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.

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.

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.

Purpose: Occupational road freight drivers in Europe are crucial to the logistics chain. They deal with difficulties such as deadlines, demanding clients and traffic, which can lead to stress. Due to the driver shortages in Europe, it is crucial to gain more insight into the issues occupational drivers experience to improve their working conditions. Design/methodology/approach: This study gathers data on stressors and strategies to counter stress through two online focus group discussions. In total, eleven drivers participated. The data were analyzed through a semantic inductive approach of thematic analysis and integrated into the JDC model. Findings: The themes that emerged from the analysis were stressors related to company operations, job tasks, clients, and non-work-related stressors. Coping strategies include self-performed strategies, including music or social contact, and solutions such as an improved attitude towards freight drivers, optimized communication between drivers and other parties, better equipment and facilities, and legislation. Practical implications: Recommendations for organizations include (1) a stress management platform, (2) increased peer support, (3) driver appreciation campaigns (including tangible support), (4) improved communication between drivers and other employees and (5) providing proper equipment and information about proper facilities. Originality/value: The results provide deeper insights into driver stressors and strategies against stress. Based on these results, recommendations were made to improve freight driver well-being.

This study assessed the feasibility of a technology-supported high-intensity interval training (HIIT) program for occupational drivers. A multiphase feasibility study incorporating participant feedback was conducted in Flanders, Belgium, including consultation, pilot, and intervention phases. Occupational drivers were randomized to a HIIT group, an active control group (mindfulness program), or a passive control group (no intervention). The 16-week, equipment-free HIIT program consisted of two 15-min sessions per week, delivered via a technology-supported mobile application, preceded by a 2-week baseline. Primary feasibility outcomes included program completion, session adherence, intervention fidelity, adverse events, and participant experiences, while changes in health-related outcomes were assessed as secondary exploratory outcomes using validated questionnaires. During the consultation (n = 11) and pilot (n = 2) phases, participants reported positive attitudes toward the intervention. Key barriers, including scheduling constraints (n = 5) and bicycle transportation (n = 4), informed refinements such as an equipment-free, technology-supported protocol and flexible scheduling. Of the 23 randomized participants across all groups, 16 participants initiated their assigned study protocol, and 10 completed all assessment points and were included in the final analysis. Of the nine participants allocated to the HIIT group, four initiated the intervention (44.4%), and three completed the program (33.3% of those allocated; 75% of those who initiated). Among the three completers, mean session attendance was 80.2%. Session fidelity was well maintained, but exercise-intensity fidelity could not be objectively confirmed. No exercise-related adverse events were reported. Participant feedback indicated generally positive usability but mixed acceptability of the technology-supported HIIT program among the three participants who completed the intervention. Exploratory individual-level changes in health-related outcomes were heterogeneous, with lower mean scores for depression, anxiety, stress, and fatigue. A technology-supported HIIT program was feasible among the small subgroup of occupational drivers who initiated it, with high session attendance. However, considerable pre-initiation attrition and low retention limit its feasibility for the broader target population. Observed health-related outcomes were heterogeneous and should be interpreted descriptively, warranting evaluation in a fully powered randomized controlled trial.