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BackgroundPast mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information. ObjectiveThe primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense’s Mobile Health Care Environment (MHCE) in a patient-centered medical home setting. MethodsA multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes. ResultsThe final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA1c) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA1c (control mean −0.53, intervention mean −0.11; P=.006), and low-density lipoprotein cholesterol (control mean −7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA1c (control mean −0.09, intervention mean −0.52; P=.04), BMI (control mean 0.58, intervention mean −1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean −4.86, intervention mean 3.56; P

BackgroundThe potential of mHealth technologies in the care of patients with diabetes and other chronic conditions has captured the attention of clinicians and researchers. Efforts to date have incorporated a variety of tools and techniques, including Web-based portals, short message service (SMS) text messaging, remote collection of biometric data, electronic coaching, electronic-based health education, secure email communication between visits, and electronic collection of lifestyle and quality-of-life surveys. Each of these tools, used alone or in combination, have demonstrated varying degrees of effectiveness. Some of the more promising results have been demonstrated using regular collection of biometric devices, SMS text messaging, secure email communication with clinical teams, and regular reporting of quality-of-life variables. In this study, we seek to incorporate several of the most promising mHealth capabilities in a patient-centered medical home (PCMH) workflow.ObjectiveWe aim to address underlying technology needs and gaps related to the use of mHealth technology and the activation of patients living with type 2 diabetes. Stated differently, we enable supporting technologies while seeking to influence patient activation and self-care activities.MethodsThis is a multisite phased study, conducted within the US Military Health System, that includes a user-centered design phase and a PCMH-based feasibility trial. In phase 1, we will assess both patient and provider preferences regarding the enhancement of the enabling technology capabilities for type 2 diabetes chronic care management. Phase 2 research will be a single-blinded 12-month feasibility study that incorporates randomization principles. Phase 2 research will seek to improve patient activation and self-care activities through the use of the Mobile Health Care Environment with tailored behavioral messaging. The primary outcome measure is the Patient Activation Measure scores. Secondary outcome measures are Summary of Diabetes Self-care Activities Measure scores, clinical measures, comorbid conditions, health services resource consumption, and technology system usage statistics.ResultsWe have completed phase 1 data collection. Formal analysis of phase 1 data has not been completed. We have obtained institutional review board approval and began phase 1 research in late fall 2016.ConclusionsThe study hypotheses suggest that patients can, and will, improve their activation in chronic care management. Improved activation should translate into improved diabetes self-care. Expected benefits of this research to the scientific community and health care services include improved understanding of how to leverage mHealth technology to activate patients living with type 2 diabetes in self-management behaviors. The research will shed light on implementation strategies in integrating mHealth into the clinical workflow of the PCMH setting.Trial registrationClinicalTrials.gov NCT02949037. https://clinicaltrials.gov/ct2/show/NCT02949037. (Archived by WebCite at http://www.webcitation.org/6oRyDzqei).

A multidisciplinary team developed and pilot-tested a curriculum, delivered by trained lay educators, to increase self-efficacy for physical activity among elderly food stamp recipients. Curriculum development was guided by a comprehensive literature review. Process evaluation was used to revise the curriculum and to assess lay educator training and pilot test fidelity. Three hundred sixteen low-income older adults participated in curriculum development and pilot-testing activities. One hundred twenty four participants completed pretest/posttest impact evaluation surveys. The average age of participants was 75 years old with 55.28% having less than high school education. Significant increases in self-efficacy regarding walking most days (p = 0.0022), using exercise bands (p less than 0.0001), being physically active to increase strength (p = 0.0012), being active without injury (p less than 0.0001), being able to wear appropriate clothing and shoes (p less than 0.0001), and getting information about community physical activity programs (p = 0.0007) were observed. Participants also endorsed higher ratings of general health at posttest (p less than 0.0001). (Contains 3 tables.)

Objective: Develop scales to assess the impact of the "Cooking with a Chef" program on several psychosocial constructs. Methods: Cross-sectional design in which parents and caregivers were recruited from child care settings (Head Start, faith-based, public elementary schools), and cooks were recruited from church and school kitchens. Analysis involved descriptive statistics, correlations, and exploratory factor analysis. Results: Test-retest reliability coefficients ranged from r = 0.63 to r = 0.88. Exploratory factor analysis indicated a simple structure for 3 scales: Cooking Techniques Self-Efficacy, Negative Cooking Attitude, and Self-Efficacy Fruit and Vegetables and accounted for 85.0% of the total variance. Mean responses to each scale were not statistically different between parents and cooks. Conclusions and Implications: Evidence of face validity and construct validity were provided through expert review, factor analysis, and scale correlations. Analyses presented constitute the first step in developing psychosocial scales for evaluating the "Cooking with a Chef" program and provide preliminary evidence that these scales work well with diverse groups. (Contains 2 tables.)

Minority and low socioeconomic status groups have the highest rates of overweight and obesity. Children from these families face a greater risk for weight-related health problems such as diabetes, heart disease and cancer as compared to their nonminority and more affluent peers. Factors from the larger physical, social, and cultural environments, and within families, influence weight status among children. Many of these factors are yet to be identified and relationships among known factors are complex and not well understood. The National Institutes of Health and U.S. Department of Agriculture have proposed a research agenda for social and behavioral scientists to "examine how social contexts influence health and elucidate the mechanisms through with those mechanisms operate", and to "develop ways to intervene on those factors for preventing obesity." This editorial presents a social ecological view of child obesity and proposes several steps necessary to identify how community and family domain factors affect the weight-regulating behaviors of children. An empiricallybased, theoretically-driven approach will lead to efficacious interventions which can be translated and widely disseminated into community-based programs, thus increasing the probability of success of population-based obesity prevention and control interventions. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a tool for assessing the quality of public recreation facilities' amenities. Knowing the location, distribution, and quality of recreation facilities can assist administrators in developing more effective programs for encouraging physical activity across city, county, and state systems. Based on a literature review, expert opinions, and professional standards, a tool was developed for assessing the safety, condition, and maintenance of recreation facilities. A Geographic Information System (GIS) database was developed to locate public recreation facilities in a southeastern county of the United States. Objective ratings of recreation facility quality were computed from the tool. Items on the tool were tested for inter-rater reliability among three independent raters. The findings demonstrated that overall the items in the tool were reliable and effective in evaluating objective physical features of the recreation facilities. Total Quality Management systems would benefit from the addition of an objective component to the evaluation of facility quality. The tool also provides a means of evaluating park quality over time, and can be modified to focus on users of particular ages, or for families versus individual users. Parks and recreation agencies have an important role to play in developing environmental supports for physical activity that may aid in reducing the national epidemic of obesity and related chronic diseases. [ABSTRACT FROM AUTHOR]

Background: The Stopping Elderly Accidents, Deaths and Injuries (STEADI) screening algorithm aligns with current fall prevention guidelines and is easy to administer within clinical practice. However, the stratification into low, moderate and high risk categories limits the meaningful interpretation of the fall-related risk factors., Methods: Baseline measures from a modified STEADI were used to predict self-reported falls over 4 years in 3170 respondents who participated in the 2011-2015 National Health and Aging Trends Study. A point method was then applied to find coefficient-based integers and 4-year fall risk estimates from the predictive model. Sensitivity and specificity estimates from the point method and the combined moderate and high fall risk STEADI categories were compared., Results: There were 886 (27.95%) and 387 (12.21%) respondents who were classified as moderate and high risk, respectively, when applying the stratification method. Falls in the past year (OR: 2.16; 95% CI: 1.61 to 2.89), multiple falls (OR: 2.94; 95% CI: 1.89 to 4.55) and a fear of falling (OR: 1.77; 95% CI: 1.45 to 2.16) were among the significant predictors of 4-year falls in older adults. The point method revealed integers that ranged from 0 (risk: 27.21%) to 44 (risk: 99.71%) and a score of 10 points had comparable discriminatory capacity to the combined moderate and high STEADI categories., Conclusion: Coefficient-based integers and their risk estimates can provide an alternative interpretation of a predictive model that may be useful in determining fall risk within a clinical setting, tracking changes longitudinally and defining the effectiveness of an intervention., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)