Page 927 - Cardiac Nursing

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C HAPTER 4 0 / Adherence to Cardiovascular Treatment Regimens 903
exercise, and physical activity and weight loss treatment outcomes. An- 96. Jakicic, J. M., Marcus, M., Gallagher, K. I., et al. (2004). Evaluation of
nals of Behavioral Medicine, 30(3), 182–190. the SenseWear Pro Armband[TM] to assess energy expenditure during
6
6
73. Burke, L. E., Warziski, M., Styn, M. A., et al. (2008). A randomized clin- exercise. Medicine & Science in Sports & Exercise, 36(5), 897–904.
ical trial of a standard versus vegetarian diet for weight loss: The impact 97. Malavolti, M., Pietrobelli, A., Dugoni, M., et al. (2007). A new device
2
of treatment preference. International Journal of Obesity, 32, 166–176. for measuring resting energy expenditure (REE) in healthy subjects. Nu-
2
7
74. Wickel, E. E., Welk, G. J., & Eisenmann, J. C. (2006). Concurrent val- trition, Metabolism and Cardiovascular Diseases, 17(5), 338–343.
7
idation of the Bouchard Diary with an accelerometry-based monitor. 98. Papazoglou, D., Augello, G., Tagliaferri, M., et al. (2006). Evaluation of
Medicine & Science in Sports & Exercise, 38(2), 373–379. a multisensor armband in estimating energy expenditure in obese indi-
4
75. Walsh, M. C., Hunter, G. R., Sirikul, B., et al. (2004). Comparison of viduals. Obesity, 14(12), 2217–2223.
4
self-reported with objectively assessed energy expenditure in black and 99. Welk, G. J., McClain, J. J., Eisenmann, J. C., et al. (2007). Field valida-
white women before and after weight loss. American Journal of Clinical tion of the MTI actigraph and BodyMedia Armband monitor using the
Nutrition, 79(6), 1013–1019. IDEEA monitor. Obesity, 15(4), 918–928.
76. Johnson, R. K., Friedman, A. B., Harvey-Berino, J., et al. (2005). Par- 100. le Grange, D., Gorin, A., Dymek, M., et al. (2002). Does ecological mo-
ticipation in a behavioral weight-loss program worsens the prevalence mentary assessment improve cognitive behavioural therapy for binge eat-
and severity of underreporting among obese and overweight women. ing disorder? A pilot study. European Eating Disorders Review, 10,
0
Journal of the American Dietetic Association, 105(12), 1948–1951. 316–328.
77. Yon, B. A., Johnson, R. K., Harvey-Berino, J., et al. (2006). The use of 101. Glanz, K., Murphy, S., Moylan, J., et al. (2006). Improving dietary self-
a personal digital assistant for dietary self-monitoring does not improve monitoring and adherence with hand-held computers: a pilot study.
the validity of self-reports of energy intake. Journal of the American Di- American Journal of Health Promotion, 20(3), 165–170.
etetic Association, 106(8), 1256–1259. 102. Sevick, M. A., Zickmund, S., Korytkowski, M., et al. (2007). Design,
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78. Colley, R. C., Hills, A. P., O’Moore-Sullivan, T. M., et al. (2008). Vari- feasibility, and acceptability of an intervention using personal digital
ability in adherence to an unsupervised exercise prescription in obese assistant-based self-monitoring in managing type 2 diabetes. Contempo-
women. International Journal of Obesity (London), 32(5), 837–844. rary Clinical Trials, 29(3), 396–409. Epub 2007 Sep 26.
79. Braam, R. L., van Uum, S. H., Lenders, J. W., et al. (2008). Bromide as 103. Stone, A. A., & Shiffman, S. (2002). Capturing momentary, self–report
marker for drug adherence in hypertensive patients. British Journal of data: A proposal for reporting guidelines. Annals of Behavioral Medicine,
4
Clinical Pharmacology, 65(5), 733–737. 24, 236–243.
4
80. Leiba, A., Vald, A., Peleg, E., et al. (2005). Does dietary recall adequately 104. Burke, L. E., Music, E., Styn, M. A., et al. (2006). Using technology to
assess sodium, potassium, and calcium intake in hypertensive patients? improve self-monitoring in weight loss. International Journal of Behav-
Nutrition, 21(4), 462–466. ioral Medicine, 13(Suppl.), 192.
81. Pärna, K., Rahu, M., Youngman, L. D., et al. (2005). Self-reported and 105. Music, E., Choo, J., Styn, M. A., et al. (2006). Feasibility study: Using
serum cotinine-validated smoking in pregnant women in Estonia. Ma- PDA-based DietMate Pro® for dietary self-monitoring. Annals of Be-
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9
ternal and Child Health Journal, 9(4), 385–392. havioral Medicine, 31(Suppl.), S058.
82. St-Onge, M., Mignault, D., Allison, D. B., et al. (2007). Evaluation of a 106.Burke, L. E., Sereika, S., Choo, J., et al. (2006). Ancillary study to the PRE-
portable device to measure daily energy expenditure in free-living adults. FER trial: A descriptive study of participants’ patterns of self-monitoring:
American Journal of Clinical Nutrition, 85(3), 742–749. Rationale, design and preliminary experiences. Contemporary Clinical
7
83. Schoeller, D. A. (1988). Measurement of energy expenditure in free- Trials, 27(1), 23–33.
7
living humans by using doubly labeled water. Journal of Nutrition, 107. Baer, A., Saroiu, S., & Koutsky, L. A. (2002). Obtaining sensitive data
118(11), 1278–1289. through the Web: An example of design and methods. Epidemiology,
84. Allison, D. B. (1995). Handbook of assessment methods for eating behav- 13(6), 640–645.
iors and weight related problems: Measures, theory, and research. Thousand 108. Tate, D. F., Jackvony, E. H., & Wing, R. R. (2006). A randomized trial
Oaks, CA: Sage. comparing human e-mail counseling, computer-automated tailored
85. Charlton, K. E., Steyn, K., Levitt, N. S., et al. (2005). Ethnic differences counseling, and no counseling in an Internet weight loss program.
in intake and excretion of sodium, potassium, calcium and magnesium Archives of Internal Medicine, 166, 1620–1625.
6
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in South Africans. European Journal of Cardiovascular Prevention and Re- 109. Prado, J. C., Jr., Kupek, E., & Mion, D., Jr. (2007). Validity of four in-
habilitation, 12(4), 355–362. direct methods to measure adherence in primary care hypertensives.
86. Stone, A. A., Shiffman, S., Schwartz, J. E., et al. (2002). Patient non- Journal of Human Hypertension, 21(7), 579–584.
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compliance with paper diaries. BMJ, 324(7347), 1193–1194. 110. DiMatteo, M. R., Haskard, K. B., & Williams, S. L. (2007). Health be-
87. Girvin, B. G., & Johnston, G. D. (2004). Comparison of the effects of a liefs, disease severity, and patient adherence: A meta-analysis. Medical
7-day period of non-compliance on blood pressure control using three dif- Care, 45(6), 521–528.
ferent antihypertensive agents. Journal of Hypertension, 22(7), 1409–1414. 111. Vlachopoulos, S. P., & Neikou, E. (2007). A prospective study of the re-
88. Santschi, V., Wuerzner, G., Schneider, M. P., et al. (2007). Clinical evalu- lationships of autonomy, competence, and relatedness with exercise at-
ation of IDAS II, a new electronic device enabling drug adherence moni- tendance, adherence, and dropout. The Journal of Sports Medicine and
toring. European Journal of Clinical Pharmacology, 63(12), 1179–1184. Physical Fitness, 47(4), 475–482.
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89. Gamelin, F. X., Baquet, G., Berthoin, S., et al. (2008). Validity of the po- 112. Fuertes, J. N., Mislowack, A., Bennett, J., et al. (2007). The physician-
lar s810 to measure R-R intervals in children. International Journal of patient working alliance. Patient Education and Counseling, 66(1),
6
6
Sports Medicine, 29(2), 134–138. 29–36.
90. Gamelin, F. X., Berthoin, S., & Bosquet, L. (2006). Validity of the polar 113. Capdevila Ortís, L., Niñerola Maymí, J., Cruz Feliu, J., et al. (2007). Ex-
S810 heart rate monitor to measure R-R intervals at rest. Medicine & Sci- ercise motivation in university community members: A behavioural in-
ence in Sports & Exercise, 38(5), 887–893. tervention. Psicothema, 19(2), 250–255.
91. Cyarto, E. V., Myers, A. M., & Tudor-Locke, C. (2004). Pedometer ac- 114. Jones, M., Jolly, K., Raftery, J., et al. (2007). ‘DNA’ may not mean ‘did
curacy in nursing home and community-dwelling older adults. Medicine not participate’: A qualitative study of reasons for non-adherence at
4
& Science in Sports & Exercise, 36(2), 205–209. home- and centre-based cardiac rehabilitation. Family Practice, 24(4),
6
6
4
92. Melanson, E. L., Knoll, J. R., Bell, M. L., et al. (2004). Commercially 343–357.
available pedometers: Considerations for accurate step counting. Preven- 115. Gazmararian, J. A., Kripalani, S., Miller, M. J., et al. (2006). Factors
tive Medicine, 39(2), 361–368. associated with medication refill adherence in cardiovascular-related
93. Storti, K. L., Pettee, K. K., Brach, J. S., et al. (2008). Gait speed and step- diseases: A focus on health literacy. Journal of General Internal Medicine,
count monitor accuracy in community-dwelling older adults. Medicine 21(12), 1215–1221.
& Science in Sports & Exercise, 40(1), 59–64. 116. Stilley, C. S., Sereika, S., Muldoon, M. F., et al. (2004). Psychological
94. Dua, J. S., Cooper, A. R., Fox, K. R., et al. (2007). Physical activity lev- and cognitive function: Predictors of adherence with cholesterol lowering
els in adults with congenital heart disease. European Journal of Cardio- treatment. Annals of Behavioral Medicine, 27(2), 117–124.
7
7
4
vascular Prevention and Rehabilitation, 14(2), 287–293. 117. Tinker, L. F., Rosal, M. C., Young, A. F., et al. (2007). Predictors of di-
4
95. Troiano, R. P., Berrigan, D., Dodd, K. W., et al. (2008). Physical activ- etary change and maintenance in the Women’s Health Initiative Dietary
7
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ity in the United States measured by accelerometer. Medicine & Science Modification Trial. Journal of the American Dietetic Association, 107(7),
in Sports & Exercise, 40(1), 181–188. 1155–1166.