Visual Modeling: A Socialization-Based Intervention to Improve Nutritional Intake Among Nursing Home Residents

Abstract

Purpose Malnutrition is a widespread, dangerous, and costly condition among institutionalized older adults and can be both a contributor to and consequence of dysphagia for individuals with cognitive impairment. However, interventions to maximize intake in individuals with dementia are limited and frequently problematic, with negative implications for independence and quality of life. The goal of this study was to examine a novel, socialization-grounded intervention based on visual modeling, utilizing the theoretical underpinnings of motor resonance and mimicry. Method To examine the impact of environment on intake, data were collected from four nursing home residents ( M age = 83.5 years, SD = 4.2; three women) with dementia. Weight of food and liquid intake was measured across 15 meals and three different mealtime conditions: the “baseline condition” in which the individual ate alone, the “watch condition” in which the individual ate in the company of a “mealtime buddy,” and the “eat” condition in which the individual consumed a meal while the “mealtime buddy” did the same. Results Data visualization supported a weak functional relation between eating environment and amount of intake consumed across participants. Log response ratio estimates suggested a trend for increased weight of food consumed during the eat condition as compared to baseline and the eat condition as compared to the watch condition for some participants. Conclusions These results preliminarily support the benefit of a visual model for increased consumption in some individuals with dementia. The presence and magnitude of the effect across conditions varied based on individual-level factors, such as cognitive status, which has implications for implementation. Overall, this study provides initial proof of concept regarding the use of visual modeling as an intervention approach, laying the foundation for larger scale future studies.