BACKGROUND Nursing home (NH) residents who require assistance during mealtimes are at risk for malnutrition. Supportive handfeeding is recommended, yet there is limited evidence supporting use of a specific handfeeding technique to increase meal intake. OBJECTIVES To compare efficacy of three handfeeding techniques for assisting NH residents with dementia with meals: Direct Hand (DH), Over Hand (OH), and Under Hand (UH). DESIGN A prospective pilot study using a within-subjects experimental Latin square design with randomization to one of three handfeeding technique sequences. SETTING AND PARTICIPANTS 30 residents living with advanced dementia in 11 U.S. NHs. MEASUREMENTS Time required for assistance; meal intake (% eaten); and feeding behaviors, measured by the Edinburgh Feeding Evaluation in Dementia (EdFED) scale. INTERVENTION Research Assistants provided feeding assistance for 18 video-recorded meals per resident (N=540 meals). Residents were assisted with one designated technique for 6 consecutive meals, changing technique every two days. RESULTS Mean time spent providing meal assistance did not differ significantly between techniques. Mean meal intake was greater for DH (67±15.2%) and UH (65±15.0%) with both significantly greater than OH (60±15.1%). Feeding behaviors were more frequent with OH (8.3±1.8%), relative to DH (8.0±1.8) and UH (7.7±1.8). CONCLUSION All three techniques are time neutral. UH & DH are viable options to increase meal intake among NH residents with advanced dementia and reduce feeding behaviors relative to OH feeding.

Objective The purpose of this study was to examine the use of multiple mobile health technologies to generate and transmit data from diverse patients with type 2 diabetes mellitus (T2DM) in between clinic visits. We examined the data to identify patterns that describe characteristics of patients for clinical insights. Methods We enrolled 60 adults with T2DM from a US healthcare system to participate in a 6-month longitudinal feasibility trial. Patient weight, physical activity, and blood glucose were self-monitored via devices provided at baseline. Patients also responded to biweekly medication adherence text message surveys. Data were aggregated in near real-time. Measures of feasibility assessing total engagement in device submissions and survey completion over the 6 months of observation were calculated. Results It was feasible for participants from different socioeconomic, educational, and racial backgrounds to use and track relevant diabetes-related data from multiple mobile health devices for at least 6 months. Both the transmission and engagement of the data revealed notable patterns and varied by patient characteristics. Discussion Using multiple mobile health tools allowed us to derive clinical insights from diverse patients with diabetes. The ubiquitous adoption of smartphones across racial, educational, and socioeconomic populations and the integration of data from mobile health devices into electronic health records present an opportunity to develop new models of care delivery for patients with T2DM that may promote equity as well.

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Decibel levels were analyzed to identify changes in noise levels following alterations in the NICU environment. Installation of motion-sensing motorized paper towel holders significantly increased levels at beds closest to the towel dispensers, as did the trial of a new communication system. Decibel levels in four different incubators revealed varying noise levels. This study suggests that all environmental changes must be monitored to ensure that they reduce rather than increase noise levels.

This study identified facilitators and barriers pertaining to the use of multiple mobile health (mHealth) devices (Fitbit Alta® fitness tracker, iHealth® glucometer, BodyTrace® scale) that support self-management behaviors in individuals with type 2 diabetes mellitus (T2DM). Design: This qualitative descriptive study presents study participants' perceptions of using multiple mobile devices to support T2DM self-management. Additionally, this study assessed whether participants found visualizations, generated from each participant's health data as obtained from the three separate devices, useful and easy to interpret. Methods: Semistructured interviews were completed with a convenience sample of participants (n = 20) from a larger randomized control trial on T2DM self-management. Interview questions focused on participants' use of three devices to support T2DM self-management. A study team member created data visualizations of each interview participant's health data using RStudio. Results: We identified two themes from descriptions of study participants: feasibility and usability. We identified one theme about visualizations created from data obtained from the mobile devices. Despite some challenges, individuals with T2DM found it feasible to use multiple mobile devices to facilitate engagement in T2DM self-management behaviors. Discussion: As mHealth devices become increasingly popular for diabetes self-management and are integrated into care delivery, we must address issues associated with the use of multiple mHealth devices and the use of aggregate data to support T2DM self-management. Clinical Relevance: Real-time patient-generated health data that are easily accessible and readily available can assist T2DM self-management and