1 Transport Research Arena 2014, Paris Factors influencing risk and severity of cycling accidents Ariane von Below * Bundesanstalt für Straßenwesen (BASt), Brüderstraße 53,51427 Bergisch Gladbach, Germany Abstract In cyclists died in road accidents in Germany and another cyclists have been injured. Since 2000 the number of injured or fatally injured cyclists remained on the same high level. Cyclists form 19% of all casualties in German road traffic, although the modal split rate of this transport mode in 2008 was only 10% of trips and resp. 3% of kilometres per day. The high proportion of fatally and seriously injured shows the high vulnerability of cyclists. The demographic shift towards an older population in Germany and the governmental recommendation of increased bicycle use as an ecological, economical and healthy alternative to other modes of transport lead to the assumption that bicycle use especially of elderly people will increase. Based on these facts about the German cyclists situation a representative survey of cyclists was conducted. Results display use patterns and accident involvement on a descriptive level. Overall 7.7% of cyclists report to have been involved in at least one road traffic accident within the last three years. The performed path model analysis reveals an acceptable model fit. Though the explained variance of accident involvement of the applied psychological factors was low, significant relations between several of these factors and especially risky behaviour could be found. Keywords: cycling; accident; attitudes; risk perception; cycling behaviour. Résumé En 2011, 399 cyclistes sont décédés dans des accidents de la route en Allemagne, et cyclistes ont été blessés. Depuis 2000, le nombre de blessés ou tués cyclistes est demeuré à un même niveau élevé. Les cyclistes constituent 19% de l'ensemble des victimes du trafic routier en Allemagne, bien que le taux de répartition modale de ce mode de transport en 2008 n'était que de 10% parmi tous les moyens et resp. 3% des kilomètres parcourus par jour. La proportion élevée de conducteurs décédés ou grièvement blessés exprime la grande vulnérabilité des cyclistes. L évolution démographique vers un vieillissement de la population en Allemagne et les recommandations gouvernementales d intensification de l utilisation de la bicyclette comme moyen de déplacement écologique, économique et sain alternatif à d'autres modes de transport permettent d émettre l'hypothèse que l'emploi de bicyclettes surtout par des personnes âgées va augmenter. En partant de ces hypothèses, un sondage représentatif des cyclistes a été réalisé. Les résultats sont basés sur des schémas d'utilisation et d implication dans les accidents à un niveau descriptif. Globalement 7.7% de cyclistes rapportent qu ils ont été impliqués dans au moins un accident de la circulation routière au cours des trois dernières années. L analyse du modèle de chemin révèle un modèle d ajustement acceptable. Bien que la variance expliquée de l implication dans un accident des facteurs psychologiques soit faible, des relations significatives entre plusieurs de ces facteurs et en particulier des comportements à risque ont pu être mises en avant. Mots-clés: cyclisme ; l'accident ; les attitudes ; la perception du risque ; le comportement du vélo. * Corresponding author Tel.: ; fax: address:
2 Ariane von Below/ Transport Research Arena 2014, Paris 2 1. Introduction Cycling use as an ecological, economical and healthy alternative to other modes of transport is promoted by the national governments. In fact cycling is a very popular transport mode as well as leisure time activity in almost all social stratums and age groups. Within the huge (n = ) research study mobility in Germany ( Mobilitaet in Deutschland (MiD)) the number of trips and kilometres travelled per day by several modes of transport was recorded in 2002 and According to these data in % of all kilometres and 10% of all trips travelled per day were done by cycle. Between 2002 and 2008 the number of trips increased about 8% and the number of kilometres travelled by cycle about 6% (cf. figure 1). The road safety situation of cyclists in Germany has not improved in the same way as for other road users, especially car drivers. In cyclists died in cause of a road accident, were seriously (which is staying in hospital at least 24 hours) and slightly injured. While the number of all injured or fatally injured in road traffic decreased about 20% between 2002 and 2012 the number of cyclists that were injured or fatally injured increased about 6% in the same period of time. Although there is a positive trend in the reduction of the number of fatally injured cyclists, about -30% between 2002 and 2012, the number of injured cyclists increased (+6%) as well as the number of accidents with involvement of cyclists (+5%) (cf. figure 1). Figure 1. Difference of accident involvement between 2002 and 2012 and difference of ways resp. kilometres travelled per day between 2002 and 2008 (in percent) (Statistisches Bundesamt (Federal Statistical Office), 2013; MiD, 2008). Almost 17% of cycle accidents are single accidents in which no further road users were involved. For accidents with the involvement of two parties the second party mainly is a car (74%). Out of the cyclists being involved in an accident, 42% were blamed on being the responsible party of the accident. The cyclists major misbehaviours leading to accidents were improper street use, failure while turning, backing etc., right of way violation and cycling under the influence of alcohol (cf. figure 2).
3 Ariane von Below/ Transport Research Arena 2014, Paris Influence of alcohol Improper street use Failure while backing, turning etc. Right of way violation under and above At the age of... to... years Figure 2. Misbehaviour per accident involved cyclists by age groups. (Satistisches Bundesamt (Federal Statistical Office), 2012) While there is detailed knowledge on the relation between psychological aspects like attitudes, motives, risk perception, self-efficacy, traffic behaviour and accident involvement of car drivers (e.g. Schulze, 1996, 1999; Ulleberg, 2002; Holte, 2012; Tranter & Warn, 2008; Dzewaltowski, 1999; Schwarzer, 1992; DeVries, Dijkstra & Kuhlman, 1988; Armitage & Conner, 1999, 2001; Broadhead-Fearn & White, 2006) little research was done on such relations for cyclists. A study dealing with some psychological constructs concerning cyclists was carried out by Marning and van Schagen (1990). The authors focused on age related differences of knowledge of priority rules, importance of rule compliance, self-reported behaviour and opinions about car drivers, other cyclists and themselves. Based on Dutch accident statistics which show a U-shaped curve for age, they compared four age groups with a higher risk (9-11 years, years, years and years) with the reference group (19-59 years) regarding the above mentioned psychological aspects. Results show that knowledge of priority rules increases with age but decreases again after the age of 60. Self-reported behaviour, the importance of rule compliance, and the opinions of other cyclists and themselves show U-shaped curves similar to accident involvement. DeWaard, Schepers, Ormel and Brookhuis (2010) and DeWaard, Edlinger and Brookhuis (2011) studied the influence of listening to music or using a handheld phone while cycling on cycling performance. They observed a prevalence of talking on a phone of 2.2% and typing on a phone of 0.6% in Groningen (NL). 0.5% of accident involved cyclists reported that they used a handheld phone when the accident happened. An additional experimental study showed that several aspects of cycling performance were affected when using a handheld phone compared to non distracted cycling. These aspects were reduced speed, reduced peripheral vision performance and increased risk and mental effort ratings. Entering text to the phone had the most negative influence on the performance. Furthermore the auditory performance and the response time to an auditory stop signal were derogated by using a phone as well as by listening to music. 68% of auditory stop signals were missed when listening to music with in-earbuds. Terzano (2013) also assessed whether the safety of cyclists who are performing a secondary task like using a handheld phone is affected. Comparing cyclists performing a secondary task and cyclists not performing a secondary task observed at intersections the author found that a higher rate of secondary task performing cyclists showed unsafe behaviour and forced other road users to evade or to brake to avoid a collision. The objective of this study was to gather more detailed information on German cyclists, especially on demographics, use patterns, purpose of cycle use etc. Furthermore predictors of accident involvement of cyclists were focused in order to identify special risk groups and to define appropriate road safety measures for them.
4 Ariane von Below/ Transport Research Arena 2014, Paris 4 2. Methodology 2.1. Data collection The study was conducted with the goal to describe the German cyclists population and to investigate the influence of psychological aspects on accident involvement. Data of a representative sample of German cyclists (n = 2 158) from the age of 14 and above who have been cycling at least once within the last 12 month was collected by an opinion research institute. The questionnaire was presented face-to-face by 585 interviewers in May The quota sample included male (51%) and female (49%) cyclists. The mean age of the participants was 45.6 years with the maximum age of 84 years. The highest proportion of subjects was aged between 55 and 64 years (23.3%) Questionnaire The questionnaire contained 48 questions, of which only parts will be discussed in the paper at hand. The first part of the questionnaire focused on demographical and educational characteristics. Further questions focused on cycling behaviour, rule violations and accident involvement. Motives for cycle use were assessed by 14 dichotomous items partly adapted from SARTRE 4-questionnaire (Cestac & Delhomme, 2012). To measure risk perception in different situations and behaviours that could be dangerous for cyclists, 21 items were developed. Subjects were asked to rate the riskiness of these situations and behaviours on a four-point Likert-scale from nonhazardous to very hazardous. The five items of the self-efficacy scale are modifications of items from studies of Holte (2012) and von Below & Holte (in preparation). Subjects were asked to rate their own ability to handle different situations while cycling on a rating-scale from 0 ( I don t think I am capable ) to 10 ( I totally think I am capable ). Attitude items were adapted from other studies as well as the self-efficacy and motive items. The questionnaire contained 16 items on attitudes towards cycling in form of statements. The approval to these statements was measured on a four-point Likert-scale from I don t approve at all to I totally approve. Finally cycling behaviour was measured by a list of 30 items asking for the frequency of such behaviours (never, rarely, sometimes or often). 3. Results 3.1. Descriptive results 33.4% of the subjects use the cycle (almost) daily, 23.2% several times a week, another 25.5% several times a month and the other 17.9% use a cycle less than monthly. There is no significant sex specific difference in the frequency of cycle use but there are age specific differences: younger cyclists between 14 and 17 years and 18 and 24 years state more often to use to cycle (almost) daily than the other age groups. The majority of cyclists own or have the opportunity to use at least one cycle (75.9%), 22.8% own or may use two or more cycles and only 1.3% don t have a cycle or the possibility tu use a cycle at all. The most common cycle type is a conventional city cycle (or Dutch, touring or trekking cycle) with a proportion of 61.1% of all reported owned cycles. 24.5% of the cycles are mountain bikes, 8.5% are racing cycles, 2.3% are pedelecs (pedal electric cycle), 2.2% are folding cycles and 1.4% other cycle types. The owned cycle types differ significantly between men and women. Women own a conventional cycle above average whereas men have disproportionately high rates of mountain bikes, racing cycles and pedelecs. Cyclists who own a mountain bike, racing cycle or pedelec and cyclists who own more than one cycle type use to cycle more often than the owners of other cycle types. Only a small proportion of subjects cycle independent of weather conditions or seasons. More than 60% use their cycles exclusively or predominantly in the warmer time of the year. Furthermore 74.1% report that there are weather conditions or times of a day at which they do not use to cycle. These are snow or glaze (95.6%), rain (68.9%), coldness (53.4%), strong wind (40.7%), mist (38.9%) and darkness (33.1%). Most frequently cycles are used on weekends (55.1%), followed by running errands (45.3%), using the cycle for short ways (44.8%), on holidays (32.3%), travelling to school or work (31.4%), for sports (4.7%), attending children (4.5%), and job-related use (2.7%). The subjects were also asked to report if they have been warned or fined by the police within the last two to three years because of disobeying traffic rules while cycling. 23.6% report such a warning or fine. The most often
5 Ariane von Below/ Transport Research Arena 2014, Paris stated rule violations are cycling on a pedestrian path (14.4%), cycling against the driving direction (9.1%), jumping the lights (7.0%), cycling without headlight in darkness (6.3%), disregarding a compulsory cycle path (6.3%), handheld phone use while cycling (4.3%), violating right of way (3.7%) and other violations beyond 3.5% of share. In Germany the observed helmet wearing rate is relatively low. Therefore the subjects were asked to report their helmet use 62.6% never wear a helmet, 21.1% always and 15.6% use a helmet just for specific purposes. Situations in which cyclists wear a helmet are long distances or cycle tours (68.6%), using heavily travelled (48.5%) or dirt roads (43.8%), cycling within a group (32.7%), attending children (31.8%), cycling in urban areas (24.1%), using the cycle for sports (18.8%) and cycling with high speed (15.5%). The distribution between different age shows large variance of helmet use. In particular the age group of 65 years and older most often never wear a helmet (71.8%) whereas only 47.2% of teenagers between 14 and 17 years do not wear a helmet while cycling (cf. table 1). Table 1. Helmet use by age groups in percent Age groups Helmet use Total Always For specific purposes Never No answer A cycle accident within the last three years was reported by 7.7% of the subjects (male: 8.8%, female: 6.5%). Young adults between 18 and 24 years have the highest proportion of accident occurrence (11.4%). With 6.4% the year-olds show the lowest percentage (cf. figure 3). 80.7% of the accident involved cyclists were injured. Over the half (51.5%) of those had to undergo an ambulant treatment, 42.5% treated the injuries by themselves and 9.0% had to get a hospital treatment a. Figure 3. Accident involvement rate by age groups Factor analysis To analyze the dimensions behind motives, attitudes, risk perception and cycling behaviour, exploratory factor analyses were conducted. Items were excluded from further analyses if the factor loadings were under.40 or if factor loadings were similarly high on two factors. Mean values, standard deviations, Cronbach s α values and number of items for all reported factors are shown in table 2. a Differences to 100% result because five respondents choose more than one answer category.
6 Ariane von Below/ Transport Research Arena 2014, Paris 6 The 13 items of motives result in three factors. The first factor contains mobile and favourable alternative and consists of five items with a mean inter-item-correlation of.38 and a Cronbach s α of.75 ( no parking problem ; getting ahead faster in the city ; avoiding to be caught up in a traffic jam ; cheaper than a car, train or bus ; protecting the environment ). The second motive factor describes the dimension fun and fitness with five items and a mean inter-item-correlation of.25 and a Cronbach s α of.63 ( staying healthy and fit ; having fun ; loosing or keeping weight ; gives a feeling of freedom ; it is fun to cycle together with others ). Factor three includes three items with a mean inter-item-correlation of.28 and Cronbach s α of.54. Items loading on this factor embody cycling being the only mobility option ( no driving license ; no car ; no other opportunity to get to work ). For risk perception also three factors result from 11 items. The three factors are risk by interaction which includes six items with a Cronbach s α of.80 and an inter-item-correlation of.41 ( How dangerous do you perceive : cycling on a high travelled road ; cycling on a road without a cycle path ; turning at high travelled crossroads ; cycling on a rural main road ( Bundesstraße ) without a side strip ; cycling on a narrow street where cars can hardly overtake ; overtaking cyclist or pedestrians ), risk by rule violation with seven items with a Cronbach s α of.78 and an inter-item-correlation of.34 ( How dangerous do you perceive : running a red light, when you made sure nobody is arriving ; using a handheld phone while cycling ; cycling on a pedestrian path ; listening to music while cycling ; passing cars that are waiting at a red light ; cycling against the travel direction ; cycling after drinking alcohol ) and risk by external factors with three items with a Cronbach s α of.56 and an inter-item-correlation of.30 ( How dangerous do you perceive : cycling with damaged brakes ; cycling without light in the dark ; cycling at glaze ). The factor analysis of the 12 items on attitudes towards cycling again shows three factors. Factor one attitude towards high speed contains four items ( it is fun to cycle fast ; cycling helps to calm down ; I m taking risks while cycling when I m in a hurry ; you can go fast if you cycle carefully ) with a mean inter-item-correlation of.43 and a Cronbach s α of. 75. The second factor attitudes towards rule violation has six items with a mean inter-item-correlation of.21 and Cronbach s α of.61 ( you shouldn t cycle if you had drunk* b ; with a couple of beer or glasses of wine it is still possible to cycle ; it is not allowed to run a red light even if nobody is arriving* ; you shouldn t cycle without light in the dark in any case* ; I mind to wear high visible clothes* ; it is ok to bend the rules ). Factor three is called uncertainty of rules. The two items that load on this factor ( sometimes I m not sure if it is allowed to cycle where I cycle ; I sometimes violate traffic rules by mistake ) have an inter-item-correlation of.33 and Cronbach s α of.50. The five items on self-efficacy ( How much do you think you are able to : cycle on a very narrow path ; overtaking other cyclists ; keeping balance at low speed or when stopping ; passing cars waiting at a red light ; cycling after drinking alcohol ) all load on a single factor with a mean inter-item-correlation of.53. The scale s Cronbach s α is.84. The 27 items on cycling behaviour are split to five factors. The first factor with nine items ( using a handheld phone while cycling ; listening to music while cycling ; cycling after taking illegal drugs ; almost sliding off a curve because going to fast ; running a red light when there is no traffic ; cycling after drinking alcohol ; passing cars that are waiting at a red light ; running a stop sign ; cycling without light in the dark ) is called intentional risky behaviour. The mean inter-item-correlation of factor one is.31 and Cronbach s α is.80. Factor two risky behaviour of other road users includes six items ( abrupt braking because of a pedestrian suddenly stepping on the cycle path ; being overtaken by a car too closely ; need to brake though having right of way because a car driver fails to see me ; car doors being opened towards the street ; cycle path ends all at ones ; ringing angrily because of pedestrians or cyclists blocking the way ) with a mean inter-item-correlation of.35 and a Cronbach s α of.76. Wrong use of roads, the third factor, contains of four items ( cycling on a pedestrian path ; using a pedestrian path to avoid waiting at a red light ; cycling on a cycle path against the travel direction ; cycling against the travel direction in a one-way street without permission ). The mean inter-itemcorrelation is.38 and Cronbach s α is.71. The fourth factor is incautiousness with an inter-item-correlation of. 34 and Cronbach s α of.66 for the four items ( unintentional missing to look back before overtaking or avoiding b * Items were recoded. High values correspond to riskier attitudes.
7 Ariane von Below/ Transport Research Arena 2014, Paris an obstacle ; not looking back before crossing a street or overtaking ; turning without indicating by a hand signal ; fast crossing a street without making sure that no car is arriving ). The last factor of cycling behaviour is called handling error. The four items ( loosing balance at start or stop ; choosing the wrong gear and getting out of step ; not noticing an obstacle and having difficulties to stop ; almost falling over the handlebar because of putting on the front-wheel brake to hard ) have a mean inter-item-correlation of.26 and a Cronbach s α of.58. T-tests show that there are significant differences between male and female cyclists for several of the factors (cf. table 2). Men use the cycle more often because they want to stay fit or to have fun. Additionally they have higher values on attitudes towards high speed as well as towards rule violation. Men more frequently report intentional risky behaviour, risky behaviour of other road users and incautiousness. Female cyclists significantly more often cycle because they have no other mobility option. They have higher values on all three risk perception scales and they more often report handling errors. Pearson correlations with age are highly significant for the most of the scales but the effect sizes are low to medium. With higher age cyclists more often cycle to stay fit and for fun and less often use a cycle because it is the only mobility option. Risk perception is higher the older the cyclists are, for all risk perception factors. Whereas the attitudes towards high speed and towards rule violations and the uncertainty of rules is higher the younger the cyclists are. Also self-efficacy, intentional risky behaviour, risky behaviour of others, wrong use of roads and incautiousness are higher for younger cyclists. A small positive correlation of handling error is shown for age. Table 2. Descriptive factor statistics, t-values for gender differences and correlation coefficients with age. Factors Motives Mean Standard deviation Cronbach α Number of items t-values gender Correlation with age Mobile and favourable alternative n.s. n.s. Fun and fitness ** Only mobility option ** Risk perception Risk by interaction ** Risk by rule violation ** Risk by external factors ** Attitudes Attitude towards high speed ** Attitude towards rule violation ** Uncertainty of rules n.s. -.13** Self-efficacy ** Cycling behaviour Intentional risky behaviour ** Risky behaviour of others ** Wrong use of roads n.s. -.25** Incautiousness ** Handling error * 3.3. Path analysis Based on the theoretical assumptions of Holte (2012) a working model of the relationships of motives, risk perception, attitudes, self-efficacy and cycling behaviour on accident involvement was developed. To assess the influence of these psychological constructs on accident involvement a path analysis was conducted with the program MPlus (Muthén & Muthén, 2010). Three factors of cycling behaviour (wrong use of roads, incautiousness, handling error) were not significant related to either police warning or accident involvement and were therefore not included in the final model.
8 Ariane von Below/ Transport Research Arena 2014, Paris 8 In a first analysis of the final model the accident variable was used as a numerical criterion (dummy coded) in order to evaluate the goodness of fit for the assumed model. The values show that model fit is very good (Chi2 = ; df =40; p =.000; RMSEA =.035; CFI =.968). Figure 4 presents the results of the second analysis of the final model which was conducted for accident involvement as a categorical variable. Path coefficients lower than.10 are not shown in the figure as well as significant correlations between attitude, motive and risk perception factors and between those factors and age. Overall the included variables and factors explained 11% of the variance of accident involvement of cyclists within the last three years. Police warning or fine, risky behaviour of others and attitude towards high speed have direct effects on accident involvement, but there is no direct effect of intentional risky behaviour on accident involvement. Thus the more often cyclists report that they need to react on risky behaviour of other road users the more often they have been involved in an accident. On the other hand the probability of accident involvement is higher for cyclists having been warned or fined by the police. Further on positive attitudes towards high speed are related to a higher accident involvement. As the explained variance of R 2 =.57 documents that the included psychological factors do explain intentional risky behaviour very good, misbehaviour of cyclists can be predicted by attitudes, risk perception and selfefficacy, although a direct influence of this intentional misbehaviour on accidents cannot be found. Figure 4. Path model of the relationship of age, motives, risk perception, attitudes, self-efficacy, cycling behaviour, police warning or fine, and accident involvement. 4. Summary and conclusions The present study provides insight in the current situation of cyclists in Germany. For cyclists of different gender and age groups behavioural patterns including cycling frequency, cycle type, purpose of use, dependence on weather conditions and helmet usage as well as police warnings or fines and accident involvement can be
9 Ariane von Below/ Transport Research Arena 2014, Paris distinguished showing significant age and sex specific differences. To define appropriate road safety measures for the highly endangered but very heterogeneous group of cyclists this group of road users has to be divided into specific subgroups to be focused on by specific road safety measures. Compared to the numbers of other road users only a small number of cyclists report road accidents. But among these there is a considerable high amount of injured people. There are two possible explanations for this: First the endangerment of being injured within a cycle accident is in fact very high. A second possible explanation is that cyclists do not remember accidents without injury or they do not constitute those minor accidents as such. The chosen model of psychological constructs influencing accident involvement of cyclists proofed to be sufficient, though the explained variance of the accident variable was as expected from the results of studies with other road users low. Accident involvement is directly impacted by the attitude toward high cycling speed, the reported reaction to risky behaviour of other road users and by police warning or fine. Other behavioural factors could not be found affecting cycling accidents. But accidents are rare events, influenced by the interaction of several situational factors and are therefore not perfectly suitable as a criterion for road safety. Epstein (1979) suggested independent of the road safety context to use an aggregation of several behaviours upon different situations instead of a single occasion. The five factors of cycling behaviour that are presented in this paper can be used as aggregations in terms of Epstein. Both factors of behaviour that are included in the path model are affected by different factors of motives, risk perception, attitudes and self-efficacy. Consequently the chosen behavioural factors are appropriate to be used as criteria for the differentiation of subgroups. References Armitage, C. J. & Conner, M. (1999). Distinguishing perceptions of control from self-efficacy: predicting consumption of a low-fat diet using the theory of planned behavior. Journal of Applied Social Psychology, 29, Armitage, C. J. & Conner, M. (2001). Efficacy of the theory of planned behaviour: a meta-analytic review. British Journal of Social Psychology, 40, Broadhead-Fearn, D. & White, K. (2006) Perceptions of self efficacy in predicting rule-following behaviours in shelters for homeless youth: A test of the theory of planned behaviour. Journal of Social Psychology, 146, Cestac, J. & Delhomme, P. (Eds.) (2012). European road users risk perception and mobility. The SARTRE 4 survey. Available under DeVries, H., Dijkstra, M. & Kuhlman, P. (1988). Self-efficacy: the third factor besides attitude and subjective norm as a predictor of behavioral intentions. Health Education Research, 3, DeWaard, D., Edlinger, K. & Brookhuis, K. (2011). Effects of listening to music, and of using a handheld and handsfree telephone on cycling behaviour. Transportation Research Part F, 14, DeWaard, D., Schepers, P., Ormel, W. & Brookhuis, K. (2010). Mobile phone use while cycling: Incidence and effects on behaviour and safety. Ergonomics, 53, Dzewaltowski, D.A. (1999). Towards a model of exercise motivation. Journal of Sport and Exercise Psychology, 12, Epstein, S. (1979) The stability of behaviour: on predicting most of the people much of the time. Journal of Personality and Social Psychology, 37, Holte, H. (2012). Einflussfaktoren auf das Fahrverhalten und das Unfallrisiko junger Fahrerinnen und Fahrer. Berichte der Bundesanstalt für Straßenwesen, Mensch und Sicherheit, Heft M 229. Bremerhaven: Wirtschaftsverlag NW.
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