Partners Community Healthcare, Inc.
Study Design-A structured literature review. Summary of the Background Data-Widely recognized classification criteria for rheumatologic disorders have resulted in well-defined patient populations for clinical investigation.Objectives-We sought to determine whether similar criteria were needed for back pain disorders by examining variability in eligibility criteria in published studies Methods-Studies involving radiculopathy due to lumbar herniated disc (HD) and for neurogenic claudication due to lumbar spinal stenosis (LSS) were identified. Randomized controlled trials published between January 1, 2006 and October 1, 2008 in select peer reviewed journals were retrieved, their eligibility criteria were identified and categorized.Results-Twelve eligible HD studies were identified. Thirteen unique categories of eligibility criteria were identified with a mean of 3.9 (+/−2.0) and a range from 0 to 8 categories per study. More categories were present for studies that included nonsurgical (5.6 +/− 2.5) treatment for studies with only surgical treatment (2.6 +/− 1.7) p= 0.04). Seven LSS studies met eligibility criteria, and 9 unique categories were identified. A mean of 5.0 (+/−2.2) categories with a range from 2 to 7 was used per study.Conclusion-Wide variation in the number and type of eligibility criteria from randomized clinical trials of well defined back pain syndromes was identified. These results support the need for developing and disseminating international classification criteria for these clinical conditions.
BackgroundMobility limitations among older adults increase the risk for disability and healthcare utilization. Rehabilitative care is identified as the most efficacious treatment for maintaining physical function. However, there is insufficient evidence identifying a healthcare model that targets prevention of mobility decline among older adults. The objective of this study is to evaluate the preliminary effectiveness of a physical therapy program, augmented with mobile tele-health technology, on mobility function and healthcare utilization among older adults.MethodsThis is a quasi-experimental 12-month clinical trial conducted within a metropolitan-based healthcare system in the northeastern United States. It is in parallel with an existing longitudinal cohort study evaluating mobility decline among community-dwelling older adult primary care patients over one year. Seventy-five older adults (≥ 65–95 years) are being recruited using identical inclusion/exclusion criteria to the cohort study. Three aims will be evaluated: the effect of our program on 1) physical function, 2) healthcare utilization, and 3) healthcare costs. Changes in patient-reported function over 1 year in those receiving the intervention (aim 1) will be compared to propensity score matched controls (N = 150) from the cohort study. For aims 2 and 3, propensity scores, derived from logistic regression model that includes demographic and diagnostic information available through claims and enrollment information, will be used to match treatment and control patients in a ratio of 1:2 or 1:3 from a Medicare Claims Registry derived from the same geographic region. The intervention consists of a one-year physical therapy program that is divided between a combination of outpatient and home visits (6–10 total visits) and is augmented on a computerized tablet using of a commercially available application to deliver a progressive home-based exercise program emphasizing lower-extremity function and a walking program.DiscussionIncorporating mobile health into current healthcare models of rehabilitative care has the potential to decrease hospital visits and provide a longer duration of care. If the hypotheses are supported and demonstrate improved mobility and reduced healthcare utilization, this innovative care model would be applicable for optimizing the maintenance of functional independence among community-dwelling older adults.Trial registrationClinicalTrial.gov Identifier: NCT02580409 (Date of registration October 14, 2015).
IntroductionDue to the recognized importance of social psychological factors in health, a premium has been placed on the elucidation of preventive health theories like the Health Belief Model (HBM). The HBM is one of the most commonly used theories in health education and health promotion. According to the original HBM, health behavior is determined by personal beliefs and perceptions [1][2][3]. Health psychologists have shown that to respond to problems associated with a condition such as an illness people create their own beliefs regarding the condition [4][5][6]. These beliefs are the primary determinants of coping strategies, i.e., the behavioral actions to manage health threats [5,7]. Numerous studies have been conducted in the past highlighting the efficacy of HBM to identify and examine the factors related to health behavior [8][9][10][11]. However, very little information has been derived from appropriate interventions designed with the HBM. Techniques that can help the individual change perceptions and beliefs about health-related issues need to be devised and studied.ZYTO is a technology company that produces biocommunication software and equipment to facilitate decision making about wellness and human performance. The company has developed a software application called "Reframe Technology" that incorporates biocommunication to reframe perceptions. The foundation of Reframe Technology and biocommunication is similar to the principles used in the formulation of techniques such as biofeedback. Biofeedback uses the idea that by harnessing the power of the mind one can increase awareness of and self-monitor internal body processes [12,13]. Neurofeedback is a new variation of biofeedback. It also works on the same principle, i.e., once brain activities are shown to an individual, the person can self-regulate and bring appropriate changes in the patterns of the activities [14].The person using Reframe Technology speaks about any chosen topic, such as health, a relationship, or personal performance. As the person speaks, Reframe analyzes the voice for frequency patterns. Using a proprietary algorithm, these patterns generate an information field that is embedded into a fractal image. The information is not discernible by the conscious mind, but the person's body is sensitive to the data. This information field is activated for a 30-second period, while the person focuses thoughts on the selected topic. During that period, the fractal image containing the information field is visible to the person. Conscious access to the image is not significant to the process beyond helping the person stay focused on the topic. The person then talks again about the topic, Reframe analyzes the voice, and the information field is embedded and presented to the person AbstractBackground: The recognized importance of psychological and behavioral factors in human performance highlights the need for devising new techniques that positively affect perceptions to achieve better outcomes. New software called Reframe Technology imbedde...
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