Cigarette smoking remains the leading preventable cause of illness and death in the United States and young adults are a priority population. This study aimed to investigate the feasibility of conducting a pilot micro-randomized trial (MRT) among young adults, using smartphone messages to reduce smoking urges in high-risk situations and combining Ecological Momentary Assessments (EMA) and geofence-based delivery. Participants were recruited online and completed surveys on Qualtrics and an EMA app to report smoking situations over 14 days (assessment phase). Assessment phase data were used to generate individual risk profiles [by combining timestamps, Global Positioning System (GPS), and self-reported data] with the highest likelihood of smoking for each participant. We used geofencing to generate geospatial buffers around these high-risk locations and intervention messages were triggered when a mobile device entered a geofence during specific time windows. In the following 30-day intervention phase, participants were prompted to complete up to 3 geofence-triggered EMAs daily. Each geofence-triggered EMA was followed by an intervention message and the type of message (distraction, acceptance, control) was randomized at each time point (within-subject). Urge level was the primary proximal outcome assessed in a follow-up EMA up to 15 minutes after message delivery. Analyses investigated the feasibility of study procedures, including geofence triggers, within-subject randomization, EMA survey completion rates, and smoking urge reports before and after message delivery. Cigarette smoking cessation and reduction outcomes at 45-day follow-up were also investigated. A total of 8 participants were included in analyses (mean age 26.3 years; 50% male; 37.5% non-Hispanic White). In the assessment phase, participants completed between 4 and 51 real-time smoking reports. At least 2 geofences were created per participant, with a maximum of 9 geofences for one participant. In the intervention phase, between 11 and 90 geofence EMAs were triggered per participant. Compliance with EMAs was high (90.1% of geofence-triggered, 93.9% of follow-up EMAs). Within-subject randomization was successful and urge ratings declined from pre- to post-message assessments for control (k=98; mean difference =0.20), acceptance (k=99, mean difference =0.36) and distraction messages (k=94, mean difference =0.37). At 45-day follow-up, 1 participant (12.5%) reported no cigarette smoking during the past 7 days and abstinence was confirmed remotely using saliva cotinine testing. Half of participants (n=4, 50%) reduced their number of cigarettes per day (CPD) from baseline to follow-up by 50% or more. Findings also demonstrated the need for robust prevention of fraudulent research participant enrollment, as 10 participants were excluded due to GPS locations outside of the United States. Results indicate potential technical feasibility of an app-based MRT using intervention messages triggered by geofence locations. Findings will inform a fully powered MRT to investigate message efficacy to reduce smoking urges and smoking in young adults. ClinicalTrials.gov (NCT05991934).
Interest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences-spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual's phone enters the perimeter. Despite growth in personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information. This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4 case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries. Data came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft2 fishnet grids, and 1000 ft2 fishnet grids). Descriptive comparisons were made across the 4 cases to better understand the strengths and limitations of each geofence construction method. The number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft2 fishnet grid captured the highest percentage of smoking events compared to census blocks across the 4 cases. Across 3-hour periods except for 3:00 AM-5:59 AM for 1 case, geofences contained an average of 36.4%-100% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to census blocks. Our findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages.
Smartphone alerting systems (SAS) alert volunteers in close vicinity of suspected out-of-hospital cardiac arrest. Some systems use sophisticated algorithms to select those who will probably arrive first. Precise estimation of departing times and travel times may help to further improve algorithms. We developed a global positioning system (GPS) based method for automatic measurements of departing times. The aim of this pilot study was to evaluate feasibility and precision of the method. Region of Lifesavers alerting app (iOS/ Android, version 3.0, FirstAED ApS, Denmark) was used in this study. 27 experiments were performed with 9 students, who were instructed to stay in their flats during the study days. A geofence was set for each alarm in the alerting system with a radius of 10 m (8 cases), 15 m (10 cases), and 20 m (9 cases) around the GPS position at which the alarm was accepted in the app. The system logged responders as being departed when the smartphone position was registered outside the geofence. The students were instructed to manually start a stopwatch at the time of the alert and to stop the stopwatch once they had entered the street in front of their flat. The median difference between automatically and manually retrieved times were -16 seconds [interquartile range IQR 50 seconds] (geofence 10 m), 30 seconds [IQR 25 seconds] (15 m), and 20 seconds [IQR 13 seconds] (20 m), respectively. The 20 m geofence was associated with the smallest interquartile range. Departing times of volunteer responders in SAS can be retrieved automatically using GPS and a geofence.
The rapid convergence of physical and digital environments is redefining user interactions in both professional and retail sectors. While the concept of the Metaverse offers new avenues for immersive remote collaboration, complex physical venues such as shopping malls require intelligent optimization to mitigate navigational inefficiencies and enhance user satisfaction. This research integrates augmented reality (AR), virtual reality (VR), and the Metaverse alongside machine learning (ML) and Federated Learning (FL) to create virtual spaces for workplace meetings in the Meta Workplaces Monitoring System (MetaWMS) and an active navigation application for shopping malls, the Meta Shopping Navigation System (MetaSNS). To ensure data integrity within these IoT environments, anomaly detection is applied prior to geofencing to filter out spurious Wi-Fi network signatures, such as mobile hotspots. Validated against the Aegean Wi-Fi Intrusion Dataset 3 (AWID3), the proposed One-Class SVM gatekeeper achieves a detection accuracy of 93.5%, significantly outperforming KNN (86.6%) and Isolation Forest (67.4%). Geofencing is then used to define virtual perimeters, enabling location-specific AR experiences. Building on previous work in indoor geofence detection, this paper extends the framework to support intelligent navigation using the large-scale Microsoft Research Indoor Location dataset. Sequence Prediction is performed using a Long Short-Term Memory (LSTM) architecture to forecast users’ next likely destinations, achieving prediction accuracies of 59%, 77%, and 83% for top-1, top-3, and top-5 recommendations, respectively. To preserve privacy, Federated Learning (FL) is employed so that only model weights, rather than raw data, are shared with the server, introducing a marginal accuracy loss of 2–5% while ensuring privacy-preserving personalization.
BACKGROUND: Aerial entry points are critical in the international spread of infectious diseases, underscoring the need for effective management of acute respiratory viral infections (ARVIs). Recent global outbreaks, including COVID‑19, have highlighted the importance of strengthened public health measures at air borders. However, evidence on ARVI management strategies at these points remains fragmented. This scoping review mapped and synthesized existing approaches to quarantine protocols, contact‑tracing methods, and advanced or innovative technologies used at aerial entry points. METHODS: We conducted a scoping review following the Arksey and O’Malley framework, refined by Levac et al., and reported in accordance with PRISMA ScR guidelines. Scientific databases were systematically searched for English language studies published between 2003 and 2024 that described the management of ARVIs at air borders. Data from eligible studies were charted and analyzed using thematic analysis to identify and categorize quarantine approaches, contact tracing strategies, and advanced or innovative technologies applied in these settings. RESULTS: A total of 80 studies were included in the review, addressing diseases such as COVID 19, influenza, SARS, and Ebola. Most studies were conducted in high income countries and in regions of Southeast Asia and the Western Pacific. Various quarantine approaches were identified, including mandatory quarantine, risk based quarantine, and home based isolation strategies. Contact tracing methods ranged from traditional manual approaches using passenger information to technology supported systems that improved the speed of identifying exposed individuals. Several studies reported the use of advanced digital technologies such as digital health passports, geofencing monitoring systems, and electronic reporting platforms to support monitoring, compliance, and data management during public health responses at air borders. CONCLUSION: The findings highlight the diverse range of quarantine protocols, contact tracing approaches, and emerging technological tools used to manage ARVIs at air borders. Despite these developments, evidence regarding the effectiveness and long term implementation of these strategies remains limited. Further research is needed to strengthen the evidence base and support evidence informed policies for managing respiratory infectious diseases at international points of entry.
Posttraumatic stress, along with comorbid mental health challenges and hazardous alcohol use, disproportionately affects people living with HIV. The drivers of these stressors are both intraindividual, rooted in early life adversity and firsthand violence exposures, and contextual, often place-based. Imparting effective coping skills and distinguishing between changeable and unchangeable stressors can improve stress management in the short term, with cascading effects on key HIV continuum of care end points, such as antiretroviral therapy adherence. However, problem- and emotion-based coping skills, delivered via traditional linear in-person group modalities, may falter in the moment. To address this, we adapted the evidence-based Living in the Face of Trauma intervention into an iOS- and Android-native app, featuring daily diary-triggered coping skills recommendations, self-guided Living in the Face of Trauma psychoeducational sessions, and a customizable geofencing function. This mixed methods study aimed to examine the acceptability, feasibility, and user experiences of NOLA (New Orleans, Louisiana) Gem, focusing on user interaction costs relative to geographic ecological momentary assessment (GEMA) alone and refining future optimization options. People living with HIV (N=32) were recruited across New Orleans and initially randomized 1:1 to treatment (NOLA Gem + GEMA) versus control (GEMA) for 21 days. Feasibility was assessed via enrollment and attrition rates. At the immediate postassessment, participants completed acceptability and usability measures and a brief structured usability interview. Analyses included descriptive statistics, bivariate logit modeling, and synergistic human-large language model deductive coding. In total, 30 participants (n=22 in the GEMA + NOLA Gem treatment arm) completed the pilot, representing 94% (n=29) of baseline enrollees. Acceptability was very high across the board: 100% (n=30) of users considered NOLA Gem "very" or "somewhat" successful in addressing their daily lives, with 91% (n=28) endorsing increased calm and emotional well-being. In addition, 50% (n=11) of NOLA Gem users were "extremely likely" (Net Promoter Score=10/10) to recommend the app to friends. Eight (27%) GEMA and GEMA + NOLA Gem users reported privacy concerns. Eleven (50%) NOLA Gem users received geofencing alerts; perceptions of this feature's helpfulness were mixed. No statistically significant sociodemographic or clinical predictors of disparate acceptability or increased privacy concerns were found. No additional frictions were evidenced by GEMA + NOLA Gem versus GEMA users. Qualitatively, NOLA Gem users praised the just-in-time mindfulness, breathing, problem-solving skills delivery, and broader stress control and self-insight benefits. A subset of users pointed out the burdensome length and sometimes inconvenient timing of the daily diaries. Recommendations for next-generation personalization included user-specific dynamic daily diary and geofencing prompt tailoring. Our small pilot study demonstrated high NOLA Gem acceptability and feasibility, as well as a rich and beneficial user experience among people living with HIV, with clear and actionable opportunities for improvement.
The ubiquitous existence of COVID-19 has required the management of congested areas such as workplaces. As a result, the use of a variety of inspiring tools to deal with the spread of COVID-19 has been required, including internet of things, artificial intelligence (AI), machine learning (ML), and geofencing technologies. In this work, an efficient approach based on the use of ML and geofencing technology is proposed to monitor and control the density of persons in workplaces during working hours. In particular, the workplace environment is divided into a number of geofences in which each person is associated with a set of geofences that make up their own cluster using a dynamic user-centric clustering scheme. Different metrics are used to generate a unique geofence digital signature (GDS) such as Wi-Fi basic service set identifier, Wi-Fi received signal strength indication, and magnetic field data, which can be collected using the person's smartphone. Then, these metrics are utilized by different ML techniques to generate the GDS for each indoor geofence and each building geofence as well as to detect whether the person is in their cluster. In addition, a Layered-Architecture Geofence Division method is considered to reduce the processing overhead at the person's smartphone. Our experimental results demonstrate that the proposed approach can perform well in a real workplace environment. The results show that the system accuracy is about 98.25% in indoor geofences and 76% in building geofences.
Smartphones have become a widely used tool for delivering digital health interventions and conducting observational research. Many digital health studies adopt an ecological momentary assessment (EMA) methodology, which can be enhanced by collecting participant location data using built-in smartphone technologies. However, there is currently a lack of customizable software capable of supporting geographically explicit research in EMA. To address this gap, we developed the Healthy Environments and Active Living for Translational Health (HEALTH) Platform. The HEALTH Platform is a customizable smartphone application that enables researchers to deliver geographic ecological momentary assessment (GEMA) prompts on a smartphone in real-time based on spatially complex geofence boundaries, to collect audiovisual data, and to flexibly adjust system logic without requiring time-consuming updates to participants' devices. We illustrate the HEALTH Platform's capabilities through a study of park exposure and well-being. This study illustrates how the HEALTH Platform improves upon existing GEMA software platforms by offering greater customization and real-time flexibility in data collection and prompting participants. We observed survey prompt adherence is associated with participant motivation and the complexity of the survey instrument itself, following past EMA research findings. Overall, the HEALTH Platform offers a flexible solution for implementing GEMA in digital health research and practice.
Commercial motor vehicles (CMVs) are disproportionately involved in crashes around work zones. We seek to reduce these crashes via location-based in-cab alerts ahead of work zone activity. It was hypothesized that alerted drivers would reduce their speed, and that they would remain alert for traffic hazards as they passed through work zones. This would in turn reduce CMV-involved crashes and associated injuries and fatalities. Participants comprised existing users of a mobile application that provides free safety alerts exclusively to commercial drivers. Experimental group vehicles received a pop-up notification and audible chime 500 m before work zones, control group vehicles received no such alert. Location data was collected for all CMVs 30 s before alert through 5 min after the alert geofence, which was used to determine vehicle speed. An anonymous driver survey was also deployed in November - December 2024 via email to assess driver perceptions of the alerting experience and the safety impact of these alerts. Analyses from April 1 - December 23, 2024 for 228,713 vehicle visits at 4,080 unique work zones across nine counties in California indicate that, within the first 10 s post-alert, alerted drivers traveling above 55 mph reduce their speed by up to 0.5 mph more than the control group (p = .02), with a 30% greater magnitude of speed reduction. Lane-specific alerts may also be more effective than generic alerts, with slopes of deceleration up to 1.5 times steeper (p < .001). A survey of drivers participating in the present study (N = 422) found that 82% of drivers reported slowing down when receiving these alerts and 83% reported paying increased attention to their surroundings. In-cab notifications of active work zones in California appear to promote safer driving behaviors among commercial drivers exceeding the CMV speed limit. More informative alerting may have a more pronounced impact. Drivers appear to perceive these alerts as helpful in promoting safer driving behaviors.
Recently, e-scooters have proliferated worldwide. Municipalities have been struggling with regulating e-scooters due to public concerns that the injuries from the new crashes outweigh the health and environmental benefits of micromobility use. Indeed, several studies have reported crash risk for e-scooters 4 to 10 times higher than that for bicycles. We had unprecedented access to crash and exposure data collected in 2022 and 2023 from a rental service of e-scooters and e-bicycles in seven European cities. We conducted a retrospective cross-sectional study to compute injury rates and incidence-rate ratios for each city while directly controlling for geography, ownership, and exposure (measured in three different ways). We analyzed 686 e-scooterist and 35 e-cyclist crashes. Injury rates were higher for e-cyclists than e-scooterists in most of the cities, for all exposure measures. Further, the incidence-rate ratios indicate that the injury risk was 2.5-10 times lower for e-scootering than e-cycling. E-scootering may not be riskier than cycling as several studies have claimed. In fact, by exploiting technology to control for location, exposure, ownership, and usage, our analysis shows that e-scooterists experience lower crash rates than e-cyclists. While our analysis has some limitations and cannot be considered conclusive evidence, taking location, usage, ownership, and high-resolution exposure into account-which our analysis did contrary to previous studies-is crucial for a more accurate comparison among (micromobility) transport modes. In general, our research suggests incorporating geofencing and GPS-derived exposure metrics in future safety assessments. The results and methodologies presented in this paper may help urban planning of rental micromobility services within cities.
Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities-properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns.
The use of standing electric motorized scooters (eScooters) has skyrocketed since its first release in 2016. This quickly popularized form of transportation has been associated with significant injury and even death. These eScooter-related traumatic injuries led to local advocacy efforts, resulting in safety restrictions including speed limit geofencing, sidewalk restrictions, and limiting the number of eScooter providers in high-density population areas. We hypothesized that these local safety restrictions decreased the number of eScooter-related injuries presenting to our trauma center. . This is a retrospective cohort study of eScooter-crash patients presenting to our Level 1 trauma center from July 2018 to June 2023. Variables included patient demographics, injury severity score (ISS), and mortality. The primary outcome was the rate of eScooter patients presenting over time in relation to the implementation of local-regional safety regulations. A total of 381 patients presented after eScooter crashes. Males were 73.8% of patients. The average age was 38.6 years; 45+ years was the most common age group at 33%, followed by ages 25-34 (31%). The mean ISS was 9±6, with ISS 0-9 (65.1%), 10-15 (24.4%), 16-24 (8.4%), and >25 (20.1%). There were three (0.8%) deaths. The median number of eScooter patients per month with prespeed limits was nine and post five (p=0.005), showing a 44.4% decrease in injured patients. After February 2022 restrictions, the rate precipitously declined with a median of two (p=0.033), reflecting an additional 60% decrease in injured patients. Local advocacy resulting in increased safety regulations was associated with a significant reduction in injured patients secondary to eScooter use. This demonstrates the importance of advocacy efforts in response to changes in injury patterns and mechanisms of injury. We believe that our work can serve as a model for other urban centers seeking to reduce eScooter-related injuries and implement effective safety measures. IV, prognostic/epidemiologic.
Electric bikes (e-bikes) are increasingly popular in the United States, with studies documenting increased injuries associated with their use. U.S. laws vary widely with licensure required in only 7 states, age restriction and helmet use varying by bike class in 35. This differs from the stricter regulations applied to higher-speed vehicles like mopeds. This study examines numbers of injuries and characteristics of serious injury events, comparing e-bikes to the more regulated moped. Emergency department data from 2019 to 2023 was extracted from the U.S. Consumer Product Safety Commission's National Electronic Injury Surveillance System. Event narratives were parsed using text search algorithms to classify cases into e-bike and moped groups. Frequency, rider age, involvement of motor vehicles, drugs/alcohol use, and pedestrian involvement were examined. Regression analyses were conducted using R. Rao-Scott Pearson Chi-Square tests were used to compare case characteristics between vehicle types. E-bikes accounted for 28.2% of the weighted 268,828 two-wheeled vehicle injury cases, with mopeds at 53.3%. E-bike injury case counts significantly increased with 7948 additional cases/year (r2 = 0.96), significantly outpacing increases in moped injury counts. For both, head injuries were most common, however, helmet use/non-use documentation in the data set was low (37.9% for e-bike and 38.9% for moped) limiting assessment. Counts of e-bike injury in the 13-19 year age group showed rapid increase, becoming the second greatest frequency age group by 2023. More serious e-bike injuries also significantly increased. Drug (2.2%) and alcohol involvement (7.6%), as well as pedestrian involvement (1.6%), was rare. Motor vehicle interactions were a significantly higher proportion of the serious moped injuries (50.7%) in comparison to e-bikes (32.5%). Interaction with motor vehicles was noted more than twice as frequently in cases treated at urban versus rural hospitals for both e-bikes and mopeds. There was one e-bike and 16 moped fatalities (raw counts), with all but one associated with impact with a motor vehicle. Injury counts for e-bikes increased significantly greater than moped injury counts and increasingly involved younger riders. Alcohol involvement was significantly lower in e-bike injuries compared to moped injuries. Low reported rates of drug or pedestrian involvement were observed. Helmet use was poorly documented, despite head injuries being the most common injury type. Given the high prevalence of internal head trauma, universal helmet laws for e-bike and moped users should be considered. While fatalities remain rare for e-bikes compared to mopeds, the consistent role of motor vehicle interactions in the most severe cases points to a systemic issue in roadway safety. These findings suggest that e-bikes are comparatively under regulated compared to mopeds. Policies, such as universal helmet laws, geofencing, pedestrian airbags, and infrastructure improvements could help mitigate both e-bike and moped injuries.
Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and wearables. Participant intervention fatigue may happen when DBCIs become too frequent, repetitive, demanding, or lack perceived relevance, and this may result in participants' reduced motivation and adherence over time. Advancing technology-supported engagement mechanisms is therefore of utmost importance. To address this problem, we present a reference and solution architecture based on open-source technologies and open Application Programming Interfaces (Open APIs). First, we integrated a Large Language Model (LLM) component into the DBCI design. Second, to support context-awareness, we enhanced this integration by adding a Geographic Information Systems (GIS) element. Our pilot implemented AI4Motion platform targets both personalization and contextualization aspects of DBCIs. Our work contributes to the emerging discussion on LLM/GIS-related system design patterns for digital platforms supporting Ecological Momentary Assessment (EMA), Experience Sampling Method (ESM), and Just-in-Time Adaptive Interventions (JITAIs).
Safety and performance validation of autonomous agricultural robots is critically dependent on realistic, mobile test environments that provide high-fidelity ground truth. Existing infrastructures focus on either component-level sensor evaluation in fixed setups or system-level black-box testing under constrained conditions, lacking true mobility, multi-object capability and tracking or detecting objects in multiple Degrees Of Freedom (DOFs) in unstructured fields. In this paper, we present a sensor station network designed to overcome these limitations. Our mobile testbed consists of self-powered stations, each equipped with a high-resolution 3D-Light Detection And Ranging (LiDAR) sensor, dual-antenna Global Navigation Satellite System (GNSS) receivers and on-board edge computers. By synchronising over GNSS time and calibrating rigid LiDAR-to-LiDAR transformations, we fuse point clouds from multiple stations into a coherent geometric representation of a real agricultural environment, which we sample at up to 20 Hz. We demonstrate the performance of the system in field experiments with an autonomous robot traversing a 26,000 m2 area at up to 20 km/h. Our results show continuous and consistent detections of the robot even at the field boundaries. This work will enable a comprehensive evaluation of geofencing and environmental perception capabilities, paving the way for safety and performance benchmarking of agricultural robot systems.
Caregivers of people living with dementia (PLWD) often experience burden based on their care recipients' symptoms of wandering, disorientation, and agitation. To examine the utilization and perceived value of technology-based solutions for caregiving among caregivers of PLWD. In collaboration with three Texas sites, PLWD and family caregiver dyads were recruited from clinical and community sites to assess the feasibility of a caregiving technology. PLWDs were asked to wear a GPS-based wearable device, which was paired with caregivers' smartphone application, that enabled location monitoring and was equipped with call functions. After three months, researchers called caregivers to ask about their utilization of the "system" (i.e., wearable paired with smartphone application) and the perceived value of this technology. Forty-one caregivers completed follow-up telephone interviews. About 70% of caregivers reported their care recipient wore the wearable device daily, and 39.1% used the smartphone application daily. Approximately 31% of caregivers reported daily use of the tracking feature, 30.8% reported daily use of the "safe zone" feature (i.e., geo-fencing), and 17.1% reported daily use of the two-way calling feature. About 39% of caregivers were extremely satisfied with the "system," 43.6% found it extremely easy to use, and 46.2% found it extremely useful for caregiving. On average, caregivers with higher baseline Zarit Burden Interview scores found the "system" to be more useful with their caregiving (f = 5.97, p = 0.006) and were more satisfied with the "system" (f = 3.75, p = 0.034). Findings suggest caregiver burden may drive the perceived usefulness of, and satisfaction with, technology-based solutions.
This manuscript presents a novel geofencing method in behavioral research. Geofencing, built upon geolocation technology, constitutes virtual fences around specific locations. Every time a participant crosses the virtual border around the geofenced area, an event can be triggered on a smartphone, e.g., the participant may be asked to complete a survey. The geofencing method can alleviate the problems of constant location tracking, such as recording sensitive geolocation information and battery drain. In scenarios where locations for geofencing are determined by participants (e.g., home, workplace), no location data need to be transferred to the researcher, so this method can ensure privacy and anonymity. Given the widespread use of smartphones and mobile Internet, geofencing has become a feasible tool in studying human behavior and cognition outside of the laboratory. The method can help advance theoretical and applied psychological science at a new frontier of context-aware research. At the same time, there is a lack of guidance on how and when geofencing can be applied in research. This manuscript aims to fill the gap and ease the adoption of the geofencing method. We describe the current challenges and implementations in geofencing and present three empirical studies in which we evaluated the geofencing method using the Samply application, a tool for mobile experience sampling research. The studies show that sensitivity and precision of geofencing were affected by the type of event, location radius, environment, operating system, and user behavior. Potential implications and recommendations for behavioral research are discussed.
With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.
Invasive Escherichia coli disease (IED) encompasses a diverse range of sterile site infections. This study evaluated the feasibility of capturing IED among community-dwelling older adults to inform the implementation of a phase 3 efficacy trial of a novel vaccine against IED (NCT04899336). EXPECT-1 (NCT04087681) was a prospective, multinational, observational study conducted in medically stable participants aged ≥ 60 years. At least 50% of participants were selected based on a history of urinary tract infection (UTI) in the previous 10 years. The main outcomes were the incidence of IED and the number of hospitalisations reported by the site vs participant. The length of follow-up was 12 months. In a US-based substudy, a smartphone-based geofencing was evaluated to track hospital entries. In total, 4470 participants were enrolled (median age, 70.0 years); 59.5% (2657/4469) of participants had a history of UTI in the previous 10 years. Four IED events were captured through deployment of different tracking methods: a self-report, a general practitioner (GP) report, and a follow-up call. The incidence rate of IED was 98.6 events per 100,000 person-years. The number of reported hospitalisations was 2529/4470 (56.6%) by the site and 2177/4470 (48.7%) by participants; 13.8% of hospitalisations would have been missed if utilising only site reports. Geofencing detected 72 hospital entries. Deployment of multiple tracking methods can optimise detection of IED among community-dwelling older adults. Older adults with a history of UTI could be feasibly targeted for a phase 3 vaccine efficacy trial through a network of GPs.
Technological advancements that use global positioning system (GPS), such as geofencing, provide the opportunity to examine place-based context in population health research. This review aimed to systematically identify, assess and synthesise the existing evidence on geofencing intervention design, acceptability, feasibility and/or impact. Scoping review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidance for reporting. PubMed, CINAHL, EMBASE, Web of Science, Cochrane and PsycINFO for articles in English published up to 31 December 2021. Articles were included if geofencing was used as a mechanism for intervention delivery. (1) a component or combination of GPS, geographical information system or ecological momentary assessment was used without delivery of an intervention; (2) did not include a health or health-related outcome from the geofencing intervention; or (3) was not a peer-reviewed study. Several researchers independently reviewed all abstracts and full-text articles for final inclusion. A total of 2171 articles were found; after exclusions, nine studies were included in the review. The majority were published in 5 years preceding the search (89%). Geofences in most studies (n=5) were fixed and programmed in the mobile application carried by participants without their input. Mechanisms of geofencing interventions were classified as direct or indirect, with five studies (56%) using direct interventions. There were several different health outcomes (from smoking to problematic alcohol use) across the five studies that used a direct geofencing intervention. This scoping review found geofencing to be an emerging technology that is an acceptable and feasible intervention applied to several different populations and health outcomes. Future studies should specify the rationale for the locations that are geofenced and user input. Moreover, attention to mechanisms of actions will enable scientists to understand not only whether geofencing is an appropriate and effective intervention but why it works to achieve the outcomes observed.