Clinical determinants of resting metabolic rate in geriatric outpatients

Purpose: Accurate estimation of the energy requirements including resting metabolic rate (RMR) is important for optimal nutritional care, yet its clinical determinants are unknown. This study examined the associations between clinical determinants of the Comprehensive Geriatric Assessment (CGA) domains with RMR among geriatric outpatients. Materials & methods: Data were retrieved from cohorts of community-dwelling older adults (n = 84, 54 female) referring to geriatrics outpatient mobility clinics in both Amsterdam, The Netherlands and Melbourne, Australia. Determinants within domains of the CGA included diseases (number, type and severity of diseases, polypharmacy), nutrition (body weight, body mass index, absolute and relative skeletal muscle mass, fat-free mass and fat mass, risk of malnutrition), physical function (handgrip strength, Short Physical Performance Battery, Timed Up & Go), cognition (Mini-Mental State Examination), psychological wellbeing (Geriatric Depression Scale) and blood pressure. RMR was objectively measured using indirect calorimetry with a canopy hood. Association between the clinical determinants with standardized RMR (country and sex-specific z-score) were analysed with linear regression adjusted for age, sex and body weight. Results: Determinants within the nutritional domain were associated with RMR; body weight showed the strongest association with RMR. Significant associations between determinants within the nutritional domain with RMR disappeared after further adjustment for body weight. None of the other domains were associated with RMR. Conclusions: Body weight is the strongest clinical determinant of RMR and should be taken into account when estimating RMR in geriatric care

Malnutrition is associated with dynamic physical performance

Background Malnutrition and poor physical performance are both conditions that increase in prevalence with age; however, their interrelation in a clinically relevant population has not been thoroughly studied. Aims This study aimed to determine the strength of the association between malnutrition and measures of both static and dynamic physical performance in a cohort of geriatric outpatients. Methods This cross-sectional study included 286 older adults (mean age 81.8, SD 7.2 years, and 40.6% male) who were referred to geriatric outpatient mobility clinics. The presence of malnutrition was determined using the Short Nutritional Assessment Questionnaire (SNAQ, cut-off ≥ 2 points). Measures of dynamic physical performance included timed up and go (TUG), 4-m walk test, and chair stand test (CST). Static performance encompassed balance tests and hand grip strength (HGS). Physical performance was standardized into sex-specific Z-scores. The association between malnutrition and each individual measure of physical performance was assessed using linear regression analysis. Results 19.9% of the cohort was identified as malnourished. Malnutrition was most strongly associated with CST and gait speed; less strong but significant associations were found between malnutrition and TUG. There was no significant association between malnutrition and HGS or balance. Discussion Physical performance was associated with malnutrition, specifically, dynamic rather than static measures. This may reflect muscle power being more impacted by nutritional status than muscle strength; however, this needs to be further addressed. Conclusions Malnutrition is associated with dynamic physical performance in geriatric outpatients, which should inform diagnosis and treatment/prevention strategies

Instrumented Assessment of Physical Activity Is Associated With Muscle Function but Not With Muscle Mass in a General Population

Objectives: Self-reported physical activity has shown to affect muscle-related parameters. As self-report is likely biased, this study aimed to assess the association between instrumented assessment of physical activity (I-PA) and muscle-related parameters in a general population. Method: Included were 156 young-to-middle-aged and 80 older community-dwelling adults. Seven days of trunk accelerometry (DynaPort MoveMonitor, McRoberts B.V.) quantified daily physical activity (i.e., active/inactive duration, number and mean duration of active/inactive periods, and number of steps per day). Muscle-related parameters included muscle mass, handgrip strength, and gait speed. Results: I-PA was associated with handgrip strength in young-to-middle-aged adults and with gait speed in older adults. I-PA was not associated with muscle mass in either age group. Discussion: The association between I-PA and muscle-related parameters was age dependent. The lack of an association between I-PA and muscle mass indicates the relevance of muscle function rather than muscle mass

Lack of Knowledge Contrasts the Willingness to Counteract Sarcopenia Among Community-Dwelling Adults

OBJECTIVE: Sarcopenia is highly prevalent in older adults. Knowledge among community-dwelling adults is important for effective prevention and treatment of sarcopenia. This study aims to assess current knowledge about sarcopenia, investigate willingness for treatment and prevention, and awareness of muscle health. METHOD: Participants who attended health educational events completed a questionnaire on knowledge about sarcopenia. Self-perceived muscle health was assessed by visual analog scale. Objective muscle measures included muscle mass, handgrip strength, and gait speed. RESULTS: Included participants were 197 (median aged 67.9 years [interquartile range = 57.0-75.1]). Eighteen participants (9%) reported to know what sarcopenia is. Participants' self-perceived muscle health showed a low correlation with all objective muscle measures. 76% were willing, in case of sarcopenia diagnosis, to start treatment and 71% were willing to prevent sarcopenia. DISCUSSION: Knowledge about sarcopenia is limited while participants were willing to start treatment and prevention. Strategies to increase knowledge among community-dwelling adults are needed

Gait speed assessed by a 4-m walk test is not representative of daily-life gait speed in community-dwelling adults

Objectives: Standardized tests of gait speed are regarded as being of clinical value, but they are typically performed under optimal conditions, and may not reflect daily-life gait behavior. The aim of this study was to compare 4-m gait speed to the distribution of daily-life gait speed. Study design: The cross-sectional Grey Power cohort included 254 community-dwelling participants aged 18 years or more. Main outcome measures: Pearson's correlations were used to compare gait speed assessed using a timed 4-m walk test at preferred pace, and daily-life gait speed obtained from tri-axial lower-back accelerometer data over seven consecutive days. Results: Participants (median age 66.7 years [IQR 59.4–72.5], 65.7% female) had a mean 4-m gait speed of 1.43 m/s (SD 0.21), and a mean 50th percentile of daily-life gait speed of 0.90 m/s (SD 0.23). Ninety-six percent had a bimodal distribution of daily-life gait speed, with a mean 1st peak of 0.61 m/s (SD 0.15) and 2nd peak of 1.26 m/s (SD 0.23). The percentile of the daily-life distribution that corresponded best with the individual 4-m gait speed had a median value of 91.2 (IQR 75.4–98.6). The 4-m gait speed was very weakly correlated to the 1st and 2nd peak (r = 0.005, p = 0.936 and r=0.181, p = 0.004), and the daily-life gait speed percentiles (range: 1st percentile r = 0.076, p = 0.230 to 99th percentile r = 0.399, p < 0.001; 50th percentile r = 0.132, p = 0.036). Conclusions: The 4-m gait speed is only weakly related to daily-life gait speed. Clinicians and researchers should consider that 4-m gait speed and daily-life gait speed represent two different constructs

Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model

Objective: Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM-UE) scale. However, this rule is criticized for overestimating the predictability of FM-UE recovery. Our objectives were to develop a longitudinal mixture model of FM-UE recovery, identify FM-UE recovery subgroups, and internally validate the model predictions. Methods: We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient rk, time constant in weeks τk, and distribution of the initial FM-UE scores. We fitted the model to FM-UE measurements of 412 first-ever ischemic stroke patients and cross-validated endpoint predictions and FM-UE recovery cluster assignment. Results: The model distinguished 5 subgroups with different recovery parameters (r1 = 0.09, τ1 = 5.3, r2 = 0.46, τ2 = 10.1, r3 = 0.86, τ3 = 9.8, r4 = 0.89, τ4 = 2.7, r5 = 0.93, τ5 = 1.2). Endpoint FM-UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3–12.8) at 1 week poststroke and 4.2 (IQR = 1.3–9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM-UE recovery clusters was 0.79 (95% equal-tailed interval [ETI] = 0.78–0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80–0.82) at 2 weeks. Interpretation: FM-UE recovery reflects different subgroups, each with its own recovery profile. Cross-validation indicates that FM-UE endpoints and FM-UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020

Validity of Nutritional Screening Tools for Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis

Objectives The aim of this systematic review was to summarize the validity of nutritional screening tools to detect the risk of malnutrition in community-dwelling older adults. Design A systematic review and meta-analysis. The protocol for this systematic review was registered in the PROSPERO database (CRD42017072703). Setting and participants A literature search was performed in PubMed, EMBASE, CINAHL, and Cochrane using the combined terms “malnutrition,” “aged,” “community-dwelling,” and “screening.” The time frame of the literature reviewed was from January 1, 2001, to May 18, 2018. Older community-dwellers were defined as follows: individuals with a mean/median age of >65 years who were community-dwellers or attended hospital outpatient clinics and day hospitals. All nutritional screening tools that were validated in community-dwelling older adults against a reference standard to detect the risk of malnutrition, or with malnutrition, were included. Measures Meta-analyses were performed on the diagnostic accuracy of identified nutritional screening tools validated against the Mini Nutritional Assessment-Long Form (MNA-LF). The symmetric hierarchical summary receiver operating characteristic models were used to estimate test performance. Results Of 7713 articles, 35 articles were included in the systematic review, and 9 articles were included in the meta-analysis. Seventeen nutritional screening tools and 10 reference standards were identified. The meta-analyses showed average sensitivities and specificities of 0.95 (95% confidence interval [CI] 0.75–0.99) and 0.95 (95% CI 0.85–0.99) for the Mini Nutritional Assessment-Short Form (MNA-SF; cutoff point ≤11), 0.85 (95% CI 0.80–0.89) and 0.87 (95% CI 0.86–0.89) for the MNA-SF-V1 (MNA-SF using body mass index, cutoff point ≤11), 0.85 (95% CI 0.77–0.89) and 0.84 (95% CI 0.79–0.87) for the MNA-SF-V2 (MNA-SF using calf circumference instead of body mass, cutoff point ≤11), respectively, using MNA-LF as the reference standard. Conclusions and Implications The MNA-SF, MNA-SF-V1, and MNA-SF-V2 showed good sensitivity and specificity to detect community-dwelling older adults at risk of malnutrition validated against the MNA-LF. Clinicians should consider the use of the cutoff point ≤11 on the MNA-SF, MNA-SF-V1, and MNA-SF-V2 to identify community-dwelling older adults at risk of malnutrition

Physical Activity and Nutrition INfluences In ageing (PANINI): consortium mission statement

First paragraph: Current demographic trends indicate that by the year 2020, almost one in five of the European population will be aged 65 years or over. Although life expectancy is increasing by 2 years per decade, the period of life spent in good health is not keeping pace and most Europeans spend their last decade in poor health. Consequently, there is an urgent need to understand how lifestyle factors can influence age-related changes from gene to society level and how they may be integrated into a net effect of healthy ageing. It is also crucial to develop and validate interventions and health policies to ensure that more of our older adults have a healthy and active later life. This is an urgent and cross-cutting research priority in Europe, and to achieve this, it is vital to increase research capacity in this area to push forward the frontiers of scientific understanding. The Horizon 2020 funded Marie Curie Sklodowska Innovative Training Network—PANINI is addressing this capacity issue by focusing on research and training in two major interacting lifestyle factors with impact at multiple levels, namely, physical activity and nutrition

The association of objectively measured physical activity and sedentary behavior with skeletal muscle strength and muscle power in older adults: a systematic review and meta-analysis

Background Engaging in physical activity (PA) and avoiding sedentary behavior (SB) are important for healthy ageing with benefits including the mitigation of disability and mortality. Whether benefits extend to key determinants of disability and mortality, namely muscle strength and muscle power, is unclear. Aims This systematic review aimed to describe the association of objective measures of PA and SB with measures of skeletal muscle strength and muscle power in community-dwelling older adults. Methods Six databases were searched from their inception to June 21st, 2020 for articles reporting associations between objectively measured PA and SB and upper body or lower body muscle strength or muscle power in community dwelling adults aged 60 years and older. An overview of associations was visualized by effect direction heat maps, standardized effect sizes were estimated with albatross plots and summarized in box plots. Articles reporting adjusted standardized regression coefficients (β) were included in meta-analyses. Results A total of 112 articles were included representing 43,796 individuals (range: 21 to 3726 per article) with a mean or median age from 61.0 to 88.0 years (mean 56.4 % female). Higher PA measures and lower SB were associated with better upper body muscle strength (hand grip strength), upper body muscle power (arm curl), lower body muscle strength, and lower body muscle power (chair stand test). Median standardized effect sizes were consistently larger for measures of PA and SB with lower compared to upper body muscle strength and muscle power. The meta-analyses of adjusted β coefficients confirmed the associations between total PA (TPA), moderate-to-vigorous PA (MVPA) and light PA (LPA) with hand grip strength (β = 0.041, β = 0.057, and β = 0.070, respectively, all p ≤ 0.001), and TPA and MVPA with chair stand test (β = 0.199 and β = 0.211, respectively, all p ≤ 0.001). Conclusions Higher PA and lower SB are associated with greater skeletal muscle strength and muscle power, particularly with the chair stand test

Computerised patient-specific prediction of the recovery profile of upper limb capacity within stroke services: The next step

Introduction: Predicting upper limb capacity recovery is important to set treatment goals, select therapies and plan discharge. We introduce a prediction model of the patient-specific profile of upper limb capacity recovery up to 6 months poststroke by incorporating all serially assessed clinical information from patients. Methods: Model input was recovery profile of 450 patients with a first-ever ischaemic hemispheric stroke measured using the Action Research Arm Test (ARAT). Subjects received at least three assessment sessions, starting within the first week until 6 months poststroke. We developed mixed-effects models that are able to deal with one or multiple measurements per subject, measured at non-fixed time points. The prediction accuracy of the different models was established by a fivefold cross-validation procedure. Results: A model with only ARAT time course, finger extension and shoulder abduction performed as good as models with more covariates. For the final model, cross-validation prediction errors at 6 months poststroke decreased as the number of measurements per subject increased, from a median error of 8.4 points on the ARAT (Q1-Q3:1.7-28.1) when one measurement early poststroke was used, to 2.3 (Q1-Q3:1-7.2) for seven measurements. An online version of the recovery model was developed that can be linked to data acquisition environments. Conclusio