Ecological momentary assessment (EMA) is a data collection method involving real-time, repeated sampling of behavior in the natural environment. Having gained traction over the years in areas of psychology and public health, EMA’s application in individuals with acquired brain injuries (ABIs) is now beginning to be explored. Our review aims to introduce ABI researchers to EMA and the work conducted to date, specifically in traumatic brain injury and stroke cohorts. First, we outline the essential methodological components used, including sampling schemes, measures, and statistical analyses. Second, we synthesize diverse applications of EMA into five overarching themes, encompassing: (a) contextual-temporal relationships between symptoms, (b) intraindividual variability, (c) individual differences in daily life experiences, (d) comparison with traditional measures, and (e) combined use with wearables. Next, we discuss challenges faced by the field around the feasibility and optimization of EMA protocols, as well as emerging applications in clinical assessment, intervention, cognitive assessment, and brain–behavior investigations. Our review highlights EMA’s potential for ABI, encouraging wider adoption for enhanced patient care and research advancement.
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References
1.
World Health Organization. Neurological Disorders: Public Health Challenges. World Health Organization: Geneva; 2006.
2.
GoldmanL, SiddiquiEM, KhanA, et al.Understanding acquired brain injury: A review. Biomedicines, 2022; 10(9):2167; doi: 10.3390/biomedicines10092167
3.
PonsfordJL, DowningMG, OlverJ, et al.Longitudinal follow-up of patients with traumatic brain injury: Outcome at two, five, and ten years post-injury. J Neurotrauma, 2014; 31(1):64–77; doi: 10.1089/neu.2013.2997
4.
Ebner-PriemerUW, TrullTJ. Ambulatory assessment: An innovative and promising approach for clinical psychology. European Psychologist, 2009; 14(2):109–119; doi: 10.1027/1016-9040.14.2.109
5.
KahnemanD, FredricksonBL, SchreiberCA, et al.When more pain is preferred to less: Adding a better end. Psychol Sci, 1993; 4(6):401–405.
6.
KihlstromJF, EichE, SandbrandD, et al.Emotion and Memory: Implications for Self-Report. In: The Science of Self-Report Psychology Press. Taylor; 1999; pp. 93–112.
7.
Ebner-PriemerUW, TrullTJ. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol Assess, 2009; 21(4):463–475; doi: 10.1037/a0017075
8.
McKeonA, McCueM, SkidmoreE, et al.Ecological momentary assessment for rehabilitation of chronic illness and disability. Disabil Rehabil, 2018; 40(8):974–987; doi: 10.1080/09638288.2017.1280545
9.
MaasA, MenonDK, ManleyGT, et al.; InTBIR Participants and Investigators. Traumatic brain injury: Progress and challenges in prevention, clinical care, and research. Lancet Neurol, 2022; 21(11):1004–1060; doi: 10.1016/S1474-4422(22)00309-X
10.
World Health Organization. International Classification of Functioning, Disability and Health (ICF). World Health Organization: Geneva; 2001.
11.
SallisJF, OwenN, FisherE. Ecological Models of Health Behavior. In: Health Behavior: Theory, Research, and Practice. Wiley; 2015; pp. 43–64.
12.
SchiepekG. Complexity and nonlinear dynamics in psychotherapy. European Review, 2009; 17(2):331–356; doi: 10.1017/S1062798709000763
13.
WichersM, WigmanJTW, Myin-GermeysI. Micro-Level affect dynamics in psychopathology viewed from complex dynamical system theory. Emotion Review, 2015; 7(4):362–367; doi: 10.1177/1754073915590623
Aan Het RotM, HogenelstK, SchoeversRA. Mood disorders in everyday life: A systematic review of experience sampling and ecological momentary assessment studies. Clin Psychol Rev, 2012; 32(6):510–523; doi: 10.1016/j.cpr.2012.05.007
18.
de VriesLP, BaselmansBML, BartelsM. Smartphone-Based ecological momentary assessment of well-being: A systematic review and recommendations for future studies. J Happiness Stud, 2021; 22(5):2361–2408; doi: 10.1007/s10902-020-00324-7
19.
KampshoffCS, Verdonck-de LeeuwIM, van OijenMG, et al.Ecological momentary assessments among patients with cancer: A scoping review. Eur J Cancer Care, 2019; 28(3):e13095; doi: 10.1111/ecc.13095).
20.
MayM, JunghaenelDU, OnoM, et al.Ecological momentary assessment methodology in chronic pain research: A systematic review. J Pain, 2018; 19(7):699–716; doi: 10.1016/j.jpain.2018.01.006
21.
ShiffmanS. Ecological Momentary Assessment (EMA) in studies of substance use. Psychol Assess, 2009; 21(4):486–497; doi: 10.1037/a0017074
22.
ParksAC, DavisJ, SpresserCD, et al.Validity of in-home teleneuropsychological testing in the wake of COVID-19. Arch Clin Neuropsychol, 2021; 36(6):887–896; doi: 10.1093/arclin/acab002
23.
BroenMPG, MarsmanVAM, KuijfML, et al.Unraveling the relationship between motor symptoms, affective states and contextual factors in parkinson’s disease: A feasibility study of the experience sampling method. Branchi I. ed. PLoS ONE, 2016; 11(3):e0151195; doi: 10.1371/journal.pone.0151195
24.
FernieBA, SpadaMM, BrownRG. Motor fluctuations and psychological distress in Parkinson’s disease. Health Psychol, 2019; 38(6):518–526; doi: 10.1037/hea0000736
25.
HabetsJ, HeijmansM, HerffC, et al.Mobile health daily life monitoring for parkinson disease: Development and validation of ecological momentary assessments. JMIR Mhealth Uhealth, 2020; 8(5):e15628; doi: 10.2196/15628
26.
KratzAL, MurphySL, BraleyTJ. Ecological momentary assessment of pain, fatigue, depressive, and cognitive symptoms reveals significant daily variability in multiple sclerosis. Arch Phys Med Rehabil, 2017; 98(11):2142–2150; doi: 10.1016/j.apmr.2017.07.002
RealRGL, DickhausT, LudolphA, et al.Well-being in amyotrophic lateral sclerosis: A pilot experience sampling study. Front Psychol, 2014; 5:704; doi: 10.3389/fpsyg.2014.00704
29.
FringsL, WagnerK, MaiwaldT, et al.Early detection of behavioral side effects of antiepileptic treatment using handheld computers. Epilepsy Behav, 2008; 13(2):402–406; doi: 10.1016/j.yebeh.2008.04.022
30.
HautSR, HallCB, BorkowskiT, et al.Clinical features of the pre-ictal state: Mood changes and premonitory symptoms. Epilepsy Behav, 2012; 23(4):415–421; doi: 10.1016/j.yebeh.2012.02.007
31.
HautSR, HallCB, BorkowskiT, et al.Modeling seizure self-prediction: An e-diary study. Epilepsia, 2013; 54(11):1960–1967; doi: 10.1111/epi.12355
32.
MaiwaldT, BlumbergJ, TimmerJ, et al.Are prodromes preictal events? A prospective PDA-based study. Epilepsy Behav, 2011; 21(2):184–188; doi: 10.1016/j.yebeh.2011.02.004
33.
JohnsonEI, SibonI, RenouP, et al.Feasibility and validity of computerized ambulatory monitoring in stroke patients. Neurology, 2009; 73(19):1579–1583; doi: 10.1212/WNL.0b013e3181c0d466
34.
JuengstSB, GrahamKM, PulantaraIW, et al.Pilot feasibility of an mHealth system for conducting ecological momentary assessment of mood-related symptoms following traumatic brain injury. Brain Inj, 2015; 29(11):1351–1361; doi: 10.3109/02699052.2015.1045031
35.
BuiQ, KaufmanKJ, PhamV, et al.Ecological momentary assessment of real-world functional behaviors in individuals with stroke: A longitudinal observational study. Arch Phys Med Rehabil, 2022a;103(7):1327–1337; doi: 10.1016/j.apmr.2022.02.011
36.
BuiQ, KaufmanKJ, MunsellEG, et al.Smartphone assessment uncovers real-time relationships between depressed mood and daily functional behaviors after stroke. J Telemed Telecare, 2022b;30(5):871–884; doi: 10.1177/1357633X221100061
37.
ChenY-A, DemersM, LewthwaiteR, et al.A novel combination of accelerometry and ecological momentary assessment for post-stroke paretic arm/hand use: Feasibility and validity. Jcm, 2021; 10(6):1328; doi: 10.3390/jcm10061328
38.
ChenY-A, LewthwaiteR, SchweighoferN, et al.Essential role of social context and self-efficacy in daily paretic arm/hand use after stroke: An ecological momentary assessment study with accelerometry. Arch Phys Med Rehabil, 2023; 104(3):390–402; doi: 10.1016/j.apmr.2022.09.003
39.
ForsterSD, GauggelS, PetershoferA, et al.Ecological momentary assessment in patients with an acquired brain injury: A pilot study on compliance and fluctuations. Front Neurol, 2020; 11:115; doi: 10.3389/fneur.2020.00115
40.
ForsterSD, GauggelS, LoevenichR, et al.A microanalysis of mood and self-reported functionality in stroke patients using ecological momentary assessment. Front Neurol, 2022; 13:854777; doi: 10.3389/fneur.2022.854777
41.
JeanFAM, SwendsenJD, SibonI, et al.Daily life behaviors and depression risk following stroke: A preliminary study using ecological momentary assessment. J Geriatr Psychiatry Neurol, 2013; 26(3):138–143; doi: 10.1177/0891988713484193
42.
Lassalle-LagadecS, AllardM, DilharreguyB, et al.Linking MRI to daily life experience: The example of poststroke depression. Neurology, 2012; 78(5):322–325; doi: 10.1212/WNL.0b013e31824528b2
43.
Lassalle-LagadecS, CathelineG, MayoW, et al.Cerebellum involvement in post-stroke mood: A combined ecological and MRI study. Psychiatry Res, 2013; 212(2):158–160; doi: 10.1016/j.pscychresns.2013.01.003
44.
LauSCL, ConnorLT, KingAA, et al.Multimodal ambulatory monitoring of daily activity and health-related symptoms in community-dwelling survivors of stroke: Feasibility, acceptability, and validity. Arch Phys Med Rehabil, 2022; 103(10):1992–2000; doi: 10.1016/j.apmr.2022.06.002
45.
LauSCL, ConnorLT, SkidmoreER, et al.The moderating role of motivation in the real-time associations of fatigue, cognitive complaints, and pain with depressed mood among stroke survivors: An ecological momentary assessment study. Arch Phys Med Rehabil, 2023a;104(5):761–768; doi: 10.1016/j.apmr.2022.11.012
46.
LauSCL, Tabor ConnorL, BaumCM. Motivation, physical activity, and affect in community-dwelling stroke survivors: An ambulatory assessment approach. Ann Behav Med, 2023b;57(4):334–343; doi: 10.1093/abm/kaac065
47.
LauSCL, ConnorLT, BaumCM. Associations between basic psychological need satisfaction and motivation underpinning daily activity participation among community-dwelling survivors of stroke: An ecological momentary assessment study. Arch Phys Med Rehabil, 2023c;104(2):229–236; doi: 10.1016/j.apmr.2022.07.011
48.
Lazeron-SavuE, LenaertB, PondsR, et al.The association of personality traits with poststroke fatigue in daily life: An exploratory experience sampling method and cross-sectional study. Neuropsychol Rehabil, 2023; 33(6):1074–1089; doi: 10.1080/09602011.2022.2059524
49.
LenaertB, ColombiM, van HeugtenC, et al.Exploring the feasibility and usability of the experience sampling method to examine the daily lives of patients with acquired brain injury. Neuropsychol Rehabil, 2019; 29(5):754–766; doi: 10.1080/09602011.2017.1330214
50.
LenaertB, NeijmeijerM, van KampenN, et al.Poststroke fatigue and daily activity patterns during outpatient rehabilitation: An experience sampling method study. Arch Phys Med Rehabil, 2020; 101(6):1001–1008; doi: 10.1016/j.apmr.2019.12.014
51.
LenaertB, van KampenN, van HeugtenC, et al.Real-time measurement of post-stroke fatigue in daily life and its relationship with the retrospective Fatigue Severity Scale. Neuropsychol Rehabil, 2022; 32(6):992–1006; doi: 10.1080/09602011.2020.1854791
52.
MazureCM, WeinbergerAH, PittmanB, et al.Gender and stress in predicting depressive symptoms following stroke. Cerebrovasc Dis, 2014; 38(4):240–246; doi: 10.1159/000365838
53.
NeffAJ, LeeY, MettsCL, et al.Ecological momentary assessment of social interactions: Associations with depression, anxiety, pain, and fatigue in individuals with mild stroke. Arch Phys Med Rehabil, 2021; 102(3):395–405; doi: 10.1016/j.apmr.2020.08.007
54.
SibonI, Lassalle-LagadecS, RenouP, et al.Evolution of depression symptoms following stroke: A prospective study using computerized ambulatory monitoring. Cerebrovasc Dis, 2012; 33(3):280–285; doi: 10.1159/000334663
55.
VillainM, SibonI, RenouP, et al.Very early social support following mild stroke is associated with emotional and behavioral outcomes three months later. Clin Rehabil, 2017; 31(1):135–141; doi: 10.1177/0269215515623600
56.
VillainM, SibonI, RenouP, et al.Depression and routinization following stroke. Rev Neurol (Paris), 2021; 177(8):964–968; doi: 10.1016/j.neurol.2020.08.012).
57.
AnthonyCA, PetersonAR. Utilization of a Text-messaging robot to assess intraday variation in concussion symptom severity scores. Clin J Sport Med, 2015; 25(2):149–152; doi: 10.1097/JSM.0000000000000115
58.
BetthauserLM, ForsterJE, BortzA, et al.Strength and awareness in action: Feasibility of a yoga-based intervention for post-acute mild TBI headaches among veterans. Contemp Clin Trials Commun, 2021; 22:100762; doi: 10.1016/j.conctc.2021.100762
59.
CorwinDJ, OrchinikJ, D’AlonzoB, et al.A randomized trial of incentivization to maximize retention for real-time symptom and activity monitoring using ecological momentary assessment in pediatric concussion. Pediatr Emer Care, 2023; 39(7):488–494.
60.
JuengstSB, TerhorstL, KewCL, et al.Variability in daily self-reported emotional symptoms and fatigue measured over eight weeks in community dwelling individuals with traumatic brain injury. Brain Inj, 2019; 33(5):567–573; doi: 10.1080/02699052.2019.1584333
61.
JuengstSB, WrightB, SanderAM, et al.The behavioral assessment screening tool for mobile health (BASTmHealth): Development and compliance in 2 weeks of daily reporting in chronic traumatic brain injury. Arch Phys Med Rehabil, 2023; 104(2):203–210; doi: 10.1016/j.apmr.2022.07.016
62.
LewandowskiL, RiegerB, SmythJ, et al.Measuring post-concussion symptoms in adolescents: Feasibility of ecological momentary assessment. Arch Clin Neuropsychol, 2009; 24(8):791–796; doi: 10.1093/arclin/acp087
63.
LiuK, MadrigalE, ChungJS, et al.Headache diary mobile application for monitoring and characterizing posttraumatic headaches. J Head Trauma Rehabil, 2022; 37(3):171–177; doi: 10.1097/HTR.0000000000000771
64.
NabasnyA, RabinowitzA, WrightB, et al.Neurobehavioral symptoms and heart rate variability: Feasibility of remote collection using mobile health technology. J Head Trauma Rehabil, 2022; 37(3):178–188; doi: 10.1097/HTR.0000000000000764
65.
PacellaM, PrabhuA, MorleyJ, et al.Postconcussive symptoms over the first 14 days after mild traumatic brain injury: An experience sampling study. The Journal of Head Trauma Rehabilitation, 2018; 33(3):E31–E39; doi: 10.1097/HTR.0000000000000335
66.
RabinowitzAR, FisherAJ. Person-Specific methods for characterizing the course and temporal dynamics of concussion symptomatology: A pilot study. Sci Rep, 2020; 10(1):1248; doi: 10.1038/s41598-019-57220-1
67.
RabinowitzA, HartT, WilsonJ. Ecological momentary assessment of affect in context after traumatic brain injury. Rehabil Psychol, 2021; 66(4):442–449; doi: 10.1037/rep0000403
68.
SmithB, HarmsWD, BurresS, et al.Enhancing behavioral health treatment and crisis management through mobile ecological momentary assessment and SMS messaging. Health Informatics J, 2012; 18(4):294–308; doi: 10.1177/1460458212445349
69.
SuffolettoB, WagnerAK, ArenthPM, et al.Mobile phone text messaging to assess symptoms after mild traumatic brain injury and provide self-care support: A pilot study. J Head Trauma Rehabil, 2013; 28(4):302–312; doi: 10.1097/HTR.0b013e3182847468
70.
SufrinkoAM, HowieEK, CharekDB, et al.Mobile ecological momentary assessment of postconcussion symptoms and recovery outcomes. J Head Trauma Rehabil, 2019; 34(6):E40–E48; doi: 10.1097/HTR.0000000000000474
71.
TrbovichAM, HowieEK, ElbinRJ, et al.The relationship between accelerometer-measured sleep and next day ecological momentary assessment symptom report during sport-related concussion recovery. Sleep Health, 2021; 7(4):519–525; doi: 10.1016/j.sleh.2021.03.006
72.
WiebeDJ, NanceML, HouseknechtE, et al.Ecologic momentary assessment to accomplish real-time capture of symptom progression and the physical and cognitive activities of patients daily following concussion. JAMA Pediatr, 2016; 170(11):1108–1110; doi: 10.1001/jamapediatrics.2016.1979
73.
WiebeDJ, StoreyEP, OrchinikJE, et al.Measuring recovery with ecological momentary assessment in a randomized trial of exercise after sport-related concussion. Clinical Journal of Sport Medicine, 2022; 32(4):345–353; doi: 10.1097/JSM.0000000000000946
74.
Worthen-ChaudhariL, McGonigalJ, LoganK, et al.Reducing concussion symptoms among teenage youth: Evaluation of a mobile health app. Brain Inj, 2017; 31(10):1279–1286; doi: 10.1080/02699052.2017.1332388
75.
YeK, FleysherR, LiptonRB, et al.Repetitive soccer heading adversely impacts short-term learning among adult women. J Sci Med Sport, 2022; 25(11):935–941; doi: 10.1016/j.jsams.2022.08.011
76.
RuwaardJ, KooistraL, ThongMSY. Ecological Momentary Assessment in Mental Health Research: A Practical Introduction, with Examples in R. 1st ed. APH Mental Health: Amsterdam; 2018.
77.
Myin-GermeysI, KuppensP. (eds). The Open Handbook of Experience Sampling Methodology: A Step-by-Step to Designing, Conducting, and Analyzing ESM Studies. 2nd ed. Amazon Kindle Direct Publishing: Center for Research on Experience Sampling and Ambulatory Methods Leuven: Leuven; 2022.
78.
PiaseckiTM, HuffordMR, SolhanM, et al.Assessing clients in their natural environments with electronic diaries: Rationale, benefits, limitations, and barriers. Psychological Assessment, 2007; 19(1):25–43; doi: 10.1037/1040-3590.19.1.25
79.
SilviaPJ, CotterKN. Researching Daily Life: A Guide to Experience Sampling and Daily Diary Methods. American Psychological Association: Washington; 2021; doi: 10.1037/0000236-000
80.
EiseleG, KasanovaZ, HoubenM. Questionnaire Design and Evaluation. In: The Open Handbook of Experience Sampling Methodology: A Step-by-Step Guide to Designing, Conducting, and Analyzing ESM Studies. (Myin-GermeysI, KuppensP. eds). Leuven University Press; 2022; pp. 71–89.
81.
HuffordMR. Special Methodological Challenges and Opportunities in Ecological Momentary Assessment. In: The Science of Real-Time Data Capture: Self-Reports in Health Research. Oxford Academic; 2007; pp. 54–75.
82.
WrightAGC, ZimmermannJ. Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement. Psychol Assess, 2019; 31(12):1467–1480; doi: 10.1037/pas0000685
83.
StoneAA, SchneiderS, SmythJM. Evaluation of pressing issues in ecological momentary assessment. Annu Rev Clin Psychol, 2023; 19(1):107–131; doi: 10.1146/annurev-clinpsy-080921-083128
84.
OlesonJJ, JonesMA, JorgensenEJ, et al.Statistical considerations for analyzing ecological momentary assessment data. J Speech Lang Hear Res, 2022; 65(1):344–360; doi: 10.1044/2021_JSLHR-21-00081
85.
ViechtbauerW. Statistical Methods for ESM Data. In: The Science of Real-Time Data Capture: Self-Reports in Health Research. Oxford Academic; 2007; pp. 153–183.
86.
BorsboomD, DesernoMK, RhemtullaM, et al.Network analysis of multivariate data in psychological science. Nat Rev Methods Primers, 2021; 1(1):58; doi: 10.1038/s43586-021-00055-w
87.
DoraJ, McCabeC, van LissaCJ, et al.A tutorial on bayesian analysis of ecological momentary assessment data in psychological research. Adv Methods Pract Psychol Sci, 2024; 7(1); doi: 10.3123/4/osf.io/jc938
88.
KimH, LeeS, LeeS, et al.Depression prediction by using ecological momentary assessment, actiwatch data, and machine learning: Observational study on older adults living alone. JMIR Mhealth Uhealth, 2019; 7(10):e14149; doi: 10.2196/14149
89.
SchwartzJE, StoneAA. Strategies for analyzing ecological momentary assessment data. Health Psychol, 1998; 17(1):6–16; doi: 10.1037/0278-6133.17.1.6
EzekielL, CollettJ, MayoNE, et al.factors associated with participation in life situations for adults with stroke: A systematic review. Arch Phys Med Rehabil, 2019; 100(5):945–955; doi: 10.1016/j.apmr.2018.06.017
92.
McKennaK, LiddleJ, BrownA, et al.Comparison of time use, role participation and life satisfaction of older people after stroke with a sample without stroke. Aust Occup Ther J, 2009; 56(3):177–188; doi: 10.1111/j.1440-1630.2007.00728.x
93.
WinklerD, UnsworthC, SloanS. Time use following a severe traumatic brain injury. Journal of Occupational Science, 2005; 12(2):69–81; doi: 10.1080/14427591.2005.9686550
94.
PagulayanKF, HoffmanJM, TemkinNR, et al.Functional limitations and depression after traumatic brain injury: Examination of the temporal relationship. Arch Phys Med Rehabil, 2008; 89(10):1887–1892; doi: 10.1016/j.apmr.2008.03.019
95.
MacDonaldSWS, NybergL, BäckmanL. Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity. Trends Neurosci, 2006; 29(8):474–480; doi: 10.1016/j.tins.2006.06.011
96.
MacDonaldSWS, LiS-C, BäckmanL. Neural underpinnings of within-person variability in cognitive functioning. Psychol Aging, 2009; 24(4):792–808; doi: 10.1037/a0017798
97.
Ebner-PriemerUW, KoudelaS, MutzG, et al.Interactive multimodal ambulatory monitoring to investigate the association between physical activity and affect. Front Psychology, 2013; 3; doi: 10.3389/fpsyg.2012.00596
98.
RaughIM, ChapmanHC, BartolomeoLA, et al.A comprehensive review of psychophysiological applications for ecological momentary assessment in psychiatric populations. Psychol Assess, 2019; 31(3):304–317; doi: 10.1037/pas0000651
99.
LeeY, WalshRJ, FongMWM, et al.Heart rate variability as a biomarker of functional outcomes in persons with acquired brain injury: Systematic review and meta-analysis. Neurosci Biobehav Rev, 2021; 131:737–754; doi: 10.1016/j.neubiorev.2021.10.004
100.
BangerterA, ManyakovNV, LewinD, et al.Caregiver daily reporting of symptoms in autism spectrum disorder: Observational study using web and mobile apps. JMIR Ment Health, 2019; 6(3):e11365; doi: 10.2196/11365
101.
Claros-SalinasD, BratzkeD, GreitemannG, et al.Fatigue-related diurnal variations of cognitive performance in multiple sclerosis and stroke patients. J Neurol Sci, 2010; 295(1–2):75–81; doi: 10.1016/j.jns.2010.04.018
102.
KolanowskiA, HoffmanL, HoferSM. Concordance of self-report and informant assessment of emotional well-being in nursing home residents with dementia. J Gerontol B Psychol Sci Soc Sci, 2007; 62(1)P2:P20–P27; doi: 10.1093/geronb/62.1.p20
103.
MadsøKG, Flo-GroeneboomE, PachanaNA, et al.Assessing momentary well-being in people living with dementia: A systematic review of observational instruments. Front Psychol, 2021; 12:742510; doi: 10.3389/fpsyg.2021.742510
104.
NiculescuI, QuirtH, AroraT, et al.Ecological momentary assessment of depression in people with advanced dementia: Longitudinal pilot study. JMIR Aging, 2021; 4(3):e29021; doi: 10.2196/29021
105.
TrullTJ, Ebner-PriemerUW. Ambulatory assessment in psychopathology research: A review of recommended reporting guidelines and current practices. J Abnorm Psychol, 2020; 129(1):56–63; doi: 10.1037/abn0000473
106.
DejonckheereE, ErbasY. Designing an Experience Sampling Study. In: The Open Handbook of Experience Sampling Methodology: A Step-by-Step Guide to Designing, Conducting, and Analyzing ESM Studies. (Myin-GermeysI, KuppensP. eds) Leuven University Press: 2022; pp. 33–70.
107.
RamN, BrinbergM, PincusAL, et al.The questionable ecological validity of ecological momentary assessment: Considerations for design and analysis. Res Hum Dev, 2017; 14(3):253–270; doi: 10.1080/15427609.2017.1340052
108.
HopwoodCJ, BleidornW, WrightAGC. Connecting theory to methods in longitudinal research. Perspect Psychol Sci, 2022; 17(3):884–894.
109.
StoneAA, ShiffmanS. Capturing momentary, self-report data: A proposal for reporting guidelines. Ann Behav Med, 2002; 24(3):236–243; doi: 10.1207/S15324796ABM2403_09
110.
HallM, SchernerPV, KreidelY, et al.A systematic review of momentary assessment designs for mood and anxiety symptoms. Front Psychol, 2021; 12:642044; doi: 10.3389/fpsyg.2021.642044
111.
MaasA, Harrison-FelixCL, MenonD, et al.Common data elements for traumatic brain injury: Recommendations from the interagency working group on demographics and clinical assessment. Arch Phys Med Rehabil, 2010; 91(11):1641–1649; doi: 10.1016/j.apmr.2010.07.232
112.
SaverJL, WarachS, JanisS, et al.; National Institute of Neurological Disorders and Stroke (NINDS) Stroke Common Data Element Working Group. Standardizing the structure of stroke clinical and epidemiologic research data. Stroke, 2012; 43(4):967–973; doi: 10.1161/STROKEAHA.111.634352
113.
GershonRC, WagsterMV, HendrieHC, et al.NIH toolbox for assessment of neurological and behavioral function. Neurology, 2013; 80(Suppl 3):S2–S6; doi: 10.1212/WNL.0b013e3182872e5f
114.
WeintraubS, DikmenSS, HeatonRK, et al.Cognition assessment using the NIH Toolbox. Neurology, 2013; 80(Suppl 3):S54–S64; doi: 10.1212/WNL.0b013e3182872ded
115.
CampbellLM, PaolilloEW, HeatonA, et al.Daily activities related to mobile cognitive performance in middle-aged and older adults: An ecological momentary cognitive assessment study. JMIR Mhealth Uhealth, 2020; 8(9):e19579; doi: 10.2196/19579
116.
Schmitter-EdgecombeM, SumidaC, CookDJ. Bridging the gap between performance-based assessment and self-reported everyday functioning: An ecological momentary assessment approach. Clin Neuropsychol, 2020; 34(4):678–699; doi: 10.1080/13854046.2020.1733097
117.
SchusterRM, MermelsteinRJ, HedekerD. Acceptability and feasibility of a visual working memory task in an ecological momentary assessment paradigm. Psychol Assess, 2015; 27(4):1463–1470; doi: 10.1037/pas0000138
118.
VerhagenSJW, DaniëlsNEM, BartelsSL, et al.Measuring within-day cognitive performance using the experience sampling method: A pilot study in a healthy population. PLoS One, 2019; 14(12):e0226409; doi: 10.1371/journal.pone.0226409
119.
WarrenCM, PentzMA. The feasibility and acceptability of assessing inhibitory control and working memory among adolescents via an ecological momentary assessment approach. Child Neuropsychol, 2019; 25(8):1022–1034; doi: 10.1080/09297049.2018.1556624
120.
MooreRC, SwendsenJ, DeppCA. Applications for self-administered mobile cognitive assessments in clinical research: A systematic review. Int J Methods Psychiatr Res, 2017; 26(4):e1562; doi: 10.1002/mpr.1562
121.
MooreRC, AckermanRA, RussellMT, et al.Feasibility and validity of ecological momentary cognitive testing among older adults with mild cognitive impairment. Front Digit Health, 2022; 4:946685.
122.
WeizenbaumE, TorousJ, FulfordD. Cognition in context: Understanding the everyday predictors of cognitive performance in a new era of measurement. JMIR Mhealth Uhealth, 2020; 8(7):e14328; doi: 10.2196/14328
123.
MazzuccoS, LiL, TunaMA, et al.; Oxford Vascular Study. Time-of-Day could affect cognitive screening performance in older patients with TIA and stroke. Cerebrovasc Dis, 2017; 43(5–6):290–293; doi: 10.1159/000456673
124.
BrodatyH, WithallA, AltendorfA, et al.Rates of depression at 3 and 15 months poststroke and their relationship with cognitive decline: The Sydney stroke study. Am J Geriatr Psychiatry, 2007; 15(6):477–486; doi: 10.1097/JGP.0b013e3180590bca
125.
JoosubN, CassimjeeN, CramerA. The relationship between neuropsychological performance and depression in patients with traumatic brain injury. South African Journal of Psychology, 2017; 47(2):171–183; doi: 10.1177/0081246316654327
126.
RapoportM, McCullaghS, ShammiP, et al.Cognitive impairment associated with major depression following mild and moderate traumatic brain injury. J Neuropsychiatry Clin Neurosci, 2005; 17(1):61–65; doi: 10.1176/jnp.17.1.61
127.
RasquinS, LodderJ, VerheyF. The association between psychiatric and cognitive symptoms after stroke: A prospective study. Cerebrovasc Dis, 2005; 19(5):309–316; doi: 10.1159/000084499
128.
SpitzG, SchönbergerM, PonsfordJ. The relations among cognitive impairment, coping style, and emotional adjustment following traumatic brain injury. J Head Trauma Rehabil, 2013; 28(2):116–125; doi: 10.1097/HTR.0b013e3182452f4f
129.
MarekS, Tervo-ClemmensB, CalabroFJ, et al.Reproducible brain-wide association studies require thousands of individuals. Nature, 2022; 603(7902):654–660; doi: 10.1038/s41586-022-04492-9
130.
TiegoJ, FornitoA. Putting behaviour back into brain-behaviour correlation analyses. OSF Preprints, 2022; doi: 10.3121/9/osf.io/g84j2
131.
TiegoJ, MartinEA, DeYoungCG, et al.; HiTOP Neurobiological Foundations Work Group. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. Nat Ment Health, 2023; 1(5):304–315; doi: 10.1038/s44220-023-00057-5
132.
GadassiR, PaganiniG, WinschelJ, et al.Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment. PsyArXiv, 2021; doi: 10.3123/4/osf.io/mxznb
133.
McGowanAL, ParkesL, HeX, et al.Controllability of structural brain networks and the waxing and waning of negative affect in daily life. Biol Psychiatry Glob Open Sci, 2022; 2(4):432–439; doi: 10.1016/j.bpsgos.2021.11.008
134.
McGowanAL, SayedF, BoydZM, et al.Dense sampling approaches for psychiatry research: Combining scanners and smartphones. Biol Psychiatry, 2023; 93(8):681–689; doi: 10.1016/j.biopsych.2022.12.012
135.
AvinaC. The Use of Self-Monitoring as a Treatment Intervention. In: Evidence-Based Adjunctive Treatments. Elsevier Academic Press: San Diego, CA, US; 2008; pp. 207–219; doi: 10.1016/B978-012088520-6.50011-1
136.
KingBR, BoswellJF. Therapeutic strategies and techniques in early cognitive-behavioral therapy. Psychotherapy (Chic), 2019; 56(1):35–40; doi: 10.1037/pst0000202
137.
DaniëlsNEM, VerhagenSJW, Van BokhovenMA, et al.How to use experience‐sampling technology to understand daily functioning: A practical guide for mental health professionals. Clin Psychol Psychother, 2023; 30(2):357–372; doi: 10.1002/cpp.2798
138.
Myin-GermeysI, KlippelA, SteinhartH, et al.Ecological momentary interventions in psychiatry. Curr Opin Psychiatry, 2016; 29(4):258–263; doi: 10.1097/YCO.0000000000000255
139.
SchneiderS, JunghaenelDU, SmythJM, et al.Just-in-time adaptive ecological momentary assessment (JITA-EMA). Behav Res Methods, 2024; 56(2):765–783; doi: 10.3758/s13428-023-02083-8
140.
Krasny-PaciniA, EvansJ. Single-case experimental designs to assess intervention effectiveness in rehabilitation: A practical guide. Ann Phys Rehabil Med, 2018; 61(3):164–179; doi: 10.1016/j.rehab.2017.12.002
141.
ClementeA, AttyéA, RenardF, et al.Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients. Brain Res, 2023; 1806:148289; doi: 10.1016/j.brainres.2023.148289
142.
BentleyKH, KleimanEM, ElliottG, et al.Real-time monitoring technology in single-case experimental design research: Opportunities and challenges. Behav Res Ther, 2019; 117:87–96; doi: 10.1016/j.brat.2018.11.017
143.
VilardagaR, BrickerJ, McDonellM. The promise of mobile technologies and single case designs for the study of individuals in their natural environment. J Contextual Behav Sci, 2014; 3(2):148–153; doi: 10.1016/j.jcbs.2014.03.003
144.
WrightAGC, WoodsWC. Personalized models of psychopathology. Annu Rev Clin Psychol, 2020; 16(1):49–74; doi: 10.1146/annurev-clinpsy-102419-125032
145.
RoefsA, FriedEI, KindtM, et al.A new science of mental disorders: Using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology. Behav Res Ther, 2022; 153:104096; doi: 10.1016/j.brat.2022.104096
146.
CookJ, Kabubo-MariaraJ, KimuyuP. Happy at work in Africa? Measuring hedonic well-being among water carriers in rural Kenya using the experience sampling method. J Happiness Stud, 2022; 23(6):2449–2468; doi: 10.1007/s10902-022-00509-2
147.
HansonJD, HarrisA, GilbertsonRJ, et al.Feasibility and acceptability of using ecological momentary assessment to evaluate alcohol use with American Indian Women. Int J Environ Res Public Health, 2023; 20(12):6071; doi: 10.3390/ijerph20126071
148.
HubachRD, O’NeilA, StoweM, et al.Perceived confidentiality risks of mobile technology-based ecologic momentary assessment to assess high-risk behaviors among rural men who have sex with men. Arch Sex Behav, 2021; 50(4):1641–1650; doi: 10.1007/s10508-019-01612-x
149.
SeekinsT, IpsenC, ArnoldNL. Using ecological momentary assessment to measure participation: A preliminary study. Rehabilitation Psychology, 2007; 52(3):319–330; doi: 10.1037/0090-5550.52.3.319
150.
EbertDD, CuijpersP, MuñozRF, et al.Prevention of mental health disorders using internet- and mobile-based interventions: A narrative review and recommendations for future research. Front Psychiatry, 2017; 8:116.
151.
KleimanEM, NockMK. Advances in scientific possibilities offered by real-time monitoring technology. Psychiatry, 2017; 80(2):118–124; doi: 10.1080/00332747.2017.1325661
152.
HarariGM, LaneND, WangR, et al.Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspect Psychol Sci, 2016; 11(6):838–854; doi: 10.1177/1745691616650285
153.
WeermeijerJ, KiekensG, WampersM. Experience Sampling Platforms. In: The Open Handbook of Experience Sampling Methodology: A Step-by-Step to Designing, Conducting, and Analyzing ESM Studies. (Myin-GermeysI, KuppensP. eds) Leuven University Press: 2022; pp. 103–118.
154.
DohertyK, BalaskasA, DohertyG. The design of ecological momentary assessment technologies. Interacting with Computers, 2020; 32(3):257–278; doi: 10.1093/iwcomp/iwaa019
155.
WenzeSJ, MillerIW. Use of ecological momentary assessment in mood disorders research. Clin Psychol Rev, 2010; 30(6):794–804; doi: 10.1016/j.cpr.2010.06.007
