- Email: [email protected]
Implications of light-intensity physical activity in improving health-related quality of life among congestive heart failure patients
International Journal of Cardiology 212 (2016) 16–17
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Correspondence
Implications of light-intensity physical activity in improving health-related quality of life among congestive heart failure patients Paul D. Loprinzi ⁎ Jackson Heart Study Vanguard Center of Oxford, Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, University, MS 38677, United States
a r t i c l e
i n f o
Article history: Received 3 December 2015 Accepted 12 March 2016 Available online 14 March 2016
A considerable amount of research demonstrates that participation in moderate-to-vigorous physical activity (MVPA) is favorably associated with health-related quality of life (HRQOL) [1–10]. Emerging research is demonstrating that, independent of MVPA, light-intensity physical activity (LIPA) is associated with various cardiometabolic and mental health outcomes [11–14]. However, there is a paucity of research examining the association between LIPA and HRQOL [15–18], and to my knowledge, no study has examined the association between LIPA and HRQOL among congestive heart failure patients. Such an investigation in this targeted population is warranted as congestive heart failure is associated with poorer HRQOL [19] and less physical activity engagement [20]. Thus, if a relationship between LIPA and HRQOL is observed for this population, promotion of LIPA may be an attractive alternative to higher intensity physical activity for improving HRQOL among congestive heart failure patients. Data from the population-based 2003–2006 National Health and Nutrition Examination Survey (NHANES) were used; these cycles were evaluated as, at the time of this writing, these are the only cycles with objectively-measured physical activity data. Briefly, NHANES employs a population-based sample of Americans via household interviews and examinations in a mobile examination center. Using a multistage, complex probability design, non-institutionalized U.S. civilians are selected for participation. Further details about NHANES can be found on their website (http://www.cdc.gov/nchs/nhanes.htm). In these cycles, 190 participants self-reported a physician-diagnosis of congestive heart failure and had complete data on the study variables. Among these congestive heart failure patients, the mean age was 66.7 yrs; mean body mass index was 31.1 kg/m2; 56.2% were males; and 79.6% were non-Hispanic white. ⁎ Jackson Heart Study Vanguard Center of Oxford, Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science, and Recreation Management, 229 Turner Center, The University of Mississippi, University, MS 38677, United States. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.ijcard.2016.03.015 0167-5273/© 2016 Elsevier Ireland Ltd. All rights reserved.
Physical activity was assessed for up to 7 days using an ActiGraph 7164 accelerometer; activity counts/min between and inclusive of 100 and 2019 were used to define LIPA [12], with counts/min ≥2020 defined as participation in MVPA [21]. Only those having at least 4 days of 10 + h/day of monitoring were included in the analyses. Nonwear time was identified as ≥60 consecutive minutes of zero activity counts, with allowance for 1–2 min of activity counts between 0 and 100. Specific details regarding the NHANES accelerometer protocol have been previously published [22]. The mean LIPA in this sample was 261.6 min/day, with the mean MVPA being 8.56 min/day. The CDC HRQOL measure was assessed from 4 questions, including 1 question about self-rated health status and 3 about the number of unhealthy days during the past 30 days [23,24]: 1. “Would you say that in general your health is excellent, very good, good, fair, or poor?” 2. “Now thinking about your physical health, which includes physical illness and injury, how many days during the past 30 days was your physical health not good?” 3. “Now thinking about your mental health, which includes stress, depression, and problems with emotions, how many days during the past 30 days was your mental health not good?” 4. “During the past 30 days, approximately how many days did poor physical or mental health keep you from doing usual activities, such as self-care, work, or recreation?” The 4 CDC HRQOL items were categorized according to CDC's recommendations, which included question 1 dichotomized as good/excellent health (coded as 0) or poor/fair health (coded as 1). The latter 3 items were dichotomized as 14 or more days (coded as 1) and less than 14 days (coded as 0). Thus, the recoded 4 HRQOL items ranged from 0 to 1. An overall HRQOL score was created by summing the responses from each of the 4 individual items (range: 0–4), with higher HRQOL scores indicating worse HRQOL. In this sample of congestive heart failure patients, the mean HRQOL was 1.05; 41.0%, 28.8%, 18.1%, 7.9% and 4.2%, respectively, had a HRQOL score of 0, 1, 2, 3, and 4. The HRQOL-4 developed by CDC has undergone extensive reliability and validity testing and has demonstrated adequate psychometric properties [25–29]. All statistical analyses were computed in Stata (v. 12) and accounted for the complex survey design of NHANES to adjust for non-compliance, non-response and to render nationally representative estimates. A single multivariable ordinal regression was employed to examine the
P.D. Loprinzi / International Journal of Cardiology 212 (2016) 16–17
association between LIPA and HRQOL. The following covariates were included: age (yrs; continuous); gender; race–ethnicity (non-Hispanic white vs. other); self-reported smoking status (current smoker vs. not); measured body mass index (kg/m2; continuous); educational attainment (college or more vs. less); C-reactive protein (mg/dL; continuous); and physician diagnosed coronary artery disease (yes vs. no). Statistical significance was established as P b 0.05. After adjusting for age, gender, race–ethnicity, smoking, body mass index, education, C-reactive protein and coronary artery disease status, LIPA was associated with a better HRQOL score (βadjustment = −0.009; 95% CI: −0.01 to −0.005; P b 0.001). After adding MVPA as a covariate in the model, LIPA remained significantly associated with HRQOL (βadjustment = −0.01; 95% CI: −0.01 to −0.005; P b 0.001). Additionally, LIPA remained significantly associated with HRQOL among those above the median (2.6 min/day) MVPA level (βadjustment = − 0.01; 95% CI: −0.01 to −0.005; P = 0.001; N = 95). Similarly, LIPA remained significantly associated with HRQOL among those in the top MVPA quartile (8.2 min/day) MVPA level (βadjustment = − 0.01; 95% CI: −0.02 to −0.004; P = 0.01; N = 46). A limitation of this study is the cross-sectional design, thus necessitating the need for future prospective work within this population. Strengths include the population-based study design and objective measure of physical activity. If confirmed by future prospective and experimental work, then promotion of LIPA, in addition to higher intensity physical activity, may be a useful strategy to improve HRQOL among this vulnerable population. Conflict of interest The author reports no relationships that could be construed as a conflict of interest. Acknowledgments No funding was used to prepare this manuscript. References [1] J. Klavestrand, E. Vingard, The relationship between physical activity and healthrelated quality of life: a systematic review of current evidence, Scand. J. Med. Sci. Sports 19 (3) (2009) 300–312. [2] G.W. Heath, D.W. Brown, Recommended levels of physical activity and healthrelated quality of life among overweight and obese adults in the United States, 2005, J. Phys. Act. Health 6 (4) (2009) 403–411. [3] R. Bize, J.A. Johnson, R.C. Plotnikoff, Physical activity level and health-related quality of life in the general adult population: a systematic review, Prev. Med. 45 (6) (2007) 401–415. [4] J. Freelove-Charton, H.R. Bowles, S. Hooker, Health-related quality of life by level of physical activity in arthritic older adults with and without activity limitations, J. Phys. Act. Health 4 (4) (2007) 481–494. [5] R.A. Kekkonen, T.J. Vasankari, T. Vuorimaa, T. Haahtela, I. Julkunen, R. Korpela, The effect of probiotics on respiratory infections and gastrointestinal symptoms during training in marathon runners, Int. J. Sport Nutr. Exerc. Metab. 17 (4) (2007) 352–363. [6] J. Kruger, H.R. Bowles, D.A. Jones, B.E. Ainsworth, H.W. Kohl 3rd., Health-related quality of life, BMI and physical activity among US adults (N/=18 years): National Physical Activity and Weight Loss Survey, 2002, Int. J. Obes. 31 (2) (2007) 321–327. [7] K.J. Mukamal, E.L. Ding, L. Djousse, Alcohol consumption, physical activity, and chronic disease risk factors: a population-based cross-sectional survey, BMC Public Health 6 (2006) 118.
17
[8] J.R. Pierce Jr., A.V. Denison, A.A. Arif, J.E. Rohrer, Living near a trail is associated with increased odds of walking among patients using community clinics, J. Community Health 31 (4) (2006) 289–302. [9] J.E. Abell, J.M. Hootman, M.M. Zack, D. Moriarty, C.G. Helmick, Physical activity and health related quality of life among people with arthritis, J. Epidemiol. Community Health 59 (5) (2005) 380–385. [10] D.W. Smith, S.L. McFall, The relationship of diet and exercise for weight control and the quality of life gap associated with diabetes, J. Psychosom. Res. 59 (6) (2005) 385–392. [11] P.D. Loprinzi, Objectively measured light and moderate-to-vigorous physical activity is associated with lower depression levels among older US adults, Aging Ment. Health 17 (7) (2013) 801–805. [12] P.D. Loprinzi, Effects of light-intensity physical activity on red blood cell distribution width: implications for a novel mechanism through which light-intensity physical activity may influence cardiovascular disease, Int. J. Cardiol. 203 (2015) 724–725. [13] P.D. Loprinzi, H. Lee, B.J. Cardinal, Evidence to support including lifestyle lightintensity recommendations in physical activity guidelines for older adults, Am. J. Health Promot. (2014). [14] P.D. Loprinzi, H. Lee, B.J. Cardinal, Dose response association between physical activity and biological, demographic, and perceptions of health variables, Obes. Facts 6 (4) (2013) 380–392. [15] K.M. Thraen-Borowski, A. Trentham-Dietz, D.F. Edwards, K.F. Koltyn, L.H. Colbert, Dose–response relationships between physical activity, social participation, and health-related quality of life in colorectal cancer survivors, J. Cancer Surviv. 7 (3) (2013) 369–378. [16] M.P. Buman, E.B. Hekler, W.L. Haskell, et al., Objective light-intensity physical activity associations with rated health in older adults, Am. J. Epidemiol. 172 (10) (2010) 1155–1165. [17] E.J. Coups, B.J. Park, M.B. Feinstein, et al., Physical activity among lung cancer survivors: changes across the cancer trajectory and associations with quality of life, Cancer Epidemiol. Biomarkers Prev. 18 (2) (2009) 664–672. [18] S.M. Phillips, E.A. Awick, D.E. Conroy, C.A. Pellegrini, E.L. Mailey, E. McAuley, Objectively measured physical activity and sedentary behavior and quality of life indicators in survivors of breast cancer, Cancer (2015). [19] H.B. Bosworth, K.E. Steinhauser, M. Orr, J.H. Lindquist, S.C. Grambow, E.Z. Oddone, Congestive heart failure patients' perceptions of quality of life: the integration of physical and psychosocial factors, Aging Ment. Health 8 (1) (2004) 83–91. [20] K.R. Nilsson, B.D. Duscha, P.M. Hranitzky, W.E. Kraus, Chronic heart failure and exercise intolerance: the hemodynamic paradox, Curr. Cardiol. Rev. 4 (2) (2008) 92–100. [21] R.P. Troiano, D. Berrigan, K.W. Dodd, L.C. Mâsse, T. Tilert, M. McDowell, Physical activity in the United States measured by accelerometer, Med. Sci. Sports Exerc. 40 (1) (2008) 181. [22] R.P. Troiano, J.J. McClain, R.J. Brychta, K.Y. Chen, Evolution of accelerometer methods for physical activity research, Br. J. Sports Med. 48 (13) (2014) 1019–1023. [23] S.J. Dankel, J.P. Loenneke, P.D. Loprinzi, Physical activity and diet on quality of life and mortality: the importance of meeting one specific or both behaviors, Int. J. Cardiol. 202 (2016) 328–330. [24] P.D. Loprinzi, Joint associations of objectively-measured sedentary behavior and physical activity with health-related quality of life, Prev. Med. Reports 2 (2015) 959–961. [25] Y. Jiang, J.E. Hesser, Using item response theory to analyze the relationship between health-related quality of life and health risk factors, Prev. Chron. Dis. 6 (1) (2009) A30. [26] W. Horner-Johnson, G. Krahn, E. Andresen, T. Hall, Rehabilitation R, Training Center Expert Panel on Health Status M, Developing summary scores of health-related quality of life for a population-based survey, Public Health Rep. 124 (1) (2009) 103–110. [27] R.D. Hays, J.B. Bjorner, D.A. Revicki, K.L. Spritzer, D. Cella, Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items, Qual. Life Res. Int. J. Qual. Life Asp. Treat. Care Rehab. 18 (7) (2009) 873–880. [28] T. Mielenz, E. Jackson, S. Currey, R. DeVellis, L.F. Callahan, Psychometric properties of the Centers for Disease Control and Prevention Health-Related Quality of Life (CDC HRQOL) items in adults with arthritis, Health Qual. Outcomes 4 (2006) 66. [29] W. Linden, D. Yi, M.C. Barroetavena, R. MacKenzie, R. Doll, Development and validation of a psychosocial screening instrument for cancer, Health Qual. Outcomes 3 (2005) 54.
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Correspondence
Implications of light-intensity physical activity in improving health-related quality of life among congestive heart failure patients Paul D. Loprinzi ⁎ Jackson Heart Study Vanguard Center of Oxford, Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, University, MS 38677, United States
a r t i c l e
i n f o
Article history: Received 3 December 2015 Accepted 12 March 2016 Available online 14 March 2016
A considerable amount of research demonstrates that participation in moderate-to-vigorous physical activity (MVPA) is favorably associated with health-related quality of life (HRQOL) [1–10]. Emerging research is demonstrating that, independent of MVPA, light-intensity physical activity (LIPA) is associated with various cardiometabolic and mental health outcomes [11–14]. However, there is a paucity of research examining the association between LIPA and HRQOL [15–18], and to my knowledge, no study has examined the association between LIPA and HRQOL among congestive heart failure patients. Such an investigation in this targeted population is warranted as congestive heart failure is associated with poorer HRQOL [19] and less physical activity engagement [20]. Thus, if a relationship between LIPA and HRQOL is observed for this population, promotion of LIPA may be an attractive alternative to higher intensity physical activity for improving HRQOL among congestive heart failure patients. Data from the population-based 2003–2006 National Health and Nutrition Examination Survey (NHANES) were used; these cycles were evaluated as, at the time of this writing, these are the only cycles with objectively-measured physical activity data. Briefly, NHANES employs a population-based sample of Americans via household interviews and examinations in a mobile examination center. Using a multistage, complex probability design, non-institutionalized U.S. civilians are selected for participation. Further details about NHANES can be found on their website (http://www.cdc.gov/nchs/nhanes.htm). In these cycles, 190 participants self-reported a physician-diagnosis of congestive heart failure and had complete data on the study variables. Among these congestive heart failure patients, the mean age was 66.7 yrs; mean body mass index was 31.1 kg/m2; 56.2% were males; and 79.6% were non-Hispanic white. ⁎ Jackson Heart Study Vanguard Center of Oxford, Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science, and Recreation Management, 229 Turner Center, The University of Mississippi, University, MS 38677, United States. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.ijcard.2016.03.015 0167-5273/© 2016 Elsevier Ireland Ltd. All rights reserved.
Physical activity was assessed for up to 7 days using an ActiGraph 7164 accelerometer; activity counts/min between and inclusive of 100 and 2019 were used to define LIPA [12], with counts/min ≥2020 defined as participation in MVPA [21]. Only those having at least 4 days of 10 + h/day of monitoring were included in the analyses. Nonwear time was identified as ≥60 consecutive minutes of zero activity counts, with allowance for 1–2 min of activity counts between 0 and 100. Specific details regarding the NHANES accelerometer protocol have been previously published [22]. The mean LIPA in this sample was 261.6 min/day, with the mean MVPA being 8.56 min/day. The CDC HRQOL measure was assessed from 4 questions, including 1 question about self-rated health status and 3 about the number of unhealthy days during the past 30 days [23,24]: 1. “Would you say that in general your health is excellent, very good, good, fair, or poor?” 2. “Now thinking about your physical health, which includes physical illness and injury, how many days during the past 30 days was your physical health not good?” 3. “Now thinking about your mental health, which includes stress, depression, and problems with emotions, how many days during the past 30 days was your mental health not good?” 4. “During the past 30 days, approximately how many days did poor physical or mental health keep you from doing usual activities, such as self-care, work, or recreation?” The 4 CDC HRQOL items were categorized according to CDC's recommendations, which included question 1 dichotomized as good/excellent health (coded as 0) or poor/fair health (coded as 1). The latter 3 items were dichotomized as 14 or more days (coded as 1) and less than 14 days (coded as 0). Thus, the recoded 4 HRQOL items ranged from 0 to 1. An overall HRQOL score was created by summing the responses from each of the 4 individual items (range: 0–4), with higher HRQOL scores indicating worse HRQOL. In this sample of congestive heart failure patients, the mean HRQOL was 1.05; 41.0%, 28.8%, 18.1%, 7.9% and 4.2%, respectively, had a HRQOL score of 0, 1, 2, 3, and 4. The HRQOL-4 developed by CDC has undergone extensive reliability and validity testing and has demonstrated adequate psychometric properties [25–29]. All statistical analyses were computed in Stata (v. 12) and accounted for the complex survey design of NHANES to adjust for non-compliance, non-response and to render nationally representative estimates. A single multivariable ordinal regression was employed to examine the
P.D. Loprinzi / International Journal of Cardiology 212 (2016) 16–17
association between LIPA and HRQOL. The following covariates were included: age (yrs; continuous); gender; race–ethnicity (non-Hispanic white vs. other); self-reported smoking status (current smoker vs. not); measured body mass index (kg/m2; continuous); educational attainment (college or more vs. less); C-reactive protein (mg/dL; continuous); and physician diagnosed coronary artery disease (yes vs. no). Statistical significance was established as P b 0.05. After adjusting for age, gender, race–ethnicity, smoking, body mass index, education, C-reactive protein and coronary artery disease status, LIPA was associated with a better HRQOL score (βadjustment = −0.009; 95% CI: −0.01 to −0.005; P b 0.001). After adding MVPA as a covariate in the model, LIPA remained significantly associated with HRQOL (βadjustment = −0.01; 95% CI: −0.01 to −0.005; P b 0.001). Additionally, LIPA remained significantly associated with HRQOL among those above the median (2.6 min/day) MVPA level (βadjustment = − 0.01; 95% CI: −0.01 to −0.005; P = 0.001; N = 95). Similarly, LIPA remained significantly associated with HRQOL among those in the top MVPA quartile (8.2 min/day) MVPA level (βadjustment = − 0.01; 95% CI: −0.02 to −0.004; P = 0.01; N = 46). A limitation of this study is the cross-sectional design, thus necessitating the need for future prospective work within this population. Strengths include the population-based study design and objective measure of physical activity. If confirmed by future prospective and experimental work, then promotion of LIPA, in addition to higher intensity physical activity, may be a useful strategy to improve HRQOL among this vulnerable population. Conflict of interest The author reports no relationships that could be construed as a conflict of interest. Acknowledgments No funding was used to prepare this manuscript. References [1] J. Klavestrand, E. Vingard, The relationship between physical activity and healthrelated quality of life: a systematic review of current evidence, Scand. J. Med. Sci. Sports 19 (3) (2009) 300–312. [2] G.W. Heath, D.W. Brown, Recommended levels of physical activity and healthrelated quality of life among overweight and obese adults in the United States, 2005, J. Phys. Act. Health 6 (4) (2009) 403–411. [3] R. Bize, J.A. Johnson, R.C. Plotnikoff, Physical activity level and health-related quality of life in the general adult population: a systematic review, Prev. Med. 45 (6) (2007) 401–415. [4] J. Freelove-Charton, H.R. Bowles, S. Hooker, Health-related quality of life by level of physical activity in arthritic older adults with and without activity limitations, J. Phys. Act. Health 4 (4) (2007) 481–494. [5] R.A. Kekkonen, T.J. Vasankari, T. Vuorimaa, T. Haahtela, I. Julkunen, R. Korpela, The effect of probiotics on respiratory infections and gastrointestinal symptoms during training in marathon runners, Int. J. Sport Nutr. Exerc. Metab. 17 (4) (2007) 352–363. [6] J. Kruger, H.R. Bowles, D.A. Jones, B.E. Ainsworth, H.W. Kohl 3rd., Health-related quality of life, BMI and physical activity among US adults (N/=18 years): National Physical Activity and Weight Loss Survey, 2002, Int. J. Obes. 31 (2) (2007) 321–327. [7] K.J. Mukamal, E.L. Ding, L. Djousse, Alcohol consumption, physical activity, and chronic disease risk factors: a population-based cross-sectional survey, BMC Public Health 6 (2006) 118.
17
[8] J.R. Pierce Jr., A.V. Denison, A.A. Arif, J.E. Rohrer, Living near a trail is associated with increased odds of walking among patients using community clinics, J. Community Health 31 (4) (2006) 289–302. [9] J.E. Abell, J.M. Hootman, M.M. Zack, D. Moriarty, C.G. Helmick, Physical activity and health related quality of life among people with arthritis, J. Epidemiol. Community Health 59 (5) (2005) 380–385. [10] D.W. Smith, S.L. McFall, The relationship of diet and exercise for weight control and the quality of life gap associated with diabetes, J. Psychosom. Res. 59 (6) (2005) 385–392. [11] P.D. Loprinzi, Objectively measured light and moderate-to-vigorous physical activity is associated with lower depression levels among older US adults, Aging Ment. Health 17 (7) (2013) 801–805. [12] P.D. Loprinzi, Effects of light-intensity physical activity on red blood cell distribution width: implications for a novel mechanism through which light-intensity physical activity may influence cardiovascular disease, Int. J. Cardiol. 203 (2015) 724–725. [13] P.D. Loprinzi, H. Lee, B.J. Cardinal, Evidence to support including lifestyle lightintensity recommendations in physical activity guidelines for older adults, Am. J. Health Promot. (2014). [14] P.D. Loprinzi, H. Lee, B.J. Cardinal, Dose response association between physical activity and biological, demographic, and perceptions of health variables, Obes. Facts 6 (4) (2013) 380–392. [15] K.M. Thraen-Borowski, A. Trentham-Dietz, D.F. Edwards, K.F. Koltyn, L.H. Colbert, Dose–response relationships between physical activity, social participation, and health-related quality of life in colorectal cancer survivors, J. Cancer Surviv. 7 (3) (2013) 369–378. [16] M.P. Buman, E.B. Hekler, W.L. Haskell, et al., Objective light-intensity physical activity associations with rated health in older adults, Am. J. Epidemiol. 172 (10) (2010) 1155–1165. [17] E.J. Coups, B.J. Park, M.B. Feinstein, et al., Physical activity among lung cancer survivors: changes across the cancer trajectory and associations with quality of life, Cancer Epidemiol. Biomarkers Prev. 18 (2) (2009) 664–672. [18] S.M. Phillips, E.A. Awick, D.E. Conroy, C.A. Pellegrini, E.L. Mailey, E. McAuley, Objectively measured physical activity and sedentary behavior and quality of life indicators in survivors of breast cancer, Cancer (2015). [19] H.B. Bosworth, K.E. Steinhauser, M. Orr, J.H. Lindquist, S.C. Grambow, E.Z. Oddone, Congestive heart failure patients' perceptions of quality of life: the integration of physical and psychosocial factors, Aging Ment. Health 8 (1) (2004) 83–91. [20] K.R. Nilsson, B.D. Duscha, P.M. Hranitzky, W.E. Kraus, Chronic heart failure and exercise intolerance: the hemodynamic paradox, Curr. Cardiol. Rev. 4 (2) (2008) 92–100. [21] R.P. Troiano, D. Berrigan, K.W. Dodd, L.C. Mâsse, T. Tilert, M. McDowell, Physical activity in the United States measured by accelerometer, Med. Sci. Sports Exerc. 40 (1) (2008) 181. [22] R.P. Troiano, J.J. McClain, R.J. Brychta, K.Y. Chen, Evolution of accelerometer methods for physical activity research, Br. J. Sports Med. 48 (13) (2014) 1019–1023. [23] S.J. Dankel, J.P. Loenneke, P.D. Loprinzi, Physical activity and diet on quality of life and mortality: the importance of meeting one specific or both behaviors, Int. J. Cardiol. 202 (2016) 328–330. [24] P.D. Loprinzi, Joint associations of objectively-measured sedentary behavior and physical activity with health-related quality of life, Prev. Med. Reports 2 (2015) 959–961. [25] Y. Jiang, J.E. Hesser, Using item response theory to analyze the relationship between health-related quality of life and health risk factors, Prev. Chron. Dis. 6 (1) (2009) A30. [26] W. Horner-Johnson, G. Krahn, E. Andresen, T. Hall, Rehabilitation R, Training Center Expert Panel on Health Status M, Developing summary scores of health-related quality of life for a population-based survey, Public Health Rep. 124 (1) (2009) 103–110. [27] R.D. Hays, J.B. Bjorner, D.A. Revicki, K.L. Spritzer, D. Cella, Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items, Qual. Life Res. Int. J. Qual. Life Asp. Treat. Care Rehab. 18 (7) (2009) 873–880. [28] T. Mielenz, E. Jackson, S. Currey, R. DeVellis, L.F. Callahan, Psychometric properties of the Centers for Disease Control and Prevention Health-Related Quality of Life (CDC HRQOL) items in adults with arthritis, Health Qual. Outcomes 4 (2006) 66. [29] W. Linden, D. Yi, M.C. Barroetavena, R. MacKenzie, R. Doll, Development and validation of a psychosocial screening instrument for cancer, Health Qual. Outcomes 3 (2005) 54.