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Physical Activity Assessment
Physical Activity Assessment Validation of a Clinical Assessment Tool Rebecca Ann Meriwether, MD, MPH, Pamela M. McMahon, PhD, MPH, Nahid Islam, MBBS, MPH, William C. Steinmann, MD, MSc Background: Physicians report that they fail to counsel patients about physical activity due to a lack of practical tools, time, reimbursement, knowledge, and confidence. This paper reports concurrent and criterion validation of the Physical Activity Assessment Tool (PAAT), designed to rapidly assess patient physical activity in clinical settings and reduce time for assessment, and thus to facilitate counseling. Methods:
Adult volunteers (n⫽68) completed the PAAT and International Physical Activity QuestionnaireLong Form (IPAQ-Long) twice and wore a Manufacturing Technology, Inc. (MTI) accelerometer for 14 days in 2003. Continuous and categorical measures of physical activity by PAAT were compared to MTI accelerometer and IPAQ-Long in analyses conducted in 2003 to 2006. Consistent with national recommendations, participants were classified as active if they accumulated more than 150 minutes per week of moderate to vigorous physical activity (MVPA) or more than 60 minutes per week of vigorous physical activity.
Results:
The PAAT was significantly correlated with the IPAQ (r⫽0.562, p⬍0.001) and MTI (r⫽0.392, p⫽0.015) for MVPA. Seven-day test–retest reliability was comparable for PAAT (r⫽0.618, p⬍0.001) and MTI (r⫽0.527, p⬍0.001). PAAT classified participants as “active” or “under-active” concordantly with MTI for 69.8% of participants and with IPAQ for 66.7%; strength of agreement was fair (⫽0.338 and 0.212, respectively). The PAAT classified fewer participants as active than either the MTI (p⫽0.169) or IPAQ (p⬍0.001), and measured physical activity more like the direct objective measure (MTI) than did IPAQ.
Conclusions: The concurrent and criterion validity of the PAAT are comparable to self-report instruments used in epidemiologic research. (Am J Prev Med 2006;31(6):484 – 491) © 2006 American Journal of Preventive Medicine
Introduction
B
ecause of the salutary health effects of physical activity,1–11 the U.S. Surgeon General, Centers for Disease Control and Prevention, and American College of Sports Medicine recommend that American adults should get at least 30 minutes of moderate-intensity physical activity (MPA) in bouts of 10 or more minutes on most days of the week or 20 minutes of vigorous physical activity (VPA) on 3 or more days each week.1,2 Although physician counseling appears to be modestly effective for short-term increases in patient physical activity, it remains uncertain how counseling should be done to elicit more From the Department of Family and Preventive Medicine, University of South Carolina School of Medicine (Meriwether), Columbia, South Carolina; and Department of Family and Community Medicine (McMahon, Islam), and Department of Internal Medicine and Center for Clinical Effectiveness and Prevention (Steinmann), Tulane University School of Medicine, New Orleans, Louisiana Address correspondence and reprint requests to: Rebecca Ann Meriwether, MD, MPH, Department of Family & Preventive Medicine, University of South Carolina School of Medicine, 3209 Colonial Drive, Columbia SC 29203. E-mail: [email protected].
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durable changes in physical activity.12–28 Nonetheless, Healthy People 2010 29 recommended physician counseling for physical activity, and because 82% of people see a physician every year, the potential public health impact of physician counseling could be significant even if it is only modestly effective. Yet 52% to 78% of patients report that they have not been counseled about physical activity by their physicians.30 –35 Physicians report that they do not counsel patients because of a lack of practical tools, time, reimbursement, knowledge, and confidence that counseling will trigger behavior change.1,2,32,36 The self-administered Physical Activity Assessment Tool (PAAT) (Figure 1) was developed to rapidly assess patient physical activity in primary care settings, reduce physician time for assessment, facilitate counseling, and address physician knowledge and confidence deficits. The PAAT defines moderate and vigorous physical activity, and incorporates questions used in International Physical Activity Questionnaire (IPAQ) Short form37 with lists of common types of physical activity that are stratified by intensity according to the updated
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0749-3797/06/$–see front matter doi:10.1016/j.amepre.2006.08.021
Figure 1. Physical activity assessment tool (PAAT).
Compendium of physical activity.38 The PAAT measures type, frequency, and duration of moderate and vigorous physical activity from all four domains of physical activity—leisure, occupational, household, and transportation—in the last 7 days, and asks if this is “more, less, or about the same as usual” activity. The PAAT can be completed in 5 to 7 minutes while patients wait to see a physician. It includes questions about possible contraindications to physical activity, stage of change, patient-oriented benefits, and psychosocial facilitators of physical activity. This paper describes validation of self-reported physical activity by PAAT against a direct, objective measure of physical activity—the Manufacturing Technology, Inc. (MTI, formerly Computer Science and Applications, Inc. [CSA]) accelerometer39 – 46—and a previously validated, self-report instrument—the IPAQ-Long Form, Self Administered.37
Methods Subjects After human subjects approval, 69 adult volunteers from a university community in New Orleans were recruited in 2003 using flyers, word-of-mouth referrals, and one university-wide e-mail. Volunteers were screened for eligibility by phone and scheduled for enrollment and baseline data collection at one of two on-campus sites. Volunteers were eligible if they were aged 18 to 64 years, not pregnant, had a body mass index
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(BMI) ⬍40, and no contraindications to moderate physical activity based on the Physical Activity Readiness Questionnaire.47–50 Volunteers were English-speaking and primarily white; data were not collected on race/ethnicity or educational attainment. A balanced-block design was used to assure that half the volunteers were active and half under-active to enhance the study’s ability to test discrimination between active and under-active individuals by PAAT. At screening, activity level was assessed using the IPAQ, Short Form– Telephone Administered.37 Informed consent was obtained at the baseline visit, and participants who completed data collection received a $20 grocery-store gift certificate. The study was powered (1⫺⫽0.83) to detect significant (␣⫽0.05) correlations between measures of 0.37 with 60 participants, a level chosen based on correlations for commonly used self-reported physical activity instruments.
Data Collection Participants self-reported physical activity using the PAAT and IPAQ for the previous 7 days and physical-activity stage of change at data collection visits 7 days apart during 2003. The PAAT was administered first and only once at each visit to simulate conditions at a physician office visit and minimize impact of the longer, more detailed IPAQ on PAAT results and because repeating the PAAT in a session lasting only 10 to 15 minutes would likely have captured recalled answers rather than independent recall of physical activity during the last 7 days. The MTI accelerometer was used as a direct objective measure of physical activity between visits. The MTI uniaxial accelerometer is a small, pager-like device that is
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worn at the waist and records accelerations in the vertical plane per epoch of time and has been validated against VO2, doubly-labeled water, and directly observed physical activity.39 – 46 At the first visit (Week 0), participants were given an MTI accelerometer set to capture accelerations in 1-minute epochs and a brochure explaining the use of the MTI. Participants were asked to wear the MTI daily while awake except when showering, bathing, or during other water activities and to return it in 7 days. MTI data were downloaded at each subsequent visit and the MTI re-initialized.
Measurement For the PAAT, minutes of MPA and VPA were calculated by multiplying the number of minutes per day by the number of days per week; these were summed for moderate-to-vigorous physical activity (MVPA). For the IPAQ-Long, minutes of MVPA were calculated by multiplying the number of minutes per day by the number of days per week reported on each of 11 questions asking about moderate and vigorous types of physical activity and summing the results. These questions cover physical activity at work, for transportation, leisure-time activities, and household chores and yard work. MTI activity counts were transformed using software provided by Actigraph, Manufacturing Technologies, Inc. (Fort Walton Beach FL) and the Freedson equation39 into minutes per week of light (⬍3 metabolic equivalents [METS]), moderate (3 to 4.9 METS), and vigorous (hard and very hard, ⱖ5 METS) physical activity.
Data Management Self-reported physical activity was abstracted from the PAAT and IPAQ and entered into an Excel database. The last 7 days of MTI data were compared with self-reported physical activity for the last 7 days. Participants wore the MTI for at least part of a mean of 6.9 days in Week 1 and 6.4 days in Week 2. Seven participants failed to wear the accelerometer on ⱖ1 days. Sensitivity analysis showed no statistically significant differences between correlation coefficients for data when 6, 8, or 12 hours of activity counts were available for 4, 5, 6, or 7 days per week. The amount of data retained for analysis was optimized and participant-weeks with activity counts for ⱖ6 hours/day on ⱖ4 days were included. The main outcome (dependent variable) was minutes per week of physical activity. A categorical measure of physical activity, in which participant-weeks were classified as active or under-active was a secondary outcome. A participant-week was classified according to national recommendations as active if 60 minutes per week VPA or 150 minutes per week of MVPA combined were reported or measured. Otherwise, participantweeks were classified as under-active. Scatter plots were used to visually identify six outliers. Results are presented with outliers included for all measures.
Analysis To measure test–retest reliability and concurrent and criterion validity, Spearman coefficients were used because data were not normally distributed.51 Test–retest reliability was measured for PAAT and MTI by comparing physical activity minutes per week for sequential weeks. Concurrent and criterion validity were measured by comparing physical activ-
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Table 1. Final participant characteristics (n ⫽ 69) Characteristic Main campus Health sciences campus Staff Faculty Students Trainees Completed study Dropouts During Week 1 During Week 3 Females Age, mean years (range) Body mass index mean (range)
n (%) 23 (33.3) 46 (66.7) 43 (62.3) 11 (15.9) 11 (15.9) 4 (5.8) 67 (97.1) 1 (1.4) 1 (1.4) 58 (84.1) 37.5 (20–61) 25.7 (18.3–37.6)
ity minutes per week measured in the first week of the study by PAAT with IPAQ and MTI, respectively. Percent agreement, sensitivity, specificity, and positive and negative predictive values were calculated comparing the categorical variable (active vs under-active) on PAAT to IPAQ and MTI. PAAT– MTI comparisons were stratified by intensity (MPA, VPA, MVPA) and the percent of over- and under-reporting on PAAT compared with MTI were calculated. Because no criteria were published for defining MPA or VPA using IPAQ at the time this study was undertaken, only MVPA is reported when comparing PAAT to IPAQ. Data management, analyses, and statistical testing were conducted in 2003 to 2006 using Excel (Microsoft) and SAS, version 9.1 (SAS Institute Inc., Cary NC, 2002–2003). The McNemar test for paired data with continuity correction (two-tailed) was used to determine whether the referent measures were significantly more likely to classify participants as active compared with the PAAT. Kappas were used to evaluate the extent of agreement between measures surpassing that due to chance alone.
Results Sixty-nine individuals were enrolled, and 34 (49.3%) reported that they were active at screening. One participant withdrew during the first week of the study; 68 participants completed two data-collection visits. Because of missing data, MTI data were available for analysis for only 63 participants. Participants and enrollment and retention are described in Table 1 and Figure 2. More than half of participants were classified as active by each of the three measures. The PAAT classified fewer participants as active (60.3%) than did MTI (71.4%), but this was not significant (p⫽0.169), and markedly fewer than IPAQ (80.3%, p⬍0.001). Participants reported that physical activity in the last 7 days was same as usual for 68.3%, more than usual for 9.5%, and less than usual for 22.2%. Participants selfreported their stage of change as follows: 1.6% precontemplation, 7.9% contemplation, 47.6% preparation, 14.3% action, and 28.6% maintenance. Forty-one percent reported that they were “very confident” they
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Mean minutes per week of MPA reported on the PAAT were 14.7% higher than by the MTI, mean VPA was 15.1% lower than the MTI, and mean MVPA was 6.1% higher. PAAT was significantly correlated with MTI for MVPA (r⫽0.392, p⬍0.01), VPA (r⫽0.380, p⬍0.01), and MPA (r⫽0.392, p⬍0.01). The PAAT and MTI classified activity level concordantly for 69.8%; strength of agreement was fair (⫽0.338). The PAAT under-estimated the number of active participants (n⫽38) compared with MTI (n⫽45), and misclassified 13 of 19 (68.4%) discordant participants as under-active, but the difference was not significant (p⫽0.169). PAAT sensitivity and specificity compared with the MTI were 71.1% and 66.7%, respectively; positive predictive value was 84.2% and negative predictive value, 48.0%.
Discussion
Figure 2. Data collection procedures and timeline, and participant characteristics. BMI, body mass index; PAAT, Physical Activity Assessment Tool; Wk, week.
could increase their physical activity, 44.4% were “fairly confident,” and 12.7% were “not at all confident.”
Test–Retest Reliability Physical activity measured by PAAT varied from week to week as expected, but correlations between weeks were significant (p⬍0.001) for total (r⫽0.618), vigorous (r⫽0.771), and moderate physical activity (r⫽0.489), and similar to week-to-week correlations for physical activity objectively measured by MTI (MVPA, r⫽0.527; VPA, r⫽0.574; MPA, r⫽0.567) and self-reported by IPAQ (MVPA r⫽0.627, p⬍0.001).
Concurrent Validity Participants reported a mean of 357.7 minutes/week of MVPA by PAAT and 1093.8 by IPAQ. PAAT was significantly (p⬍0.001) correlated with IPAQ (r⫽0.585) for MVPA. PAAT and IPAQ agreed on classification as active or under-active for two thirds (66.7%), but the IPAQ was significantly more likely to classify participants as active (p⬍0.001). Strength of agreement was fair (⫽0.212).
Criterion Validity Participants reported a mean of 357.7 minutes/week of MVPA by PAAT, and the MTI measured a mean of 337.0 minutes/week. The PAAT classified 77.0% of physical activity as moderate, and the MTI, 71.3%. December 2006
The PAAT meets criteria for concurrent and criterion validity described by Sallis and Saelens,52 and incorporates a wide range of types of physical activity thought to be acceptable to and understood by culturally diverse patients.53,54 The PAAT assesses physical activity frequency, intensity, type, and time; elicits recall for moderate and vigorous physical activity from all four domains of physical activity—leisure, occupational, household chores, and transportation52,55; is able to measure compliance with national health recommendations (i.e., minutes per week of MPA and VPA)52; and demonstrates acceptable test–retest reliability. It is designed to be completed by patients while waiting to see the physician and rapidly assessed by clinicians, and it supports the delivery of a brief counseling message in the 90 to 180 seconds available for health promotion in typical primary care visits.56 Reliability of the PAAT is comparable to that reported for other self-report instruments,37,55,57–71 and similar to what was measured for the MTI accelerometer. Because people naturally vary the type and amount of their physical activity from day to day and week to week,54,55,57 test–retest measures for different time frames describe stability of physical activity and include both measurement error and natural variation.52,55,60 The similarity of the test–retest correlations for the MTI suggest that most of the measured variation was true variation in physical activity rather than measurement error. Criterion validity of the PAAT is supported by significant correlations with MTI, which were comparable to those published for IPAQ (pooled rho⫽0.33, 95% confidence interval⫽0.26 – 0.39)37 and other self-report instruments that measure usual or last 7 days of physical activity and have been validated against direct objective measures of physical activity.52,55,58,59,62,69,70,72–74 The PAAT over-estimated MVPA minutes per week compared with the MTI by 6.1%, while the Seven Day Physical Activity Recall Am J Prev Med 2006;31(6)
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over-estimated physical activity energy expenditure in a small group of active young women by 46% compared with the MTI in one study73 and 20% compared with doubly-labeled water in obese women in another.75 However, studies suggest that accelerometers may under-estimate energy expenditure from physical activity by as much as 40%.76,77 The tendency of the PAAT to under-estimate the number of active participants was surprising, while the tendency of self-report instruments to over-estimate total physical activity and the intensity of physical activity has been frequently noted.52,62,78 Several studies have shown that VPA tends to be overestimated, while MPA is often under-estimated,11,64,79 – 81 although this varies by gender, BMI, and level of habitual physical activity.54,82 In this study, the PAAT over-estimated MPA by 14.7% compared with the accelerometer and under-estimated VPA by 15.1%, although these differences were not significant. It is of note that, when compared with the direct objective measure of physical activity (MTI), participants in this study were significantly less likely to over-report total physical activity on the PAAT than on IPAQ, and that the PAAT classified patients as active or under-active more like the objective measure, MTI, than did IPAQ, which is thought to measure baseline physical activity in addition to conditioning activity. Overall, the PAAT performs as well as self-report instruments commonly used in research, while the accuracy of open-ended questions commonly used in clinical practice is unknown. Additional work is currently underway to strengthen the PAAT’s content and construct validity among lower-activity individuals and to confirm criterion validity in a more poorly educated, lower socioeconomic-status population. Jacobs et al.55 observed that the construct of questions in physical activity assessment tools may be more important than the length and complexity of the questionnaire. The PAAT is a brief, simple tool for identifying individuals who are not meeting national recommendations. Most persons misclassified by PAAT are active individuals misclassified as under-active. Since increasing the amount of physical activity for patients who are already active carries additional benefits, counseling active patients to increase their physical activity, particularly when MPA is recommended and potential contraindications are addressed, is likely to be beneficial rather than harmful. While physician counseling alone is often not sufficient to enable patients to begin and sustain the level of physical activity needed for optimal health benefits,24 and the precise content and style of delivery of optimal counseling has yet to be described, Kreuter et al.16 have shown that inactive patients who receive physician counseling are 50% more likely to increase their physical activity than patients who are not counseled. 488
Limitations Because physical activity recall and reporting involve a complex cognitive process,60,83,84 misclassification by participants may occur due to errors in the interpretation of questions; estimation of duration, frequency, and intensity of physical activity bouts; or failure to recognize some activities as physical activity due to either a lack of salience or cultural differences in activities and terminology used to describe them.52–54,85,86 In addition to these recall and misclassification biases, self-reported physical activity is also subject to social acceptability bias.54,82,85 Instruments constructed differently may result in different measurement errors because of differences in interpretation and the complex cognitive process required to generate responses.52,54,84 The PAAT and IPAQ both measure physical activity during the previous 7 days and can be expected to have lower levels of recall bias than instruments attempting to measure physical activity that occurred over longer periods of time, such as a month, a year, or a lifetime.54,59,60,62,87 However, because physical activity varies from day to day and week to week, physical activity during the last 7 days may not be typical of usual physical activity.57,60,61 This could result in misclassification of usually under-active patients as active or the reverse. To address this in clinical settings, the PAAT also asks if physical activity during last 7 days was the “same, less, or more than usual.” The results of this study may not be generalizable to all populations. Because adults aged 65 and older and with BMIs of 40 or more were excluded, and participants were drawn from a university community, they were probably better educated and more affluent than the general population and younger and leaner than typical primary care patients. Other studies have reported that more poorly educated, older, sicker, and more-obese patients over-report physical activity more frequently than other participants.54,79,82 In addition, this study population was intentionally recruited to be approximately half active and half under-active, substantially above overall population prevalence. Therefore, sensitivity, specificity, and positive and negative predictive values, which are dependent on prevalence, should be interpreted with caution. Overall, the PAAT is likely to misclassify more patients as active than it did in this study population. A follow-up study is underway to refine and re-validate the PAAT in lower-income, lower-literacy populations. Accelerometers are commonly used as criterion measures because they provide a direct, objective measure of physical activity. However, they are not “gold standards” because they are also subject to measurement error,52,55,83,88,89 since they do not capture all types of physical activity with equal precision. Activities such as
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walking, running, and jogging are well captured, but many of the lifestyle physical activities that have been shown to be better sustained than structured leisuretime physical activity,90 such as gardening, playing with children, and housework are poorly captured by these devices.44,52,91 Consequently, while accelerometers have been used traditionally as criterion measures against which self-reported physical activity is validated,52,83 they actually measure a smaller subset of physical activity.
Conclusion The PAAT demonstrates concurrent and criterion validity comparable to other physical activity self-report instruments, and agreement with the criterion measure MTI is fair. Test–retest reliability was similar to that of our criterion measure, MTI accelerometers. In this study, the PAAT slightly under-estimated the number of active participants compared with the criterion measure MTI, but performed more like the criterion measure than did the IPAQ. The PAAT warrants further evaluation. This work was supported by a Generalist Physician Faculty Scholar award from the Robert Wood Johnson Foundation (RAM). We wish to express our thanks for the assistance of Loretta Wilson, PhD, assistant professor, and Shane Sanne and Eva Lustigova, students in the Tulane Department of Sports and Exercise Science, Tulane University, for assistance collecting data; Allyssia Sam-Dairies, MSPH, Tulane University Department of Biostatistics, and Cheryl Addy, PhD, University of South Carolina Arnold School of Public Health, for statistical advice; and Amanda Johnson, MS, for assistance with data analysis. No financial conflict of interest was reported by the authors of this paper.
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34. Centers for Disease Control and Prevention. Physician advice and individual behaviors about cardiovascular disease risk reduction—seven states and Puerto Rico, 1997. MMWR Morb Mortal Wkly Rep 1999; 48:74 –7. 35. Damush TM, Stewart AL, Mills KM, King AC, Ritter PL. Prevalence and correlates of physician recommendations to exercise among older adults. J Gerontol A Biol Sci Med Sci 1999;54:M423–7. 36. Pinto BM, Goldstein MG, Marcus BH. Activity counseling by primary care physicians. Prev Med 1998;27:506 –13. 37. Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire:12-country reliability and validity. Med Sci Sports Exerc 2003;35:1381–95. 38. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32(suppl 9):S498 –504. 39. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998;30:777– 81. 40. Trost SG, Ward DS, Moorehead SM, Watson PD, Riner W, Burke JR. Validity of the computer science and applications (CSA) activity monitor in children. Med Sci Sports Exerc 1998;30:629 –33. 41. Ekelund U, Sjostrom M, Yngve A, et al. Physical activity assessed by activity monitor and doubly labeled water in children. Med Sci Sports Exerc 2001;33:275– 81. 42. Bassett DRJr, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 2000;32(suppl 9):S471– 80. 43. Brage S, Wedderkopp N, Franks PW, Andersen LB, Froberg K. Reexamination of validity and reliability of the CSA monitor in walking and running. Med Sci Sports Exerc 2003;35:1447–54. 44. Hendelman D, Miller K, Baggett C, Debold E, Freedson P. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc 2000;32(suppl 9):S442–9. 45. Eston RG, Rowlands AV, Ingledew DK. Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children’s activities. J Appl Physiol 1998;84:362–71. 46. Swartz AM, Strath SJ, Bassett DRJr, O’Brien WL, King GA, Ainsworth BE. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc 2000;32(suppl 9):S450 – 6. 47. Shephard RJ. PAR-Q, Canadian Home Fitness Test and exercise screening alternatives. Sports Med 1988;5:185–95. 48. Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Can J Sport Sci 1992;17:338 – 45. 49. Cardinal BJ, Esters J, Cardinal MK. Evaluation of the revised physical activity readiness questionnaire in older adults. Med Sci Sports Exerc 1996;28:468 –72. 50. Cardinal BJ, Cardinal MK. Preparticipation physical activity screening within a racially diverse, older adult sample: comparison of the original and Revised Physical Activity Readiness Questionnaires. Res Q Exerc Sport 2000;71:302–7. 51. Neter J, Kutner MH, Nachtsheim CJ, et al. Applied linear statistical models. 4th ed. New York: McGraw-Hill, 1996. 52. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport 2000;71(suppl 2):S1–14 (erratum appears in Res Q Exerc Sport 2000;71:409). 53. Ainsworth BE, Irwin ML, Addy CL, Whitt MC, Stolarczyk LM. Moderate physical activity patterns of minority women: the Cross-Cultural Activity Participation Study. J Womens Health Gend Based Med 1999;8:805–13. 54. Durante R, Ainsworth BE. The recall of physical activity: using a cognitive model of the question-answering process. Med Sci Sports Exerc 1996;28:1282–91. 55. Jacobs DR Jr, Ainsworth BE, Hartman TJ, Leon AS. A simultaneous evaluation of 10 commonly used physical activity questionnaires. Med Sci Sports Exerc 1993;25:81–91. 56. Stange KC, Woolf SH, Gjeltema K. One minute for prevention: the power of leveraging to fulfill the promise of health behavior counseling. Am J Prev Med 2002;22:320 –3. 57. Booth ML, Owen N, Bauman AE, Gore CJ. Retest reliability of recall measures of leisure-time physical activity in Australian adults. Int J Epidemiol 1996;25:153–9. 58. Rauh MJ, Hovell MF, Hofstetter CR, Sallis JF, Gleghorn A. Reliability and validity of self-reported physical activity in Latinos. Int J Epidemiol 1992;21:966 –71.
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59. Sallis JF, Buono MJ, Roby JJ, Micale FG, Nelson JA. Seven-day recall and other physical activity self-reports in children and adolescents. Med Sci Sports Exerc 1993;25:99 –108. 60. Washburn RA, Montoye HJ. The assessment of physical activity by questionnaire. Am J Epidemiol 1986;123:563–76. 61. Kriska AM, Knowler WC, LaPorte RE, et al. Development of questionnaire to examine relationship of physical activity and diabetes in Pima Indians. Diabetes Care 1990;13:401–11. 62. Kriska AM, Caspersen CJ. Introduction to the collection of physical activity questionnaires. Med Sci Sports Exerc 1997;29(suppl 6):S5–9. 63. Ainsworth BE, Jacobs DR Jr, Leon AS. Validity and reliability of selfreported physical activity status: the Lipid Research Clinics questionnaire. Med Sci Sports Exerc 1993;25:92– 8. 64. Sallis JF, Haskell WL, Wood PD, et al. Physical activity assessment methodology in the Five-City Project. Am J Epidemiol 1985;121:91–106. 65. Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol 1993;46:153– 62. 66. Washburn RA, Smith KW, Goldfield SRW, McKinlay JB. Reliability and physiologic correlates of the Harvard Alumni activity survey in a general population. J Clin Epidemiol 1991;44:1319 –26. 67. Dishman RK, Steinhardt M. Reliability and concurrent validity for a 7-d re-call of physical activity in college students. Med Sci Sports Exerc 1988;20:14 –25 (erratum appears in Med Sci Sports Exerc 1988;20:211). 68. Sallis JF, Patterson TL, Buono MJ, Nader PR. Relation of cardiovascular fitness and physical activity to cardiovascular disease risk factors in children and adults. Am J Epidemiol 1988;127:933– 41. 69. LaPorte RE, Black-Sandler R, Cauley JA, Link M, Bayles C, Marks B. Assessment of physical activity in older women: Analysis of the interrelationship and reliability of activity monitoring, activity surveys, and caloric intake. J Gerontol 1983;38:394 –7. 70. Ainsworth BE, Leon AS, Richardson MT, Jacobs DR, Paffenbarger RS Jr. Accuracy of the College Alumnus Physical Activity questionnaire. J Clin Epidemiol 1993;46:1403–11. 71. Godin G, Shepherd RJ. Simple method to assess exercise behavior in the community. Can J Appl Sport Sci 1985;10:141– 6. 72. Miller DJ, Freedson PS, Kline GM. Comparison of activity levels using the Caltrac accelerometer and five questionnaires. Med Sci Sports Exerc 1994;26:376 – 82. 73. Leenders N, Sherman WM, Nagaraja HN, Kien CL. Comparisons of four methods of estimating physical activity in adult women. Med Sci Sports Exerc 2000;32:1320 – 6. 74. Richardson MT, Leon AS, Jacobs DR Jr, Ainsworth BE, Serfass R. Ability of the Caltrac accelerometer to assess daily physical activity levels. J Cardiopulm Rehabil 1995;15:107–13. 75. Racette SB, Schoeller DA, Kushner RF. Comparison of heart rate and physical activity recall with doubly labeled water in obese women. Med Sci Sports Exerc 1995;27:126 –33. 76. Bouten CV, Westerterp KR, Verduin M, Janssen JD. Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med Sci Sports Exerc 1994;26:1516 –23. 77. Bouten CV, Verboelet-Van de Venne WPHG, Westerterp KR, Verduin M, Janssen JD. Daily physical activity assessment: comparison between movement registration and doubly labeled water. J Appl Physiol 1996;81:1019 –26. 78. Sallis JF, Strikmiller PK, Harsha DW, et al. Validation of interviewer- and self-administered physical activity checklists for fifth grade students. Med Sci Sports Exerc 1996;28:840 –51. 79. Cumming RG, Klineberg RJ. Study of the reproducibility of long-term recall in the elderly. Epidemiology 1994;5:116 –9. 80. Blair SN, Dowda M, Pate RR, et al. Reliability of long-term recall of participation in physical activity by middle-aged men and women. Am J Epidemiol 1991;133:266 –75. 81. Hayden-Wade HA, Coleman KJ, Sallis JF, Armstrong C. Validation of the telephone and in-person interview versions of the 7-Day PAR. Med Sci Sports Exerc 2003;35:801–9. 82. Klesges RC, Eck LH, Mellon MW, Fulliton W, Somes GW, Hanson CL. The accuracy of self-reports of physical activity. Med Sci Sports Exerc 1990;22:690 –7. 83. Montoye HJ. Introduction: evaluation of some measurements of physical activity and energy expenditure. Med Sci Sports Exerc 2000;32(suppl 9):S439 – 41.
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84. Baranowski T. Validity and reliability of self-report measures of physical activity: an information-processing perspective. Res Q Exerc Sport 1988;59:314 –27. 85. Warnecke RB, Johnson TP, Chavez N, Sudman S, O’Rourke DP, Lacey L, Horm J. Improving question wording in surveys of culturally diverse populations. Ann Epidemiol 1997;7:334 – 42. 86. Taylor CB, Coffey T, Berra K, Iaffaldano R, Casey K, Haskell WL. Seven-day activity and self-report compared to a direct measure of physical activity. Am J Epidemiol 1984;120:818 –24. 87. Blair SN, Haskell WL, Ho P, et al. Assessment of habitual physical activity by a seven-day recall in a community survey and controlled experiments. Am J Epidemiol 1985;122:794 – 804.
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88. Bassett DRJr. Validity and reliability issues in objective monitoring of physical activity. Res Q Exerc Sport 2000;71(suppl 2):S30 – 6. 89. Montoye HJ, Washburn R, Servais S, Ertl A, Webster JG, Nagle FJ. Estimation of energy expenditure by a portable accelerometer. Med Sci Sports Exerc 1983;15:403–7. 90. Dunn AL, Marcus BH, Kampert JB, Garcia ME, Kohl HW 3rd, Blair SN. Comparison of lifestyle and structured interventions to increase physical activity and cardiorespiratory fitness: a randomized trial. JAMA 1999;281:327–34. 91. Welk GJ, Blair SN, Wood K, Jones S, Thompson RW. A comparative evaluation of three accelerometry-based physical activity monitors. Med Sci Sports Exerc 2000;32(suppl 9):S489 –97.
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Adult volunteers (n⫽68) completed the PAAT and International Physical Activity QuestionnaireLong Form (IPAQ-Long) twice and wore a Manufacturing Technology, Inc. (MTI) accelerometer for 14 days in 2003. Continuous and categorical measures of physical activity by PAAT were compared to MTI accelerometer and IPAQ-Long in analyses conducted in 2003 to 2006. Consistent with national recommendations, participants were classified as active if they accumulated more than 150 minutes per week of moderate to vigorous physical activity (MVPA) or more than 60 minutes per week of vigorous physical activity.
Results:
The PAAT was significantly correlated with the IPAQ (r⫽0.562, p⬍0.001) and MTI (r⫽0.392, p⫽0.015) for MVPA. Seven-day test–retest reliability was comparable for PAAT (r⫽0.618, p⬍0.001) and MTI (r⫽0.527, p⬍0.001). PAAT classified participants as “active” or “under-active” concordantly with MTI for 69.8% of participants and with IPAQ for 66.7%; strength of agreement was fair (⫽0.338 and 0.212, respectively). The PAAT classified fewer participants as active than either the MTI (p⫽0.169) or IPAQ (p⬍0.001), and measured physical activity more like the direct objective measure (MTI) than did IPAQ.
Conclusions: The concurrent and criterion validity of the PAAT are comparable to self-report instruments used in epidemiologic research. (Am J Prev Med 2006;31(6):484 – 491) © 2006 American Journal of Preventive Medicine
Introduction
B
ecause of the salutary health effects of physical activity,1–11 the U.S. Surgeon General, Centers for Disease Control and Prevention, and American College of Sports Medicine recommend that American adults should get at least 30 minutes of moderate-intensity physical activity (MPA) in bouts of 10 or more minutes on most days of the week or 20 minutes of vigorous physical activity (VPA) on 3 or more days each week.1,2 Although physician counseling appears to be modestly effective for short-term increases in patient physical activity, it remains uncertain how counseling should be done to elicit more From the Department of Family and Preventive Medicine, University of South Carolina School of Medicine (Meriwether), Columbia, South Carolina; and Department of Family and Community Medicine (McMahon, Islam), and Department of Internal Medicine and Center for Clinical Effectiveness and Prevention (Steinmann), Tulane University School of Medicine, New Orleans, Louisiana Address correspondence and reprint requests to: Rebecca Ann Meriwether, MD, MPH, Department of Family & Preventive Medicine, University of South Carolina School of Medicine, 3209 Colonial Drive, Columbia SC 29203. E-mail: [email protected].
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durable changes in physical activity.12–28 Nonetheless, Healthy People 2010 29 recommended physician counseling for physical activity, and because 82% of people see a physician every year, the potential public health impact of physician counseling could be significant even if it is only modestly effective. Yet 52% to 78% of patients report that they have not been counseled about physical activity by their physicians.30 –35 Physicians report that they do not counsel patients because of a lack of practical tools, time, reimbursement, knowledge, and confidence that counseling will trigger behavior change.1,2,32,36 The self-administered Physical Activity Assessment Tool (PAAT) (Figure 1) was developed to rapidly assess patient physical activity in primary care settings, reduce physician time for assessment, facilitate counseling, and address physician knowledge and confidence deficits. The PAAT defines moderate and vigorous physical activity, and incorporates questions used in International Physical Activity Questionnaire (IPAQ) Short form37 with lists of common types of physical activity that are stratified by intensity according to the updated
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0749-3797/06/$–see front matter doi:10.1016/j.amepre.2006.08.021
Figure 1. Physical activity assessment tool (PAAT).
Compendium of physical activity.38 The PAAT measures type, frequency, and duration of moderate and vigorous physical activity from all four domains of physical activity—leisure, occupational, household, and transportation—in the last 7 days, and asks if this is “more, less, or about the same as usual” activity. The PAAT can be completed in 5 to 7 minutes while patients wait to see a physician. It includes questions about possible contraindications to physical activity, stage of change, patient-oriented benefits, and psychosocial facilitators of physical activity. This paper describes validation of self-reported physical activity by PAAT against a direct, objective measure of physical activity—the Manufacturing Technology, Inc. (MTI, formerly Computer Science and Applications, Inc. [CSA]) accelerometer39 – 46—and a previously validated, self-report instrument—the IPAQ-Long Form, Self Administered.37
Methods Subjects After human subjects approval, 69 adult volunteers from a university community in New Orleans were recruited in 2003 using flyers, word-of-mouth referrals, and one university-wide e-mail. Volunteers were screened for eligibility by phone and scheduled for enrollment and baseline data collection at one of two on-campus sites. Volunteers were eligible if they were aged 18 to 64 years, not pregnant, had a body mass index
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(BMI) ⬍40, and no contraindications to moderate physical activity based on the Physical Activity Readiness Questionnaire.47–50 Volunteers were English-speaking and primarily white; data were not collected on race/ethnicity or educational attainment. A balanced-block design was used to assure that half the volunteers were active and half under-active to enhance the study’s ability to test discrimination between active and under-active individuals by PAAT. At screening, activity level was assessed using the IPAQ, Short Form– Telephone Administered.37 Informed consent was obtained at the baseline visit, and participants who completed data collection received a $20 grocery-store gift certificate. The study was powered (1⫺⫽0.83) to detect significant (␣⫽0.05) correlations between measures of 0.37 with 60 participants, a level chosen based on correlations for commonly used self-reported physical activity instruments.
Data Collection Participants self-reported physical activity using the PAAT and IPAQ for the previous 7 days and physical-activity stage of change at data collection visits 7 days apart during 2003. The PAAT was administered first and only once at each visit to simulate conditions at a physician office visit and minimize impact of the longer, more detailed IPAQ on PAAT results and because repeating the PAAT in a session lasting only 10 to 15 minutes would likely have captured recalled answers rather than independent recall of physical activity during the last 7 days. The MTI accelerometer was used as a direct objective measure of physical activity between visits. The MTI uniaxial accelerometer is a small, pager-like device that is
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worn at the waist and records accelerations in the vertical plane per epoch of time and has been validated against VO2, doubly-labeled water, and directly observed physical activity.39 – 46 At the first visit (Week 0), participants were given an MTI accelerometer set to capture accelerations in 1-minute epochs and a brochure explaining the use of the MTI. Participants were asked to wear the MTI daily while awake except when showering, bathing, or during other water activities and to return it in 7 days. MTI data were downloaded at each subsequent visit and the MTI re-initialized.
Measurement For the PAAT, minutes of MPA and VPA were calculated by multiplying the number of minutes per day by the number of days per week; these were summed for moderate-to-vigorous physical activity (MVPA). For the IPAQ-Long, minutes of MVPA were calculated by multiplying the number of minutes per day by the number of days per week reported on each of 11 questions asking about moderate and vigorous types of physical activity and summing the results. These questions cover physical activity at work, for transportation, leisure-time activities, and household chores and yard work. MTI activity counts were transformed using software provided by Actigraph, Manufacturing Technologies, Inc. (Fort Walton Beach FL) and the Freedson equation39 into minutes per week of light (⬍3 metabolic equivalents [METS]), moderate (3 to 4.9 METS), and vigorous (hard and very hard, ⱖ5 METS) physical activity.
Data Management Self-reported physical activity was abstracted from the PAAT and IPAQ and entered into an Excel database. The last 7 days of MTI data were compared with self-reported physical activity for the last 7 days. Participants wore the MTI for at least part of a mean of 6.9 days in Week 1 and 6.4 days in Week 2. Seven participants failed to wear the accelerometer on ⱖ1 days. Sensitivity analysis showed no statistically significant differences between correlation coefficients for data when 6, 8, or 12 hours of activity counts were available for 4, 5, 6, or 7 days per week. The amount of data retained for analysis was optimized and participant-weeks with activity counts for ⱖ6 hours/day on ⱖ4 days were included. The main outcome (dependent variable) was minutes per week of physical activity. A categorical measure of physical activity, in which participant-weeks were classified as active or under-active was a secondary outcome. A participant-week was classified according to national recommendations as active if 60 minutes per week VPA or 150 minutes per week of MVPA combined were reported or measured. Otherwise, participantweeks were classified as under-active. Scatter plots were used to visually identify six outliers. Results are presented with outliers included for all measures.
Analysis To measure test–retest reliability and concurrent and criterion validity, Spearman coefficients were used because data were not normally distributed.51 Test–retest reliability was measured for PAAT and MTI by comparing physical activity minutes per week for sequential weeks. Concurrent and criterion validity were measured by comparing physical activ-
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Table 1. Final participant characteristics (n ⫽ 69) Characteristic Main campus Health sciences campus Staff Faculty Students Trainees Completed study Dropouts During Week 1 During Week 3 Females Age, mean years (range) Body mass index mean (range)
n (%) 23 (33.3) 46 (66.7) 43 (62.3) 11 (15.9) 11 (15.9) 4 (5.8) 67 (97.1) 1 (1.4) 1 (1.4) 58 (84.1) 37.5 (20–61) 25.7 (18.3–37.6)
ity minutes per week measured in the first week of the study by PAAT with IPAQ and MTI, respectively. Percent agreement, sensitivity, specificity, and positive and negative predictive values were calculated comparing the categorical variable (active vs under-active) on PAAT to IPAQ and MTI. PAAT– MTI comparisons were stratified by intensity (MPA, VPA, MVPA) and the percent of over- and under-reporting on PAAT compared with MTI were calculated. Because no criteria were published for defining MPA or VPA using IPAQ at the time this study was undertaken, only MVPA is reported when comparing PAAT to IPAQ. Data management, analyses, and statistical testing were conducted in 2003 to 2006 using Excel (Microsoft) and SAS, version 9.1 (SAS Institute Inc., Cary NC, 2002–2003). The McNemar test for paired data with continuity correction (two-tailed) was used to determine whether the referent measures were significantly more likely to classify participants as active compared with the PAAT. Kappas were used to evaluate the extent of agreement between measures surpassing that due to chance alone.
Results Sixty-nine individuals were enrolled, and 34 (49.3%) reported that they were active at screening. One participant withdrew during the first week of the study; 68 participants completed two data-collection visits. Because of missing data, MTI data were available for analysis for only 63 participants. Participants and enrollment and retention are described in Table 1 and Figure 2. More than half of participants were classified as active by each of the three measures. The PAAT classified fewer participants as active (60.3%) than did MTI (71.4%), but this was not significant (p⫽0.169), and markedly fewer than IPAQ (80.3%, p⬍0.001). Participants reported that physical activity in the last 7 days was same as usual for 68.3%, more than usual for 9.5%, and less than usual for 22.2%. Participants selfreported their stage of change as follows: 1.6% precontemplation, 7.9% contemplation, 47.6% preparation, 14.3% action, and 28.6% maintenance. Forty-one percent reported that they were “very confident” they
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Mean minutes per week of MPA reported on the PAAT were 14.7% higher than by the MTI, mean VPA was 15.1% lower than the MTI, and mean MVPA was 6.1% higher. PAAT was significantly correlated with MTI for MVPA (r⫽0.392, p⬍0.01), VPA (r⫽0.380, p⬍0.01), and MPA (r⫽0.392, p⬍0.01). The PAAT and MTI classified activity level concordantly for 69.8%; strength of agreement was fair (⫽0.338). The PAAT under-estimated the number of active participants (n⫽38) compared with MTI (n⫽45), and misclassified 13 of 19 (68.4%) discordant participants as under-active, but the difference was not significant (p⫽0.169). PAAT sensitivity and specificity compared with the MTI were 71.1% and 66.7%, respectively; positive predictive value was 84.2% and negative predictive value, 48.0%.
Discussion
Figure 2. Data collection procedures and timeline, and participant characteristics. BMI, body mass index; PAAT, Physical Activity Assessment Tool; Wk, week.
could increase their physical activity, 44.4% were “fairly confident,” and 12.7% were “not at all confident.”
Test–Retest Reliability Physical activity measured by PAAT varied from week to week as expected, but correlations between weeks were significant (p⬍0.001) for total (r⫽0.618), vigorous (r⫽0.771), and moderate physical activity (r⫽0.489), and similar to week-to-week correlations for physical activity objectively measured by MTI (MVPA, r⫽0.527; VPA, r⫽0.574; MPA, r⫽0.567) and self-reported by IPAQ (MVPA r⫽0.627, p⬍0.001).
Concurrent Validity Participants reported a mean of 357.7 minutes/week of MVPA by PAAT and 1093.8 by IPAQ. PAAT was significantly (p⬍0.001) correlated with IPAQ (r⫽0.585) for MVPA. PAAT and IPAQ agreed on classification as active or under-active for two thirds (66.7%), but the IPAQ was significantly more likely to classify participants as active (p⬍0.001). Strength of agreement was fair (⫽0.212).
Criterion Validity Participants reported a mean of 357.7 minutes/week of MVPA by PAAT, and the MTI measured a mean of 337.0 minutes/week. The PAAT classified 77.0% of physical activity as moderate, and the MTI, 71.3%. December 2006
The PAAT meets criteria for concurrent and criterion validity described by Sallis and Saelens,52 and incorporates a wide range of types of physical activity thought to be acceptable to and understood by culturally diverse patients.53,54 The PAAT assesses physical activity frequency, intensity, type, and time; elicits recall for moderate and vigorous physical activity from all four domains of physical activity—leisure, occupational, household chores, and transportation52,55; is able to measure compliance with national health recommendations (i.e., minutes per week of MPA and VPA)52; and demonstrates acceptable test–retest reliability. It is designed to be completed by patients while waiting to see the physician and rapidly assessed by clinicians, and it supports the delivery of a brief counseling message in the 90 to 180 seconds available for health promotion in typical primary care visits.56 Reliability of the PAAT is comparable to that reported for other self-report instruments,37,55,57–71 and similar to what was measured for the MTI accelerometer. Because people naturally vary the type and amount of their physical activity from day to day and week to week,54,55,57 test–retest measures for different time frames describe stability of physical activity and include both measurement error and natural variation.52,55,60 The similarity of the test–retest correlations for the MTI suggest that most of the measured variation was true variation in physical activity rather than measurement error. Criterion validity of the PAAT is supported by significant correlations with MTI, which were comparable to those published for IPAQ (pooled rho⫽0.33, 95% confidence interval⫽0.26 – 0.39)37 and other self-report instruments that measure usual or last 7 days of physical activity and have been validated against direct objective measures of physical activity.52,55,58,59,62,69,70,72–74 The PAAT over-estimated MVPA minutes per week compared with the MTI by 6.1%, while the Seven Day Physical Activity Recall Am J Prev Med 2006;31(6)
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over-estimated physical activity energy expenditure in a small group of active young women by 46% compared with the MTI in one study73 and 20% compared with doubly-labeled water in obese women in another.75 However, studies suggest that accelerometers may under-estimate energy expenditure from physical activity by as much as 40%.76,77 The tendency of the PAAT to under-estimate the number of active participants was surprising, while the tendency of self-report instruments to over-estimate total physical activity and the intensity of physical activity has been frequently noted.52,62,78 Several studies have shown that VPA tends to be overestimated, while MPA is often under-estimated,11,64,79 – 81 although this varies by gender, BMI, and level of habitual physical activity.54,82 In this study, the PAAT over-estimated MPA by 14.7% compared with the accelerometer and under-estimated VPA by 15.1%, although these differences were not significant. It is of note that, when compared with the direct objective measure of physical activity (MTI), participants in this study were significantly less likely to over-report total physical activity on the PAAT than on IPAQ, and that the PAAT classified patients as active or under-active more like the objective measure, MTI, than did IPAQ, which is thought to measure baseline physical activity in addition to conditioning activity. Overall, the PAAT performs as well as self-report instruments commonly used in research, while the accuracy of open-ended questions commonly used in clinical practice is unknown. Additional work is currently underway to strengthen the PAAT’s content and construct validity among lower-activity individuals and to confirm criterion validity in a more poorly educated, lower socioeconomic-status population. Jacobs et al.55 observed that the construct of questions in physical activity assessment tools may be more important than the length and complexity of the questionnaire. The PAAT is a brief, simple tool for identifying individuals who are not meeting national recommendations. Most persons misclassified by PAAT are active individuals misclassified as under-active. Since increasing the amount of physical activity for patients who are already active carries additional benefits, counseling active patients to increase their physical activity, particularly when MPA is recommended and potential contraindications are addressed, is likely to be beneficial rather than harmful. While physician counseling alone is often not sufficient to enable patients to begin and sustain the level of physical activity needed for optimal health benefits,24 and the precise content and style of delivery of optimal counseling has yet to be described, Kreuter et al.16 have shown that inactive patients who receive physician counseling are 50% more likely to increase their physical activity than patients who are not counseled. 488
Limitations Because physical activity recall and reporting involve a complex cognitive process,60,83,84 misclassification by participants may occur due to errors in the interpretation of questions; estimation of duration, frequency, and intensity of physical activity bouts; or failure to recognize some activities as physical activity due to either a lack of salience or cultural differences in activities and terminology used to describe them.52–54,85,86 In addition to these recall and misclassification biases, self-reported physical activity is also subject to social acceptability bias.54,82,85 Instruments constructed differently may result in different measurement errors because of differences in interpretation and the complex cognitive process required to generate responses.52,54,84 The PAAT and IPAQ both measure physical activity during the previous 7 days and can be expected to have lower levels of recall bias than instruments attempting to measure physical activity that occurred over longer periods of time, such as a month, a year, or a lifetime.54,59,60,62,87 However, because physical activity varies from day to day and week to week, physical activity during the last 7 days may not be typical of usual physical activity.57,60,61 This could result in misclassification of usually under-active patients as active or the reverse. To address this in clinical settings, the PAAT also asks if physical activity during last 7 days was the “same, less, or more than usual.” The results of this study may not be generalizable to all populations. Because adults aged 65 and older and with BMIs of 40 or more were excluded, and participants were drawn from a university community, they were probably better educated and more affluent than the general population and younger and leaner than typical primary care patients. Other studies have reported that more poorly educated, older, sicker, and more-obese patients over-report physical activity more frequently than other participants.54,79,82 In addition, this study population was intentionally recruited to be approximately half active and half under-active, substantially above overall population prevalence. Therefore, sensitivity, specificity, and positive and negative predictive values, which are dependent on prevalence, should be interpreted with caution. Overall, the PAAT is likely to misclassify more patients as active than it did in this study population. A follow-up study is underway to refine and re-validate the PAAT in lower-income, lower-literacy populations. Accelerometers are commonly used as criterion measures because they provide a direct, objective measure of physical activity. However, they are not “gold standards” because they are also subject to measurement error,52,55,83,88,89 since they do not capture all types of physical activity with equal precision. Activities such as
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walking, running, and jogging are well captured, but many of the lifestyle physical activities that have been shown to be better sustained than structured leisuretime physical activity,90 such as gardening, playing with children, and housework are poorly captured by these devices.44,52,91 Consequently, while accelerometers have been used traditionally as criterion measures against which self-reported physical activity is validated,52,83 they actually measure a smaller subset of physical activity.
Conclusion The PAAT demonstrates concurrent and criterion validity comparable to other physical activity self-report instruments, and agreement with the criterion measure MTI is fair. Test–retest reliability was similar to that of our criterion measure, MTI accelerometers. In this study, the PAAT slightly under-estimated the number of active participants compared with the criterion measure MTI, but performed more like the criterion measure than did the IPAQ. The PAAT warrants further evaluation. This work was supported by a Generalist Physician Faculty Scholar award from the Robert Wood Johnson Foundation (RAM). We wish to express our thanks for the assistance of Loretta Wilson, PhD, assistant professor, and Shane Sanne and Eva Lustigova, students in the Tulane Department of Sports and Exercise Science, Tulane University, for assistance collecting data; Allyssia Sam-Dairies, MSPH, Tulane University Department of Biostatistics, and Cheryl Addy, PhD, University of South Carolina Arnold School of Public Health, for statistical advice; and Amanda Johnson, MS, for assistance with data analysis. No financial conflict of interest was reported by the authors of this paper.
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