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

Technical validation of real-world monitoring of gait : a multicentric observational study

Introduction: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users’ perspective on the device. Methods and analysis: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs. After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users’ perspective on the deployed technology and relevance of the mobility assessment. Ethics and dissemination: The study has been granted ethics approval by the centre’s committees (London—Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. Trial registration number ISRCTN (12246987)