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Quality of Life Research
Gepubliceerd in: Quality of Life Research 12/2015

13-06-2015

Differences in Patient Health Questionnaire and Aachen Depression Item Bank scores between tablet versus paper-and-pencil administration

Auteurs: Lena Spangenberg, Heide Glaesmer, Maren Boecker, Thomas Forkmann

Gepubliceerd in: Quality of Life Research | Uitgave 12/2015

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Abstract

Purpose

To extend knowledge about measurement equivalence of depression measures assessed by tablet and paper–pencil administration, the present study evaluated the effect of mode of administration (MoA) on scale and item level for the Patient Health Questionnaire (PHQ-9) and the Aachen Depression Item Bank (ADIB) in elderly patients.

Methods

Primary care patients (N = 193, ≥60 years) were assessed following a crossover design in Leipzig, Germany. All participants filled out the PHQ-9 and the ADIB in both MoAs under study. Effects of MoA were analyzed by intra-class correlation, mixed-effects regression, and differential item functioning (DIF). Additionally, detection rates between both MoAs were compared using receiver operating characteristics analysis compared to a diagnostic interview (SCID-I, N = 163).

Results

No effect of MoA was found in the PHQ-9 on scale score or item level. Two ADIB items showed DIF according to MoA. In terms of discriminatory power, MoA did not influence detection rates of both instruments.

Conclusions

In summary, our findings suggest that no severe effect of mode of administration on self-report assessments of depression should be expected. It can be concluded that tablets provide a valid way to electronically assess depressive symptoms in elderly patients. Yet changes in item presentation can influence the psychometric properties and require equivalence testing using sophisticated analyses on item level such as DIF.
vorige artikel Psychometric properties of the EQ-5D-5L in Thai patients with chronic diseases
volgende artikel Steven P. Reise and Dennis A. Revicki: Handbook of item response theory modeling: applications to typical performance assessment. Multivariate applications series
Literatuur
1.
Gwaltney, C. J., Shields, A. L., & Shiffman, S. (2008). Equivalence of electronic and paper-and-pencil administration of patient-reported outcome measures: A meta-analytic review. Value in Health, 11, 322–333.CrossRefPubMed
2.
Ebner-Priemer, U. W., Kubiak, T., & Pawlik, K. (2009). Ambulatory assessment introduction. European Psychologist, 14, 95–97.CrossRef
3.
Gibbons, R. D., Weiss, D. J., Kupfer, D. J., Frank, E., Fagiolini, A., Grochocinski, V. J., et al. (2008). Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatric Services, 59, 361–368.PubMedCentralCrossRefPubMed
4.
Forkmann, T., Boecker, M., Wirtz, M., Frey, C., & Gauggel, S. (2010). Adaptives testen in der psychotherapie: Das Rasch-basierte adaptive depressionsscreening (A-DESC). Zeitschrift für Klinische Diagnostik und Evaluation, 3, 59–75.
5.
Kurt, R., Bogner, H. R., Straton, J. B., Tien, A. Y., & Gallo, J. J. (2004). Computer-assisted assessment of depression and function in older primary care patients. Computer Methods and Programs in Biomedicine, 73, 165–171.PubMedCentralCrossRefPubMed
6.
Goldstein, L. A., Gibbons, M. B. C., Thompson, S. M., Scott, K., Heintz, L., Green, P., et al. (2011). Outcome assessment via handheld computer in community mental health: Consumer satisfaction and reliability. Journal of Behavioral Health Services & Research, 38, 414–423.CrossRef
7.
Velikova, G., Wright, E. P., Smith, A. B., Cull, A., Gould, A., Forman, D., et al. (1999). Automated collection of quality-of-life data: A comparison of paper and computer touch-screen questionnaires. Journal of Clinical Oncology, 17, 998–1007.PubMed
8.
Coons, S. J., Gwaltney, C. J., Hays, R. D., Lundy, J. J., Sloan, J. A., Revicki, D. A., et al. (2009). Recommendations on evidence needed to support measurement equivalence between electronic and paper-based patient-reported outcome (pro) measures: ISPOR ePRO good research practices task force report. Value in Health, 12, 419–429.CrossRefPubMed
9.
Bjorner, J. B., Rose, M., Gandek, B., Stone, A. A., Junghaenel, D. U., & Ware, J. E. (2014). Method of administration of PROMIS scales did not significantly impact score level, reliability, or validity. Journal of Clinical Epidemiology, 67, 108–113.PubMedCentralCrossRefPubMed
10.
Alfonsson, S., Maathz, P., & Hursti, T. (2014). Interformat reliability of digital psychiatric self-report questionnaires: A systematic review. Journal of Medical Internet Research, 16, 86–97.CrossRef
11.
Bushnell, D. M., Martin, M. L., & Parasuraman, B. (2003). Electronic versus paper questionnaires: A further comparison in persons with asthma. Journal of Asthma, 40, 751–762.CrossRefPubMed
12.
Bliven, B. D., Kaufman, S. E., & Spertus, J. A. (2001). Electronic collection of health-related quality of life data: Validity, time benefits, and patient preference. Quality of Life Research, 10, 15–21.CrossRefPubMed
13.
Fritz, F., Balhorn, S., Riek, M., Breil, B., & Dugas, M. (2012). Qualitative and quantitative evaluation of EHR-integrated mobile patient questionnaires regarding usability and cost-efficiency. International Journal of Medical Informatics, 81, 303–313.CrossRefPubMed
14.
Swartz, R. J., de Moor, C., Cook, K. F., Fouladi, R. T., Basen-Engquist, K., Eng, C., et al. (2007). Mode effects in the center for epidemiologic studies depression (CES-D) scale: Personal digital assistant vs. paper and pencil administration. Quality of Life Research, 16, 803–813.CrossRefPubMed
15.
Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals, 32, 509–515.CrossRef
16.
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9—validity of a brief depression severity measure. Journal of General Internal Medicine, 16, 606–613.PubMedCentralCrossRefPubMed
17.
Forkmann, T., Boecker, M., Norra, C., Eberle, N., Kircher, T., Schauerte, P., et al. (2009). Development of an item bank for the assessment of depression in persons with mental illnesses and physical diseases using Rasch analysis. Rehabilitation Psychology, 54, 186–197.CrossRefPubMed
18.
Tennant, A., & Conaghan, P. G. (2007). The Rasch measurement model in rheumatology: What is it and why use it? When should it be applied, and what should one look for in a Rasch paper? Arthritis & Rheumatism-Arthritis Care & Research, 57, 1358–1362.CrossRef
19.
Forkmann, T., Kroehne, U., Wirtz, M., Norra, C., Baumeister, H., Gauggel, S., et al. (2013). Adaptive screening for depression—recalibration of an item bank for the assessment of depression in persons with mental and somatic diseases and evaluation in a simulated computer-adaptive test environment. Journal of Psychosomatic Research, 75, 437–443.CrossRefPubMed
20.
Forkmann, T., Boecker, M., Wirtz, M., Glaesmer, H., Brahler, E., Norra, C., et al. (2010). Validation of the Rasch-based Depression Screening in a large scale German general population sample. Health and Quality of Life Outcomes, 8, 105.PubMedCentralCrossRefPubMed
21.
Boecker, M., Elhan., A. H., Tennant, A., Wirtz, M., Eberle N., Gauggel, S. (2010). On the way to NeuroCAT: development and initial evaluation of the Aachen-ADL-item bank. Abstract.
22.
Bayliss, E. A., Ellis, J. L., & Steiner, J. F. (2005). Subjective assessments of comorbidity correlate with quality of life health outcomes: Initial validation of a comorbidity assessment instrument. Health and Quality of Life Outcomes, 3, 51.PubMedCentralCrossRefPubMed
23.
Bock, R. D., & Mislevy, R. J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6, 431–444.CrossRef
24.
van der Linden, W. J., & Pashley, P. J. (2000). Item selection and ability estimation in adaptive testing. In W. J. van der Linden & C. A. W. Glas (Eds.), Computerized adaptive testing: Theory and practice. Dordrecht: Kluwer.CrossRef
25.
26.
Penfield, R. D. (2006). Applying Bayesian item selection approaches to adaptive tests using polytomous items. Applied Measurement in Education, 19, 1–20.CrossRef
27.
Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Lowe, B. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. General Hospital Psychiatry, 32, 345–359.CrossRefPubMed
28.
Bayliss, E. A., Ellis, J. L., & Steiner, J. F. (2009). Senior´s self-reported multimorbidity captured biopsychosocial factors not incorporated into two other databased morbidity measures. Journal of Clinical Epidemiology, 62, 550–557.PubMedCentralCrossRefPubMed
29.
Wittchen, H. U., Zaudig, M., & Fydrich, T. (1997). SKID-I und SKID-II. Strukturiertes klinisches interview für DSM-IV (1st ed.). Göttingen: Hogrefe.
30.
Hox, J. J. (2010). Multilevel analysis: techniques and applications (2nd ed.). New York: Routledge.
31.
Macmillan, N. A. (2002). Signal detection theory. In J. Wixted (Ed.), Methodology in experimental psychology. New York: Wiley.
32.
33.
Andrich, D., Lyne, A., Sheridan, B., & Luo, G. (2010). RUMM: Rasch unidimensional measurement models software (2030). Computer software. Perth: RUMM Laboratory.
34.
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174.CrossRef
35.
36.
Pallant, J. F., & Tennant, A. (2007). An introduction to the Rasch measurement model: An example using the Hospital Anxiety and Depression Scale (HADS). British Journal of Clinical Psychology, 46, 1–18.CrossRefPubMed
37.
Elhan, A. H., Oztuna, D., Kutlay, S., Kucukdeveci, A. A., & Tennant, A. (2008). An initial application of computerized adaptive testing (CAT) for measuring disability in patients with low back pain. BMC Musculoskeletal Disorders, 9, 166.PubMedCentralCrossRefPubMed
38.
Andrich, D. (1988). Rasch models for measurement. London: Sage.
39.
Linacre, J. M. (1998). Structure in Rasch residuals: why principal component analysis (PCA). Rasch Measurement Transactions, 12, 636.
40.
Smith, R. M., & Miao, C. Y. (1994). Assessing unidimensionality for Rasch measurement. In M. Wilson (Ed.), Objective measurement: Theory into practice. Norwood NJ: Ablex Publishing Corporation.
41.
Smith, E. V. J. (2002). Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals. Journal of applied measurement, 3, 205–231.PubMed
42.
Maddala, R. (1986). Limit-dependent and qualitative variables in economics (3rd ed.). Cambridge: Cambridge University Press.
43.
Forkmann, T., Gauggel, S., Spangenberg, L., Brahler, E., & Glaesmer, H. (2013). Dimensional assessment of depressive severity in the elderly general population: Psychometric evaluation of the PHQ-9 using Rasch analysis. Journal of Affective Disorders, 148, 323–330.CrossRefPubMed
44.
Phelan, E., Williams, B., Meeker, K., Bonn, K., Frederick, J., LoGerfo, J., et al. (2010). A study of the diagnostic accuracy of the PHQ-9 in primary care elderly. BMC Family Practice, 11, 63.PubMedCentralCrossRefPubMed
45.
Chen, S. L., Chiu, H. L., Xu, B. H., Ma, Y., Jin, T., Wu, M. H., et al. (2010). Reliability and validity of the PHQ-9 for screening late-life depression in Chinese primary care. International Journal of Geriatric Psychiatry, 25, 1127–1133.CrossRefPubMed
46.
Lamers, F., Jonkers, C. C. M., Bosma, H., Penninx, B. W. J. H., Knottnerus, J. A., & van Eijk, J. T. (2008). Summed score of the Patient Health Questionnaire-9 was a reliable and valid method for depression screening in chronically ill elderly patients. Journal of Clinical Epidemiology, 61, 679–687.CrossRefPubMed
47.
Spangenberg, L., Forkmann, T., Braehler, E., & Glaesmer, H. (2011). The association of depression and multimorbidity in the elderly: Implications for the assessment of depression. Psychogeriatrics, 11, 227–234.CrossRefPubMed
48.
Gilbody, S., Richards, D., Brealey, S., & Hewitt, C. (2007). Screening for depression in medical settings with the patient health questionnaire (PHQ): A diagnostic meta-analysis. Journal of General Internal Medicine, 22, 1596–1602.PubMedCentralCrossRefPubMed
49.
Spangenberg, L., Forkmann, T., & Glaesmer, H. (2014). Actually I am not a computer literate …”: Usability and acceptance of depression measurement by tablet administration versus paper-pencil mode in German primary care patients >60 years. Nervenheilkunde, 33, 631–637.
50.
Riedel-Heller, S. G., Busse, A., & Angermeyer, M. C. (2006). The state of mental health in old-age across the ‘old’ European Union—a systematic review. Acta Psychiatrica Scandinavica, 113, 388–401.CrossRefPubMed
51.
Djernes, J. K. (2006). Prevalence and predictors of depression in populations of elderly: A review. Acta Psychiatrica Scandinavica, 113, 372–387.CrossRefPubMed
Metagegevens
Titel
Differences in Patient Health Questionnaire and Aachen Depression Item Bank scores between tablet versus paper-and-pencil administration
Auteurs
Lena Spangenberg
Heide Glaesmer
Maren Boecker
Thomas Forkmann
Publicatiedatum
13-06-2015
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 12/2015
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-015-1040-5

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