Several occupations require workers to spend long periods of time driving road vehicles. This occupational task is associated with musculoskeletal disorders. The purpose of this review was to collate, synthesize, and analyze research reporting on musculoskeletal disorders associated with occupational driving, in order to develop a volume of evidence to inform occupational disorder mitigation strategies. A systematic search of academic databases (PubMed, EBSCO host, CINAHL, SPORTDiscus, and Web of Science) was performed using key search terms. Eligible studies were critically appraised using the Joanna Briggs Institute critical appraisal checklists. A Cohen’s kappa analysis was used to determine interrater agreement between appraisers. Of the 18,254 identified studies, 25 studies were selected and appraised. The mean critical appraisal score is 69% (range 38–100%), with a fair level of agreement (k = 0.332). The studies report that musculoskeletal disorders, most commonly lower back pain, is of concern in this population, particularly in truck, bus, and taxi drivers. Risk factors for these occupations include long hours in a sitting position, years in the profession, vehicle ergonomics, and vibration.

Background Evidence-based digital health tools allow clinicians to keep up with the expanding medical literature and provide safer and more accurate care. Understanding users’ online behavior in low-resource settings can inform programs that encourage the use of such tools. Our program collaborates with digital tool providers, including UpToDate, to facilitate free subscriptions for clinicians serving in low-resource settings globally. Objective We aimed to define segments of clinicians based on their usage patterns of UpToDate, describe the demographics of those segments, and relate the segments to self-reported professional climate measures. Methods We collected 12 months of clickstream data (a record of users’ clicks within the tool) as well as repeated surveys. We calculated the total number of sessions, time spent online, type of activity (navigating, reading, or account management), calendar period of use, percentage of days active online, and minutes of use per active day. We defined behavioral segments based on the distributions of these statistics and related them to survey data. Results We enrolled 1681 clinicians from 75 countries over a 9-week period. We based the following five behavioral segments on the length and intensity of use: short-term, light users (420/1681, 25%); short-term, heavy users (252/1681, 15%); long-term, heavy users (403/1681, 24%); long-term, light users (370/1681, 22%); and never-users (252/1681, 15%). Users spent a median of 5 hours using the tool over the year. On days when users logged on, they spent a median of 4.4 minutes online and an average of 71% of their time reading medical content as opposed to navigating or managing their account. Over half (773/1432, 54%) of the users actively used the tool for 48 weeks or more during the 52-week study period. The distribution of segments varied by age, with lighter and less use among those aged 35 years or older compared to that among younger users. The speciality of medicine had the heaviest use, and emergency medicine had the lightest use. Segments varied strongly by geographic region. As for professional climate, most respondents (1429/1681, 85%) reported that clinicians in their area would view the use of a online tool positively, and compared to those who reported other views, these respondents were less likely to be never-users (286/1681, 17% vs 387/1681, 23%) and more likely to be long-term users (655/1681, 39% vs 370/1681, 22%). Conclusions We believe that these behavioral segments can help inform the implementation of digital health tools, identify users who may need assistance, tailor training and messaging for users, and support research on digital health efforts. Methods for combining clickstream data with demographic and survey data have the potential to inform global health implementation. Our forthcoming analysis will use these methods to better elucidate what drives digital health tool use.

Background: The use of point-of-care, evidence-based tools is becoming increasingly popular. They can provide easy-to- use, high-quality information which is regularly updated and has been shown to improve clinical outcomes. Integrating such tools into clinical practice is an important component of improving the quality of health care. However, because such tools are rarely used in resource-limited settings, there is limited research on uptake especially among medical students. Objective: This paper explores the uptake of one such tool, Up-To-Date, when provided free of cost at a medical school in Africa. Methods: In partnership with the Better Evidence at Ariadne Labs free access to UpToDate was granted through the MakCHS IP address. On-site librarians facilitated training sessions and spread awareness of the tool. Usage data was aggre- gated, based on log ins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows evidence of meaningful usage, with 43,043 log ins and 15,591 registrations between August 2019 and August 2020. The most common topics viewed were in obstetrics and gynecology, pediatrics, drug information, and infectious diseases. Access occurred mainly through the mobile phone app. Conclusion: Findings show usage by various user categories, but with inconsistent uptake and low usage. Librarians can draw upon these results to encourage institutions to support uptake of point-of-care tools in clinical practice. Keywords: UpToDate clinical decision support tool; Makerere University College of Health Sciences; Uganda.

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