Measurements of energy intake and expenditure in people with dementia living in care homes: the use of wearable technology.

Authors: Murphy, J. and Holmes, J.

http://eprints.bournemouth.ac.uk/23232/

Start date: 18 June 2015

Journal: Journal of Nutrition, Health and Aging

ISSN: 1279-7707

Background Nutritional problems, especially unexplained weight loss is often observed in dementia leading to loss of muscle mass and strength, greater risk of falls, functional dependence and worsening quality of life. Weight loss differs with the various stages and types of dementia. The mechanisms potentially inducing weight loss include lower energy intake, higher energy expenditure, exaggerated physical activity or combinations of these factors. To prevent the consequences of weight loss and cognitive decline, there is a need to identify new strategies and approaches to monitor and better manage nutritional status and offer appropriate dietary measures. The purpose of this study was to assess the relationship between daily energy expenditure and patterns of activity with energy intake in people with dementia. Methods Twenty elderly residents were recruited into a cross sectional study from two care homes that specialised in dementia care. The mean age was 78.7 ± 11.8 years, 50% were women with confirmed diagnosis of a range of dementia types. We used an innovative light weight physical activity monitor (Sensewear TM Armband, Body Media, Pittsburgh, PA) to objectively determine total energy expenditure (TEE), sleep duration and physical activity. The device measures tri-axial acceleration, skin temperature, galvanic skin response and heat flux and has been shown to be valid in resting, exercise and free-living conditions in older people. The armband was placed around the left upper triceps for up to 7 days. During this period energy intake was determined by weighing all food and drink items over 5 days (4 weekdays and 1 weekend day) including measurements of food wastage. Results Body Mass Index (BMI) was 23.0 ± 4.2 (range 13.7-30.0) kg/m2. Mean energy intake was 7.4 ± 2.6 MJ/d with a 4 fold difference observed between residents (3.0-12.5 MJ/d). Energy intake was associated with body weight (r =0.50, p<0.05) and TEE (r=0.49, p<0.05). Duration of sleeping ranged from 0.4-12.5 (mean 5.9) hrs/d and time spent lying down was 1.0-16.0 (8.1) hrs/d. On average residents spent 17.4 (6.0-23.7) hrs/d undertaking sedentary activity. There was no relationship between time spent sleeping or lying down and energy intake. Conclusion The data suggests that physical activity may be stimulating energy intake to restore energy balance but the variable activity and sleep patterns observed may be contributing to lower energy intakes in some residents. Wearable technology has the potential to offer real-time monitoring to provide food and nutrition that is person-centred and prevent weight loss.

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