Nudge-Based Intervention for Cognitive Enhancement of Elderly in Long-Term Care Facilities During Fire Evacuation According to Urgent-Level Circumstances

Abstract

The cognitive ability of the elderly significantly influences evacuation performance in urgent situations. Despite its importance, many fire evacuation studies overlook the impact of cognitive ability on elderly evacuation performance. To address this gap, this study employs multicriteria decision-making to identify nudging factors that enhance the cognitive abilities of the elderly during fire evacuations in long-term care facilities. Based on a literature review, key nudging factors include guidance lights, guide lines, handrails, and guidance equipment, with sub-criteria such as location, color, size, and intervals. Experts from academic and practical fields analyzed the nudging factors, followed by a hybrid analytic hierarchy process (AHP–TOPSIS) analysis. The findings emphasize the necessity of providing auditory information through guidance equipment (e.g., voice evacuation system) in high-level scenarios (practice experts AHP: 0.31) and visual information through the continuous installation of guide lines in strategic locations (academic experts AHP: 0.35) to facilitate efficient evacuation. As a result, this study confirms both the differing and concordant opinions among expert groups while recognizing the absolute necessity of elderly evacuation research and considering the unique challenges that prevent actual evacuation experiments with elderly individuals. By synthesizing these perspectives, the study derives the weights and ranks of nudging factors based on urgent-level circumstances, thereby conducting a quantitative assessment of factors that enhance cognitive ability during elderly evacuation. The findings of this study can serve as a basis for future evacuation policy formulation for elderly-related facilities and, as a derivative effect, contribute to ensuring the life safety of elderly individuals within the local community.

1. Introduction

In societies with aging populations, ensuring safety in long-term care settings, such as convalescent hospitals, is becoming increasingly vital [1]. The elderly, who face challenges in mobility and cognitive functions, are particularly vulnerable during evacuations [2,3,4]. They typically move slower and decide slower than younger groups, owing to physical and cognitive declines, prolonging pre-evacuation and wayfinding times [5,6,7]. Research focusing on these unique characteristics is crucial, yet practical challenges limit experimental and survey-based approaches [8,9,10,11,12,13].

Previous research has shown that targeted support can significantly aid evacuations of the elderly with issues such as deteriorations in sensory functions, walking abilities, and balance and cognitive declines, including judgment impairments and disorientation [5,6,9,10,13,14,15,16,17,18,19,20,21,22,23,24]. These studies suggest that enhancements, such as using walking aids and providing clear evacuation information, can improve mobility and cognitive responses during emergencies [8,10,15,18,21,22,24,25]. Despite these advancements, the impact of this information on cognitive enhancement has not been extensively studied. Additionally, changes in psychological states due to varying urgency levels have been noted as significant influences on elderly behavior during evacuations [1]. Nevertheless, existing studies on elderly evacuation continue to use standardized quantitative variables, such as walking speed, as simulation input data, despite significant variability among elderly individuals depending on their physical capabilities [8,9,10,11,12,13].

The concept of a “nudge”, derived from the theory of affordances, suggests that subtle, non-coercive interventions can effectively alter behavior by simplifying and clarifying necessary actions and information [26,27]. By enhancing cognitive processing, nudges can reduce decision-making time and encourage appropriate evacuation actions. Thus, employing nudge-based strategies could be a promising method to improve evacuation safety by supporting the elderly’s decision-making capabilities through non-invasive cognitive enhancements [26,27].

Therefore, since previous studies did not clearly identify the impact of evacuation information on cognitive enhancement during elderly evacuation and did not adequately consider varying emergency levels, this study aims to identify effective nudging factors for cognitive enhancement in elderly evacuation according to urgent-level circumstances. Various methods, such as guidance lights, guide lines, handrail signs, and voice alarms, are essential for creating a nudge effect [22,24,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48]. We evaluate how the forms of these nudging factors, including continuity [28,29,33], size [22,28,31,36,39,40], color [28,37,43], and height [28,29,30,32,33,35,36,41], influence cognitive enhancement in the elderly. Reflecting expert opinions on elderly care and fire evacuation is crucial [49,50]; hence, this study engages 41 experts, including experts from academic fields (n = 20; firefighting and disaster prevention) and professionals (n = 21) from elderly care facilities.

The study employs a hybrid analytic hierarchy process (AHP), which is a technique for order of preference by similarity to ideal solution (TOPSIS) approach, as the analysis method [51,52,53]. The hybrid AHP–TOPSIS method is suitable for evaluating multiple alternatives against various criteria by determining how closely each alternative aligns with the ideal solution, thus identifying the optimal choice. This method can be applied to evaluate and prioritize the importance of nudging factors (visual and auditory) influencing cognitive enhancement during elderly evacuation based on expert group opinions. It unfolds in five main steps: (1) reviewing the literature to summarize elderly evacuation-related nudging factors and their forms, (2) defining urgent-level circumstances and preparing scenarios, (3) conducting expert surveys, (4) assessing the impact of nudging factors on cognitive enhancement under urgent scenarios, and (5) validating the effects of these factors on elderly evacuation.

The effective provision of evacuation information through nudge-based interventions can enhance the cognitive abilities of the elderly during emergencies. Moreover, using nudging factors can mitigate the adverse effects of psychological state changes caused by emergency scenarios, thus potentially reducing pre-evacuation times by simplifying decision-making processes. This research adopts an “agent”-oriented approach, considering the unique cognitive traits of the elderly under urgent conditions, moving away from generalized simulation inputs. The findings are expected to guide future research on dynamic evacuation signage for the elderly, utilizing effective nudging factors.

2. Nudge-Based Evacuation-Inducing Factors (Nudging Factors)

Prior studies were examined to identify the nudging factors that enhance the cognitive functions of the elderly during fire evacuations. The key findings are summarized in Table 1. The main nudging factors identified are guide lines, guidance lights, handrails, and guidance equipment, with their installation intervals, locations, sizes, and colors serving as specific sub-criteria.

Exit sign systems, such as guide lines and guidance lights, influence evacuation times by their placement, environmental conditions, and the evacuee’s cognitive abilities [32]. Visibility under smoke conditions is crucial, as people rely on these signs to navigate evacuation routes [18]. Floor-level exit signs, in particular, support cognitive processing in smoke-filled environments [27], and clear evacuation signs reduce disorientation and uncertainty in evacuation behavior [18,32].

Research on guidance lights includes aspects such as optimal placement, size, design, and color, which can affect evacuation [18,22,54,55]. Effective guidance light specifications have been determined by considering visual cognition and viewing distance, revealing that response times vary with different color combinations [54,55]. The color of guidance lights carries contextual meanings—green for safety, red for danger, and orange for warning—which can psychologically impact urgency perception [22]. However, unconventional colors may confuse evacuees, and color combinations can render pictograms difficult to interpret, potentially obscuring the guidance light’s function in evacuation assistance [22]. Despite extensive research on guidance light cognition, specific studies focusing on elderly evacuation are lacking.

A guide line influences cognition based on its installation location, color, and size [28,29,30]. The continuity of exit sign markings is crucial; shorter intervals between continuous exit signs enhance evacuee route guidance [28], and positioning them as continuous strips at lower levels along the evacuation route aids proximity guidance [29]. For instance, experiment participants followed a path marked by a line of white dots on a hallway floor [29]. Furthermore, using photoluminescent materials on guide lines can enhance visibility, increasing evacuation speed by up to 50% and reducing total evacuation time by 25% [28]. Guide lines also assist in identifying floor height variations, such as steps [29]. However, the potential of guide lines to influence the cognitive and evacuation behaviors of the elderly remains underexplored.

Handrails, initially used as barrier-free walking aids, now also convey evacuation information [56,57]. During evacuations, over 80% of evacuees rely on handrails for support on stairs [28], indicating their significant role. Marking handrails with continuous information strips has been shown to improve evacuation efficiency [29]. For visually impaired evacuees, braille boards attached to handrails are crucial for obtaining evacuation information [28,58]. Thus, handrails not only aid mobility but also enhance psychological stability, evacuation efficiency, and route cognition [29].

Path guidance devices, fire alarm systems, and voice evacuation systems deliver critical auditory evacuation information. These systems alert occupants about fires and highlight dangers [59,60]. Auditory cues from alarms typically prompt occupants to gather belongings and prepare to evacuate, suggesting that auditory guidance can enhance evacuation efficiency by shortening pre-evacuation times [14,18,61].

Table 1. Derivation of nudging factors by analyzing earlier studies.

Literature	Nudging Factor
	Guide Lines				Guidance Light				Hand-Rails	Path Guidance Device	Fire Alarm System	Voice Evacuation System
	Interval	Location	Color	Shape	Interval	Height	Color	Size
D’Orazio et al. [28]	●	●	●	●	●			●	●	●	●
Proulx and Bénichou [29]	●	●							●
Kobes et al. [30]		●				●
Ran et al. [31]				●	●					●
Kobes et al. [32]						●
Kubota et al. [33]					●	●
ISO 3864-1 [34]					●		●	●
Kubota et al. [35]						●
Smedberg et al. [36]						●
Kinateder et al. [37]							●
Balboa et al. [38]											●
Li et al. [62]												●
Kim et al. [39]								●
Olander et al. [22]								●
Galea et al. [40]								●			●
Zhang et al. [41]						●			●	●	●
Rigos et al. [42]										●	●
Rousek and Hallbeck [43]							●
Menzemer et al. [24]										●	●
Wang et al. [44]									●

Note: Dots (•) indicate the presence or relevance of the corresponding item in the referenced prior studies.

Table 1. Derivation of nudging factors by analyzing earlier studies.

Literature	Nudging Factor
	Guide Lines				Guidance Light				Hand-Rails	Path Guidance Device	Fire Alarm System	Voice Evacuation System
	Interval	Location	Color	Shape	Interval	Height	Color	Size
D’Orazio et al. [28]	●	●	●	●	●			●	●	●	●
Proulx and Bénichou [29]	●	●							●
Kobes et al. [30]		●				●
Ran et al. [31]				●	●					●
Kobes et al. [32]						●
Kubota et al. [33]					●	●
ISO 3864-1 [34]					●		●	●
Kubota et al. [35]						●
Smedberg et al. [36]						●
Kinateder et al. [37]							●
Balboa et al. [38]											●
Li et al. [62]												●
Kim et al. [39]								●
Olander et al. [22]								●
Galea et al. [40]								●			●
Zhang et al. [41]						●			●	●	●
Rigos et al. [42]										●	●
Rousek and Hallbeck [43]							●
Menzemer et al. [24]										●	●
Wang et al. [44]									●

Note: Dots (•) indicate the presence or relevance of the corresponding item in the referenced prior studies.

The literature review revealed that existing research primarily focuses on factors such as guidance lights and guide lines for inducing evacuation, as summarized in Table 1, with fewer studies exploring additional factors. Most research has detailed aspects such as the location and color of guidance lights and guide lines but has not comprehensively evaluated their effectiveness in conveying evacuation information. Moreover, no study has specifically identified factors that can enhance the evacuation cognitive abilities of the elderly. Effective communication of evacuation information requires it to be perceivable, understandable, and persuasive. Thus, this study seeks to identify nudging factors that effectively enhance cognitive abilities during evacuation in the elderly.

3. Methodology

3.1. Intervention Forms of the Nudging Factors

In this study, four main criteria—guidance lights, guide lines, handrails, and guidance equipment—and 15 sub-criteria, representing different forms of nudge-based interventions, were applied to evaluate nudging factors related to elderly evacuation in long-term care facilities. Details of each nudging factor used in this study are outlined in

Table 2. Intervention description of nudging factors.

Main Criteria		Sub-Criteria
Guidance light	Sign lamp indicating the location of nearby exits or evacuation routes
		Evacuation information continuity based on guidance light installation intervals			Guidance light installation height
		(a) Interval			(b) Height
		Guidance light color			Guidance light size
		(c) Color			(d) Size
Guide line	Line that emits light, either stored or current, facilitating evacuation in dark environments
		Evacuation information continuity based on guide line installation intervals			Installation locations: left and right on the floor, center of the floor, and left and right on the walls
		(e) Interval			(f) Location
		Guide line color			Guide line shape (arrow, dotted line, and straight line)
		(g) Color			(h) Shape
Handrail	Structure on corridor and stair walls, used as walking support and displaying evacuation information
		Evacuation information continuity based on display intervals on the handrail surface			Display color on the handrail surface
		(i) Interval			(j) Color
		Display size on the handrail surface			Display shape on the handrail surface (arrow, dotted line, and straight line)
		(k) Size			(l) Shape
Guidance equipment	Auditory device to prompt evacuation and issue fire alarms
		Presence/absence of a path guidance system		Traditional fire alarm system (e.g., fire alarm bell and blaring horns). Presence/absence of a machine that detects smoke or heat and issues a fire alarm.		System that allows a designated individual to inform evacuation procedures through speakers. Presence/absence of a system that can deliver real-time emergency information using a voice evacuation system.
		(m) Path guidance system		(n) Fire alarm system		(o) Voice evacuation system

.

3.2. Scenarios Under Urgent-Level Circumstances

The evacuation process and behaviors during a fire in long-term care facilities vary based on urgent-level circumstances [1]. Under normal conditions, residents exhibit typical walking behaviors [63]. However, in emergency situations, such as building fires, evacuation is influenced by differing psychological [8] and environmental factors [64], as well as the walking speeds of individuals [5,6,7]. It is critical to consider these variations to enhance safety and evacuation efficiency [65]. This study develops scenarios that reflect building, occupant, and fire characteristics to assess evacuation efficiency [66,67] based on insights from previous research [1,65]. Initially, it is necessary to establish scenario objects, such as building occupants [67]. In this study, scenarios focus on “building-occupant” dynamics, reflecting the characteristics of long-term care facilities and their elderly residents under both normal and emergency conditions related to fire risks [67]. These characteristics are informed by fire survey data from earlier studies [68,69].

Table 3. Scenarios for urgent-level circumstances.

Scenario	Circumstance	Description
Scenario 1	High-level circumstance	Elderly evacuate early owing to severe fire risks. Elevators are inoperable. Continuous fire alarms and alerts guide the evacuation process. Despite differences in cognition and response to evacuation alerts, the elderly simultaneously move toward the exits. The presence of fire is clear, prompting immediate evacuation.
Scenario 2	Low-level circumstance	Fire risks are not immediately threatening; elevators are operational. Those who cannot walk use elevators; others use stairs. All exits are open, with less time variance in heading to exits than in high-risk scenarios. Minor fire anticipated; emergency alerts prompt evacuation.
Scenario 3	Normal circumstance	Nudging factors recognizable in daily life are emphasized. Elderly seek exits and elevators without psychological pressure; fewer simultaneous movements. Departure times to exits are random, with no time constraints.

outlines scenarios for three levels of urgent circumstances to explore how nudging factors influence evacuation route recognition and affordances for the elderly.

Figure 1 shows the derivation of nudging factors that enhance cognitive capabilities during elderly evacuations under these circumstances. Four main nudging factors were categorized into sub-intervention forms and evaluated across the three scenarios, identifying key influences on elderly cognitive enhancement based on the level of urgency and specific intervention forms.

3.3. Data Collection

A questionnaire was developed to summarize the nudging factors identified in Section 3.1, focusing on items that promote the safe and rapid evacuation of the elderly in long-term care facilities. The questionnaire incorporated scenarios reflecting urgent-level circumstances. The questionnaire utilized pairwise comparisons of each factor to facilitate hybrid AHP–TOPSIS analysis. The survey was conducted from 7 February to 11 April 2024 in Daegu, Korea, involving 41 experts divided into academic (n = 20) and practical field experts (n = 21). The survey was conducted in Daegu Metropolitan City, Republic of Korea, from 7 February to 11 April 2024. As of 2024, the elderly population aged 65 or older in Daegu accounts for 21.1% of the total population, exceeding the national average of 20% [70]. Notably, Daegu has rapidly transitioned into a super-aged society, making it an appropriate study location that reflects elderly characteristics and necessitates diverse welfare policies. To capture diverse perspectives among decision-makers and enhance the robustness of the decision-making process, expert groups were classified into academic experts and practical field experts. The academic group included professors from four universities specializing in firefighting and disaster prevention, as well as researchers from three disaster and evacuation research institutes. The practical field experts were managers from five elderly care hospitals. Experts were initially contacted by phone to request their cooperation and subsequently visited in person to conduct the surveys, enhancing the reliability of the responses through detailed explanations and face-to-face interactions.

Table 4. Survey participant sample.

Variable	Item	Academic Field Experts (n = 20)		Practical Field Experts (n = 21)
		n	Percentage (%)	n	Percentage (%)
Gender	Male	10	50	8	38
	Female	10	50	13	62
Academic background	Bachelor’s degree or higher	2	10	18	85.7
	Master’s degree or higher	8	40	3	14.3
	Doctoral degree or higher	10	50	0
Expert groups	Professor	4	20	-	-
	Research	16	80	-	-
	Field expert	-	-	21	100
Work experience	Less than one year	4	20	-	-
	One to three years	9	45	9	42.8
	More than five years	7	35	12	57.2

details the demographic characteristics of the expert groups.

3.4. Hybrid AHP–TOPSIS

Hybrid AHP–TOPSIS is a sophisticated decision-making model that integrates subjective judgments and objective measurements by compensating for the limitations of each model [51,53,71]. This combination of AHP and TOPSIS allows decision-makers to systematically evaluate criteria and alternatives based on expert opinions and data. However, a single criterion is insufficient to assess the levels of factors impacting elderly evacuation. The multicriteria decision-making approach facilitates the evaluation of various criteria, encompassing the visual, auditory, and cognitive aspects relevant to emergency situations.

In this study, expert judgments were analyzed using the hybrid AHP–TOPSIS method to identify the key nudging factors for elderly evacuation. The AHP analysis involved pairwise comparisons to ascertain the relative importance of each alternative, employing Saaty’s scale [52]. The consistency of responses was checked using the consistency index (CI) and consistency ratio (CR), calculated by dividing the CI by the random consistency index (RI). A CR of 0.1 or less indicates consistent responses; a CR of 0.2 or less is considered acceptable [52]. The analysis steps are as follows:

Step 1: Construct a decision matrix with criteria/attributes via pairwise comparison. The matrix is represented as

$$c=\left[\begin{array}{ccccc}{c}_{11}& c_{12}& c_{13}& \cdots & c_{1n}\\ c_{21}& c_{22}& c_{23}& \cdots & c_{2n}\\ ⋮& ⋮& ⋮& \ddots & ⋮\\ c_{n1}& c_{n2}& c_{n3}& \cdots & c_{nn}\end{array}\right],$$

(1)

where c_ij represents the comparative importance of the ith attribute with respect to the jth attribute vis-a-vis the overall objective.

Step 2: Normalize the decision matrix using

$$m_{ij}={\displaystyle \frac{c_{ij}}{\sum _{j=1}^n{c}_{ij}}}.$$

(2)

Step 3: Calculate the local weights of the criteria/sub-criteria and test the consistency.

Prepare the weighted normalized decision matrix as follows:

$$W={\left[w_i\right]}_{n\times 1},$$

(3)

$$where w_i={\sum }_{j=1}{\displaystyle \frac{m_{ij}}{n}},$$

(4)

$$i=\mathrm{1,2},3\dots n,j=\mathrm{1,2},3\dots n.$$

Calculate the consistency vector CV = [cv_i] (i = 1, 2, …, n) used to denote the consistency values for different criteria, where ${cv}_i={\displaystyle \frac{c_{wi}}{wi}}$ for i = 1, 2, …, n.

Determine the maximum Eigen value λ_max using

$${\lambda }_{max}={\displaystyle \frac{{\sum }_{i=1}^n{cv}_i}{n}}.$$

(5)

Calculate the consistency index and consistency ratio. The consistency index is found using

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}},$$

(6)

where n represents the number of criteria. Pairwise comparison is consistently evaluated if the consistency index is 0. The consistency ratio (CR) is calculated to check the consistency using

$$CR={\displaystyle \frac{CI}{RI}}$$

(7)

where RI represents the average random index; the value is determined by different orders of pairwise comparison matrix. If the CR value obtained is smaller or equal to 10% or 0.10, the evaluation of the attribute importance is accepted, and the inconsistency is ignored. Otherwise, the evaluators are asked to revisit their judgments to increase consistency.

Step 4: Construct the normalized decision matrix that has attributes with different units. This matrix is transformed into a dimensionless unit that enables comparisons across criteria. The data are normalized as

$$r_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{\sum x_{ij}^2}}},$$

(8)

where x_ij represents the value of the ith alternative with respect to the jth criterion for i = 1, …, m; j = 1, …, n.

Step 5: Determine the weighted normalized matrix v_ij by multiplying each column of the matrix r_ij by weight w_j obtained by AHP.

$$v_{ij}=w_j{r}_{ij}.$$

(9)

Step 6: Determine the ideal (best) and negative-ideal (worst) solutions using

$$A^{\mathrm{*}}=\left\{\left({\sum }_i^{max}{v}_{ij}|j\mathrm{\epsilon }J\right),\left({\sum }_i^{min}{v}_{ij}|j\mathrm{\epsilon }J\prime \right)|=\mathrm{1,2},\cdots m\right\}=\left\{v_1^{+},v_2^{+},v_3^{+},\cdots v_n^{+}\right\},$$

(10)

$$A^{-}=\left\{\left({\sum }_i^{min}{v}_{ij}|j\mathrm{\epsilon }J\right),\left({\sum }_i^{max}{v}_{ij}|j\mathrm{\epsilon }J\prime \right)|=\mathrm{1,2},\cdots m\right\}=\left\{v_1^{-},v_2^{-},v_3^{-},\cdots v_n^{-}\right\},$$

(11)

where J = (j = 1, 2, …, n); j is associated with the beneficial attributes, and J′ = (j′ = 1, 2, …, n)/j is associated with the non-beneficial attributes. The maximum value of the benefit attributes and the minimum value of the cost attributes are considered for the positive ideal solution (A*), whereas the minimum value of benefit attributes and the maximum value of cost attributes is considered for the negative ideal solution ($A^{-}$).

Step 7: Measure the distance of each alternative from the ideal solution using (for i = 1, 2, …, m)

$$S_i^{\mathrm{*}}=\sqrt{{\sum }_{j=1}^n{(v_{ij}-v_j^{+})}^2}.$$

(12)

The distance from the negative ideal solution is measured as

$$S_i^{-}=\sqrt{{\sum }_{j=1}^n{(v_{ij}-v_j^{-})}^2}.$$

(13)

Step 8: Determine the relative closeness to the ideal solution using

$$C_i^{\mathrm{*}}=\left(S_i^{-}\right)/(S_i^{\mathrm{*}}+S_i^{-})$$

(14)

Step 9: Rank the set of alternatives according to the relative closeness values C_i^* in descending order. A higher relative closeness value is the better alternative.

Figure 2 shows the process used to derive nudging factors influencing elderly evacuation under urgent-level circumstances.

4. Results

4.1. Nudging Factor

This study aims to identify the nudging factors affecting cognitive enhancement among elderly individuals under varying levels of evacuation scenarios through the involvement of expert groups. Considering the uncertainty in responses among expert groups, we evaluated the relative importance of nudging factors through pairwise comparisons based on a quantitative numerical scale. The TOPSIS analysis, based on AHP analysis to ensure evaluation reliability, identified key nudging factors for decision-makers. For the analysis, this study aims to determine the detailed factors of the most effective nudging factors for enhancing elderly cognition across different evacuation scenarios (high-level, low-level, and normal circumstances). Accordingly, four nudging factors were selected as the main criteria. The main and sub-criteria were derived based on the literature review in chapter 2 and are summarized in Table 2 (Section 3.1). The specific scenarios for each evacuation level were established in Section 3.2. The overall hierarchical structure is illustrated in Figure 3.

The consistency ratio (CR) for these factors was verified at 0.1, indicating reliable responses.

Table 5. Weight calculation for main- and sub- criteria using AHP.

Scenario	Main Criteria	Weight of Main Criteria		Sub-Criteria	Weight of Sub-Criteria		Weight of Main Criteria X Weight of Sub-Criteria
		Academic Experts	Practical Experts		Academic Experts	Practical Experts	Academic Experts		Practical Experts
							Weight	Rank	Weight	Rank
Scenario 1	Guidance light	0.31770	0.25504	Interval	0.34823	0.23072	0.11063	2	0.05884	8
				Height	0.19058	0.23114	0.06054	8	0.05894	7
				Color	0.18216	0.19706	0.05787	9	0.05025	10
				Size	0.27904	0.34108	0.08865	3	0.08698	3
	Guide line	0.35753	0.24970	Interval	0.37578	0.24803	0.13435	1	0.06193	5
				Location	0.24245	0.36457	0.08668	5	0.09103	2
				Color	0.23853	0.21344	0.08528	6	0.05329	9
				Shape	0.14324	0.17395	0.05121	10	0.04343	13
	Handrail	0.15446	0.17936	Interval	0.40060	0.25373	0.06187	7	0.04550	12
				Color	0.20680	0.26570	0.03194	14	0.04765	11
				Size	0.26358	0.33928	0.04071	12	0.06085	6
				Shape	0.12901	0.14129	0.01992	15	0.02534	15
	Guidance equipment	0.17031	0.31590	Path guidance device	0.28348	0.21824	0.04827	11	0.06894	4
				Fire alarm system	0.51563	0.13084	0.08781	4	0.04133	14
				Voice evacuation system	0.20089	0.65092	0.03421	13	0.20562	1
Scenario 2	Guidance light	0.23319	0.31372	Interval	0.29854	0.21340	0.06961	6	0.06694	6
				Height	0.21122	0.23661	0.04925	10	0.07422	5
				Color	0.20366	0.19737	0.04749	11	0.06191	8
				Size	0.28658	0.35263	0.06682	7	0.11062	2
	Guide line	0.36750	0.24421	Interval	0.34124	0.24761	0.12540	1	0.06046	9
				Location	0.31079	0.31844	0.11421	2	0.07776	4
				Color	0.22212	0.27215	0.08162	4	0.06646	7
				Shape	0.12585	0.16180	0.04624	12	0.03951	11
	Handrail	0.18976	0.14452	Interval	0.32154	0.26700	0.06101	8	0.03858	12
				Color	0.22233	0.25495	0.04218	14	0.03684	13
				Size	0.29918	0.30731	0.05677	9	0.04441	10
				Shape	0.15695	0.17074	0.02978	15	0.02467	15
	Guidance equipment	0.20955	0.29755	Path guidance device	0.33528	0.33524	0.07025	5	0.09975	3
				Fire alarm system	0.45059	0.11011	0.09442	3	0.03276	14
				Voice evacuation system	0.21413	0.55465	0.04487	13	0.16503	1
Scenario 3	Guidance light	0.21080	0.25200	Interval	0.36323	0.20223	0.07656	5	0.05096	9
				Height	0.26181	0.24425	0.05518	9	0.06155	7
				Color	0.16129	0.22707	0.03399	13	0.05722	8
				Size	0.21367	0.32646	0.04504	11	0.08226	3
	Guide line	0.34675	0.18296	Interval	0.35646	0.20985	0.12360	1	0.03839	14
				Location	0.32064	0.34469	0.11118	3	0.06306	6
				Color	0.19441	0.25294	0.06741	7	0.04627	10
				Shape	0.12849	0.19251	0.04455	12	0.03522	15
	Handrail	0.20244	0.19867	Interval	0.38468	0.22770	0.07787	4	0.04523	11
				Color	0.16293	0.21929	0.03298	14	0.04356	13
				Size	0.29601	0.33267	0.05992	8	0.06609	5
				Shape	0.15639	0.22034	0.03165	15	0.04377	12
	Guidance equipment	0.24000	0.36637	Path guidance device	0.21594	0.21675	0.05182	10	0.07941	4
				Fire alarm system	0.48011	0.53477	0.11522	2	0.19592	1
				Voice evacuation system	0.30395	0.24848	0.07294	6	0.09103	2

presents the AHP results, highlighting the importance of nudging factors under different urgency circumstances as perceived by academic experts. The analysis differentiated factors critical for high-level, low-level, and normal circumstances. In high-level circumstances, the guide line (0.36), guidance light (0.32), guidance equipment (0.17), and handrail (0.15) were deemed most important. For low-level circumstances, the importance shifted marginally to the guide line (0.37), guidance light (0.23), guidance equipment (0.21), and handrail (0.19). In normal circumstances, the prioritization was the guide line (0.35), guidance equipment (0.24), guidance light (0.21), and handrail (0.20), indicating the consistent relevance of the guide line across all scenarios.

Field experts found guidance equipment (avg. 0.32) and guidance light (avg. 0.27) to significantly enhance cognitive capabilities during evacuations under all conditions, though the guide line (avg. 0.17) and handrail (avg. 0.22) were viewed as less effective.

Overall, guidance lights (avg. 0.26) were considered important for cognitive enhancement, whereas handrails (avg. 0.17) were deemed less crucial for conveying evacuation information. Opinions varied between the expert groups: academic experts rated the guide line highly (avg. 0.35) in contrast to the practical field experts (avg. 0.22). Practical field experts valued guidance equipment more highly (avg. 0.32) compared to academic experts (0.36) under normal circumstances.

4.2. Intervention Forms of Nudging Factors

The analysis of intervention types, or sub-criteria, based on Table 5 shows that the influence of nudging factors on elderly cognitive enhancement during evacuation varies significantly across different urgent-level circumstances and between academic and practice experts.

According to the academic experts’ perspectives, across different scenarios, specific intervention forms emerged as particularly effective. In high-level circumstances, the guide line interval (0.134) was the most impactful, followed by the guidance light interval (0.110), guidance light size (0.088), guidance equipment with a fire alarm system (0.087), guide line location (0.086), and guide line color (0.085). For low-level scenarios, the most effective factors were the guide line interval (0.125) and location (0.114), and the guidance equipment’s fire alarm system (0.094) was also notable. Under normal circumstances, the pattern continued with the guide line interval (0.123) leading, supported by the guidance equipment’s fire alarm system (0.115) and guide line location (0.111). All circumstances revealed the importance of the guide line interventions (interval, location, and color), guidance light interval, and the guidance equipment’s fire alarm system, indicating the significance of continuous information exposure. For auditory-based guidance equipment, the fire alarm system was highlighted as especially effective, assisting the elderly in intuitively recognizing fire emergencies owing to the familiarity of the alarm sound.

According to insights from the practice experts, in high-level circumstances, the guidance equipment’s voice evacuation system (0.205) was deemed most effective, indicating the value of diverse auditory information in emergencies. This was also prominent in low-level scenarios, where the same system (0.165) was most influential, along with guidance light size (0.110) and the guidance equipment’s path guidance device (0.099). In normal circumstances, the guidance equipment with a fire alarm system (0.195) took precedence, reflecting its importance in standard conditions.

Overall, guidance equipment, particularly the voice evacuation system, was identified as crucial, with its importance increasing with the urgency of the scenario. The larger light-emitting surfaces of guidance lights and strategic guide line locations were also emphasized. Additionally, the findings suggest that in high- and low-level circumstances, the voice evacuation system, providing detailed voice information such as ignition points and evacuation paths, can significantly enhance elderly evacuation compared to standard fire alarm alerts.

To facilitate the understanding of the AHP results, Figure 4 shows the influence of the main and sub-criteria of nudging factors, highlighting the differences between expert groups. Factors close to the X and Y axes represent divergent views between the groups. For practice experts, the importance of providing auditory information based on the urgency level is emphasized. Marks near gray-dashed lines indicate factors with similar influence levels across groups, such as the location of the guide line and the size of the guidance light in high-level circumstances, and the interval of the guidance light in low-level circumstances.

Table 6. TOPSIS analysis results for nudging factors.

Scenario	Main Criteria	Sub-Criteria	TOPSIS
			di+	di−	CCi	Rank
Scenario 1	Guidance light	Interval	0.027	0.190	0.126	7
		Height	0.011	0.192	0.055	12
		Color	0.011	0.194	0.054	13
		Size	0.028	0.179	0.134	6
	Guide line	Interval	0.042	0.185	0.186	3
		Location	0.030	0.175	0.146	5
		Color	0.025	0.188	0.117	8
		Shape	0.012	0.194	0.059	11
	Handrail	Interval	0.020	0.189	0.097	9
		Color	0.010	0.191	0.048	14
		Size	0.019	0.182	0.095	10
		Shape	0.000	0.199	0.000	15
	Guidance equipment	Path guidance device	0.034	0.169	0.168	4
		Fire alarm system	0.056	0.176	0.240	2
		Voice evacuation system	0.194	0.026	0.881	1
Scenario 2	Guidance light	Interval	0.010	0.153	0.060	10
		Height	0.010	0.153	0.060	11
		Color	0.007	0.156	0.042	12
		Size	0.023	0.139	0.144	6
	Guide line	Interval	0.030	0.148	0.167	5
		Location	0.030	0.141	0.175	4
		Color	0.019	0.146	0.117	7
		Shape	0.004	0.159	0.023	14
	Handrail	Interval	0.012	0.155	0.073	9
		Color	0.006	0.158	0.035	13
		Size	0.015	0.151	0.092	8
		Shape	0.000	0.162	0.002	15
	Guidance equipment	Path guidance device	0.057	0.107	0.347	2
		Fire alarm system	0.057	0.140	0.289	3
		Voice evacuation system	0.156	0.024	0.868	1
Scenario 3	Guidance light	Interval	0.012	0.140	0.082	9
		Height	0.007	0.140	0.047	12
		Color	0.004	0.143	0.024	15
		Size	0.012	0.135	0.080	10
	Guide line	Interval	0.033	0.134	0.196	4
		Location	0.030	0.126	0.194	5
		Color	0.014	0.137	0.095	8
		Shape	0.005	0.144	0.034	14
	Handrail	Interval	0.022	0.133	0.143	7
		Color	0.006	0.141	0.040	13
		Size	0.024	0.124	0.162	6
		Shape	0.009	0.137	0.062	11
	Guidance equipment	Path guidance device	0.040	0.107	0.271	3
		Fire alarm system	0.147	0.000	1.000	1
		Voice evacuation system	0.100	0.050	0.666	2

details the TOPSIS analysis results, which are used to prioritize nudging factors by integrating expert opinions. Across scenarios 1 and 2, similar effective nudging factors were identified. Priority is given to auditory information through guidance equipment (e.g., voice evacuation system, fire alarm system, and path guidance device) during elderly evacuation. The arrangement of the guide line at strategic locations and the use of guidance lights with large light-emitting surfaces are also crucial. In scenario 3, under normal circumstances, the location and interval of guidance equipment and guide lines are highly ranked, while the priority for guidance lights diminishes. Providing information via handrails is found to be relatively effective under normal circumstances. The results suggest that auditory information significantly enhances elderly cognitive capabilities, and the continuity and size of visual information exposure are critical.

5. Discussion

5.1. Nudging Factors in Terms of Cognitive Enhancement During Elderly Evacuation

For effective fire evacuation, it is crucial to first recognize the risk, then enhance cognitive ability through evacuation information, and familiarize evacuees with the evacuation process through education [24]. Providing a guidance system can minimize pre-evacuation delays and trigger evacuation behaviors. However, the impact of education may be limited for evacuation-vulnerable groups such as the elderly, who may struggle with understanding and responding to the guidance system owing to cognitive and functional limitations. Thus, further research is needed to explore these groups’ specific needs and enhance evacuation efficiency [72]. This study examined nudging factors that can improve cognitive abilities related to evacuation and wayfinding [24], focusing on identifying and applying effective nudging factors to aid elderly evacuation [72].

A key finding is elderly cognitive enhancement varies depending on the intervention forms of the nudging factors. Effective nudging interventions, such as interval (continuous strip) and size (light-emitting surface and mark), align with findings that continuous photoluminescent tiles along a path can significantly increase evacuation speed by over 20% [9]. This study confirms that continuous information strips such as guidance light interval, guide line interval, and handrail interval are vital, carrying significant weight.

The size of the light-emitting surface of the guidance light is crucial, but caution is required with the continuity of guidance light placement. There is a risk that the elderly may misinterpret guidance lights installed above exits, potentially leading to erroneous behaviors [73,74,75]. This study’s recommendation to install guidance lights at regular intervals contrasts with previous findings suggesting that lights should only be placed above final exits [73,74,75]. Enhancing the clarity of guidance light pictograms could mitigate these misunderstandings, improving exit and wayfinding recognition.

To improve elderly cognitive abilities, comprehensive information utilizing vision, hearing, and touch is necessary. Auditory cues such as shouting “fire” or sounding an alarm can trigger evacuation by inducing psychological tension and anxiety [49]. Among the guidance equipment, academic experts find the fire alarm system most effective, although it may delay actual evacuation owing to the “cry-wolf effect” [42]. In contrast, practice experts favor the voice evacuation system for enhancing elderly cognition in urgent situations, consistent with a previous research finding that larger buildings benefit from efficient warning systems such as emergency communication systems [76]. These nudging factors offer simple methods to reduce uncertainty in elderly evacuation behavior and decrease evacuation times.

5.2. Elderly Cognitive Enhancement According to Urgent-Level Circumstances

There is a notable difference in evacuation performance between the elderly and younger individuals under varying urgent-level circumstances. These circumstances can significantly influence elderly evacuation behavior, decision-making, and psychological responses [1], indicating the need to consider these factors in elderly evacuation research. In this study, scenarios representing different levels of urgency were utilized to evaluate nudging factors.

Analysis revealed that the intervention forms of nudging factors that affect cognitive enhancement during elderly evacuation differed significantly between urgent (high and low levels) and normal circumstances. However, no significant difference was observed between the high and low urgency levels. This may be attributed to the intense psychological responses—such as tension, anxiety, and impatience—experienced during urgent circumstances. The inability to fully capture the real-time psychological state of the elderly in these scenarios highlights a limitation of this study, as the nudging factors were derived through expert surveys. Future research needs to validate the effectiveness of these factors, considering the specific dynamics of urgent-level circumstances.

Moreover, strong stimuli present in high-urgency scenarios can disrupt cognitive processes, potentially leading to unresponsive or lethargic behavior, such as failing to notice evacuation signs [77] or fainting [49]. Thus, employing evacuation signage that counters the elderly’s typical preference for stability is crucial in high-urgency settings [78]. Evacuation signs cannot ensure elderly cognition in high-level circumstances [1]. Previous research has shown that dynamic signage can significantly enhance cognitive recognition—from 38% to 77%—by providing clearer evacuation cues [22]. Dynamic signs, therefore, hold promise for increasing sensory affordances, making evacuation information more noticeable to the elderly.

This study primarily considered static signs owing to methodological constraints, which may have diluted the perceptual differences between high and low urgency levels. Recognizing this limitation, future research involving experimental investigations with dynamic signage could offer valuable insights into enhancing elderly evacuation effectiveness.

5.3. Bridging the Knowledge Gap Between Academic and Practice Experts

Fire risk awareness, influenced by the individual–environment-risk paradigm, necessitates integrating the insights of diverse expert groups—including the elderly, individuals with fire experience, firefighters, disaster prevention theorists, and fire medical experts [49]. This approach is similar to the methodology of previous studies, which have emphasized understanding various perspectives [49,79,80]. Academic experts often focus on theoretical knowledge, while practice experts engage directly with real-world applications, leading to differing viewpoints. Recognizing and balancing these perspectives, rather than favoring one over the other, is essential [79,80].

Previous studies have highlighted these differences: building and firefighting engineers have varied opinions on fire safety [79], and research on optimal evacuation routes using architectural spaces has shown discrepancies between expert groups [79]. Additionally, surveys on fire risk awareness between the elderly and medical experts have revealed divergent views [49]. Nevertheless, consensus has been achieved on key design factors in building design and disaster prevention planning through extensive expert consultation [79]. Installing firefighting equipment in the right place based on the knowledge of firefighting experts is necessary; however, the concept of evacuation support can be strengthened by reflecting parts recognized through the opinions of experts in each field.

In this study, there was a consensus between academic and practice experts regarding the importance of the continuity and size of auditory and visual information in nudging factors. If implemented in long-term care facilities, these factors could significantly enhance the elderly’s cognition of evacuation information. However, opinions on “guidance equipment” differed: academic experts favored the “fire alarm system” (a traditional warning system) but were skeptical of the “voice evacuation system” based on the assumption that traditional methods are more suitable given the elderly’s information literacy [76]. In contrast, practice experts believed that providing specific auditory information through the “voice evacuation system” could enhance elderly cognitive abilities, as direct, actionable information facilitates quicker decision-making and information acceptance. Addressing these divergent views will require further in-depth analysis of nudging factors to assess their cognitive and functional affordances and usage preferences in supporting elderly evacuation.

6. Conclusions

This study demonstrated that the application of nudging factors based on urgent-level circumstances significantly influences elderly cognitive enhancement during fire evacuations in long-term care facilities. The research utilized a hybrid AHP–TOPSIS method, analyzing responses from groups of academic and practice experts. The findings indicate that differences in understanding nudging factors related to elderly evacuation exist between these groups. Key factors identified include the continuity of information provided by the installation intervals of guide lines and guidance lights, as well as the size of the light-emitting surfaces.

This study found that direct and frequent signage in urgent situations enhances the effectiveness of nudging factors by reinforcing information continuity. Similarly, larger sizes of light-emitting surfaces in guidance lights are beneficial for elderly cognition. Additionally, auditory information was identified as crucial, with notable differences in preferred methods of information delivery. Significantly, this study quantitatively assessed factors that enhance cognitive abilities during elderly evacuations, deriving weights and ranks for various nudging factors. These results can improve evacuation efficiency by addressing the extensive time elderly individuals spend in the pre-evacuation stage, including wayfinding and route selection. This can serve as a basis for establishing evacuation policies in elderly-related facilities and, as a derivative effect, potentially enhance life safety for elderly individuals within the local community. The findings contribute to the development of more accurate elderly agent models for fire evacuation simulations, providing a reliable database for such models. Future research should aim to objectively verify (e.g., pre-evacuation time, response time measurement, etc.) the effectiveness of these nudging factors for elderly residents in long-term care facilities. Furthermore, a personalized approach is deemed necessary for analyzing the effectiveness of nudging factors, considering the cognitive, physical, and sensory differences among elderly individuals. This individualized perspective may contribute to a more precise assessment and implementation of nudging strategies in elderly evacuation scenarios.