CRAIG, C. L., A. L. MARSHALL, M. SJOSTROM, A. E. BAUMAN, M. L. BOOTH, B. E. AINSWORTH, M. PRATT, U. EKELUND, A. YNGVE, J. F. SALLIS, and P. OJA. International Physical Activity Questionnaire: 12-Country Reliability and Validity. Med. Sci. Sports Exerc., Vol. 35, No. 8, pp. 1381-1395, 2003. Background: Physical inactivity is a global concern, but diverse physical activity measures in use prevent international comparisons. The International Physical Activity Questionnaire (IPAQ) was developed as an instrument for cross-national monitoring of physical activity and inactivity. Methods: Between 1997 and 1998, an International Consensus Group developed four long and four short forms of the IPAQ instruments (administered by telephone interview or self-administration, with two alternate reference periods, either the "last 7 d" or a "usual week" of recalled physical activity). During 2000, 14 centers from 12 countries collected reliability and/or validity data on at least two of the eight IPAQ instruments. Test-retest repeatability was assessed within the same week. Concurrent (inter-method) validity was assessed at the same administration, and criterion IPAQ validity was assessed against the CSA (now MTI) accelerometer. Spearman's correlation coefficients are reported, based on the total reported physical activity. Results: Overall, the IPAQ questionnaires produced repeatable data (Spearman's clustered around 0.8), with comparable data from short and long forms. Criterion validity had a median of about 0.30, which was comparable to most other self-report validation studies. The "usual week" and "last 7 d" reference periods performed similarly, and the reliability of telephone administration was similar to the self-administered mode. Conclusions: The IPAQ instruments have acceptable measurement properties, at least as good as other established self-reports. Considering the diverse samples in this study, IPAQ has reasonable measurement properties for monitoring population levels of physical activity among 18- to 65-yr-old adults in diverse settings. The short IPAQ form "last 7 d recall" is recommended for national monitoring and the long form for research requiring more detailed assessment. Key Words: MEASUREMENT, SURVEILLANCE, EPIDEMIOLOGY

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International physical activity questionnaire: 12-country reliability and validity

We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.

http://www.lisaworkman.com/uploads/3/1/7/1/3171788/ainsworth_et_al._2000_-_msse.pdf

Compendium of physical activities: an update of activity codes and MET intensities.

ABI
: ankle–brachial index
ACCORD
: Action to Control Cardiovascular Risk in Diabetes
ADVANCE
: Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation
AGREE
: Appraisal of Guidelines Research and Evaluation
AHA
: American Heart Association
apoA1
: apolipoprotein A1
apoB
: apolipoprotein B
CABG
: coronary artery bypass graft surgery
CARDS
: Collaborative AtoRvastatin Diabetes Study
CCNAP
: Council on Cardiovascular Nursing and Allied Professions
CHARISMA
: Clopidogrel for High Athero-thrombotic Risk and Ischemic Stabilisation, Management, and Avoidance
CHD
: coronary heart disease
CKD
: chronic kidney disease
COMMIT
: Clopidogrel and Metoprolol in Myocardial Infarction Trial
CRP
: C-reactive protein
CURE
: Clopidogrel in Unstable Angina to Prevent Recurrent Events
CVD
: cardiovascular disease
DALYs
: disability-adjusted life years
DBP
: diastolic blood pressure
DCCT
: Diabetes Control and Complications Trial
ED
: erectile dysfunction
eGFR
: estimated glomerular filtration rate
EHN
: European Heart Network
EPIC
: European Prospective Investigation into Cancer and Nutrition
EUROASPIRE
: European Action on Secondary and Primary Prevention through Intervention to Reduce Events
GFR
: glomerular filtration rate
GOSPEL
: Global Secondary Prevention Strategies to Limit Event Recurrence After MI
GRADE
: Grading of Recommendations Assessment, Development and Evaluation
HbA1c
: glycated haemoglobin
HDL
: high-density lipoprotein
HF-ACTION
: Heart Failure and A Controlled Trial Investigating Outcomes of Exercise TraiNing
HOT
: Hypertension Optimal Treatment Study
HPS
: Heart Protection Study
HR
: hazard ratio
hsCRP
: high-sensitivity C-reactive protein
HYVET
: Hypertension in the Very Elderly Trial
ICD
: International Classification of Diseases
IMT
: intima-media thickness
INVEST
: International Verapamil SR/Trandolapril
JTF
: Joint Task Force
LDL
: low-density lipoprotein
Lp(a)
: lipoprotein(a)
LpPLA2
: lipoprotein-associated phospholipase 2
LVH
: left ventricular hypertrophy
MATCH
: Management of Atherothrombosis with Clopidogrel in High-risk Patients with Recent Transient Ischaemic Attack or Ischaemic Stroke
MDRD
: Modification of Diet in Renal Disease
MET
: metabolic equivalent
MONICA
: Multinational MONItoring of trends and determinants in CArdiovascular disease
NICE
: National Institute of Health and Clinical Excellence
NRT
: nicotine replacement therapy
NSTEMI
: non-ST elevation myocardial infarction
ONTARGET
: Ongoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial
OSA
: obstructive sleep apnoea
PAD
: peripheral artery disease
PCI
: percutaneous coronary intervention
PROactive
: Prospective Pioglitazone Clinical Trial in Macrovascular Events
PWV
: pulse wave velocity
QOF
: Quality and Outcomes Framework
RCT
: randomized clinical trial
RR
: relative risk
SBP
: systolic blood pressure
SCORE
: Systematic Coronary Risk Evaluation Project
SEARCH
: Study of the Effectiveness of Additional Reductions in Cholesterol and
SHEP
: Systolic Hypertension in the Elderly Program
STEMI
: ST-elevation myocardial infarction
SU.FOL.OM3
: SUpplementation with FOlate, vitamin B6 and B12 and/or OMega-3 fatty acids
Syst-Eur
: Systolic Hypertension in Europe
TNT
: Treating to New Targets
UKPDS
: United Kingdom Prospective Diabetes Study
VADT
: Veterans Affairs Diabetes Trial
VALUE
: Valsartan Antihypertensive Long-term Use
VITATOPS
: VITAmins TO Prevent Stroke
VLDL
: very low-density lipoprotein
WHO
: World Health Organization

### 1.1 Introduction

Atherosclerotic cardiovascular disease (CVD) is a chronic disorder developing insidiously throughout life and usually progressing to an advanced stage by the time symptoms occur. It remains the major cause of premature death in Europe, even though CVD mortality has …

/pdf/european-guidelines-on-cardiovascular-disease-prevention-in-4z4nw4311i.pdf

European Guidelines on Cardiovascular Disease Prevention in Clinical Practice (Version 2012)

WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Purpose:To describe physical activity levels of children (6-11 yr), adolescents (12-19 yr), and adults (20+ yr), using objective data obtained with accelerometers from a representative sample of the U.S. population.Methods:These results were obtained from the 2003-2004 National Health and Nu

Physical activity in the United States measured by accelerometer.

The purpose of this study was to develop a VO2peak prediction model derived from nonexercise (N-EX) based predictors. VO2peak was measured using a walking treadmill protocol with 229 females and 210 males between 20 and 79 yr of age (mean +/- SD: 38.62 +/- 10.36 ml.kg-1.min-1). Subjects were randomly divided into validation (V) (85% of total; N = 374) and cross-validation (CV) (15% of total; N = 65) groups. The V group was used to validate generalized and gender-specific models using stepwise multiple regression procedures with gender, age and age2, percent body fat, and a physical activity code (AC). The generalized ml.kg-1.min-1 (R2 = 0.77, SEE = 4.90 ml.kg-1.min-1, SEE% = 12.7%) and gender-specific (females: R2 = 0.72, SEE = 4.64 ml.kg-1.min-1; males: R2 = 0.72, SEE = 5.02 ml.kg-1.min-1) models were highly accurate relative to N-EX and exercise based models in the literature. Cross-validation procedures were used to evaluate model stability. The generalized model was stable across the total CV group and various CV subsamples (by gender, decade-wide age groups, and AC groups), but not across groups similar in VO2peak. These results suggest that N-EX models can be valid predictors of VO2peak for heterogenous samples.

Nonexercise regression models to estimate peak oxygen consumption

AB Introduction: Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. Purpose: The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting. Methods: Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation. Results: Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time. Conclusions: Compared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.

/pdf/a-method-to-estimate-free-living-active-and-sedentary-3lx0bld60q.pdf

A method to estimate free-living active and sedentary behavior from an accelerometer.

Purpose: Participation in physical activity during pregnancy may reduce the risk of maternal and fetal disorders However, few studies have validated physical activity questionnaires for use during pregnancy, a time characterized by different patterns of activity than nonpregnancy Therefore, the aim of this study was to assess the validity and reliability of the Kaiser Physical Activity Survey (KPAS) for use during pregnancy Methods: The KPAS, adapted from the Baecke physical activity survey, was designed specifically to assess physical activity in women Unique features of the KPAS include the assessment of multiple domains of physical activity (household/caregiving, occupational, active living, and sports/exercise) as well as total activity Summary KPAS indices were compared with objective (ActiGraph accelerometer by ActiGraph LLC) and subjective (Pregnancy Physical Activity Questionnaire (PPAQ)) measures of physical activity Participants completed the self-administered PPAQ followed by the interviewer-administered KPAS and then wore the accelerometer for the following 7 d At the end of the 7-d period, the questionnaires were repeated Results: Intraclass correlation coefficients used to measure reproducibility of the KPAS were r = 084 for total activity and ranged from r = 076 for active living activities to r = 086 for occupational activity Spearman correlations between the KPAS and three published cut points used to classify accelerometer data ranged from r = 049-059 for total activity, r = 012-026 for household/caregiving, r = 026-033 for occupational activity, r = 031-036 for active living, and r = 034-051 for sports/exercise Spearman correlations between the KPAS and the PPAQ ranged from r = 071 for household/caregiving to r = 084 for sports/exercise Conclusions: The KPAS is a reliable and reasonably accurate instrument for estimating physical activity among pregnant women

Validation of the Kaiser Physical Activity Survey in pregnant women.

Recent epidemiologic evidence suggests that lifetime physical activity is an important factor in the development of many chronic diseases. The authors assessed the reproducibility of a self-administered physical activity questionnaire designed to assess the duration, frequency, and intensity of lifetime household and recreational activities. The study was conducted among 134 female college alumnae from two colleges in western Massachusetts who were aged 39-65 years in 1998. A modified version of the Historical Leisure Activity Questionnaire was used to assess physical activity over four prior age periods (menarche to 21 years and 22-34, 35-50, and 51-65 years). The questionnaire was administered to participants by mail twice 1 year apart. The intraclass correlation coefficients used to measure reproducibility were 0.82 for total lifetime physical activity, 0.80 for lifetime moderate-intensity activities, 0.86 for lifetime vigorous-intensity activities, 0.87 for lifetime recreational activities, and 0.78 for lifetime household activities. Correlations were 0.73 for total activity during the earliest prior age period (menarche to 21 years), 0.70 for ages 22-34 years, 0.78 for ages 35-50 years, and 0.83 for ages 51-65 years. These data indicate that this physical activity questionnaire is reproducible and provides a useful measure of average lifetime activity.

/pdf/reproducibility-of-a-self-administered-lifetime-physical-2mcmn4vuau.pdf

Reproducibility of a Self-administered Lifetime Physical Activity Questionnaire among Female College Alumnae

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods.

Patty S. Freedson

Papers

Nonexercise regression models to estimate peak oxygen consumption

A method to estimate free-living active and sedentary behavior from an accelerometer.

Validation of the Kaiser Physical Activity Survey in pregnant women.

Reproducibility of a Self-administered Lifetime Physical Activity Questionnaire among Female College Alumnae

Multisensor Data Fusion for Physical Activity Assessment