Embedded motivational interviewing combined with a smartphone app to increase physical activity in people with sub-acute low back pain: study protocol of a cluster randomised control trial

Background: Motivational Interviewing is an evidence-based, client-centred counselling technique that has been used effectively to increase physical activity, including for people with low back pain. One barrier to implementing Motivational Interviewing in health care settings more broadly is the extra treatment time with therapists. The aim of this paper is to describe the design of a cluster randomised controlled trial evaluating the effect of an intervention that pairs Motivational Interviewing embedded into usual physiotherapy care with a specifically designed app to increase physical activity in people with sub-acute low back pain. Methods: The study is a cluster randomised controlled in which patients aged over 18 years who have sub-acute low back pain (3–12 weeks duration) are recruited from four public hospital outpatient clinics. Based on the recruitment site, participants either receive usual physiotherapy care or the Motivational Interviewing intervention over 6 consecutive weekly outpatient sessions with a specifically designed app designed to facilitate participant-led physical activity behaviour change in between sessions. Outcome measures assessed at baseline and 7 weeks are: physical activity as measured by accelerometer (primary outcome), and pain-related activity restriction and pain self-efficacy (secondary outcomes). Postintervention interviews with physiotherapists and participants will be conducted as part of a process evaluation. Discussion: This intervention, which comprises trained physiotherapists conducting conversations about increasing physical activity with their patients in a manner consistent with Motivational Interviewing as part of usual care combined with a specifically designed app, has potential to facilitate behaviour change with minimal extra therapist time

Components of the indirect effect in vaccine trials: identification of contagion and infectiousness effects

Vaccination of one person may prevent the infection of another either because (i) the vaccine prevents the first from being infected and from infecting the second or because (ii) even if the first person is infected, the vaccine may render the infection less infectious. We might refer to the first of these mechanisms as a contagion effect and the second as an infectiousness effect. In this paper, for the simple setting of a randomized vaccine trial with households of size two, we use counterfactual theory under interference to provide formal definitions of a contagion effect and an infectiousness effect. Using ideas analogous to mediation analysis, we show that the indirect effect (the effect of one individual\u27s vaccine on another\u27s outcome) can be decomposed into a contagion effect and an infectiousness effect on the risk difference, risk ratio, odds ratio and vaccine efficacy scales. We provide identification assumptions for such contagion and infectiousness effects, and describe a simple statistical techniques to estimate these effects when they are identified. We also give a sensitivity analysis techniques to assess how inferences would change under violations of the identification assumptions. The concepts and results of this paper are illustrated with sample vaccine trial data

The role of 1,25-dihydroxyvitamin D in the inhibition of bone formation induced by skeletal unloading

Skeletal unloading results in osteopenia. To examine the involvement of vitamin D in this process, the rear limbs of growing rats were unloaded and alterations in bone calcium and bone histology were related to changes in serum calcium (Ca), inorganic phosphorus (P sub i), 25-hydroxyvitamin D (25-OH-D), 24,25-dihydroxyvitamin D (24,25(OH)2D and 1,25-dihydroxyvitamin D (1,25(OH)2D. Acute skeletal unloading induced a transitory inhibition of Ca accumulation in unloaded bones. This was accompanied by a transitory rise in serum Ca, a 21% decrease in longitudinal bone growth (P 0.01), a 32% decrease in bone surface lined with osteoblasts (P .05), no change in bone surface lined with osteoclasts and a decrease in circulating (1,25(OH)2D. No significant changes in the serum concentrations of P sub i, 25-OH-D or 24,25(OH)2D were observed. After 2 weeks of unloading, bone Ca stabilized at approximately 70% of control and serum Ca and 1,25(OH)2D returned to control values. Maintenance of a constant serum 1,25(OH)2D concentration by chronic infusion of 1,25(OH)2D (Alza osmotic minipump) throughout the study period did not prevent the bone changes induced by acute unloading. These results suggest that acute skeletal unloading in the growing rat produces a transitory inhibition of bone formation which in turn produces a transitory hypercalcemia

Exploring Agricultural Production Systems and Their Fundamental Components with System Dynamics Modelling

Agricultural production in the United States is undergoing marked changes due to rapid shifts in consumer demands, input costs, and concerns for food safety and environmental impact. Agricultural production systems are comprised of multidimensional components and drivers that interact in complex ways to influence production sustainability. In a mixed-methods approach, we combine qualitative and quantitative data to develop and simulate a system dynamics model that explores the systemic interaction of these drivers on the economic, environmental and social sustainability of agricultural production. We then use this model to evaluate the role of each driver in determining the differences in sustainability between three distinct production systems: crops only, livestock only, and an integrated crops and livestock system. The result from these modelling efforts found that the greatest potential for sustainability existed with the crops only production system. While this study presents a stand-alone contribution to sector knowledge and practice, it encourages future research in this sector that employs similar systems-based methods to enable more sustainable practices and policies within agricultural production

Spatial regression and spillover effects in cluster randomized trials with count outcomes.

This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modeled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. We use spatial regression models with Gaussian random effects, where the individual outcomes have distributions overdispersed with respect to the Poisson, and the corresponding direct and indirect effects have a marginal interpretation. To avoid spatial confounding, we use orthogonal regression, in which random effects represent spatial dependence using a homoscedastic and dimensionally reduced modification of the intrinsic conditional autoregression model. We illustrate the methodology using spatial data from a pair-matched cluster randomized trial against the dengue mosquito vector Aedes aegypti, done in Trujillo, Venezuela

A comparison of nursing and medical diagnoses in predicting hospital outcomes.

The main premise of the Nursing Minimum Data Set (NMDS) is that nursing data should be included in the hospital discharge abstract. Yet to date, little empirical evidence has been published to measure the efficacy or usefulness of these nursing data elements. We report the results of a comparison between a daily collection of nursing assessments using nursing diagnoses (NDX) to the Diagnostic Related Group (DRG) and the All Payer Refined DRG (APR-DRG) in their ability to predict three common outcome variables: hospital days, ICU day, and total charges. A secondary data analysis was performed from a large existing data set off our years patient data from a Midwest University hospital. Findings: NDX is significantly associated with hospital length of stay, ICU length of stay, and total charges. NDX also improves explanatory power when added to models with DRG or APR-DRG. This suggests that nursing data compliments existing data and is not redundant with the DRG or APR-DRG. The findings also suggest that NDX explains a different portion of the variance of the three outcome variables in this series. The results of this study support the argument that nursing data should be included in the hospital discharge abstract