Decision fatigue of surrogate decision-makers: a scoping review

Abstract

Background

Surrogate decision-makers are prone to decision fatigue when faced with medical decisions, which might compromise decision quality.

Objective

Conducting a scoping review of studies related to decision fatigue in surrogate decision-makers, this work synthesizes the current state of decision fatigue, its influencing factors, assessment tools, and coping strategies, providing a reference for the implementation of decision support.

Methods

Guided by scoping review methodology, we systematically searched databases including the Cochrane Library, PubMed, Embase, Web of Science, CINAHL, CNKI, COVIP, and Wanfang databases as well as pertinent grey literature was carried out. Two researchers were responsible for the selection, extraction, and analysis of the literature, and Cohen’s kappa was calculated to determine interrater reliability.

Results

The initial search retrieved 2048 documents, from which 25 articles were ultimately selected for inclusion. Among the included articles, 15 (60%) were cross-sectional studies, 1 (4%) was a longitudinal study, 6 (24%) were qualitative research, 1 (4%) was a randomized controlled trial, and 2 (8%) were quasi-experimental studies. The decision fatigue scores of surrogate decision makers in critically ill patients varied from 5.2 to 14.18, whereas those in non-critically sick patients ranged from 10.53 to 23.97. The primary evaluation tools were the Decision Fatigue Scale, which included versions DFS-7, DFS-9, and DFS-10. In surrogate decision-making, decision fatigue is influenced by decision behaviour factors, self-regulation factors, and situational factors, suggesting the multidimensional character of decision fatigue. The coping strategies included health education and intrinsic motivation interventions.

Conclusions

Although decision fatigue is common among surrogate decision makers, there are variations in study scores that require further investigation to confirm. Assessment tools lack diversity and are inconsistent. Decision fatigue is influenced by multiple factors. In the future, new assessment tools should be introduced and developed with the goal of quickly identifying and thoroughly assessing the causes and mechanisms of decision-making fatigue among surrogate decision makers. The intervention outcomes lacked generalizability. Future research should focus on developing targeted, multi-centre interventions with large sample sizes to reduce decision fatigue, improve decision quality, and promote a healthcare decision-making system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-025-03198-y.

Introduction

Surrogate Decision-Maker (SDM) is a group of people who make medical decisions on behalf of the patient when the patient is severely ill or has lost decision-making capacity [1]. They can be the patient’s relatives, legally qualified representatives (legal guardians, power of attorney), or healthcare professionals [2]. According to studies, around 70% of patients are unable to make autonomous choices at the end of their lives [3]. So these patients must depend on surrogate decision-makers for some or all of their medical choices [3]. Surrogate decision-makers play an important part in this process. This quick, massive, and frequent decision-making might overload the substitute decision maker’s brain, resulting in Decision Fatigue (DF) [4].

Baumeister pioneered the concept of decision fatigue based on the Strength Model of Self-Control [5]. Subsequent research defined the definition of decision fatigue, which is a state of decreased decision-making ability and control behavior experienced by individuals as a result of repetitive decision-making [6]. Different from ego depletion, decision fatigue is a symptom or manifestation of ego depletion [7]. Surrogate decision-maker that experience decision fatigue show behavioral, cognitive, and physiological deficiencies. They will make impulsive, reluctant, or avoidant decisions, leading to impaired executive functioning and reasoning ability [8]. This is followed by a decline in physical endurance, which reduces the quality of decision-making. Decisions are even made that are harmful to the patient’s interests and impair illness healing [8].

Fortunately, the phenomenon of decision fatigue of surrogate decision-makers has gradually attracted the attention of scholars in recent years [6]. They carried out an initial investigation on the prevalence of decision fatigue, influencing elements, and evaluation instruments [9]. However, we discovered that the results lacked systematic summarizing and analysis, and there were inconsistencies [6, 9]. Therefore, it is essential to systematize decision fatigue among surrogate decision makers better in order to investigate the phenomena. A scoping review may swiftly discover current sources of evidence and research shortfalls by conducting a thorough search of the research progress in this topic [10]. Based on this, the scope review standards created by Arksey and O’Malley were employed in this study to provide a framework for analyzing relevant literature on decision fatigue among surrogate decision makers [11]. The goal was to thoroughly search the relevant literature on surrogate decision makers’ decision fatigue both domestically and internationally in order to support the efficacy and scientific nature of nursing interventions for surrogate decision makers and to serve as a guide for future research and clinical practice.

Aim

This scoping review’s objectives are to provide an overview of the current state of affairs and the instruments used to assess decision fatigue in relation to surrogate decision makers, identify the factors that influence decision fatigue in relation to surrogate decision makers, and evaluate intervention strategies aimed at reducing decision fatigue in alternative decision makers.

Methods

The study is structured in accordance with the scoping review framework devised by Arksey and O’Malley [11], which encompasses five distinct stages: (1) Defining the research question; (2) Locating relevant studies; (3) Conducting study selection; (4) Extracting and charting the data; (5) Synthesizing, summarizing, and disseminating the results. This scoping review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews(PRISMA-ScR) checklist [12] (Additional file 1).

Defining the research question

The research questions for this literature search were: (1) What is the current distribution of surrogate decision makers’ decision fatigue levels? (2) What elements influence surrogate decision makers’ degree of decision fatigue? (3) Which instruments may be used to measure surrogate decision makers’ degree of decision fatigue? (4) What interventions are available for decision fatigue among surrogate decision-makers?

Locating relevant studies

Computers were used to search databases such as the Cochrane Library, PubMed, Embase, Web of Science, CINAHL, CNKI, CQVIP, and WAN-FANG database. The search period covered the time from when each of these databases was established to July 2025. Additionally, reference lists and various grey literature sites were searched for relevant articles. Retrieval was done using a mix of topic and free words, and each database retrieval should have its search terms modified accordingly. The operators “AND,” “OR,” and “NOT” in Boolean logic were used for thorough retrieval. In order to build the search strategy, we looked for “Decision Fatigue” and “Surrogate Decision Maker,” along with all other pertinent keywords, MeSH, and index phrases, as well as combinations of these terms and suitable synonyms. For usage in the Chinese databases, we next converted the English keywords into their equivalent Chinese terms. The complete search algorithms are available in Additional File 2.

Conducting study selection

The inclusion criteria were the following: ①The subjects were surrogate decision makers of patients with various diseases; ②The research focuses on surrogate decision makers’ decision fatigue (e.g., evaluation tools to measure the degree of decision fatigue, significant elements in decision fatigue, etc.). ③Research types included quantitative, qualitative, and hybrid methods; ④The articles must be published in Chinese or English. The exclusion criteria were the following: ①Incomplete or inaccessible information. ②Reviews, conference papers, systematic reviews, and secondary investigations.

Extracting and charting the data

All obtained literature was imported into Endnote 9.1, and duplicates were removed. Two researchers with systematic evidence-based training separately read the literature’s title and abstract for preliminary screening, followed by the complete text for re-screening. In the event of a dispute between the two researchers, a third viewpoint was added, and the third researcher was contacted before making a decision. We computed Cohen’s Kappa statistics to assess inter-rater reliability [13].

After establishing the number of included literatures, data need to be extracted, including authors, nations, publication dates, sample sizes, evaluation indicators, influencing variables, research subjects, study kinds, intervention measures, intervention outcomes, and so on.

Synthesizing, summarizing, and disseminating the results

The included literature was presented in the form of a table, and the four dimensions of decision fatigue were discussed from the research status, influencing factors, assessment tools, and intervention measures.

Results

Study characteristics

A total of 2048 articles were retrieved. Following the exclusion of 894 duplicate publications, the titles and abstracts of the remaining 1154 publications were examined for preliminary screening. The remaining 45 pieces of literature were then studied in their entirety for re-screening, and 25 articles were finally included. Figure 1 depicts the method of screening the literature.

Fig. 1.

Literature screening process(PRISMA flow diagram)

Characteristics of sources of evidence

This research includes 25 papers, including 14 Chinese articles (56%) and 11 English articles (44%). There were 15 cross-sectional studies (60%), 1 longitudinal study (4%), 6 qualitative studies (24%), 1 randomized controlled trial (4%), and 2 quasi-experiments (8%) among the study designs in the evaluated articles. The literature was published between 2012 and 2025, with 18 of the relevant works (72%) appearing during the last five years (Fig. 2). The included studies enrolled between 10 and 856 participants. Quantitative research included between 30 and 856 participants, and qualitative studies had between 10 and 174. Table 1 displays the fundamental features of the literature.

Fig. 2.

Country and temporal distribution of included research

Table 1.

Basic characteristics of the included literature

Author	Country	Year	Sample size (control/intervention group)	Evaluation indicator	DF score (control/intervention group)	Research subject	Study design
Ji FF [14]	China	2024	183	DFS-9	14.18 ± 4.86	ICU patients’ spouses, parents, and kids	Cross-Sectional survey
Zhang Y [15]	China	2024	209	DFS-9	13.65 ± 4.50	Parents and grandparents of leukemia-stricken children	Cross-Sectional survey
Xiong GC [16]	China	2024	182	DFS-9	10.53 ± 1.65	Parents of leukemia-stricken children	Cross-Sectional survey
Li SS [17]	China	2024	123	DFS-9	12.16 ± 3.81	Parents and grandparents of critically ill children	Cross-Sectional survey
Zhang ZY [4]	China	2023	192	DFS-9	13.00(11.00,16.00)	Children and spouses of cancer patients	Cross-Sectional survey
An X [18]	China	2023	314	DFS-9	12.33 ± 4.88	ICU patients’ spouses, parents, and kids	Cross-Sectional survey
Wang JN [19]	China	2022	310	DFS-9	9.13 ± 1.73	Parents of critically ill children	Cross-Sectional survey
Pignatiello GA [20]	the US	2020	215	DFS-10	/	spouses, kids, or relatives of patients in critical condition	Cross-Sectional survey
Pignatiello GA [8]	the US	2019	97	DFS-10	/	spouses, kids, or relatives of patients in critical condition	Cross-Sectional survey
Hickman RL Jr [1]	the US	2018	101	DFS-10	5.2 ± 4.0	spouses, kids, or relatives of patients in critical condition	Cross-Sectional survey
Li L [21]	China	2023	86	DFS-9	12.47 ± 4.52	Surrogate decision-makers for critically ill people	Cross-Sectional survey
Pignatiello GA [22]	the US	2023	32	DFS-9	5.8(5.5)	ICU patients’ spouses and kids	Longitudinal Study
Pignatiello GA [23]	the US	2022	160	DFS-9	9.6	Nurse	Cross-Sectional survey
Fernández-MG [24]	Colombia	2023	856	DFS-7	5.32(5.11)	Health care workers	Cross-Sectional survey
Pan GC [25]	China	2020	191	DFS-9	/	ICU patients’ spouses, parents, and kids	Cross-Sectional survey
Hur Y [26]	South Korea	2024	211	DFS-9	13.43 ± 6.21 11.55 ± 4.70	Nurse	Cross-Sectional survey
Iverson E [27]	the US	2013	34	/	/	Parents, partners, and children of critically ill patients	Qualitative Study
An X [28]	China	2023	12	/	/	ICU patients’ parents	Qualitative Study
Qi CH [29]	China	2023	15	/	/	ICU patients’ spouses, parents, and kids	Qualitative Study
Iverson E [30]	the US	2012	74	/	/	Parents, partners, and children of critically ill patients	Qualitative Study
White DB [31]	the US	2016	174	/	/	Parents, partners, children, or relatives of critically sick patients	Qualitative Study
Maier M [32]	Britain	2025	10	/	/	Health care workers	Qualitative Study
Li XL [33]	China	2024	35/35	DFS-9	22.15 ± 2.48/ 22.18 ± 2.77	The spouses and relatives of individuals with cognitive impairment	RCT
Yin JL [34]	China	2023	30/30	DFS-9	21.17 ± 3.23/ 20.43 ± 4.12	Siblings, spouses, or kids of elderly COPD patients	quasi-experiment
Zhang YH [35]	China	2022	30/31	DFS-9	23.62 ± 21.1/ 23.97 ± 3.07	Parents, partners, or children of older COPD patients	quasi-experiment

Note:“/“ means no mention; DFS-7 refers to the 7-item Decision Fatigue Scale; DFS-9 refers to the 9-item Decision Fatigue Scale; DFS-10 refers to the 10-item Decision Fatigue Scale

The current situation of decision fatigue of surrogate decision-makers

We conducted a systematic evaluation of 25 publications that covered research from five different nations. Of these, 14 studies were from China (56%), eight were from the United States (32%), and one each from South Korea (4%), Colombia (4%), and the United Kingdom (4%). In this research, 21 publications (84%) involved the patient’s legal guardian (e.g., parent, spouse, child, or next-of-kin) in substitute decision-making, whereas 4 papers (16%) involved the healthcare practitioner. Additionally, 16 publications provided decision fatigue scores of substitute decision makers. Given the clinical heterogeneity, we explored subgroup analyses. When grouped by assessment tool, the Decision Fatigue scores were 5.32 using Decision Fatigue Scale-7 (DFS-7), 5.8-23.97 using Decision Fatigue Scale-9 (DFS-9), and 5.2 using Decision Fatigue Scale-10 (DFS-10). Grouped by study subjects, the decision fatigue scores for those who made decisions for critically ill patients varied from 5.2 to 14.18, while those who made decisions for non-critically sick patients ranged from 10.53 to 23.97. Grouped by country grouping, decision fatigue scores ranged from 9.13 to 23.97 in China, 5.2 to 9.6 in the United States, 5.3 in Colombia, and 11.55 to 13.43 in Korea.

Factors impacting decision fatigue of surrogate decision-makers

This study includes 22 studies that examine the relevant factors affecting decision fatigue among surrogate decision-makers. The research suggests decision behavior, self-regulation, and situational factors are the antecedents of decision fatigue, based on the Strength Model of Self-Control [6]. This not only highlights the multidimensional character of decision fatigue but also gives guidance for developing focused intervention measures. Accordingly, four parameters influencing the decision fatigue of surrogate decision-makers in this study were chosen: demographic factors, decision behavior factors, self-regulating factors, and situational factors. The primary demographic factors were gender (5/22, 22.7%), household income (9/22, 40.9%), and educational attainment (3/22, 13.6%). The main factors influencing decision behavior were disease knowledge (5/22, 22.7%), decision-making conflict (3/22, 13.6%), and number of decisions (2/22, 9.1%). Decision-making self-efficacy (4/22, 18.2%) was the primary cognitive burden element of self-regulation. The primary emotional load elements of self-regulation were negative psychology (7/22, 31.8%), willpower belief (5/22, 22.7%), and social support level (5/22, 22.7%). The primary situational factor was days spent in the ward or intensive care unit (7/22, 31.8%) (Table 2).

Table 2.

Factors influencing decision fatigue in surrogate decision-makers

Categories	Specific factors		Number of studies(%)
Demographic factor	household income [4, 14, 16–19, 21, 28, 29]		9(40.9%)
	gender [14, 17–19, 25]		5(22.7%)
	disease severity [15, 27, 30]		3(13.6%)
	occupational status [18, 24, 32]		3(13.6%)
	educational attainment [4, 14, 18]		3(13.6%)
Decision behavior factor	decision-making conflict [1, 28, 29]		3(13.6%)
	number of decisions [29, 30]		2(9.1%)
	degree of disease knowledge [14, 18, 27–29]		5(22.7%)
Self-regulation factor	cognitive stress	decision-making self-efficacy [15, 17, 18, 20]	4(18.2%)
		sleep problems [16, 19, 22]	3(13.6%)
		Internal cognitive load [1, 8, 32]	3(13.6%)
		decision-making readiness [18, 21]	2(9.1%)
	emotional stress	family resistance level [14]	1(4.5%)
		stress level [14, 23]	2(9.1%)
		self-control [16, 19]	2(9.1%)
		willpower belief [16, 19, 27, 30, 31]	5(22.7%)
		Negative psychology [1, 22, 26, 28–31]	7(31.8%)
		expression inhibition [1, 8]	2(9.1%)
		mental toughness [24]	1(4.5%)
		intolerance of uncertainty [4]	1(4.5%)
		social support level [15, 18, 27, 28, 30]	5(22.7%)
Situational factor	temporal dimension	days of ward / ICU stay [1, 16–19, 25, 30]	7(31.8%)
		decision-making time [28, 29]	2(9.1%)
		duration of diagnosis [4]	1(4.5%)
	environmental dimension	working environment [23, 26, 32]	3(13.6%)

Demographic factor

Regarding demographics, the primary factors were household income, gender, disease severity, educational attainment, and occupational status. Five studies found that gender influenced the amount of decision fatigue among surrogate decision makers. Female decision makers had a greater decision fatigue score than male decision makers [14, 17–19, 25], which may be because they are more likely to experience anxiety and sadness when faced with unpleasant occurrences [36]. Nine studies found a negative correlation between family per capita monthly income and surrogate decision makers’ decision fatigue levels [4, 14, 16–19, 21, 28, 29]. Family wealth influences an individual’s economic stability, choice complexity, and psychological stress level, which in turn impacts decision fatigue. Three research studies found that the more serious the patient’s sickness, the greater the caregiver burden, with surrogate decision makers feeling higher degrees of decision fatigue [15, 27, 30]. However, there is variation in how surrogate decision-makers’ educational attainments affect decision fatigue. Two research studies revealed that surrogate decision makers with high education may employ critical thinking skills to fully examine medical information resources, effectively control emotions, and decrease decision fatigue to some degree [4, 14]. However, the Anxiao team argues that persons with a high level of education, due to their greater access to information channels, may experience information overload during decision-making, interfering with decision-making clarity and increasing the likelihood of decision fatigue [18].

Decision behavior factor

Decision behavior factors to the problem of decision fatigue induced by repeated choices, conflicting options, and complicated decisions made by people continuously [6]. In terms of variables linked with decision behavior, research focuses on the number of decisions and decision-making conflict and has underlined that the degree of disease knowledge directly influences the ability to process information in the decision behavior [37]. Five studies thought that surrogate decision makers should contact with medical professionals on time and thoroughly grasp the patient’s condition and other pertinent medical facts. Such information makes it easier to make objective and logical decisions, which lowers ego depletion and decision fatigue [14, 18, 27–29]. Two qualitative studies found that due to the severity of the patient’s illness, family members were forced to make hard judgments frequently, resulting in avoidance or passive decision-making behavior. Furthermore, when presented with several decision-making possibilities, family members were difficult to assess and quantify, leading to “choice overload” and decision fatigue [29, 30]. Three studies found that when people make decisions, they encounter various decision-making conflicts, including patient benefit and risk conflict, decision preference conflict, and role conflict [1, 28, 29]. Such situations will result in ongoing psychological tension and ego depletion among surrogate decision-makers [29]. It not only reduces the efficiency and quality of decision-making, but it may also influence the timeliness of patient treatment [38].

Self-regulating factor

Self-regulation is the ability to sustain and manage individual execution and mood, including cognitive and emotional burden [6]. The ability to self-regulate is essential in medical decision-making, particularly for surrogate decision makers [6]. The main factor influencing decision fatigue with respect to the cognitive burden dimension was decision-making self-efficacy. Four studies found a negative correlation between decision-making self-efficacy, which is a measure of confidence in one’s own decision-making abilities, and decision fatigue. This means that strong self-efficacy may efficiently process information while reducing cognitive load and decision fatigue [15, 17, 18, 20]. Decision fatigue is particularly impacted by the negative psychology and social support of surrogate decision-makers when it comes to the emotional burden factor. Among them, seven research studies found that negative psychological states such as anxiety, fear, and depression had a direct impact on the emotional load of surrogate decision makers [1, 22, 26, 28–31]. When people are unable to bear the uncertainty caused by the sickness [4] or choose to conceal and suppress their feelings [1, 8], these unpleasant emotions will be exacerbated, exhausting psychological resources and finally leading to decision fatigue. Conversely, surrogate decision makers who possess mental resilience are more equipped to control their emotions and lessen their emotional load, which lowers the likelihood of decision fatigue [24]. Furthermore, research found that the social support obtained by surrogate decision makers from family, friends, social institutions, and experts, including emotional, informational, and material assistance, helped them feel understood and valued. This emotional comfort serves to cushion their psychological strain throughout the decision-making process, encourages involvement in decision-making, reduces decision-making weariness, and improves decision quality and efficiency [15, 18, 27, 28, 30].

Situational factor

Situational factors are those connected to time and environment that might influence individual behaviors, decision-making processes, and psychological states [6]. Surrogate decision makers’ decision fatigue is influenced by time dimension parameters such as days spent in the ward or ICU, decision-making time, and illness diagnostic time, as well as environmental dimension factors such as the working atmosphere. Decision fatigue and the number of days spent in the ward or intensive care unit(ICU) were positively correlated in seven trials [1, 16–19, 25, 30]. Prolonged hospitalization increases decision makers’ emotional attrition. Additionally, as diseases change, there is a greater frequency of urgent decisions, which surely adds to the psychological load of decision makers by making them tired and making decisions more difficult [28, 29]. Despite the widely held belief that the length of time to diagnosis of disease is positively linked with decision fatigue, one study presents a different view [4]. This study showed that long-term illness experience can help surrogate decision makers better understand the nature of the disease and the wishes of patients, as well as more effectively determine decision-making priorities and rationally arrange the order and focus of decision-making, thereby reducing decision fatigue [4].

Decision fatigue evaluation tool of surrogate decision makers

Among the studies addressed, DFS-10 was chosen in three, DFS-9 in fifteen, DFS-7 in one, and the other six articles made no mention of the measuring instrument. Hickman created the DFS as a measure to quantify decision-making fatigue based on the Strength Model of Self-Control. Concentrating on the population of substitute decision-makers for critically ill patients [1]. The Cronbach’s α value was 0.87, while the test-retest reliability was 0.90. The scale consists of ten items, each of which is rated from “strongly disagree (scoring = 0)” to “strongly agree (score = 3)”, with a total score ranging from 0 to 30, with higher scores suggesting greater degrees of decision fatigue. Following psychometric examination, the initial scale (DFS-10) was changed to the 9-item DFS (DFS-9). Pan Guocui translated the revised scale into Chinese in 2020. The Cronbach’s α value was 0.854, and the test-retest reliability was 0.863, indicating strong internal consistency [25]. Hur Y translated it into Korean and implemented it there in 2024. With a Cronbach’s score of 0.88, the internal consistency was the same good [26]. Based on the low factor loadings found in the initial validation study, Fernández-Miranda G created the 7-item DFS (DFS-7) after removing three items from the original survey [24]. However, its reliability and validity have not yet been established.

Interventions to relieve decision fatigue in surrogate decision makers

Health education intervention

One research study used a health education intervention [33]. The “4Y in place” photo-elicitation interview approach was used to provide targeted health education to surrogate decision makers. Based on the photo’s tale, the patient received a follow-up plan with the basic components of “plan in place,” “responsibility in place,” “check in place,” and “incentive in place”. Video conferencing, peer sharing, self-reflection, and other methods were used to increase decision support in various settings, emphasising the relevance of decision-making complexity, repeatability, and emotion control. It seeks to increase the surrogate decision maker’s grasp of the disease and perception of the choice, lowering decision fatigue that may occur throughout the continuous decision-making process.

Intrinsic motivation intervention

Two studies used motivational interventions. Yin Jianli [34] integrated the FOUCS and information-motivation-behavioral (IMB) models to create a multi-dimensional intervention of information, motivation, and behaviour across five dimensions: family involvement, optimistic attitude, uncertainty reduction, coping effectiveness and symptom management. It attempts to direct carers’ decision-making stages, decrease the negative impact of recurrent decision-making, increase intrinsic motivation, and enhance self-efficacy in order to successfully alleviate decision fatigue. Zhang Yuanhui [35] used the FOUCS model in conjunction with ATDE to include the four parts of asking, thinking, doing, and evaluating into the development of thinking capacity, emphasising the importance of individual emotion and cognition in decision-making. From a cognitive standpoint, “need identification,” “cognitive deepening,” “behavioural change,” and “goal setting” were employed to enable carers to make choices, increase coping efficacy, and reduce uncertainty(Table 3).

Table 3.

Interventions related to decision fatigue in surrogate decision-makers

Author	Sample size (control/intervention group)	Evaluation indicator	DF score (control/intervention group)	Research subject	Study design	intervention measure	intervention outcome
Li XL [33]	35/35	DFS-9	22.15 ± 2.48/ 22.18 ± 2.77	The spouses and relatives of individuals with cognitive impairment	RCT	Control group: routine wechat follow-up Intervention group: “4Y in Place” photo-inspired interview follow-up	The “4Y in place” photo-inspired interview can effectively reduce the decision fatigue of caregivers of patients with post-stroke cognitive impairment
Yin JL [34]	30/30	DFS-9	21.17 ± 3.23/ 20.43 ± 4.12	Siblings, spouses, or kids of elderly COPD patients	quasi-experiment	Control group: routine follow-up Intervention group: IMB combined with FOUCS model follow-up	IMB combined with FOUCS model can effectively reduce the decision fatigue of caregivers of elderly patients with COPD in the community
Zhang YH [35]	30/31	DFS-9	23.62 ± 21.1/ 23.97 ± 3.07	Parents, partners, or children of older COPD patients	quasi-experiment	Intervention group: FOUCS model combined with ATDE follow-up Intervention group: FOUCS model combined with ATDE follow-up	FOUCS model combined with ATDE can effectively improve the decision fatigue of caregivers of elderly COPD patients

Discussion

A scoping review of the literature on decision fatigue in surrogate decision-makers is presented in this paper. American scholars first addressed the possibility of decision fatigue among surrogate decision makers in 2012, doing relevant research [30]. Since the introduction of related concepts in China in 2020, research on this topic has been increasing [25]. However, it is still in the early phases of investigation. Currently, research on decision fatigue is dominated by observational research designs, with cross-sectional studies being more common. However, due to the dynamic nature of decision fatigue, cross-sectional studies struggle to capture its patterns across time, much alone infer causative pathways. Although some researchers sought to address this problem through longitudinal studies, the study’s sample attrition rate was 41% [22]. This may result in biased findings that must be cautiously interpreted and confirmed. As a result, it is proposed that future studies improve their study design by combining several research methodologies, such as mixed-methods studies and prospective cohort studies. This will increase the depth and breadth of the study and provide a more comprehensive perspective.

Decision fatigue is common among surrogate decision makers, according to the research that reported decision fatigue scores. However, there are discrepancies in the scores obtained from the studies that require more testing. Notably, subgroup analyses revealed that groups making decisions for critically ill patients had relatively low decision fatigue scores when measured on a uniform scale, and there was less variation across trials. On the one hand, DFS was originally developed based on surrogate decision makers for critically ill patients and may be more disease-specific [1]. On the other hand, non-critical patients tend to have impaired decision-making capacity due to chronic illness [35]. It is frequently necessary for their surrogate decision makers to take on decision-making duties for extended periods of time [39]. This may increase the risk of decision-making endurance depletion and decision-making fatigue [4]. Given the limited number of studies and population heterogeneity, the findings may not be comparable. As a result, future investigations need to be undertaken in homogeneous groups to investigate intrinsic patterns. Second, we should increase the emphasis on surrogate decision makers for chronic illnesses while continuing research into acute and critical diseases. Finally, investigate the effect of the temporal dimension on decision fatigue. Consider the dose-effect study of decision duration and tiredness level.

The DFS is the primary tool to assess decision fatigue among surrogate decision-makers, which included versions DFS-7, DFS-9, and DFS-10. However, in practice, there are differences in the assessment results of these three versions. Compared to the DFS-9, decision fatigue scores measured with the DFS-10 and DFS-7 were generally lower. This might be because the DFS-9 measures decision fatigue with higher sensitivity and specificity and more precisely reflects research participants’ experiences with decision fatigue. Currently, DFS-9 has been translated and used in study by Chinese and Korean researchers, demonstrating high internal consistency (Cronbach’s α value > 0.8) [25, 26]. However, we discovered that the decision fatigue scores evaluated using the translated version in China and Korea were generally higher than those obtained from research utilising the original scale in the United States and Colombia. This might be connected to the scale’s translation and adaption in various cultural contexts. Because the original scale was designed in a Western cultural setting, its essential notions and expressions may differ from those in Chinese and Korean cultures. As a result, future study might investigate and improve the scale’s structural validity in order to eliminate disparities between versions and raise the scale’s generalisability. Moreover, although several versions are currently in use, all of them are based on the DFS, which lacks diversity. Additionally, because of its retrospective design, the DFS is susceptible to recall bias and may underestimate tiredness [40]. Thus, based on the available information, it is advised that future study focus on designing evaluation scales that are appropriate for the local cultural context, while including objective indicators, in order to more fully determine the real extent of decision fatigue.

In the research covered, the amount of decision fatigue among surrogate decision-makers is impacted by a combination of demographic characteristics, decision-making behaviours, self-regulation abilities, and situational circumstances. Among them, the impacts of cultural literacy and sickness diagnosis time on surrogate decision-makers’ decision fatigue are debatable and require more investigation with bigger samples or meta-analyses. Self-regulation is a variable that is commonly used in influencing factor research. Because this ability may be enhanced by acquired intervention, it is especially important to comprehend its underlying causal process [41]. However, there is no study that investigates the influence route of various variables on decision fatigue, and it is impossible to define the mediating variables and effect size, limiting the in-depth implementation of intervention research to some extent.

Currently, intervention research on decision fatigue is at the exploratory stage. The three studies included in this analysis, despite adopting the intervenable elements of self-regulation and situational variables as entrance points and constructing particular intervention programmes based on theoretical models. Some effects were detected in small-scale trials, but the overall methodological design of the studies was inadequate, lacking rigor and scientific integrity. These are single-center, non-randomized controlled trials with limited sample numbers, limiting the generalizability and accessibility of the intervention outcomes. Moreover, the existing studies have not yet delved deeply into the underlying mechanisms of decision fatigue, and the theoretical frameworks employed are relatively singular. To further evaluate the effectiveness of the intervention plan, future research should conduct multi-centre, large-sample randomized controlled trials. Simultaneously, several cognitive decision-making theoretical models could have combined to thoroughly analyze the mechanism of decision-making fatigue and develop a more scientific and systematic strategies to increase the program’s dependability.

Limitations

This scoping review adheres precisely to the PRISMA-ScR checklist, yet three limitations remain. First, while grey literature was examined during the literature search and inclusion procedure, only research published in English or Chinese were included. This may result in the exclusion of significant research or data published in other minor languages, increasing the danger of linguistic bias. Second, the 25 included research showed an unequal distribution among nations. Although subgroup analyses were conducted, the results may still be biased. As a result, while referring to the study’s conclusions, various nations need to take into account their cultural differences. Finally, the inclusion of studies with variations in evaluation methods, research subjects, and other factors resulted in greater inter-study heterogeneity, which may have an influence on the accuracy and generalizability of the findings.

Conclusion

Surrogate decision makers confront serious challenges in healthcare environments, and decision fatigue is a significant concern. There has been some progress in contemporary research on decision fatigue among surrogate decision makers. However, there is still a lack of longitudinal studies with multiple dimensions and time points, inconsistency and lack of diversity in assessment tools, complexity and partial disagreement in influencing factors, and a lack of scientific validity of interventions, making the study results incomparable and generalizable. It is recommended that standardized, multidimensional measuring techniques be developed and implemented in the future to detect decision fatigue in a timely and accurate manner. Furthermore, undertake a current state study in homogenous populations to investigate the underlying reasons and mechanisms, resulting in scientific and precise intervention measures. These initiatives are anticipated to reduce decision fatigue, enhance decision quality, and advance the medical decision-making system.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

SSC: Conceptualization, Methodology, Formal analysis, drafting of the first version of the manuscript, critical revision of the manuscript, Validation, Visualization. MZ: Formal analysis, Supervision, Validation. ZHS: Conceptualization, Methodology. YMYH: Methodology, Validation, Visualization. MCH: Conceptualization, Formal analysis, Validation. QYS: Conceptualization, Methodology, Writing -review & editing.

Funding

The authors received no specific funding for this work.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.