Impact of Provider Self-Management Education, Patient Self-Efficacy, and Health Status on Patient Adherence in Heart Failure in a Veterans Administration Population

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73786/1/j.1751-7133.2008.07174.x.pd

Impact of vital signs screening & clinician prompting on alcohol and tobacco screening and intervention rates: a pre-post intervention comparison

<p>Abstract</p> <p>Background</p> <p>Though screening and intervention for alcohol and tobacco misuse are effective, primary care screening and intervention rates remain low. Previous studies have increased intervention rates using vital signs screening for tobacco misuse and clinician prompts for screen-positive patients for both alcohol and tobacco misuse. This pilot study's aims were: (1) To determine the feasibility of combined vital signs screening for tobacco and alcohol misuse, (2) To assess the impact of vital signs screening on alcohol and tobacco screening and intervention rates, and (3) To assess the additional impact of tobacco assessment prompts on intervention rates.</p> <p>Methods</p> <p>In five outpatient practices, nurses measuring vital signs were trained to routinely ask a single tobacco question, a prescreening question that identified current drinkers, and the single alcohol screening question for current drinkers. After 4-8 weeks, clinicians were trained in tobacco intervention and nurses were trained to give tobacco abusers a tobacco questionnaire which also served as a clinician intervention prompt. Screening and intervention rates were measured using patient exit interviews (n = 622) at baseline, during the "screening only" period, and during the tobacco prompting phase. Changes in screening and intervention rates were compared using chi square analyses and test of linear trends. Clinic staff were interviewed regarding patient and staff acceptability. Logistic regression was used to evaluate the impact of nurse screening on clinician intervention, the impact of alcohol intervention on concurrent tobacco intervention, and the impact of tobacco intervention on concurrent alcohol intervention.</p> <p>Results</p> <p>Alcohol and tobacco screening rates and alcohol intervention rates increased after implementing vital signs screening (p < .05). During the tobacco prompting phase, clinician intervention rates increased significantly for both alcohol (12.4%, p < .001) and tobacco (47.4%, p = .042). Screening by nurses was associated with clinician advice to reduce alcohol use (OR 13.1; 95% CI 6.2-27.6) and tobacco use (OR 2.6; 95% CI 1.3-5.2). Acceptability was high with nurses and patients.</p> <p>Conclusions</p> <p>Vital signs screening can be incorporated in primary care and increases alcohol screening and intervention rates. Tobacco assessment prompts increase both alcohol and tobacco interventions. These simple interventions show promise for dissemination in primary care settings.</p

Correction to: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium (<em>Journal of NeuroEngineering and Rehabilitation</em>, (2023), 20, 1, (78), 10.1186/s12984-023-01198-5)

\ua9 The Author(s) 2024.Following publication of the original article [1], the author noticed the errors in Table 1, and in Discussion section. In Table 1 under Metric (Gait sequence detection) column, the algorithms GSDB was updated with wrong description, input, output, language and citation and GSDc with wrong description has been corrected as shown below: (Table presented.) Description of algorithms for each metric: gait sequence detection (GSD), initial contact event detection (ICD), cadence estimation (CAD) and stride length estimation (SL) Metric Name Description Input Output Language References GSDA Based on a frequency-based approach, this algorithm is implemented on the vertical and anterior–posterior acceleration signals. First, these are band pass filtered to keep frequencies between 0.5 and 3 Hz. Next, a convolution of a 2 Hz sinewave (representing a template for a gait cycle) is performed, from which local maxima will be detected to define the regions of gait acc_v: vertical acceleration acc_ap: anterior–posterior acceleration WinS = 3 s; window size for convolution OL = 1.5 s; overlap of windows Activity_thresh = 0.01; Motion threshold Fs: sampling frequency Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Iluz, Gazit [40] GSDB This algorithm, based on a time domain-approach, detects the gait periods based on identified steps. First, the norm of triaxial acceleration signal is low-pass filtered (FIR, fc = 3.2 Hz), then a peak detection procedure using a threshold of 0.1 [g] is applied to identify steps. Consecutive steps, detected using an adaptive step duration threshold are associated to gait sequences acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.1 (g) Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Paraschiv-Ionescu, Newman [41] GSDc This algorithm utilizes the same approach as GSDBthe only difference being a different threshold for peak detection of 0.15 [g] acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.15 (g) Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Paraschiv-Ionescu, Newman [41] In Discussion section, the paragraph should read as "Based on our findings collectively, we recommend using GSDB on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings. Moreover, the use of adaptive threshold, that are derived from the features of a subject’s data and applied to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns" instead of “Based on our findings collectively, we recommend using GSDB on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings [41]. Moreover, the use of adaptive thresholds, that are derived from the features of a subject’s data and applied to the amplitude of acceleration norm and to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns”

Design and methods for a randomized clinical trial comparing three outreach efforts to improve screening mammography adherence

<p>Abstract</p> <p>Background</p> <p>Despite the demonstrated need to increase screening mammography utilization and strong evidence that mail and telephone outreach to women can increase screening, most managed care organizations have not adopted comprehensive outreach programs. The uncertainty about optimum strategies and cost effectiveness have retarded widespread acceptance. While 70% of women report getting a mammogram within the prior 2 years, repeat mammography rates are less than 50%. This 5-year study is conducted though a Central Massachusetts healthcare plan and affiliated clinic. All womenhave adequate health insurance to cover the test.</p> <p>Methods/Design</p> <p>This randomized study compares 3 arms: reminder letter alone; reminder letter plus reminder call; reminder letter plus a second reminder and booklet plus a counselor call. All calls provide women with the opportunity to schedule a mammogram in a reasonable time. The invention period will span 4 years and include repeat attempts. The counselor arm is designed to educate, motivate and counsel women in an effort to alleviate PCP burden.</p> <p>All women who have been in the healthcare plan for 24 months and who have a current primary care provider (PCP) and who are aged 51-84 are randomized to 1 of 3 arms. Interventions are limited to women who become ≥18 months from a prior mammogram. Women and their physicians may opt out of the intervention study.</p> <p>Measurement of completed mammograms will use plan billing records and clinic electronic records. The primary outcome is the proportion of women continuously enrolled for ≥24 months who have had ≥1 mammogram in the last 24 months. Secondary outcomes include the number of women who need repeat interventions. The cost effectiveness analysis will measure all costs from the provider perspective.</p> <p>Discussion</p> <p>So far, 18,509 women aged 51-84 have been enrolled into our tracking database and were randomized into one of three arms. At baseline, 5,223 women were eligible for an intervention. We anticipate that the outcome will provide firm data about the maximal effectiveness as well as the cost effectiveness of the interventions both for increasing the mammography rate and the repeat mammography rate.</p> <p>Trial registration</p> <p><url>http://clinicaltrials.gov/</url><a href="http://www.clinicaltrials.gov/ct2/show/NCT01332032">NCT01332032</a></p

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement–the Mobilise-D study protocol

Background The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility