Mobility Metrics Research Articles

The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.

The objective, unobtrusively collected GPS features (eg, homestay and distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date, there is no systematic and meta-analytical evidence on the associations between GPS features and depression. This study aimed to investigate the between-person and within-person correlations between GPS mobility and activity features and depressive symptoms, and to critically review the quality and potential publication bias in the field. We searched MEDLINE, PsycINFO, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression from December 6, 2022, to March 24, 2023. Inclusion and exclusion criteria were applied in a 2-stage inclusion process conducted by 2 independent reviewers (YT and JK). To be eligible, studies needed to report correlations between wearable-based GPS variables (eg, total distance) and depression symptoms measured with a validated questionnaire. Studies with underage persons and other mental health disorders were excluded. Between- and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines. Publication bias was investigated using Egger test and funnel plots. A total of k=19 studies involving N=2930 participants were included in the analysis. The mean age was 38.42 (SD 18.96) years with 59.64% (SD 22.99%) of participants being female. Significant between-person correlations between GPS features and depression were identified: distance (r=-0.25, 95% CI -0.29 to -0.21), normalized entropy (r-0.17, 95% CI -0.29 to -0.04), location variance (r-0.17, 95% CI -0.26 to -0.04), entropy (r=-0.13, 95% CI -0.23 to -0.04), number of clusters (r=-0.11, 95% CI -0.18 to -0.03), and homestay (r=0.10, 95% CI 0.00 to 0.19). Studies reporting within-correlations (k=3) were too heterogeneous to conduct meta-analysis. A deficiency in study quality and research standards was identified: all studies followed exploratory observational designs, but no study referenced or fully adhered to the international guidelines for reporting observational studies (STROBE). A total of 79% (k=15) of the studies were underpowered to detect a small correlation (r=.20). Results showed evidence for potential publication bias. Our results provide meta-analytical evidence for between-person correlations of GPS mobility and activity features and depression. Hence, depression diagnostics may benefit from adding GPS mobility and activity features as an integral part of future assessment and expert tools. However, confirmatory studies for between-person correlations and further research on within-person correlations are needed. In addition, the methodological quality of the evidence needs to improve. OSF Registeries cwder; https://osf.io/cwder.

Long-term validation of inner-urban mobility metrics derived from Twitter/X

Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the. metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 1011 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to. June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter - rebranded to X in 2023 - is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance.

Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study

The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID). The Covid Collab study was a longitudinal, self-enrolled, community, case-control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0-4 weeks), ongoing COVID-19 (4-12 weeks), and post-COVID-19 (12-16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis). By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03-1·09]; p<0·0001), ongoing (0·99 beats per min; 1·11 [1·08-1·14]; p<0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02-1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient -0·017 [95% CI -0·030 to -0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01-1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07-1·22]; p<0·0001). Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19. National Institute for Health and Care Research (NIHR), NIHR Maudsley Biomedical Research Centre, UK Research and Innovation, and Medical Research Council.

Quantifying local mobility patterns in urban human mobility data

Understanding fine-scale dynamics of human mobility patterns is pivotal for effective urban planning, public health strategies, and retail analysis. This study introduces a novel mobility measure – the Local Mobility Index (LMI) – combining geometry-based mobility metrics and accessibility measures. The LMI can be considered a measure of ‘relative localness’ by integrating preferences into the assessment of local mobility patterns, offering a novel measure for understanding mobility behavior in urban contexts. The LMI improves upon existing measures as it captures individual choice for local destinations through measuring whether individuals select nearby destinations; accounting for the unequal spatial distribution of urban amenities. Our contribution is mainly methodological, advancing the field by introducing a metric that captures different aspects of mobility compared to conventional mobility metrics. Leveraging mobile-phone-based GPS data, we examine the LMI using 759 individuals across three cities in England. We found that the LMI captures a new and distinct dimension of urban mobility, as evidenced by its weak correlation with established metrics. Therefore, LMI's capacity to highlight previously undetected aspects of mobility behavior, underscores its importance for advancing research and urban planning.

Predictability of human mobility during the COVID-19 pandemic in the United States.

Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics.

Worm-Inspired, Untethered, Soft Crawling Robots for Pipe Inspections.

The increasing demand for inspection, upkeep, and repair of pipeline and tunnel infrastructures has catalyzed research into the creation of robots with superior flexibility, adaptability, and load-bearing capacities. This study introduces an autonomous soft robot designed for navigating both straight and curved pipelines of 90 mm diameter. The soft robot is enabled by an elongation pneumatic actuator (EPA) as its body and multiple radial expansion pneumatic actuators (REPAs) as its feet to provide adhesion and support on the pipe walls. It achieves a horizontal movement speed of 1.27 mm/s and ascends vertically at 0.39 mm/s. An integrated control mechanism, merging both pneumatic and electrical systems is employed to facilitate unrestrained movement. A novel control tactic has been formulated to ensure synchronized coordination between the robot's body deformation and leg anchoring, ensuring stable movement. This soft robot demonstrates remarkable mobility metrics, boasting an anchoring strength of over 100 N, a propelling force of 43.8 N when moving vertically, and a pulling strength of 31.4 N during navigation in curved pipelines. It can carry a camera to capture the internal view of the pipe and remove obstacles autonomously. The unconstrained and autonomous movement of the untethered soft robot presents new opportunities for various applications at different scales.

Cycling into the workshop: e-bike and m-bike mobility patterns for predictive maintenance in Barcelona’s bike-sharing system

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona’s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system’s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

The Economic Dynamics of Desktop and Mobile Customer Analytics in Advancing Digital Branding Strategies: Insights from the Agri-Food Industry

In the agri-food industry, strategic digital branding and digital marketing are essential for maintaining competitiveness. This study examines the economic dynamics and impact of desktop and mobile customer analytics on digital branding strategies within the sector. Through a comprehensive literature review, this research utilizes empirical evidence to validate hypotheses regarding the influence of desktop and mobile analytics metrics on key digital branding metrics and value creation. This study explores various branding indicators by utilizing descriptive statistics, correlation analyses, regression models, and fuzzy cognitive mapping (FCM). The findings reveal significant correlations between desktop and mobile analytics and digital branding outcomes, underscoring the critical role of digital analytics and Decision Support Systems (DSSs) in shaping modern branding strategies in the agri-food industry. This study highlights the economic implications of desktop and mobile customer analytics on digital branding, providing insights to enhance market performance and foster sustainable growth in the agri-food sector.

Daily life mobility detects frailty, falls, and functioning in ADT-treated prostate cancer survivors.

Androgen deprivation therapy (ADT) increases the risk of frailty, falls, and, poor physical functioning in prostate cancer survivors. Detection of frailty is limited to self-report instruments and performance measures, so unbiased tools are needed. We investigated relationships between an unbiased measure - daily life mobility - and ADT history, frailty, falls, and functioning in ADT-treated prostate cancer survivors. ADT-treated prostate cancer survivors (N=99) were recruited from an exercise clinical trial, an academic medical center, and the community. Participants completed performance measures and surveys to assess frailty, fall history, and physical functioning, then wore instrumented socks to continuously monitor daily life mobility. We performed a principal component analysis on daily life mobility metrics and used regression analyses to investigate relationships between domains of daily life mobility and frailty, fall history, and physical functioning. Daily life mobility metrics clustered into four domains: Gait Pace, Rhythm, Activity, and Balance. Worse scores on Rhythm and Activity were associated with increased odds of frailty (OR 1.59, 95% CI: 1.04, 2.49 and OR 1.81, 95% CI: 1.19, 2.83, respectively). A worse score on Rhythm was associated with increased odds of ≥1 falls in the previous year (OR 1.60, 95% CI: 1.05, 2.47). Worse scores on Gait Pace, Rhythm, and Activity were associated with worse physical functioning. Mobility metrics were similar between current and past users of ADT. Continuous passive monitoring of daily life mobility may identify prostate cancer survivors who have or are developing risk for frailty, falls, and declines in physical functioning.

Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections

BackgroundWhile many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. MethodsContact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. ResultsA total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: −0.239 [95 % CI: −0.431 to −0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. ConclusionsThis study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

Patients with High Pre-Operative Physical Activity Take Longer to Return to Baseline

Patients with end-stage osteoarthritis are recommended to engage in physical activity (PA) to reduce pain and improve function but may avoid PA due to joint pain. Our goal was to investigate patient-reported outcomes and objective mobility metrics (step counts) in total hip arthroplasty (THA) patients as a function of pre-operative PA levels. In total, 1647 patients enrolled in a multicenter prospective cohort study investigating a smartphone-based care management platform for self-directed rehabilitation that underwent THA and were included in analysis. The entire cohort’s step count was divided into quartiles to categorize patients with low, moderate, and high baseline PA. Outcomes including pain, EQ-5D-5L, HOOS JR, and step counts were compared according to activity group by ANOVA. Pre-operative pain scores were lowest, with smallest improvements, in the high-baseline PA group. Low-PA patients demonstrated the greatest improvements in EQ-5D-5L, while changes in EQ-VAS, HOOS JR, and satisfaction were similar between groups. Low- and moderate-PA patients increased physical activity by six weeks, reaching 180% and 114% of pre-operative steps; high-PA patients did not return to full step counts until one-year post-operation. Patients who perform high levels of PA undergoing THA report lower levels of pain and higher function pre-operatively but may appreciate less improvement in PA up to one year post-operatively. These results may be helpful in appropriate counseling of patient expectations prior to surgery.

Cadaver preservative properties of a solution composed of honey, ethyl alcohol, liquid paraffin, distilled water and citric acid: Experiments on rabbit cadavers.

The objective of this study is to assess the efficacy of a solution including honey, ethyl alcohol, liquid paraffin, distilled water and citric acid (HEFS) as a preservative for rabbit cadavers, serving as a potential substitute for formaldehyde. The cadavers underwent preservation using three distinct solutions: 10% formalin, 35% alcohol and HEFS. The cadavers were subjected to a total of four sampling events, occurring at 4-month intervals, in order to collect specimens for microanatomical, histological, microbiological, mycological, colourimetric, texture and odour analysis. In terms of hardness, suitability for dissection and joint mobility metrics, the cadavers fixed with HEFS had superior qualities to those fixed with formalin. The fixation quality of HEFS for histological analyses was deemed acceptable, except kidney and intestinal tissues. In texture analysis, differences only in the elasticity parameter (p < 0.05) in the same sampling period. A total of 10 (13.9) bacteria isolates were identified among which, Metasolibacillus meyeri 3 (30%) was predominantly followed by Staphylococcus aureus 2 (20%), Bacillus siamensis, Bacillus subtilis, Pseudarthrobacter oxydans, Bacillus licheniformis, Bacillus subtilis subsp. subtilis with a proportion of 1 (10%), respectively, by both microbiological and molecular analysis. However, no anaerobic bacteria and fungi were isolated. A considerable percentage of the students had the perception that HEFS was appropriate for utilization in laboratory settings due to its absence of unpleasant odours and detrimental impact on ocular and respiratory functions. In conclusion, we consider that HEFS may serve as a viable substitute for formalin solution in the preservation of rabbit cadavers.

A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability

ObjectiveTo establish the concurrent validity, acceptability, and sensor optimization of a consumer-grade, wearable, multi-sensor system to capture quantity and quality metrics of mobility and upper limb movements in stroke survivors. DesignSingle-session, cross-sectional. SettingClinical research laboratory. ParticipantsThirty chronic stroke survivors (age 57 (10) years; 33% female) with mild to severe motor impairments participated. InterventionsNot Applicable. Main Outcome MeasuresParticipants donned 5 sensors and performed standardized assessments of mobility and upper limb (UL) movement. True/false, positive/negative time in active movement for the UL were calculated and compared to criterion-standards using an accuracy rate. Bland-Altman plots and linear regression models were used to establish concurrent validity of UL movement counts, step counts, and stance time symmetry of MiGo against established criterion-standard measures. Acceptability and sensor optimization were assessed through an end-user survey and decision matrix. ResultsMobility metrics showed excellent association with criterion-standards for step counts (video: r=0.988, P<.001, IMU: r=0.921, P<.001) and stance-time symmetry (r=0.722, P<.001). In the UL, movement counts showed excellent to good agreement (paretic: r=0.849, P<.001, nonparetic: r=0.672, P<.001). Accuracy of active movement time was 85.2% (paretic) and 88.0% (nonparetic) UL. Most participants (63.3%) had difficulty donning/doffing the sensors. Acceptability was high (4.2/5). ConclusionsThe sensors demonstrated excellent concurrent validity for mobility metrics and UL movements of stroke survivors. Acceptability of the system was high, but alternative wristbands should be considered.

An optimal cooperative relay selection strategy for delay aware image transmission in large scale multi-radio multi-channel multimedia wireless sensor network

High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%.

The influence of the urban model on civic involvement and public time. A study applied to the commuting population of the Greater Metropolitan Area of San José, Costa Rica

A major problem for policymakers is to ensure that levels of citizen participation reflect the society in its diversity, but they must also ensure that those citizens who want to get involved have the means to do so. Despite empirical evidence of the role of urban planning in health, well-being, and livability, studies have not delved into the implications of how low-density sprawled urban forms influence civic involvement. This paper adds new evidence regarding the significance of urban form, built environment characteristics and more in general city planning, as intermediary factors between public and private life connected to civic engagement and public space use. Based on surveys and geographic data from the Greater Metropolitan Area of San José, in Costa Rica, the article investigates how the results of citizen participation change depending on whether residents are from a compact or dispersed study area, relating to urban rhythms and mobility metrics. Findings suggest that inhabitants of group A (lower-density neighborhoods) differ significantly from group B (compact-city areas) in four variables: means of mobility, commuting time, public space use, and civic activism, while having similar sociodemographic composition. Compared with residents of group A, group B report higher levels of use and knowledge of public spaces and civic participation. These two aspects constitute, the Urban Civic Participation Index, which, related to self-reported free time, forms what we define as Public Time. The main characteristics of the compact city (higher population and building density, high street connectivity, grid presence and proximity of services, mixed land use) were found to be positively related to the outcomes of accessibility to public space and community participation. In contrast, there are no significant differences in the availability of free time between the two groups. This study, along with others on the compact city, shows that there are other reasons to reduce daily commuting distances in cities and the need of urban time policies to redesign urban society's temporal dimension besides the known environmental impacts. It also raises the question of how low urban density affects people's time and disposition to participate in public life.

Nutraceutical supplementation increases mobility in aged captive non-domesticated felids.

Musculoskeletal diseases (MSDs) are an increasing issue as the lifespan of captive animals increases. Extracts of green-lipped mussels have been linked to alleviation of MSDs in domestic carnivores. Understanding their efficacy in non-domestic felids could provide another tool to improve the welfare of aged individuals in collections. A within-subject study design quantified steps per minute in each of 18 cats of 13 species before and after the addition of a nutraceutical containing green-lipped mussel extract (Antinol) to their diets. The age structure of four commonly kept subspecies of non-domestic cats was quantified to provide a demographic context to the need for managing aged individuals. Each of the 18 cats exhibited a higher number of steps per minute after the addition of Antinol to their diet. At the group level, a paired t-test showed that the step rate was significantly increased after the addition of Antinol to the diet. While our results showed a strong significant increase in step rate following Antinol supplementation, further studies that incorporate a placebo, more individuals and more detailed metrics of mobility would provide a more detailed evidence base for practitioners. Nutraceuticals may yield benefits to aged individual felids, including species kept widely in European collections. Their use warrants further, detailed research in collections.

Impact of Chronic Opioid Use on Postoperative Mobility Recovery and Patient-Reported Outcomes: A Propensity-Matched Study

BackgroundOpioid use prior to total joint arthroplasty may be associated with poorer postoperative outcomes. However, few studies have reported the impact on postoperative recovery of mobility. We hypothesized that chronic opioid users would demonstrate impaired objective and subjective mobility recovery compared to nonusers. MethodsA secondary data analysis of a multicenter, prospective observational cohort study in which patients used a smartphone-based care management platform with a smartwatch for self-directed rehabilitation following hip or knee arthroplasty was performed. Patients were matched 2:1 based on age, body mass index, sex, procedure, Charnley class, ambulatory status, orthopedic procedure history, and anxiety. Postoperative mobility outcomes were measured by patient-reported ability to walk unassisted at 90 days, step counts, and responses to the 5-level EuroQol-5 dimension 5-level, compared by Chi-square and student’s t-tests. Unmatched cohorts were also compared to investigate the impact of matching. ResultsA total of 153 preoperative chronic opioid users were matched to 306 opioid-naïve patients. Age (61.9 ± 10.5 versus 62.1 ± 10.3, P = .90) and sex (53.6 versus 53.3% women, P = .95) were similar between groups. The proportion of people who reported walking unassisted for 90 days did not vary in the matched cohort (87.8 versus 90.7%, P = .26). Step counts were similar preoperatively and 1-month postoperatively but were lower in opioid users at 3 and 6 months postoperatively (4,823 versus 5,848, P = .03). More opioid users reported moderate to extreme problems with ambulation preoperatively on the 5-level EuroQol-5 dimension 5-level (80.6 versus 69.0%, P = .02), and at 6 months (19.2 versus 9.3%, P = .01). ConclusionsSubjective and objective measures of postoperative mobility were significantly reduced in patients who chronically used opioid medications preoperatively. Even after considering baseline factors that may affect ambulation, objective mobility metrics following arthroplasty were negatively impacted by preoperative chronic opioid use.

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system.

The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters.

Predictors of early life residential mobility in urban and rural Pennsylvania children with acute lymphoblastic leukemia and implications for environmental exposure assessment.

Residential mobility can introduce exposure misclassification in pediatric epidemiology studies using birth address only. We examined whether residential mobility varies by sociodemographic factors and urbanicity/rurality among children with cancer. Our study included 400 children born in Pennsylvania during 2002-2015 and diagnosed with leukemia at ages 2-7 years. Addresses were obtained from state registries at birth and diagnosis. We considered three aspects of mobility between birth and diagnosis: whether a child moved, whether a mover changed census tract, and distance moved. We evaluated predictors of these aspects in urban- and rural-born children using chi-square, t-tests, and regression analyses. Overall, 58% of children moved between birth and diagnosis; suburban/rural-born children were more likely to move than urban-born children (67% versus 57%). The mean distance moved was 16.7 km in suburban/rural-born and 14.8 km in urban-born movers. In urban-born children, moving between birth and diagnosis was associated with race, education, participation in the Nutrition Program for Women, Infants and Children (WIC), and census tract-level income (all χ2 p < 0.01). Urban-born movers tended to be born in a census tract with a higher Social Vulnerability Index than non-movers (t-test p < 0.01). No factors were statistically significantly associated with any of the residential mobility metrics in suburban/rural-born children, although the sample size was small. In this study of a vulnerable population of children with cancer, we found that rural-born children were more likely to move than urban-born children, however, the frequency of movers changing census tracts was equivalent. Mobility in urban-born children, but not rural-born, was associated with several social factors, although the sample size for rural-born children was small. Mobility could be an important source of misclassification depending on the spatial heterogeneity and resolution of the exposure data and whether the social factors are related to exposures or health outcomes. Our results highlight the importance of considering differences in mobility between urban and rural populations in spatial research.

The Digital Walls of the University: Translating New Metrics of Labour, Education, and Mobility in the COVID-19 Crisis

The COVID-19 crisis has drastically accelerated the reconfiguration of the borders between the university and the outside world. In this scenario, the walls of the university, whose function is the discrimination of the university’s external and internal elements, are satisfied via units of artificial measure (metrics) that organise the social division of labour and continuously retrace the boundary between the “foreign” and the “internal.” If earlier reforms in higher education aimed at widening access and improving academic success, on the one hand, and adapting to the demand of the labour market for a more “flexible” workforce, on the other, the exponential use of digital platforms in education strongly focuses on establishing a real-time translatability among different demands within a single tool of governance. While educational institutions exponentially rely on indexes, which are intended to both measure the fluctuations and the demands of the labour market in real time, it is urgent to rethink how we translate technology across social, economic, and cultural fields and to formulate teaching and learning machines capable of responding to the complexities and the challenges of the present.