Prediction of Rear-Seat-Belt Use among Older Adults

Abstract

The efficacy of rear seat belts in safeguarding both rear- and front-seat passengers is well established; however, the use of rear seat belts in China remains suboptimal. Furthermore, automotive transportation holds significant importance for seniors in China. Consequently, it is necessary to analyze the factors affecting intention to use rear seat belts among Chinese seniors. This study applies the hierarchical regression model to investigate the effects of sociodemographic characteristics and constructs on older adults’ backseat seat-belt use intentions. A questionnaire survey was conducted in Pingdingshan City and 551 valid questionnaires were obtained. Results show that sociodemographic characteristics exhibit a limited influence on the intention to use rear seat belts, accounting for only 16.7% of the variance in intention. However, the incorporation of additional constructs can substantially augment the model’s explanatory capacity. After incorporating the exogenous constructs of the theory of planned behavior, the model’s explanatory power for behavioral intention increased to 46.9%. Subsequently, on the inclusion of susceptibility, severity, perceived benefit, and perceived law enforcement as additional factors, the model’s capacity to account for variance in behavioral intention further improved to 62.7%. Attitude, subjective norm, perceived behavioral control, susceptibility, severity, and law enforcement have significant impact on behavioral intention.

Keywords

data and data science statistical methods safety occupant protection seat belts/safety belts transportation safety management systems safe mobility of older persons

Annually, about 1.3 million individuals, of whom about 29% are car occupants, lose their lives in road traffic collisions, in addition to the large number of people injured or incapacitated ( 1 , 2 ). The use of seat belts is a significant intervention in reducing the risk of injury or death for car occupants. According to the World Health Organization ( 2 ), wearing seatbelts decreases the likelihood of fatality by approximately 45% to 50% for front-seat occupants, and by approximately 25% for rear-seat occupants. In a frontal collision, an unrestrained rear-seat passenger is propelled forward and makes contact with the front occupant. Consequently, an unrestrained rear-seat passenger endangers not only his or her own life but also the life of the front occupant (3 –6). While seat belts have been shown to significantly enhance passenger safety, research indicates that rear-seat passengers exhibit lower seat-belt usage compared with front-seat passengers (7 –11).

Background

Factors Associated with the Use of Belts among Rear-Seat Occupants

The factors influencing the use of seat belts by front-seat occupants have received a lot of attention, while the factors influencing the use of seat belts by rear-seat occupants have received far less attention. Prior studies have found that sociodemographic factors, including gender ( 8 , 12 , 13 ), age ( 9 , 12 –14), and education ( 13 ), may exhibit a correlation with rear-seat-belt use. In addition to sociodemographic factors, some studies have found that an individual’s attitude affects whether or not that person uses a rear seat belt ( 7 , 8 , 11 ).

Some studies have found that rear-seat passengers are more likely to wear seat belts in countries or regions that have laws requiring rear-seat passengers to wear seat belts than in countries or regions that do not have such laws ( 7 , 15 , 16 ). In addition, if a person believes that there is a law in his or her country or region that requires rear-seat passengers to use seat belts, that person is significantly more likely to use a seat belt in the rear seat ( 9 , 17 ).

Models Employed to Analyze the Use of Seat Belts

In the existing literature on the factors influencing seat-belt usage, the majority of studies have employed quantitative analytical methods, while there are also articles, such as that of Anund et al. ( 18 ), that have utilized qualitative approaches, such as focus group discussions.

On reviewing existing literature, it was observed that some studies employed the test model to investigate the influence of factors (such as gender and education) on seat-belt usage, including the chi-square test ( 7 , 9 , 19 –21) and Tukey-type multiple comparison test ( 22 ).

Many studies have employed the logistic regression model to examine the factors linked to seat-belt usage ( 8 , 11 –13, 16 , 17 , 23 –25). Additionally, certain papers have employed more sophisticated logistic regression models, such as the nested and mixed logit model ( 26 ) and the latent class binary logit model ( 27 ).

The aforementioned models primarily investigate the influence of demographic characteristics and other observable variables on seat-belt usage. In addition to these, some studies have employed social cognitive models to examine the impact of psychological factors and latent constructs on seat-belt usage behavior or intention, such as the theory of planned behavior (TPB) ( 28 ) and the health belief model (HBM) ( 29 , 30 ) and combined models incorporating both the TPB and the HBM (31 –36).

The Current Study

This study investigates the impact of psychological factors on the use of rear seat belts among older persons, motivated by the following rationales:

1) China is undergoing a progressive transition toward an aging society. As reported by the National Bureau of Statistics of China, 18.70% of the overall population consists of individuals aged 60 and over. Furthermore, the senior population is experiencing a significantly greater growth rate compared with the natural growth rate of China’s population ( 37 ).

2) As China’s economy grows, car travel has become an important mode of transportation for Chinese people. In Beijing, for example, the share of car travel in motorized trips by older people is 15% ( 38 ).

3) In the “People’s Republic of China Road Traffic Safety Law” enacted in 2021, Article 51 stipulates that drivers and passengers must use seat belts as required. However, this law does not specify the penalties for rear-seat passengers who fail to fasten their seat belts. At present, in most Chinese cities, there is either no or minimal enforcement of penalties for rear-seat passengers who do not wear seat belts. In the city where this research is conducted, traffic police rarely impose penalties on rear-seat passengers who do not fasten their seat belts.

4) Insufficient research exists into the determinants of rear-seat-belt utilization among older adults. Rear-seat-belt use is associated with age ( 9 , 12 –14). Therefore, the generalizability of existing research findings to older people may not be assured.

The paper is structured as follows. The analysis methodology and data are detailed in the next section. The results are presented in the third section, followed by discussions and conclusions in the subsequent section.

Survey Design and Data Collection

Conceptual Framework and Method

Except for demographic characteristics, and the exogenous constructs in the TPB (attitude, perceived behavioral control, and subjective norm), previous studies have found that risk perception ( 39 , 40 ), perceived benefit ( 41 , 42 ), and law enforcement ( 43 , 44 ) may affect one’s behavioral intention. Risk perception refers to people’s beliefs, judgments, and feelings toward risk. Perceived benefit refers to the perception of a positive consequence of a behavior. Law enforcement refers to the traveler’s subjective perception of the strictness of police enforcement. Also, prior research argued that risk perception can be decomposed into two sub-constructs: susceptibility and severity ( 45 , 46 ), in which susceptibility refers to the likelihood of something occurring, and severity refers to the severity of the consequences of something occurring.

Therefore, this study aims to investigate the effect of factors on the behavioral intention of older adults to use rear seat belts through hierarchical regression modeling. And three models are considered:

In Model 1, only sociodemographic characteristics are considered as predictors.

In Model 2, the TPB predictors are added to Model 1.

In Model 3, susceptibility, severity, perceived benefit, and law enforcement are added to Model 2.

Data Collection

This study investigated the central city of Pingdingshan, Henan Province, China. The survey was conducted in July to August 2023. All sampling was conducted in densely populated public places (senior activity centers, squares, vegetable markets, parks, etc.). The survey was conducted using face-to-face interviews. Some respondents filled out the questionnaire themselves. Respondents who had some problems with reading or writing were “interviewed” and marked on the questionnaire by the investigator.

After excluding incomplete questionnaires, a total of 551 valid questionnaires remained. The age of the respondents ranged from 55 to 73 (mean = 63.91, standard deviation = 3.39). Of the respondents, 42.29% were female, 82.94% had a junior-high-school education or less, and 10.89% of respondents had been involved in a traffic collision in the last 5 years.

Measures

The exogenous constructs (including attitude, perceived behavioral control, and subjective norm, susceptibility, severity, perceived benefit, and law enforcement) and behavioral intention are latent variables. Latent variables, by nature, cannot be directly assessed and necessitate characterization through indicator items. The indicator items utilized for evaluating the constructs were drawn from prior research and subsequently modified for the present study. The responses for all items were recorded using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree), and these items can be found in the Appendix.

Results

A measurement model is used to describe how well the observed indicator variables serve as a measurement instrument for the constructs. Before investigating the links between constructs, it is imperative to evaluate the measurement model’s validity and reliability ( 47 , 48 ).

Measurement Model Analysis

The reliability and validity of the measurement model are assessed by the examination of the outcomes derived from the confirmatory factor analysis model (CFA). A satisfactory overall fit to the collected data is indicated by the CFA fit indices: root mean square error of approximation (RMSEA) = 0.069, standardized root mean square residual (SRMR) = 0.077, comparative fit index (CFI) = 0.952, Tucker–Lewis index (TLI) = 0.941 ( 49 ).

A construct’s indicator variables are appropriate when they all have standardized factor loadings greater than 0.6 ( 50 ), and the composite reliability (CR) of this construct is greater than 0.7 ( 51 , 52 ), and the average variance extracted (AVE) for this construct is greater than 0.5 ( 51 , 53 ). All the values in Table 1 meet the cut-off point, indicating that the indicator variables represent the constructs well.

Table 1.

Results of the CFA

Indicators	Coefficient	Std err	P>z	CR	AVE
Behavioral intention
bi1	0.883	0.012	73.150	0.000	0.908
bi2	0.928	0.010	93.500	0.000
bi3	0.814	0.017	48.760	0.000
Susceptibility
sus1	0.711	0.024	29.120	0.000	0.869
sus2	0.921	0.014	65.640	0.000
sus3	0.846	0.016	52.590	0.000
Severity
sev1	0.916	0.008	113.900	0.000	0.960
sev2	0.969	0.005	193.580	0.000
sev3	0.942	0.006	149.600	0.000
Perceived benefit
ben1	0.738	0.020	36.700	0.000	0.922
ben2	0.975	0.006	150.670	0.000
ben3	0.950	0.007	130.370	0.000
Attitude
att1	0.864	0.017	50.670	0.000	0.847
att2	0.722	0.025	28.950	0.000
att3	0.825	0.018	44.580	0.000
Subjective norm
sn1	0.606	0.027	22.190	0.000	0.904
sn2	0.996	0.005	182.110	0.000
sn3	0.970	0.006	166.590	0.000
Perceived behavioral control
pbc1	0.991	0.002	558.340	0.000	0.984
pbc2	0.962	0.004	271.780	0.000
pbc3	0.976	0.003	379.580	0.000
Law enforcement
cta1	0.811	0.018	44.310	0.000	0.885
cta2	0.903	0.014	63.460	0.000
cta3	0.828	0.017	47.520	0.000

Note: CFA = confirmatory factor analysis; std err = standard error; CR = composite reliability; AVE = average variance extracted (one value per construct).

The discriminant validity of the indicators is further evaluated using the AVE. Table 2 displays the diagonal elements as the square roots of the AVE, while the off-diagonal elements represent the correlations between the constructs. The diagonal elements possess higher values than the other elements within the corresponding row and column, thereby confirming the presence of discriminant validity ( 47 ).

Table 2.

Discriminant Validity Analysis

	1	2	3	4	5	6	7	8
1	0.876
2	0.550*	0.831
3	0.413*	0.323*	0.942
4	0.307*	0.267*	0.383*	0.894
5	0.590*	0.413*	0.440*	0.679*	0.806
6	0.317*	0.008	0.186*	−0.022	0.239*	0.875
7	0.504*	0.241*	0.274*	0.262*	0.487*	0.117*	0.977
8	0.539*	0.340*	0.205*	0.122*	0.291*	0.303*	0.304*	0.849

Note: 1 = Behavioral intention; 2 = susceptibility; 3 = severity; 4 = perceived benefit; 5 = attitude; 6 = subjective norm; 7 = perceived behavioral control; 8 = law enforcement.

p < 0.05.

Based on the findings from the aforementioned tests, it can be inferred that the measurement model exhibits a satisfactory level of conformity with the data. Additionally, the reliability and validity of the indicators are substantiated.

Hierarchical Regression Analysis

The hierarchical regression models were run using Stata 17.0. The model fit indices, as shown in Table 3, suggest that the three models exhibit a reasonable level of fit. The results of these models are shown in Table 4 and Figure 1.

Table 3.

The Fitness Indices

Index	Cut-off value	Model 1	Model 2	Model 3
CFI	> 0.9	0.985	0.960	0.951
TLI	> 0.9	0.971	0.943	0.936
RMSEA	< 0.08	0.065	0.082	0.065
SRMR	< 0.08	0.022	0.067	0.067

Note: CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Table 4.

Results of the Hierarchical Regression

Factors	Model 1	Model 2	Model 3
Sociodemographic characteristics
Gender (ref: male)	0.025	0.022	0.021
Age	−0.033*	−0.018*	−0.013
Education (ref: primary school or lower)	0.650*	0.222*	0.166*
Involved in a traffic crash (ref: no)	0.384*	0.199*	0.083
Construct
Attitude		0.248*	0.178*
Subjective norm	na	0.221*	0.157*
Perceived behavioral control	na	0.141*	0.105*
Susceptibility	na	na	0.252*
Severity	na	na	0.124*
Perceived benefit	na	na	−0.123
Law enforcement	na	na	0.287*
R ² of behavioral intention	0.167	0.469	0.627

p < 0.05. na = not applicable.

According to the result in Table 4, the sociodemographic factors account for 16.7% of the variability in behavioral intention (Model 1). Age (−0.033), education (0.650), and whether involved in a traffic crash or not (0.384) are significantly associated with behavioral intention, while gender is not. After incorporating the exogenous construct in the TPB to Model 1, 46.9% of the variability in behavioral intention is explained by Model 2. In Model 2, age (−0.018), education (0.222), whether involved in a traffic crash or not (0.199), attitude (0.248), subjective norm (0.221), and perceived behavioral control (0.141), are significantly associated with behavioral intention. In Model 3, education (0.166), attitude (0.178), subjective norm (0.157), perceived behavioral control (0.105), susceptibility (0.252), severity (0.124), and law enforcement (0.287) are significantly associated with behavioral intention. The model explained 62.7% of the variability in behavioral intention.

Discussion

The efficacy of the rear seat safety belt in protecting passengers’ safety has been widely recognized ( 2 ). However, the utilization rate of the rear-seat safety belt is still low (7 –11). Most of the existing studies focus on the use of a belt by front-seat passengers or drivers, while relatively few articles are available for the study of rear-seat-belt use. In addition, most of the studies related to rear-seat-belt use are based on analyzing the influence of indicators (such as demographic characteristics) on the use of rear seat belts. A latent construct is an important factor that affects people’s behavioral intention or behavior. Currently, there are few articles that use social cognitive models to study the influence of latent constructs on the intention or behavior of using a rear seat belt. With the aging of the Chinese population, older people in China rely on cars for mobility. Based on the above background, in the present study, we investigated older people’s rear-seat-belt use intention with the hierarchical regression model.

Comparison of the Three Models

Three models are evaluated in this study. Model 1 solely considers sociodemographic characteristics, and it accounts for 16.7% of the variability in behavioral intention. Consequently, the sociodemographic characteristics exhibit limited explanatory capacity.

The inclusion of constructs as predictors significantly enhances the model’s capacity to elucidate behavioral intention: Model 2 and Model 3 account for 46.9% and 62.7% of the variability in behavioral intention, respectively. The result of the likelihood-ratio test shows that Model 2 has a significantly higher explanatory power than Model 1: Δχ²(45) = −7539.58. Similarly, Model 3 has a significantly higher explanatory power than Model 2: Δχ²(74) = −8082.85.

Analysis of the Impact of Sociodemographic Characteristics

Gender is insignificant in Model 1 and in Model 2 and Model 3. Previous studies have found that gender is correlated with the use of rear seat belts ( 8 , 12 , 13 ). The result of this study is inconsistent with previous studies.

Age is significant in Model 1 (−0.033) and Model 2 (−0.018), and the result is consistent with previous studies ( 9 , 12 –14). The intention to wear rear seat belts diminishes with advancing age among older adults.

Education is significant in all three models, and the result is consistent with the study of Bendak and Alnaqbi ( 13 ). Older adults with a high-school education or higher have a greater intent to wear a rear seat belt.

Whether or not someone has been involved in a traffic crash (ref: no) is significant in Model 1 (0.384) and Model 2 (0.199). If an older person has previously been involved in a traffic collision, that person is more likely to wear a rear seat belt.

Analysis of the Impact of Exogenous Constructs

Susceptibility (0.192) and severity (0.093) are both significant. Several previous studies have stated that risk perception is a significant predictor of behavioral intention ( 46 , 54 , 55 ). The result of this study is consistent with these studies. The use of rear seat belts is more prevalent among older individuals who perceive a high rate of car collision and severe collision consequences.

Law enforcement (0.124) is significant. The result is consistent with previous studies ( 7 , 15 , 16 ). The implementation of rigorous traffic-police enforcement measures has the potential to enhance the intention of older adults to use rear seat belts.

Numerous studies have consistently found that attitude plays a substantial role in shaping the intentions and behaviors of travelers about risk ( 32 , 39 , 56 ). The present study aligns with these findings. The term “attitude” pertains to the extent to which an individual holds a positive or negative assessment of a particular behavior. Therefore, there exists a positive correlation between older individuals’ favorable ratings of rear seat belts and their likelihood of utilizing them.

There is currently no consensus on whether perceived behavioral control is correlated with behavioral intention. Some studies have argued that perceived behavioral control is significantly related to behavioral intention ( 32 , 39 , 56 ), while others do not agree (57 –59). In this study, perceived behavioral control is significantly correlated with behavioral intention. There is a positive correlation between the perceived ease of wearing a seat belt in the rear seat and the likelihood of older individuals wearing a rear seat belt.

Prior research has yielded inconsistent findings about the question of how subjective norm is associated with unsafe traffic behavioral intention. In this study, the subjective norm is significant. The level of approval or disapproval toward wearing the rear seat belt significantly influences the behavioral intention of seniors to wear it.

Conclusion

Research Findings

Analysis reveals that sociodemographic factors have a restricted influence on usage intention. Nevertheless, incorporating additional psychological factors can markedly improve the model’s explanatory ability. Attitude, subjective norm, perceived behavioral control, susceptibility, severity, and law enforcement are significantly associated with behavioral intention of using rear seat belts among older adults, after controlling sociodemographic factors.

Policy Implications

Risk perception (including susceptibility and severity) and attitude are significantly associated with seniors’ intention of using rear seat belts. To enhance seniors’ inclination toward utilizing rear seat belts, it is imperative to disseminate information that raises awareness of the potentially life-threatening consequences of not wearing seat belts in the rear seat during car collisions. By highlighting the efficacy of rear seat belts in safeguarding their lives, seniors may be encouraged to use their rear seat belts. One effective method to raise awareness about the significance of rear seat belts is through various media channels such as television and radio. Additionally, strategically placing posters in locations frequented by older individuals, such as parks, senior citizen activity centers, and hospitals, can also help to promote this message.

Subjective norm is significantly associated with seniors’ intention of using rear seat belts. Therefore, there exists a correlation between the approval of others with respect to the utilization of rear seat belts by older individuals and the behavioral intention of these older individuals to use rear seat belts. Authorities must underscore the importance of rear seat belts across all age cohorts.

Perceived behavioral control is significantly associated with seniors’ intention of using rear seat belts. Perceived behavioral control refers to an individual’s subjective assessment of the level of effort or challenge associated with engaging in a specific behavior. Based on our research findings, the process of fastening the rear seat belts in certain cars is comparatively less convenient compared with the front seat belts. Therefore, it is crucial to address through industrial design interventions the issue of rear-seat passengers encountering challenges when wearing seat belts.

Law enforcement is significantly associated with seniors’ intention of using rear seat belts. The traffic police should increase enforcement and penalties for not wearing a seat belt in the rear seat, as well as inform seniors that they will be fined if they are found not wearing a seat belt in the back seat.

Limitations and Future Work

The research in this paper is based on survey data from the city of Pingdingshan. Pingdingshan is a medium-sized city in northern China. It is worth exploring whether the conclusions of this study apply to other cities in China, especially southern and smaller cities, because of differences between regions and economic levels.

The mean age of the participants in this study is 63.9 years, although the current average life expectancy in China exceeds 63.9 years. The majority of the samples included in this study consisted of seniors in their early years. Further investigation is warranted to determine the extent to which the conclusions reported in this study can be extrapolated to the older geriatric population.

Supplemental Material

sj-docx-1-trr-10.1177_03611981241230305 – Supplemental material for Prediction of Rear-Seat-Belt Use among Older Adults

Supplemental material, sj-docx-1-trr-10.1177_03611981241230305 for Prediction of Rear-Seat-Belt Use among Older Adults by Tangyi Guo, Lihua Liu and Jianrong Liu in Transportation Research Record

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: Jianrong Liu, Tangyi Guo; data collection: Lihua Liu; analysis and interpretation of results: Tangyi Guo, Lihua Liu, Jianrong Liu; draft manuscript preparation: Tangyi Guo. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Tangyi Guo

Lihua Liu

Jianrong Liu

Data Accessibility Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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