Classification tree for the assessment of sedentary lifestyle among hypertensive/Arbol de clasificacion para la evaluacion del estilo de vida sedentario entre las personas con hipertension/Arvore de classificacao para a avaliacao do estilo de vida sedentario entre pessoas com hipertensao.
Trees Classification (TC) are graphic tools that allow a clearer and large view and needs the different directions they can take to multiple decision alternatives based on conditional probabilities for assistance in the diagnosis inference. (1) This help in the decision-making process, allowing a quick visualization of the occurrence of diagnosis probabilities and can be used in clinical trial processes maintaining the accuracy of diagnostic inference. The development of TC for diagnostic inference has been described in previous studies as a tool to support the clinical reasoning of nurses. A previous study developed classification trees for the inference of Ineffective clearance of airways and Ineffective breathing pattern in children with acute respiratory infection. (1) Other researchers developed a classification tree for differentiation of nursing diagnoses activity Intolerance and Impaired physical mobility among elderly. (2)
In particular, the use of TC can be a useful tool for the care of individuals with clinical conditions including changes in their health habits. The early identification and subsequent evaluations of these changes enable a quick assessment of the effectiveness of interventions and analysis of the achieved health results. Although assist in this process and based on clinical indicators with greater interaction with a diagnosis, studies with this approach are still scarce. A sedentary lifestyle is a nursing diagnosis example linked to health habits and various chronic conditions that require prolonged clinical monitoring. The NANDA International (NANDA-I) included this diagnosis in the domain 1: Health promotion and it has three defining characteristics: "Choose a daily routine without exercise"; "It shows a lack of physical fitness"; "Verbalizes preference for activities with little exercise"; and five related factors, "Deficient knowledge about the benefits that physical activity brings to health"; "Lack of interest"; "Lack of motivation"; "Lack of resources"; "Lack of training to exercise". (3)
A recent review of this diagnosis for people with hypertension identified three other defining characteristics which are: "Does not perform physical activity during leisure"; "Overweight" and "Deficient performance in instrumental activities of daily living", in addition to proposing the division of the characteristic shows a lack of physical fitness in three: "Decreased cardiorespiratory capacity"; "Diminished muscle strength" and "Diminished flexibility of joints". (4) The sedentary lifestyle is considered one of the main risk factors for the development of hypertension and other cardiovascular problems and may result in severe consequences to the body. Hypertension is considered a serious public health problem, generating high costs of hospitalization, the patient's disability, and early retirement. (5) The main form of prevention and control of HTN is the adoption of healthy habits. (6-9) Few studies have proposed the use of classification trees in the diagnostic inference based on defining characteristics. Given this context, this study aimed to generate a Tree Classification (TC) from clinical indicators for classification and correct prediction of the nursing diagnosis "Sedentary lifestyle" in patients with Hypertension.
It is a cross-sectional study conducted in an outpatient care center for individuals with high blood pressure and diabetes. 285 patients with hypertension were included in the study, aged between 19 and 59 years old, registered in the institution's monitoring program where the study was developed. Due to the inexistence of statistical formulas to determine the sample size in classification trees development studies, an approach similar to the one used in the development of scales has applied this study, considering the relationship between the number of indicators and the number of subjects to be evaluated. (10) Thus, was initially determined a minimum of 35 individuals per indicator to be included in the analysis. In this study, a total of 8 clinical indicators were included in the analysis generating the need for at least 280 individuals (35 x 8). However, the final sample was expanded to 285 individuals.
The data collection was performed by a team previously submitted to training, lasting eight hours. It used an instrument with the variables related to identification, socio-demographic profile, to the related factors and signs and symptoms that constitute the defining characteristics of the nursing diagnosis "Sedentary lifestyle" of NANDA-I.
The diagnostic inference was made by four nurses previously trained at a course lasting eight hours. These nurses were evaluated for efficiency, trend, false-positive taxes and false negative in their diagnostic inferences as recommended in the specialized literature. (11) Data were compiled in an Excel spreadsheet and forwarded subsequently to every diagnostician nurse. The occurrence or not of the diagnosis has been determined by the absolute agreement between the diagnosticians. The agreement between evaluators was measured by Kappa coefficient and ranged from 0.790 to 0.979 indicating excellent concordance. (11)
The data obtained from the nurse diagnosticians were compiled in Excel, and the statistical analysis was done in 2105 with the help of SPSS programs version 20.0 and R version 2.12.1. The tree was generated based on CHAID algorithm (Chi-square Automatic Interaction Detection), sequentially adding in three steps the indicator with higher initial interaction. The CHAID algorithm is based on Chi-square test for detection of interactions between variables. The independent variable that has greater interaction with the dependent variable is elected every step of induction of TC. For the goodness of fit verification, the cross-validation method was adopted, in which delimited a maximum depth of 8 levels (node) with a minimum of 50 cases to the primary node and 10 cases to the secondary. The study was previously approved by the local Ethics Committee, and all participants were informed about the research objectives and signed an Informed Consent form.
The 285 patients in this study had a mean age of 51.34 [+ or -] 07.09 years old, were mostly female (55.4%), 62.8% lived with a partner, and had an average of 9.52 [+ or -] 4.41 years of schooling. A total of 55.8% had a sedentary lifestyle. The CHAID algorithm generated a classification tree with two defining characteristics: "Choose daily routine without exercise" and "Does not perform the physical activity during leisure" of the nursing diagnosis Sedentary Lifestyle, as illustrated in Figure 1. This tree contains five nodes in total, being three terminal nodes, presenting even two deep levels.
Before the defining characteristic "Choose a daily routine without exercise", the probability of occurrence of the nursing diagnosis Sedentary Lifestyle (SL) was 88.8%. By associating the defining characteristic before the "Does not perform physical activity during leisure", obtained the probability of 99.4%. The power of global prediction of this tree using the cross-validation method was 69.5%, as shown in Table 1.
Conserning socio-demographic data, the results obtained in this study are similar to those found in literatura (12-14) since most individuals with hypertension are female, live with a partner, are aged between 40 and 50 years old, per capita income slightly more than a salary and schooling of up to 9 years for the most part. The literature (12) shows that sedentary lifestyle is present in 35.5% of patients with hypertension and/or Mellitus diabetes treated at a Family Health Center. Another study about the risk factors and coronary artery disease, showed prevalence of sedentary lifestyle with values above 60% of the sample, being this disease closely associated with hypertension. (15) Regarding the nursing diagnosis of Sedentary Lifestyle, it is noticed that this was present in 60% of patients enrolled in a Basic Health Unit. (16) In the same study, it was observed that 81% of hypertension patients were female consonant with the elaborate the study. This is because women seek more medical attention than men even in primary care.
As for the classification tree generated to aid in the inference of the nursing diagnosis Sedentary Lifestyle as seen in Figure 1, the presence of the defining characteristics Choose daily routine without exercise and Does not perform physical activity during leisure predict the occurrence of SL in 99.4%, in contrast, the absence of the first one is sufficient to determine the absence of the nursing diagnosis in question. The defining feature Does not perform physical activity during leisure is not included in the Taxonomy of NANDA-I however it increases the probability of SL. Thus, a sedentary lifestyle can be identified as non-participation in physical activities during leisure, considering physical activity as any bodily movement produced by skeletal muscles that result in energy expenditure, with components and determinants of biopsychosocial, cultural and behavioral. (17)
Nurses who care for people with hypertension should be sensitive to the signs and symptoms presented by this population that may evidence the presence of nursing diagnosis Sedentary Lifestyle. When adopted the classification trees for diagnostic inference, nurses can make inferences based on a limited set of defining characteristics. These trees help in decision-making, that relate which present or absent defining characteristics significantly increase the probability or not of the nursing diagnosis, thereby optimizing the time for diagnostic inference. Also, the construction of decision trees generates a set of interactions between clinical indicators that allows a probabilistic analysis of multiple situations in which it is possible to quantify the opportunity for an individual presenting a Sedentary Lifestyle.
The limitation of this study is based on the fact having done with a specific sample of adults, suffering high blood pressure and ambulatory monitored. Thus, the results should not be extrapolated to the general population; there is need for further research on the same nursing diagnosis, but involving different populations. Noteworthy is that there are few studies in the literature using classification trees, and is, therefore, difficult to compare with other samples.
(1.) Chaves DBR. Arvores de Decisao para inferencia de Desobstrucao ineficaz de vias aereas e Padrao respiratorio ineficaz de Criancas com infeccao Respiratoria Aguda. [Dissertation]. Fortaleza: Universidade Federal do Ceara; 2011.
(2.) Hur H, Park S, Kim S, Storey MJ, Kim G. Activity intolerance and impaired physical mobility in elders. Int J Nurs Terminol Classif. 2005; 16(34):47-53.
(3.) NANDA International (NANDA-I). Diagnosticos de Enfermagem da NANDA: Definicoes e classificacoes, 2009-2011. Porto Alegre: Artmed; 2010.
(4.) Guedes NG, Lopes MVO, Cavalcante TF, Moreira RP Araujo TL. Revisao do diagnostico de enfermagem Estilo de vida sedentario em pessoas com hipertensao arterial: analise conceitual. Rev Esc Enferm USP 2013; 47(3):742-49.
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(10.) Pasquali L. Instrumentacao psicologica: fundamentos e praticas. Porto Alegre: Artmed; 2010.
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(14.) Silva NT, Giacon TR, Costa MP Vitor ALR, Vanderlei LCM. Moreira TMM. Prevalencia de correlacao entre obesidade, hipertensao arterial e a pratica de atividade fisica. Colloquium Vitae. 2011; 3(1):32-6.
(15.) Nascente FMN, Jardim PCBV, Peixoto RMG, Monego ET, Moreira HG, Vitorino PVO, et al. Hipertensao arterial e sua correlacao com alguns fatores de risco em uma cidade brasileira de pequeno porte. Arq Bras Cardiol. 2010; 95(4):502-9.
(16.) Gus I, Fischmann A, Medina C. Prevalencia dos fatores de risco da doenca arterial coronariana no Estado do Rio Grande do Sul. Arq Bras Cardiol. 2002; 78(5):478-83.
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Larissa Castelo Guedes Martins 
Marcos Venicios de Oliveira Lopes 
Nirla Gomes Guedes 
Angelica Paixao de Menezes 
Odaleia de Oliveira Farias 
Naftale Alves dos Santos 
 Nurse, MSc. Home care nurse, Fortaleza-CE, Brazil. email: firstname.lastname@example.org
 Nurse, Doctor. Professor, Federal University of Ceara--UFC -, Fortaleza-CE, Brazil. email: email@example.com
 Nurse, Doctor. Professor, UFC, Fortaleza-CE, Brazil. Email: firstname.lastname@example.org
 Nursing Student. UFC, Fortaleza-CE, Brazil. email: email@example.com
 Nursing Student. UFC, Fortaleza-CE, Brazil. email: firstname.lastname@example.org
 Nurse, Master degree. UFC, Fortaleza-CE, Brazil. email: email@example.com
Article derived from the research: Review of the defining characteristics and factors related to nursing diagnoses sedentary lifestyle in individuals with hypertension.
Conflicts of interest: none.
Received on: January 21, 2015.
Approved on: December 4, 2015.
Table 1. Adjusting Features and prediction of classification tree constructed for Sedentary Lifestyle Classification Present Absent Expected Correct Percentage Present 158 1 99.4% Observed Absent 86 40 31.7% General Percentage 85.6% 14.4% 69.5% Figure 1. Classification tree generated with the defining characteristics of the nursing diagnosis. Sedentary Lifestyle using the CHAID method. Node 0 Category % n Present 55.8 159 Absent 44.2 126 Total 100 285 CD1 Value p <,001 Qui-square = 212,97 Present Absent Node 1 Node 2 Category % n Category % n Present 88.8 159 Present 0.0 0 Absent 11.2 20 Absent 100.0 8 Total 62.8 179 Total 37.2 106 CD2 Value p <,001 Qui-square = 135,69 Present Absent Node 3 Node 4 Category % n Category % n Present 99.4 155 Present 17.4 4 Absent 0.6 1 Absent 82.6 19 Total 54.7 156 Total 8.1 23 * CD1: Choose daily routine without exercise; CD2: Does not perform physical activity during leisure
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|Title Annotation:||Original article|
|Author:||Martins, Larissa Castelo Guedes; Lopes, Marcos Venicios de Oliveira; Guedes, Nirla Gomes; de Menezes|
|Publication:||Investigacion y Educacion en Enfermeria|
|Date:||Mar 1, 2016|
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