Body Mass Index (BMI)
Statistical Technique in Review
Most research reports describe the subjects or participants who comprise the study sample. This description of the sample is called the sample characteristics, which may be presented in a table and/or the narrative of the article. The sample characteristics are often presented for each of the groups in a study (i.e., intervention and control groups). Descriptive statistics are calculated to generate sample characteristics, and the type of statistic conducted depends on the level of measurement of the demographic variables included in a study (Grove, Burns, & Gray, 2013). For example, data collected on gender is nominal level and can be described using frequencies, percentages, and mode. Measuring educational level usually produces ordinal data that can be described using frequencies, percentages, mode, median, and range. Obtaining each subject’s specific age is an example of ratio data that can be described using mean, range, and standard deviation. Interval and ratio data are analyzed with the same statistical techniques and are sometimes referred to as interval/ratio-level data in this text.
Research Article
Source
Oh, E. G., Yoo, J. Y., Lee, J. E., Hyun, S. S., Ko, I. S., & Chu, S. H. (2014). Effects of a three-month therapeutic lifestyle modification program to improve bone health in postmenopausal Korean women in a rural community: A randomized controlled trial. Research in Nursing & Health, 37(4), 292–301.
Introduction
Oh and colleagues (2014) conducted a randomized controlled trial (RCT) to examine the effects of a therapeutic lifestyle modification (TLM) intervention on the knowledge, self-efficacy, and behaviors related to bone health in postmenopausal women in a rural community. The study was conducted using a pretest-posttest control group design with a sample of 41 women randomly assigned to either the intervention (n = 21) or control group (n = 20). “The intervention group completed a 12-week, 24-session TLM program of individualized health monitoring, group health education, exercise, and calcium–vitamin D supplementation. Compared with the control group, the intervention group showed significant increases in knowledge and self-efficacy and improvement in diet and exercise after 12 weeks, providing evidence that a comprehensive TLM program can be effective in improving health behaviors to maintain bone health in women at high risk of osteoporosis” (Oh et al., 2014, p. 292).
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Relevant Study Results
“Bone mineral density (BMD; g/cm2) was measured by dual energy x-ray absorptiometry (DXA) with the use of a DEXXUM T machine . . . . A daily calibration inspection was performed. The error rate for these scans is less than 1%. Based on the BMD data, the participants were classified into three groups: osteoporosis (a BMD T-score less than −2.5); osteopenia (a BMD T-score between −2.5 and −1.0); and normal bone density (a BMD T-score higher than −1.0)” (Oh et al. 2014, p. 295).
“Characteristics of Participants
The study participants were 51–83 years old, and the mean age was 66.2 years (SD = 8.2). The mean BMI was 23.8 kg/m2 (SD = 3.2). Most participants did not consume alcoholic drinks, and all were nonsmokers. Antihypertensives and analgesics such as aspirin and acetaminophen were the most common medications taken by the participants. Less than 20% of participants had a regular routine of exercise at least three times per week. Daily calcium- and vitamin D-rich food intake (e.g., dairy products, fish oil, meat, and eggs) was low. Seventy-five percent (n = 31) of the participants had osteoporosis or osteopenia. There were no differences in the baseline characteristics of the groups (Table 2). The adherence rate to the TLM program was 99.6%” (Oh et al., 2014, p. 296).
TABLE 2
BASELINE CHARACTERISTICS AND HOMOGENEITY OF THE TREATMENT AND CONTROL GROUPS
Intervention (n = 21)Control (n = 20)CharacteristicMean ± SD Mean ± SD t or χ2 a
Anthropometric Age (years)65.95 ± 8.5966.35 ± 7.940.154 Height (cm)152.33 ± 6.53150.57 ± 6.010.896 Weight (kg)57.90 ± 10.8554.66 ± 9.481.016 BMI (kg/m2)24.17 ± 3.1423.38 ± 3.320.782Lifestyle Years since menopause20.21 ± 10.4417.5 ± 11.050.767 Calcium-rich food intake (times/week)27.3 ± 11.423.8 ± 8.81.110 Vitamin D-rich food intake (times/week)2.4 ± 2.53.1 ± 3.10.705Intervention (n = 21) Control (n = 20) Characteristic n % n % t or χ2 a History of fracture8385251.026 Regular exercise (≥3 times/week)4194200.006 Non-drinker (alcohol)2095201000.024 Non-smoker21100201000.024Bone statusb Normal (T ≥ −1.0)6294201.995 Osteopenia (−1.0 > T > −2.5)8381260 Osteoporosis (T ≤ −2.5)733420Intervention (n = 21) Control (n = 20) Characteristic Mean ± SD Mean ± SD t or χ2 a BMD Lumbar 2–40.83 ± 0.120.85 ± 0.200.526 Femur neck0.67 ± 0.150.67 ± 0.130.055Bone biomarkers Serum osteocalcin (ng/ml)13.97 ± 4.9015.85 ± 5.641.135 Serum calcium (mg/dl)9.47 ± 0.409.54 ± 0.590.405 Serum phosphorus (mg/dl)3.68 ± 0.443.70 ± 0.500.165 Serum alkaline phosphatase (IU/L)68.43 ± 21.5266.70 ± 13.240.308 Serum 25-OH-Vitamin D (ng/ml)14.03 ± 4.3412.38 ± 4.651.177 Urine deoxypyridinoline (nM/mM creatinine)5.70 ± 1.705.95 ± 1.120.555
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a All group differences p > 0.05.
b Defined from T-score of femur neck site based on World Health Organization criteria.
Note. SD, standard deviation; BMD, bone mineral density (g/cm2).
Oh, E. G., Yoo, J. Y., Lee, J. E., Hyun, S. S., Ko, I. S., & Chu, S. H. (2014). Effects of a three-month therapeutic lifestyle modification program to improve bone health in postmenopausal Korean women in a rural community: A randomized controlled trial. Research in Nursing & Health, 37(4), p. 297.
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Study Questions
1. What demographic variables were described in this study?
2. Which variable was measured at the ordinal level? Provide a rationale for your answer.
3. What level of measurement is the data for history of fracture? Provide a rationale for your answer.
4. What statistics were calculated to describe history of fracture? Were these appropriate? Provide a rationale for your answer.
5. Could a mean be calculated on the history of fracture data? Provide a rationale for your answer.
6. What statistics were calculated to describe the regular exercise (≥3 times per week) for the intervention and control groups? Calculate the frequency and percentage of the total sample who exercised regularly. Round your answer to the nearest tenth of a percent.
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7. What statistics were calculated to describe age in this study? Were these appropriate? Provide a rationale for your answer.
8. Were the intervention and control groups significantly different for age? Provide a rationale for your answer.
9. What was the mode for bone status for the total sample (N = 41)? Determine the frequency and percentage for the bone status mode for the sample. Round your answer to the nearest whole percent. Why is this clinically important?
10. Based on the bone status of the study participants, discuss the clinical importance of this study. Document your response.
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Answers to Study Questions
1. Demographic variables described in the study were age, height, weight, body mass index (BMI), lifestyle (years since menopause, calcium-rich food intake, vitamin D-rich food intake), history of fracture, regular exercise, alcohol consumption, and smoking. You might have identified the bone status, bone mineral density (BMD), and bone biomarkers but these are dependent variables for this study (Grove et al., 2013).
2. The variable bone status provided ordinal-level data. The researchers classified the participants into three groups, normal (T-score higher than −1.0), osteopenia (T-score between −1.0 and −2.5), and osteoporosis (T-score less than −2.5), based on their BMD scores. These categories are exhaustive, mutually exclusive, and can be ranked from greatest or normal BMD to osteoporosis or least BMD.
3. The data collected for history of fracture are nominal level, including the two categories of no history of fracture and yes history of fracture. These categories are exhaustive and mutually exclusive, since all study participants will fit into only one category. These yes and no categories of history of fracture cannot be ranked so the data are nominal versus ordinal level (see Exercise 1).
4. Frequencies and percentages were used to describe history of fracture for the intervention and control groups. Since the data are nominal, frequencies and percentages were appropriate. The researchers might have also identified the mode, which was no history of fracture since 13 participants had a history of fracture and 28 had no history of fracture.
5. No, a mean cannot be calculated on the history of fracture data, which are nominal-level data that can only be organized into categories (see Exercise 1). A mean can only be calculated on interval- and ratio-level data that are continuous and have numerically equal distances between intervals.
6. Regular exercise was described for both the intervention and control groups using frequencies and percentages. A total of 8 or 19.5% of the participants exercised regularly. Eight of the participants (4 in the intervention and 4 in the control groups) were involved in regular exercise of the sample (N = 41). Percentage = (8 ÷ 41) × 100% = 0.1951 × 100% = 19.51% = 19.5%. Researchers also indicated in the narrative that less than 20% of the participants were involved in regular exercise, supporting the importance of providing these individuals with an exercise program.
7. Age was described with means and SDs for the intervention and control groups (see Table 2). In the narrative, the…
