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Recovery housing provides substance-free living environments that use peer-support to empower individuals in recovery from substance use disorder. This study estimated the total revenue of the recovery housing industry from recovery houses, nationwide.Using survey data collected in June and July of 2020, we calculate the total revenue of the recovery housing industry and determine the share of revenue that comes from different sources for rural and nonrural houses.We find that individual recovery houses operate with an annual revenue of $250,000 and the whole industry accounts for $4.5 billion annually. COVID-19 has reduced industry revenue by 4%. Rural and nonrural houses differ significantly in their sources of revenue.Our results suggest that COVID-19 reduced the size of the recovery housing industry. Houses in nonrural regions may need more federal support due to the relatively high reliance on resident fees, which may be unreliable during COVID-19.

Introduction Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disease that places a substantial burden on patients and caregivers. Aducanumab is the first AD therapy approved by the US Food and Drug Administration to reduce a defining pathophysiological feature of the disease, brain amyloid plaques. In the phase 3 clinical trial EMERGE (NCT02484547), aducanumab reduced clinical decline in patients with mild cognitive impairment (MCI) due to AD and mild AD dementia and confirmed amyloid pathology. Methods We used a Markov modeling approach to predict the long-term clinical benefits of aducanumab for patients with early AD based on EMERGE efficacy data. In the model, patients could transition between AD severity levels (MCI due to AD; mild, moderate, and severe AD dementia) and care settings (community vs. institution) or transition to death. The intervention was aducanumab added to standard of care (SOC), and the comparator was SOC alone. Data sources for base-case and scenario analyses included EMERGE, published National Alzheimer’s Coordinating Center analyses, and other published literature. Results Per patient over a lifetime horizon, aducanumab treatment corresponded to 0.65 incremental patient quality-adjusted life-years (QALYs) and 0.09 fewer caregiver QALYs lost compared with patients treated with SOC. Aducanumab treatment translated to a lower lifetime probability of transitioning to AD dementia, a lower lifetime probability of transitioning to institutionalization (25.2% vs. 29.4%), delays in the median time to transition to AD dementia (7.50 vs. 4.92 years from MCI to moderate AD dementia or worse), and an incremental median time in the community of 1.32 years compared with SOC. Conclusion The model predicted long-term benefits of aducanumab treatment in patients with MCI due to AD and mild AD dementia and their caregivers. The predicted outcomes provide a foundation for healthcare decision-makers and policymakers to understand the potential clinical and socioeconomic value of aducanumab. Supplementary Information The online version contains supplementary material available at 10.1007/s40120-021-00273-0.

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens in new locations, which requires effective surveillance through the implementation of appropriate monitoring programs. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. ‘Sentinel’ plants – alternative susceptible host species that display visible symptoms of infection more rapidly – could be introduced to at-risk populations and included in monitoring programs to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.

Although new cases of monkeypox have been expected in the Western Pacific Region (WPR) since the virus emerged in Europe earlier this year, there have been only a few reported cases across the WPR (New Zealand 2, Singapore 6, South Korea 1, Taiwan 2), other than a limited number of cases (compared to numbers of cases seen elsewhere in the world) in Australia (33), as of July 15, 2022. In our short communication, we highlight two key reasons for this: i) international travel has still not fully resumed in the WPR following the COVID-19 pandemic, and ii) local public health measures to counter the spread of COVID-19 have not been completely relaxed. We provide supporting evidence for both of these reasons.\ud \ud

Defeating peripheral neuropathy The mechanisms underlying peripheral neuropathies are not well understood. Spaulding et al . studied mouse models of the inherited Charcot-Marie-Tooth (CMT) disease, which is caused by mutations in transfer RNA (tRNA) synthetases. Changes in gene expression and the rate of protein synthesis in neurons in the spinal cord triggered the cell stress response activated by the protein sensor GCN2. When GCN2 was genetically deleted or inhibited with drugs, the stress response was blocked, and the neuropathy was much milder. Zuko et al . found that mutant glycyl-tRNA synthetases bind tRNA Gly but fail to release it, thus depleting the cellular tRNA Gly pool. This process caused stalling of translating ribosomes on glycine codons and activated the integrated stress response. Transgenic tRNA Gly overexpression prevented peripheral neuropathy and protein synthesis defects in mouse and fruit fly models. Thus, elevating tRNA Gly levels or targeting GCN2 may have therapeutic potential for this currently untreatable disease (see the Perspective by Mellado and Willis). —SMH

Purpose With the impending changes to state Medicaid programs and other health reform policies, it is imperative to understand the factors at play in promoting consumer health insurance literacy and health system engagement. This study examines the availability of health system and community‐based programs promoting health insurance literacy and supporting informed consumer health care decision making in rural communities in Kentucky. Methods Forty‐six health systems, community‐based providers, and outreach workers participated in 4 focus groups and 10 semistructured interviews. Descriptive and analytic coding techniques were used to identify 5 major themes and subthemes from interview and focus group transcripts. Findings Consumers were generally identified as having low health insurance literacy, especially in rural communities, serving as a barrier to accessing health care insurance and services. Participants identified their own lack of knowledge and understanding around health systems, resulting from lack of training and challenges with staying updated on constant changes in health systems and policies. Overall, consumer demand or need for health insurance literacy resources and programs far exceeded supply or availability. Constant changes in the status of Kentucky's Medicaid program and the proposed changes to eligibility, specifically work requirements and copays, have caused increased confusion among both providers and consumers. Conclusions Findings indicate a pressing need for implementing programs that provide training, tools, and resources to outreach workers to help them better assist consumers with accessing and using health insurance, especially in low‐income, rural areas. Health reform policies need to be responsive to the health insurance literacy needs and abilities of consumers.

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

The reproductive number is an important metric that has been widely used to quantify the infectiousness of communicable diseases. The time-varying instantaneous reproductive number is useful for monitoring the real time dynamics of a disease to inform policy making for disease control. Local estimation of this metric, for instance at a county or city level, allows for more targeted interventions to curb transmission. However, simultaneous estimation of local reproductive numbers must account for potential sources of heterogeneity in these time-varying quantities – a key element of which is human mobility. We develop a statistical method that incorporates human mobility between multiple regions for estimating region-specific instantaneous reproductive numbers. The model also can account for exogenous cases imported from outside of the regions of interest. We propose two approaches to estimate the reproductive numbers, with mobility data used to adjust incidence in the first approach and to inform a formal priori distribution in the second (Bayesian) approach. Through a simulation study, we show that region-specific reproductive numbers can be well estimated if human mobility is reasonably well approximated by available data. We use this approach to estimate the instantaneous reproductive numbers of COVID-19 for 14 counties in Massachusetts using CDC case report data and the human mobility data collected by SafeGraph. We found that, accounting for mobility, our method produces estimates of reproductive numbers that are distinct across counties. In contrast, independent estimation of county-level reproductive numbers tends to produce similar values, as trends in county case-counts for the state are fairly concordant. These approaches can also be used to estimate any heterogeneity in transmission, for instance, age-dependent instantaneous reproductive number estimates. As people are more mobile and interact frequently in ways that permit transmission, it is important to account for this in the estimation of the reproductive number.Author summaryTo control the spreading of an infectious disease, it is very important to understand the real-time infectiousness of the pathogen that causes the disease. An existing metric called instantaneous reproductive number is often used to quantify the average number of secondary cases generated by individuals who are infectious at a certain time point, assuming no changes to current conditions. In practice, we might be interested in using the metric to describe the infectiousness in multiple regions. However, this is challenging when there are visitors traveling between these regions, since this could lead to a misclassification of where an individual is actually infected and create biased estimates for the instantaneous reproductive numbers. We developed a method that takes account of human mobility to estimate the instantaneous reproductive numbers for multiple regions simultaneously, which could reveal the heterogeneity of the metric. This method aims to provide helpful information on region-specific infectiousness for disease control measures that focus on the region with higher pathogen infectiousness. This approach is also applicable for estimating the reproductive number in the presence of other sources of heterogeneity, including by age.

Background Spinal muscular atrophy (SMA) is a rare, genetic, progressive neuromuscular disorder characterized by severe muscle atrophy and weakness and is a leading genetic cause of death in infants and children. Nusinersen was the first treatment targeting the underlying cause of disease approved by the FDA, EMA and other countries for patients with SMA. There are currently very limited data available on the health-related quality of life (HRQoL) burden of SMA suitable for use in a cost-effectiveness analysis. Objective This study was designed to estimate quality of life weights or utilities for different SMA states. Methods SMA case studies were developed describing Type I (infantile onset) and Type II (later-onset) patients and different outcomes from treatment. These were developed so that quality of life weights or utilities (where the value of health ranges from 1 - full health to 0 - dead) could be estimated for cost-effectiveness analysis. Clinical experts (n=5) rated each of the case studies using standardized HRQoL instruments - the EQ-5D-Y and PedsQL-NMM (baseline states only). Results The SMA Type I utilities ranged from -0.33 (requires ventilation) to 0.71 (Type I patient reclassified as Type III following treatment), with quite substantial differences between some states. Most Type I states had a utility score below zero indicating the severity of the states. The SMA Type II utilities ranged from -0.13 (worsened) to 0.72 (stands/walks unaided). In general, the results showed HRQoL improved in line with better health states. Conclusion The utility scores obtained in this study highlight the very substantial burden experienced by SMA patients. Despite the limitations in the methods used, this study produced data with face validity and is a useful starting point for understanding the burden of SMA Types I and II in cost-effectiveness analysis.

We address the use of accelerometery to automatically monitor lying behaviour in free-farrowing sows; due to their freedom of movement and the consequent increased variety of movements the sows are able to exhibit, the challenges in automating this are greater than in sows housed in movement restricting farrowing environments. The methodology developed was applied to two salient applications: that of farrowing prediction through detection of nest building activity, and comparison of maternal lying behaviour in conventional movement-restricting and free-farrowing systems. Two sensors were attached at both the front and hind end to each of eight periparturient sows. Movement behaviour was recorded for a period of five days around parturition. Activity transitions were classified by a Support Vector Machine classifier, using data from both sensors individually, and combined; classifier output was validated against ground truth annotations collected from video data. We draw conclusions about the benefits of using multiple sensors over a single sensor, as well as the suitability of different sensor locations on the sow. Activity classification was found to improve through the use of multiple sensors, with a mean F 1 score (a measure of predictive performance between 0 and 1) of 0.84, compared to use of the front sensor alone (mean F 1 = 0.49) and the hind sensor alone (mean F 1 = 0.57). Activity transitions were classified using the dual sensor setup with a mean F 1 score of 0.77. Using a threshold-based approach, taking transition frequency as an indicator of nesting behaviour, we were able to detect the onset of nest building with an average latency to farrowing of 11.1 (±4.65) hours, and an average of 1 premature detection per sow; however, the majority of these premature were in a particular sow. We draw comparisons between the lying behaviour of free-farrowing and restricted sows. Using a mixed-design ANOVA we found a main effect of farrowing environment on transition duration ( p = 0.003 ), peak acceleration ( p = 0.007 ) and rate of change in pitch ( p = 0.009 ) . Improving the classification accuracy of sow activity transitions through the addition of multiple sensors allows for improved performance in applications such as farrowing prediction, which has the capacity to reduce piglet mortality through enabling farrowing supervision. Understanding how movement restriction affects the lying behaviour of farrowing sows has the potential to inform decisions regarding restriction of sows and development of free-farrowing environments.

Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting millions of people worldwide and imposing heavy economic burdens to societies. Currently, only symptomatic treatments are available for patients, but there is ongoing research on potential therapies that can modify the course of disease. The main objective of this work is to identify and explore the challenges surrounding decision modeling for economic evaluation of interventions for AD.This article discusses the challenges in modeling the natural history of disease, particularly regarding the selection of disease progression and outcome measures, the inclusion of biomarker status in models, and the approach to model mortality. Challenges stemming from the use of long-term assumptions regarding treatment effects and the need for real-world evidence to fill data gaps are discussed. Lastly, the overwhelming economic impact of disease and the challenges in estimating these costs for modeling are addressed.Value assessment frameworks need to be reconsidered in order to demonstrate the full benefit of new disease-modifying therapies spanning beyond the scope of health systems. Data collection efforts that expand the evidence base, upon which economic models are based, will reduce the uncertainties surrounding the long-term outcomes of interventions in AD.

This article explores Jesus' healing of the disabled man at the Pool of Bethesda. This healing is only reported in the Gospel of John, and the location of the healing is described in some detail. After addressing the textual and archaeological challenges in locating the Pool of Bethesda, this article discusses both the tradition of healing associated with this site and the well-known Greco-Roman god of healing, Asclepius. While this healing may serve more than one purpose in John's Gospel, I propose that the location of the healing serves as a challenge to the Gentiles in John's audience: Jesus was more than a god who could heal—he was a God who could provide eternal life.

This paper presents a novel approach to biomass variability assessment within a crop field, and to zone management in Precision Agriculture. The k-means clustering and the fuzzy c-means clustering algorithms are used to create homogeneous management zones in a crop field based on within-field biomass variation, acquired from multispectral data and processed using a vegetation index. To test the accuracy of the delineation of management zones, the segmented maps are compared against an application plan based on approved agricultural models and expert knowledge used as a reference. It is shown that the k-means clustering algorithm is a viable approach to highlighting biomass variability and defining management zones. The proposed zoning functionality is aimed to be added as an extra module to an existing Fertiliser Assessment tool in a plug-and-play principle to enhance its functionality.

Tracheal intubation is common in the care of critically ill adults and is frequently complicated by hypotension, cardiac arrest, or death. We aimed to evaluate administration of an intravenous fluid bolus to prevent cardiovascular collapse during intubation of critically ill adults.We did a pragmatic, multicentre, unblinded, randomised trial in nine sites (eight ICUs and one emergency department) around the USA. Critically ill adults (≥18 years) undergoing tracheal intubation were randomly assigned (1:1, block sizes of 2, 4, and 6, stratified by study site) to either an intravenous infusion of 500 mL of crystalloid solution or no fluid bolus. The primary outcome, assessed in the intention-to-treat population, was cardiovascular collapse, defined as a new systolic blood pressure65 mm Hg; new or increased vasopressor receipt between induction and 2 min after tracheal intubation; or cardiac arrest or death within 1 h of tracheal intubation. Adverse events were assessed in the as-treated population. This trial, which is now complete, is registered with ClinicalTrials.gov, number NCT03026777.Patients were enrolled from Feb 6, 2017, to Jan 9, 2018, when the data and safety monitoring board stopped the trial on the basis of futility. By trial termination, 337 (63%) of 537 screened adults had been randomly assigned. Cardiovascular collapse occurred in 33 (20%) of 168 patients in the fluid bolus group compared with 31 (18%) of 169 patients in the no fluid bolus group (absolute difference 1·3% [95% CI -7·1% to 9·7%]; p=0·76). The individual components of the cardiovascular collapse composite outcome did not differ between groups (new systolic blood pressure65 mm Hg 11 [7%] in the bolus group vs ten [6%] in the no-bolus group, new or increased vasopressor 32 [19%] vs 31 [18%], cardiac arrest within 1 h seven [4%] vs two [1%], death within 1 h of intubation two [1%] vs one [1%]). In-hospital mortality was not significantly different in the fluid bolus group (48 [29%]) compared with no fluid bolus (59 [35%]).Administration of an intravenous fluid bolus did not decrease the overall incidence of cardiovascular collapse during tracheal intubation of critically ill adults compared with no fluid bolus in this trial.US National Institutes of Health.

Spinal muscular atrophy is a rare neuromuscular disorder with a spectrum of severity related to age at onset and the number of SMN2 gene copies. Infantile-onset (≤ 6 months of age) is the most severe spinal muscular atrophy and is the leading monogenetic cause of infant mortality; patients with later-onset (> 6 months of age) spinal muscular atrophy can survive into adulthood. Nusinersen is a new treatment for spinal muscular atrophy. The objective of this study was to evaluate the cost effectiveness of nusinersen for the treatment of patients with infantile-onset spinal muscular atrophy and later-onset spinal muscular atrophy in Sweden. One Markov cohort health-state transition model was developed for each population. The infantile-onset and later-onset models were based on the efficacy results from the ENDEAR phase III trial and the CHERISH phase III trial, respectively. The cost effectiveness of nusinersen in both models was compared with standard of care in Sweden. For a time horizon of 40 years in the infantile-onset model and 80 years in the later-onset model, treatment with nusinersen resulted in 3.86 and 9.54 patient incremental quality-adjusted life-years and 0.02 and 2.39 caregiver incremental quality-adjusted life-years and an incremental cost of 21.9 and 38.0 million SEK (Swedish krona), respectively. These results translated into incremental cost-effectiveness ratios (including caregiver quality-adjusted life-years) of 5.64 million SEK (€551,300) and 3.19 million SEK (€311,800) per quality-adjusted life-year gained in the infantile-onset model and later-onset model, respectively. Treatment with nusinersen resulted in overall survival and quality-adjusted life-year benefits but with incremental costs above 21 million SEK (€2 million) [mainly associated with maintenance treatment with nusinersen over a patient’s lifespan]. Nusinersen was not cost effective when using a willingness-to-pay threshold of 2 million SEK (€195,600), which has been considered in a recent discussion by the Dental and Pharmaceutical Benefits Agency as a reasonable threshold for rare disease. Nonetheless, nusinersen gained reimbursement in Sweden in 2017 for paediatric patients (below 18 years old) with spinal muscular atrophy type I–IIIa.

Field trials for variety selection often exhibit spatial correlation between plots. When multivariate data are analysed from these field trials, there is the added complication in having to simultaneously account for correlation between the traits at both the residual and genetic levels. This may be temporal correlation in the case of multi-harvest data from perennial crop field trials, or between-trait correlation in multi-trait data sets. Use of parsimonious yet plausible models for the variance–covariance structure of the residuals for such data is a key element to achieving an efficient and inferentially sound analysis. In this paper, a model is developed for the residual variance–covariance structure firstly by considering a multivariate autoregressive model in one spatial direction and then extending this to two spatial directions. Conditions for ensuring that the processes are directionally invariant are presented. Using a canonical decomposition, these directionally invariant processes can be transformed into a set of independent separable processes. This simplifies the estimation process. The new model allows for flexible modelling of the spatial and multivariate interaction and allows for different spatial correlation parameters for each harvest or trait. The methods are illustrated using data from lucerne breeding trials at several environments.

Objective The purpose of this study was to assess the feasibility and effectiveness of live telemedicine applications in hearing amplification and cochlear implantation. Data sources and study selection A systematic search was performed in PubMed, MEDLINE, PsychINFO, CINALH, and Web of Science to identify peer-reviewed research. Inclusion criteria were titles containing words from the search terms 1) audiology, otolaryngology, and hearing impairment, 2) rehabilitative methods, and 3) telemedicine. Exclusion criteria were: 1) non-English articles, and 2) non-original research. Data extraction and synthesis Twelve eligible studies were identified. The studies employed a prospective design in nine of the articles and retrospective case series in three. The use of telemedicine for the provision of cochlear implant services was examined in eight of the articles and with hearing aids in four of the articles. The types of services include intraoperative cochlear implant telemetry; implant programming and assessment of electrode-specific measures and speech recognition after implantation. Hearing aid programming and remote gain assessments were also reported. Many studies assess patient and provider satisfaction along with encounter time comparison. The studies occurred from 2009 to 2014 and took place in seven countries. Conclusions This review examined the feasibility of remote telemedicine connection to provide in auditory rehabilitation services through hearing aids and cochlear implants. There are significant concerns regarding Internet bandwidth limitations for remote clinics. There is a paucity of research examining reimbursement and cost-effectiveness for services. Further prospective research investigating cost-effectiveness and bandwidth limitations is warranted to assess long-term sustainability of remote audiological rehabilitative service delivery.

Human activity recognition (HAR), i.e., the automated detection and classification of specific activities that a person pursues, is one of the core concerns of mobile and ubiquitous computing. Multimodal sensing facilities of modern mobile devices allow for detailed capture of contextual information, most importantly movement data recorded with inertial measurement units that are now standard in most mobile devices. The majority of HAR applications aim at automatically documenting when something of interest has happened and what that was. For example, the popular moves app on iOS and Android devices "automatically records any walking, cycling, and running [a user does]" [7] and as such automatically generates a life log for those interested in their daily movement patterns. Beyond the mere recognition of certain activities of interest, few applications currently go a step further and analyze the quality of a person's activities, i.e., how (well) their activities were performed, which directly corresponds to a person's abilities or skills .

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.

I review my scientific career in terms of eight areas and 16 papers. The first two areas are associated with childhood. The other six are associated with residual maximum likelihood (REML), canonical transformation, inbreeding in selected populations, average information residual maximum likelihood (AIREML), the computer program ASReml and sampling-based estimation.

Vaccination is an important tool for decreasing the spread and disease burden of the widespread bacterial pathogen Streptococcus pneumoniae. However, current vaccines target only a subset of pneumococcal serotypes, and the nonvaccine types often remain at high frequencies after vaccination. In some cases, nonvaccine types display a disconcerting increase in antibiotic resistance frequencies after a population has been vaccinated by the commonly used PCV7 or PCV13, and the reason for this is unclear. Using mathematical modeling, we show that removal of competition with the vaccine types may induce this rise in antibiotic resistance. Our model explores the conditions leading to such an increase and contributes to a better understanding of this process.

Veterinarians and pet owners have limited ability to assess pruritic behaviors in dogs. This pilot study assessed the capacity of the Vetrax® triaxial accelerometer to measure these behaviors in six dogs with pruritus likely due to environmental allergens. Dogs wore the activity monitor for two weeks while consuming their usual pet food (baseline), then for eight weeks while consuming a veterinary-exclusive pet food for dogs with suspected non-food-related skin conditions (Hill’s Prescription Diet® Derm DefenseTM Canine dry food). Veterinarians and owners completed questionnaires during baseline, phase 1 (days 1–28) and phase 2 (days 29–56) without knowledge of the activity data. Continuous 3-axis accelerometer data was processed using proprietary behavior recognition algorithms and analyzed using general linear mixed models with false discovery rate-adjusted p values. Veterinarian-assessed overall clinical signs of pruritus were significantly predicted by scratching (β 0.176, p = 0.008), head shaking (β 0.197, p < 0.001) and sleep quality (β −0.154, p < 0.001), while owner-assessed quality of life was significantly predicted by scratching (β −0.103, p = 0.013) and head shaking (β −0.146, p < 0.001). Among dogs exhibiting pruritus signs eating the veterinary-exclusive food, the Vetrax® sensor provided an objective assessment of clinically relevant pruritic behaviors that agreed with owner and veterinarian reports.