Activity Intolerance and Impaired Physical Mobility in Elders

Hur, Hea-Kung; Park, So-Mi; Kim, So-Sun; Storey, M; Kim, GiYon

doi:10.1111/j.1744-618x.2005.00011.x

Cited by 4 publications

(8 citation statements)

References 9 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Classification tree for the assessment of sedentary lifestyle among hypertensive

et al. 2016

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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. Classification tree for the assessment of sedentary lifestyle among hypertensiveObjective. To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). Methods. A crosssectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining sociodemographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). Results. The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Conclusion. Decision trees help nurses who care HTN people in decisionmaking in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

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Section: Introductionmentioning

confidence: 99%

Classification tree for the assessment of sedentary lifestyle among hypertensive

et al. 2016

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“…and activity intolerance (00093) (Hur et al, 2005), impaired memory (00131) and chronic confusion (00129) (Souza et al, 2015), situational low self-esteem (00120) and chronic low self-esteem (00119) (Castro et al, 2021), sexual dysfunction (00059) and ineffective sexuality pattern (00065) (Melo et al, 2008), fear (00148) and anxiety (00146) (Bergamasco et al, 2004;Whitley, 1994Whitley, , 1997, ineffective airway clearance (00031) (Pascoal et al, 2016), acute pain (00132), and chronic pain (00133) (Simon et al, 1995).…”

Section: Resultsmentioning

confidence: 99%

“…The differential diagnostic validation model was used to evaluate different types of diagnoses from different domains: domain 3—elimination and exchange (Pascoal et al., 2016); domain 4—activity/rest (Hur, Park, Kim, Storey, & Kim, 2005; Pascoal et al., 2016; Seganfredo et al., 2017); domain 5—perception/cognition (Souza, Santana, & Cassiano, 2015); domain 6—self‐perception (Castro et al., 2021); domain 8—sexuality (Melo, Carvalho, & Haas, 2008); domain 9—coping/stress tolerance (Bergamasco, Rossi, de Carvalho, & Dalri, 2004; Whitley, 1994, 1997); domain 11—safety/protection (Pascoal et al., 2016); and domain 12—comfort (Simon, Baumann, & Nolan, 1995).…”

Section: Resultsmentioning

confidence: 99%

“…The nursing diagnoses studied were Impaired gas exchange (00030) (Pascoal et al., 2016), ineffective breathing pattern (00032) (Pascoal et al., 2016; Seganfredo et al., 2017), impaired spontaneous ventilation (00033) (Seganfredo et al., 2017), impaired physical mobility (00085) and activity intolerance (00093) (Hur et al., 2005), impaired memory (00131) and chronic confusion (00129) (Souza et al., 2015), situational low self‐esteem (00120) and chronic low self‐esteem (00119) (Castro et al., 2021), sexual dysfunction (00059) and ineffective sexuality pattern (00065) (Melo et al., 2008), fear (00148) and anxiety (00146) (Bergamasco et al., 2004; Whitley, 1994, 1997), ineffective airway clearance (00031) (Pascoal et al., 2016), acute pain (00132), and chronic pain (00133) (Simon et al., 1995).…”

Section: Resultsmentioning

confidence: 99%

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Differential validation of nursing diagnoses: An integrative literature review on the use of the method

Miguel

Caldeira

Morais

et al. 2021

Int J of Nursing Knowl

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Purpose This review aimed to analyze the use of differential validation of nursing diagnosis. Methods Integrative literature review with search on international databases. The diagnoses, the type of the diagnosis differential validation, the sample, the method, main results, and the limitations were extracted. Ten studies were included. Findings Differential diagnostic validation publication dates from 1994, and Brazil was the predominant country. Conclusions This method seems helpful in improving diagnosis accuracy, particularly those related to subjective, behavioral, or complex human responses. Implications for nursing practice Using this model may facilitate understanding the specificity of nursing diagnosis, which is critical for teaching clinical reasoning and for new opportunities to research.

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“…Impaired physical mobility has been defined as limited independent movement (Hur, Park, Kim, Storey, & Kim, 2005). Nursing interventions are included in Table 2.…”

Section: Management Of Impaired Physical Mobilitymentioning

confidence: 99%

Management of the Child With Polymicrogyria

Turkelson

Martin

2009

Journal of Neuroscience Nursing

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Polymicrogyria is one of several neuronal migrational defects. This disorder is derived from a malformation in cortical development characterized by many small gyri, shallow sulci, and abnormal cortical layering. The potential causes of polymicrogyria include genetic disorders, intrauterine cytomegalovirus infection, and fetal vascular supply disruption. A wide variation in the extent of findings exists, ranging from minor deficits to profound neurological dysfunction. Seizures, feeding problems, elimination, physical mobility, and psychosocial issues must be managed for children with polymicrogyria. Children with this disorder require collaborative care from healthcare practitioners and parents to achieve their highest level of health. The incidence of neuronal migrational defects is approximately 1 in 2,500 live births (L. Villard et al., 2002). The diagnosis of these disorders has increased with improvement of magnetic resonance imaging (MRI) technology (A.

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Activity Intolerance and Impaired Physical Mobility in Elders

Abstract: Clarification of nursing diagnoses serves to direct students and nurses to choosing appropriate interventions.

Cited by 4 publications

References 9 publications

Classification tree for the assessment of sedentary lifestyle among hypertensive

Classification tree for the assessment of sedentary lifestyle among hypertensive

Differential validation of nursing diagnoses: An integrative literature review on the use of the method

Management of the Child With Polymicrogyria

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