An Integrative Review of Self-Report Instruments for Measuring Physical Activity Among African American Women

Abstract

Lack of physical activity is associated with increased risk for coronary heart disease, the main cause of death in African American women. This integrative review aims to evaluate self-report instruments to assess physical activity in African American women, within the context of coronary heart disease. A systematic literature search was conducted using PubMed, CINAHL, PsycINFO, and PubMed Central databases. Only peer-reviewed studies (a) that included African American women and (b) that assessed the psychometric properties of physical activity instruments in the context of heart disease were included in the search. Initial search located 691 articles but only 7 studies were included in the final review. Of the 7 studies, 6 utilized a combination of self-report and objective measures. While most self-report instruments yielded modest validity and reliability, results were inconsistent and modest at best for African American women. Further studies are needed to identify psychometrically and culturally competent instruments for African American women.

Keywords

physical activity self-report instruments women African American coronary heart disease

The impact of physical activity (PA) as a major indicator of health and wellness has been well-established. Lack of PA is associated with increased risk for obesity, hypertension, hyperlipidemia, diabetes, and coronary heart disease (CHD; Masse et al., 2012; Thompson & Barksdale, 2010). African American (AA) women have one of the highest death rates from CHD among all ethnic groups (Roger et al., 2012). The high prevalence of risk factors contributes to high mortality and morbidity from CHD in AA women (Oexmann et al., 2000; Roger et al., 2012). Despite the known risks of inactive lifestyle, a large proportion of AA women do not engage in regular PA (Crespo, Smit, Andersen, Carter-Pokras, & Ainsworth, 2000; Marshall et al., 2007; Peterson, 2011). Studies show that adopting an active lifestyle reduces the risk for CHD and improves overall health outcomes (Elsawy & Higgins, 2010).

Increasingly, health initiatives such as Heart and Soul, Racial and Ethnic Approaches to Community Health (REACH), and Project Joy have focused on strategies to address PA in AA women due to their high risk for CHD (Peterson & Cheng, 2011; Plescia, Herrick, & Chavis, 2008; Yanek, Becker, Moy, Gittelsohn, & Koffman, 2001). Studies conducted to examine PA in AA women have primarily focused on the influence of socioecological factors in reducing risk factors for CHD (Ainsworth, Wilcox, Thompson, Richter, & Henderson, 2003; Fleury & Lee, 2006). The benefit of PA in reducing risk for CHD is well-established but how the different aspects of PA are associated with certain health outcomes remains unclear (Aadahl & Jorgensen, 2003). A greater understanding about the relationship between PA and health outcomes among AA women warrants the need to evaluate the reliability and validity of instruments to ensure accurate assessment of the impact of PA on CHD.

There are two main methods used for measuring PA: (a) objective measures and (b) subjective measures or self-report (Warren et al., 2010). Objective measures include direct calorimetry, indirect calorimetry, doubly labeled water (DLW), accelerometers, and pedometers. DLW provides measurement of PA by assessing the rate of metabolism of isotopes over a period of time. The difference in elimination rates of the isotopes provides a measure of energy expenditure represented by carbon dioxide production that directly relates to PA (Mindell, Coombs, & Stamatakis, 2014; Vanhees et al., 2005). Indirect calorimetry accurately measures energy expenditure from oxygen consumption and carbon dioxide production in a ventilated hood (Vanhees et al., 2005). Direct calorimetry is more accurate in assessing metabolic rate than indirect calorimetry and DLW but is primarily utilized for laboratory-based studies (Kaiyala & Ramsay, 2011). Although considered to be the gold standard for validating PA measures, these three methods are too complicated and too expensive to administer in large-scale studies (Kaiyala & Ramsay, 2011; Vanhees et al., 2005). Accelerometers and pedometers are wearable motion sensors that can objectively measure acceleration, duration, and intensity of PA. Previous studies have demonstrated the utility of pedometers and accelerometers as valid measures of PA (Crouter, Schneider, Karabulut, & Bassett, 2003; Freedson, Melanson, & Sirard, 1998). Relatively inexpensive and easy to use compared with DLW and indirect calorimetry, they require technical expertise to manage and analyze data (Mindell et al., 2014). While newer models have greatly improved their accuracy, the utility of wearable motion sensors on assessing improvement in health outcomes remains inconclusive (Heil, Brage, & Rothney, 2012). Self-reports are the most widely used measure for PA. They include PA questionnaires, surveys, logs, and diaries. Self-reports are inexpensive, easy to administer, and the instrument of choice in large population studies (Aadahl & Jorgensen, 2003). The validity of the instrument is based on the subjective assessment, recall, and interpretation of different aspects of PA over a period of time (Vanhees et al., 2005). Studies examining validity and reliability of self-report against objective measures of PA have concluded that self-report methods provide a valid measure of PA (Philippaerts, Westerterp, & Lefevre, 1999; Schuit, Schouten, Westerterp, & Saris, 1997; Singh, Fraser, Knutsen, Lindsted, & Bennett, 2001). However, validation studies on AA women are lacking. Therefore, the purpose of this integrative review is to evaluate the different self-report instruments used to measure PA in AA women in the context of their risk for CHD. Assessment of validity and reliability of self-report instruments used in AA women will provide additional insight to help designate which PA interventions offer the most significant health benefits for AA women.

Theoretical Definition and Operational Definition

The World Health Organization (WHO) defines PA “as any bodily movement produced by skeletal muscles that requires energy expenditure” (WHO, 2014). Although the term “physical activity” is sometimes used interchangeably with “exercise,” the WHO defines exercise as a subclass of PA in which physical fitness is the objective (WHO, 2014). PA encompasses exercise as well as other activities involving bodily movement including play, work, active transportation, house chores, and other recreational activities. The Department of Health and Human Services (DHHS) PA Guidelines defines “physical activity” as “any body movement that works your muscles and requires more energy than resting” (U.S. Department of Health and Human Services, 2014). In their early work investigating interventions to promote physical activity in AA women, Banks-Wallace and Conn (2002) expanded the definition to include bodily movements that produce progressive healthy benefits. Given the variability in how PA is defined, the following conceptual definition was used for this integrative review: Any bodily movement produced by skeletal muscles that results in energy expenditure. Operationalization is the process of outlining how a concept can be measured; this process is used to take an abstract idea and make it into an observable and measurable concept (Waltz, Strickland, & Lenz, 2010). The variability among the different instruments used to measure PA makes operationalization of the concept problematic. Previous studies have highlighted this challenge by advocating for greater precision and more clarity for the term PA in order to operationalize the concept in research (Cook, 2009). The DHHS PA Guidelines provide an operational definition of PA as “at least 150 minutes (2 hours and 30 minutes) a week of moderate-intensity, or 75 minutes (1 hour and 15 minutes) a week of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- and vigorous-intensity aerobic activity” (U.S. Department of Health and Human Services, 2014). However, in this review for PA among AA women within a CHD perspective, PA will be operationally defined as bodily activities performed for at least 10 minutes a day resulting in energy expenditure quantified using the following measures and standards: DHHS 2008 PA Guidelines, Centers for Disease Control and American College of Sports Medicine (CDC ACSM) PA Guidelines, DLW, indirect calorimetry, treadmill testing, actigraph, accelerometer, and pedometer; it is expressed as minutes of exercise per day or week, steps per day or week, metabolic equivalent (MET), maximum oxygen consumption (VO_2max), kilocalories per day or week, and resting energy expenditure (REE). The self-report methods and objective measures used in AA women will be evaluated in the context of CHD.

Method

Search Strategy

The authors conducted a systematic search of the literature to review publications evaluating instruments used to measure PA in AA women. PubMed, CINAHL, PsycINFO, and PubMed Central databases were used to search for literature reporting on the reliability and validity of the instruments using various combinations of the following key terms: PA, motor activity, exercise, AAs, Blacks, African Continental Ancestry Group, female, women, data collection, surveys, questionnaires, measurement, reproducibility of results, and test validity. All located studies that described some form of PA among AA women and reported some form of measurement data were initially included. Located articles were excluded if there was no detailed description of reliability or validity measures, no full text English version, no association with CHD or chronic diseases, sample younger than 18 years of age, or fewer than 18% of AA women in the total sample. All the articles were collected in 2013. A summary of the systematic search of literature is presented in Figure 1.

Figure 1.

Search strategy diagram.

Level of Evidence

The level of evidence for each study was appraised using the Center for Evidence Based Medicine Levels of Evidence (2009). Studies were evaluated and rated based on their strength of evidence.

Results

Initial search used a combination of key terminologies including Medical Subject Headings (MeSH®) terms and subject headings. Out of the initial 691 articles, 7 studies were included in the final review as summarized in Table 1.

Table 1.

Self-Report and Objective Measures of Physical Activity in African American Women.

Instrument/references	Research subjects	Description	Method of measure	Validity	Reliability	Findings	Level of evidence
7-Day PAR, AAFQ, and Women’s Health Initiative PHQ (Neuhouser et al., 2013)	Enrolled participants from 9 of 40 WHI clinical centers between 2007 and 2009 N = 450 AA = 84 Subsample of 88 women repeated entire protocol 6 months later to provide repeatability measures	PAR questions included time spent in sleep and performing moderate, hard, and very hard intensity activities for each segment of each day over the 7 days before the interview. AAFQ is a self-administered questionnaire previously validated using DLW and includes specific activities grouped by domain with categorical responses for frequency and duration of activities. WHI PHQ is a short, self-administered questionnaire that inquires about usual frequency, intensity, and duration of different types of activity.	Self-report Biomarker objective measures: DLW and indirect calori-meter	Regressing log biomarker AREE revealed that self-report explained minimal biomarker variance (R² = 7.6, 4.8, and 3.4 for AAFQ, PAR, and PHQ, respectively).	Unadjusted Pearson correlation coefficients estimating reliability between primary (self-report) and reliability measures for TEE from DLW, REE from indirect calorimetry, AREE (TEE − REE), and calculated AREE are .59, .75, .42, and .42, respectively. Intraclass correlation coefficients for primary and reliability measures of TEE, REE, AREE, and calculated AREE were .58, .77, .42, and .42, respectively.	Use of biomarker-calibrated estimates of self-report provides objective assessment of physical activity as revealed in the study and should be used to reduce measurement error.	Level 3b
CHAMPS Physical Activity Questionnaire, YPAS–Modified Version (Resnicow et al., 2003)	AA adults in Atlanta, GA. N = 138, females = 109	Adapted from the original 41-item measure, CHAMPS PAR is self-administered instrument developed for underactive populations and tested primarily on older adults. YPAS comprises five individual frequency items, namely, vigorous activity, leisurely walking, moving around on your feet, standing and sitting duration are assessed. The treadmill measure used Balke protocol.	Self-report Treadmill test (Balke protocol)	CHAMPS indices and estimated VO_2max significant correlation in vigorous and sports-related activities (r = .19 and r = .32, respectively). For females, sports index (r = .19) significantly correlated with estimated VO_2max. For females, YPAS (r = .24) significantly correlated with estimated VO_2max in diastolic BP.	YPAS test-retest correlations of total activity index of r = .55 and r = .65.	CHAMPS Physical Activity Questionnaire is a valid measure of physical activity with higher validity for more intensive physical activity measures and may be useful for assessing physical activity among AAs. May not be feasible for AA women without medical clearance from health care provider. Treadmill is expensive and requires technical skills for data analysis.	Level 3b
Checklist Questionnaire and Global Questionnaire (Masse et al., 2012)	N = 260 130 AA women and 130 Hispanic women	The Checklist Questionnaire is a 64-item, self-administered questionnaire that assessed the frequency and duration of physical activities performed in previous 7 days. The Global Questionnaire is a brief 8-item, interviewer-administered survey that was developed to assess moderate-to-vigorous intensity activities for six domains of physical activity (occupation, household, yard, family, church/volunteer work, and transportation). Two self-report questionnaires validated with DLW, Accelerometer, and Diary.	Self-report DLW Accelerometer 7-Day Diary Physical Activity Estimation Questionnaire	Checklist correlated modestly with Diary for all domains (.32-.67). Overall correlation for moderate-intensity activities was .62. Global Questionnaire correlations (.36-.73). Overall correlation between Global and Diary was .42. Overall, correlations with DLW TEE and questionnaires (.54-.62). Correlations among DLW PAEE with questionnaires (.23-.26). Associations between Checklist and total accelerometer counts were .30 (p < .05) and .23 for accelerometer min ≥ METs (p < .05). Associations with Global Questionnaire were .23 for accelerometer counts (p < .05) and .22 for accelerometer min ≥ 3 METs (p < .05).		The Checklist and Global Questionnaires have measurement properties consistent with other validated questionnaires but the Checklist had slightly better psychometric properties than the Global Questionnaire across validation standards. Findings show that both questionnaires provide valid ranking of participants in their level of physical activity but less ideal for estimating amount of physical activity performed. Instruments may not be feasible to use due to length, time constraint, cost, and participant burden.	Level 3b
IPAQ-S (Wolin, Heil, Askew, Matthews, & Bennett, 2008)	AA residents living in two public housing developments in Boston, MA.N = 157, females = 91	IPAQ-S assesses activities performed for at least 10 minutes during the last 7 days. Participants report time spent in physical activity performed at each of three intensities: walking, moderate, and vigorous.IPAQ-S used with accelerometer.	Self-reportAccelerometer	1-minute bout length, moderate agreement IPAQ-S and accelerometer-determined activity counts (r = .36, p < .001).10-minute bout definition, fair correlation between IPAQ-S and accelerometer-measured activity counts (r = .48, p = .003).		Findings suggest that IPAQ-S may be appropriate for use in measuring physical activity with a continuous scale variable among low-income Black men. IPAQ-S performed less well among AA women. IPAQ-S is a short version of IPAQ-Long which reduces burden to respondents and is more feasible to use than the original version	Level 3b
PAQ (Buchowski et al., 2010)	Women enrolled in Southern Community Cohort Study between 2002 and 2006 who self-report race as either Black or White. N = 31,502AA = 22,948	Measure wide range of active and sedentary behaviors done at home, work, and leisure time	Self-report	Spearman correlation coefficient of sitting items at baseline and 1 year later (.24-.53).No meaningful differences between Blacks and Whites based on questionnaire responses.		Women spent majority of time on sedentary behaviors, higher among obese women, associated with White than Black women. Use of questionnaire is feasible as previously validated.	Level 3b
SEE, OEE, and YPAS (Resnick, Luisi, Vogel, & Junaleepa, 2004)	Older minority adults participating in self-efficacy-based exercise intervention program, SESEP.N = 166,134 females, 73% AA	SEE is a 9-item scale measuring self-efficacy expectations related to the ability to exercise in the face of barriers to exercising.OEE is a 9-item scale developed from a previously validated scale on outcome expectations and benefits associated with exercise in adults.YPAS is an interviewer-administered questionnaire focusing on five major categories of physical activity performed during a typical week: yard work, caretaking, work-related physical activity, exercise, and recreational activities.	Self-report	Factor analyses show that all items were significantly related to the appropriate construct, either self-efficacy or outcome expectations, with the exception of Items 1 (.44) and 2 (.46) on the OEE at follow-up.	Internal consistency for SEE and OEE with alpha coefficients of .89 and .90, and .72 and .88, respectively.	Study supported use of SEE and OEE when used in minority adults. Scales are easy and simple to use. List of questions in scales is easy to understand.	Level 3b
Two-Part Survey Item to Assess Adherence to MPA Recommendation (Whitt, Levin, Ainsworth, & Dubose, 2003)	N = 377168 AA,157 Native American and 52 Caucasian women	Survey assessed the usual frequency and duration of MPA. Survey is worded to directly reflect CDC ACSM recommendation for ≥30 minutes of MPA on ≥5 days per week.Survey was evaluated using three dependent measures: PAR, Caltrac accelerometer, and pedometer.	Self-reportCaltrac accelerometerPedometer	Least squares methodParticipation rate differences between those who reported meeting and not meeting MPA (χ², p = .02)Mean minutes of MPA between those who reported meeting and not meeting CDC ACSM recommendation (t test, p < .0001)		Findings reveal that the two-part survey item can reliably differentiate between higher and lower levels of physical activity. The survey’s simplicity and relative ease make it feasible for widespread use in larger populations.	Level 3b

Note. PAR = Physical Activity Recall; AAFQ = Arizona Activity Frequency Questionnaire; PHQ = Personal Habit Questionnaire; WHI = Women’s Health Initiative; DLW = doubly labeled water; AREE = activity-related energy expenditure; TEE = total energy expenditure; REE = resting energy expenditure; YPAS = Yale Physical Activity Survey; PAEE = Physical activity-related energy expenditure; IPAQ-S = International Physical Activity Questionnaire–Short; PAQ = Physical Activity Questionnaire; SEE = Self-Efficacy for Exercise Scale; OEE = Outcome Expectations for Exercise Scale; SESEP = Senior Exercise Self-Efficacy Pilot; MPA = Moderate Physical Activity; MET = metabolic equivalent; CDC ACSM = Centers for Disease Control and American College of Sports Medicine.

All seven studies included in this integrative review used self-report as the main measurement method. Six studies utilized a combination of self-reports and objective measures to assess validity and reliability of the primary instrument in the study (Buchowski et al., 2010; Masse et al., 2012; Neuhouser et al., 2013; Resnick, Luisi, Vogel, & Junaleepa, 2004; Resnicow et al., 2003; Whitt, Levin, Ainsworth, & Dubose, 2003; Wolin, Heil, Askew, Matthews, & Bennett, 2008). Only one study, “Reliability and Validity of the Self Efficacy for Exercise and Outcome Expectations for Exercise Scales with Minority Older Adults,” explicated a theoretical framework (Resnick et al., 2004).

Overall, 14 self-report questionnaires and surveys were used: Self-Efficacy for Exercise Scale (SEE), Outcome Expectations for Exercise Scale (OEE), Yale Physical Activity Survey (YPAS), Community Healthy Activities Model Program for Seniors (CHAMPS), Arizona Activity Frequency Questionnaire (AAFQ), 7-Day Physical Activity Recall (7-D PAR), Women’s Health Initiative Personal Habit Questionnaire (PHQ), Two-Part Survey Item to Assess Adherence to Moderate Physical Activity Recommendation (MPA), Physical Activity Records (PAR), Checklist Questionnaire, Global Questionnaire, 7-Day Diary Physical Activity Estimation Questionnaire, International Physical Activity Questionnaire–Short (IPAQ-S), and Physical Activity Questionnaire (PAQ; Buchowski et al., 2010; Masse et al., 2012; Neuhouser et al., 2013; Resnick et al., 2004; Resnicow et al., 2003; Whitt et al., 2003; Wolin et al., 2008). The self-report instruments were used to assess PA in sample of AAs along with other ethnic groups. None of the instruments were utilized exclusively on AA women.

Criterion validity was examined in the instruments used in this review. Five objective measures of PA were used to validate self-reports: DLW (Masse et al., 2012; Neuhouser et al., 2013), indirect calorimetry (Masse et al., 2012; Neuhouser et al., 2013), treadmill protocol (Resnicow et al., 2002), accelerometer (Masse et al., 2012; Whitt et al., 2003; Wolin et al., 2008), and pedometer (Whitt et al., 2003). Criterion validity correlations tended to be modest and inconsistent among AA women. The highest correlations were demonstrated between DLW total energy expenditure (TEE) and the Checklist and Global Questionnaires (.54-.62) and between diary and the questionnaires (.32-.67; Masse et al., 1998). Yet, correlations between accelerometer counts and the questionnaires in the same study were low (.30-.22; Masse et al., 1998). Higher correlations were found among males between maximum oxygen consumption (VO_2max) and CHAMPS indices than among females; only the sports index significantly correlated among females (r = .19; Resnicow et al., 2002). Likewise, the IPAQ-S performed worse among AA women than among AA men when validated against accelerometer (Wolin et al., 2008). However, a study validating a two-part survey against accelerometer and pedometer revealed that the survey can reliably differentiate between higher and lower PA levels among AAs (Whitt et al., 2003). Participants who reported meeting the PA recommendation in the survey had significantly higher steps per day and kilocalories per day (all p < .0001) compared with those who reported not meeting the recommendation (Whitt et al., 2003).

Construct validity was established using factor analysis in one study (Resnick et al., 2004). Overall, construct validity demonstrated acceptable validity for body mass index (BMI), blood pressure, cholesterol, ethnicity, age, gender, educational attainment, and income (Buchowski et al., 2010; Masse et al., 2012; Neuhouser et al., 2013; Resnick et al., 2004; Resnicow et al., 2003; Whitt et al., 2003; Wolin et al., 2008). BMI and age were inversely correlated with steps per day, kilocalories per day, and self-reported PA among AA women (Buchowski et al., 2010; Masse et al., 2012; Neuhouser et al., 2013; Resnick et al., 2004; Resnicow et al., 2003; Whitt et al., 2003; Wolin et al., 2008). The study using the Modified CHAMPS Questionnaire reported high correlations with oxygen consumption for participants with income below US$30,000 and no college degree (Resnicow et al., 2002).

Internal consistency and reliability were reported in studies using the YPAS, SEE, and OEE, and in a study establishing reliability between the primary study with the reliability study group (Neuhouser et al., 2013; Resnick et al., 2004; Resnicow et al., 2003). Resnicow et al. (2003) reported 2-week test-retest correlations of total activity index of r = .5 and r = .65 for the YPAS. Although AA women and Hispanics under-reported PA in AAFQ and PAR, reproducibility of biomarker measures was reported examining energy expenditures using the self-report questionnaires, namely, AAFQ, PAR, and PHQ (R² = 25.2, 21.5, and 21.8, respectively; Neuhouser et al., 2013). Furthermore, internal consistency was established for SEE and OEE with alpha coefficients of .89 and .90, and .72, and .88, respectively (Resnick et al., 2004).

Discussion, Gaps in the Literature, and Implications

The aim of this review was to evaluate self-report instruments that have been used to measure PA in AA women in the context of the risk for CHD. In general, the reviewed studies reported correlations between self-report and objective measures that were within previously observed ranges for determining PA levels. However, some of the self-report instruments used in the reviewed studies exhibited slight inconsistencies in validity among AA women. This was evident in both the self-report methods and objective measures. The use of objective measures was valuable for detecting systematic errors and bias. One study using indirect calorimeter and DLW as validation standards revealed differential bias by the instrument that resulted in over-reporting and under-reporting of some measures in the study (Neuhouser et al., 2013). Use of indirect calorimetry and DLW involve highly specified protocols using sophisticated laboratory equipment to analyze PA; if error is present among these methods, it is more likely to be systematic resulting from imprecise instrumentation across all participants.

Although all the studies examined the validity of the self-reports, only three studies evaluated reliability. A self-report instrument cannot be useful for what it intends to measure unless the instrument is able to assign scores consistently. Resnicow et al. (2003) reported 2-week test-retest correlations of total activity index of r = .5 and r = .65 for the YPAS. However, the authors did not perform test-retest reliability study on CHAMPS, the main self-report instrument used in the study. Reliability is an essential prerequisite of validity (Waltz, Strickland, Lenz, & Waltz, 2005). The variability and inconsistency in PA behavior adds additional challenges to establish reliability when administering self-report instruments.

Studies that utilized objective measures such as DLW and indirect calorimetry as criterion standards provide robust evidence of the test validity of self-report instruments in assessing PA in AA women. Moreover, DLW and indirect calorimetry are gold standards in measuring PA providing physiological evidence for precise validation (Vanhees et al., 2005). Logistical and financial constraints reduce feasibility and preclude widespread use of objective measures in many field-based studies (Aadahl & Jorgensen, 2003). In contrast, self-report methods are inexpensive, practical, and easy to administer in large-scale studies (Vanhees et al., 2005). Self-report instruments allow PA recommendations to be easily communicated and understood by the public. Issues such as under-reporting, over-reporting, or misreporting of actual PAs can restrict the reliability and validity of self-report methods (Mindell et al., 2014). As a result, establishing the validity of self-report instruments based on subjective assessment is extremely important.

Overall, findings in this review indicate that most self-report instruments evaluated in this review yielded modest validity and reliability consistent with previous studies. However, the limited number of AA women included in the studies used in this review may limit understanding about the direct effects of PA behaviors on specific health outcomes.

Conclusion and Limitations

The integrative review included a small spectrum of self-report instruments used to evaluate PA in AA women. The number of studies identified in this review may be limited by the search strategy method and publication date. Although a number of validation studies of self-report measures were included, none were conducted solely on AA women. However, the results are promising with the focus toward identifying the most accurate and practical instrument to measure PA in this population.

In conclusion, the findings highlight the need to identify and evaluate self-report instruments that can accurately assess PA behaviors among AA women and specifically support positive health outcomes. In addition, it is possible that AA women may have divergent views about PA and can influence their behavior toward PA measurement. Future studies should focus on developing culturally competent PA instruments and adapting self-report measures specifically for use on AA women. Identifying the most valid and reliable self-report instrument for AA women will help designate the most effective intervention to achieve their PA goals and ultimately reduce the risk for CHD.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

John Paguntalan is a fourth year PhD student at the Medical University of South Carolina. His extensive clinical knowledge both as a critical care nurse and nurse practitioner has given him a broad base to approach the prevention and management of coronary heart disease. He is currently engaged as a primary investigator in a study about physical activity and reduction of risk factors for heart disease among African American women.

Mathew Gregoski is an Assistant Professor in the College of Nursing at the Medical University of South Carolina. He has formal training in Kinesiology, Vascular Biololgy, mHealth, Genetics, and Experimental Psychology. He maintains several leading roles in the pursuit of developing personalized yet practical interventions to aid in the reduction of health disparities among minority populations.

References

Aadahl

Jorgensen

(2003). Validation of a new self-report instrument for measuring physical activity. Medicine & Science in Sports & Exercise, 35, 1196-1202. doi:10.1249/01.MSS.0000074446.02192.14

Ainsworth

B. E.

Wilcox

Thompson

W. W.

Richter

D. L.

Henderson

K. A.

(2003). Personal, social, and physical environmental correlates of physical activity in African-American women in South Carolina. American Journal of Preventive Medicine, 25(3, Suppl. 1), 23-29.

Banks-Wallace

Conn

(2002). Interventions to promote physical activity among African American women. Public Health Nursing, 19(5), 321-335.

Buchowski

M. S.

Cohen

S. S.

Matthews

C. E.

Schlundt

D. G.

Signorello

L. B.

Hargreaves

M. K.

Blot

W. J.

(2010). Physical activity and obesity gap between black and white women in the southeastern U.S. American Journal of Preventive Medicine, 39, 140-147. doi:10.1016/j.amepre.2010.03.025

Center for Evidence-Based Medicine. (2009). Oxford center for evidence based-medicine levels of evidence. Retrieved from http://www.cebm.net/oxford-centre-evidence-based-medicine-levels-evidence-march-2009/

Cook

(2009). Physical activity as a mechanism for treatment: A call for an acceptable operational definition. American Journal of Lifestyle Medicine, 3, 71-72. doi:10.1177/1559827608325454

Crespo

C. J.

Smit

Andersen

R. E.

Carter-Pokras

Ainsworth

B. E.

(2000). Race/ethnicity, social class and their relation to physical inactivity during leisure time: Results from the Third National Health and Nutrition Examination Survey, 1988-1994. American Journal of Preventive Medicine, 18, 46-53.

Crouter

S. E.

Schneider

P. L.

Karabulut

Bassett

D. R.

Jr. (2003). Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Medicine & Science in Sports & Exercise, 35, 1455-1460. doi:10.1249/01.MSS.0000078932.61440.A2

Elsawy

Higgins

K. E.

(2010). Physical activity guidelines for older adults. American Family Physician, 81, 55-59.

10.

Fleury

Lee

S. M.

(2006). The social ecological model and physical activity in African American women. American Journal of Community Psychology, 37, 129-140. doi:10.1007/s10464-005-9002-7

11.

Freedson

P. S.

Melanson

Sirard

(1998). Calibration of the computer science and applications, Inc. accelerometer. Medicine & Science in Sports & Exercise, 30, 777-781.

12.

Heil

D. P.

Brage

Rothney

M. P.

(2012). Modeling physical activity outcomes from wearable monitors. Medicine & Science in Sports & Exercise, 44(1, Suppl. 1), S50-S60. doi:10.1249/MSS.0b013e3182399dcc

13.

Kaiyala

K. J.

Ramsay

D. S.

(2011). Direct animal calorimetry, the underused gold standard for quantifying the fire of life. Comparative Biochemistry Physiology Part A: Molecular Integrated Physiology, 158, 252-264. doi:10.1016/j.cbpa.2010.04.013

14.

Marshall

S. J.

Jones

D. A.

Ainsworth

B. E.

Reis

J. P.

Levy

S. S.

Macera

C. A.

(2007). Race/ethnicity, social class, and leisure-time physical inactivity. Medicine & Science in Sports & Exercise, 39(1), 44-51. doi:10.1249/01.mss.0000239401.16381.37

15.

Masse

L. C.

Ainsworth

B. E.

Tortolero

Levin

Fulton

J. E.

Henderson

K. A.

Mayo

(1998). Measuring physical activity in midlife, older, and minority women: Issues from an expert panel. Journal of Women’s Health, 7, 57-67.

16.

Masse

L. C.

Fulton

J. E.

Watson

K. B.

Tortolero

Kohl

H. W.

III Meyers

M. C.

. . . Wong

W. W.

(2012). Comparing the validity of 2 Physical Activity Questionnaire formats in African-American and Hispanic women. Journal of Physical Activity & Health, 9, 237-248.

17.

Mindell

J. S.

Coombs

Stamatakis

(2014). Measuring physical activity in children and adolescents for dietary surveys: Practicalities, problems and pitfalls. Proceedings of the Nutrition Society, 73, 218-225. doi:10.1017/S0029665-113003820

18.

Neuhouser

M. L.

Tinker

L. F.

Thomson

Sternfeld

Mossavar-Rahmani

. . . Prentice

R. L.

(2013). Physical activity assessment: Biomarkers and self-report of activity-related energy expenditure in the WHI. American Journal of Epidemiology, 177, 576-585. doi:10.1093/aje/kws269

19.

Oexmann

M. J.

Thomas

J. C.

Taylor

K. B.

O’Neil

P. M.

Garvey

W. T.

Lackland

D. T.

Egan

B. M.

(2000). Short-term impact of a church-based approach to lifestyle change on cardiovascular risk in African Americans. Ethnicity & Disease, 10(1), 17-23.

20.

Peterson

J. A.

(2011). Evaluation of the Heart and Soul Physical Activity Program by African American women. The ABNF Journal: Official Journal of the Association of Black Nursing Faculty in Higher Education, 22(3), 64-72.

21.

Peterson

J. A.

Cheng

A. L.

(2011). Heart and Soul Physical Activity Program for African American women. Western Journal of Nursing Research, 33, 652-670. doi:10.1177/0193945910383706

22.

Philippaerts

R. M.

Westerterp

K. R.

Lefevre

(1999). Doubly labelled water validation of three Physical Activity Questionnaires. International Journal of Sports Medicine, 20, 284-289. doi:10.1055/s-2007-971132

23.

Plescia

Herrick

Chavis

(2008). Improving health behaviors in an African American community: The Charlotte racial and ethnic approaches to community health project. American Journal of Public Health, 98, 1678-1684. doi:10.2105/AJPH.2007.125062

24.

Resnick

Luisi

Vogel

Junaleepa

(2004). Reliability and validity of the self-efficacy for exercise and outcome expectations for exercise scales with minority older adults. Journal of Nursing Measurement, 12, 235-247.

25.

Resnicow

DiIorio

Soet

J. E.

Ernst

Borrelli

Hecht

(2002). Motivational interviewing in health promotion: It sounds like something is changing. Health Psychology, 21, 444-451.

26.

Resnicow

McCarty

Blissett

Wang

Heitzler

Lee

R. E.

(2003). Validity of a modified CHAMPS Physical Activity Questionnaire among African-Americans. Medicine & Science in Sports & Exercise, 35, 1537-1545. doi:10.1249/01.MSS.0000084419.64044.2B

27.

Roger

V. L.

A. S.

Lloyd-Jones

D. M.

Benjamin

E. J.

Berry

J. D.

Borden

W. B.

. . . Turner

M. B.

(2012). Heart disease and stroke statistics–2012 update: A report from the American Heart Association. Circulation, 125(1), e2-e220. doi:10.1161/CIR.0b013e31823ac046

28.

Schuit

A. J.

Schouten

E. G.

Westerterp

K. R.

Saris

W. H.

(1997). Validity of the Physical Activity Scale for the Elderly (PASE): According to energy expenditure assessed by the doubly labeled water method. Journal of Clinical Epidemiology, 50, 541-546.

29.

Singh

P. N.

Fraser

G. E.

Knutsen

S. F.

Lindsted

K. D.

Bennett

H. W.

(2001). Validity of a Physical Activity Questionnaire among African-American seventh-day Adventists. Medicine & Science in Sports & Exercise, 33, 468-475.

30.

Thompson

W. M.

Barksdale

D. J.

(2010). Physical inactivity in female African-American adolescents: Consequences, costs, & care. Journal of National Black Nurses’ Association, 21(1), 39-45.

31.

U.S. Department of Health and Human Services. (2014). 2008 Physical activity guidelines for Americans summary. Retrieved from http://www.health.gov/paguidelines/guidelines/summary.aspx

32.

Vanhees

Lefevre

Philippaerts

Martens

Huygens

Troosters

Beunen

(2005). How to assess physical activity? How to assess physical fitness? European Journal of Cardiovascular Preventive & Rehabilitation, 12, 102-114.

33.

Waltz

C. F.

Strickland

Lenz

E. R.

(2010). Measurement in nursing and health research (4th ed.). New York, NY: Springer.

34.

Waltz

C. F.

Strickland

Lenz

E. R.

Waltz

C. F.

(2005). Measurement in nursing and health research (3rd ed.). New York, NY: Springer.

35.

Warren

J. M.

Ekelund

Besson

Mezzani

Geladas

Vanhees

& Experts Panel. (2010). Assessment of physical activity-a review of methodologies with reference to epidemiological research: A report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European Journal of Cardiovascular Preventive & Rehabilitation, 17, 127-139. doi:10.1097/HJR.0b013e32832ed875

36.

Whitt

M. C.

Levin

Ainsworth

B. E.

Dubose

K. D.

(2003). Evaluation of a two-part survey item to assess moderate physical activity: The Cross-Cultural Activity Participation Study. Journal of Women’s Health, 12, 203-212. doi:10.1089/154099903321667537

37.

Wolin

K. Y.

Heil

D. P.

Askew

Matthews

C. E.

Bennett

G. G.

(2008). Validation of the International Physical Activity Questionnaire-Short among Blacks. Journal of Physical Activity & Health, 5, 746-760.

38.

World Health Organization. (2014). Health topics: Physical activity. Retrieved from http://www.who.int/diet-physicalactivity/pa/en/

39.

Yanek

L. R.

Becker

D. M.

Moy

T. F.

Gittelsohn

Koffman

D. M.

(2001). Project joy: Faith based cardiovascular health promotion for African American women. Public Health Reports, 116(Suppl. 1), 68-81.