Can activity monitors predict outcomes in patients with heart failure? A systematic review.

Actigraphy is increasingly incorporated into clinical practice to monitor intervention effectiveness and patient health in congestive heart failure (CHF). We explored the prognostic impact of actigraphy-quantified physical activity (AQPA) on CHF outcomes. PubMed and Medline databases were systematically searched for cross-sectional studies, cohort studies or randomised controlled trials from January 2007 to December 2017. We included studies that used validated actigraphs to predict outcomes in adult HF patients. Study selection and data extraction were performed by two independent reviewers. A total of 17 studies (15 cohort, 1 cross-sectional, 1 randomised controlled trial) were included, reporting on 2,759 CHF patients (22-89 years, 27.7% female). Overall, AQPA showed a strong inverse relationship with mortality and predictive utility when combined with established risk scores, and prognostic roles in morbidity, predicting cognitive function, New York Heart Association functional class and intercurrent events (e.g. hospitalisation), but weak relationships with health-related quality of life scores. Studies lacked consensus regarding device choice, time points and thresholds of PA measurement, which rendered quantitative comparisons between studies difficult. AQPA has a strong prognostic role in CHF. Multiple sampling time points would allow calculation of AQPA changes for incorporation into risk models. Consensus is needed regarding device choice and AQPA thresholds, while data management strategies are required to fully utilise generated data. Big data and machine learning strategies will potentially yield better predictive value of AQPA in CHF patients.

Reallocating sitting time to standing or stepping through isotemporal analysis: associations with markers of chronic low-grade inflammation.

Although high levels of sitting time are adversely related to health, it is unclear whether moving from sitting to standing provides a sufficient stimulus to elicit benefits upon markers of chronic low-grade inflammation in a population at high risk of type 2 diabetes (T2DM). Three hundred and seventy two participants (age = 66.8 ± 7.5years; body mass index (BMI) = 31.7 ± 5.5kg/m2; Male = 61%) were included. Sitting, standing and stepping was determined using the activPAL3TM device. Linear regression modelling employing an isotemporal substitution approach was used to quantify the association of theoretically substituting 60 minutes of sitting per day for standing or stepping on interleukin-6 (IL-6), C-reactive protein (CRP) and leptin. Reallocating 60 minutes of sitting time per day for standing was associated with a -4% (95% CI -7%, -1%) reduction in IL-6 (p = 0.048). Reallocating 60 minutes of sitting time for light stepping was also associated with lower IL-6 levels (-28% (-46%, -4%; p = 0.025)). Substituting sitting for moderate-to-vigorous (MVPA) stepping was associated with lower CRP (-41% (-75%, -8%; p = 0.032)), leptin (-24% (-34%, -12%; p ≤ 0.001)) and IL-6 (-16% (-28%, 10%; p = 0.036). Theoretically replacing 60 minutes of sitting per day with an equal amount of either standing or stepping yields beneficial associations upon markers of chronic-low grade inflammation.

Associations Between Sedentary Behaviors and Cognitive Function: Cross-Sectional and Prospective Findings From the UK Biobank.

We investigated the cross-sectional and prospective associations between different sedentary behaviors and cognitive function in a large sample of adults with data stored in the UK Biobank. Baseline data were available for 502,643 participants (2006-2010, United Kingdom). Cognitive tests included prospective memory (baseline only: n = 171,585), visual-spatial memory (round 1: n = 483,832; round 2: n = 482,762), fluid intelligence (n = 165,492), and short-term numeric memory (n = 50,370). After a mean period of 5.3 years, participants (numbering from 12,091 to 114,373, depending on the test) also provided follow-up cognitive data. Sedentary behaviors (television viewing, driving, and nonoccupational computer-use time) were measured at baseline. At baseline, both television viewing and driving time were inversely associated with cognitive function across all outcomes (e.g., for each additional hour spent watching television, the total number of correct answers in the fluid intelligence test was 0.15 (99% confidence interval: 0.14, 0.16) lower. Computer-use time was positively associated with cognitive function across all outcomes. Both television viewing and driving time at baseline were positively associated with the odds of having cognitive decline at follow-up across most outcomes. Conversely, computer-use time at baseline was inversely associated with the odds of having cognitive decline at follow-up across most outcomes. This study supports health policies designed to reduce television viewing and driving in adults.

Association of walking pace and handgrip strength with all-cause, cardiovascular, and cancer mortality: a UK Biobank observational study.

AIMS: To quantify the association of self-reported walking pace and handgrip strength with all-cause, cardiovascular, and cancer mortality. METHODS AND RESULTS: A total of 230 670 women and 190 057 men free from prevalent cancer and cardiovascular disease were included from UK Biobank. Usual walking pace was self-defined as slow, steady/average or brisk. Handgrip strength was assessed by dynamometer. Cox-proportional hazard models were adjusted for social deprivation, ethnicity, employment, medications, alcohol use, diet, physical activity, and television viewing time. Interaction terms investigated whether age, body mass index (BMI), and smoking status modified associations. Over 6.3 years, there were 8598 deaths, 1654 from cardiovascular disease and 4850 from cancer. Associations of walking pace with mortality were modified by BMI. In women, the hazard ratio (HR) for all-cause mortality in slow compared with fast walkers were 2.16 [95% confidence interval (CI): 1.68-2.77] and 1.31 (1.08-1.60) in the bottom and top BMI tertiles, respectively; corresponding HRs for men were 2.01 (1.68-2.41) and 1.41 (1.20-1.66). Hazard ratios for cardiovascular mortality remained above 1.7 across all categories of BMI in men and women, with modest heterogeneity in men. Handgrip strength was associated with cardiovascular mortality in men only (HR tertile 1 vs. tertile 3 = 1.38; 1.18-1.62), without differences across BMI categories, while associations with all-cause mortality were only seen in men with low BMI. Associations for walking pace and handgrip strength with cancer mortality were less consistent. CONCLUSION: A simple self-reported measure of slow walking pace could aid risk stratification for all-cause and cardiovascular mortality within the general population.

Associations of moderate-to-vigorous-intensity physical activity and body mass index with glycated haemoglobin within the general population: a cross-sectional analysis of the 2008 Health Survey for England.

OBJECTIVES: To investigate the associations of objectively measured moderate-to-vigorous-intensity physical activity (MVPA) and body mass index (BMI) with glycated haemoglobin (HbA1c) in a national sample of English adults. METHODS: The 2008 Health Survey for England data were used with 1109 participants aged ≥18 providing complete data. MVPA time was assessed using an accelerometer. Weighted linear regression models, adjusted for several confounders, quantified the associations between continuous measures of MVPA and BMI with HbA1c. Interaction analyses were implemented to observe whether the association of MVPA with HbA1c was modified by BMI or vice versa. Further weighted linear regression models examined the differences in HbA1c across four mutually exclusive categories of MVPA and BMI: (1) 'physically active and non-obese', (2) 'physically active and obese', (3) 'physically inactive and non-obese' and (4) 'physically inactive and obese'. 'Physically active' was defined as: ≥150 min/week of MVPA. 'Obese' was defined as: BMI ≥30.0 kg/m2. A wide range of sensitivity analyses were also implemented. RESULTS: Every 30 min/day increment in MVPA was associated with a 0.7 mmol/mol (0.07% (p<0.001)) lower HbA1c level. Each 1 kg/m2 increment in BMI was associated with a 0.2 mmol/mol (0.02% (p<0.001)) higher HbA1c level. The association of MVPA with HbA1c was stronger in obese individuals (-1.5 mmol/mol (-0.13% (p<0.001))) than non-obese individuals (-0.7 mmol/mol (-0.06% (p<0.001))); p=0.004 for interaction. The association of BMI with HbA1c remained stable across MVPA categories. Compared with individuals categorised as 'physically inactive and obese', only those categorised as 'physically active and obese' or 'physically active and non-obese' had lower HbA1c levels by 2.1 mmol/mol (0.19% (p=0.005)) and 3.5 mmol/mol (0.32% (p<0.001)), respectively. Sensitivity analyses indicated robustness and stability. CONCLUSIONS: This study emphasises the importance of physical activity as a determinant of HbA1c, and suggests that the associations may be stronger in obese adults.

Associations of objectively measured moderate-to-vigorous-intensity physical activity and sedentary time with all-cause mortality in a population of adults at high risk of type 2 diabetes mellitus.

The relationships of physical activity and sedentary time with all-cause mortality in those at high risk of type 2 diabetes mellitus (T2DM) are unexplored. To address this gap in knowledge, we examined the associations of objectively measured moderate-to-vigorous-intensity physical activity (MVPA) and sedentary time with all-cause mortality in a population of adults at high risk of T2DM. In 2010-2011, 712 adults (Leicestershire, U.K.), identified as being at high risk of T2DM, consented to be followed up for mortality. MVPA and sedentary time were assessed by accelerometer; those with valid data (≥ 10 hours of wear-time/day with ≥ 4 days of data) were included. Cox proportional hazards regression models, adjusted for potential confounders, were used to investigate the independent associations of MVPA and sedentary time with all-cause mortality. 683 participants (250 females (36.6%)) were included and during a mean follow-up period of 5.7 years, 26 deaths were registered. Every 10% increase in MVPA time/day was associated with a 5% lower risk of all-cause mortality [Hazard Ratio (HR): 0.95 (95% Confidence Interval (95% CI): 0.91, 0.98); p = 0.004]; indicating that for the average adult in this cohort undertaking approximately 27.5 minutes of MVPA/day, this benefit would be associated with only 2.75 additional minutes of MVPA/day. Conversely, sedentary time showed no association with all-cause mortality [HR (every 10-minute increase in sedentary time/day): 0.99 (95% CI: 0.95, 1.03); p = 0.589]. These data support the importance of MVPA in adults at high risk of T2DM. The association between sedentary time and mortality in this population needs further investigation.

Associations of reallocating sitting time into standing or stepping with glucose, insulin and insulin sensitivity: a cross-sectional analysis of adults at risk of type 2 diabetes.

OBJECTIVE: To quantify associations between sitting time and glucose, insulin and insulin sensitivity by considering reallocation of time into standing or stepping. DESIGN: Cross-sectional. SETTING: Leicestershire, UK, 2013. PARTICIPANTS: Adults aged 30-75 years at high risk of impaired glucose regulation (IGR) or type 2 diabetes. 435 adults (age 66.8±7.4 years; 61.7% male; 89.2% white European) were included. METHODS: Participants wore an activPAL3 monitor 24 hours/day for 7 days to capture time spent sitting, standing and stepping. Fasting and 2-hour postchallenge glucose and insulin were assessed; insulin sensitivity was calculated by Homeostasis Model Assessment of Insulin Secretion (HOMA-IS) and Matsuda-Insulin Sensitivity Index (Matsuda-ISI). Isotemporal substitution regression modelling was used to quantify associations of substituting 30 min of waking sitting time (accumulated in prolonged (≥30 min) or short (<30 min) bouts) for standing or stepping on glucose regulation and insulin sensitivity. Interaction terms were fitted to assess whether the associations with measures of glucose regulation and insulin sensitivity was modified by sex or IGR status. RESULTS: After adjustment for confounders, including waist circumference, reallocation of prolonged sitting to short sitting time and to standing was associated with 4% lower fasting insulin and 4% higher HOMA-IS; reallocation of prolonged sitting to standing was also associated with a 5% higher Matsuda-ISI. Reallocation to stepping was associated with 5% lower 2-hour glucose, 7% lower fasting insulin, 13% lower 2-hour insulin and a 9% and 16% higher HOMA-IS and Matsuda-ISI, respectively. Reallocation of short sitting time to stepping was associated with 5% and 10% lower 2-hour glucose and 2-hour insulin and 12% higher Matsuda-ISI. Results were not modified by IGR status or sex. CONCLUSIONS: Reallocating a small amount of short or prolonged sitting time with standing or stepping may improve 2-hour glucose, fasting and 2-hour insulin and insulin sensitivity. Findings should be confirmed through prospective and intervention research. TRIAL REGISTRATION NUMBER: ISRCTN31392913, Post-results.

Accelerometer wear-site detection: When one site does not suit all, all of the time.

OBJECTIVES: Choice of accelerometer wear-site may facilitate greater compliance in research studies. We aimed to test whether a simple method could automatically discriminate whether an accelerometer was worn on the hip or wrist from free-living data. DESIGN: Cross-sectional. METHODS: Twenty-two 10-12y old children wore a GENEActiv at the wrist and at the hip for 7-days. The angle between the forearm and the total acceleration vector for the wrist-worn monitor and between the pelvis and the total acceleration vector for the hip-worn monitor (i.e. the angle between the Y-axis component of the acceleration and the total acceleration vector) was calculated for each 5s epoch. The standard deviation of this angle (SDangle) was calculated over time for the wrist-worn and hip-worn monitor for windows of varying lengths. We hypothesised that the wrist angle would be more variable than the hip angle. RESULTS: Wear site could be discriminated based on SDangle; the shorter the time window the lower the optimal threshold and Area under the Receiver-Operating-Characteristic curve (AUROC) for discrimination of wear-site (AUROC=0.833 (1min) - 0.952 (12h)). Classification accuracy was good for windows of 8min (sensitivity=90%, specificity=87%, AUROC=0.92) and plateaued for windows of ≥60min (sensitivity and specificity >90%, AUROC=0.95-0.96). CONCLUSIONS: We have presented a robust, computationally simple method that detects whether an accelerometer is being worn on the hip or wrist from 8 to 60min of data. This facilitates the use of wear-site specific algorithms to analyse accelerometer data.

Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches.

OBJECTIVES: (1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics. METHODS: Thirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m2; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations. RESULTS: For both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands. CONCLUSIONS: Researchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol.

The activPAL monitor, often worn 24 h d-1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries-burdensome to participants and researchers-are commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear. We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL 'Events' files as 'sleep'/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18-40 years) then evaluated in AusDiab 2011/12 participants (n = 741, 44% men, aged >35 years, mean ± SD 58.5 ± 10.4 years) who wore the activPAL3™ (7 d, 24 h d-1 protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (κ > 0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p < 0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d-1 higher than by the diary method, with 95% limits of agreement of approximately this amount ±2 h d-1. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.

Associations of mutually exclusive categories of physical activity and sedentary time with markers of cardiometabolic health in English adults: a cross-sectional analysis of the Health Survey for England.

BACKGROUND: Both physical activity and sedentary behaviour have been individually associated with health, however, the extent to which the combination of these behaviours influence health is less well-known. The aim of this study was to examine the associations of four mutually exclusive categories of objectively measured physical activity and sedentary time on markers of cardiometabolic health in a nationally representative sample of English adults. METHODS: Using the 2008 Health Survey for England dataset, 2131 participants aged ≥ 18 years, who provided valid accelerometry data, were included for analysis and grouped into one of four behavioural categories: (1) 'Busy Bees': physically active & low sedentary, (2) 'Sedentary Exercisers': physically active & high sedentary, (3) 'Light Movers': physically inactive & low sedentary, and (4) 'Couch Potatoes': physically inactive & high sedentary. 'Physically active' was defined as accumulating at least 150 min of moderate-to-vigorous physical activity (MVPA) per week. 'Low sedentary' was defined as residing in the lowest quartile of the ratio between the average sedentary time and the average light-intensity physical activity time. Weighted multiple linear regression models, adjusting for measured confounders, investigated the differences in markers of health across the derived behavioural categories. The associations between continuous measures of physical activity and sedentary levels with markers of health were also explored, as well as a number of sensitivity analyses. RESULTS: In comparison to 'Couch Potatoes', 'Busy Bees' [body mass index: -1.67 kg/m(2) (p < 0.001); waist circumference: -1.17 cm (p = 0.007); glycated haemoglobin: -0.12% (p = 0.003); HDL-cholesterol: 0.09 mmol/L (p = 0.001)], 'Sedentary Exercisers' [body mass index: -1.64 kg/m(2) (p < 0.001); glycated haemoglobin: -0.11 % (p = 0.009); HDL-cholesterol: 0.07 mmol/L (p < 0.001)] and 'Light Movers' [HDL-cholesterol: 0.11 mmol/L (p = 0.004)] had more favourable health markers. The continuous analyses showed consistency with the categorical analyses and the sensitivity analyses indicated robustness and stability. CONCLUSIONS: In this national sample of English adults, being physically active was associated with a better health profile, even in those with concomitant high sedentary time. Low sedentary time independent of physical activity had a positive association with HDL-cholesterol.

Nonexercise Equations to Estimate Fitness in White European and South Asian Men.

PURPOSE: Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-age white European and South Asian men. METHODS: Multiple linear regression models (n = 168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness (V˙O2max, mL·kg⁻¹·min⁻¹): age (yr), body mass index (kg·m⁻²), resting HR (bpm); smoking status (0, never smoked; 1, ex or current smoker), physical activity expressed as quintiles (0, quintile 1; 1, quintile 2; 2, quintile 3; 3, quintile 4; 4, quintile 5), categories of moderate- to-vigorous intensity physical activity (MVPA) (0, <75 min·wk⁻¹; 1, 75-150 min·wk⁻¹; 2, >150-225 min·wk⁻¹; 3, >225-300 min·wk⁻¹; 4, >300 min·wk⁻¹), or minutes of MVPA (min·wk⁻¹); and, ethnicity (0, South Asian; 1, white). The leave-one-out cross-validation procedure was used to assess the generalizability, and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models. RESULTS: Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: V˙O2max = 77.409 - (age × 0.374) - (body mass index × 0.906) - (ex or current smoker × 1.976) + (physical activity quintile coefficient) - (resting HR × 0.066) + (white ethnicity × 8.032), where physical activity quintile 1 is 0, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias. CONCLUSION: These data demonstrate the importance of incorporating ethnicity in nonexercise equations to estimate cardiorespiratory fitness in multiethnic populations.