Skip to main content
Mijn bibliotheek
Shop
Nieuws Boeken Tijdschriften E-learning Web-tv Video Audio
Tips voor Mijn BSL
UITGEBREID ZOEKEN
Inloggen
Top
Quality of Life Research
Gepubliceerd in: Quality of Life Research 3/2016

23-06-2015

Ambulatory and diary methods can facilitate the measurement of patient-reported outcomes

Auteurs: Stefan Schneider, Arthur A. Stone

Gepubliceerd in: Quality of Life Research | Uitgave 3/2016

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Abstract

Purpose

Ambulatory and diary methods of self-reported symptoms and well-being have received increasing interest in recent years. These methods are a valuable addition to traditional strategies for the assessment of patient-reported outcomes (PROs) in that they capture patients’ recent symptom experiences repeatedly in their natural environments. In this article, we review ways that incorporating diary methods into PRO measurement can facilitate research on quality of life.

Methods

Several diary methods are currently available, and they include “real-time” (Ecological Momentary Assessment) and “near-real-time” (end-of-day assessments, Day Reconstruction Method) formats. We identify the key benefits of these methods for PRO research.

Results

(1) In validity testing, diary assessments can serve as a standard for evaluating the ecological validity and for identifying recall biases of PRO instruments with longer-term recall formats. (2) In research and clinical settings, diaries have the ability to closely capture variations and dynamic changes in quality of life that are difficult or not possible to obtain from traditional PRO assessments. (3) In test construction, repeated diary assessments can expand understanding of the measurement characteristics (e.g., reliability, dimensionality) of PROs in that parameters for differences between people can be compared with those for variation within people.

Conclusions

Diary assessment strategies can enrich the repertoire of PRO assessment tools and enhance the measurement of patients’ quality of life.
vorige artikel Introduction to special section on patient-reported outcomes in nonstandard settings
volgende artikel Advantages and psychometric validation of proximal intensive assessments of patient-reported outcomes collected in daily life
Literatuur
1.
Stone, A. A., & Shiffman, S. (1994). Ecological Momentary Assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16, 199–202.
2.
Bhattacharyya, M. R., Whitehead, D. L., Rakhit, R., & Steptoe, A. (2008). Depressed mood, positive affect, and heart rate variability in patients with suspected coronary artery disease. Psychosomatic Medicine, 70, 1020–1027.CrossRefPubMed
3.
Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A. (2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences, 107, 9985–9990.CrossRef
4.
Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences, 107, 16489–16493.CrossRef
5.
Steptoe, A., & Wardle, J. (2011). Positive affect measured using ecological momentary assessment and survival in older men and women. Proceedings of the National Academy of Sciences, 108, 18244–18248.CrossRef
6.
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The Day Reconstruction Method. Science, 306, 1776–1780.CrossRefPubMed
7.
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.CrossRefPubMed
8.
Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616.CrossRefPubMed
9.
Stone, A. A., Shiffman, S., Atienza, A., & Nebling, L. (Eds.). (2007). The science of real-time data capture: Self-reports in health research. New Yrok: Oxford University Press.
10.
Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21, 463–475.CrossRefPubMed
11.
aan het Rot, M., Hogenelst, K., & Schoevers, R. A. (2012). Mood disorders in everyday life: A systematic review of experience sampling and ecological momentary assessment studies. Clinical Psychology Review, 32, 510–523.CrossRefPubMed
12.
Walz, L. C., Nauta, M. H., & aan het Rot, M. (2014). Experience sampling and ecological momentary assessment for studying the daily lives of patients with anxiety disorders: A systematic review. Journal of Anxiety Disorders, 28, 925–937.CrossRefPubMed
13.
Stone, A. A., Schwartz, J. E., Schkade, D., Schwarz, N., Krueger, A., & Kahneman, D. (2006). A population approach to the study of emotion: Diurnal rhythms of a working day examined with the Day Reconstruction Method. Emotion, 6, 139–149.CrossRefPubMed
14.
Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., & Ryan, R. M. (2000). Daily well-being: The role of autonomy, competence, and relatedness. Personality and Social Psychology Bulletin, 26, 419–435.CrossRef
15.
Carstensen, L. L., Pasupathi, M., Mayr, U., & Nesselroade, J. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79, 644–655.CrossRefPubMed
16.
Ram, N., & Gerstorf, D. (2009). Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24, 778–791.PubMedCentralCrossRefPubMed
17.
Burton, C., Weller, D., & Sharpe, M. (2007). Are electronic diaries useful for symptoms research? A systematic review. Journal of Psychosomatic Research, 62, 553–561.CrossRefPubMed
18.
Conner, T. S., & Barrett, L. F. (2012). Trends in ambulatory self-report: The role of momentary experience in psychosomatic medicine. Psychosomatic Medicine, 74, 327–337.PubMedCentralCrossRefPubMed
19.
Kubiak, T., & Stone, A. A. (2012). Ambulatory monitoring of biobehavioral processes in health and disease. Psychosomatic Medicine, 74, 325–326.CrossRefPubMed
20.
Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128, 934–960.CrossRefPubMed
21.
Stull, D. E., Leidy, N. K., Parasuraman, B., & Chassany, O. (2009). Optimal recall periods for patient-reported outcomes: Challenges and potential solutions. Current Medical Research and Opinion, 25, 929–942.CrossRefPubMed
22.
Dunn, S. M., Bryson, J. M., Hoskins, P. L., Alford, J. B., Handelsman, D. J., & Turtle, J. R. (1984). Development of the diabetes knowledge (DKN) scales: forms DKNA, DKNB, and DKNC. Diabetes Care, 7, 36–41.CrossRefPubMed
23.
Craig, B. M., Busschbach, J. J., & Salomon, J. A. (2009). Modeling ranking, time trade-off and visual analogue scale values for EQ-5D health states: A review and comparison of methods. Medical Care, 47, 634–641.PubMedCentralCrossRefPubMed
24.
Hays, R. D., Bjorner, J. B., Revicki, D. A., Spritzer, K. L., & Cella, D. (2009). Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Quality of Life Research, 18, 873–880.PubMedCentralCrossRefPubMed
25.
Bruce, B., & Fries, J. (2005). The health assessment questionnaire (HAQ). Clinical and Experimental Rheumatology, 23, S14–S18.PubMed
26.
Schwarz, N. (2007). Retrospective and concurrent self-reports: The rationale for real-time data capture. In A. A. Stone, S. S. Shiffman, A. Atienza, & L. Nebeling (Eds.), The science of real-time data capture: Self-reports in health research (pp. 11–26). New York: Oxford University Press.
27.
Ware, J. E. (1993). SF-36 health survey: Manual & interpretive guide. Boston: The Health Institute, New England Medical Center.
28.
Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63, 1179–1194.PubMedCentralCrossRefPubMed
29.
McFarland, C., Ross, M., & DeCourville, N. (1989). Women’s theories of menstruation and biases in recall of menstrual symptoms. Journal of Personality and Social Psychology, 57, 522–531.CrossRefPubMed
30.
Barrett, L. F. (1997). The relationships among momentary emotion experiences, personality descriptions, and retrospective ratings of emotion. Personality and Social Psychology Bulletin, 23, 1100–1110.CrossRef
31.
U.S. Department of Health and Human Services Food and Drug Administration. (2009). Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims. http://​www.​fda.​gov. Accessed March 22, 2010.
32.
33.
34.
Bussmann, J. B., & Ebner-Priemer, U. W. (2012). Ambulatory assessment of movement behavior. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 235–250). New York, NY: The Guilford Press.
35.
Broderick, J. E., Schwartz, J. E., Schneider, S., & Stone, A. A. (2009). Can end-of-day reports replace momentary assessment of pain and fatigue? Journal of Pain, 10, 274–281.PubMedCentralCrossRefPubMed
36.
Broderick, J. E., Schneider, S., Schwartz, J. E., & Stone, A. A. (2010). Interference with activities due to pain and fatigue: Accuracy of ratings across different reporting periods. Quality of Life Research, 19, 1163–1170.PubMedCentralCrossRefPubMed
37.
Schlatter, M. C., & Cameron, L. D. (2010). Emotional suppression tendencies as predictors of symptoms, mood, and coping appraisals during AC chemotherapy for breast cancer treatment. Annals of Behavioral Medicine, 40, 15–29.CrossRefPubMed
38.
Junghaenel, D. U., Schneider, S., Stone, A. A., Christodoulou, C., & Broderick, J. E. (2014). Ecological validity and clinical utility of patient-reported outcomes measurement information system (PROMIS®) instruments for detecting premenstrual symptoms of depression, anger, and fatigue. Journal of Psychosomatic Research, 76, 300–306.PubMedCentralCrossRefPubMed
39.
Murphy, S. L., Smith, D. M., Clauw, D. J., & Alexander, N. B. (2008). The impact of momentary pain and fatigue on physical activity in women with osteoarthritis. Arthritis Care Res (Hoboken), 59, 849–856.CrossRef
40.
Broderick, J. E., Schneider, S., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Validity and reliability of patient-reported outcomes measurement information system (PROMIS) instruments in osteoarthritis. Arthritis Care & Research, 65, 1625–1633.
41.
Daly, M., Delaney, L., Doran, P. P., Harmon, C., & MacLachlan, M. (2010). Naturalistic monitoring of the affect-heart rate relationship: A day reconstruction study. Health Psychology, 29, 186–195.CrossRefPubMed
42.
Daly, M., Baumeister, R. F., Delaney, L., & MacLachlan, M. (2014). Self-control and its relation to emotions and psychobiology: Evidence from a Day Reconstruction Method study. Journal of Behavioral Medicine, 37, 81–93.CrossRefPubMed
43.
Dolan, P., & Kudrna, L. (2015). More years, less yawns: Fresh evidence on tiredness by age and other factors. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70, 576–580.
44.
Smith, J., Ryan, L. H., Queen, T. L., Becker, S., & Gonzalez, R. (2014). Snapshots of mixtures of affective experiences in a day: Findings from the health and retirement study. Journal of population ageing, 7, 55–79.PubMedCentralCrossRefPubMed
45.
Broderick, J. E., Schwartz, J. E., Vikingstad, G., Pribbernow, M., Grossman, S., & Stone, A. A. (2008). The accuracy of pain and fatigue items across different reporting periods. Pain, 139, 146–157.PubMedCentralCrossRefPubMed
46.
Stone, A. A., Broderick, J. E., Shiffman, S. S., & Schwartz, J. E. (2004). Understanding recall of weekly pain from a momentary assessment perspective: Absolute agreement, between- and within-person consistency, and judged change in weekly pain. Pain, 107, 61–69.CrossRefPubMed
47.
Miron-Shatz, T., Stone, A., & Kahneman, D. (2009). Memories of yesterday’s emotions: Does the valence of experience affect the memory-experience gap? Emotion, 9, 885–891.CrossRefPubMed
48.
Shrier, L. A., Shih, M. C., & Beardslee, W. R. (2005). Affect and sexual behavior in adolescents: A review of the literature and comparison of momentary sampling with diary and retrospective self-report methods of measurement. Pediatrics, 115, e573–e581.PubMedCentralCrossRefPubMed
49.
Bennett, A. V., Amtmann, D., Diehr, P., & Patrick, D. L. (2012). Comparison of 7-day recall and daily diary reports of COPD symptoms and impacts. Value in Health, 15, 466–474.CrossRefPubMed
50.
Houtveen, J. H., & Oei, N. Y. (2007). Recall bias in reporting medically unexplained symptoms comes from semantic memory. Journal of Psychosomatic Research, 62, 277–282.CrossRefPubMed
51.
Van den Brink, M., Bandell-Hoekstra, E., & Abu-Saad, H. H. (2001). The occurrence of recall bias in pediatric headache: A comparison of questionnaire and diary data. Headache: The Journal of Head and Face Pain, 41, 11–20.CrossRef
52.
Homma, Y., Ando, T., Yoshida, M., Kageyama, S., Takei, M., Kimoto, K., et al. (2002). Voiding and incontinence frequencies: Variability of diary data and required diary length. Neurourology and Urodynamics, 21, 204–209.CrossRefPubMed
53.
Shiffman, S. (2009). How many cigarettes did you smoke? Assessing cigarette consumption by global report, time-line follow-back, and ecological momentary assessment. Health Psychology, 28, 519–526.PubMedCentralCrossRefPubMed
54.
Carney, M. A., Tennen, H., Affleck, G., del Boca, F. K., & Kranzler, H. R. (1998). Levels and patterns of alcohol consumption using timeline follow-back, daily diaries and real-time. Journal of Studies on Alcohol and Drugs, 59, 447–454.CrossRef
55.
Litt, M. D., Cooney, N. L., & Morse, P. (1998). Ecological momentary assessment (EMA) with treated alcoholics: Methodological problems and potential solutions. Health Psychology, 17, 48–52.CrossRefPubMed
56.
Bennett, A., Patrick, D., Bushnell, D., Chiou, C., & Diehr, P. (2011). Comparison of 7-day and repeated 24-h recall of type 2 diabetes. Quality of Life Research, 20, 769–777.CrossRefPubMed
57.
Jamison, R. N., Raymond, S. A., Slawsby, E. A., McHugo, G. J., & Baird, J. C. (2006). Pain assessment in patients with low back pain: Comparison of weekly recall and momentary electronic data. Journal of Pain, 7, 192–199.CrossRefPubMed
58.
Stel, V. S., Smit, J. H., Pluijm, S. M. F., Visser, M., Deeg, D. J. H., & Lips, P. (2004). Comparison of the LASA physical activity questionnaire with a 7-day diary and pedometer. Journal of Clinical Epidemiology, 57, 252–258.CrossRefPubMed
59.
Cella, D., & Stone, A. A. (2015). Health-related quality of life measurement in oncology: Advances and opportunities. American Psychologist, 70, 175–185.CrossRefPubMed
60.
Bogaerts, K., Wan, L., Van Diest, I., Stans, L., Decramer, M., & Van den Bergh, O. (2012). Peak-end memory bias in laboratory-induced dyspnea: A comparison of patients with medically unexplained symptoms and healthy controls. Psychosomatic Medicine, 74, 974–981.CrossRefPubMed
61.
Stone, A. A., Broderick, J. E., Kaell, A. T., DelesPaul, P. A. E. G., & Porter, L. E. (2000). Does the peak-end phenomenon observed in laboratory pain studies apply to real-world pain in rheumatoid arthritics? Journal of Pain, 1, 212–217.CrossRefPubMed
62.
Redelmeier, D. A., Katz, J., & Kahneman, D. (2003). Memories of colonoscopy: A randomized trial. Pain, 104, 187–194.CrossRefPubMed
63.
64.
Stone, A. A., Schwartz, J. E., Broderick, J. E., & Shiffman, S. S. (2005). Variability of momentary pain predicts recall of weekly pain: A consequence of the peak (or salience) memory heuristic. Personality and Social Psychology Bulletin, 31, 1340–1346.CrossRefPubMed
65.
Sohl, S. J., & Friedberg, F. (2008). Memory for fatigue in chronic fatigue syndrome: Relationships to fatigue variability, catastrophizing, and negative affect. Behavioral Medicine, 34, 29–38.PubMedCentralCrossRefPubMed
66.
Schneider, S., Broderick, J. E., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Temporal trends in symptom experience predict the accuracy of recall PROs. Journal of Psychosomatic Research, 75, 160–166.PubMedCentralCrossRefPubMed
67.
Smith, W. B., & Safer, M. A. (1993). Effects of present pain level on recall of chronic pain and medication use. Pain, 55, 355–361.CrossRefPubMed
68.
Begg, A., Drummond, G., & Tiplady, B. (2003). Assessment of postsurgical recovery after discharge using a pen computer diary. Anaesthesia, 58, 1101–1105.CrossRefPubMed
69.
Chapman, C. R., Davis, J., Donaldson, G. W., Naylor, J., & Winchester, D. (2011). Postoperative pain trajectories in chronic pain patients undergoing surgery: The effects of chronic opioid pharmacotherapy on acute pain. Journal of Pain, 12, 1240–1246.CrossRefPubMed
70.
Jim, H. S., Small, B., Faul, L. A., Franzen, J., Apte, S., & Jacobsen, P. B. (2011). Fatigue, depression, sleep, and activity during chemotherapy: Daily and intraday variation and relationships among symptom changes. Annals of Behavioral Medicine, 42, 321–333.PubMedCentralCrossRefPubMed
71.
Junghaenel, D. U., Cohen, J., Schneider, S., Neerukonda, A. R., & Broderick, J. E. (2015). Identification of distinct fatigue trajectories in patients with breast cancer undergoing adjuvant chemotherapy. Supportive Care in Cancer. doi:10.​1007/​s00520-015-2616-x.
72.
73.
Almeida, D. M. (2005). Resilience and vulnerability to daily stressors assessed via diary methods. Current Directions in Psychological Science, 14, 62–68.CrossRef
74.
Edwards, R. R., Almeida, D. M., Klick, B., Haythornthwaite, J. A., & Smith, M. T. (2008). Duration of sleep contributes to next-day pain report in the general population. Pain, 137, 202–207.PubMedCentralCrossRefPubMed
75.
Cranford, J. A., Shrout, P. E., Iida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin, 32, 917–929.PubMedCentralCrossRefPubMed
76.
Schneider, S., Junghaenel, D. U., Keefe, F. J., Schwartz, J. E., Stone, A. A., & Broderick, J. E. (2012). Individual differences in the day-to-day variability of pain, fatigue, and well-being in patients with rheumatic disease: Associations with psychological variables. Pain, 153, 813–822.PubMedCentralCrossRefPubMed
77.
Barge-Schaapveld, D. Q., Nicolson, N. A., & Berkhof, J. (1999). Quality of life in depression: Daily life determinants and variability. Psychiatry Research, 88, 173–189.CrossRefPubMed
78.
Maes, I. H., Delespaul, P. A., Peters, M. L., White, M. P., van Horn, Y., Schruers, K., et al. (2015). Measuring health-related quality of life by experiences: The experience sampling method. Value in Health, 18, 44–51.CrossRefPubMed
79.
Nesselroade, J. R., & Ram, N. (2004). Studying intraindividual variability: What we have learned that will help us understand lives in context. Research in Human Development, 1, 9–29.CrossRef
80.
Zautra, A. J., Fasman, R., Parish, B. P., & Davis, M. C. (2007). Daily fatigue in women with osteoarthritis, rheumatoid arthritis, and fibromyalgia. Pain, 128, 128–135.CrossRefPubMed
81.
Trull, T. J., Solhan, M. B., Tragesser, S. L., Jahng, S., Wood, P. K., Piasecki, T. M., et al. (2008). Affective instability: Measuring a core feature of borderline personality disorder with ecological momentary assessment. Journal of Abnormal Psychology, 117, 647–661.CrossRefPubMed
82.
MacDonald, S. W., Hultsch, D. F., & Dixon, R. A. (2003). Performance variability is related to change in cognition: Evidence from the Victoria Longitudinal Study. Psychology and Aging, 18, 510–523.CrossRefPubMed
83.
Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., & Rowe, J. W. (1997). Intraindividual variability in perceived control in a older sample: The MacArthur successful aging studies. Psychology and Aging, 12, 489–502.CrossRefPubMed
84.
Chow, S.-M., Ram, N., Boker, S. M., Fujita, F., & Clore, G. (2005). Emotion as a thermostat: Representing emotion regulation using a damped oscillator model. Emotion, 5, 208–225.CrossRefPubMed
85.
Boker, S. M., & Nesselroade, J. R. (2002). A method for modeling the intrinsic dynamics of intraindividual variability: Recovering the parameters of simulated oscillators in multi-wave panel data. Multivariate Behavioral Research, 37, 127–160.CrossRefPubMed
86.
Smolensky, M. H., & D’alonzo, G. E. (1993). Medical chronobiology: Concepts and applications. American Review of Respiratory Disease, 147, S2–S19.CrossRefPubMed
87.
Jamison, R. N., & Brown, G. K. (1991). Validation of hourly pain intensity profiles with chronic pain patients. Pain, 45, 123–128.CrossRefPubMed
88.
Stone, A. A., Broderick, J. E., Schneider, S., & Schwartz, J. E. (2012). Expanding options for developing outcome measures from momentary assessment data. Psychosomatic Medicine, 74, 387–397.CrossRefPubMed
89.
Affleck, G., Tennen, H., Urrows, S., & Higgins, P. (1991). Individual differences in the day-to-day experience of chronic pain: A prospective daily study of rheumatoid arthritis patients. Health Psychology, 10, 419–426.CrossRefPubMed
90.
Fayers, P., & Machin, D. (2013). Quality of life: The assessment, analysis and interpretation of patient-reported outcomes. Wichester, West Sussex: Wiley.
91.
Mehta, P. D., & Neale, M. C. (2005). People are variables too: Multilevel structural equations modeling. Psychological Methods, 10, 259–284.CrossRefPubMed
92.
Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201–218.
93.
Velicer, W. F., Babbin, S. F., & Palumbo, R. (2014). Idiographic applications: Issues of ergodicity and generalizability. In P. C. M. Molenaar, R. M. Lerner, & K. M. Newell (Eds.), Handbook of developmental systems theory and methodology (pp. 425–441). New York, NY: Guilford Press.
94.
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19, 72–91.CrossRefPubMed
95.
Mäkikangas, A., Kinnunen, S., Rantanen, J., Mauno, S., Tolvanen, A., & Bakker, A. B. (2014). Association between vigor and exhaustion during the workweek: A person-centered approach to daily assessments. Anxiety, Stress, & Coping, 27, 555–575.CrossRef
96.
Church, A. T., Katigbak, M. S., Ching, C. M., Zhang, H., Shen, J., Arias, R. M., et al. (2013). Within-individual variability in self-concepts and personality states: Applying density distribution and situation-behavior approaches across cultures. Journal of Research in Personality, 47, 922–935.CrossRef
97.
Roesch, S. C., Aldridge, A. A., Stocking, S. N., Villodas, F., Leung, Q., Bartley, C. E., et al. (2010). Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation. Multivariate Behavioral Research, 45, 767–789.PubMedCentralCrossRefPubMed
98.
Schneider, S., Choi, S. W., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Psychometric characteristics of daily diaries for the patient-reported outcomes measurement information system (PROMIS®): a preliminary investigation. Quality of Life Research, 22, 1859–1869.PubMedCentralCrossRefPubMed
99.
Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Medical Care, 44, S115–S123.CrossRefPubMed
100.
Roussos, L., & Stout, W. (1996). A multidimensionality-based DIF analysis paradigm. Applied Psychological Measurement, 20, 355–371.CrossRef
101.
Zumbo, B. D. (2007). Three generations of DIF analyses: Considering where it has been, where it is now, and where it is going. Language Assessment Quarterly, 4, 223–233.CrossRef
102.
Asparouhov, T., & Muthén, B. (2012). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. Paper presented at the annual meeting of the National Council on Measurement in Education.
103.
104.
Metagegevens
Titel
Ambulatory and diary methods can facilitate the measurement of patient-reported outcomes
Auteurs
Stefan Schneider
Arthur A. Stone
Publicatiedatum
23-06-2015
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 3/2016
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-015-1054-z

Andere artikelen Uitgave 3/2016

Quality of Life Research 3/2016 Naar de uitgave

Brief Communication

Well-being and environmental quality: Does pollution affect life satisfaction?

Special Section: PROs in Non-Standard Settings (by invitation only)

Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

Special Section: PROs in Non-Standard Settings (by invitation only)

AmbuFlex: tele-patient-reported outcomes (telePRO) as the basis for follow-up in chronic and malignant diseases

OriginalPaper

Impact of Alzheimer’s disease on the family caregiver’s long-term quality of life: results from an ALSOVA follow-up study

Special Section: PROs in Non-Standard Settings (by invitation only)

Advantages and psychometric validation of proximal intensive assessments of patient-reported outcomes collected in daily life

Original Paper

The effectiveness of a preventive health program and vitamin D status in improving health-related quality of life of older Canadians