q20Q18Q25Q23Q21Q17Q24Q26Q22Q19
Lack of self-controlQ112.23                          
Q131.002.00                         
Eating in moderationQ80.220.251.69                        
Q100.230.140.301.80                       
Q30.180.240.650.761.60                      
WakefulnessQ20.240.250.180.250.282.05                     
Q10.210.210.050.200.271.271.65                    
Correction of bad habitQ70.250.220.220.210.190.290.281.60                   
Q150.150.280.11−0.060.000.080.050.411.33                  
Q60.170.240.30−0.090.010.220.190.320.271.34                 
Q90.160.210.070.040.090.270.270.520.340.261.38                
Care about one's healthQ120.200.290.090.440.350.330.370.310.120.050.231.21               
Q140.280.210.080.400.370.420.420.400.130.060.300.701.23              
Q160.260.270.020.450.290.300.330.350.07−0.010.210.690.671.49             
Q50.220.26−0.020.270.240.220.290.270.100.060.300.490.520.821.72            
Q40.090.220.120.360.360.370.410.320.100.070.210.660.530.450.451.81           
Social supportQ270.16−0.06−0.050.230.220.300.290.300.08−0.040.430.310.370.420.340.161.54          
Q200.190.10−0.050.190.150.170.240.280.070.100.410.280.320.360.350.100.951.33         
Q180.270.16−0.030.160.100.230.240.390.130.050.390.270.380.400.320.190.810.941.51        
Q250.170.05−0.120.130.050.250.230.270.060.090.500.250.300.300.320.050.851.030.941.46       
Sense of fulfillmentQ230.280.290.010.180.110.470.450.320.220.110.340.370.370.430.370.340.500.480.580.431.23      
Q210.160.220.030.150.120.370.360.330.130.050.280.310.310.380.300.320.290.310.460.310.791.39     
Q170.340.29−0.020.160.140.420.430.310.190.030.380.360.410.430.390.310.490.460.560.460.860.741.58    
HopeQ240.200.110.130.250.220.250.230.280.090.080.260.400.440.490.370.330.400.390.500.370.580.510.451.87   
Q260.160.140.110.280.190.240.260.290.120.090.260.340.360.480.330.320.320.310.430.270.520.600.370.991.45  
Q220.160.220.170.210.240.280.240.260.06−0.030.170.330.360.350.390.430.240.240.360.220.600.650.440.870.751.46 
Q190.230.240.11−0.020.050.260.220.290.180.160.300.220.190.280.300.190.190.290.380.310.480.440.340.720.640.531.62

As for the fit indices of this model, GFI, AGFI, CFI, and SRMR showed good fitness, while RMSEA showed acceptable fitness. The lower bound of the 90% confidence interval was less than 0.05 and the upper bound was close to the value of RMSEA. However, the value of χ2 and χ2/d.f. (χ2/d.f. = 4.33) were too large (Engel, Moosbrugger, & Muller, 2003; Kline, 2011; Toyoda, 2007). We interpreted this to indicate that our model did not fit the data.

All of the standardized coefficients were significant (P < 0.05). “Positive attitude” influenced “attitude toward health” (0.80), which in turn influenced “everyday behavior” (0.94). “Positive attitude” consisted of the secondary factors “hope” and “sense of fulfillment”, whose standardized coefficients were 0.65 and 0.77, respectively. “Positive attitude” was influenced by “social support” (0.55). “Attitude toward health” consisted of the secondary factors “care about one's health” and “correction of bad habits”. Their standardized coefficients were 0.70 and 0.44, respectively. “Everyday behavior” consisted of the secondary factors “wakefulness” (0.49), “eating in moderation” (0.35), and “lack of self-control” (−0.35).

Multiple linear regression analysis of the SCAI-W

To examine whether the SCAI-W was related to a psychological and a behavioral concept, demographic data, health-related data, and subscales of the JUMACL (energetic arousal and tense arousal) were used as independent variables in the multiple linear regression analysis.

Ten variables were selected by the stepwise method: (i) energetic arousal; (ii) tense arousal; (iii) exercise; (iv) smoking; (v) body mass index (BMI); (vi) drinking more than three alcoholic drinks per day; (vii) gambling; (viii) household; (ix) sex; and (x) age (Table 5). These 10 variables explained 54.7% of the variance in the SCAI-W scores. The BDI score was excluded from the analysis because of its similarity to the tense arousal subscale of the JUMACL.

Table 5. Multiple linear regression analysis of the self-care assessment inventory for workers (n = 893)
Independent variablesβ
  1. *P < 0.05, ***P < 0.001. β = standard partial regression coefficient. Exercise: 0 = no, 1 = yes. Smoking: 0 = non-smoking, 1 = 1–9, 2 = 10–19, 3 = 20–29, 4 = 30–39, 5 = ≥40. Body mass index: 0 = underweight, 1 = normal, 2 = overweight, 3 = obese. Drinking more than three alcoholic drinks: 0 = never, 1 = 1–2 times a year, 2 = 3–5 times a year, 3 = 6–11 times a year, 4 = 1–3 times a month, 5 = once a week, 6 = more than 2 times a week. Gambling: 0 = no, 1 = yes. Household: 0 = live alone, 1 = live with family. Sex: 0 = male, 1 = female. R = 0.739, R2 = 0.547.

Energetic arousal0.422***
Tense arousal−0.177***
Exercise0.197***
Smoking−0.166***
Body mass index−0.113***
Drinking more than three alcoholic drinks−0.048*
Gambling−0.048*
Household0.152***
Sex0.103***
Age0.063*

anova of the SCAI-W for health-related data

To examine the characteristics of the secondary factors, the scores of secondary factors on the SCAI-W were compared between groups classified by health-related data: BMI (BMI ≥25, BMI <25), smoking (yes, no), drinking more than three alcoholic drinks per day (yes, no), gambling (yes, no), exercise (yes, no), and depression (BDI ≥11, BDI <11). A one-way anova and a multiple comparison (Tukey's test) were used. Significant differences in “social support” scores were found for BMI (F[1, 891] = 4.18, P = 0.041), smoking (F[1, 891] = 34.29, P < 0.001), drinking more than three alcoholic drinks per day (F[1, 891] = 8.35, P = 0.004), gambling (F[1, 891] = 11.56, P < 0.001), and depression (F[1, 891] = 80.68, P < 0.001). Significant differences in “care about one's health” scores were also found for BMI (F[1, 891] = 8.07, P = 0.005), exercise (F[1, 891] = 152.78, P < 0.001), smoking (F[1, 891] = 39.33, P < 0.001), gambling (F[1, 891] = 20.58, P < 0.001), and depression (F[1, 891] = 62.80, P < 0.001). Finally, significant differences in “correction of bad habits” scores were found for BMI (F[1, 891] = 15.59, P < 0.001), exercise (F[1, 891] = 11.26, P < 0.001), gambling (F[1, 891] = 4.18, P = 0.041), and depression (F[1, 891] = 57.75, P < 0.001).

SEM of the SCAI-W and depression

Depression was not considered a component of “self-care”, because depression may be the cause or the result of poor self-care. To investigate the possibility that the SCAI-W predicts depression and to explore the relationships with three primary factors of “self-care”, SEM was performed again for the model with a variable of BDI (Fig. 3). Using the BDI score, 255 participants (28.6%) were judged as depressive (BDI score ≥11) and 638 participants (71.4%) were judged as non-depressive (BDI score <11).

Figure 3.

Structural equation model of self-care assessment inventory for workers (SCAI-W) and depression. Depression was influenced by “positive attitude” and “everyday behavior”. Grey, primary factor; white, secondary factor; Q, an item; e, an error variable; AGFI, adjusted goodness of fit index; CFI, comparative fit index; GFI, goodness of fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual.

As for the fit indices of this model, GFI, AGFI, and CFI showed good fitness. SRMR and RMSEA showed acceptable fitness. The value of χ2 and χ2/d.f. (χ2/d.f. = 5.93) indicated a poor fit. Like earlier, the fit indices of this model showed poor fit.

All of the standardized coefficients were significant (P < 0.05). Depression was influenced by “positive attitude” (−0.48) and “everyday behavior” (−0.27).

DISCUSSION

The factor analysis shows that the cumulative proportion of these eight factors was not large. In other words, the 27 items did not adequately explain the state of workers' self-care. In particular, three factors connected with addictive tendencies –“lack of self-control”, “eating in moderation”, and “correction of bad habits”– showed a low proportion of variance explained. Moreover, these factors showed insufficient reliability, which is possibly due to the denial of addictive behavior. The SCAI-W is unique in that it includes items concerning addictive tendencies. Therefore, it is necessary to improve the SCAI-W through revision and addition of items regarding addictive behavior. In addition, the contents of items should be checked even in addictive populations.

The model of the SCAI-W, comprising three primary factors and eight secondary factors, did not fit the data. The authors believe that one of the reasons why the model did not fit well could be because of the influence of three secondary factors. The standardized coefficients among primary factors –“positive attitude”, “attitude toward health”, and “everyday behavior”– were relatively high in this model. However, the standardized coefficients of “correction of bad habits”, “eating in moderation”, and “lack of self-control” were insufficient. Thus, these three secondary factors had to be improved. Another reason could be because χ2 was influenced by the large sample size. The model of “self-care” set by the authors was rather complex; therefore, a large sample was used. However, subsequent studies should be performed with an appropriate sample size determined through power analysis.

Regarding primary factors, realizing one's own “positive attitude” led to an “attitude toward health” and then to healthy “everyday behavior”. Conversely, failure to maintain a “positive attitude” led to unhealthy actions for “everyday behavior” and “lack of self-control”. Affleck, Tennen and Croog (1987) reported that the meaning of life changed for the better in heart attack survivors who perceived the experience of a heart attack in a positive light, which led them to preventive behavior and motivated them to correct their lifestyle. This subsequently reduced the recurrence rate of heart attacks. This suggests that psychological support focusing on the meaning of life leads to a “positive attitude” and is an effective method for promoting self-care in persons under stressful conditions. People tend to prioritize behavior that gives pleasure and stimulation in daily living activities when a life purpose is unclear, which may lead to addictive behaviors such as smoking and overeating (Crandall & Rasmussen, 1975; Simmons, 1980). These reports indicate that a high-level “positive attitude” promotes healthy behavior, whereas a low-level or lack of “positive attitude” leads to addictive behavior.

Regarding the relationship between the primary factor “positive attitude” and its secondary factors, the American Psychiatric Association (2005) argued for the importance of the relationship between “hope” and a “positive attitude” by explaining that the concept of recovery from mental illness emphasizes a person's capacity to have hope and lead a meaningful life. In this study, “social support” influenced a “positive attitude” and enhanced the secondary factors of “sense of fulfillment” and “hope”. Previous studies reported that “social support” influenced health through self-esteem/self-efficacy, concepts that are closely related to “sense of fulfillment” (Kim, Shimada, & Sakano, 1998; Sullivan, 1953). Matsushita (2007) also stated that a feeling of “social support” in stressful conditions increases the perception of one's own value and of stress as part of one's own growth, thereby promoting the ability to overcome stressful experiences. A one-way anova revealed that the score for “social support” was high in the groups with favorable scores for BMI, exercise, smoking, drinking more than three alcoholic drinks, gambling, and depression. This was consistent with studies that reported that receiving social support reduced problem-avoidance coping, such as overeating, smoking, drinking alcohol, and gambling (Kosugi et al., 2004; Usami & Kosugi, 2008). In contrast, a lack of social support led to discouragement (Matsushita, 2007). A psychological approach to promoting a feeling of support from and connection with others in daily personal relations may be effective in preventing addiction and improving self-care.

Regarding the multiple regression analysis for the SCAI-W, energetic arousal (β = 0.422, P < 0.001) and tense arousal (β = −0.177, P < 0.001) were selected, which were subscales of the JUMACL (Table 5). Conditions with high energetic arousal and low tense arousal represent a good mood (Matthews, Jones, & Chamberlain, 1990). Accordingly, the results of the multiple regression analysis showed that a good mood supports favorable self-care conditions. Kuromaru (2001) reported that conditions engendering a good mood, such as relaxation and a sense of fulfillment, induce spontaneous behavioral changes. Interventions to promote a good mood accompanied by relaxation and a sense of fulfillment (i.e. interventions to activate energetic arousal and reduce tense arousal, including exercise (Hall, Ekkekakis, & Petruzzello, 2002), aromatherapeutic massage (Ogasawara et al., 2007), music (Hirokawa, 2004), and life review (Ando, 2003) have been reported, and these may effectively improve self-care.

Health-related data selected indicate that the adjustment of everyday behavior to habitual exercise, not smoking, maintaining a healthy bodyweight, not drinking, not gambling, and avoiding addictive habits were associated with favorable self-care conditions. Miyamoto and Yasuda (2008) stated that everyone has an addictive tendency and has to consciously control their behavior. Rosenbaum (1980) described the ability to delay immediate gratification as one component of controlling behavior. Accordingly, in order to improve self-care, which is the awareness and regulation of one's lifestyle, it is important to have awareness and regulation of one's mood as well as control of one's behavior.

Regarding secondary factors of the SCAI-W constituting self-care conditions, the score of “care about one's health” was low when obesity, smoking, gambling, and depression were present, whereas the score was high in the group with a habit of exercising. These findings were consistent with those reported by Koyano, Ueno, and Imaeda (2006): a health-oriented attitude influences balanced eating habits, exercise, and avoidance of problematic alcohol drinking and smoking. The score for “lack of self-control” was high when obesity, smoking, gambling, depression, and drinking more than three alcoholic drinks were present. Because the score for “correction of bad habits” was low when obesity, gambling, and depression were present, the healthy group without these conditions may be conscious of continuous health management in daily living activities. However, no significant differences were noted in “correction of bad habits” with regard to smoking or drinking. Namely, the group that smoked or drank was aware of “lack of self-control”, but did not show “correction of bad habits”. This can be interpreted as one of the psychological features of addiction. Therefore, this group should be regarded as having at least one characteristic of substance dependence and be supported in the early stages of addiction.

Regarding the association between the state of self-care and the tendency toward depression, the model composed of the SCAI-W and the BDI identified the evidence for this relationship through SEM (Fig. 3). Of the primary factors of the SCAI-W, “positive attitude” and “everyday behavior” were associated with depression. Depression was low when “positive attitude” was high and “everyday behavior” was healthy. Previous studies have also reported similar findings, noting that symptoms of depression were mild in persons with favorable self-care (Christensen, Ehlers, Raichle, Bertolatus, & Lawton, 2000; Weng, Dai, Wang, Huang, & Chiang, 2008). It was suggested that support to improve self-care and, in particular, support promoting the perception of a “positive attitude” and improving “everyday behavior”, is effective for alleviating the symptoms of depression. Although the SCAI-W model did not show a sufficiently good fit, we found that “positive attitude” and “everyday behavior” may reduce depression but “attitude toward health” did not.

Despite the insights gained from the current results, there is a problem from a preventive medicine standpoint that considers the workplace collectively. In this study, a cut-off point for the differentiation of a poor self-care group according to the SCAI-W was not determined. Therefore, a poor self-care group, which requires support, could not be differentiated from a good one. For such collective applications, it will be necessary to determine a cut-off point.

That limitation notwithstanding, the SCAI-W may nonetheless be used in the workplace and may be a useful measure of the effectiveness of interventions intended to improve self-care. In order to do so, longitudinal studies of parameters, including blood test data (such as blood sugar and cholesterol), as indices of lifestyle-related disorders will be necessary. It will also be necessary to examine how life events, such as job promotion, marriage, and maternity, influence self-care. Also, the SCAI-W appears to be helpful to assess the self-care of patients in clinical nursing. For instance, physical care by nurses, such as bed baths or footbaths, engenders patients' good mood, which induces spontaneous behavioral changes toward self-care. In addition, through psychological care provided by nurses, such as counseling, patients can also work to find their own meaning of life, despite the burden of disease or functional deficits. This intervention motivates patients to take care of themselves. We therefore believe that these interventions promote self-care and can play an important role in nursing.

CONCLUSION

In this study, a self-care assessment inventory for workers (SCAI-W) consisting of 27 self-care items was developed. The conceptual structure of self-care consisted of three primary factors: “positive attitude”, “attitude toward health”, and “everyday behavior”. “Positive attitude” consisted of “hope” and “sense of fulfillment”, and was influenced by “social support”. “Attitude toward health” consisted of “care about one's health” and “correction of bad habits”. “Everyday behavior” consisted of “wakefulness”, “eating in moderation”, and “lack of self-control”. It was found that “positive attitude” promoted “attitude toward health”, while “attitude toward health” improved “everyday behavior”.

This assessment inventory establishes a relationship between psychological and behavioral concepts that is important for health promotion. This suggests that it is important to have awareness and regulation of one's mood as well as control of one's behavior in order to improve self-care.

Ancillary