The Breakfast and Dressing Conflict Task: Preliminary Evidence of Its Validity to Measure Online Self-Awareness After Acquired Brain Injury

Abstract

Importance: Impaired self-awareness (SA) of deficits after an acquired brain injury (ABI) severely affects patients’ independence in activities of daily living (ADLs). However, any assessment tool permits an exhaustive evaluation of SA in the context of ADLs.

Objective: To study the validity of the Breakfast and Dressing Conflict Task (BD Conflict Task) to assess online SA (awareness of performance in the context of a given task) in patients with ABI; to study its interactions with offline SA (general awareness); and to test the validity of a simplified measure of performance monitoring, the ADL Conflict-Monitoring Index.

Design: Convergent validity and correlational study.

Setting: Research laboratory, hospitals, and homes.

Participants: Thirty patients with ABI and 28 neurologically healthy controls.

Outcomes and Measures: Using the BD Conflict Task, measures of emergent awareness, self-regulation, anticipatory awareness, and self-evaluation were assessed and their convergent validity and relationship with offline SA were analyzed. The ADL Conflict-Monitoring Index was calculated, and its convergent validity was tested.

Results: The online SA variables of the BD Conflict Task showed convergent validity with traditional online SA measures. Offline SA correlated with emergent and anticipatory awareness in the Breakfast Task. The ADL Conflict-Monitoring Index proved to be a valid measure of patients’ performance monitoring.

Conclusions and Relevance: These preliminary findings suggest that the BD Conflict Task is a valid tool to assess online SA in patients with ABI and provide further understanding of the online SA–offline SA interaction. Furthermore, the ADL Conflict-Monitoring Index may be a valid and easy-to-use monitoring measure in clinical settings.

Plain-Language Summary: Patients with acquired brain injury (ABI) and reduced awareness of their cognitive deficits face problems performing activities of daily living (ADLs) and may show signs of unsafe behaviors. Being aware of one’s own abilities involves anticipating problems before starting a task, detecting and correcting errors during the task, and evaluating performance afterward. This study provides preliminary validity for the Breakfast and Dressing Conflict Task, a new tool that assesses aspects of self-awareness simultaneously in the context of familiar and significant ADLs. Furthermore, the tool simplifies the assessment of detecting and correcting errors with an easy-to-use index, making it suitable for use in clinical settings.

This study provides preliminary validity for the Breakfast and Dressing Conflict Task, a new tool that assesses aspects of self-awareness simultaneously in the context of familiar and significant activities of daily living.

Impaired self-awareness (SA) after acquired brain injury (ABI) is a severe complication that compromises people’s functional outcomes (Fischer et al., 2004). The Dynamic Comprehensive Model of Awareness (DCMA; Toglia & Kirk, 2000) describes SA as a multicomponent construct. Metacognitive knowledge is the offline SA component that contains almost-stable semantic knowledge stored in long-term memory about task characteristics and one’s ability to perform them. The online SA component constitutes a group of processes activated during the execution of a given task and is composed of emergent awareness (error detection), self-regulation (error correction), anticipatory awareness (the ability to predict upcoming task performance), and self-evaluation (the appraisal of one’s performance immediately after the task). One key aspect of the model is the interaction between offline SA and online SA through an updating process, which consists of integrating information derived from one’s present performance with one’s prior metacognitive knowledge.

A recent review of the DCMA (Toglia & Goverover, 2022) highlights the importance of developing new assessment tools that enable the evaluation of all online SA subcomponents in the context of the same functional task. Most studies have used laboratory tasks or neuropsychological tests to measure online SA (O’Keeffe et al., 2007; Robertson & Schmitter-Edgecombe, 2015). Patients’ lack of previous experience with these artificial tasks limits their validity to measure online SA because performance prediction, monitoring, and self-evaluation cannot be based on prior task knowledge. Additionally, these tasks may not reflect real online SA deficits when patients face familiar and meaningful tasks in their daily life. A few studies have proposed tools to assess online SA within the context of activities of daily living (ADL) tasks (Arora et al., 2021; Chudoba & Schmitter-Edgecombe, 2020; Doig et al., 2017; Giovannetti et al., 2002). Nevertheless, to the best of our knowledge, none of them have measured all online SA components proposed by the DCMA.

Furthermore, to study the potential online SA– offline SA relationship, a tool that assesses all online SA components in the context of functional tasks would be more appropriate, because offline SA is typically assessed with questionnaires that ask about the patient’s difficulties with functional activities (Al Banna et al., 2016).

However, an inherent difficulty of the evaluation of online SA with performance-based ADL tasks is that emergent awareness and self-regulation are usually measured on the basis of the patient’s ability to detect and correct errors that occur spontaneously along with performance of the task and cannot be predicted by the clinician beforehand. For this reason, patients’ performance needs to be video recorded and coded offline, which makes the evaluation highly resource- and time-consuming, preventing its applicability in clinical settings.

We have recently proposed an evaluation protocol (Merchán-Baeza et al., 2020) in which all online SA components can be evaluated in the context of the same ADL. The tool is composed of two ADL tasks (an instrumental activity of daily living [IADL], preparing breakfast, and a basic ADL, getting dressed), both of which include a set of distracting objects and unexpected conflicting situations that must be resolved to successfully complete the tasks. These distracting and conflicting situations will constitute evoked situations that the evaluator can focus on during the assessment of error and conflict detection and correction.

The main aim of the current study was to provide preliminary evidence of the validity of the protocol to assess online SA components with patients with ABI. Second, we aimed to test the interactional nature of the DCMA by analyzing the relationship of all online SA components to offline SA, measured in the same functional domain. Last, we aimed to test whether the assessment of emergent awareness and self-regulation abilities based exclusively on patients’ behavior toward evoked distracting and conflicting situations is suitable to assess online SA in a simplified manner.

We expected to find convergent validity for the online SA variables of the proposed protocol with traditional online SA measures. In addition, we expected a pattern of interaction between several online SA components and offline SA. Finally, we expected emergent awareness and self-regulation to be validly assessed, focusing exclusively on the patient’s actions toward distractors and conflicting situations.

Method

Participants

A total of 41 patients with ABI were recruited from neurological rehabilitation services and ABI associations in the cities of Granada and Málaga, Spain. The eligibility criteria were (1) older than 18 yr, (2) diagnosis of ABI, and (3) absence of premorbid psychiatric disease. The exclusion criteria were (1) presence of aphasia, (2) hemineglect, (3) perception deficits, (4) severe motor impairments in both upper limbs, and (5) Mini-Mental State Examination (Lobo et al., 1999) score of less than 18. These criteria led to the exclusion of 5 patients, and another 6 patients did not complete the evaluation. Therefore, a final sample of 30 patients (11 women, 19 men; M age = 56.7 yr, SD = 12.4; M years of education = 9.7, SD = 3.5) was included in the study. Of these, 26 patients had a cerebrovascular accident (19 ischemic, 7 hemorrhagic), and 4 patients had a traumatic brain injury. A sample of 28 healthy controls (HCs; 21 women, 7 men; M age = 62.0 yr, SD = 11.6; M years of education = 11.1, SD = 2.9) also participated in the study. One-way analyses of variance revealed no statistically significant differences between groups in terms of age and years of education, F(1, 57) = 2.8, p = .10, and F(1, 57) = 2.7, p = .10, respectively. HCs’ performance was used as reference to assess patients’ performance and calculate anticipatory awareness and self-evaluation variables (see Merchán-Baeza et al., 2020, for a detailed description of variable calculation).

The study protocol was registered with ClinicalTrials.gov (NCT03712839) and approved by the Andalusian Ethics Committee for Biomedical Research (AnosognosiaAVD2017, 0056-N-17). Before participation, all participants received verbal and written information about the study and gave their written informed consent to participate and to be video recorded during the evaluation sessions.

Procedure

Sociodemographic (gender, age, and years of education) and clinical (brain damage etiology and time since injury) information was collected from all participants.

Measures

Familiarity with the protocol’s tasks was assessed by asking each patient how often they performed each ADL before and after the ABI, rated on a scale on which 1 = never, 2 = rarely, 3 = weekly, and 4 = every day.

The Rey Verbal Auditory Learning Test (Rey, 1964), Color Trails Test (D’Elia et al., 1996), Key Search test (Wilson et al., 1996), Institute of Cognitive Neurology (INECO) Frontal Screening tool (Torralva et al., 2009), and Phonetic Fluency (FAS) and Category Fluency (animals) of the Controlled Oral Word Association Test (COWAT, Benton & Hamsher, 1976) were administered to assess memory and executive functions. A Cognitive Index was calculated as the average of age-adjusted scores of all neuropsychological tests (excluding the measures used for convergent validity analyses).

Offline SA was calculated as the discrepancy between the total scores on the patient and significant-other forms of the Patient Competency Rating Scale (PCRS; Prigatano, 1996). In addition, the mean rating of the significant-other form was used as a measure of the patient’s functionality. Online SA was assessed with the Breakfast and Dressing Conflict Task (BD Conflict Task).

During the BD Conflict Task the participants had to complete (1) the Breakfast Task (BT), by preparing orange juice with a teaspoon of sugar and one piece of toast with butter and jelly, and (2) the Dressing Task (DT), by dressing the upper body to go out on a rainy day. Four distractor objects semantically related to the target task (i.e., objects to make a cup of coffee with milk in the BT and objects to wear to go to bed or to dress the lower body in the DT) and four unexpected conflicting situations (e.g., unplugged appliances in the BT and clothes presented inside out in the DT) were introduced in each task. All mistakes made during performance of the tasks were classified according to the participant’s level of awareness. Immediately before and immediately after task performance, the participant was asked to predict and evaluate, respectively, their performance on a 5-point Likert-scale ranging from 0 to 4 (0=I won’t be able to do it/I couldn’t do it; 1 = I can do it, but I will make many mistakes/I could do it, but I made many mistakes; 2 = I can do it, but I will make some mistakes/I could do it, but I made some mistakes; 3 = I will do it quite well, with few errors/I did it quite well, with few errors; 4 = I will do it perfectly without making any mistakes/I did it perfectly without making any mistakes). See Merchán-Baeza et al. (2020) for a detailed description of target objects, distractors, conflicting situations, administration, error classification, and calculation of online SA variables. The online SA variables derived from the BD Conflict Task and the corresponding traditional measures used for convergent validity are described in Table 1.

Table 1.

Online SA Measures of the BD Conflict Task and Traditional Online SA Measures

BD Conflict Task Measures	Traditional Measures
Emergent awareness: percentage of detected errors (sum of all spontaneous errors and conflicting situations detected or anticipated by the participant divided by the total errors, including all conflicting situations) Self-regulation: percentage of corrected errors (sum of spontaneous errors and conflicting situations efficiently corrected or anticipated by the participant divided by the total detected or anticipated errors and conflicting situations)	COWAT Monitoring: sum of intrusion and perseveration generated divided by total words generated in COWAT Phonetic subtask (Robertson & Schmitter-Edgecombe, 2015).
Anticipatory awareness: difference between patient’s estimation of their performance before the task and the patient’s actual performance, scored on a scale ranging from 4 (maximum anticipatory overestimation) to −4 (maximum anticipatory underestimation)
Self-evaluation: difference between patient’s evaluation of their performance immediately after the task and their actual performance, rated on a scale ranging from 4 (maximum self-evaluation overestimation) to −4 (maximum self-evaluation underestimation).	Short-term Memory score: sum of the words recalled immediately after list presentations in the RVLT (5 trials).

Note. BD Conflict Task = Breakfast and Dressing Conflict Task; COWAT = Controlled Oral Word Association Test; RVLT = Rey Verbal Auditory Learning Test; SA = self-awareness.

An ADL Conflict-Monitoring Index was calculated for the patient’s behavior toward distractors and conflicting situations in the BD Conflict Task, taking into account the patient’s detection and correction capacity. Each conflicting situation and action with distractors was scored as follows: 0 = the participant anticipated the conflicting situation/no use of distractor, 1 = the participant detected the conflicting situation/use of distractor and efficiently corrected it, 2 = the participant detected the conflicting situation/use of distractor but could not efficiently correct it, and 3 = the participant did not detect the conflicting situation/use of distractor or detected it but did not try to correct it. The percentage of errors with distractors was calculated as the patient’s score on distractor use divided by the total possible score on distractor use. Although the number of conflicting situations within the task was always the same, not all participants faced each situation. Therefore, the percentage of errors with conflicting situations was calculated as the patient’s score on conflicting situations divided by the maximum score the patient could obtain on the basis of the number of conflicting situations they faced. The ADL Conflict-Monitoring Index was the sum of the percentages of errors with distractors and conflicting situations, where higher scores indicate worse conflict or error monitoring.

We also calculated the updating variable as the difference between the delayed self-evaluation (20–30 min after task completion) and the immediate self-evaluation. However, the delayed self-evaluation was available for only 15 patients in the BT and 13 patients in the DT, and it showed small variance. Therefore, this measure was excluded from the final analyses.

Of the patients that participated in the study, 29 patients performed the BT (1 patient who, because of cultural background, was not familiar with the task, was excluded), and 23 patients performed the DT. Few neuropsychological data were missing (8.5%) and were not replaced with any values. We did not have data for 11 patients for the COWAT Monitoring Index (see Table 1) and 4 patients the significant-other PCRS form and were thus excluded from the corresponding analysis.

Analyses

All analyses were performed with IBM SPSS Statistics (Version 28.0). Interrater reliability for error classification was analyzed for 20% of the sample (randomly selected). Spearman’s correlations were used because of the non-normal distribution of the data. The relationship between the online SA variables of the BD Conflict Task and the sociodemographic and clinical variables was analyzed with two-tailed correlations. Convergent validity for each of the online SA variables of the BD Conflict Task was tested with one-tailed correlations. For variables that significantly correlated with demographic or clinical variables, these correlations were repeated controlling for such variables. The online SA–offline SA relationship was studied with one-tailed correlations. The convergent validity of the ADL Conflict-Monitoring Index was analyzed with one-tailed correlation with the COWAT Monitoring.

Alpha was set at .05, and Benjamini-–Hochberg correction was applied to control for multiple comparisons because it demonstrates higher power than other correction methods (Benjamini & Hochberg, 1995; Olejnik et al., 1997).

Results

The patients’ and HCs’ neuropsychological information is reported in Table 2. Compared with HCs, the patients showed significantly impaired performance on five of the six neuropsychological tests administered. The raters obtained an ICC greater than .85 in codifying errors as detected and corrected in both tasks. Patients’ scores on the BD Conflict Task are reported in Table 3.

Table 2.

Neuropsychological and Self-Awareness Information and Familiarity With the BD Conflict Task of Patients and Healthy Controls

Measures	M (SD)		F(1, 57)
Measures	Patients (n = 30)	HCs (n = 28)	F(1, 57)
MMSE	27 (2.3)	28.9 (1)	8.4*
INECO Frontal Screening Tool	16.5 (5.4)	22 (4.9)	13.1*
Color Trails Test
Errors	0.7 (0.9)	0.1 (0.4)	7.9*
Near misses	0.3 (0.6)	0.2 (0.5)	<1
Part B time – Part A time, s	95.2 (109.4)	68 (43.2)	2.5
Key Search test	1.3 (1.4)	2 (1.3)	3.1
COWAT
Phonetic	16 (10.2)	38.6 (15.5)	24.6*
Semantic	12.6 (5.4)	22.7 (9.6)	14*
RVLT
Short-term memory	33.4 (13.4)	47.3 (9.4)	18*
Delayed memory	5.4 (4.6)	10.6 (2.5)	23.1*
Time since injury, mo	33.4 (54.5)
Functionality	3.5 (0.8)
Patient’s Competency Rating Scale Discrepancy Index	20.8 (28.9)
Familiarity
Breakfast Task		3
Before brain injury	2.9
After brain injury	2.02
Dressing Task		4
Before brain injury	4
After brain injury	4

Note. BD Conflict Task = Breakfast and Dressing Conflict Task; COWAT = Controlled Oral Word Association Test; INECO = Institute of Cognitive Neurology; MMSE = Mini-Mental State Examination; RVLT = Rey Verbal Auditory Learning Test.

*p < .05

Table 3.

Patients’ Scores on the Breakfast and Dressing Conflict Task

BD Conflict Task Variables	Median (Range)
BD Conflict Task Variables	Breakfast Task	Dressing Task
Total errors	8 (1 to 27)	3 (0 to 9)
Emergent awareness	66.7 (7.1 to 100)	75 (33.3 to 100)
Self-regulation	80 (0 to 100)	100 (33.3 to 100)
Anticipatory awareness	1 (−2 to 4)	1 (0 to 3)
Self-evaluation	1 (−1 to 4)	1 (−1 to 3)
ADL Conflict-Monitoring Index	22.2 (5.9 to 119.6)	33.3 (0 to 79.2)

Note. BD Conflict Task = Breakfast and Dressing Conflict Task.

Correlations between the BD Conflict Task online SA variables and demographic and clinical variables are reported in Table 4. None of the variables were significantly correlated with years of education or functionality as reported by significant others. Age correlated with emergent awareness, self-regulation, and anticipatory awareness in the BT. Time since injury correlated with anticipatory awareness and self-evaluation in the DT. The Cognitive Index significantly correlated with all online SA variables in both tasks, except for self-regulation in the DT. Only the correlations involving the Cognitive Index and the correlation between anticipatory awareness and time since injury in the DT survived the Benjamini–Hochberg correction.

Table 4.

Online SA Correlations With Sociodemographic, Clinical, and Cognitive Variables

Task	Age	Years of Education	Time Since Injury	Functionality	Cognitive Index
Breakfast Task
Emergent awareness	−.39*	.15	.26	.27	.55** a
Self-regulation	−.42*	.23	−.03	.13	.52** a
Anticipatory awareness	.37*	−.28	.02	−.29	−.60** a
Self-evaluation	.35	−.16	−.16	−.29	−.54** a
ADL Conflict-Monitoring Index	.26	−.25	.04	−.33	−.54** a
Dressing Task
Emergent awareness	−.27	.36	.17	.28	.81** a
Self-regulation	−.19	.20	.17	−.01	.31
Anticipatory awareness	.31	−.13	−.54 a	.05	−.60** a
Self-evaluation	.37	−.25	−.51*	.02	−.66** a
ADL Conflict-Monitoring Index	.24	−.33	−.03	−.08	−.69** a

Note. ADL = activities of daily living; SA = self-awareness.

Significant after Benjamini–Hochberg correction

*p < .05. **p < .01 (two-tailed).

Convergent Validity

Emergent awareness significantly correlated with the COWAT Monitoring in both the BT (N = 18; ρ = −.64, p = .002) and the DT (N = 14; ρ = −.52, p = .027). The correlations of self-regulation with COWAT Monitoring did not reach significance (BT, ρ = −.30, p = .12; DT, ρ = −.19, p = .26). Self-evaluation significantly correlated with short-term memory in the BT (N = 28; ρ = −.45, p = .008) and the DT (N = 22; ρ = −.69, p < .001). All significant correlations survived the Benjamini–Hochberg correction. When controlling for the Cognitive Index, the correlation between emergent awareness and COWAT Monitoring remained significant in the BT (ρ = −.58, p = .007) but not in the DT (ρ = −.13, p = .33). The correlations between self-evaluation and short-term memory were not significant after controlling for the Cognitive Index in either the BT (ρ = −.03, p = .44) or the DT (ρ = −.35, p = .06).

Online SA–Offline SA Relationship

Regarding the BT (N = 26), offline SA significantly correlated with emergent awareness (ρ = −.35, p = .038) and anticipatory awareness (ρ = .47, p = .008). Offline SA’s correlation with self-evaluation was marginally significant (ρ = .33, p = .05), whereas its correlation with self-regulation was far from significant (ρ = −.01, p = .47). Offline SA’s correlation with anticipatory awareness was the only correlation that survived the Benjamini–Hochberg correction. None of the correlations between offline SA and online SA components in the DT (N = 19) reached significance (emergent awareness, ρ = −.28, p = .24; self-regulation, ρ = .09, p = .72; anticipatory awareness, ρ = .18, p = .45; self-evaluation, ρ = .15, p = .53).

ADL Conflict-Monitoring Index

The ADL Conflict-Monitoring Index was significantly correlated with the COWAT Monitoring in both tasks (BT, ρ = .65, p = .002; DT, ρ = .57, p = .016). After controlling for the Cognitive Index, the correlation maintained its significance in the BT (ρ = .59, p = .007) but not the DT (ρ = .19, p = .269).

Discussion

The main aim of the current study was to provide preliminary evidence of the validity of an evaluation protocol based on ADL performance to assess online SA in patients with ABI. To this end, the patients performed the BD Conflict Task, and measures of online SA were analyzed.

The patients were familiar with the BT and the DT, favoring the general applicability of the protocol used in this study. The online SA variables in the two tasks did not show significant correlations with years of education, indicating that the protocol may be a valid tool to assess online SA in patients with ABI regardless of their education level.

Moderately significant correlations were found between online SA and age in the BT, which is in line with previous studies that identified age as affecting some online SA components in complex functional tasks (Arora et al., 2021). Time since injury strongly correlated with anticipatory awareness and self- evaluation in the DT, favoring the idea that task practice improves online SA (Doig et al., 2017; Goverover et al., 2014). Dressing is one of the first ADLs trained during rehabilitation, and patients who had more practice with the task after ABI onset might be more aware of their abilities and difficulties, showing more accurate prediction and evaluation of performance. The lack of significant correlations between online SA and functionality is at odds with previous studies that described SA as being related to functionality (Goverover, 2004; Villalobos et al., 2020). However, these studies also revealed that the relationship between SA and functionality is highly influenced by other cognitive factors. Indeed, online SA variables strongly correlated with the Cognitive Index (including executive and memory functions), which is fully congruent with previous studies that reported executive and memory associations with online SA in patients with ABI (Bivona et al., 2008; Ciurli et al., 2010; Goverover, 2004; O’Keeffe et al., 2007; Zimmermann et al., 2017). Future research is necessary to elucidate the potential relationship among cognitive factors, functionality, and online SA measured with the current ADL tasks.

Convergent Validity

It is important that the online SA measures derived from the BD Conflict Task showed convergent validity with traditional measures assessing the corresponding components of online SA. As predicted, emergent awareness strongly correlated with COWAT Monitoring in both tasks, indicating that these indexes are valid to measure performance monitoring processes in the context of familiar ADLs. However, self-regulation measures did not significantly correlate with COWAT Monitoring for any of the tasks. This dissociation is in line with the DCMA, which indicates that emergent awareness and self-regulation are two different subcomponents of online SA. The correlation of emergent awareness with COWAT Monitoring maintained its significance even after controlling for the Cognitive Index in the BT, which shows that the emergent awareness index of this task, despite its strong correlation with other cognitive processes, constitutes a measure of a genuine monitoring process that can be specifically altered after ABI. The fact that the convergent validity of emergent awareness disappeared for the DT when controlling for the Cognitive Index may indicate that, although error detection relies on more specific monitoring processes in complex instrumental tasks, in easier basic tasks such as dressing, error detection seems to be largely mediated by other cognitive deficits. Self-evaluation strongly correlated with short-term memory in both tasks, which is highly congruent with our predictions. However, these correlations disappeared when controlling for the Cognitive Index in both tasks. These results are in agreement with the DCMA, for which self-evaluation is the immediate evaluation of the performance, although it also involves other cognitive processes, such as the comparison of actual performance with expected performance, its integration with previous knowledge, and self-reflection to understand the consequences of performance difficulties.

Online SA–Offline SA Relationship

The proposed protocol also allowed testing in a more direct manner whether and which online SA components are related to offline SA, with all measures being related to the functional domain. In the BT, a significant correlation emerged between emergent awareness and offline SA. This finding is congruent with the DCMA, which affirms that a dynamic interaction exists between these components, because, based on our experiences with specific tasks, people create or change their beliefs about their ability to perform a task. Therefore, patients who show poor performance monitoring may not integrate an appropriate reflection of their current abilities into previous metacognitive knowledge.

A significant positive correlation was also found between anticipatory awareness and offline SA. This correlation is also congruent with the DCMA. Anticipatory awareness allows prediction of performance on a specific task on the basis of the information one has in metacognitive knowledge (Toglia & Kirk, 2000). Therefore, anticipatory awareness strictly depends on offline SA, and patients who underestimate their deficits offline may also underestimate the difficulties they will face when performing a given task.

No significant online SA–offline SA correlations were found in the basic DT. A possible explanation for the lack of significant results in the DT may be that the questionnaire used in the current study (i.e., the PCRS) assesses offline SA by asking about abilities involved to a greater extent in IADLs. Furthermore, as is proposed by the DCMA, SA might change depending on the task performed, and different factors may affect the online SA–offline SA relationship in basic ADLs and IADLs (Toglia & Goverover, 2022).

ADL Conflict-Monitoring Index

We also tested the ADL Conflict-Monitoring Index, which is a new, simplified index of online SA. This index showed the same convergent validity with COWAT Monitoring as the most extensive assessment of patients’ detection of spontaneous errors. It is important that the assessment of this index is much simpler during task execution, because the assessor’s attention needs to focus on a closed number of patient behaviors. For this reason, this index can be a valuable and easy-to-use measure of performance monitoring in clinical practice.

Limitations

The current study has several limitations. First, because of the coronavirus disease 2019 pandemic, we could not recruit the planned sample and administer all the proposed tests described in the published protocol (Merchán-Baeza et al. 2020). Consequently, our small sample size diminished the power to detect small effects, preventing the generalization of the findings. Nevertheless, an a posteriori sensitivity analysis with G*Power (Faul et al., 2007) showed that our smallest samples (those regarding the convergent validity with COWAT Monitoring) were sufficient to detect moderate effects of r = .40 in the BT (n = 18) and r = .46 in the DT (n = 14), with α = .5 and 1 − β = .80. Furthermore, we could test the a priori established hypothesis, enhancing the statistical power of the analyses. However, further studies are required to confirm the reliability and generalizability of these preliminary results. In addition, the fact that the updating measure showed little variance in both tasks, and that it was only available to few patients, limited the possibility to study it. Future studies including a longer time span between immediate and delayed self-evaluation (i.e., 1 hr) might be more sensitive to isolate specific alterations in delayed self-evaluation (Stewart et al., 2010).

Implications for Occupational Therapy Practice

The current study offers an ecological ADL-based evaluation protocol to assess online SA in patients with ABI during naturalistic tasks. Additionally, it proposes a new method to easily identify patients’ monitoring deficits that is useful in clinical settings. The study shows promising results, although further research with larger samples is needed to confirm the protocol’s reliability and generalizability.

Conclusion

The current study provides preliminary evidence of the validity of an evaluation protocol to assess all online SA components in patients with ABI in the context of the same ADL. In addition, it provides empirical evidence supporting the DCMA, enhancing the understanding of the interaction pattern between online SA and offline SA. In addition, we propose a new, valid simplified measure (i.e., the ADL Conflict-Monitoring Index) to assess performance monitoring that can be easily used in clinical practice (in which only offline SA is usually evaluated) by occupational therapists and other health professionals.

Footnotes

Acknowledgments

We thank the patients and their caregivers for their participation in this study, as well as the professionals (occupational therapists and neuropsychologists) of the rehabilitation centers who collaborated on this research. This study was funded by the Spanish Ministry of Economy and Competitiveness (M.J.F. Research Project PSI2016-80331-P) and supported by the Spanish Ministry of Science, Innovation and Universities with a predoctoral fellowship within the Formación de Profesorado Universitario program (G.R. Grant No: FPU17/02536).

References

Al Banna

Redha

N. A.

Abdulla

Nair

, & Donnellan

(2016). Metacognitive function poststroke: A review of definition and assessment. Journal of Neurology, Neurosurgery, and Psychiatry, 87, 161–166. https://doi.org/10.1136/jnnp-2015-310305

Arora

Frantz

, & Toglia

(2021). Awareness of performance on a functional cognitive performance-based assessment across the adult lifespan. Frontiers in Psychology, 12, 753016. https://doi.org/10.3389/fpsyg.2021.753016

Benjamini

, & Hochberg

(1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B: Methodological, 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

Benton

, & Hamsher

(1976). Multilingual aphasia examination. University of Iowa Press.

Bivona

Ciurli

Barba

Onder

Azicnuda

Silvestro

et al. Formisano, R. (2008). Executive function and metacognitive self-awareness after severe traumatic brain injury. Journal of the International Neuropsychological Society, 14, 862–868. https://doi.org/10.1017/S1355617708081125

Chudoba

L. A.

, & Schmitter-Edgecombe

(2020). Insight into memory and functional abilities in individuals with amnestic mild cognitive impairment. Journal of Clinical and Experimental Neuropsychology, 42, 822–833. https://doi.org/10.1080/13803395. 2020.1817338

Ciurli

Bivona

Barba

Onder

Silvestro

Azicnuda

et al. Formisano, R. (2010). Metacognitive unawareness correlates with executive function impairment after severe traumatic brain injury. Journal of the International Neuropsychological Society, 16, 360–368. https://doi.org/10.1017/S135561770999141X

D’Elia

L. F.

Satz

Uchiyama

C. L.

, & White

(1996). Color Trails Test (CTT). Psychological Assessment Resources.

Doig

Fleming

Ownsworth

, & Fletcher

(2017). An occupation-based, metacognitive approach to assessing error performance and online awareness. Australian Occupational Therapy Journal, 64, 137–148. https://doi.org/10.1111/1440-1630. 12322

10.

Faul

Erdfelder

Lang

A. G.

, & Buchner

(2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. https://doi.org/10.3758/BF03193146

11.

Fischer

Gauggel

, & Trexler

L. E.

(2004). Awareness of activity limitations, goal setting and rehabilitation outcome in patients with brain injuries. Brain Injury, 18, 547–562. https://doi.org/10.1080/02699050310001645793

12.

Giovannetti

Libon

D. J.

, & Hart

(2002). Awareness of naturalistic action errors in dementia. Journal of the Neuropsychological Society, 8, 633–644. https://doi.org/10.1017/S135561770280131X

13.

Goverover

(2004). Categorization, deductive reasoning, and self-awareness: Association with everyday competence in persons with acute brain injury. Journal of Clinical and Experimental Neuropsychology, 26, 737–749. https://doi.org/10.1080/13803390490509321

14.

Goverover

Genova

Griswold

Chiaravalloti

, & DeLuca

(2014). Metacognitive knowledge and online awareness in persons with multiple sclerosis. NeuroRehabilitation, 35, 315–323. https://doi.org/10.3233/NRE-141113

15.

Lobo

Saz

Marcos

Día

J. L.

de la Cámara

Ventura

et al. Aznar, S. (1999). Revalidación y normalización del Mini-Examen Cognoscitivo (primera versión en castellano del Mini-Mental Status Examination) en la población general geriátrica [Revalidation and standardization of the cognition mini-exam (first Spanish version of the Mini-Mental Status Examination)]. Medicina Clínica, 112, 767–774.

16.

Merchán-Baeza

J. A.

Rodriguez-Bailon

Ricchetti

Navarro-Egido

, & Funes

M. J.

(2020). Awareness of cognitive abilities in the execution of activities of daily living after acquired brain injury: An evaluation protocol. BMJ Open, 10, e037542. https://doi.org/10.1136/bmjopen-2020-037542

17.

O’Keeffe

F. M.

Dockree

P. M.

Moloney

Carton

, & Robertson

I. H.

(2007). Characterising error-awareness of attentional lapses and inhibitory control failures in patients with traumatic brain injury. Experimental Brain Research, 180, 59–67. https://doi.org/10.1007/s00221-006-0832-9

18.

Olejnik

Supattathum

, & Huberty

C. J.

(1997). Multiple testing and statistical power with modified Bonferroni procedures. Journal of Educational and Behavioral Statistics, 22, 389–406. https://doi.org/10.2307/1165229

19.

Prigatano

G. P.

(1996). Behavioral limitations TBI patients tend underestimate: A replication and extension to patients with lateralized cerebral dysfunction. Clinical Neuropsychologist, 10, 191–201. https://doi.org/10.1080/13854049608406680

20.

Rey

(1964). L’examen clinique en psychologie [Clinical examination in psychology]. Presses Universitaires de France.

21.

Robertson

, & Schmitter-Edgecombe

(2015). Self-awareness and traumatic brain injury outcome. Brain Injury, 29, 848–858. https://doi.org/10.3109/02699052.2015.1005135

22.

Stewart

McGeown

W. J.

Shanks

M. F.

, & Venneri

(2010). Anosognosia for memory impairment in Alzheimer’s disease. Acta Neuropsychiatrica, 22, 180–187. https://doi.org/10.1111/j.1601-5215. 2010.00463.x

23.

Toglia

, & Goverover

(2022). Revisiting the dynamic comprehensive model of self-awareness: a scoping review and thematic analysis of its impact 20 years later. Neuropsychological Rehabilitation, 32, 1676–1725. https://doi.org/10.1080/09602011.2022.2075017

24.

Toglia

, & Kirk

(2000). Understanding awareness deficits following brain injury. NeuroRehabilitation, 15, 57–70. https://doi.org/10.3233/NRE-2000-15104

25.

Torralva

Roca

Gleichgerrcht

López

, & Manes

(2009). INECO Frontal Screening (IFS): A brief, sensitive, and specific tool to assess executive functions in dementia. Journal of the International Neuropsychological Society, 15, 777–786. https://doi.org/10.1017/S1355617709990415

26.

Villalobos

Caperos

J. M.

Bilbao

Á.

Bivona

Formisano

, & Pacios

(2020). Self-awareness moderates the association between executive dysfunction and functional independence after acquired brain injury. Archives of Clinical Neuropsychology, 35, 1059–1068. https://doi.org/10.1093/arclin/acaa048

27.

Wilson

B. A.

Alderman

Burgess

P. W.

Emslie

, & Evans

J. J.

(1996). Behavioural assessment of the dysexecutive syndrome . Harcourt Assessment.

28.

Zimmermann

Mograbi

D. C.

Hermes-Pereira

Fonseca

R. P.

, & Prigatano

G. P.

(2017). Memory and executive functions correlates of self-awareness in traumatic brain injury. Cognitive Neuropsychiatry, 22, 346–360. https://doi.org/10.1080/13546805.2017.1330191