Energy expenditure estimation in cardiac patients by ...



Energy expenditure estimation in beta-blocker medicated cardiac patients by combining heart rate and body movement dataJos J Kraala, Francesco Sartorb, Gabriele Papinib, Wim Stutb, Niels Peekc, Hareld M.C. Kempsd, Alberto G Bonomiba Department of Medical Informatics, Academic Medical Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlandsb Personal Health department, Philips Research, Eindhoven, The Netherlandsc Health eResearch Centre, Farr Institute for Health Informatics Research, University of Manchester, Manchester, United Kingdomd Department of Cardiology, Máxima Medical Center Veldhoven, Veldhoven, The NetherlandsWord count: 5196Corresponding author:Jos J Kraal, Department of Medical Informatics, Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The NetherlandsEmail: j.kraal@mmc.nlAbstractBackground: Accurate assessment of energy expenditure provides the opportunity to monitor physical activity during cardiac rehabilitation. However, available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker medicated cardiac rehabilitation patients. Methods: Sixteen male cardiac rehabilitation patients (age: 55.8±7.3 years, weight: 93.1±11.8 kg) underwent a physical activity protocol with 11 low to moderate intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom limited exercise test and resting metabolic rate assessment. Energy expenditure estimation models were developed using multivariate regression analyses based on HR, body movement data and/or patient characteristics. In addition, a HR-flex model was developed.Results: The model combining HR, body movement and patient characteristics showed the highest correlation and lowest error (R2=0.84, RMSE=0.834 kcal/min) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accuracy (R2=0.83, RMSE=0.992 kcal/min).Conclusions: Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods developed for healthy subjects. The proposed methodology does not require individual calibration and is based on data available in clinical practice. Word count: 228Keywords: Accelerometry, heart rate monitoring, physical activity, energy expenditure, cardiac rehabilitation, beta-blockers.IntroductionPhysical inactivity is an expanding problem and is related to several chronic diseases including cardiovascular diseases, diabetes and obesity ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/S0140-6736(12)61031-9", "ISBN" : "1474-547X (Electronic)\\n0140-6736 (Linking)", "ISSN" : "01406736", "PMID" : "22818936", "abstract" : "Background Strong evidence shows that physical inactivity increases the risk of many adverse health conditions, including major non-communicable diseases such as coronary heart disease, type 2 diabetes, and breast and colon cancers, and shortens life expectancy. Because much of the world's population is inactive, this link presents a major public health issue. We aimed to quantify the effect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level. Methods For our analysis of burden of disease, we calculated population attributable fractions (PAFs) associated with physical inactivity using conservative assumptions for each of the major non-communicable diseases, by country, to estimate how much disease could be averted if physical inactivity were eliminated. We used life-table analysis to estimate gains in life expectancy of the population. Findings Worldwide, we estimate that physical inactivity causes 6% (ranging from 3.2% in southeast Asia to 7.8% in the eastern Mediterranean region) of the burden of disease from coronary heart disease, 7% (3.9-9.6) of type 2 diabetes, 10% (5.6-14.1) of breast cancer, and 10% (5.7-13.8) of colon cancer. Inactivity causes 9% (range 5.1-12.5) of premature mortality, or more than 5.3 million of the 57 million deaths that occurred worldwide in 2008. If inactivity were not eliminated, but decreased instead by 10% or 25%, more than 533 000 and more than 1.3 million deaths, respectively, could be averted every year. We estimated that elimination of physical inactivity would increase the life expectancy of the world's population by 0.68 (range 0.41-0.95) years. Interpretation Physical inactivity has a major health effect worldwide. Decrease in or removal of this unhealthy behaviour could improve health substantially. Funding None.", "author" : [ { "dropping-particle" : "", "family" : "Lee", "given" : "I. 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worldwide: An analysis of burden of disease and life expectancy", "type" : "article-journal", "volume" : "380" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹", "plainTextFormattedCitation" : "1", "previouslyFormattedCitation" : "¹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }1. Lack of physical activity may lead to a chronic imbalance between energy intake and energy expenditure, which is associated with all-cause and cardiovascular mortality ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.amjmed.2004.06.047", "ISBN" : "0002-9343", "ISSN" : "00029343", "PMID" : "15629729", "abstract" : "PURPOSE: To compare the contributions of fitness level and physical activity patterns to all-cause mortality. METHODS: Of 6213 men referred for exercise testing between 1987 and 2000, 842 underwent an assessment of adulthood activity patterns. The predictive power of exercise capacity and activity patterns, along with clinical and exercise test data, were assessed for all-cause mortality during a mean (+/-SD) follow-up of 5.5 +/- 2 years. RESULTS: Expressing the data by age-adjusted quartiles, exercise capacity was a stronger predictor of mortality than was activity pattern (hazard ratio [HR] = 0.56; 95% confidence interval [CI]: 0.38 to 0.83; P < 0.001). In a multivariate analysis that considered clinical characteristics, risk factors, exercise test data, and activity patterns, exercise capacity (HR per quartile = 0.62; CI: 0.47 to 0.82; P < 0.001) and energy expenditure from adulthood recreational activity (HR per quartile = 0.72; 95% CI: 0.58 to 0.89; P = 0.002) were the only significant predictors of mortality; these two variables were stronger predictors than established risk factors such as smoking, hypertension, obesity, and diabetes. Age-adjusted mortality decreased per quartile increase in exercise capacity (HR for very low capacity = 1.0; HR for low = 0.59; HR for moderate = 0.46; HR for high = 0.28; P < 0.001) and physical activity (HR for very low activity = 1.0; HR for low = 0.63; HR for moderate = 0.42; HR for high = 0.38; P < 0.001). A 1000-kcal/wk increase in activity was approximately similar to a 1 metabolic equivalent increase in fitness; both conferred a mortality benefit of 20%. CONCLUSION: Exercise capacity determined from exercise testing and energy expenditure from weekly activity outperform other clinical and exercise test variables in predicting all-cause mortality.", "author" : [ { "dropping-particle" : "", "family" : "Myers", "given" : "Jonathan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kaykha", "given" : "Amir", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "George", "given" : "Sheela", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Abella", "given" : "Joshua", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zaheer", "given" : "Naima", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lear", "given" : "Scott", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Yamazaki", "given" : "Takuya", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Froelicher", "given" : "Victor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "The American journal of medicine", "id" : "ITEM-1", "issue" : "12", "issued" : { "date-parts" : [ [ "2004" ] ] }, "page" : "912-918", "title" : "Fitness versus physical activity patterns in predicting mortality in men.", "type" : "article-journal", "volume" : "117" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "ISBN" : "0002-9262 (Print)\\r0002-9262 (Linking)", "ISSN" : "00029262", "PMID" : "685971", "abstract" : "In a 22-year followup of 3686 San Francisco longshoremen, the roles of physical activity, cigarette smoking habit, and systolic blood pressure level were evaluated independently in relation to risk of death from a broad range of diseases. Smoking pattern and blood pressure status were established in 1951 and job activity was assessed annually during the followup period. Lower levels of energy expenditure predicted increased risk of fatal heart attack and perhaps of stroke. Heavy cigarette smoking predicted increased risk of death from heart attack, cancer, chronic obstructive respiratory disease, and pneumonia. Higher levels of systolic blood pressure were associated with death from all cardiovascular diseases, diabetes mellitus, and cirrhosis. Tacit to these findings: sedentary living takes its toll largely through heart disease and stroke; the toxicity of cigarette smoking is associated with a broader range of diseases, including heart attack, cancer, and respiratory disease; and higher level of blood pressure related to an even broader range of cardiovascular disease than either of the other characteristics studied.", "author" : [ { "dropping-particle" : "", "family" : "Paffenbarger", "given" : "R S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brand", "given" : "R J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sholtz", "given" : "R I", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Jung", "given" : "D L", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "American journal of epidemiology", "id" : "ITEM-2", "issue" : "1", "issued" : { "date-parts" : [ [ "1978" ] ] }, "page" : "12-18", "title" : "Energy expenditure, cigarette smoking, and blood pressure level as related to death from specific diseases.", "type" : "article-journal", "volume" : "108" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^2,3", "plainTextFormattedCitation" : "2,3", "previouslyFormattedCitation" : "^2,3" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }2,3. After a cardiac incident, patients are recommended to participate in cardiac rehabilitation to improve their physical fitness level and engage in a healthy lifestyle ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1161/01.CIR.0000151788.08740.5C", "ISBN" : "8006116083", "ISSN" : "1524-4539", "PMID" : "15668354", "abstract" : "This article updates the 1994 American Heart Association scientific statement on cardiac rehabilitation. It provides a review of recommended components for an effective cardiac rehabilitation/secondary prevention program, alternative ways to deliver these services, recommended future research directions, and the rationale for each component of the rehabilitation/secondary prevention program, with emphasis on the exercise training component.", "author" : [ { "dropping-particle" : "", "family" : "Leon", "given" : "Arthur S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franklin", "given" : "Barry a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Costa", "given" : "Fernando", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Balady", "given" : "Gary J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Berra", "given" : "Kathy a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Stewart", "given" : "Kerry J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Paul D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Williams", "given" : "Mark a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lauer", "given" : "Michael S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Circulation", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2005", "1", "25" ] ] }, "page" : "369-76", "title" : "Cardiac rehabilitation and secondary prevention of coronary heart disease: an American Heart Association scientific statement from the Council on Clinical Cardiology (Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention) and the Council on Nut", "type" : "article-journal", "volume" : "111" }, "uris" : [ "", "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1097/HJR.0b013e3283313592", "ISBN" : "1741-8275; 1741-8267", "ISSN" : "1741-8275", "PMID" : "19952757", "abstract" : "Increasing awareness of the importance of cardiovascular prevention is not yet matched by the resources and actions within health care systems. Recent publication of the European Commission's European Heart Health Charter in 2008 prompts a review of the role of cardiac rehabilitation (CR) to cardiovascular health outcomes. Secondary prevention through exercise-based CR is the intervention with the best scientific evidence to contribute to decrease morbidity and mortality in coronary artery disease, in particular after myocardial infarction but also incorporating cardiac interventions and chronic stable heart failure. The present position paper aims to provide the practical recommendations on the core components and goals of CR intervention in different cardiovascular conditions, to assist in the design and development of the programmes, and to support healthcare providers, insurers, policy makers and consumers in the recognition of the comprehensive nature of CR. Those charged with responsibility for secondary prevention of cardiovascular disease, whether at European, national or individual centre level, need to consider where and how structured programmes of CR can be delivered to all patients eligible. Thus a novel, disease-oriented document has been generated, where all components of CR for cardiovascular conditions have been revised, presenting both well-established and controversial aspects. A general table applicable to all cardiovascular conditions and specific tables for each clinical disease have been created and commented.", "author" : [ { "dropping-particle" : "", "family" : "Piepoli", "given" : "Massimo Francesco", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Corr\u00e0", "given" : "Ugo", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Benzer", "given" : "Werner", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bjarnason-Wehrens", "given" : "Birna", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Dendale", "given" : "Paul", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gaita", "given" : "Dan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McGee", "given" : "Hannah", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mendes", "given" : "Miguel", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Niebauer", "given" : "Josef", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zwisler", "given" : "Ann-Dorthe Olsen", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Schmid", "given" : "Jean-Paul", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology", "id" : "ITEM-2", "issued" : { "date-parts" : [ [ "2010" ] ] }, "page" : "1-17", "title" : "Secondary prevention through cardiac rehabilitation: from knowledge to implementation. A position paper from the Cardiac Rehabilitation Section of the European Association of Cardiovascular Prevention and Rehabilitation.", "type" : "article-journal", "volume" : "17" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^4,5", "plainTextFormattedCitation" : "4,5", "previouslyFormattedCitation" : "^4,5" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }4,5. Cardiac rehabilitation is a multidisciplinary intervention to improve physical, psychological and social well being of patients after a cardiac incident ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/HJR.0b013e32833a1c95", "ISSN" : "1741-8275", "PMID" : "20458247", "abstract" : "AIM: To summarize the current evidence with regard to the effectiveness of nonpharmacological secondary prevention strategies of coronary heart disease (CHD) and to investigate the comparative effectiveness of interventions of different categories, specific intervention components and the effectiveness in patient subgroups. METHODS: A structured search of databases and manual search were conducted. Clinical trials and meta-analyses published between January 2003 and September 2008 were included if they targeted adults with CHD, had a follow-up of at least 12 months, and reported mortality, cardiac events or quality of life. Two researchers assessed eligibility and methodological quality, in which appropriate, pooled effect estimates were calculated and tested in sensitivity analyses. RESULTS: Of 4798 publications 43 met the inclusion criteria. Overall study quality was satisfactory, but only about half of the studies reported mortality. Follow-up duration varied between 12 and 120 months. Despite substantial heterogeneity, there was strong evidence of intervention effectiveness overall. The evidence for exercise and multimodal interventions was more conclusive for reducing mortality, whereas psychosocial interventions seemed to be more effective in improving the quality of life. Rigorous studies investigating dietary and smoking cessation interventions, specific intervention components and important patient subgroups, were scarce. CONCLUSION: Nonpharmacological secondary prevention is safe and effective, with exercise and multimodal interventions reducing mortality most substantially. There is a lack of studies concerning dietary and smoking cessation interventions. In addition, intervention effectiveness in patient subgroups and of intervention components could not be evaluated conclusively. Future research should investigate these issues in rigorous studies with appropriate follow-up duration to improve the current poor risk factor control of CHD patients.", "author" : [ { "dropping-particle" : "", "family" : "M\u00fcller-Riemenschneider", "given" : "Falk", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Meinhard", "given" : "Charlotte", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Damm", "given" : "Kathrin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vauth", "given" : "Christoph", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bockelbrink", "given" : "Angelina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Greiner", "given" : "Wolfgang", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Willich", "given" : "Stefan N", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European Journal of Cardiovascular Prevention and Rehabilitation", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2010", "12" ] ] }, "page" : "688-700", "title" : "Effectiveness of nonpharmacological secondary prevention of coronary heart disease.", "type" : "article-journal", "volume" : "17" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁶", "plainTextFormattedCitation" : "6", "previouslyFormattedCitation" : "⁶" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }6. However, the majority of the cardiac rehabilitation programs focus on the improvement and enhancement exercise capacity and quality of life, but do not target sedentary time and overall physical activity behavior in daily life. This may be related to the fact that to date, no standardized methods exist to assess physical activity accurately and unobtrusively in cardiac patients in daily life ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/HCR.0000000000000191", "ISBN" : "0000000000000", "ISSN" : "1932-7501", "author" : [ { "dropping-particle" : "", "family" : "Kaminsky", "given" : "Leonard A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brubaker", "given" : "Peter H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Guazzi", "given" : "Marco", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lavie", "given" : "Carl J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Montoye", "given" : "Alexander H. K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sanderson", "given" : "Bonnie K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Savage", "given" : "Patrick D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Cardiopulmonary Rehabilitation and Prevention", "id" : "ITEM-1", "issue" : "4", "issued" : { "date-parts" : [ [ "2016" ] ] }, "page" : "217-229", "title" : "Assessing Physical Activity as a Core Component in Cardiac Rehabilitation", "type" : "article-journal", "volume" : "36" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁷", "plainTextFormattedCitation" : "7" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }7. Physical activity is a multi-dimensional human behavior characterized by factors such as type, pattern, duration and intensity of physical tasks and can be quantified by determining energy expenditure ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.2307/20056429", "ISBN" : "0033-3549", "ISSN" : "0033-3549", "PMID" : "3920711", "abstract" : "\"Physical activity,\" \"exercise,\" and \"physical fitness\" are terms that describe different concepts. However, they are often confused with one another, and the terms are sometimes used interchangeably. This paper proposes definitions to distinguish them. Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. The energy expenditure can be measured in kilocalories. Physical activity in daily life can be categorized into occupational, sports, conditioning, household, or other activities. Exercise is a subset of physical activity that is planned, structured, and repetitive and has as a final or an intermediate objective the improvement or maintenance of physical fitness. Physical fitness is a set of attributes that are either health- or skill-related. The degree to which people have these attributes can be measured with specific tests. These definitions are offered as an interpretational framework for comparing studies that relate physical activity, exercise, and physical fitness to health.", "author" : [ { "dropping-particle" : "", "family" : "Caspersen", "given" : "C J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Powell", "given" : "K E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Christenson", "given" : "G M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Public health reports", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "1985" ] ] }, "page" : "126-131", "title" : "Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research.", "type" : "article-journal", "volume" : "100" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁸", "plainTextFormattedCitation" : "8", "previouslyFormattedCitation" : "⁷" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }8. Gold standard methods for the assessment of energy expenditure are indirect calorimetry and doubly labelled water. Although these methods were shown to be accurate, they are costly and obtrusive, making them unsuitable for population-based studies or integration in clinical practice ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "PMID" : "9771875", "abstract" : "Scientists have been measuring energy expenditure by using gas exchange for the past 200 y. This technique is based on earlier work in the 1660s. Gas exchange in respirometers provides accurate and repeatable measures of resting metabolic rate. However, it is impossible to duplicate in a respirometry chamber the diversity of human behaviors that influence energy expenditure. The doubly labeled water technique is an isotope-based method that measures the energy expenditure of unencumbered subjects from the divergence in enrichments of 2 isotopic labels in body water--1 of hydrogen and 1 of oxygen. The method was invented in the 1950s and applied to small animals only until the early 1980s, mostly because of the expense. Since 1982, when the first study in humans was published, its use has expanded enormously. Although there is some debate over the precise calculation protocols that should be used, the differences between alternative calculations result in relatively minor effects on total energy expenditure estimates (approximately 6%). Validation studies show that for groups of subjects the method works well, but that precision is still relatively poor (8-9%) and consequently the method is not yet sufficiently refined to provide estimates of individual energy expenditures.", "author" : [ { "dropping-particle" : "", "family" : "Speakman", "given" : "J R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Am J Clin Nutr", "id" : "ITEM-1", "issue" : "4", "issued" : { "date-parts" : [ [ "1998" ] ] }, "page" : "932S-938S", "title" : "The history and theory of the doubly labeled water technique", "type" : "article-journal", "volume" : "68" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "⁹", "plainTextFormattedCitation" : "9", "previouslyFormattedCitation" : "⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }9.Over the past decade, the use of wearable movement sensors for measuring physical activity in free-living conditions has become widespread. Heart rate (HR) monitors provide insight into the intensity of the aerobic work during physical activity. During activities with moderate to vigorous intensity, HR and oxygen consumption (VO2) are linearly related ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/00005768-200009001-00005", "ISBN" : "0195-9131", "ISSN" : "0195-9131", "PMID" : "10993416", "abstract" : "To further develop our understanding of the relationship between habitual physical activity and health, research studies require a method of assessment that is objective, accurate, and noninvasive. Heart rate (HR) monitoring represents a promising tool for measurement because it is a physiological parameter that correlates well with energy expenditure (EE). However, one of the limitations of HR monitoring is that training state and individual HR characteristics can affect the HR-VO2 relationship. PURPOSE: The primary purpose of this study was to examine the relationship between HR (beats x min(-1)) and VO2 (mL x kg(-1 x -1) min(-1)) during field- and laboratory-based moderate-intensity activities. In addition, we examined the validity of estimating EE from HR after adjusting for age and fitness. This was done by expressing the data as a percent of heart rate reserve (%HRR) and percent of VO2 reserve (%VO2R). METHODS: Sixty-one adults (18-74 yr) performed physical tasks in both a laboratory and field setting. HR and VO2 were measured continuously during the 15-min tasks. Mean values over min 5-15 were used to perform linear regression analysis on HR versus VO2. HR data were then used to predict EE (METs), using age-predicted HRmax and estimated VO2max. RESULTS: The correlation between HR and VO2 was r = 0.68, with HR accounting for 47% of the variability in VO2. After adjusting for age and fitness level, HR was an accurate predictor of EE (r = 0.87, SEE = 0.76 METs). CONCLUSION: This method of analyzing HR data could allow researchers to more accurately quantify physical activity in free-living individuals.", "author" : [ { "dropping-particle" : "", "family" : "Strath", "given" : "S J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Swartz", "given" : "a M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bassett", "given" : "D R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "O'Brien", "given" : "W L", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "King", "given" : "G a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ainsworth", "given" : "B E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Medicine and science in sports and exercise", "id" : "ITEM-1", "issue" : "9 Suppl", "issued" : { "date-parts" : [ [ "2000" ] ] }, "page" : "S465-S470", "title" : "Evaluation of heart rate as a method for assessing moderate intensity physical activity.", "type" : "article-journal", "volume" : "32" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Astrand", "given" : "P. -O.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ryhming", "given" : "Irma", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "J Appl Physiol", "id" : "ITEM-2", "issue" : "2", "issued" : { "date-parts" : [ [ "1954", "9" ] ] }, "page" : "218-221", "title" : "A Nomogram for Calculation of Aerobic Capacity (Physical Fitness) From Pulse Rate During Submaximal Work", "type" : "article-journal", "volume" : "7" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "^10,11", "plainTextFormattedCitation" : "10,11", "previouslyFormattedCitation" : "^9,10" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }10,11. Therefore energy expenditure, which is dependent on VO2ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Weir", "given" : "J B", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issue" : "5", "issued" : { "date-parts" : [ [ "1948" ] ] }, "page" : "1-9", "title" : "New methods for calculating metabolic rate with special reference to protein metabolism", "type" : "article-journal", "volume" : "109" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "¹²", "plainTextFormattedCitation" : "12", "previouslyFormattedCitation" : "¹¹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }12, can be predicted using HR recordings. However, in free-living conditions energy expenditure assessment using HR has some important limitationsADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1139/H08-117", "author" : [ { "dropping-particle" : "", "family" : "Hay", "given" : "Dean Charles", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wakayama", "given" : "Akinobu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sakamura", "given" : "Ken", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fukashiro", "given" : "Senshi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2008" ] ] }, "page" : "1213-1222", "title" : "Improved estimation of energy expenditure by artificial neural network modeling", "type" : "article-journal", "volume" : "1222" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "¹³", "plainTextFormattedCitation" : "13", "previouslyFormattedCitation" : "¹²" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }13. First, the HR vs. energy expenditure relation is not linear during rest and low-intensity activities and is substantially influenced by internal and external factors (i.e. temperature, emotional stress, caffeine) ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1079/BJN19970205", "ISBN" : "0007-1145", "ISSN" : "0007-1145", "PMID" : "9497439", "abstract" : "The benefits of physical activity are well-documented and exercise is now included in most health promotion recommendations. However, before adopting a population strategy it is important to establish baseline patterns of physical activity on which properly informed recommendations can be made and behaviour change measured. At present few would argue that it is difficult to draw meaningful conclusions and detect trends from studies that measure physical activity using different measurement instruments.", "author" : [ { "dropping-particle" : "", "family" : "Livingstone", "given" : "M.B.E.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Nutrition", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "1997" ] ] }, "page" : "869-871", "title" : "Heart-rate monitoring: the answer for assessing energy expenditure and physical activity in population studies?", "type" : "article-journal", "volume" : "78" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹⁴", "plainTextFormattedCitation" : "14", "previouslyFormattedCitation" : "¹³" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }14. In addition, inter-individual variation in HR is large, suggesting the need for individual calibration processes to build a valid HR-based energy expenditure model ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/01.hjr.0000161551.73095.9c", "ISBN" : "0000161551", "ISSN" : "1741-8267", "author" : [ { "dropping-particle" : "", "family" : "Vanhees", "given" : "L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lefevre", "given" : "J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Philippaerts", "given" : "R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Martens", "given" : "M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Huygens", "given" : "W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Troosters", "given" : "T.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Beunen", "given" : "G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European Journal of Cardiovascular Prevention & Rehabilitation", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "2005", "4", "1" ] ] }, "page" : "102-114", "title" : "How to assess physical activity? How to assess physical fitness?", "type" : "article-journal", "volume" : "12" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹⁵", "plainTextFormattedCitation" : "15", "previouslyFormattedCitation" : "¹⁴" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }15.Yet, combining HR and accelerometer data may reduce these limitations ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Haskell", "given" : "William", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Yee", "given" : "Martin C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Evans", "given" : "Anthony", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Irby", "given" : "Pamela J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Medicine and Science in Sport and Exercise", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "1993" ] ] }, "page" : "109-115", "title" : "Simultaneous measurement of heart rate and body motion to quantitate physical activity", "type" : "article-journal", "volume" : "25" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "¹⁶", "plainTextFormattedCitation" : "16", "previouslyFormattedCitation" : "¹⁵" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }16. Tri-axial accelerometers have been used to quantify body movement and predict energy expenditure using linear models in healthy subjects and patients with a chronic disease ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/00005768-199805000-00021", "ISBN" : "0195-9131 (Print)\\n0195-9131 (Linking)", "ISSN" : "0195-9131", "PMID" : "9588623", "abstract" : "PURPOSE: We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. METHODS: Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). RESULTS: Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were < or = 1951, 1952-5724, 5725-9498, > or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). CONCLUSIONS: These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.", "author" : [ { "dropping-particle" : "", "family" : "Freedson", "given" : "P S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Melanson", "given" : "E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sirard", "given" : "J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Medicine and science in sports and exercise", "id" : "ITEM-1", "issue" : "5", "issued" : { "date-parts" : [ [ "1998" ] ] }, "page" : "777-781", "title" : "Calibration of the Computer Science and Applications, Inc. accelerometer.", "type" : "article-journal", "volume" : "30" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1038/oby.2007.281", "ISBN" : "1930-7381 (Print)\\n1930-7381 (Linking)", "ISSN" : "1930-7381", "PMID" : "17925461", "abstract" : "This review focuses on the ability of different accelerometers to assess daily physical activity as compared with the doubly labeled water (DLW) technique, which is considered the gold standard for measuring energy expenditure under free-living conditions. The PubMed Central database (U.S. NIH free digital archive of biomedical and life sciences journal literature) was searched using the following key words: doubly or double labeled or labeled water in combination with accelerometer, accelerometry, motion sensor, or activity monitor. In total, 41 articles were identified, and screening the articles' references resulted in one extra article. Of these, 28 contained sufficient and new data. Eight different accelerometers were identified: 3 uniaxial (the Lifecorder, the Caltrac, and the CSA/MTI/Actigraph), one biaxial (the Actiwatch AW16), 2 triaxial (the Tritrac-R3D and the Tracmor), one device based on two position sensors and two motion sensors (ActiReg), and the foot-ground contact pedometer. Many studies showed poor results. Only a few mentioned partial correlations for accelerometer counts or the increase in R(2) caused by the accelerometer. The correlation between the two methods was often driven by subject characteristics such as body weight. In addition, standard errors or limits of agreement were often large or not presented. The CSA/MTI/Actigraph and the Tracmor were the two most extensively validated accelerometers. The best results were found for the Tracmor; however, this accelerometer is not yet commercially available. Of those commercially available, only the CSA/MTI/Actigraph has been proven to correlate reasonably with DLW-derived energy expenditure.", "author" : [ { "dropping-particle" : "", "family" : "Plasqui", "given" : "Guy", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Westerterp", "given" : "Klaas R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Obesity (Silver Spring, Md.)", "id" : "ITEM-2", "issue" : "10", "issued" : { "date-parts" : [ [ "2007" ] ] }, "page" : "2371-2379", "title" : "Physical activity assessment with accelerometers: an evaluation against doubly labeled water.", "type" : "article-journal", "volume" : "15" }, "uris" : [ "" ] }, { "id" : "ITEM-3", "itemData" : { "DOI" : "10.1177/2047487316634883", "ISBN" : "2047-4873", "ISSN" : "2047-4873", "PMID" : "26907794", "abstract" : "BACKGROUND: Accurate physical activity monitoring is important for cardiac patients. Novel activity monitoring devices may enable precise measurement of physical activity. This study aimed to validate Fitbit-Flex against Actigraph accelerometer for monitoring physical activity.\\n\\nDESIGN: A validation study with a comparative design.\\n\\nMETHODS: Cardiac patients and family members participating in community-based exercise programs wore Fitbit-Flex and Actigraph simultaneously over four days to monitor daily step counts and minutes of moderate to vigorous physical activity (MVPA).\\n\\nRESULTS: Participants (N\u2009=\u200948) comprised 52.1% males, with a mean age of 65.6\u2009\u00b1\u20096.9 years and 58.9% had a cardiac diagnosis. Fitbit-Flex and Actigraph were significantly correlated in males, females, total participants and cardiac patients for step counts (r\u2009=\u2009.96; r\u2009=\u2009.95; r\u2009=\u2009.95; r\u2009=\u2009.95), though less so for MVPA (r\u2009=\u2009.81; r\u2009=\u2009.65, r\u2009=\u2009.74; r\u2009=\u2009.71). As step counts increased the differences between Fitbit-Flex and Actigraph also increased. Fitbit-Flex over-estimated step counts in females (556 steps/day), males (1462 steps/day) and total participants (1038 steps/day) as well as for minutes of MVPA in females (4\u2009min/day), males (15\u2009min/day) and total participants (10\u2009min/day). Fitbit-Flex had high sensitivity and specificity in classifying participants who achieved the recommended physical activity guidelines.\\n\\nCONCLUSION: Fitbit-Flex is accurate in assessing attainment of physical activity guideline recommendations and is useful for monitoring physical activity in cardiac patients. The device does, however, slightly over-estimate step counts and MVPA.", "author" : [ { "dropping-particle" : "", "family" : "Alharbi", "given" : "M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bauman", "given" : "A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Neubeck", "given" : "L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gallagher", "given" : "R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European Journal of Preventive Cardiology", "id" : "ITEM-3", "issued" : { "date-parts" : [ [ "2016" ] ] }, "page" : "1-10", "title" : "Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^17\u201319", "plainTextFormattedCitation" : "17\u201319", "previouslyFormattedCitation" : "^16\u201318" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }17–19. Accelerometers are small and minimally obtrusive, with battery life and storage capacity enabling long-term monitoring from several days to weeks. Brage et al. described a branched equation model in which a combination of HR and accelerometer recordings was used to estimate physical-activity related energy expenditure in healthy subjects ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1152/japplphysiol.00703.2003", "ISSN" : "8750-7587", "PMID" : "12972441", "abstract" : "The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brage", "given" : "Niels", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wong", "given" : "Man-Yu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Andersen", "given" : "Lars Bo", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Froberg", "given" : "Karsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Applied Physiology", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2004", "1" ] ] }, "page" : "343-51", "title" : "Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.", "type" : "article-journal", "volume" : "96" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁰", "plainTextFormattedCitation" : "20", "previouslyFormattedCitation" : "¹⁹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }20. Other studies showed that models combining HR and accelerometer data significantly improved energy expenditure estimation accuracy as compared to single-input only models in children and healthy adults ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1249/01.mss.0000176466.", "ISBN" : "0195-9131 (Print)\\n0195-9131 (Linking)", "ISSN" : "01959131", "PMID" : "16260978", "abstract" : "PURPOSE: Accurate measurement of physical activity in children is a challenge. Combining physiological (e.g., heart rate (HR)) and body movement registration (e.g., accelerometry) may overcome limitations with either method used alone. This study aimed to compare the estimated physical activity energy expenditure (PAEE) from hip- and ankle-mounted MTI Actigraphs, a hip-mounted Actical, and a new combined HR and movement sensor, the Actiheart (Cambridge Neurotechnology, Papworth, UK). METHODS: Resting EE and submaximal EE (treadmill walking and running) were measured in 39 children (13.2 +/- 0.3 yr) by indirect calorimetry during a progressive treadmill exercise bout. Associations between monitor outputs (activity counts, HR, and activity counts + HR) and the criterion were examined by linear regression models. The agreement between measured and predicted PAEE was examined by modified Bland-Altman plots in a subsample of participants. RESULTS: The combined Actiheart model (activity counts + HR) had the strongest relationship with PAEE (R2 = 0.86), compared with those from the single-measure models (R2 = 0.69 and 0.82 for the activity model and HR model). The explained variances from the other activity monitors were lower (R2 = 0.50, 0.37, and 0.67) for the hip MTI, ankle MTI, and Actical, respectively. In cross-validation analyses, significant correlations were observed between estimation errors of the methods with the criterion (r = 0.49 to 0.90) in all models using only activity counts indicating a large systematic error. The HR and combined models indicated less systematic error (r = 0.41 and 0.33, respectively). CONCLUSIONS: Of the techniques considered, combined HR and movement sensing is the most valid for estimating PAEE in children during treadmill walking and running, compared with movement or HR alone. It also has the lowest level of systematic error.", "author" : [ { "dropping-particle" : "", "family" : "Corder", "given" : "Kirsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Medicine and Science in Sports and Exercise", "id" : "ITEM-1", "issue" : "10", "issued" : { "date-parts" : [ [ "2005" ] ] }, "page" : "1761-1767", "title" : "Comparison of PAEE from combined and separate heart rate and movement models in children", "type" : "article-journal", "volume" : "37" }, "uris" : [ "", "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1079/BJN20051527", "ISSN" : "0007-1145", "author" : [ { "dropping-particle" : "", "family" : "Johansson", "given" : "H. Patrik", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rossander-Hulth\u00e9n", "given" : "Lena", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Slinde", "given" : "Frode", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekblom", "given" : "Bj\u00f6rn", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Nutrition", "id" : "ITEM-2", "issue" : "03", "issued" : { "date-parts" : [ [ "2007", "3", "8" ] ] }, "page" : "631", "title" : "Accelerometry combined with heart rate telemetry in the assessment of total energy expenditure", "type" : "article-journal", "volume" : "95" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "^21,22", "plainTextFormattedCitation" : "21,22", "previouslyFormattedCitation" : "^20,21" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }21,22. Although combining HR and accelerometer data for energy expenditure assessment seems to lead to higher estimation accuracy in healthy adults, these prediction models cannot be translated directly to cardiac patients. Beta-blocker therapy, which aims at reducing myocardial oxygen demand by lowering heart rate and blood pressure ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.ehj.2004.06.002", "ISSN" : "0195-668X", "PMID" : "15288162", "author" : [ { "dropping-particle" : "", "family" : "L\u00f3pez-Send\u00f3n", "given" : "Jos\u00e9", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Swedberg", "given" : "Karl", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McMurray", "given" : "John", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Tamargo", "given" : "Juan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Maggioni", "given" : "Aldo P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Dargie", "given" : "Henry", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Tendera", "given" : "Michal", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Waagstein", "given" : "Finn", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kjekshus", "given" : "Jan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lechat", "given" : "Philippe", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Torp-Pedersen", "given" : "Christian", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European heart journal", "id" : "ITEM-1", "issue" : "15", "issued" : { "date-parts" : [ [ "2004" ] ] }, "page" : "1341-1362", "title" : "Expert consensus document on beta-adrenergic receptor blockers.", "type" : "article-journal", "volume" : "25" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²³", "plainTextFormattedCitation" : "23", "previouslyFormattedCitation" : "²²" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }23, plays an important role in the treatment of cardiovascular diseases and the prevention of future cardiac events. The heart rate-lowering effect of beta-blockers, which is highly variable between subjects, may impact the ability of HR to predict energy expenditure. Therefore, the main objective of this study was to develop an energy expenditure prediction model for beta-blocker medicated cardiac rehabilitation patients based on both HR and accelerometer data. For this aim, multivariate linear regression models were developed, and different combinations of features were evaluated. In addition, a method based on a calibration protocol for model personalization, called the flex-HR method ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "0002-9165 (Print)\\n0002-9165 (Linking)", "ISSN" : "0002-9165", "PMID" : "3414570", "abstract" : "Total daily energy expenditure (TDEE) and energy expended in activity (EAC) were estimated by the minute-by-minute heart-rate method in 22 (16 men, 6 women) individually calibrated subjects and compared with values obtained by whole-body indirect calorimetry. Subjects followed four activity protocols during the 22 h in the calorimeter; no exercise (n = 6) and 2 (n = 5), 4 (n = 4), and 6 (n = 6) 30-min bouts of exercise on a bicycle ergometer at varying intensities. There were no statistically significant differences between the two methods in TDEE or EAC in any of the sex or protocol groupings. The regression of TDEE by heart rate on TDEE in the calorimeter was y = 0.92x + 1.0 MJ; (r = 0.87, SEE = 0.91 MJ). The heart-rate method also follows the varying activity patterns of individuals and can be used to closely estimate the TDEE and EAC of even small (n = 4-6) groups of subjects. In the present measurements, it gave a maximum error of TDEE for individuals of +20% and -15%.", "author" : [ { "dropping-particle" : "", "family" : "Spurr", "given" : "G B", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Reina", "given" : "J C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Prentice", "given" : "a M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Murgatroyd", "given" : "P. R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goldberg", "given" : "G.R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Christman", "given" : "N. T.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "The American Journal of Clinical Nutrition", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1988" ] ] }, "page" : "552-559", "title" : "Energy expenditure from minute-by-minute recording : comparison with indirect calorimetry", "type" : "article-journal", "volume" : "48" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁴", "plainTextFormattedCitation" : "24", "previouslyFormattedCitation" : "²³" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }24, was evaluated as a benchmark solution.MethodsStudy populationPatients were eligible for participation when they were admitted to cardiac rehabilitation two to four weeks after hospitalisation for myocardial infarction, unstable angina, or a revascularization procedure (percutaneous coronary intervention or coronary artery bypass grafting) at the Máxima Medical Center, Veldhoven (The Netherlands). High risk of further events was an exclusion criterion (i.e. patients with symptomatic heart failure, complex congenital heart disease, severe depression, arrhythmias or co-morbidity limiting exercise performance). Recruitment was performed between November 2013 and August 2014. Sixteen male patients that were participating in referred to outpatientexercise-based cardiac rehabilitation were recruited. Fourteen patients were taking beta-blockers (metoprolol) at the time of the test. Each participant signed an informed consent form. The study was approved by the local Medical Ethical Committee of Máxima Medical Center, Veldhoven, The Netherlands. Symptom limited exercise test A symptom limited exercise test was carried out on a cycle ergometer in an upright-seated position on an electromagnetically braked cycle ergometer (Lode Corrival, Lode BV, Groningen), using an individualized ramp protocol aiming at a test duration of 8-12 minutes. Ventilatory parameters were measured breath-by-breath (Masterscreen CPX, CareFusion, Hoechberg, Germany). The test was ended when the patient was not able to maintain the required pedaling frequency. PeakVO2 was recorded as the final 30-second averaged value of the test. Physical activity protocolThe physical activity protocol, as described in Table 1, consisted of a randomly ordered set of 11 daily living activities of low to moderate intensity. Resting HR (RHR) was measured in the beginning of the test, and after each activity the patient had a recovery break, which lasted until the HR reached the resting value. Energy expenditure measurementsOxygen uptake (VO2) and carbon dioxide production (VCO2) were measured for the entire duration of the activity protocol by the Cosmed K4 b2 (Cosmed, Rome, Italy) portable metabolic system, which is a breath-by-breath pulmonary gas exchange measurement system consisting of a face mask, an analyzer unit, and a battery. Total energy expenditure (TEE) was then obtained using the Weir equation from the breath-by-breath measurements of O2 and CO2, averaged on a minute-by-minute basis and divided by resting metabolic rate (RMR) to determine the physical activity level (PAL). PAL is a popular parameter used to represent energy expenditure adjusted for RMR, which allows to compare measurements of TEE for subjects with different body size and composition. PAL can be used to determine activity intensity. Light, moderate, and vigorous intensity activities are characterized by a PAL < 3, < 6, and ≥ 6, respectively ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "0781745063", "author" : [ { "dropping-particle" : "", "family" : "Whaley", "given" : "Mitchell H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brubaker", "given" : "Peter H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Otto", "given" : "Robert M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Armstrong", "given" : "Lawrence E.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "edition" : "7th editio", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2006" ] ] }, "title" : "ACSM's guidelines for exercise testing and prescription", "type" : "book" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "²⁵", "plainTextFormattedCitation" : "25", "previouslyFormattedCitation" : "²⁴" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }25. The average daily PAL for the healthy adult population is 1.7, a PAL of 1.2 represents the activity level of a bed-bound subjects ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Black AE, Coward WA, Cole TJ", "given" : "Prentice AM.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European Journal of Clinical Nutrition", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1996" ] ] }, "page" : "72-92", "title" : "Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements", "type" : "article-journal", "volume" : "50" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "²⁶", "plainTextFormattedCitation" : "26", "previouslyFormattedCitation" : "²⁵" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }26, while an athlete participating in the Tour de France can reach a PAL of 5 ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "KR", "given" : "Westerterp", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "WH", "given" : "Saris", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "M", "given" : "van Es", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "F", "given" : "ten Hoor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Applied Physiology", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1986" ] ] }, "page" : "2162\u20132167", "title" : "Use of the doubly labeled water technique in humans during heavy sustained exercise", "type" : "article-journal", "volume" : "61" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "²⁷", "plainTextFormattedCitation" : "27", "previouslyFormattedCitation" : "²⁶" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }27. RMR measurementPrior to the RMR assessment, patients were instructed to fast for 12 hours and to avoid sports activities for 24 hours. During the assessment, patients were asked to stay awake. RMR was measured using the Cosmed metabolic equipment with the Canopy option using the mean values of VO2 and VCO2 data collected over a five minutes stability interval according to Matarese’s recommendations ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Matarese", "given" : "L E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "J Am Diet Assoc", "id" : "ITEM-1", "issue" : "10 suppl 2", "issued" : { "date-parts" : [ [ "1997" ] ] }, "page" : "154-160", "title" : "Indirect calorimetry: technical aspects", "type" : "article-journal", "volume" : "97" }, "uris" : [ "", "" ] } ], "mendeley" : { "formattedCitation" : "²⁸", "plainTextFormattedCitation" : "28", "previouslyFormattedCitation" : "²⁷" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }28.Physical activity and heart rate measurementsPhysical activity was measured using an ActiGraph wGT3X+ activity monitor positioned at the waist (ActiGraph, Pensacola, Florida), which is a tri-axial accelerometer with dynamic range of ±6G and sampling frequency set to 40Hz. Acceleration data were used to determine activity counts per minute (ACmin) according to the following equation: QUOTE where, for each i-sample in a minute period (T) the vector magnitude signal (v) is subtracted to the mean v over that minute to determine ACmin. The v signal is obtained from the Euclidean norm of the x, y, z signal representing each sensing axis of the accelerometer. HR was recorded on a second-by-second frequency using a commercially available ECG chest belt (Garmin Ltd.) that wirelessly transmitted HR data to the ActiGraph accelerometer unit for synchronous data storage. Minute-by-minute average for HR and ACmin were computed to predict energy expenditure. HR above rest (HRnet) was obtained by subtracting RHR from each value of the HR. Data were visually inspected and annotated to determine start and stop for each activity over the recorded acceleration signals. The recorded signals were processed and organized using MatLab (Matlab, Mathworks, Cambridge, MA).Energy expenditure modelingTEE and PAL estimation models were developed using multivariate linear regression analysis. This method allowed for automatically and iteratively selecting which independent variables to include in the prediction models. Three different groups of independent variables were considered: i) patient characteristics which included age, body weight, height, BMI, RMR, Beta-blocker dose (i.e. metoprolol 0, 50, 75 or 100mg/day), and peakVO2; ii) Body movement as described by ACmin, and the logarithm transform of ACmin; and iii) HR and HRnet information. Five different energy expenditure prediction models were derived using the following combination of independent variables: a) Body movement features; b) Body movement + patient characteristics; c) HR features; d) HR features + patient characteristics; e) Body movement + HR features + patient characteristics. Patient characteristics were included in the modeling process to account for between-individual variability in the relationship between HR or ACmin and energy expenditure (TEE or PAL) ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/EMBC.2015.7320162", "ISBN" : "9781424492718", "ISSN" : "1557170X", "author" : [ { "dropping-particle" : "", "family" : "Bonomi", "given" : "Alberto G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goldenberg", "given" : "Sharon", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Papini", "given" : "Gabriele", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kraal", "given" : "Jos", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Stut", "given" : "Wim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sartor", "given" : "Francesco", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kemps", "given" : "Hareld", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "7642-7646", "title" : "Predicting energy expenditure from photo-plethysmographic measurements of heart rate under beta blocker therapy: Data driven personalization strategies based on mixed models", "type" : "article-journal", "volume" : "2015-Novem" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁹", "plainTextFormattedCitation" : "29", "previouslyFormattedCitation" : "²⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }29. HR and body movement features were included to describe the within-individual variability in energy expenditure.Additionally, we used the HR-flex method as alternative solution to account for between-patient variability in the energy expenditure prediction models ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "0002-9165 (Print)\\n0002-9165 (Linking)", "ISSN" : "0002-9165", "PMID" : "3414570", "abstract" : "Total daily energy expenditure (TDEE) and energy expended in activity (EAC) were estimated by the minute-by-minute heart-rate method in 22 (16 men, 6 women) individually calibrated subjects and compared with values obtained by whole-body indirect calorimetry. Subjects followed four activity protocols during the 22 h in the calorimeter; no exercise (n = 6) and 2 (n = 5), 4 (n = 4), and 6 (n = 6) 30-min bouts of exercise on a bicycle ergometer at varying intensities. There were no statistically significant differences between the two methods in TDEE or EAC in any of the sex or protocol groupings. The regression of TDEE by heart rate on TDEE in the calorimeter was y = 0.92x + 1.0 MJ; (r = 0.87, SEE = 0.91 MJ). The heart-rate method also follows the varying activity patterns of individuals and can be used to closely estimate the TDEE and EAC of even small (n = 4-6) groups of subjects. In the present measurements, it gave a maximum error of TDEE for individuals of +20% and -15%.", "author" : [ { "dropping-particle" : "", "family" : "Spurr", "given" : "G B", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Reina", "given" : "J C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Prentice", "given" : "a M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Murgatroyd", "given" : "P. R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goldberg", "given" : "G.R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Christman", "given" : "N. T.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "The American Journal of Clinical Nutrition", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1988" ] ] }, "page" : "552-559", "title" : "Energy expenditure from minute-by-minute recording : comparison with indirect calorimetry", "type" : "article-journal", "volume" : "48" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁴", "plainTextFormattedCitation" : "24", "previouslyFormattedCitation" : "²³" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }24. This method deploys HR and energy expenditure measurements during a set of cycling activities at increasing intensity (0W to 70W) to determine parameters of a linear prediction model applicable to any other activity for HR values above the HR-flex. HR-flex is defined as the lowest HR recorded during cycling at very low effort (0W). Below the HR-flex energy expenditure is set to resting values. The HR-flex method leads to development of an energy expenditure prediction equation by personalizing model parameters using data from a cycling calibration protocol.StatisticsData are presented as mean standard deviation. Pearson correlation coefficient was calculated to describe the association between ACmin, HR features and TEE or PAL for each individual patient data or for the entire dataset. Student t-test was used to assess significant differences in the estimation error of the energy expenditure prediction models between the development and validation samples. Development of the energy expenditure prediction model was carried out with data from 11 patients, while data from the remaining 5 patients were used for validation. For the HR-flex model, patient data from both groups (from the development and validation group) were used for developing the patient-specific prediction model. Significance level was set to p < 0.05. All statistical analyses were carried out using RStudio (version 0.98.507, R Development Core Team, Free Software Foundation Boston, MA, USA).ResultsSixteen patients agreed to participate. The dataset included a set of static variables indicating the individual characteristics as well as a set of time-varying features like TEE, PAL, HR and ACmin .for an average of 64 minutes/patient (total 1027 data points for each time-varying variables). Patients were divided in two groups for model development and validation. Baseline characteristics are presented in Table 2. On the entire study population, a significant association was found between peakVO2 and resting VO2 (R = 0.65, p < 0.01) and between peakVO2 and RMR (R = 0.52, p < 0.05). RHR was not significantly correlated with any of the patient characteristics. Max HR at the symptom limited exercise test was negatively associated with age (R = - 0.57, p < 0.05) and positively associated with peakVO2 (R = 0.63, p < 0.01).A significant correlation was found between HR or body movement data (HR, HRnet, ACmin) and measures of energy expenditure (Table 3). On a group level HRnet showed the strongest association with energy expenditure as compared to ACmin or HR. On an individual level, the average correlation between HR and energy expenditure sharply increased, indicating a strong between-subject difference in the relationship between HR and TEE or PAL.Results from the stepwise multivariate regression analysis are shown in Table 4 and Figure 1. Model e) including a combination of body movement, HR features, and patient characteristics showed the lowest estimation error and the largest correlation with both TEE and PAL (> 76% and > 83%). When HR features were omitted as input to the prediction model (Models a and b) energy expenditure estimates showed larger error (Table 4).The HR-flex model showed the energy expenditure estimation accuracy was comparable with some of the models based on a combination of HR and body movement features (Table 4). However, accuracy was lower than the most accurate prediction model based on HR, body movement and patient characteristics (Figure 2). The HR-flex model accuracy was not possible to test using a hold-out group of patients (like the validation group) since the method required individual calibration to set the value of constituting parameters.. DiscussionIn this study we developed an energy expenditure prediction model for cardiac rehabilitation patients using beta-blockers, based on body movement and HR data. A multivariate regression model shows the lowest estimation error in the estimation of PAL and TEE when HR, body movement and patient characteristics are included. Personalization of the prediction model using the HR-flex model with a cycling protocol for calibration resulted in somewhat lower accuracy than the most accurate multivariate model.To our knowledge, this study is the first to validate energy expenditure estimation models based on acceleration and HR in cardiac patients taking beta-blockers. The observed correlations between body movement features and TEE as well as HR features and TEE were comparable to studies in healthy subject ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "V", "family" : "Bouten", "given" : "C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Westerterp", "given" : "K R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Verduin", "given" : "M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Janssen", "given" : "J D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Medicine and science in sports and exercise", "id" : "ITEM-1", "issue" : "12", "issued" : { "date-parts" : [ [ "1994" ] ] }, "page" : "1516-1523", "title" : "Assessment of energy expenditure for physical activity using a triaxial accelerometer", "type" : "article-journal", "volume" : "26" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Ceesay", "given" : "S.M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Prentice", "given" : "A.M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Day", "given" : "K.C.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Murgatroyd", "given" : "P. R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goldberg", "given" : "G.R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Scott", "given" : "W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Nutrition", "id" : "ITEM-2", "issued" : { "date-parts" : [ [ "1989" ] ] }, "page" : "175-186", "title" : "The use of heart rate monitoring in the estimation of energy expenditure: a validation study using indirect whole-body calorimetry", "type" : "article-journal", "volume" : "61" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^30,31", "plainTextFormattedCitation" : "30,31", "previouslyFormattedCitation" : "^29,30" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }30,31. In addition, a prediction model using individual HR features or body movement features showed comparable coefficients with studies in healthy subjects ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISSN" : "0044-264X", "PMID" : "9467213", "abstract" : "The doubly labeled water method for the measurement of average daily metabolic rate (ADMR), combined with a measurement of resting metabolic rate, permits the calculation of energy expenditure for physical activity under normal daily living conditions. This procedure was used to evaluate the use of movement registration for physical activity assessment under daily living conditions. Subjects were 16 men and 14 women with normal weight (body mass index (BMI) 24.6 +/- 2.4 kg/m2). Their body movement was registered with a triaxial accelerometer over a 7-day interval, simultaneous with an ADMR measurement with a doubly labeled water method. Resting metabolic rate was measured overnight in a respiration chamber (sleeping metabolic rate (SMR)) at the start of the ADMR measurement. Subjects did wear the accelerometer during waking hours. Accelerometer output (AO, counts/min) was related to physical activity as quantified by adjustment of ADMR for SMR. Additional studies were performed in 11 subjects with anorexia nervosa (BMI 16.7 +/- 1.7 kg/m2) and 8 subjects with morbid obesity (BMI 45.3 +/- 6.8 kg/m2). AO explained most of the variation in ADMR, after adjustment for SMR (R2 = 0.64, SEE = 0.9 MJ/d) Average AO was 1108 +/- 293, 1144 +/- 318, and 946 +/- 391 for subjects with normal weight, anorexia nervosa, and morbid obesity, respectively, and was not significantly different between the three groups. However, in the anorectics AO was significantly related to body mass index (r = 0.84, (p < 0.01), subjects with a BMI17 kg/m2 were equally or more active compared with control subjects, while subjects with a BMI < 17 kg/m2 were equally or less active compared with control subjects. In the morbid obese group, 5 of the 8 subjects had a low activity level (AO < 900 counts/day) and the other 3 had a high activity level (AO1150 counts/day). The triaxial accelerometer is an objective method that can be used to quantify physical activity related energy expenditure and to distinguish differences in activity levels between individuals.", "author" : [ { "dropping-particle" : "", "family" : "Westerterp", "given" : "K R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "V", "family" : "Bouten", "given" : "C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Zeitschrift f\u00fcr Ern\u00e4hrungswissenschaft", "id" : "ITEM-1", "issue" : "4", "issued" : { "date-parts" : [ [ "1997" ] ] }, "page" : "263-7", "title" : "Physical activity assessment: comparison between movement registration and doubly labeled water method.", "type" : "article-journal", "volume" : "36" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "ISBN" : "0002-9165 (Print)\\n0002-9165 (Linking)", "ISSN" : "0002-9165", "PMID" : "3414570", "abstract" : "Total daily energy expenditure (TDEE) and energy expended in activity (EAC) were estimated by the minute-by-minute heart-rate method in 22 (16 men, 6 women) individually calibrated subjects and compared with values obtained by whole-body indirect calorimetry. Subjects followed four activity protocols during the 22 h in the calorimeter; no exercise (n = 6) and 2 (n = 5), 4 (n = 4), and 6 (n = 6) 30-min bouts of exercise on a bicycle ergometer at varying intensities. There were no statistically significant differences between the two methods in TDEE or EAC in any of the sex or protocol groupings. The regression of TDEE by heart rate on TDEE in the calorimeter was y = 0.92x + 1.0 MJ; (r = 0.87, SEE = 0.91 MJ). The heart-rate method also follows the varying activity patterns of individuals and can be used to closely estimate the TDEE and EAC of even small (n = 4-6) groups of subjects. In the present measurements, it gave a maximum error of TDEE for individuals of +20% and -15%.", "author" : [ { "dropping-particle" : "", "family" : "Spurr", "given" : "G B", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Reina", "given" : "J C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Prentice", "given" : "a M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Murgatroyd", "given" : "P. R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goldberg", "given" : "G.R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Christman", "given" : "N. T.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "The American Journal of Clinical Nutrition", "id" : "ITEM-2", "issued" : { "date-parts" : [ [ "1988" ] ] }, "page" : "552-559", "title" : "Energy expenditure from minute-by-minute recording : comparison with indirect calorimetry", "type" : "article-journal", "volume" : "48" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^24,32", "plainTextFormattedCitation" : "24,32", "previouslyFormattedCitation" : "^23,31" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }24,32. As discussed in previous literature, accuracy of energy expenditure estimation can improve when HR and body movement data are combined ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1152/japplphysiol.00703.2003", "ISSN" : "8750-7587", "PMID" : "12972441", "abstract" : "The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brage", "given" : "Niels", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wong", "given" : "Man-Yu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Andersen", "given" : "Lars Bo", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Froberg", "given" : "Karsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Applied Physiology", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2004", "1" ] ] }, "page" : "343-51", "title" : "Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.", "type" : "article-journal", "volume" : "96" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "ISSN" : "0954-3007", "PMID" : "10822288", "abstract" : "OBJECTIVE: Heart rate monitoring has previously been used as a technique for measuring energy expenditure (EE) in field studies. However, the combination of heart rate monitoring with movement sensoring could have theoretical advantages compared to either method used alone. Therefore, this study was undertaken to develop and validate a new combined heart rate monitor and movement sensor instrument (HR+M) for measuring EE.\n\nMETHODS: The HR+M instrument is a single-piece instrument worn around the chest which records minute-by-minute heart rate and movement. Eight subjects underwent an individual calibration in which EE and heart rate were measured at rest and during a sub-maximal bicycle ergometer test. They then wore the HR+M for 24 hours in a whole-body calorimeter and underwent a standard protocol including periods of physical activity and inactivity. Minute-by-minute heart rate was converted to EE using individual calibration curves with the motion data discriminating between periods of inactivity and activity at low heart rate levels. EE was also calculated using the HRFlex method which relies on heart rate alone. Both estimates of EE were compared to EE measured in the whole-body calorimeter.\n\nRESULTS: The mean percentage error of the HR+M method calculating TEE compared with the gold standard of the calorimeter measurement was 0.00% (95% CI of the mean error -0.25, 1. 25). The HRFlex method using the heart rate information alone resulted in a mean percentage error of 16.5% (95% CI of the mean error -0.57, 1.76).\n\nCONCLUSIONS: This preliminary test of HR+M demonstrates its ability to estimate EE and the pattern of EE and activity throughout the day. Further validation studies in free-living individuals are necessary.\n\nSPONSORSHIP: NJW is an MRC Clinician Scientist Fellow. KLR holds an MRC PhD scholarship.", "author" : [ { "dropping-particle" : "", "family" : "Rennie", "given" : "K", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rowsell", "given" : "T", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Jebb", "given" : "S a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Holburn", "given" : "D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "N J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European journal of clinical nutrition", "id" : "ITEM-2", "issue" : "5", "issued" : { "date-parts" : [ [ "2000", "5" ] ] }, "page" : "409-14", "title" : "A combined heart rate and movement sensor: proof of concept and preliminary testing study.", "type" : "article-journal", "volume" : "54" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^20,33", "plainTextFormattedCitation" : "20,33", "previouslyFormattedCitation" : "^19,32" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }20,33. A validation study in healthy subjects demonstrated a lower RMSE and higher correlation in a HR-body movement model estimating free-living PAEE (r2 = 0.78) as compared to a model using estimates from HR or body movement alone (r2 = 0.59 and r2 = 0.61 respectively) ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1152/japplphysiol.00703.2003", "ISSN" : "8750-7587", "PMID" : "12972441", "abstract" : "The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brage", "given" : "Niels", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wong", "given" : "Man-Yu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Andersen", "given" : "Lars Bo", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Froberg", "given" : "Karsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Applied Physiology", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2004", "1" ] ] }, "page" : "343-51", "title" : "Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.", "type" : "article-journal", "volume" : "96" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁰", "plainTextFormattedCitation" : "20", "previouslyFormattedCitation" : "¹⁹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }20. We observed similar results for the individual HR-body movement regression models in cardiac patients (TEE: r2 = 0.56 and r2 = 0.64 respectively), and a similar improvement in r2 and RMSE in the regression model when body movement, HR and patient characteristics were combined (r2 = 0.76 for TEE, r2 = 0.83 for PAL). Studies validating HR-body movement models in free-living conditions showed comparable results ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1371/journal.pone.0137206", "ISSN" : "1932-6203", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Westgate", "given" : "Kate", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Stegle", "given" : "Oliver", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wright", "given" : "Antony", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Plos One", "id" : "ITEM-1", "issue" : "9", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "e0137206", "title" : "Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study", "type" : "article-journal", "volume" : "10" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1038/ejcn.2014.51", "ISSN" : "1476-5640", "PMID" : "24690589", "abstract" : "BACKGROUND/OBJECTIVES: A combined heart rate (HR) and motion sensor (Actiheart) has been proposed as an accurate method for assessing total energy expenditure (TEE) and physical activity energy expenditure (PAEE). However, the extent to which factors such as caffeine may affect the accuracy by which the estimated HR-related PAEE contribution will affect TEE and PAEE estimates is unknown. Therefore, we examined the validity of Actiheart in estimating TEE and PAEE in free-living adults under a caffeine trial compared with doubly labeled water (DLW) as reference criterion. SUBJECTS/METHODS: Using a double-blind crossover trial ( ID: #NCT01477294) with two conditions (4-day each with a 3-day-washout period), randomly ordered as caffeine (5 mg/kg per day) and placebo (malt-dextrine) intake, TEE was measured by DLW in 17 physically active men (20\u201338 years) who were non-caffeine users. In each condition, resting energy expenditure (REE) was assessed by indirect calorimetry and PAEE was calculated as (TEE \u2212 (REE+0.1 TEE)). Simultaneously, PAEE and TEE were estimated by Actiheart using an individual calibration (ACC+HRstep). RESULTS: Under caffeine, ACC+HRstep explained 76 and 64% of TEE and PAEE from DLW, respectively; corresponding results for the placebo condition were 82 and 66%. No mean bias was found between ACC+HRstep and DLW for TEE (caffeine:-468 kJ per day; placebo:-407 kJ per day), although PAEE was slightly underestimated (caffeine:-856 kJ per day; placebo:-1147 kJ per day). Similar limits of agreement were observed in both conditions ranging from \u2212 2066 to 3002 and from \u2212 3488 to 1776 kJ per day for TEE and PAEE, respectively. CONCLUSIONS: Regardless of caffeine intake, the combined HR and motion sensor is valid for estimating free-living energy expenditure in a group of healthy men but is less accurate for an individual assessment.", "author" : [ { "dropping-particle" : "", "family" : "Silva", "given" : "AM", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Santos", "given" : "DA", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Matias", "given" : "CN", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "J\u00fadice", "given" : "PB", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Magalh\u00e3es", "given" : "JP", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "U", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sardinha", "given" : "LB", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "European journal of clinical nutrition", "id" : "ITEM-2", "issue" : "April 2014", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "20-27", "title" : "Accuracy of a combined heart rate and motion sensor for assessing energy expenditure in free-living adults during a double-blind crossover caffeine trial using doubly labeled water as the reference method", "type" : "article-journal", "volume" : "69" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^34,35", "plainTextFormattedCitation" : "34,35", "previouslyFormattedCitation" : "^33,34" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }34,35. Our results revealed that the accuracy of energy expenditure estimation improves when specific patient characteristics are included in the model. Although HR and oxygen consumption are linearly related, our results showed that variation in individual patient characteristics (e.g. fitness, age and gender) cause a high between-subject variation. Adding these patient characteristics and using individual calibration protocols (e.g. step-test, cycling protocol) can capture these variations and improve the accuracy of TEE/PAL estimation. In particular, the HR-Flex method, using HR data and a cycling protocol for calibration proved to be more accurate in TEE and PAL estimation than the models combining body movement and HR data only. These results are in line with the literature, indicating the beneficial effects of individual calibration to capture between-individual variances ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1371/journal.pone.0137206", "ISSN" : "1932-6203", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Westgate", "given" : "Kate", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Stegle", "given" : "Oliver", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wright", "given" : "Antony", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Plos One", "id" : "ITEM-1", "issue" : "9", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "e0137206", "title" : "Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study", "type" : "article-journal", "volume" : "10" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1152/japplphysiol.00092.2006", "ISSN" : "8750-7587", "PMID" : "17463305", "abstract" : "Combining accelerometry with heart rate (HR) monitoring may improve precision of physical activity measurement. Considerable variation exists in the relationships between physical activity intensity (PAI) and HR and accelerometry, which may be reduced by individual calibration. However, individual calibration limits feasibility of these techniques in population studies, and less burdensome, yet valid, methods of calibration are required. We aimed to evaluate the precision of different individual calibration procedures against a reference calibration procedure: a ramped treadmill walking-running test with continuous measurement of PAI by indirect calorimetry in 26 women and 25 men [mean (SD): 35 (9) yr, 1.69 (0.10) m, 70 (14) kg]. Acceleration (along the longitudinal axis of the trunk) and HR were measured simultaneously. Alternative calibration procedures included treadmill testing without calorimetry, submaximal step and walk tests with and without calorimetry, and nonexercise calibration using sleeping HR and gender. Reference accelerometry and HR models explained >95% of the between-individual variance in PAI (P < 0.001). This fraction dropped to 73 and 81%, respectively, for accelerometry and HR models calibrated with treadmill tests without calorimetry. Step-test calibration captured 62-64% (accelerometry) and 68% (HR) of the variance between individuals. Corresponding values were 63-76% and 59-61% for walk-test calibration. There was only little benefit of including calorimetry during step and walk calibration for HR models. Nonexercise calibration procedures explained 54% (accelerometry) and 30% (HR) of the between-individual variance. In conclusion, a substantial proportion of the between-individual variance in relationships between PAI, accelerometry, and HR is captured with simple calibration procedures, feasible for use in epidemiological studies.", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brage", "given" : "Niels", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hennings", "given" : "Mark a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Froberg", "given" : "Karsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of applied physiology (Bethesda, Md. : 1985)", "id" : "ITEM-2", "issue" : "2", "issued" : { "date-parts" : [ [ "2007", "8" ] ] }, "page" : "682-92", "title" : "Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity.", "type" : "article-journal", "volume" : "103" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^34,36", "plainTextFormattedCitation" : "34,36", "previouslyFormattedCitation" : "^33,35" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }34,36. However, individual calibration also requires resource-demanding procedures, and is therefore unsuitable for large-scale studies and clinical practice. In addition, branched equation models, used in several studies to distinguish between low- and high intensity activities based on the transition between walking-running ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1152/japplphysiol.00703.2003", "ISSN" : "8750-7587", "PMID" : "12972441", "abstract" : "The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.", "author" : [ { "dropping-particle" : "", "family" : "Brage", "given" : "S\u00f8ren", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brage", "given" : "Niels", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Franks", "given" : "Paul W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ekelund", "given" : "Ulf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wong", "given" : "Man-Yu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Andersen", "given" : "Lars Bo", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Froberg", "given" : "Karsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wareham", "given" : "Nicholas J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Applied Physiology", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2004", "1" ] ] }, "page" : "343-51", "title" : "Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.", "type" : "article-journal", "volume" : "96" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁰", "plainTextFormattedCitation" : "20", "previouslyFormattedCitation" : "¹⁹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }20, are unsuitable for cardiac patients with limited physical fitness levels. Therefore, we aimed at developing an energy expenditure model that could be applicable to patient data that are available at the start of a rehabilitation program to successfully monitor physical activity in these patients in daily life. Currently, general patient characteristics (i.e. age, body weight, BMI, medication use) are available in the electronic patient record and a symptom limited exercise test is performed at the start of each cardiac rehabilitationCR program to determine physical fitness (peakVO2). With this data available, only RMR data are required before the most accurate multivariate regression model described in our results (Table 4, model e) can be implemented. However, RMR can be calculated using the Harris-Benedict equation ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1073/pnas.4.12.370", "ISBN" : "0027-8424 (Print)\\n0027-8424 (Linking)", "ISSN" : "0027-8424", "PMID" : "16576330", "abstract" : "Investigators are now generally agreed that the metabolism, expressed in terms of calories per unit of time, of the normal subject shall be taken as a basis of comparison in the investigation of all the special problems of human nutrition, for example, that of the requirements for muscular activity, that of the influence of specific diseases or of the level of nutrition upon metabolism, that of the chahge of metabolic activity with age,.and so forth. Critical in- vestigations in both European and American laboratories have shown that the gaseous metabolism is so affected by various factors that determinations which are to serve as a standard must be made under very exactly controlled condi- difficulties of direct calorimetry (or of the exact measurement of gaseous ex- tions. It is not merely necessary to devise apparatus in which the physical change from which heat production may be computed) are overcome.", "author" : [ { "dropping-particle" : "", "family" : "Harris", "given" : "Ja a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Benedict", "given" : "Fg G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Proceedings of the National Academy of Sciences", "id" : "ITEM-1", "issue" : "12", "issued" : { "date-parts" : [ [ "1918" ] ] }, "page" : "370-373", "title" : "A Biometric Study of Human Basal Metabolism", "type" : "article-journal", "volume" : "4" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³⁷", "plainTextFormattedCitation" : "37", "previouslyFormattedCitation" : "³⁶" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }37, although RMR assessment as performed in this study is probably more accurate. Clinical interpretation and future directionsWith the introduction of accurate energy expenditure models based on HR and body movement for cardiac patients using beta-blockers, physical activity behavior can become a more eminent topic in cardiac rehabilitation. First, objective feedback on physical activity monitored during the intervention provides awareness concerning a patient’s current physical activity behavior. With feedback and motivational coaching based on the objective physical activity data, patients are able to develop self-management skills and improve and/or maintain their physical activity behavior ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1097/01.PHM.0000156901.95289.09", "ISSN" : "0894-9115", "author" : [ { "dropping-particle" : "", "family" : "Izawa", "given" : "Kazuhiro P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Watanabe", "given" : "Satoshi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Omiya", "given" : "Kazuto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hirano", "given" : "Yasuyuki", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Oka", "given" : "Koichiro", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Osada", "given" : "Naohiko", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Iijima", "given" : "Setsu", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "American Journal of Physical Medicine & Rehabilitation", "id" : "ITEM-1", "issue" : "5", "issued" : { "date-parts" : [ [ "2005", "5" ] ] }, "page" : "313-321", "title" : "Effect of the Self-Monitoring Approach on Exercise Maintenance During Cardiac Rehabilitation", "type" : "article-journal", "volume" : "84" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³⁸", "plainTextFormattedCitation" : "38", "previouslyFormattedCitation" : "³⁷" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }38. Second, accurate assessment of physical activity is essential for evaluating the effectiveness of an intervention program and for studying the dose-response relation between physical activity levels and health outcomes. Therefore, it is essential that accurate energy expenditure models are developed, using data that are routinely available at the start of cardiac rehabilitation without the need of additional procedures and resources. Although a slight improvement in the TEE and PAL estimation error was observed with individual calibration processes, generalizability is limited. With the developments in wearable sensors, HR and body movement data collection will become more accurate, comfortable and feasible in the home environment. Whereas this study showed that TEE and PAL can be accurately predicted using HR and body movement data in combination with patient characteristics and data derived from a symptom limited exercise test, the inclusion of activity recognition and improvement of technology may lead to an even further reduce the energy expenditure estimation error ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1152/japplphysiol.00150.2009", "ISBN" : "8750-7587 (Print)\\r0161-7567 (Linking)", "ISSN" : "8750-7587", "PMID" : "19556460", "abstract" : "Accelerometers are often used to quantify the acceleration of the body in arbitrary units (counts) to measure physical activity (PA) and to estimate energy expenditure. The present study investigated whether the identification of types of PA with one accelerometer could improve the estimation of energy expenditure compared with activity counts. Total energy expenditure (TEE) of 15 subjects was measured with the use of double-labeled water. The physical activity level (PAL) was derived by dividing TEE by sleeping metabolic rate. Simultaneously, PA was measured with one accelerometer. Accelerometer output was processed to calculate activity counts per day (AC(D)) and to determine the daily duration of six types of common activities identified with a classification tree model. A daily metabolic value (MET(D)) was calculated as mean of the MET compendium value of each activity type weighed by the daily duration. TEE was predicted by AC(D) and body weight and by AC(D) and fat-free mass, with a standard error of estimate (SEE) of 1.47 MJ/day, and 1.2 MJ/day, respectively. The replacement in these models of AC(D) with MET(D) increased the explained variation in TEE by 9%, decreasing SEE by 0.14 MJ/day and 0.18 MJ/day, respectively. The correlation between PAL and MET(D) (R(2) = 51%) was higher than that between PAL and AC(D) (R(2) = 46%). We conclude that identification of activity types combined with MET intensity values improves the assessment of energy expenditure compared with activity counts. Future studies could develop models to objectively assess activity type and intensity to further increase accuracy of the energy expenditure estimation.", "author" : [ { "dropping-particle" : "", "family" : "Bonomi", "given" : "a G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Plasqui", "given" : "G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goris", "given" : "a H C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Westerterp", "given" : "K R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of applied physiology (Bethesda, Md. : 1985)", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2009" ] ] }, "page" : "655-661", "title" : "Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer.", "type" : "article-journal", "volume" : "107" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³⁹", "plainTextFormattedCitation" : "39", "previouslyFormattedCitation" : "³⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }39. Future studies can use our results as a roadmap for accurate physical activity monitoring with wearable sensors. If the methodology is confirmed in a larger cardiac rehabilitation population with mixed conditions, it will be more applicable in the cardiac rehabilitation setting.Strengths and limitationsIn this study we developed energy expenditure estimation models using HR and body movement in cardiac patients using beta-blocker medication. Nevertheless, generalizing the results to the entire cardiac population (e.g. chronic heart failure) is not straightforward considering the composition of the study population. Furthermore, our results are based on laboratory investigation using an activity protocol including treadmill walking and ergometer cycling as modes of activity. It is not known how well the results apply to free-living conditions. The results of our study are based on a limited number of cardiac rehabilitation patients. Therefore, the results cannot be translated directly to a cardiac rehabilitation population with mixed cardiac conditions.ConclusionIn conclusion, we developed an energy expenditure estimation model for beta-blocker medicated cardiac rehabilitation patients that showed the highest accuracy when HR, body movement data and patient characteristics were combined. Personalization of the model using patient characteristics available at the start of the cardiac rehabilitation program, results in an accurate and feasible model to estimate energy expenditure and physical activity levels of beta-blocker medicated patients. Additional personalization using a cycling protocol did not result in a substantial improvement in the estimation of energy expenditure, indicating that this additional personalization protocol has little benefit in clinical practice. AcknowledgementsThis study received funding from ZonMW, The Netherlands Organisation for Health Research and Development (ZonMw project number 837001003) and was supported in kind by Philips Research.Conflict of interestNone.Authors FS, GP, WS and AB are employed by Philips Research.Author contributionsAll authors contributed to the conception and design of the work. JK, FS, GP and AB contributed to the acquisition, analysis, or interpretation of data for the work. JK and AB drafted the manuscript. All authors critically revised the manuscript, gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1. Lee IM, Shiroma EJ, Lobelo F, et al. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet [Internet]. 2012;380(9838):219–229. 2. Myers J, Kaykha A, George S, et al. 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Effect of the Self-Monitoring Approach on Exercise Maintenance During Cardiac Rehabilitation. Am J Phys Med Rehabil [Internet]. 2005 May;84(5):313–321. 39. Bonomi a G, Plasqui G, Goris a HC, et al. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol. 2009;107(3):655–661. Table 1. Routine of activity tasks randomly performed by the patients and their durationActivity taskDuration (minutes)Sitting at rest5Standing at rest2Walking 4 km/h3Walking 4 km/h, slope 5%3Walking 5.5 km/h3Cycling 0W 70 rpm3Cycling 40W 70 rpm3Cycling 70W 70 rpm3Cleaning a table3Vacuuming3Putting dishes in a dish washer3W, Watt; rpm, rounds per minuteTable 2. Baseline characteristics.Development Group (n = 11)?Mean±SdMin-MaxAge, year57.1±6.645-64Height, cm182.8±10.6173-191Weight, kg95.2±9.582-108BMI, kg/m228.6±3.523.8-33.7RHR, bpm54.4±6.742-66RMR, kcal/min1.2±0.11.0-1.4Resting VO2, ml/min324.7±43.2266.7-418.8PeakVO2, ml/min2326.8±730.81429.3-3908.6Max HR, bpm138.5±23.596.3-177.2Beta-blocker dose, mg/day48.3±32.10-100Validation Group (n = 5)?Mean±SdMin-MaxAge, y53.124±8.78242-61Height, m175.5±6.6164-182Weight, kg94.8±12.280-110BMI, kg/m230.7±3.125-33.2RHR, bpm59.1±5.352-68RMR, kcal/min1.2±0.21.0-1.4Resting VO2, ml/min351.8±48.9315.8-443.8PeakVO2, ml/min2369.3±450.01839.4-3175.1Max HR, bpm151.7±3.9148.1-156.7Beta-blocker dose, mg/day80.3±39.90-100BMI; body mass index; RHR, resting heart rate; bpm, beats per minute; RMR, resting metabolic rate; VO2, oxygen consumption; PeakVO2, maximum oxygen consumption; max HR, maximum heart rate;Table 3. Correlation between energy expenditure measures and body movement or HR features for data from the entire study population (Group) or from each individual patient (Individual).CorrelationTEE vs ACminTEE vs HRTEE vs HRnetp < 0.05MeanSdMeanSdMeanSdGroup0.470.550.68?Individual0.530.150.840.120.840.12?PAL vs ACminPAL vs HRPAL vs HRnet?MeanSdMeanSdMeanSdGroup0.490.490.56?Individual0.530.150.840.120.840.12TEE, total energy expenditure; ACmin, activity counts per minute; HR, heart rate; HRnet, (heart rate – resting heart rate); PAL, physical activity levelTable 4. Accuracy of the energy expenditure prediction models.TEEDevelopmentValidation?R2RMSE (kcal/min)Bias (kcal/min)R2RMSE (kcal/min)Bias (kcal/min)Model a) Movement features0.640.98400.651.031-0.135Model b) Movement + Patient features0.630.9900.571.156-0.249Model c) HR features0.561.08400.760.9220.107Model d) HR + Patient features0.601.03200.790.856-0.079Model e) Movement + HR + Patient features0.760.79900.790.799-0.126HR-flex model0.791.11-0.22---PALDevelopmentValidation?R2RMSE(kcal/min)Bias(kcal/min)R2RMSE(kcal/min)Bias(kcal/min)Model a) Movement features0.611.26200.621.278-0.076Model b) Movement + Patient features0.671.16100.681.1780.008Model c) HR features0.741.02600.721.1230.22Model d) HR + Patient features0.751.00600.790.9860.145Model e) Movement + HR + Patient features0.830.83500.840.8340.118HR-flex model0.800.846-0.13---TEE, total energy expenditure; RMSE: root mean squared error; Bias: mean error; HR, heart rate; PAL, physical activity level. The HR-flex model accuracy was not possible to test using a hold-out group of patients since the method required individual calibration to set the value of constituting parameters. Results from the development group were showed to demonstrate the large generalization properties of the models not affected by overfitting. Figure 1. Correlation between measured and predicted TEE for all the developed models based on a combination of HR, ACmin and patient characteristics.Figure 2. Measured and predicted PAL from a representative patient according to the HR-flex method and the multivariate model based on HR, body movement features and patient characteristics. Black line represents the measured PAL, while red and blue lines the predicted PAL according to the prediction models. ................
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