A recurrent neural network architecture to model physical activity energy expenditure in older people

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

Sleep Quality and Physical Activity as Predictors of Mental Wellbeing Variance in Older Adults during COVID-19 Lockdown: ECLB COVID-19 International Online Survey

Background. The COVID-19 lockdown could engender disruption to lifestyle behaviors, thus impairing mental wellbeing in the general population. This study investigated whether sociodemographic variables, changes in physical activity, and sleep quality from pre- to during lockdown were predictors of change in mental wellbeing in quarantined older adults. Methods. A 12-week international online survey was launched in 14 languages on 6 April 2020. Forty-one research institutions from Europe, Western-Asia, North-Africa, and the Americas, promoted the survey. The survey was presented in a differential format with questions related to responses “pre” and “during” the lockdown period. Participants responded to the Short Warwick–Edinburgh Mental Wellbeing Scale, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and the short form of the International Physical Activity Questionnaire. Results. Replies from older adults (aged >55 years, n = 517), mainly from Europe (50.1%), Western-Asia (6.8%), America (30%), and North-Africa (9.3%) were analyzed. The COVID-19 lockdown led to significantly decreased mental wellbeing, sleep quality, and total physical activity energy expenditure levels (all p < 0.001). Regression analysis showed that the change in total PSQI score and total physical activity energy expenditure (F(2, 514) = 66.41 p < 0.001) were significant predictors of the decrease in mental wellbeing from pre- to during lockdown (p < 0.001, R2: 0.20). Conclusion. COVID-19 lockdown deleteriously affected physical activity and sleep patterns. Furthermore, change in the total PSQI score and total physical activity energy expenditure were significant predictors for the decrease in mental wellbeing.

Influence of Physical Activity Energy Expenditure on Functional Fitness Among Older Adults

Remaining physically active as one ages plays a critical role in maintaining health and improving functional capacity. Further, older adults can see additional health-related benefits by increasing intensity, duration, frequency, and/or levels of physical activity. However, there is limited research examining physical activity energy expenditure (PAEE) and measures of functional fitness. Therefore, the purpose was to examine differences between older adults with varying levels of PAEE on selected measures of functional fitness. A sample of 25 adults (age: 74.0±7.1 years) were recruited from an urban area and divided into two groups. PAEE was calculated using the total caloric expenditure per week for all exercise-related activities from a self-reported PA questionnaire. Group one expended less than 3,000 calories per week and group two spent more than 3,000 calories per week performing PA. The selected measures of functional fitness were a 4-m gait speed (GS), 30-s chair stand test (CS-30), 2-min step test (ST), and the 8-foot up and go test (GUG). Data were analyzed using a one-way ANOVA. There was a statistically significant difference between the groups on GS (F1, 24 = 9.29, p &lt; .01) and CS-30 (F1, 24 = 4.37, p = .05). The results yielded a trend for the GUG (p =.06). However, there was not a difference between the groups on the ST (p = .11). These results suggest older adults expending more than 3,000 calories per week performing PA walk faster and have greater lower-body strength.

Physical activity energy expenditure and fat-free mass: Relationship with metabolic syndrome in overweight or obese postmenopausal women

Menopause transition is associated with detrimental changes in physical activity, body composition and metabolic profile. Although physical activity energy expenditure (PAEE) is inversely associated with metabolic syndrome (MetS) in individuals at higher risk of CVD, the association is unknown in low-risk individuals. The aim of the study was to investigate the association between PAEE and MetS (prevalence and severity) in inactive overweight or obese postmenopausal women with a low Framingham Risk Score (FRS:< 10%). Cross-sectional data of 126 participants were divided into quartiles based on PAEE (Q1= lowest PAEE) while fat-free mass (FFM) and fat mass (FM) were measured by DXA. MetS prevalence was significantly different between Q1 and Q4 (37.9% vs 13.3%, p= 0.03). After controlling for potential confounders, MetS severity was negatively associated with PAEE (B= -0.057, p< 0.01) and positively with FFM (B= 0.038, p< 0.001). Moderation analyses indicated that a greater FFM exacerbated the association between PAEE and MetS severity in Q1 and Q2 (PAEE*FFM; B= -0.004; p= 0.1). Our results suggest that displaying a low FRS and lower PAEE increase MetS prevalence and severity. In addition, greater FFM interacts with lower PAEE to worsens MetS severity, while higher PAEE lessened this effect. Novelty - Inactive individuals displaying higher daily PAEE also have a lower MetS prevalence - Greater fat-free mass is associated with a worse MetS severity where a higher PAEE mitigates this deleterious effect in our cohort

Effect of plain versus sugar‐sweetened breakfast on energy balance and metabolic health: A secondary data analysis stratified by participant-defined sweet preference

Background: Variability in sweet preference between people is well established, with those who have a high preference colloquially identified as having a sweet tooth. Although characteristics have been described demonstrating key differences between those with and without a sweet tooth (such as differences in body mass index), it is less clear whether sweet preference moderates appetite and health responses to stimuli of different sweetness levels.Objective: To explore appetite and health responses to a sugar-sweetened (SWEET) and unsweetened (PLAIN) porridge-based breakfast, according to whether participants identified as having a sweet tooth or not. Methods: Secondary data analysis of a previously published randomised crossover trial in which n = 29 participants consumed an isocaloric PLAIN (~8 g sugar) and SWEET (~32 g sugar) porridge-based breakfast for three weeks each. Fasted pre- and post-intervention measures included blood biomarkers of health and appetite hormones, anthropometrics and metabolic rate, and a series of questionnaires assessing psychological appetite/approach to food. During each three week intervention, four days of lifestyle monitoring were conducted on days 1-4 and days 15-18, involving weighed food diaries, physical activity measurements (ActiHeart™), and visual analogue scales of appetite across the day. After study completion, participants were asked whether they believed they had a sweet tooth or not; n = 27 responded and were included in these analyses. Analyses were exploratory with no significance testing or a priori hypothesis. Results: 16 participants reported not having a sweet tooth. Average body mass index was higher in those without a sweet tooth (25.9 ± 6.0 kg/m2 versus 24.4 ± 4.3 kg/m2 for those with a sweet tooth), but waist-to-hip ratio was lower. Having a sweet tooth was associated with a higher desire for sweet across the day during both interventions compared to those without a sweet tooth, but also lower sugar intake. Sweet sensory-specific satiety was achieved in both groups post-breakfast during SWEET, whilst savoury sensory-specific satiety was achieved post-breakfast after both PLAIN and SWEET. Fasting plasma fibroblast growth factor 21 was higher in those with a sweet tooth, whilst fasting glucagon-like peptide-1 increased pre- to post-PLAIN (Δ 6.2, 95 % CI 1.1, 11.4 pmol∙L-1), but was otherwise similar across interventions and between groups. Those without a sweet tooth reduced their energy intake from week 1 to week 3 of PLAIN (Δ -152, 95 % CI -349, 45 kcal/d), whereas those with a sweet tooth (less reliably) increased their energy intake (Δ 131, 95 % CI -131, 395 kcal/d). No changes in energy intake were noted during SWEET. Physical activity energy expenditure largely remained consistent between groups and across interventions, though those without a sweet tooth increased their physical activity energy expenditure from week 1 to week 3 of SWEET (Δ 166, 95 % CI -8, 241 kcal/d).Conclusion: Having a sweet tooth was associated with distinct characteristics, such as lower body mass index and higher fasted plasma fibroblast growth factor 21 concentrations. Sweet preference may moderate biopsychological metabolic and appetitive responses to savoury or sweet primes. Due to the small sample size and other methodological features of these analyses, future work should establish the causality of these findings, and may need to consider sweet preference a priori when designing health and appetite research relating to sweetness.

Daily running exercise may induce incomplete energy intake compensation: a 7-day crossover trial

Understanding daily exercise effects on energy balance is important. This study examined the effects of 7 days of imposed exercise (EX) and no exercise (N-EX) on free-living energy intake (EI) and physical activity energy expenditure (PAEE) in 9 men. Free-living EI was higher in EX compared with N-EX. Total and vigorous PAEE were higher, with PAEE in sedentary activities lower, during EX compared with N-EX. Daily running (for 7 days) induced EI compensation of ∼60% exercise-induced EE. Novelty Daily running for 7 days induced incomplete EI compensation accounting for ∼60% of the exercise-induced EE.

Descriptive epidemiology of energy expenditure in the UK: findings from the National Diet and Nutrition Survey 2008–15

Background Little is known about population levels of energy expenditure, as national surveillance systems typically employ only crude measures. The National Diet and Nutrition Survey (NDNS) in the UK measured energy expenditure in a 10% subsample by gold-standard doubly labelled water (DLW). Methods DLW-subsample participants from the NDNS (383 males, 387 females) aged 4–91 years were recruited between 2008 and 2015 (rolling programme). Height and weight were measured and body-fat percentage estimated by deuterium dilution. Results Absolute total energy expenditure (TEE) increased steadily throughout childhood, ranging from 6.2 and 7.2 MJ/day in 4- to 7-year-olds to 9.7 and 11.7 MJ/day for 14- to 16-year-old girls and boys, respectively. TEE peaked in 17- to 27-year-old women (10.7 MJ/day) and 28- to 43-year-old men (14.4 MJ/day), before decreasing gradually in old age. Physical-activity energy expenditure (PAEE) declined steadily with age from childhood (87 kJ/day/kg in 4- to 7-year-olds) through to old age (38 kJ/day/kg in 71- to 91-year-olds). No differences were observed by time, region and macronutrient composition. Body-fat percentage was strongly inversely associated with PAEE throughout life, irrespective of expressing PAEE relative to body mass or fat-free mass. Compared with females with &lt;30% body fat, females with &gt;40% recorded 29 kJ/day/kg body mass and 18 kJ/day/kg fat-free mass less PAEE in analyses adjusted for age, geographical region and time of assessment. Similarly, compared with males with &lt;25% body fat, males with &gt;35% recorded 26 kJ/day/kg body mass and 10 kJ/day/kg fat-free mass less PAEE. Conclusions This first nationally representative study reports levels of human-energy expenditure as measured by gold-standard methodology; values may serve as a reference for other population studies. Age, sex and body composition are the main determinants of energy expenditure. Key Messages This is the first nationally representative study of human energy expenditure, covering the UK in the period 2008-2015. Total energy expenditure (MJ/day) increases steadily with age throughout childhood and adolescence, peaks in the 3rd decade of life in women and 4th decade of life in men, before decreasing gradually in old age. Physical activity energy expenditure (kJ/day/kg or kJ/day/kg fat-free mass) declines steadily with age from childhood to old age, more steeply so in males. Body-fat percentage is strongly inversely associated with physical activity energy expenditure. We found little evidence that energy expenditure varied by geographical region, over time, or by dietary macronutrient composition.