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1.
Disabil Rehabil ; 41(23): 2758-2765, 2019 11.
Article in English | MEDLINE | ID: mdl-29893157

ABSTRACT

Purpose: This study aims to identify the most important factors that influence the independent bicycle use of visually impaired people in the Netherlands.Materials and methods: Both visually impaired people and professionals participated in a two-round online Delphi study (n = 42). In Round 1 the participants identified the factors which they ranked by relevance in Round 2.Results: The participants prioritised environmental factors related to the traffic situation, the characteristics of the infrastructure, and weather and light conditions (Kendall's W = 0.66). They indicated that the most influencing personal factors are related to personality, traffic experience, and personal background (W = 0.58). Glaucoma was ranked as the most relevant ophthalmic condition (W = 0.74), while glare was regarded as the most important factor with respect to the visual functions (W = 0.78).Conclusions: The factors provided by this study can be used to optimise the independent cycling mobility of visually impaired people. More research is needed to investigate, both, how and to what extent the mentioned factors influence the cycling behaviour.Implications for rehabilitationThe results of this study can be used to set priorities during the rehabilitation and training of visually impaired people who wish to cycle independently.Visually impaired cyclists may compensate for the consequences of their visual impairments by taking alternative routes that suit their individual abilities and limitations.Since gaining and maintaining self-confidence is important for independent cycling with a visual impairment, practitioners such as mobility trainers should not only focus on cycling-related skills and abilities, but also aim to improve the self-confidence of visually impaired people who wish to cycle.


Subject(s)
Bicycling , Environment , Self Concept , Vision Disorders/rehabilitation , Visually Impaired Persons , Accidents, Traffic/prevention & control , Bicycling/physiology , Bicycling/psychology , Bicycling/standards , Delphi Technique , Disability Evaluation , Female , Humans , Male , Middle Aged , Netherlands , Personality , Visually Impaired Persons/psychology , Visually Impaired Persons/rehabilitation
2.
Ergonomics ; 59(9): 1158-70, 2016 Sep.
Article in English | MEDLINE | ID: mdl-26653393

ABSTRACT

Usage-Based Insurances (UBI) enable policyholders to actively reduce the impact of vehicle insurance costs by adopting a safer and more eco-friendly driving style. UBI is especially relevant for younger drivers, who are a high-risk population. The effectiveness of UBI should be enhanced by providing in-car feedback optimised for individual drivers. Thirty young novice drivers were therefore invited to complete six experimental drives with an in-car interface that provided real-time information on rewards gained, their driving behaviour and the speed limit. Reward size was either displayed directly in euro, indirectly as a relatively large amount of credits, or as a percentage of the maximum available bonus. Also, interfaces were investigated that provided partial information to reduce the potential for driver distraction. Compared to a control no-UBI condition, behaviour improved similarly across interfaces, suggesting that interface personalisation after an initial familiarisation period could be feasible without compromising feedback effectiveness. Practitioner Summary: User experiences and effects on driving behaviour of six in-car interfaces were compared. The interface provided information on driving behaviour and rewards in a UBI setting. Results suggest that some personalisation of interfaces may be an option after an initial familiarisation period as driving behaviour improved similarly across interfaces.


Subject(s)
Accidents, Traffic , Automobile Driving/psychology , Risk Reduction Behavior , Safety , Token Economy , Accidents, Traffic/prevention & control , Accidents, Traffic/psychology , Age Factors , Computer Simulation , Feedback , Female , Humans , Insurance, Accident , Male , Simulation Training/methods , Young Adult
3.
Accid Anal Prev ; 75: 93-104, 2015 Feb.
Article in English | MEDLINE | ID: mdl-25460096

ABSTRACT

Pay-As-You-Drive (PAYD) insurance links an individual's driving behaviour to the insurance fee that they pay, making car insurance more actuarially accurate. The best known PAYD insurance format is purely mileage based and is estimated to reduce accidents by about 15% (Litman, 2011). However, these benefits could be further enhanced by incorporating a wider range of driving behaviours, such as lateral and longitudinal accelerations and speeding behaviour, thereby stimulating not only a safe but also an eco-friendly driving style. Currently, feedback on rewards and driver behaviour is mostly provided through a web-based interface, which is presented temporally separated from driving. However, providing immediate feedback within the vehicle itself could elicit more effect. To investigate this hypothesis, two groups of 20 participants drove with a behavioural based PAYD system in a driving simulator and were provided with either delayed feedback through a website, or immediate feedback through an in-car interface, allowing them to earn up to €6 extra. To be clear, every participant in the web group did actually view their feedback during the one week between sessions. Results indicate clear driving behaviour improvements for both PAYD groups as compared to baseline rides and an equal sized control group. After both PAYD groups had received feedback, the initial advantage of the in-car group was reduced substantially. Taken together with usability ratings and driving behaviours in specific situations these results show a moderate advantage of using immediate in-car feedback. However, the study also showed that under conditions of feedback certainty, the effectiveness of delayed feedback approaches that of immediate feedback as compared to a naïve control group.


Subject(s)
Automobile Driving , Feedback, Psychological , Insurance , Acceleration , Accidents, Traffic , Adult , Automobile Driving/statistics & numerical data , Behavior , Female , Humans , Internet , Male , Reward , Young Adult
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