The Planning Strategies for Driving on a Map test (COMAP): Initial validation in stroke patients

Abstract

Objective

To validate a novel off-road assessment tool—the Planning Strategies for Driving on a Map (COMAP)—designed to evaluate strategic predriving planning in individuals with stroke.

Design

Cross-sectional observational study following COSMIN and STROBE guidelines.

Setting

Research conducted at a university-affiliated research facility.

Participants

A total of 41 stroke survivors (≥6 months poststroke) and 42 age- and gender-matched healthy controls. Participants with significant language or cognitive impairments (Mini-Mental State Examination < 24) were excluded.

Main measures

Participants completed the COMAP and a battery of cognitive tests including tests of attention, executive functions, working memory, and visuospatial organization.

Results

The COMAP total performance showed good internal consistency (α = .885) and significant correlations with cognitive measures of executive function and memory. A cutoff score of 59 demonstrated strong diagnostic utility (area under the curve = .829; sensitivity = 78%; specificity = 87%) in identifying stroke-related cognitive impairments. Strategy use and planning time were associated with better task performance. The COMAP was more sensitive to within-group variability among stroke participants than between-group differences with healthy controls.

Conclusions

The COMAP is a valid and reliable tool for assessing strategic predriving planning after stroke. It offers clinically relevant insights into executive functioning and compensatory strategies, with potential applications in rehabilitation and driving-related decision-making.

Keywords

Fitness to drive neuropsychological assessment strategic planning occupational therapy

Introduction

Stroke is a leading global cause of mortality and disability,¹ often impairing autonomy in complex activities such as driving.^2–5 Cognitive, sensory, and motor deficits following stroke can compromise driving ability,⁶ with cognitive impairments being the most challenging to accommodate and the most critical to assess for driving safety.⁷

When assessing driving performance, Michon's hierarchical model of driving distinguishes three levels of cognitive control: operational (vehicle handling), tactical (real-time traffic management), and strategic (trip planning).⁸ While operational and tactical components are commonly addressed in on-road assessments, the strategic level—focused on predriving planning—remains largely unassessed.^9,10 This level includes setting travel goals, selecting routes and transport modes, evaluating risks and constraints, and considering passenger needs.⁸ These decisions are cognitively demanding and rely on executive functions, stored knowledge.^9,11,12 Although these decisions are typically made in low-pressure contexts before driving, impairments in the underlying cognitive processes—common after stroke—may compromise planning and increase driving risk.¹³

Strategic planning not only guides the overall trip but also influences tactical and operational decisions.¹⁰ It helps organize tasks, adapt to schedules, and mitigate risks by compensating for cognitive or physical deficits.^8,9,14 This is particularly relevant for individuals with poststroke cognitive impairments, who must often adapt their driving behavior accordingly.

Despite its importance, no assessment tool currently exists to evaluate strategic-level functioning in driving. To address this gap, we developed a novel tool: the Planning Strategies for Driving on a Map (COMAP). This study reports the initial validation of the COMAP and examines its psychometric properties in individuals with stroke.

Methods

Ethical approval for this study was obtained from the Biomedical Research Ethics Committee of Andalusia (approval number 1607-M1–22). All participants provided written informed consent prior to participation. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki for research involving human participants. The design and reporting followed the COSMIN taxonomy for studies on measurement properties¹⁵ and the STROBE guidelines for observational studies.¹⁶

Participants

Stroke survivors were recruited through convenience and snowball sampling from hospitals and neurorehabilitation clinics. Inclusion criteria included being Spanish-speaking, aged 18–70 years, in the chronic poststroke phase (>6 months), clinically stable and authorized by their referring neurologist to participate, regardless of current driving ability. Participants also needed to stand independently and retain sufficient mobility in at least one upper limb to operate basic driving controls (steering wheel, gear lever, and door handle) smoothly.

Exclusion criteria included failure to meet the minimum physical or perceptual standards established by the Spanish Directorate-General for Traffic under the General Driver Regulations,¹⁷ including epilepsy, severe visual deficits, hemispatial neglect, or balance disorders. Visual acuity was assessed with the Snellen Test.¹⁸ Participants with cognitive or language impairments interfering with task comprehension (Mini-Mental State Examination [MMSE] <24; Boston Diagnostic Aphasia Examination – Spanish Version <2) were excluded,^19,20 as were those reporting intolerable driving-related pain measured with an adapted Functional Pain Scale.²¹

A control group of healthy adults matched for age and gender was also recruited, excluding individuals with neurological or psychiatric conditions.

Procedure

The COMAP was developed following the test construction guidelines proposed by Muñiz and Fonseca-Pedrero.²² Three occupational therapists specializing in driving designed the first version of the test. The pilot assessment instrument was administered to two groups of occupational therapy students, four healthy adults, and four stroke survivors to evaluate the task's clarity, feasibility, duration, and difficulty. Based on their feedback, appropriate modifications were made to finalize the test version. Specifically, the route-drawing format was revised so that participants could trace the route directly onto the map, which was perceived as more intuitive than using a separate sheet. Additional supportive cues (e.g., a clock) were incorporated, and both the number of tasks and overall cognitive demands were reduced to ensure the test remained time-efficient and accessible for individuals with potential functional limitations.

To validate the COMAP, data were collected between 2022 and 2023. All participants completed a sociodemographic and clinical questionnaire. Inclusion and exclusion criteria were carefully verified before participants underwent a comprehensive battery of cognitive assessments. The participants did not receive any compensation for taking part in the study. The COMAP was administered by three trained occupational therapists. Each evaluation session lasted approximately 3 hours.

Measures

The participants completed the COMAP. In this assessment, the participant must plan and design a route to complete five preassigned tasks, with the goal of finishing them before spending the weekend at a village house. To do so, the participant uses several materials (Figure 1): a map to draw the route, a ruler to estimate time–distance relationships for different modes of transportation (walking, cycling, or driving), a bus schedule indicating the times the bus passes through five stops marked on the map, a clock for time management, and a list of the five tasks.

Three tasks are provided at the start: attending a dentist appointment at a specified time, making several purchases (specifically bread, fruit, and fish), and arriving at the village house by a set time. During the test, a new task is added—picking up an injured friend and bringing them to the village. At this point, a new location is added to the map, indicating the friend's house. The fifth task is never explicitly mentioned but is essential for completing the test; it involves switching the mode of transportation. Failure to do so results in errors, such as driving in restricted areas or being unable to complete the route efficiently. For example, beginning the journey on foot and trying to arrive on time at the final destination without using a motor vehicle leads to mistakes.

For scoring the test, the total performance score (range 0–96) will sum up ratings across 32 items (refer to supplementary material for a more detailed description) covering all COMAP subtasks: traffic signal recognition, adherence to instructions, and task completion accuracy. In addition, three compensatory outcome variables will be assessed during and after the test: planning time, execution time, and the number of strategies used by the participant. Planning time refers to the interval from the start of the test to the moment the participant marks the first task on the map. Execution time covers the total duration from the beginning of the test to its declared completion. Strategies reflect the diversity of planning strategies employed throughout the task to enhance their performance. The evaluator will have a list of example strategies they might observe (e.g., using an eraser to revise plans, making annotations, employing different colors, or recording the additional task on a separate sheet); however, they may also document any other strategies identified during observation.

Participants also completed a series of cognitive tests including the Useful Field of View (UFOV), Trail Making Test (TMT), Paced Auditory Serial Addition Test (PASAT), Compass and Road Sign Recognition (RSR) from Stroke Driver Screening Assessment (SDSA), and the Spanish Weekly Calendar Planning Activity-10 (Spanish WCPA10). For a more detailed description of the selected cognitive tests and the variables used in each test, refer to the supplementary material. Demographic and clinical information—such as age, sex, educational level, diagnosis, and time since stroke—was also collected.

Statistical analyses

Descriptive statistics covered continuous and categorical variables. Normality was checked, and nonparametric tests were used as appropriate. Differences in sociodemographic variables were examined between stroke patients and healthy controls, followed by Spearman's correlations to explore relationships among primary COMAP variables and potential confounding factors (age, time since stroke, and education level) within the stroke group.

To assess reliability, we evaluated internal consistency using Cronbach's alpha for COMAP items pertaining to total performance. Analyses were conducted exclusively with stroke participants to examine the internal coherence of the instrument in this population. We then analyzed relationships between total performance and planning time, execution time, and strategies.

For convergent validity, we analyzed correlations between COMAP variables and cognitive test scores within the stroke group. The Benjamini–Hochberg correction was applied to control for multiple comparisons,²³ considering r_s coefficients between 0.31 and 0.7 as moderate correlations.²⁴ Discriminant validity was established by comparing stroke patients to healthy adults using the Quade Test,²⁵ controlling for sociodemographic variables that differed between groups.

A receiver operating characteristic analysis was conducted to assess the diagnostic validity of the COMAP total performance score. The Spanish WCPA10 Accuracy was used as the criterion measure, given its relevance to executive functions, memory and planning,^26–28 key processes for strategic driving.¹³ As no established impairment cutoff exists for the Spanish WCPA10, a threshold of 1.5 standard deviations below the control group mean (n = 42) was applied, following Al-Heizan's recommendation.²⁹ The optimal COMAP cutoff was identified using the Youden index.³⁰

To evaluate the effectiveness of this cutoff in distinguishing stroke participants with cognitive deficits at the strategic level, we compared the performance of two subgroups of individuals with stroke classified according to the COMAP cutoff. This comparison was conducted using the Quade Test,²⁵ with adjustments made for sociodemographic variables that differed between the groups.

We performed a sensitivity power analysis in G*power 3.1,³¹ setting the power level at 0.80. For correlation analyses (n = 41), with a two-sided test and alpha level of 0.05, the minimum detectable effect size was r = 0.421. For between-group analyses (n = 83), the minimum detectable effect size for ANCOVA (analogous to the Quade test) with two groups and one covariate was η² = 0.089, indicating that the sample was sufficient to detect moderate effect sizes according to Cohen.³² Data analysis was performed using Jamovi 2.3.21 and R 4.5.1 software.

Results

A total of 45 stroke participants and 42 age- and gender-matched healthy controls were initially recruited. Figure 2 illustrates the participant inclusion and exclusion process.

Four participants from the stroke group were excluded: one for scoring below 24 on the MMSE and three due to aphasia. This resulted in a final sample of 41 individuals in the stroke group. No participants were excluded based on any other predefined inclusion or exclusion criteria. Demographic and clinical data for both groups are presented in Table 1.

Table 1.

Characteristics of the sample.

	Stroke patients		Healthy controls		Comparison
Sociodemographic and clinical variables
	n	Mean (SD)	n	Mean (SD)	Test	P-value	r_b
Age (years)	41	55.9 (12.6)	42	50.9 (11)	U = 634	0.039*	0.264
Time since stroke (months)	41	19.5 (19.4)
	N	%	N	%	Test	P-value	V
Sex (female)	12	29.27	12	28.57	χ² = 0.005	0.994	0.008
Education level
• Low	13	31.71	10	23.81
• Medium	11	26.83	9	21.43
• High	17	41.46	23	54.76	H = 1.480	0.477	0.134
Etiology
• Ischemic	24	58.54
• Hemorrhagic	16	39.02
• Unknown	1	2.4
Lesion location
• Left	17	41.46
• Right	13	31.71
• Bilateral	9	21.95
• Unknown	2	4.88
Cognitive tests
	N	Mean (SD)	N	Mean (SD)	U	P-value	r_b
UFOV 3 (ms)	37	182.4 (103.6)	42	115.8 (61)	427	<0.001***	0.45
TMT (B-A)	39	76 (61.5)	40	42.2 (28.4)	438	<0.001***	0.439
PASAT Accuracy	28	48.2 (7.03)	40	50.4 (6.13)	445	0.151	0.206
Compass Accuracy	41	22.4 (8.25)	41	29.4 (4.73)	391	<0.001***	0.535
RSR Accuracy	41	8.95 (2.27)	40	10.9 (1.38)	392	<0.001***	0.522
Spanish WCPA10	41		42
• Strategies		5.97 (2.26)		6.69 (2.25)	711	0.226	0.154
• Rules followed		3.88 (1.14)		4.45 (0.67)	608	0.020*	0.277
• Accuracy		5.02 (2.45)		6.95 (1.83)	469	<0.001***	0.456

r_b = Rank-biserial correlation; V = Cramer's V; H = Kruskal–Wallis; χ² = Chi-square; U = Mann–Whitney U test; *P < 0.05 and ***P < 0.001.

A significant age difference was found, with more young adults in the control group. For cognitive test scores, the healthy control group performed significantly better than the stroke group on all tests except for the PASAT accuracy and Strategies in the Spanish WCPA10, where no significant differences were detected.

Finally, a statistically significant moderate correlation was found between education level and COMAP total performance (r_s = 0.448, P = 0.003) in the stroke group. Similarly, a moderately significant relationship was found between time since stroke and the COMAP use of strategies (r_s = 0.388, P = 0.012).

Reliability

The COMAP total performance demonstrated good internal consistency (Cronbach's alpha = 0.885) for the stroke patient group, with item correlations between r = 0.874 and r = 0.890. Furthermore, significant relationships were found for total performance with strategies (r_s = 0.367, P = 0.018) and planning time (r_s = 0.311, P = 0.048).

Validity

Several cognitive measures demonstrated moderate correlations with COMAP total performance after applying the Benjamini–Hochberg correction: UFOV 3 (r_s = −0.559, P < 0.001), TMT (B-A Time) (r_s = −0.747, P = 0.002), Compass accuracy (r_s = −0.415, P = 0.007), RSR accuracy (r_s = 0.521, P < 0.001), and Spanish WCPA10 accuracy (r_s = 0.650, P < 0.001). The COMAP strategies measure was also correlated with strategies on Spanish WCPA10 (r_s = 0.466, P = 0.002). No significant correlations were found for COMAP time measures.

Regarding discriminant validity, Table 2 shows the mean and median COMAP scores by group. A Quade test, controlling for age, indicated that stroke patients had significantly lower total performance scores compared to healthy controls. No differences were found in other measures.

Table 2.

Results of COMAP for each group of participants.

	Stroke patient		Healthy control		Comparison
Variables	Mean (SD)	Median	Mean (SD)	Median	F	P-value	η²
Planning time (s)	515 (725)	198	512 (690)	343	0.659	0.419	0.008
Execution time (s)	1651 (898)	1350	1567 (908)	1311	0.049	0.825	<0.001
Strategies	4.8 (1.96)	5	5 (2.14)	5	0.15	0.70	0.015
Performance	61.2 (19.8)	65	74.2 (12)	75.5	6.256	0.014*	0.072

F = Quade's test F; η² = eta squared; *P < 0.05.

To evaluate the diagnostic validity of the COMAP, the stroke sample was divided to test its ability to detect cognitive impairments in strategic planning. The analysis yielded an area under the curve of 0.829 (Figure 3), with sensitivity of 0.78 (95% CI = [0.59, 0.97]) and specificity of 0.87 (95% CI = [0.73, 1]). These results included 14 true positives, 20 true negatives, 3 false positives, and 4 false negatives. Based on this analysis, a COMAP total performance score of 59 was identified as the optimal threshold for potential cognitive impairment, classifying 43.9% of the stroke sample (n = 18) as impaired.

To further examine the diagnostic capacity of this cutoff, stroke participants were categorized as either impaired (≤59 points) or unimpaired (>59 points) according to their COMAP's total performance scores. A significant difference in educational level was observed between the groups (P = 0.012, V = 0.465), with the unimpaired group containing a higher proportion of individuals with high education and the impaired group including more individuals with basic education. No differences were found in other measures.

A Quade test, which controlled for educational level, revealed that stroke patients classified as impaired (based on the COMAP cutoff) had significantly lower scores on specific cognitive tests compared to healthy controls. As Table 3 shows, significant differences were observed in TMT B-A Time, UFOV 3 Time, and Compass Accuracy. No significant differences were found for RSR Accuracy or PASAT Accuracy. Regarding the COMAP variables, as expected, a statistically significant difference was observed in total performance between the two groups. However, no significant differences were found in time-related variables or strategy use between the groups.

Table 3.

Results of neuropsychological test for each group of stroke participants.

	Impaired stroke patient (n = 18)		Unimpaired stroke patient (n = 23)		Comparison
Variables	Mean (SD)	Median	Mean (SD)	Median	F	p value	η2
COMAP
Planning time (s)	181 (240)	72.5	777 (865)	449	3.303	0.07	0.078
Execution time (s)	1390 (535)	1214	1855 (1071)	1603	0.238	0.629	0.006
Strategies	4.28 (1.45)	4	5.22 (2.24)	5	1.732	0.196	0.042
Total Performance	42.9 (13.9)	45	75.4 (9)	76	32.65	<.001***	0.456
Cognitive test
UFOV 3 Time	233 (144)	204	144 (77.9)	120	4.761	0.036*	0.12
TMT B-A Time	110 (77.1)	92	52.3 (32.3)	46	5.994	0.019*	0.139
PASAT Accuracy	44.3 (8.72)	44	50 (5.44)	51	1.896	0.18	0.068
Compass Accuracy	18.5 (8.10)	20	25.5 (7.12)	28	5.01	0.031*	0.114
RSR Accuracy	8.17 (2.31)	8	9.57 (2.09)	10	1.887	0.177	0.046

F = Quade's test F; η² = eta squared; *P < 0.05 and ***P < 0.001.

Discussion

Michon's hierarchical framework can help identify deficits and guide the selection of appropriate assessment tools and the design of rehabilitation programs that address poststroke impairments across all levels of driving-related functions.³³ Since no single assessment captures all the cognitive and strategic abilities required for safe driving, it is essential to develop complementary tools that cover all relevant domains and can be combined effectively.⁴ In this context, the COMAP fills a critical gap by evaluating strategic-level functioning, offering detailed information about an individual's performance that can guide tailored rehabilitation interventions and support safe reintegration into driving. The following section describes the psychometric properties of the COMAP.

The COMAP total performance was associated with higher education levels, and individuals with basic education were more represented in the impaired stroke subgroup. This supports previous findings on the protective role of education through cognitive reserve^34,35 and highlights the need to consider educational background when interpreting results. Time since stroke was also associated with greater strategy use, suggesting that individuals in later stages of recovery may develop compensatory strategies, even when cognitive performance plateaus after approximately 6 months.³⁶

Internal consistency analysis indicated that the 32 COMAP's total performance items measure a single underlying construct, supporting a unidimensional scale. Strategy use correlated significantly with total performance, supporting the idea that strategic behavior enhances task outcomes. Additionally, a relationship between planning time and total performance was observed—potentially influenced by the COMAP's practice session, which may encourage individuals with greater self-awareness to allocate more time to planning as a compensatory strategy, thereby improving efficiency.³⁷

In relation to convergent validity, COMAP total performance correlated significantly with cognitive tests assessing executive functions and memory related to traffic knowledge—key processes for strategic driving per Michon's model.^9,11,12 It did not correlate with rule-following on the Spanish WCPA10, likely due to differences in rule processing: COMAP emphasizes implicit executive demands (e.g., distraction inhibition), whereas Spanish WCPA10 relies more on explicit memory-based instruction recall.²⁶ This suggests that the COMAP places greater demand on executive functioning than on declarative memory processes. Similarly, the lack of correlation with PASAT Accuracy may be due to its high difficulty, which led to incomplete data in a third of the stroke group, highlighting the need for cognitively accessible tools.

Strategy use in COMAP correlated with strategies used in the Spanish WCPA10—another functional task allowing spontaneous strategic behavior—but not with standard cognitive tests, suggesting strategy use in COMAP captures task-specific functional adaptation rather than general cognitive ability. Time-related variables also failed to correlate with cognitive scores, consistent with previous research with other planning tests,^28,38 indicating that such metrics alone may be insufficient to detect cognitive deficits, though planning time may still support performance.

Although COMAP performance differed significantly between stroke patients and healthy controls, effect sizes were smaller than expected. This, along with the absence of group differences in strategy use or timing variables, may reflect heterogeneity within the stroke sample. Subgrouping by cognitive impairment has proven useful in other neurological populations^27,37 and may offer greater sensitivity to cognitive variability than group-level comparisons. Functional-cognitive research suggests that individuals with mild cognitive impairments may use as many or even more strategies than healthy participants, who typically do not require compensatory behaviors.^27,37 Moreover, they consistently use more strategies than individuals with severe poststroke impairments, likely due to preserved executive functions and greater self-awareness.³⁹ These findings support the use of within-group cognitive profiling to better capture individual differences in task performance and self-regulation.

Diagnostic analysis further supports the COMAP's utility. A cutoff score of 59 demonstrated high sensitivity (78%) and specificity (87%),⁴⁰ clearly separating cognitively impaired from unimpaired stroke patients on strategic planning, outperforming comparisons with healthy controls. Although planning and execution times did not differ significantly, participants without cognitive impairment in planning tended to spend more time on these phases and used more strategies, potentially reflecting better self-regulation and awareness—differences less evident in control comparisons. This threshold also aligned with significant differences in attention and executive function tests (TMT B-A Time, UFOV 3, Compass Accuracy). Conversely, no significant differences were observed for the RSR accuracy, suggesting that the cutoff is less effective at detecting deficits in traffic sign knowledge. Similarly, PASAT Accuracy did not differ between subgroups, aligning with previous analyses showing no association between PASAT Accuracy and COMAP total performance or group classification.

Previous research in driving rehabilitation has shown that clinical tests can identify rehabilitation needs and guide decisions regarding further assessment, training, or driving restriction. The Occupational Therapy–Driver Off-Road Assessment Battery, developed for older and functionally impaired drivers, helps occupational therapists integrate visual, motor, and cognitive information to determine whether clients can resume driving or require additional rehabilitation.⁴¹ Similarly, the SDSA supports fitness-to-drive evaluations for stroke survivors, showing good concurrent validity with on-road outcomes and identifying those needing comprehensive assessment or training.⁴² Cognitive screening tools such as DriveSafe and DriveAware classify drivers as safe, unsafe, or requiring further testing, reducing unnecessary on-road assessments and aiding referral decisions.⁴³ In addition, self-report measures such as the Safe Driving Behavior Measure capture everyday driving difficulties and predict on-road outcomes, identifying drivers who may benefit from education, counseling, or rehabilitation.^44,45 Our findings suggest that COMAP may play a similar role at a strategic level in driving: by highlighting difficulties in planning, self-regulation, and decision-making about when, where, and how to drive, it can complement existing off-road tools and contribute to a more comprehensive assessment to determine therapeutic goals within driving rehabilitation programs.

This study has several limitations that point to future directions. While the sample size was sufficient for correlation and between-group analyses, a larger stroke sample is needed to improve the robustness of within-group comparisons. Additionally, the COMAP was validated using the WCPA, which lacks established clinical cutoffs, limiting criterion-based interpretation. However, this test was chosen as an instrument that has demonstrated its ability to detect planning cognitive alterations in patients with stroke.³⁸ Finally, future studies should examine COMAP performance in relation to real-world driving behavior to confirm ecological validity.

Clinical message

The COMAP is a novel off-road assessment with good psychometric properties that evaluates strategic predriving planning in stroke survivors.

The COMAP total performance provides insight into executive functions and memory processes related to traffic and can differentiate individuals with executive dysfunction from those without.

The COMAP strategy use and planning time may reflect compensatory behaviors relevant for rehabilitation planning and driving readiness assessments.

Supplemental Material

sj-docx-1-cre-10.1177_02692155251410487 - Supplemental material for The Planning Strategies for Driving on a Map test (COMAP): Initial validation in stroke patients

Supplemental material, sj-docx-1-cre-10.1177_02692155251410487 for The Planning Strategies for Driving on a Map test (COMAP): Initial validation in stroke patients by Lucía Laffarga, Ana Clara Szot, Candida Castro, Daniel Salazar-Frías, Jorge Clavijo-Ruiz and María Rodríguez-Bailón in Clinical Rehabilitation

Footnotes

Acknowledgment

The authors express our gratitude to the hospitals, clinics, and participants, including stroke survivors and controls, for their valuable contributions.

ORCID iDs

Lucía Laffarga

Ana Clara Szot

Daniel Salazar-Frías

Jorge Clavijo-Ruiz

María Rodríguez-Bailón

Ethics approval

This project has been approved by the Biomedical Research Committee of Andalucía (Ethics Portal code: 1607-M1-22) and has followed the ethical principles of the Declaration of Helsinki. Participation is voluntary, and all data will be kept confidential in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council on the protection of personal data (GDPR). The study poses no risks or side effects, as it only involves verbal, manual, or written responses.

Author contributions

All authors contributed to the study design, data interpretation, and provided comments on the final manuscript. Specifically, CC and MRB secured financial support for the project leading to this publication. LL, ACS, and JCR collected the data. DSF and LL led the data analysis. LL and ACS drafted the manuscript. MRB and CC supervised the work and provided critical revisions. All authors approved the final version of the paper.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: European Regional Fund “ERDF” A way of making Europe, by the “European Union Next Generation EU/PRTR” and by the Junta de Andalucía I + D + I Programa Operativo FEDER Andalucía (P20_00338, A-SEJ-114-UGR20).

Junta de Andalucía I+D+I Programa Operativo FEDER Andalucía, European Regional Fund “ERDF” A way of making Europe, by the “European Union Next Generation EU/PRTR”, (grant number A-SEJ-114-UGR20, P20_00338).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

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