Measuring within-day cognitive performance using the experience sampling method: A pilot study in a healthy population
Autoři:
Simone J. W. Verhagen aff001; Naomi E. M. Daniëls aff001; Sara Laureen Bartels aff001; Sulina Tans aff001; Karel W. H. Borkelmans aff001; Marjolein E. de Vugt aff001; Philippe A. E. G. Delespaul aff001
Působiště autorů:
Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
aff001; Department of Family Medicine, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
aff002; Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
aff003; Mondriaan Mental Health Trust, Department of Adult Psychiatry, Heerlen, the Netherlands
aff004
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226409
Souhrn
Introduction
People with depression, anxiety, or psychosis often complain of confusion, problems concentrating or difficulties cognitively appraising contextual cues. The same applies to people with neurodegenerative diseases or brain damage such as dementia or stroke. Assessments of those cognitive difficulties often occurs in cross-sectional and controlled clinical settings. Information on daily moment-to-moment cognitive fluctuations and its relation to affect and context is lacking. The development and evaluation of a digital cognition task is presented. It enables the fine-grained mapping of cognition and its relation to mood, intrapersonal factors and context.
Methods
The momentary Digit Symbol Substitution Task is a modified digital version of the original paper-and-pencil task, with a duration of 30 seconds and implemented in an experience sampling protocol (8 semi-random assessments a day on 6 consecutive days). It was tested in the healthy population (N = 40). Descriptive statistics and multilevel regression analyses were used to determine initial feasibility and assess cognitive patterns in everyday life. Cognition outcome measures were the number of trials within the 30-second sessions and the percentage of correct trials.
Results
Subjects reported the task to be easy, pleasant and do-able. On average, participants completed 11 trials with 97% accuracy per 30-second session. Cognitive variation was related to mood, with an interaction between positive and negative affect for accuracy (% correct) (p = .001) and an association between positive affect and speed (number of trials) (p = .01). Specifically, cheerful, irritated and anxious seem to covary with cognition. Distraction and location are relevant contextual factors. The number of trials showed a learning effect (p < .001) and was sensitive to age (p < .001).
Conclusion
Implementing a digital cognition task within an experience-sampling paradigm shows promise. Fine-tuning in further research and in clinical samples is needed. Gaining insight into cognitive functioning could help patients navigate and adjust the demands of daily life.
Klíčová slova:
Cognition – Cognitive impairment – Cognitive psychology – Learning – Neuropsychological testing – Psychometrics – Regression analysis – Sleep
Zdroje
1. Planton M, Peiffer S, Albucher J, Barbeau E, Tardy J, Pastor J, et al. Neuropsychological outcome after a first symptomatic ischaemic stroke with ‘good recovery’. European Journal of Neurology. 2012;19(2):212–9. doi: 10.1111/j.1468-1331.2011.03450.x 21631652
2. Wright SL, Persad C. Distinguishing between depression and dementia in older persons: neuropsychological and neuropathological correlates. Journal of Geriatric Psychiatry and Neurology. 2007;20(4):189–98. doi: 10.1177/0891988707308801 18004006
3. Bouchard RW. Diagnostic criteria of dementia. Canadian journal of neurological sciences. 2007;34(S1):S11–SS8.
4. Castaneda AE, Tuulio-Henriksson A, Marttunen M, Suvisaari J, Lönnqvist J. A review on cognitive impairments in depressive and anxiety disorders with a focus on young adults. Journal of affective disorders. 2008;106(1–2):1–27. doi: 10.1016/j.jad.2007.06.006 17707915
5. Reichenberg A, Harvey PD, Bowie CR, Mojtabai R, Rabinowitz J, Heaton RK, et al. Neuropsychological function and dysfunction in schizophrenia and psychotic affective disorders. Schizophrenia bulletin. 2008;35(5):1022–9. doi: 10.1093/schbul/sbn044 18495643
6. Heaton RK, Pendleton MG. Use of Neuropsychological tests to predict adult patients' everyday functioning. Journal of consulting and clinical psychology. 1981;49(6):807. doi: 10.1037//0022-006x.49.6.807 7309951
7. Chaytor N, Schmitter-Edgecombe M. The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychology review. 2003;13(4):181–97. doi: 10.1023/b:nerv.0000009483.91468.fb 15000225
8. Casaletto KB, Heaton RK. Neuropsychological assessment: Past and future. Journal of the International Neuropsychological Society. 2017;23(9–10):778–90. doi: 10.1017/S1355617717001060 29198281
9. Sweeney JA, Kmiec JA, Kupfer DJ. Neuropsychologic impairments in bipolar and unipolar mood disorders on the CANTAB neurocognitive battery. Biological psychiatry. 2000;48(7):674–84. doi: 10.1016/s0006-3223(00)00910-0 11032979
10. Mitchell RL, Phillips LH. The psychological, neurochemical and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions. Neuropsychologia. 2007;45(4):617–29. doi: 10.1016/j.neuropsychologia.2006.06.030 16962146
11. Gray JR. Emotional modulation of cognitive control: Approach–withdrawal states double-dissociate spatial from verbal two-back task performance. Journal of Experimental Psychology: General. 2001;130(3):436.
12. Myin-Germeys I, Oorschot M, Collip D, Lataster J, Delespaul P, Van Os J. Experience sampling research in psychopathology: opening the black box of daily life. Psychological medicine. 2009;39(9):1533–47. doi: 10.1017/S0033291708004947 19215626
13. Dijk DJ, Duffy JF, Czeisler CA. Circadian and sleep/wake dependent aspects of subjective alertness and cognitive performance. Journal of sleep research. 1992;1(2):112–7. doi: 10.1111/j.1365-2869.1992.tb00021.x 10607036
14. Gómez-Pinilla F. Brain foods: the effects of nutrients on brain function. Nature reviews neuroscience. 2008;9(7):568. doi: 10.1038/nrn2421 18568016
15. van Os J, Verhagen S, Marsman A, Peeters F, Bak M, Marcelis M, et al. The experience sampling method as an mHealth tool to support self‐monitoring, self‐insight, and personalized health care in clinical practice. Depression and anxiety. 2017;34(6):481–93. doi: 10.1002/da.22647 28544391
16. Csikszentmihalyi M, Larson R. Validity and reliability of the experience-sampling method. Flow and the foundations of positive psychology: Springer; 2014. p. 35–54.
17. Verhagen SJ, Hasmi L, Drukker M, van Os J, Delespaul PA. Use of the experience sampling method in the context of clinical trials. Evidence-based mental health. 2016;19(3):86–9. doi: 10.1136/ebmental-2016-102418 27443678
18. Scollon CN, Prieto C-K, Diener E. Experience sampling: promises and pitfalls, strength and weaknesses. Assessing well-being: Springer; 2009. p. 157–80.
19. Moore RC, Swendsen J, Depp CA. Applications for self‐administered mobile cognitive assessments in clinical research: A systematic review. International journal of methods in psychiatric research. 2017;26(4):e1562.
20. Hung S, Li M-S, Chen Y-L, Chiang J-H, Chen Y-Y, Hung GC-L. Smartphone-based ecological momentary assessment for Chinese patients with depression: An exploratory study in Taiwan. Asian journal of psychiatry. 2016;23:131–6. doi: 10.1016/j.ajp.2016.08.003 27969071
21. Sliwinski MJ, Mogle JA, Hyun J, Munoz E, Smyth JM, Lipton RB. Reliability and validity of ambulatory cognitive assessments. Assessment. 2018;25(1):14–30. doi: 10.1177/1073191116643164 27084835
22. Suffoletto B, Goyal A, Puyana JC, Chung T. Can an app help identify psychomotor function impairments during drinking occasions in the real world? A mixed-method pilot study. Substance abuse. 2017;38(4):438–49. doi: 10.1080/08897077.2017.1356797 28723276
23. Bouvard A, Dupuy M, Schweitzer P, Revranche M, Fatseas M, Serre F, et al. Feasibility and validity of mobile cognitive testing in patients with substance use disorders and healthy controls. The American journal on addictions. 2018;27(7):553–6. doi: 10.1111/ajad.12804 30260085
24. Goodman LA. Snowball sampling. The annals of mathematical statistics. 1961:148–70.
25. Johanson GA, Brooks GP. Initial scale development: sample size for pilot studies. Educational and Psychological Measurement. 2010;70(3):394–400.
26. Verhagen SJ, Berben JA, Leue C, Marsman A, Delespaul PA, van Os J, et al. Demonstrating the reliability of transdiagnostic mHealth Routine Outcome Monitoring in mental health services using experience sampling technology. PloS one. 2017;12(10):e0186294. doi: 10.1371/journal.pone.0186294 29023568
27. van Knippenberg R, De Vugt M, Ponds R, Myin‐Germeys I, van Twillert B, Verhey F. Dealing with daily challenges in dementia (deal‐id study): an experience sampling study to assess caregiver functioning in the flow of daily life. International journal of geriatric psychiatry. 2017;32(9):949–58. doi: 10.1002/gps.4552 27464472
28. Delespaul PAEG. Assessing schizophrenia in daily life: The experience sampling method. Maastricht: Maastricht University; 1995.
29. Wechsler D. Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV): San Antonio, TX: The Psychological Corporation; 2008.
30. StataCorp L. Stata multilevel mixed-effects reference manual. College Station, TX: StataCorp LP. 2013.
31. Schuster RM, Mermelstein RJ, Hedeker D. Acceptability and feasibility of a visual working memory task in an ecological momentary assessment paradigm. Psychological assessment. 2015;27(4):1463. doi: 10.1037/pas0000138 25894710
32. Waters AJ, Li Y. Evaluating the utility of administering a reaction time task in an ecological momentary assessment study. Psychopharmacology. 2008;197(1):25–35. doi: 10.1007/s00213-007-1006-6 18064440
33. Jaeger J. Digit Symbol Substitution Test: The Case for Sensitivity Over Specificity in Neuropsychological Testing. Journal of clinical psychopharmacology. 2018;38(5):513–9. doi: 10.1097/JCP.0000000000000941 30124583
34. Simons CJ, Bartels-Velthuis AA, Pijnenborg GH, Investigators OoP. Cognitive Performance and Long-Term Social Functioning in Psychotic Disorder: A Three-Year Follow-Up Study. PloS one. 2016;11(4):e0151299. doi: 10.1371/journal.pone.0151299 27082629
35. McDermott LM, Ebmeier KP. A meta-analysis of depression severity and cognitive function. Journal of affective disorders. 2009;119(1–3):1–8. doi: 10.1016/j.jad.2009.04.022 19428120
36. Van Vugt MK, van der Velde M, Investigators EM. How does rumination impact cognition? A first mechanistic model. Topics in cognitive science. 2018;10(1):175–91. doi: 10.1111/tops.12318 29383884
37. Hoyer WJ, Stawski RS, Wasylyshyn C, Verhaeghen P. Adult age and digit symbol substitution performance: a meta-analysis. Psychology and aging. 2004;19(1):211. doi: 10.1037/0882-7974.19.1.211 15065945
38. Majeres RL. Sex differences in symbol-digit substitution and speeded matching. Intelligence. 1983;7(4):313–27.
39. Van der Elst W, van Boxtel MP, van Breukelen GJ, Jolles J. The Letter Digit Substitution Test: normative data for 1,858 healthy participants aged 24–81 from the Maastricht Aging Study (MAAS): influence of age, education, and sex. Journal of clinical and experimental neuropsychology. 2006;28(6):998–1009. doi: 10.1080/13803390591004428 16822738
40. von Stumm S. Feeling low, thinking slow? Associations between situational cues, mood and cognitive function. Cognition and Emotion. 2018;32(8):1545–58. doi: 10.1080/02699931.2017.1420632 29388502
41. Van der Linden D, Eling P. Mental fatigue disturbs local processing more than global processing. Psychological research. 2006;70(5):395–402. doi: 10.1007/s00426-005-0228-7 15968553
42. Van der Linden D, Frese M, Meijman TF. Mental fatigue and the control of cognitive processes: effects on perseveration and planning. Acta Psychologica. 2003;113(1):45–65. doi: 10.1016/s0001-6918(02)00150-6 12679043
43. Lorist MM, Klein M, Nieuwenhuis S, De Jong R, Mulder G, Meijman TF. Mental fatigue and task control: planning and preparation. Psychophysiology. 2000;37(5):614–25. 11037038
44. Green MF. Cognitive impairment and functional outcome in schizophrenia and bipolar disorder. The Journal of clinical psychiatry. 2006;67:3–8; discussion 36–42.
45. Rock P, Roiser J, Riedel W, Blackwell A. Cognitive impairment in depression: a systematic review and meta-analysis. Psychological medicine. 2014;44(10):2029–40. doi: 10.1017/S0033291713002535 24168753
46. Robinson LJ, Nicol Ferrier I. Evolution of cognitive impairment in bipolar disorder: a systematic review of cross‐sectional evidence. Bipolar disorders. 2006;8(2):103–16. doi: 10.1111/j.1399-5618.2006.00277.x 16542180
47. Cameron J, Worrall‐Carter L, Page K, Riegel B, Lo SK, Stewart S. Does cognitive impairment predict poor self‐care in patients with heart failure? European Journal of Heart Failure. 2010;12(5):508–15. doi: 10.1093/eurjhf/hfq042 20354031
48. Feil DG, Zhu CW, Sultzer DL. The relationship between cognitive impairment and diabetes self-management in a population-based community sample of older adults with Type 2 diabetes. Journal of behavioral medicine. 2012;35(2):190–9. doi: 10.1007/s10865-011-9344-6 21503710
49. Jones F. Strategies to enhance chronic disease self-management: how can we apply this to stroke? Disability and rehabilitation. 2006;28(13–14):841–7. doi: 10.1080/09638280500534952 16777771
50. Brouillette RM, Foil H, Fontenot S, Correro A, Allen R, Martin CK, et al. Feasibility, reliability, and validity of a smartphone based application for the assessment of cognitive function in the elderly. PloS one. 2013;8(6):e65925. doi: 10.1371/journal.pone.0065925 23776570
51. Schweitzer P, Husky M, Allard M, Amieva H, Pérès K, Foubert‐Samier A, et al. Feasibility and validity of mobile cognitive testing in the investigation of age‐related cognitive decline. International journal of methods in psychiatric research. 2017;26(3):e1521.
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