The mobile everyday cognition scale (mECog): development and pilot testing

References

• Allard, M., Husky, M., Catheline, G., Pelletier, A., Dilharreguy, B., Amieva, H., Pérès, K., Foubert-Samier, A., Dartigues, J. F., & Swendsen, J. (2014). Mobile technologies in the early detection of cognitive decline. PLoS One, 9(12), e112197. https://doi.org/10.1371/journal.pone.0112197
   PubMed Web of Science ®Google Scholar
• Alzheimer’s Disease Neuroimaging Initiative. (2017). Alzheimer’s Disease Neuroimaging Initiative. ADNI | Alzheimer’s Disease Neuroimaging Initiative (usc.edu).
   Google Scholar
• Alzheimer’s Disease Neuroimaging Initiative. (n.d.). Alzheimer’s Disease Neuroimaging Initiative. https://www.adni4.org/
   Google Scholar
• Besser, L., Kukull, W., Knopman, D. S., Chui, H., Galasko, D., Weintraub, S. & Neuropsychology, Work Group. (2018). Version 3 of the national Alzheimer’s coordinating center’s uniform data set. Alzheimer Disease & Associated Disorders, 32(4), 351–358. https://doi.org/10.1097/WAD.0000000000000279
   PubMed Web of Science ®Google Scholar
• Bishara, A. J., & Hittner, J. B. (2012). Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches. Psychological Methods, 17(3), 399–417. https://doi.org/10.1037/a0028087
   PubMed Web of Science ®Google Scholar
• Bornstein, M. H., Putnick, D. L., Costlow, K. M., & Suwalsky, J. T. D. (2020). Retrospective report revisited: Long-term recall in European American mothers moderated by developmental domain, child age, person, and metric of agreement. Applied Developmental Science, 24(3), 242–262. https://doi.org/10.1080/10888691.2018.1462090
   PubMed Web of Science ®Google Scholar
• Burke, L., & Naylor, G. (2022). Smartphone app-based noncontact ecological momentary assessment with experienced and naïve older participants: Feasibility study. JMIR Formative Research, 6(3), e27677. https://doi.org/10.2196/27677
   PubMedGoogle Scholar
• Costa, A. S., Dogan, I., Schulz, J. B., & Reetz, K. (2019). Going beyond the mean: Intraindividual variability of cognitive performance in prodromal and early neurodegenerative disorders. The Clinical Neuropsychologist, 33(2), 369–389. https://doi.org/10.1080/13854046.2018.1533587
   PubMed Web of Science ®Google Scholar
• Doherty, K., Balaskas, A., & Doherty, G. (2020). The design of ecological momentary assessment technologies. Interacting with Computers, 32(3), 257–278. https://doi.org/10.1093/iwcomp/iwaa019
   Web of Science ®Google Scholar
• Farias, S. T., Chou, E., Harvey, D. J., Mungas, D., Reed, B., DeCarli, C., Park, L. Q., & Beckett, L. (2013a). Longitudinal trajectories of everyday function by diagnostic status. Psychology and Aging, 28(4), 1070–1075. https://doi.org/10.1037/a0034069
   PubMed Web of Science ®Google Scholar
• Farias, S. T., Lau, K., Harvey, D., Denny, K. G., Barba, C., & Mefford, A. N. (2017). Early functional limitations in cognitively normal older adults predict diagnostic conversion to mild cognitive impairment. Journal of the American Geriatrics Society, 65(6), 1152–1158. https://doi.org/10.1111/jgs.14835
   PubMed Web of Science ®Google Scholar
• Farias, S. T., Mungas, D., Harvey, D. J., Simmons, A., Reed, B. R., & Decarli, C. (2011). The measurement of everyday cognition: Development and validation of a short form of the Everyday Cognition scales. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 7(6), 593–601. https://doi.org/10.1016/j.jalz.2011.02.007
   PubMed Web of Science ®Google Scholar
• Farias, S. T., Mungas, D., Reed, B. R., Cahn-Weiner, D., Jagust, W., Baynes, K., & DeCarli, C. (2008). Farias The measurement of everyday cognition (ECog): Scale development and psychometric properties. Neuropsychology, 22(4), 531–544. https://doi.org/10.1037/0894-4105.22.4.531
   PubMed Web of Science ®Google Scholar
• Farias, S. T., Park, L. Q., Harvey, D. J., Simon, C., Reed, B. R., Carmichael, O., & Mungas, D. (2013b). Everyday cognition in older adults: Associations with neuropsychological performance and structural brain imaging. Journal of the International Neuropsychological Society: JINS, 19(4), 430–441. https://doi.org/10.1017/S1355617712001609
   PubMed Web of Science ®Google Scholar
• Farias, S. T., Weakley, A., Harvey, D., Chandler, J., Huss, O., & Mungas, D. (2021). The measurement of Everyday Cognition (ECog): Revisions and updates. Alzheimer Disease and Associated Disorders, 35(3), 258–264. https://doi.org/10.1097/WAD.0000000000000450
   PubMed Web of Science ®Google Scholar
• Filshtein, T., Chan, M., Mungas, D., Whitmer, R., Fletcher, E., DeCarli, C., & Farias, S. (2020). Differential item functioning of the everyday cognition (ECoG) scales in relation to racial/ethnic groups. Journal of the International Neuropsychological Society: JINS, 26(5), 515–526. https://doi.org/10.1017/S1355617719001437
   PubMed Web of Science ®Google Scholar
• Galvin, J. E., Roe, C. M., Xiong, C., & Morris, J. C. (2006). Validity and reliability of the AD8 informant interview in dementia. Neurology, 67(11), 1942–1948. https://doi.org/10.1212/01.wnl.0000247042.15547.eb
   PubMed Web of Science ®Google Scholar
• Howell, T., Neuhaus, J., Glymour, M. M., Weiner, M. W., & Nosheny, R. L. (2022a). Validity of online versus in-clinic self-reported everyday cognition scale. Journal of the Prevention of Alzheimer’s Disease, 9(2), 269–276. https://doi.org/10.14283/jpad.2022.20
   PubMed Web of Science ®Google Scholar
• Howell, T., Neuhaus, J., Glymour, M. M., Weiner, M. W., & Nosheny, R. L. (2022b). Validity of online versus in-clinic self-reported everyday cognition scale. The Journal of Prevention of Alzheimer’s Disease, 9, 269–276. https://doi.org/10.14283/jpad.2022.20
   PubMed Web of Science ®Google Scholar
• Hsu, J., Hsu, W., Chang, C., Lin, K., Hsiao, I., Fan, C., & Bai, C. (2017). Everday cognition scales are related to cognitive function in the early stage of probable Alzheimer’s disease and FDG-PET findings. Scientific Reports, 7(1), 1719–1727. https://doi.org/10.1038/s41598-017-01193-6
   PubMedGoogle Scholar
• Ibnidris, A., Robinson, J. N., Stubbs, M., Piumatti, G., Govia, I., & Albanese, E. (2022). Evaluating measurement properties of subjective cognitive decline self-reported outcome measures: A systematic review. Systematic Reviews, 11(1), 144–160. https://doi.org/10.1186/s13643-022-02018-y
   PubMed Web of Science ®Google Scholar
• Ismail, Z., Gatchel, J., Bateman, D. R., Barcelos-Ferreira, R., Cantillon, M., Jaeger, J., Donovan, N. J., & Mortby, M. E. (2018). Affective and emotional dysregulation as pre-dementia risk markers: Exploring the mild behavioral impairment symptoms of depression, anxiety, irritability, and euphoria. International Psychogeriatrics, 30(2), 185–196. https://doi.org/10.1017/S1041610217001880
   PubMed Web of Science ®Google Scholar
• Jack, C. R., Jr., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., Sperling, R., Elliott, C., … Silverberg, N. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018
   PubMed Web of Science ®Google Scholar
• Jessen, F., Amariglio, R. E., van Boxtel, M., Breteler, M., Ceccaldi, M., Chételat, G., Dubois, B., Dufouil, C., Ellis, K. A., van der Flier, W. M., Glodzik, L., van Harten, A. C., de Leon, M. J., McHugh, P., Mielke, M. M., Molinuevo, J. L., Mosconi, L., Osorio, R. S., Perrotin, A., … Wagner, M. (2014). A conceptual framework for research on subjective cognitive decline in pre-clinical Alzheimer’s disease. Alzheimer’s & Dementia, 10(6), 844–852. https://doi.org/10.1016/j.jalz.2014.01.001
   PubMed Web of Science ®Google Scholar
• Kiselica, A. M. (2021). Empirically defining the preclinical stages of the Alzheimer’s continuum in the Alzheimer’s Disease Neuroimaging Initiative. Psychogeriatrics: The Official Journal of the Japanese Psychogeriatric Society, 21(4), 491–502. https://doi.org/10.1111/psyg.12697
   PubMed Web of Science ®Google Scholar
• Kiselica, A. M., Webber, T. A., & Benge, J. F. (2020). The uniform dataset 3.0 neuropsychological battery: Factor structure, invariance testing, and demographically adjusted factor score calculation. Journal of the International Neuropsychological Society: JINS, 26(6), 576–586. https://doi.org/10.1017/S135561772000003X
   PubMed Web of Science ®Google Scholar
• Koppara, A., Wagner, M., Lange, C., Ernst, A., Wiese, B., König, H.-H., Brettschneider, C., Riedel-Heller, S., Luppa, M., Weyerer, S., Werle, J., Bickel, H., Mösch, E., Pentzek, M., Fuchs, A., Wolfsgruber, S., Beauducel, A., Scherer, M., Maier, W., & Jessen, F. (2015). Cognitive performance before and after the onset of subjective cognitive decline in old age. Alzheimer’s & Dementia (Amsterdam, Netherlands), 1(2), 194–205. https://doi.org/10.1016/j.dadm.2015.02.005
   PubMedGoogle Scholar
• Krippendorff, K. (2004). Measuring the reliability of qualitative text analysis data. Quality & Quantity, 38(6), 787–800. https://doi.org/10.1007/s11135-004-8107-7
   Web of Science ®Google Scholar
• Lopez, C., Tariot, P. N., Caputo, A., Langbaum, J. B., Liu, F., Riviere, M. E., Langlois, C., Rouzade-Dominguez, M. L., Zalesak, M., Hendrix, S., Thomas, R. G., Viglietta, V., Lenz, R., Ryan, J. M., Graf, A., & Reiman, E. M. (2019). The Alzheimer’s Prevention Initiative Generation Program: Study design of two randomized controlled trials for individuals at risk for clinical onset of Alzheimer’s disease. Alzheimer’s & Dementia (New York, N. Y.), 5(1), 216–227. https://doi.org/10.1016/j.trci.2019.02.005
   PubMedGoogle Scholar
• McKeith, I. G., Boeve, B. F., Dickson, D. W., Halliday, G., Taylor, J.-P., Weintraub, D., Aarsland, D., Galvin, J., Attems, J., Ballard, C. G., Bayston, A., Beach, T. G., Blanc, F., Bohnen, N., Bonanni, L., Bras, J., Brundin, P., Burn, D., Chen-Plotkin, A., … Kosaka, K. (2017). Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology, 89(1), 88–100. https://doi.org/10.1212/WNL.0000000000004058
   PubMed Web of Science ®Google Scholar
• Mitchell, U. A., Chebli, P. G., Ruggiero, L., & Muramatsu, N. (2019). The digital divide in health-related technology use: The significance of race/ethnicity. The Gerontologist, 59(1), 6–14. https://doi.org/10.1093/geront/gny138
   PubMed Web of Science ®Google Scholar
• Morrison, C., & Oliver, M. D. (2023). Subjective cognitive decline is associated with lower baseline cognition and increased rate of cognitive decline. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 78(4), 573–584. https://doi.org/10.1093/geronb/gbac178
   PubMed Web of Science ®Google Scholar
• Nicosia, J., Aschenbrenner, A. J., Balota, D. A., Sliwinski, M. J., Tahan, M., Adams, S., Stout, S. S., Wilks, H., Gordon, B. A., Benzinger, T. L. S., Fagan, A. M., Xiong, C., Bateman, R. J., Morris, J. C., & Hassenstab, J. (2023). Unsupervised high-frequency smartphone-based cognitive assessments are reliable, valid, and feasible in older adults at risk for Alzheimer’s disease. Journal of the International Neuropsychological Society: JINS, 29(5), 459–471. https://doi.org/10.1017/S135561772200042X
   PubMed Web of Science ®Google Scholar
• Ohman, F., Hassenstab, J., Berron, D., Scholl, M., & Papp, K. V. (2021). Current advances in digital cognitive assessment for preclinical Alzheimer’s disease. Alzheimer’s & Dementia, 13(1), 1–19. https://doi.org/10.1002/dad2.12217
   Web of Science ®Google Scholar
• Parfenov, V. A., Zakharov, V. V., Kabaeva, A. R., & Vakhnina, N. V. (2020). Subjective cognitive decline as a predictor of future cognitive decline: A systematic review. Dementia & Neuropsychologia, 14(3), 248–257. https://doi.org/10.1590/1980-57642020dn14-030007
   PubMedGoogle Scholar
• Pavisic, I. M., Lu, K., Keuss, S. E., James, S.-N., Lane, C. A., Parker, T. D., Keshavan, A., Buchanan, S. M., Murray-Smith, H., Cash, D. M., Coath, W., Wong, A., Fox, N. C., Crutch, S. J., Richards, M., & Schott, J. M. (2021). Subjective cognitive complaints at age 70: Associations with amyloid and mental health. Journal of Neurology, Neurosurgery, and Psychiatry, 92(11), 1215–1221. https://doi.org/10.1136/jnnp-2020-325620
   PubMed Web of Science ®Google Scholar
• Pew Research Center. (2021). Mobile Technology and Home Broadband 2021. Retrieved from https://www.pewresearch.org/internet/2021/06/03/mobile-technology-and-home-broadband-2021/
   Google Scholar
• R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
   Google Scholar
• Rabin, L. A., Smart, C. M., Crane, P. K., Amariglio, R. E., Berman, L. M., Boada, M., Buckley, R. F., Chételat, G., Dubois, B., Ellis, K. A., Gifford, K. A., Jefferson, A. L., Jessen, F., Katz, M. J., Lipton, R. B., Luck, T., Maruff, P., Mielke, M. M., Molinuevo, J. L., … Sikkes, S. A. M. (2015). Subjective cognitive decline in older adults: An overview of self-report measures used across 19 international research studies. Journal of Alzheimer’s Disease: JAD, 48(Suppl 1), S63–S86. https://doi.org/10.3233/JAD-150154
   PubMedGoogle Scholar
• Rami, L., Mollica, M., García-Sanchez, C., Saldaña, J., Sanchez, B., Sala, I., Valls-Pedret, C., Castellví, M., Olives, J., & Molinuevo, J. L. (2014). The Subjective Cognitive Decline Questionnaire (SCD-Q): A validation study. Journal of Alzheimer’s Disease: JAD, 41(2), 453–466. https://doi.org/10.3233/JAD-132027
   PubMed Web of Science ®Google Scholar
• Revelle, W., & Wilt, J. (2019). Analyzing dynamic data: a tutorial. Personality and Individual Differences, 136(1), 38–51. https://doi.org/10.1016/j.paid.2017.08.020
   Google Scholar
• Revelle, W. (2023). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.3.6, https://CRAN.R-project.org/package=psych.
   Google Scholar
• Roehr, S., Luck, T., Pabst, A., Bickel, H., König, H.-H., Lühmann, D., Fuchs, A., Wolfsgruber, S., Wiese, B., Weyerer, S., Mösch, E., Brettschneider, C., Mallon, T., Pentzek, M., Wagner, M., Mamone, S., Werle, J., Scherer, M., Maier, W., Jessen, F., & Riedel-Heller, S. G. (2017). Subjective cognitive decline is longitudinally associated with lower health-related quality of life. International Psychogeriatrics, 29(12), 1939–1950. https://doi.org/10.1017/S1041610217001399
   PubMed Web of Science ®Google Scholar
• Sheikh, J. I., Hill, R. D., & Yesavage, J. A. (1986). Long‐term efficacy of cognitive training for age‐associated memory impairment: A six‐month follow‐up study. Developmental Neuropsychology, 2(4), 413–421. https://doi.org/10.1080/87565648609540358
   Web of Science ®Google Scholar
• Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4(1), 1–32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415
   PubMed Web of Science ®Google Scholar
• Shrout, P. E., & Lane, S. P. (2012). Psychometrics. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life. The Guilford Press.
   Google Scholar
• Singh, S., Strong, R., Xu, I., Fonseca, L. M., Hawks, Z., Grinspoon, E., Jung, L., Li, F., Weinstock, R. S., Sliwinski, M. J., Chaytor, N. S., & Germine, L. T. (2023). Ecological momentary assessment of cognition in clinical and community samples: Reliability and validity study. Journal of Medical Internet Research, 25, e45028. https://doi.org/10.2196/45028
   PubMed Web of Science ®Google Scholar
• Sliwinski, M. J., Mogle, J. A., Hyun, J., Munoz, E., Smyth, J. M., & Lipton, R. B. (2018). Reliability and validity of ambulatory cognitive assessments. Assessment, 25(1), 14–30. https://doi.org/10.1177/1073191116643164
   PubMed Web of Science ®Google Scholar
• Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https://doi.org/10.1001/archinte.166.10.1092
   PubMed Web of Science ®Google Scholar
• Stawski, R. S., MacDonald, S. W., Brewster, P. W., Munoz, E., Cerino, E. S., & Halliday, D. W. (2019). A comprehensive comparison of quantifications of intraindividual variability in response times: A measurement burst approach. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 74(3), 397–408. https://doi.org/10.1093/geronb/gbx115
   PubMed Web of Science ®Google Scholar
• Vogel, J. W., Varga Doležalová, M., La Joie, R., Marks, S. M., Schwimmer, H. D., Landau, S. M., & Jagust, W. J. (2017). Subjective cognitive decline and β-amyloid burden predict cognitive change in healthy elderly. Neurology, 89(19), 2002–2009. https://doi.org/10.1212/WNL.0000000000004627
   PubMed Web of Science ®Google Scholar
• Weintraub, S., Besser, L., Dodge, H. H., Teylan, M., Ferris, S., Goldstein, F. C., Giordani, B., Kramer, J., Loewenstein, D., Marson, D., Mungas, D., Salmon, D., Welsh-Bohmer, K., Zhou, X.-H., Shirk, S. D., Atri, A., Kukull, W. A., Phelps, C., & Morris, J. C. (2018). Version 3 of the Alzheimer Disease Centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Disease and Associated Disorders, 32(1), 10–17. https://doi.org/10.1097/WAD.0000000000000223
   PubMed Web of Science ®Google Scholar
• Whitehead, B. P., Dixon, R. A., Hultsch, D. F., & MacDonald, S. W. (2011). Are neurocognitive speed and inconsistency similarly affected in type 2 diabetes? Journal of Clinical and Experimental Neuropsychology, 33(6), 647–657. https://doi.org/10.1080/13803395.2010.547845
   PubMed Web of Science ®Google Scholar
• Xue, B., Cadar, D., Fleischmann, M., Stansfeld, S., Carr, E., Kivimäki, M., McMunn, A., & Head, J. (2018). Effect of retirement on cognitive function: The Whitehall II cohort study. European Journal of Epidemiology, 33(10), 989–1001. https://doi.org/10.1007/s10654-017-0347-7
   PubMed Web of Science ®Google Scholar