The research team, led by Zhaoping Liu and Bei Hu of the General Hospital of Central Theater Command of the People’s Liberation Army, together with colleagues from Hunan Normal University and other institutions, surveyed 700 nurses across three tertiary Grade A hospitals in Wuhan between October and December 2025. Tertiary Grade A hospitals represent the top tier of China’s hospital classification system, facilities where workloads are heavy, acuity is high, and the emotional demands on nursing staff are correspondingly intense. After excluding 20 invalid questionnaires, the researchers analyzed 680 valid responses, each completed anonymously and with informed consent under ethical approval from the Ethics Committee of Hunan Normal University.

Methodologically, the study leaned on a battery of validated instruments. Problematic short-video use was measured with the Problematic Short-Video Use Scale-9, a nine-item instrument designed to capture the loss of control, preoccupation, and functional impairment that characterize problematic use of platforms such as Douyin and TikTok. Perceived organizational support was assessed with the Perceived Organizational Support Scale, on-the-job functioning with the Stanford Presenteeism Scale, and the friction between work and home life with the five-item Work Interference with Personal Life subscale of the Work/Nonwork Interference and Enhancement Scale. Descriptive statistics and univariate comparisons were run in SPSS 27.0, while the centerpiece of the analysis, latent profile analysis, was conducted in Mplus 8.3, with multivariable multinomial logistic regression performed in R.

Latent profile analysis is a person-centered statistical technique, and its use here is what gives the study its distinctive texture. Rather than treating problematic short-video use as a single continuous score averaged across the sample, the method searches the data for subgroups of individuals who share similar symptom patterns, effectively asking whether the population contains natural clusters rather than one homogeneous blob. The answer was emphatically yes. Three distinct profiles emerged from the 680 nurses: a low-symptom use group of 383 nurses, or 56.3 percent of the sample; an escape-withdrawal prominent group of 226 nurses, or 33.2 percent; and a high-symptom comprehensive risk group of 71 nurses, or 10.5 percent.

The headline numbers are striking enough on their own. The mean total score for problematic short-video use across the sample was 21.23 with a standard deviation of 8.80, and 38.4 percent of the nurses were classified as at risk of problematic use. In other words, nearly two in five nurses at these flagship hospitals showed warning signs that their short-video habits had crossed from leisure into something potentially harmful. But the three-profile structure suggests that risk is not a single flavor. The largest cluster used short videos without notable symptoms. The middle cluster, more than a third of the sample, stood out for escape and withdrawal, patterns consistent with using the feeds to avoid negative emotions or disengage from stressors. The smallest but most concerning cluster, roughly one nurse in ten, scored high across the full spectrum of symptoms.

What pushes a nurse from one profile into another? The researchers found significant differences among the three latent classes in gender, department, perceived organizational support, work interference with personal life, and presenteeism, with P values below 0.05. These variables sketch a picture in which the workplace itself is woven into the pattern of screen use. Nurses who felt less supported by their organizations, who reported that their jobs bled more heavily into their personal lives, and who struggled to function while at work were disproportionately represented in the riskier profiles. Department mattered too, hinting that the specific texture of a nursing unit, from emergency medicine to respiratory wards, shapes the stress-recovery balance that short videos appear to fill.

The regression analysis sharpened the story further. After controlling for gender, department, perceived organizational support, and work interference with personal life, presenteeism remained significantly associated with membership in both the escape-withdrawal prominent group, with an odds ratio of 1.392 (95 percent confidence interval 1.115 to 1.738, P = 0.003), and the high-symptom comprehensive risk group, with an odds ratio of 2.365 (95 percent confidence interval 1.641 to 3.409, P < 0.001). An odds ratio above one means that higher presenteeism, the phenomenon of physically showing up for work while functioning below par, increases the odds of belonging to the riskier profiles. The effect was more than twice as large for the comprehensive risk group as for the escape-withdrawal group, suggesting a dose-response-like gradient in which the most impaired nurses at work were also the most symptomatic users of short videos.

Presenteeism is a concept that has quietly become one of the most consequential ideas in occupational health. Unlike absenteeism, which removes a worker from the schedule and is therefore visible in staffing rosters, presenteeism keeps the impaired worker on the floor, performing at reduced capacity. In nursing, where medication checks, dose calculations, and continuous monitoring leave little margin for error, the costs of reduced on-the-job effectiveness can be measured in patient safety. The new findings suggest a possible feedback loop: nurses whose recovery is insufficient may function poorly at work, and those same nurses may be the ones retreating into compulsive short-video use during their off-hours, further crowding out the restorative activities, sleep, exercise, and social contact, that would rebuild their capacity.

The study’s authors frame the results as a call for stratified, profile-informed management rather than one-size-fits-all interventions. Because the three latent classes differ in their dominant symptom patterns, they argue, nursing managers could use latent class characteristics to identify which nurses need which kind of support. The escape-withdrawal prominent group, for instance, may benefit most from interventions targeting emotional avoidance and stress coping, while the high-symptom comprehensive risk group may require more comprehensive attention to occupational functioning and recovery resources. The researchers specifically recommend incorporating presenteeism, departmental context, and recovery resources into occupational health management for nurses, an approach that treats screen behavior not as an isolated personal failing but as a signal embedded in the ecology of the workplace.

As with any cross-sectional study, the design has limits that the findings cannot escape. The data capture a single moment in time, so the direction of the arrow between presenteeism and problematic short-video use cannot be established with certainty; exhausted nurses may scroll to cope with impaired workdays, or compulsive scrolling may degrade the sleep and recovery that would have made those workdays less impaired, and both mechanisms could operate simultaneously. The convenience sampling of 680 nurses from three Wuhan hospitals also means the sample may not generalize to all nursing workforces, in China or elsewhere. Still, the scale of the risk identified, with 38.4 percent of nurses at risk of problematic use and one in ten in the highest-risk profile, gives the findings real weight for hospital administrators everywhere. Short-video platforms are engineered to capture attention with variable-reward loops that exploit the same neural circuitry as other compulsive behaviors, and workers in chronically stressed, recovery-starved professions are precisely the audiences most vulnerable to that design. The Wuhan study does not claim that short videos are uniquely dangerous among digital distractions, but it does something arguably more useful: it maps, with statistical precision, who is being pulled in, how their risk is structured, and where the leverage points for intervention may lie. For a profession already stretched thin by staffing shortages and burnout, the finding that nearly four in ten nurses show signs of problematic use of the very technology they reach for to unwind is a data point that hospital leaders, platform designers, and occupational health researchers cannot afford to scroll past.

Subject of Research: Problematic short-video use and presenteeism among clinical nurses in tertiary hospitals

Article Title: Latent profiles and associated factors of problematic short-video use among nurses in tertiary hospitals: a cross-sectional study

Article References: Liu, Z., Li, M., Liu, Y., Yang, P., He, X., Liu, W., & Hu, B. (2026). Latent profiles and associated factors of problematic short-video use among nurses in tertiary hospitals: a cross-sectional study. BMC Nursing. https://doi.org/10.1186/s12912-026-05451-9

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05451-9

Keywords: nurses, short-video use, presenteeism, latent profile analysis, occupational health, burnout, perceived organizational support, work-life interference, BMC Nursing, cross-sectional study, digital addiction, Wuhan

The research team, led by Zelin Wang and Zhiyuan Liu of Southeast University together with collaborators at the National University of Singapore, the University of Bristol, the University of Wisconsin-Madison, and Monash University, seized a rare scientific opportunity. Wuhan is among the first cities worldwide where fully driverless ride-hailing services operate at commercial scale alongside conventional, human-driven taxis. By pairing the operational records of robotaxis with trajectory data from traditional taxis in the same market, the researchers could compare, street by street and hour by hour, how autonomous and human-driven fleets actually behave in the same urban environment rather than in simulations alone.

The first major finding concerns competition. When the team mapped where robotaxi pickups and drop-offs occurred, they found that robotaxi activity overlaps spatially with human-driven taxi demand to a striking degree. In other words, driverless ride-hailing is currently serving many of the same trips, in many of the same places, that conventional taxis already serve. This indicates potential direct competition within the on-demand mobility market, with both fleets chasing the same passengers in the densest and most commercially active parts of the city. For urban planners, the result raises questions about whether unmanaged robotaxi growth will simply duplicate existing services rather than expanding mobility options where they are most needed.

The relationship with public transit, however, tells a different and more hopeful story. The researchers measured how often trip endpoints fell within the catchment areas of metro and bus stations, using the share of trips beginning and ending inside these zones as an indicator of transit connectivity. Robotaxi trips showed markedly lower station-to-station ratios than human-driven taxi trips: 46.9 percent for metro-station catchments and 61.6 percent for bus-station catchments. This suggests robotaxis are relatively more active in areas less well served by fixed-route transit, and that they may function as a complement to buses and metro lines, feeding passengers to and from stations, rather than as a substitute that siphons riders away from public transport.

The study also broke new ground by connecting robotaxi operations to the physical structure of the street network. Drawing on road-network complexity measures, the team found that autonomous ride-hailing activity concentrates on simpler, less intricate street segments, an intuitive but previously unquantified pattern reflecting how autonomous driving systems navigate environments with fewer complicated intersections and interactions. Crucially, the researchers demonstrated that incorporating network complexity into their demand model improved predictive performance, meaning that the geometry of the road network itself carries real information about where robotaxi demand will materialize. This offers cities a practical analytical tool: by understanding which network environments attract autonomous services, planners can anticipate where fleets will concentrate and how deployment patterns might evolve.

The headline results come from the optimization analysis. The team constructed an upper-bound scenario in which dispatch and ride-sharing were jointly optimized, effectively asking how efficiently a perfectly coordinated robotaxi system could serve the observed demand. The answer was dramatic. Under this scenario, the required fleet size could be reduced by 62.5 percent, and daily operational energy consumption could fall by 44.8 percent. The scale of these potential savings reflects the enormous inefficiency latent in current on-demand vehicle fleets, where a large fraction of driving consists of empty repositioning between passengers. Prior theoretical work, including the landmark minimum-fleet analysis published in Nature in 2018, had suggested such savings were possible; the Wuhan study provides some of the first evidence grounded in real driverless operations rather than human-driven proxies.

The energy implications extend well beyond the vehicles themselves. Because robotaxis are predominantly electric in most commercial deployments, reductions in fleet size and kilometers traveled translate directly into lower electricity demand, smaller charging infrastructure footprints, and reduced demands on the power grid during peak hours. A smaller fleet also means fewer batteries manufactured, fewer vehicles parked at any moment, and potentially less pressure on urban land devoted to vehicle storage. The findings connect to a broader body of research on how shared autonomous mobility reshapes vehicle lifetimes and the carbon footprint of electrified transport, and they suggest that the operational choices made by robotaxi operators today will echo through urban energy systems for decades.

Still, the authors and independent observers caution that the reported savings represent an upper bound achievable under idealized coordination, not a forecast of business as usual. Real-world dispatch involves stochastic demand, passenger waiting-time tolerances, regulatory constraints on where vehicles may operate, and the commercial realities of competing operators running independent fleets. Realizing even a fraction of the modeled 62.5 percent fleet reduction would require coordination mechanisms, data sharing, or regulatory frameworks that currently do not exist in most cities. The study’s value lies in quantifying the size of the prize, giving policymakers a concrete sense of what is at stake in how they regulate and structure autonomous mobility markets.

The competitive dynamics identified in the study add urgency to that governance question. If robotaxis and human taxis are competing for the same trips while robotaxis simultaneously complement public transit, cities face a delicate balancing act. Encouraging robotaxi services to fill transit gaps could enhance accessibility in underserved neighborhoods and support transit ridership, while unchecked market overlap could destabilize existing taxi industries and add vehicle kilometers to congested corridors. The authors argue that their empirical findings provide support for aligning robotaxi deployment with urban sustainability goals, implying that deployment policy, not just technology, will determine whether autonomous mobility helps or hinders city-level climate and efficiency targets.

Wuhan’s experience offers an early template for other cities as robotaxi operations expand globally, with services now running or planned across China, the United States, and Europe. The methodology developed by the team, combining large-scale operational data, network-complexity modeling, and fleet optimization, can be applied wherever autonomous ride-hailing operates alongside conventional modes. As driverless fleets grow, the study suggests the decisive question is no longer whether robotaxis can serve urban trips, but whether cities will harness the coordination and planning needed to capture their full sustainability potential, transforming a disruptive technology into a durable pillar of low-energy urban transport.

Subject of Research: Urban energy and transport impacts of autonomous robotaxi deployment

Article Title: Urban energy and transport impacts of autonomous robotaxi deployment

Article References: Urban energy and transport impacts of autonomous robotaxi deployment. (n.d.). https://doi.org/10.1038/s41893-026-01944-2

Image Credits: AI Generated

DOI: 10.1038/s41893-026-01944-2

Keywords: robotaxis, autonomous vehicles, urban transport, energy consumption, ride-hailing, public transit, fleet optimization, ride-sharing, Wuhan, sustainability, road network complexity, electric vehicles