Several app-based studies share similar characteristics of a light touch approach that recruit, enroll, and onboard via a smartphone app and attempt to minimize burden through low-friction active ...study tasks while emphasizing the collection of passive data with minimal human contact. However, engagement is a common challenge across these studies, reporting low retention and adherence.
This study aims to describe an alternative to a light touch digital health study that involved a participant-centric design including high friction app-based assessments, semicontinuous passive data from wearable sensors, and a digital engagement strategy centered on providing knowledge and support to participants.
The Stress and Recovery in Frontline COVID-19 Health Care Workers Study included US frontline health care workers followed between May and November 2020. The study comprised 3 main components: (1) active and passive assessments of stress and symptoms from a smartphone app, (2) objective measured assessments of acute stress from wearable sensors, and (3) a participant codriven engagement strategy that centered on providing knowledge and support to participants. The daily participant time commitment was an average of 10 to 15 minutes. Retention and adherence are described both quantitatively and qualitatively.
A total of 365 participants enrolled and started the study, and 81.0% (n=297) of them completed the study for a total study duration of 4 months. Average wearable sensor use was 90.6% days of total study duration. App-based daily, weekly, and every other week surveys were completed on average 69.18%, 68.37%, and 72.86% of the time, respectively.
This study found evidence for the feasibility and acceptability of a participant-centric digital health study approach that involved building trust with participants and providing support through regular phone check-ins. In addition to high retention and adherence, the collection of large volumes of objective measured data alongside contextual self-reported subjective data was able to be collected, which is often missing from light touch digital health studies.
ClinicalTrials.gov NCT04713111; https://clinicaltrials.gov/ct2/show/NCT04713111.
Services to families have traditionally been delivered in a medical model. This presents challenges including workforce shortages, lack of cultural diversity, lack of training in strength-based work, ...and difficulty in successfully engaging and retaining families in the therapy process. The system of care (SOC) effort has worked to establish formal roles for caregivers in SOC to improve services. This paper provides an example of one community’s efforts to change the SOC by expanding the roles available to caregivers in creating systems change. It describes the model developed by Communities of Care (CoC), a SOC in Central Massachusetts, and its evolution over a 10 year period. First person accounts by system partners, caregivers hired into professional roles as well as a family receiving services, demonstrate how hiring caregivers at all levels can change systems and change lives, not only for those being served but for the caregiver/professionals doing the work. It also demonstrates, however, that change at the system level is incremental, takes time, and can be fleeting unless an ongoing effort is made to support and sustain those changes.
Within the context of a workshop, a concept and practical guidance were developed that seek to balance potential benefits of animal experiments to humans, other animals, and the environment against ...the pain, suffering, and distress caused to the experimental animals. The aim was to achieve transparent decisions that can be communicated in a concise manner that is accessible to a layperson and is in accordance with German national law and EU Directive 2010/63/EU. The steps of the resulting decision process deal with the classification of procedures into the four severity levels, the consideration of humane endpoints, determination of the indispensability of the procedure on the basis of sound scientific argument, classification into applied or basic research, determination of the probability of success in the case of applied research, and the cost-benefit analysis, culminating in a decision on the approval or denial of the procedure.
We consider fast, provably accurate algorithms for approximating functions on the \(d\)-dimensional torus, \(f: \mathbb{ T }^d \rightarrow \mathbb{C}\), that are sparse (or compressible) in the ...Fourier basis. In particular, suppose that the Fourier coefficients of \(f\), \(\{c_{\bf k} (f) \}_{{\bf k} \in \mathbb{Z}^d}\), are concentrated in a finite set \(I \subset \mathbb{Z}^d\) so that $$\min_{\Omega \subset I s.t. |\Omega| =s } \left\| f - \sum_{{\bf k} \in \Omega} c_{\bf k} (f) e^{ -2 \pi i {\bf k} \cdot \circ} \right\|_2 < \epsilon \|f \|_2$$ holds for \(s \ll |I|\) and \(\epsilon \in (0,1)\). We aim to identify a near-minimizing subset \(\Omega \subset I\) and accurately approximate the associated Fourier coefficients \(\{ c_{\bf k} (f) \}_{{\bf k} \in \Omega}\) as rapidly as possible. We present both deterministic as well as randomized algorithms using \(O(s^2 d \log^c (|I|))\)-time/memory and \(O(s d \log^c (|I|))\)-time/memory, respectively. Most crucially, all of the methods proposed herein achieve these runtimes while satisfying theoretical best \(s\)-term approximation guarantees which guarantee their numerical accuracy and robustness to noise for general functions. These are achieved by modifying several one-dimensional Sparse Fourier Transform (SFT) methods to subsample a function along a reconstructing rank-1 lattice for the given frequency set \(I\) to rapidly identify a near-minimizing subset \(\Omega \subset I\) without using anything about the lattice beyond its generating vector. This requires new fast and low-memory frequency identification techniques capable of rapidly recovering vector-valued frequencies in \(\mathbb{Z}^d\) as opposed to simple integer frequencies in the univariate setting. Two different strategies are proposed and analyzed, each with different accuracy versus computational speed and memory tradeoffs.
In this paper we present the first known deterministic algorithm for the construction of multiple rank-1 lattices for the approximation of periodic functions of many variables. The algorithm works by ...converting a potentially large reconstructing single rank-1 lattice for some \( d \)-dimensional frequency set \( I \subset N^d \) into a collection of much smaller rank-1 lattices which allow for accurate and efficient reconstruction of trigonometric polynomials with coefficients in \( I \) (and, therefore, for the approximation of multivariate periodic functions). The total number of sampling points in the resulting multiple rank-1 lattices is theoretically shown to be less than \( \mathcal{O}\left( |I| \log^{ 2 }(N |I|) \right) \) with constants independent of \(d\), and by performing one-dimensional fast Fourier transforms on samples of trigonometric polynomials with Fourier support in \( I \) at these points, we obtain exact reconstruction of all Fourier coefficients in fewer than \( \mathcal{O}\left(d\,|I|\log^4 (N|I|)\right) \) total operations. Additionally, we present a second multiple rank-1 lattice construction algorithm which constructs lattices with even fewer sampling points at the cost of only being able to reconstruct exact trigonometric polynomials rather than having additional theoretical approximation. Both algorithms are tested numerically and surpass the theoretical bounds. Notably, we observe that the oversampling factors #samples\(/|I|\) appear to grow only logarithmically in \( |I| \) for the first algorithm and appear near-optimally bounded by four in the second algorithm.
Experimental allergic encephalomyelitis (EAE) is a model of central nervous system (CNS) inflammation that follows immunization with certain CNS antigens. The course and clinical manifestations of ...EAE are similar to those of multiple sclerosis (MS) in humans; therefore, EAE has become an accepted animal model to study MS. The purpose of this study was to demonstrate that systemic expression of murine interferon-beta (IFN-beta) (MuIFN-beta), following intramuscular (i.m.) delivery of plasmid DNA encoding MuIFN-beta to the hind limb of mice, is effective in reducing the clinical manifestations of disease in a model of EAE. The results of the study demonstrate that gene-based delivery of MuIFN-beta caused significantly decreased clinical scores compared with delivery of the null vector. A single injection of the MuIFN-beta plasmid was as effective in reducing the severity of the disease as an every other day injection of MuIFN-beta protein.
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