Physical activity (PA) is measured objectively through daily wearable monitoring and mobility capacity tests, and subjectively via patient reported outcomes (mobility perception). This study investigated longitudinal changes in, and relationships between, different measures of PA among older adults recovering from proximal femoral fracture (PFF). Participants (N=201) were classified into four groups by time since surgery at baseline (T1) and followed over two assessments (T2, T3). They wore an accelerometer for 7 consecutive days. Daily PA was measured using cut-point free metrics including Average Acceleration, Intensity Gradient, and intensity of the most active accumulated X minutes (MX: M1-M90). Mobility capacity and perception of participants were evaluated using clinical tests (e.g., 6-minute Walking Test (6MinWT)) and questionnaires (Late-Life Function and Disability Instrument (LLFDI)). MX metrics, particularly M1-M15, increased significantly across the first three groups with higher sensitivity in group 1 (p<0.001). Distance covered during the 6MinWT increased significantly (p<0.01). Three of the seven LLFDI s domains showed the largest significant changes. Overall, sustained, moderate-strong positive correlations were observed between the clinical tests, LLFDI, and short-duration MX metrics in group 3 and 4 at T1, and across all participants at T2 and T3. Thus, MX metrics (M1-M15) can reveal change for daily PA intensities, especially among PFF groups in early recovery groups at T1 and reached the late stage at follow-ups. Clinicians may focus on specific LLFDI s domains to maximize assessment efficiency. The direct links between mobility capacity, perceived mobility, and short-duration MX metrics indicate the potential of these metrics to monitor patients remotely.

To describe digital mobility outcomes in a sample of home-dwelling participants with a hip fracture at different phases of recovery (within 1 year from surgery) Overall, 90

Background:Recent advances in wearable technologies make it possible to accurately quantify real-world mobility performance through technically validated digital mobility outcomes (DMOs). The aim of the present study was to evaluate the construct validity (convergent, divergent, and known-groups validity) of 24 DMOs quantifying walking activity (amount and pattern) and gait (pace, rhythm and bout-to-bout variability) in people with COPD. Methods:Part of the Mobilise-D observational cohort study, people with COPD, recruited from seven European sites, wore an activity monitor for 7 days during daily life. Functional capacity, health status, dyspnoea, lung function, quadriceps torque and experience of difficulty with physical activity were used as constructs for convergent validity testing (Pearson/Spearman correlation coefficients). Diastolic blood pressure was used as an unrelated construct for divergent validity (criterion: |r|<0.2). Known-groups validity was evaluated across Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages (I-IV), GOLD ABE and modified Medical Research Council dyspnoea grades (linear models with p-for-trend). Results:549 participants (37% females), had mean±sd age of 68±8 years, post-bronchodilator forced expiratory volume in 1 s (FEV1) 54±20% predicted and 6-min walk distance 416±119 m. Convergent validity was supported for the majority of DMOs (17 out of 24) with correlation coefficients meeting or exceeding the a priori hypotheses by clinical experts. All DMOs supported divergent validity. 22 out of 24 DMOs distinguished between disease severity groups successfully. Expert consensus supported construct validity of 17 DMOs. Conclusions:Construct validity was supported for all walking activity (amount and pattern) DMOs, and most of the gait (pace, rhythm, and bout-to-bout variability) DMOs, indicating the clinical utility of these measures.

Background: Rotavirus vaccination programmes for infants have significantly reduced the incidence of rotavirus gastroenteritis. Vaccines which decrease disease incidence can reduce antibiotic prescribing and may help combat antimicrobial resistance. A US cohort study found a significant association between rotavirus vaccination and reduced antibiotic prescribing. However, no studies have investigated this association in the UK, where infant rotavirus vaccination was introduced in 2013. Methods: We conducted a retrospective cohort study using the Clinical Practice Research Datalink Aurum, a large representative dataset of UK primary care health records. Between 2010 and 2020, children were assessed for rotavirus vaccination status and followed from six months to seven years of age. The outcome was an antibiotic prescription within seven days following an all-cause acute gastroenteritis (AGE) diagnosis. Recurrent time-to-event modelling was used to estimate adjusted hazard ratios (aHR) accounting for sex, deprivation, urban-rural status, seasonality and health-seeking behaviour. Model estimates were used to predict the prescribing events averted by rotavirus vaccination. Results: Of 429,009 participants eligible for inclusion, 2.4% (10,328 participants) experienced an AGE-linked antibiotic prescribing event. For participants who received one or more rotavirus vaccines, after adjusting for covariates, the aHR was 0.74 (95% CI 0.71–0.77). This finding was robust across several sensitivity analyses, including changing the outcome definition to an antibiotic prescription within 3 days of an acute gastroenteritis diagnosis aHR 0.74 (95% CI 0.71–0.77). We also estimated that in a UK birth cohort born between 2014 and 2019 rotavirus vaccination has averted 12,635 (95%CI 11,363 − 14,109) AGE-linked prescriptions. Conclusions: Rotavirus vaccination was associated with a significantly lower rate of antibiotic prescribing in UK primary care. This is the first UK study to investigate this association, and our findings are consistent with the US cohort study and studies in low and middle-income countries. Our study provides further evidence that rotavirus vaccines are effective against disease in primary care and may contribute to antimicrobial stewardship.

Impaired mobility increases falls and mortality risk. However, guidelines to reliably assess real-world walking activity and gait remain undefined. We aimed to (i) determine the minimum daily wear time during waking hours (7:00–22:00) for a valid measurement day, (ii) identify the minimum number of valid days, and (iii) weekend days, to reliably assess weekly walking activity and gait parameters, and (iv) provide recommendations for reliable real-world walking assessments. Participants with chronic obstructive pulmonary disease (n = 565), multiple sclerosis (n = 558), Parkinson’s disease (n = 543) or proximal femoral fracture (n = 487) from 10 countries were asked to wear a single wearable device on the lower back, 24 h/day for seven days, resulting in 13,191 measurement days. The Mobilise-D processing pipeline was used to obtain 24 daily walking activity and gait parameters. Minimum daily wear time was determined as the highest wear time category that did not statistically change parameter values. Intraclass correlation coefficients ≥ 0.80 determined the minimum number of valid measurement and weekend days. The minimum daily wear time varied between “no requirement” (13

Spontaneous reporting of adverse events (AEs) by veterinary professionals and the public is the cornerstone of post-marketing safety surveillance for veterinary medicinal products (VMPs). However, studies suggest that most veterinary AEs remain unreported. Veterinary medicine regulators, including the United Kingdom Veterinary Medicines Directorate and the European Medicines Agency, have included the exploration of big data utilization to support pharmacovigilance efforts in their regulatory strategies. In this study, we describe the application of veterinary electronic healthcare records (EHRs) from the SAVSNET veterinary first opinion informatics system to conduct pharmacoepidemiological analyses. Five VMP-AE pairs were selected for investigation in a proof-of-concept study, where drug exposure was identified from semi-structured treatment data and AEs from the unstructured free-text clinical narrative. Dictionaries were developed to identify AEs based on standard terminology. The precision of these dictionaries improved when they were expanded using word vectorization and expert opinion. A key strength of first-opinion EHR datasets is their ability to enable cohort studies and facilitate calculations of absolute incidence and relative risk. Thus, we demonstrate that unstructured free-text clinical narratives can be used to identify outcomes for veterinary pharmacoepidemiological studies and, consequently, support and expand pharmacovigilance efforts based on spontaneous AE reports.

BACKGROUND:The amount of walking that people with COPD do is reduced. However, data on their manner of walking (i.e. gait) are still lacking. We characterised real-world gait in COPD by assessing levels and distributions of gait parameters, and comparing them across COPD severity and with healthy peers. METHODS:549 people with COPD from seven European sites and 19 healthy older adults wore single wearable devices (either Axivity AX6 or DynaPort MoveMonitor MM+) continuously for 1 week, from which we identified walking bouts, calculated 15 digital mobility outcomes (DMOs) aggregated at the weekly level, and compared them across COPD severity levels and with healthy peers. RESULTS:Of the participants with COPD, 37% were female with a mean±sd age of 68±8 years and a post-bronchodilator forced expiratory volume in 1 s of 54±20% predicted. All gait DMOs were normally distributed and exhibited variability between participants (e.g. mean±sd walking speed of 0.83±0.12 m·s-1, ranging from 0.48 to 1.20 m·s-1). Walking speed and cadence DMOs were lower with increasing disease severity (e.g. mean±sd walking speed of 0.88±0.11, 0.85±0.12, 0.80±0.12 and 0.78±0.14 m·s-1 across Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1 to 4, p<0.001; mean±sd cadence of 93±6, 91±6 and 89±7 steps·min-1 across GOLD A, B and E, p=0.013). Stride length and duration varied across COPD severity levels. Walking speed and cadence bout-to-bout variability only varied across dyspnoea severity levels. In a secondary analysis, we compared DMO data from people with COPD to a convenience sample of 19 healthy older adults (47% women, mean age 71±6 years) and found that walking speed and cadence varied between participants with COPD and healthy adults (e.g. mean±sd walking speed 0.83±0.12 versus 0.90±0.12 m·s-1, p=0.041). CONCLUSION:In people with COPD, gait DMOs are normally distributed and worsen as disease advances. Moreover, walking speed and cadence DMOs are significantly altered when compared to healthy peers. Further research should elucidate which DMOs can be improved with treatments to enhance mobility and reduce adverse events.

Background A key challenge in trials targeting disease modification in Parkinson's disease (PD) is the lack of sensitive, precise, and patient-relevant outcome measures. Digital mobility outcomes (DMOs), captured using body-worn devices, offer a novel, objective means to assess real-world gait and mobility-domains often impaired early in PD. The Mobilise-D consortium was established to develop and validate DMOs in PD and other conditions.Objective To describe DMOs in a large, representative international cohort of individuals with PD and compare to controls and across disease stage; and to determine compliance and feasibility.Methods As part of the Mobilise-D Clinical Validation and Extension Studies, real-world mobility of individuals with PD (n = 601) and matched controls (n = 232) was assessed using a single wearable device for seven days. Data were processed to yield 24 technically validated DMOs, representing different domains of real-world walking and mobility performance.Results DMO data were available for 531 PD and 221 controls. Significant differences between the groups were observed in 20 of 24 DMOs. Compared to controls, PD participants exhibited shorter daily walking duration and lower step counts, walking at a higher cadence and in fewer walking bouts per day. Findings also varied by disease severity, with differences observed particularly between controls vs. mild (Hoehn and Yahr stage I-II) and mild vs. moderate (Hoehn and Yahr stage III) disease. Compliance rates were high.Conclusions Distinct DMO patterns across PD severity and between PD and controls support their utility as sensitive, scalable outcome measures for future clinical trials and therapeutic development.

Background Influenza imposes a significant health burden on older adults, with influenza-like illness (ILI) a key driver of antibiotic prescribing, contributing to unnecessary antibiotic use and antimicrobial resistance (AMR). Influenza vaccination may reduce antibiotic prescribing; however, robust evidence remains limited. To address this, we examined the association between influenza vaccination and antibiotic prescribing in individuals aged 65 and older in England, using a self-controlled case series (SCCS) approach. Methods We conducted a SCCS analysis using electronic primary care health records from the Clinical Practice Research Datalink (CPRD), covering eight influenza seasons (2011 to 2019). Eligible individuals had at least one influenza season in which they were both vaccinated and unvaccinated. The primary outcome was days prescribed antibiotics, focusing on respiratory tract infection (RTI) linked prescribing. To account for seasonal variation in influenza circulation, we initially examined antibiotic prescribing from September to April and further restricted the analysis to January to April to assess the vaccine's effect during peak influenza activity. Incidence rate ratios (IRRs) were estimated using conditional Poisson regression models, adjusting for comorbidities, healthcare interactions, and other vaccinations. Results The analysed extract included 250,344 individuals, of whom 48,329 met the inclusion criteria. On average, individuals were prescribed antibiotics for 8 days per influenza season. Findings suggest influenza vaccination may reduce overall and RTI related antibiotic prescribing, particularly during peak influenza season. The IRR for RTI linked prescribing decreased from 0.96 (95% CI: 0.95 to 0.98) for September-April to 0.91 (95% CI: 0.89 to 0.93) for January-April. Conclusion Influenza vaccination contributes to reduced RTI related antibiotic prescribing in older adults during the influenza season. ### Competing Interest Statement Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the Wellcome Trust for the submitted work; financial relationships with organisations that might have an interest in the submitted work in the previous three years; DH, DS and NF are currently in receipt of grant support from Seqirus UK for the evaluation of influenza vaccines in the UK, ; NF is in receipt of funding from GSK in relation to malaria vaccines; DH has also received grants from Merck and Co (Kenilworth, NJ) for rotavirus strain surveillance, received honorariums for presentation at a Merck Sharp and Dohme (UK) symposium on vaccines and has consulted on rotavirus strain surveillance; TS, VD, MH have no competing interests to disclose, and no other relationships or activities that could appear to have influenced the submitted work. ### Funding Statement Funding: This work was funded by a Wellcome Trust (UK) Impact of Vaccines on Antimicrobial Resistance project grant (219798/Z/19/Z). DH was funded by a National Institute for Health and Care Research (NIHR) postdoctoral fellowship (PDF-2018-11-ST2-006. DH, are affiliated with the NIHR Health Protection Research Unit (HPRU) in Gastrointestinal Infections at the University of Liverpool in partnership with the UK Health Security Agency (UKHSA), in collaboration with the University of Warwick. NF are affiliated with the NIHR HPRU in Emerging and Zoonotic Infections at University of Liverpool in partnership with the UKHSA, in collaboration with University of Oxford. The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the UKHSA. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data availability: The datasets used in this study were extracted from Clinical Practice Research Datalink (CPRD) following CPRD approval of the study protocol (reference 21_000457: available at https://cprd.com/protocol/) and through a multi-study license and data sharing agreement between the University of Liverpool and CPRD. The Medicines and Healthcare products Regulatory Agency and National Institute for Health and Care Research sponsor CPRD. The authors are not authorised to share the datasets and are obliged to destroy the datasets according to the data-sharing agreement between the University of Liverpool and CPRD.

BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785

Objectives: Antimicrobial resistance (AMR) is a vital One Health issue; the rational use of antimicrobials is essential to preserve their efficacy. Veterinarians cite pressure from pet owners as a contributor to antimicrobial prescription. Engaging pet owners in antimicrobial stewardship could reduce this pressure. A short educational animation could facilitate communication of this message. The impact of the animation on participant's opinions relating to antimicrobial prescribing and awareness of AMR was assessed via a randomized controlled trial. Methods: A survey was created based on the health belief model. Owners attending six UK veterinary centres were randomized to the intervention or control group (ratio 1:1). All owners completed an agreement level survey of two questions followed by 18 statements scored using a Likert agreement scale. The control group responded without interruption, whereas the animation group was shown the animation after answering the first two questions and five statements. Results: In total, 647 owners participated in the study; 350 complete responses were analysed. Responses to 10 of 13 statements asked after the animation were significantly different (all P < 0.050) between groups, whereas there was no significant difference between groups in response to any of the statements asked before the animation. The animation group were more likely to agree that lower antimicrobial use would help maintain future efficacy (P < 0.001) and that requesting antimicrobials from their vet could increase unnecessary use (P < 0.001). The animation group were more likely to disagree that they would expect antimicrobials if their pet had diarrhoea (P = 0.048). Conclusions: Pet owners that watched a short AMR engagement animation displayed greater awareness of the impact of AMR and were more likely to support measures in line with antimicrobial stewardship. This behavioural-nudge resource could support owners towards contributing to a multi-faceted approach to AMR.

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987.

Introduction Veterinary clinical microbiology laboratories play a key role in antimicrobial stewardship, surveillance of antimicrobial resistance and prevention of healthcare associated-infections. However, there is a shortage of international harmonized guidelines covering all steps of veterinary bacterial culture from sample receipt to reporting.Methods In order to gain insights, the European Network for Optimization of Veterinary Antimicrobial Treatment (ENOVAT) designed an online survey focused on the practices and interpretive criteria used for bacterial culture and identification (C&ID), and antimicrobial susceptibility testing (AST) of animal bacterial pathogens.Results A total of 241 microbiology laboratories in 34 European countries completed the survey, representing a mixture of academic (37.6%), governmental (27.4%), and private (26.5%) laboratories. The C&ID turnaround varied from 1 to 2 days (77.8%) to 3-5 days (20%), and 6- 8 days (1.6%), with similar timeframes for AST. Individual biochemical tests and analytical profile index (API) biochemical test kits or similar were the most frequent tools used for bacterial identification (77% and 56.2%, respectively), followed by PCR (46.6%) and MALDI-TOF MS (43.3%). For AST, Kirby-Bauer disk diffusion (DD) and minimum inhibitory concentration (MIC) determination were conducted by 43.8% and 32.6% of laboratories, respectively, with a combination of EUCAST and CLSI clinical breakpoints (CBPs) preferred for interpretation of the DD (41.2%) and MIC (47.6%) results. In the absence of specific CBPs, laboratories used human CBPs (53.3%) or veterinary CBPs representing another body site, organism or animal species (51.5%). Importantly, most laboratories (47.9%) only report the qualitative interpretation of the result (S, R, and I). As regards testing for AMR mechanisms, 48.5% and 46.7% of laboratories routinely screened isolates for methicillin resistance and ESBL production, respectively. Notably, selective reporting of AST results (i.e. excluding highest priority critically important antimicrobials from AST reports) was adopted by 39.5% of laboratories despite a similar proportion not taking any approach (37.6%) to guide clinicians towards narrower-spectrum or first-line antibiotics.Discussion In conclusion, we identified a broad variety of methodologies and interpretative criteria used for C&ID and AST in European veterinary microbiological diagnostic laboratories. The observed gaps in veterinary microbiology practices emphasize a need to improve and harmonize professional training, innovation, bacterial culture methods and interpretation, AMR surveillance and reporting strategies.

Background: Vaccination of dogs and cats is considered a mainstay of preventive health, providing benefit to individual animals and the population. This study aimed to assess temporal trends in vaccination and factors associated with vaccination uptake in a large veterinary visiting population of UK cats and dogs. Methods: The electronic health records (EHRs) of 712,266 dogs and 306,888 cats (voluntarily contributed by 201 veterinary practices) were used to determine the proportion that had record of any type of veterinary vaccination either within one-year or within three-years of attending a participating veterinary practice. Socioeconomic and animal factors were also assessed for association with vaccination uptake using multivariable mixed effects logistic regression models. Results: The percentage of animals vaccinated within one-year of consultation declined across the years 2016 to 2022, from 76.58% (95% CI 74.82-78.33) to 69.04% (95% CI 68.02-70.07) in dogs, and 69.54% (95% CI 67.89-71.19) to 66.12% (95% CI 64.83-67.41) in cats. The proportion of animals that had a vaccination recorded within a three-year window of a consultation for dogs ranged from 81.36% (95% CI 79.74-82.99)to 84.42% (95% CI 83.31-85.54) in the period from 2016 to 2022; for cats, this measure increased from 73.90% (95% CI 72.24-75.56) in 2016 to 77.85% (95% CI 76.46-79.25) in 2022. Aging was associated with reduced odds of animals having a vaccination recorded within one-year and three-years. In both species the odds of vaccination reduced as the level of socioeconomic deprivation assigned to the animal's area of residence increased. Conclusion: The results provide a baseline that can be used as a reference to continue to track vaccination uptake in the cat and dog population. Temporal trends suggest time between vaccinations has increased in recent years. Strategies to encourage owners to vaccinate their animals should consider targeting areas of high deprivation.

Innovations by engineers and physical scientists working at the frontiers of microbiome engineering and discovery requires in-depth understanding of microbiome systems with parallel skills to apply bioinformatics and biostatistics. Despite the importance of integrating bioinformatics and biology into graduate student training in fields outside traditional biological sciences, academic institutions remain challenged with including these disciplines across departmental boundaries. Furthermore, it is critical for students in engineering, bioinformatics, and biostatistics to understand fundamentals behind the biological systems they model, and for biology students to gain competencies to apply bioinformatics and biostatistics in quantitative biology arenas. To address these needs, the Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM) graduate training partnership between Duke University and North Carolina Agricultural and Technical State University was developed and funded by the National Science Foundation Research Traineeship (NRT) program. IBIEM's goals include training interdisciplinary groups of students to: (a) transform conceptualization and develop skills for application of quantitative biology in microbiome areas; b) perform cutting edge research requiring interdisciplinary team skills; and to (b) communicate their research across disciplinary barriers and to diverse audiences. The pedagogical framework adapted to foster trainee engagement is learner-centered teaching which emphasizes the importance of self-directed learning with parallel ongoing assessment to optimize student outcomes. Since IBIEM trainee goals as well as entry-level knowledge and skills across disciplines varied greatly, program implementation was found to be challenging and required rigorous evaluation and refinements for effective training across disciplines and skill levels. A comprehensive program evaluation over five years found that the strongest learning and skills outcomes were linked to several "best practices". Early provision of depth in fundamentals in R studio and Git Hub was found to be critical to "jump start" students without coding backgrounds. Addition of an overview of microbiome experimental design and analysis added important context as to how and where in the research process informatics fits into design progression and was highly motivating to students. Course modality was found to impact trainee outcomes with in-person classes that included hands-on practice and feedback showing greater improvements in training outcomes over hybrid, flipped and virtual course modalities. Furthermore, introduction of low, medium, and high level "challenges" along with in-person tutoring was found to be impactful in building a common foundation to span expertise levels and for engaging students across entry and advanced levels. Training impacts peaked during year four with cumulative implementation of revised strategies. Innovative training revisions and inclusion of critical elements was strongly linked to program satisfaction and ratings of advances in technical, professional and career skills as well as post-training carry over into trainees' own research and leadership in their labs and careers. Furthermore, this training collaboration and partnership provided the foundation and training model for a newly funded NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) for work in the critical area of engineering the microbiome in built environments.

Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances. Trial registration ISRCTN – 12246987.