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Effect of a Wii Fit® intervention on balance, muscular fitness, and bone health in middle-aged women
This work aims to understand how the community layer, represented by the food environment, moderates the association of two other layers and dietary behaviors: the interpersonal layer, represented by receiving health care provider’s (HCP) advice to lose weight, and the policy layer, represented by participation in the Supplemental Nutrition Assistance Program (SNAP), and a policy change within the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).
Participant data were obtained from a household telephone survey of 2,211 adults in four cities in New Jersey from two cross-sectional panels in 2009-10 and 2014. Community food data were purchased and classified according to previously established protocol. Interaction and stratified analyses determined the differences in the association between HCP advice, SNAP participation, and time (for WIC participants) and eating behaviors by the food environment.
Interaction and stratified analyses revealed that HCP advice was associated with a decrease in SSB consumption when participants lived near a small grocery store, or far from a supermarket, limited service restaurant (LSR), or convenience store. SNAP participation was associated with a higher SSB consumption when respondents lived close to a small grocery store, supermarket, and LSR. There were no differences in fruit and vegetable consumption between two time points among WIC participants, or by food outlet.
The food environment, part of the community layer of SEM, moderated the relationship between the interpersonal layer and dietary behaviors and the policy layer and dietary behaviors. The association between HCP advice and dietary behaviors and SNAP participation and dietary behaviors were both influenced by the food environment in which participants lived.
Methods: Study participants (n=1469) were elementary and middle school students who ate school lunch on the day of data collection. Photographs and weights (to nearest 2 g) were taken of fruits and vegetables on students’ trays before and after lunch. Trained research assistants viewed photographs and sorted trays into variable categories: color of main tray, presence/absence of secondary fruit/vegetable container, and color of secondary fruit/vegetable container. Fruit and vegetable selection, consumption, and waste were calculated using tray weights. Negative binomial regression models adjusted for gender, grade level, race/ethnicity, free/reduced price lunch status, and within-school similarities were used to examine relationships between tray color and fruit and vegetable selection, consumption, and waste.
Results: Findings indicated that students with a light tray selected (IRR= 0.44), consumed (IRR=0.73) and wasted (IRR=0.81) less fruit and vegetables. Students without a secondary fruit/vegetable container selected (IRR=0.66) and consumed (IRR=0.49) less fruit and vegetables compared to those with a secondary container. Light or clear secondary fruit and vegetable containers were related to increased selection (IRR=2.06 light, 2.30 clear) and consumption (IRR=1.95 light, 2.78 clear) compared to dark secondary containers, while light secondary containers were related to decreased waste (IRR= 0.57).
Conclusion: Tray color may influence fruit and vegetable selection, consumption, and waste among students eating school lunch. Further research is needed to determine if there is a cause and effect relationship. If so, adjusting container colors may be a practical intervention for schools hoping to increase fruit and vegetable intake among students.
Purpose: The purpose of this study was to understand how implementing EIM influenced provider behaviors in a university-based healthcare system, using a process evaluation.
Methods: A multiple baseline, time series design was used. Providers were allocated to three groups. Group 1 (n=11) was exposed to an electronic medical record (EMR) systems change, EIM-related resources, and EIM training session. Group 2 (n=5) received the EMR change and resources but no training. Group 3 (n=6) was only exposed to the systems change. The study was conducted across three phases. Outcomes included asking about patient physical activity (PA) as a vital sign (PAVS), prescribing PA (ExRx), and providing PA resources or referrals. Patient surveys and EMR data were examined. Time series analysis, chi-square, and logistic regression were used.
Results: Patient survey data revealed the systems change increased patient reports of being asked about PA, χ2(4) = 95.47, p < .001 for all groups. There was a significant effect of training and resource dissemination on patients receiving PA advice, χ2(4) = 36.25, p < .001. Patients receiving PA advice was greater during phase 2 (OR = 4.7, 95% CI = 2.0-11.0) and phase 3 (OR = 2.9, 95% CI = 1.2-7.4). Increases were also observed in EMR data for PAVS, χ2(2) = 29.27, p <. 001 during implementation for all groups. Increases in PA advice χ2(2) = 140.90, p < .001 occurred among trained providers only. No statistically significant change was observed for ExRx, PA resources or PA referrals. However, visual analysis showed an upwards trend among trained providers.
Conclusions: An EMR systems change is effective for increasing the collection of the PAVS. Training and resources may influence provider behavior but training alone increased provider documentation. The low levels of documented outcomes for PA advice, ExRx, resources, or referrals may be due to the limitations of the EMR system. This approach was effective for examining the EIM Solution and scaled-up, longer trials may yield more robust results.
The unicellular microalga Haematococcus pluvialis has emerged as a promising biomass feedstock for the ketocarotenoid astaxanthin and neutral lipid triacylglycerol. Motile flagellates, resting palmella cells, and cysts are the major life cycle stages of H. pluvialis. Fast-growing motile cells are usually used to induce astaxanthin and triacylglycerol biosynthesis under stress conditions (high light or nutrient starvation); however, productivity of biomass and bioproducts are compromised due to the susceptibility of motile cells to stress. This study revealed that the Photosystem II (PSII) reaction center D1 protein, the manganese-stabilizing protein PsbO, and several major membrane glycerolipids (particularly for chloroplast membrane lipids monogalactosyldiacylglycerol and phosphatidylglycerol), decreased dramatically in motile cells under high light (HL). In contrast, palmella cells, which are transformed from motile cells after an extended period of time under favorable growth conditions, have developed multiple protective mechanisms - including reduction in chloroplast membrane lipids content, downplay of linear photosynthetic electron transport, and activating nonphotochemical quenching mechanisms - while accumulating triacylglycerol. Consequently, the membrane lipids and PSII proteins (D1 and PsbO) remained relatively stable in palmella cells subjected to HL. Introducing palmella instead of motile cells to stress conditions may greatly increase astaxanthin and lipid production in H. pluvialis culture.
Background: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.
Objective: To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.
Methods: Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9±9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.
Results: A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001) and a group by study phase interaction was observed (p = .017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74).
Conclusions: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a “behavior change technology” that could be incorporated into mHealth technologies and scaled to reach large populations.