Hélène Dirix

Prolonged monotonous driving is associated with hypovigilance and reduced alertness, yet its influence on subsequent visual attention and driving behavior remains insufficiently understood. This study investigated how prolonged monotonous driving influences highway control, subsequent lane change performance, visual attention to lane change signs, and the relationship between subjective state and objective driving control. Sixteen licensed drivers completed a simulated drive consisting of a pre-highway lane change task, a prolonged monotonous highway segment, and a post-highway lane change task. Driving performance and eye-tracking metrics were assessed. The findings suggest that prolonged low-demand driving weakens lateral control, while visual attention to lane-change signs became faster and more extensive in the post-highway session. This pattern is consistent with compensatory visual reallocation following underload when task demands become visually explicit and behaviorally structured. These results highlight the importance of multimodal assessment for road safety, as subtle changes in control stability and visual attention may not be captured by vehicle dynamics alone, despite their relevance for driver readiness in attention-critical situations.

Background: Truck drivers constitute a high-risk occupational group due to irregular schedules, prolonged sedentary work, fatigue, and limited access to healthcare, contributing to adverse physical and mental health outcomes. Although mobile health (mHealth) tools offer potential to support driver health, sustained engagement remains a persistent challenge. Objectives: This systematic review aimed to identify behavioural, technological, and contextual determinants influencing truck drivers’ compliance, retention, and long-term engagement with digital health interventions. Methods: Following the PRISMA 2020 guidelines, six eligible studies were identified and thematically synthesised across technology acceptance, behaviour change, and persuasive system design perspectives. Results: Across studies, sustained engagement was facilitated by self-monitoring, real-time feedback, goal-setting, coaching support, and simple, flexible system design. In contrast, technological complexity, high interaction demands, limited digital literacy, privacy concerns, misalignment with irregular schedules, and fatigue consistently undermined engagement and retention. Autonomy, trust, and voluntary participation emerged as cross-cutting determinants supporting continued use. Based on the synthesis, an integrative framework was developed to explain how behavioural, technological, and contextual factors interact to shape truck drivers’ compliance, engagement, and retention with mHealth. Despite generally moderate to high study quality, the evidence base remains fragmented and dominated by short-term evaluations. Conclusions: The findings highlight the importance of context-sensitive, user-centred design to support effective digital health interventions in the trucking sector.

Wearable devices are increasingly used for health and stress monitoring, yet their accuracy under dynamic, real-world conditions remains uncertain. This study validated the heart rate measurement accuracy of one chest-worn device (Zephyr BioHarness 3.0), a research-grade wrist-worn device (EmbracePlus), and five commercial wrist-worn wearable devices (Fitbit Charge 5, Fitbit Sense 2, Garmin Vivosmart 4, WHOOP 4.0, and Withings Scanwatch) against a 12-lead ECG using a 20 min protocol simulating real-life dynamics, including rest and varied-intensity walking. Device performance was evaluated across the full protocol and during transient states, defined as periods of rapid heart rate change. Accuracy and agreement were evaluated across per-second, 10 s, and 60 s resolutions. The Zephyr device showed a strong performance during all dynamic conditions. Among the wrist-worn devices, the Fitbit Charge 5 and Sense 2 showed the highest accuracy overall, while the Garmin Vivosmart 4 demonstrated greater stability during transitions. The WHOOP 4.0, Withings Scanwatch, and EmbracePlus devices performed acceptably during steady-state conditions, but were less accurate during transitions. Performance notably declined across all wrist-worn devices during transient states, with motion onset and large step changes in heart rate exacerbating measurement errors. Larger averaging windows improved accuracy by smoothing variability. The findings underscore that wrist-worn wearable devices may be better suited for average and trend heart rate monitoring rather than capturing acute dynamics. However, the Garmin and Fitbit devices showed suitable when requiring moderate accuracy during dynamic conditions. These results highlight the importance of context-specific validation and informed device selection to ensure effective use in health and stress-related applications.

European occupational road freight drivers face difficulties such as traffic jams, deadlines and demanding customers during their job. Therefore, they often suffer from stress, a problem that may worsen as traffic increases. Europe has a severe driver shortage, so insight into the issues of occupational drivers is needed to improve their working conditions. Through two online focus group discussions with eleven occupational drivers, this study gathered data on stressors and strategies against stress. The data was analysed through thematic analysis, revealing four different stressor types: stressors related to company operations (e.g. dispatching issues), job tasks (e.g. time-related stressors), client-related factors (e.g. disrespect from the client), and non-work-related stressors (e.g. an ill partner). Strategies against stress include coping strategies (e.g. social contact) and solutions such as an improved attitude towards freight drivers, improved communication between drivers and other parties, better equipment and facilities and changes to the law. The insights of this study about occupational driver stressors could be used for e.g. intervention development.

Background: Despite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. Objective: A systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. Methods: Per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. Results: The search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (−1.4 min), light sleep (−9.6 min), and deep sleep (−9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP. Conclusions: The findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.

Speeding is one of the leading risk factors in road safety. Not only is it one of the leading causes of accidents, but it also has an extensive effect on the impact and consequences of accidents. This is especially the case for trucks, where the enforced speed limit is often dependent on local legislation and context rather than speed limit traffic signs. This study is part of the greater i-DREAMS project and aims to explore the effectiveness of an intelligent speed assistance system for truck drivers on different road types. To achieve this, a simulator experiment was performed with 34 professional truck drivers in Belgium. Participants first made a baseline drive, followed by two more drives, where they received visual information about the enforced speed limit but also visual and auditory warnings when exceeding the speed limit. The drives included different road environments with different speed limits. The results reveal a significant reduction in relevant parameters (i.e., average speed, minimum speed, maximum speed, and percentage of distance above the speed limit) when drivers received information and warnings about speeding while driving on a rural 1 × 1 road with a speed limit of 70 km/h (60 km/h for trucks). Further research is needed to validate this effect on other road types and under more-challenging conditions.