The Morbidity of Cardiac Nondisease in Schoolchildren

Bergman, Abraham B.; Stamm, Stanley J.

doi:10.1056/nejm196705042761804

Cited by 229 publications

(83 citation statements)

References 11 publications

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“…For example in children with congenital heart disease and hemophilia, hypoactivity might be caused by overprotection by parents and educators (Bar-Or, 1987;Bergman and Stamm, 1967), whereas in juvenile arthritis, children might be hypoactive because of painful joints (Takken et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Validation of the Actiheart activity monitor for measurement of activity energy expenditure in children and adolescents with chronic disease

et al. 2010

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Background/Objectives: The purpose of this study was to develop an activity energy expenditure (AEE) prediction equation for the Actiheart activity monitor for use in children with chronic disease. Subjects/Methods: In total, 63 children, aged 8-18 years with different types of chronic disease (juvenile arthritis, hemophilia, dermatomyositis, neuromuscular disease, cystic fibrosis or congenital heart disease) participated in an activity testing session, which consisted of a resting protocol, working on the computer, sweeping, hallway walking, steps and treadmill walking at three different speeds. During all activities, actual AEE was measured with indirect calorimetry and the participants wore an Actiheart on the chest. Resting EE and resting heart rate were measured during the resting protocol and heart rate above sleep (HRaS) was calculated. Results: Mixed linear modeling produced the following prediction equation:Estimated AEE ðJ=kg= minÞ ¼ À93:7 þ ð4:8ÂHRaSÞþð0:04ÂAccelerometer CountsÞþð39:8ÂGender ðgirls ¼ 0; boys ¼ 1ÞÞ:This equation results in a nonsignificant mean difference of 2.1 J/kg/min (limits of agreement: À144.2 to 148.4 J/kg/min) for the prediction of AEE from the Actiheart compared with actual AEE. Conclusions: The Actiheart is valid for the use of AEE determination when using the new prediction equation for groups of children with chronic disease. However, the prediction error limits the use of the equation in individual subjects.

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Section: Introductionmentioning

confidence: 99%

Validation of the Actiheart activity monitor for measurement of activity energy expenditure in children and adolescents with chronic disease

et al. 2010

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“…Whether or not they are treated, simply labeling individuals as abnormal carries potentially important physical and psychological consequences. [6][7][8][9] Furthermore, apparently small numerical alterations to narrow the definition of normal can affect literally millions of people. 10 If this encourages more treatment, it does so in those individuals for whom clinical benefits are least likely to outweigh treatment harms.…”

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confidence: 99%

Impact of Diastolic and Systolic Blood Pressure on Mortality: Implications for the Definition of “Normal”

2011

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“…15 It would be helpful if simple screening through a device such as PCS would allow us to de-label these children. As indicated in this study, such a procedure would be impossible using PCS because the machine erroneously labels as "normal" children who have "suspicious" cardiac murmurs.…”

Section: Discussionmentioning

confidence: 99%