Decision Fatigue and Associated Factors Among Patients with Hematologic Malignancies: Implications for Patient-Centered Decision Support.

Introduction

Hematologic malignancies, encompassing leukemia, lymphoma, and multiple myeloma, represent a substantial global cancer burden. In China, the overall incidence and mortality of hematologic malignancies both rank among the top 10 of all cancer types, 1 , 2 with age-standardized incidence rates of approximately 8.31 per 100,000 population, 3 posing considerable challenges to public health and clinical care. In recent years, with advancements in medical technology, therapeutic approaches for hematologic malignancies have become increasingly diverse, including chemotherapy, radiotherapy, transplantation, targeted therapy, chimeric antigen receptor T-cell (CAR-T) therapy, and micro-transplantation. 4–6 Faced with multiple treatment options, patients must comprehensively evaluate numerous factors, such as therapeutic efficacy, potential risks, prognosis, physical tolerance, and financial burden, and make repeated complex decisions during illness, leading to decision fatigue (DF). DF refers to a state of impaired self-regulatory capacity and consequent physical, mental, and cognitive distress caused by frequent and complex decision-making tasks. 7 This condition not only affects patients’ adherence to treatment plans and potentially leads to suboptimal decisions, but also impacts therapeutic outcomes and quality of life, sometimes resulting in post-decision regret. 8

Globally, research on decision fatigue in oncology has largely focused on solid tumor populations, including breast, brain, and bladder cancers, with growing evidence of its prevalence and impact on treatment outcomes. 9 In China, existing studies have similarly concentrated on patients with solid tumors, such as gastrointestinal and breast cancers, 10 , 11 while research on hematologic malignancies remains scarce. Hematologic malignancies differ from solid tumors in biological behavior, treatment trajectory, and decision-making patterns, with rapid progression, high heterogeneity, and repeated treatment adjustments contributing to unique decision-making stress. To date, most studies on DF in hematologic settings have focused on surrogate decision-makers of pediatric leukemia patients 12 , 13 and evidence regarding DF among adult patients with hematologic malignancies remains limited both internationally and in China. To address this knowledge gap, the present study investigated DF levels and their associated factors in adult patients with hematologic malignancies, aiming to provide evidence for developing targeted, patient-centered decision support strategies.

Theoretical Basis

Decision fatigue was initially proposed by Baumeister et al 14 within the framework of the strength model of self-control. This model posits that self-control relies on a limited resource, and repeated decision-making depletes this resource, leading to decision fatigue. Building on this model and prior research, Pignatiello et al 7 conducted a concept analysis of decision fatigue in 2020, identifying its antecedents across three categories: decision-related factors, self-regulatory factors, and contextual factors. In the context of this study, patients with hematologic malignancies are required to make repeated decisions during treatment. Their readiness to engage in such decisions—termed decision preparation—is considered a decision-related factor. The confidence these patients have in their ability to make informed healthcare decisions—referred to as decision self-efficacy—reflects their cognitive appraisal of whether a decision exceeds their capacity and is categorized as a self-regulatory factor. Additionally, general demographic and disease-related characteristics, anxiety and depression, and sleep quality are regarded as contextual factors influencing decision-making. Therefore, this study incorporates four variables—decision preparation, decision self-efficacy, anxiety and depression, and sleep quality—to examine their relationships with decision fatigue.

Material and Methods

Study Participants

A convenience sampling method was adopted to recruit patients with hematologic malignancies who received treatment in the Department of Hematology at a tertiary first-class hospital in Anhui Province from October 2024 to May 2025. The inclusion criteria were as follows: (1) Diagnosed with leukemia, lymphoma, or multiple myeloma according to the Diagnostic and Therapeutic Criteria for Hematologic Diseases; 15 (2) currently receiving active treatment for hematologic malignancies; (3) aged ≥ 18 years; (4) possessing clear and fluent language expression and comprehension abilities; and (5) voluntarily participating in the study and signing the informed consent form. The exclusion criteria were: (1) Having clinically diagnosed or self-reported severe psychiatric disorders that could affect questionnaire completion; (2) currently experiencing severe disease exacerbation or having unstable clinical conditions and unable to complete the questionnaire; and (3) currently participating in other studies related to psychology or cognition. The sample size was calculated using the formula, \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document}$N = {{4U_\alpha ^2{S^2}} \over {{\delta ^2}}}$\end{document} 16 which is appropriate for estimating means with a specified precision in single-group studies, particularly when the primary outcome is continuous and normally distributed. In this formula, U represents the value corresponding to the significance level, α, S denotes the standard deviation, and δ is the allowable margin of error. A two-tailed test was applied with α = 0.05, yielding U = 1.96. The standard deviation ( S = 2.08) was derived from a previously published study assessing similar measures in a comparable adult population, 9 ensuring realistic effect size estimation. The allowable error ( δ ) was set at 0.25 times the standard deviation, resulting in δ = 0.52, a commonly accepted threshold in epidemiological surveys to balance feasibility and precision. 17 Substituting these values into the formula gave \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document}$N = {{4 \times {{(1.96)}^2} \times {{(2.08)}^2}} \over {{{(0.52)}^2}}} \approx 246$\end{document} . To account for potential non-response and invalid questionnaires, a 20% attrition rate was adopted, the sample size was at least 308. Ultimately, 322 individuals were enrolled, exceeding the target and thus enhancing statistical robustness.

Methods

Research Instruments

General Information Questionnaire (GIQ)

The General Information Questionnaire (GIQ) was designed based on a combination of literature review and research objectives. The questionnaire was structured around two primary dimensions: demographic characteristics and disease-related data. The demographic dimension encompassed age distribution, gender, educational level, marital status, occupation, average monthly household income, area of residence, mode of medical payment, and primary caregiver. The disease-related dimension focused on the type of hematologic malignancy, disease duration, treatment modalities, and presence of comorbid chronic conditions.

Decision Fatigue Scale (DFS)

The DFS was developed by Hickman et al 18 in 2018 and translated into Chinese by Pan et al 19 in 2020. It is used for self-assessment of fatigue levels among healthcare decision-makers. The scale is unidimensional and consists of 9 items rated on a 4-point Likert scale (0–3) with end points of “strongly disagree” and “strongly agree.” The total score ranges from 0 to 27, with higher scores indicating greater levels of DF. The Cronbach’s α coefficient of the original scale was 0.854, 19 and it was 0.881 in this study.

Preparation for Decision Making Scale (PrepDMS)

The PrepDMS was developed by Bennett et al 20 in 2010 and translated into Chinese by Li 21 in 2017. It is used to assess an individual’s readiness to engage in decision-making. The scale is unidimensional and consists of 10 items rated on a 5-point Likert scale (1–5) with end points of “not at all” and “a great deal.” The total score ranges from 10 to 50, with higher scores indicating higher levels of decision preparedness. The Cronbach’s α coefficient of the original scale was 0.946, 21 and it was 0.923 in this study.

Decision Self-Efficacy Scale (DSES)

The DSES was developed by O’Connor 22 in 1995 and translated into Chinese by Wang et al 23 in 2020. It is designed to measure an individual’s confidence in making informed choices within the healthcare process. The scale is unidimensional and consists of 11 items rated on a 5-point Likert scale (0–4) with end points of “not at all confident” and “very confident.” The total score ranges from 0 to 44, with higher scores indicating greater decision self-efficacy. The Cronbach’s α coefficient of the original scale was 0.918, 23 and it was 0.933 in this study.

Hospital Anxiety and Depression Scale (HADS)

The HADS was developed by Zigmond et al 24 in 1983 and has been widely used in Chinese clinical settings with established cross-cultural validity. The Chinese version used in this study underwent cultural validation in a previous study. 25 It is used to assess anxiety and depressive states in individuals within hospital settings. The scale includes 14 items divided into two subscales, the Anxiety Subscale (HADS-A) and the Depression Subscale (HADS-D), each containing 7 items. Each item is rated on a 4-point Likert scale (0–3), with higher scores reflecting increasing severity of anxiety or depression symptoms. The Cronbach’s α coefficient of the scale was 0.879, 25 and it was 0.885 in this study.

Pittsburgh Sleep Quality Index (PSQI)

The PSQI was developed by Buysse et al 26 in 1989 and translated into Chinese by Liu et al 27 in 1996. It is used to assess individual sleep quality. It consists of 18 self-rated items forming seven factors: sleep quality, sleep latency, sleep duration, sleep efficiency, use of sleep medication, sleep disturbances, and daytime dysfunction. Each factor scored on a 4-point Likert scale (0–3). The total score ranges from 0 to 21, with higher scores indicating poorer sleep quality. A total score > 7 suggests sleep disturbances. 26 The Cronbach’s α coefficient of the original scale was 0.842, 27 and it was 0.722 in this study.

Data Collection Method

This study was conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki and its subsequent amendments. Ethical approval was obtained from the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University. (Approval number: SL-YX2025-039). Data acquisition was carried out by credentialed nursing professionals. All data collectors received 4 hours of study-specific training prior to study initiation, covering study objectives, questionnaire content, standardized administration protocols, and communication skills for participants with comprehension difficulties. A written administration manual was used throughout the study to ensure procedural consistency. Regular calibration meetings held monthly were held to resolve discrepancies and maintain inter-interviewer reliability.

Prior to enrollment, comprehensive information about the research purpose and significance was conveyed to all participants and their family members. Written informed consent was obtained after a detailed explanation of the study procedures. All questionnaires were administered in person and collected immediately on site, with each completed form checked for completeness. Questionnaires with > 5% missing critical data were excluded from analysis.

For participants with low education levels, visual impairment, or difficulty understanding items, researchers used standardized language to explain the questionnaire and conducted face-to-face question-and-answer interviews, recording responses verbatim to ensure data accuracy. We acknowledge that assisted administration may introduce potential interviewer or measurement bias, and standardized protocols were implemented to minimize this risk.

Ultimately, 333 eligible patients were invited to participate and received the questionnaire. After excluding 11 invalid questionnaires (defined as > 20% missing critical data), 322 valid questionnaires were included in the final analysis, yielding an effective response rate of 96.69%. For the remaining cases with incomplete data (all < 20% missing), multiple imputation was applied, as the missing proportion was low and the missingness pattern appeared to be random.

Statistical Analysis

Data analysis was conducted using SPSS version 29.0 (IBM Corp., Armonk, NY, USA). Categorical variables were presented as frequencies and percentages. Data analysis was conducted using SPSS version 29.0 (IBM Corp., Armonk, NY, USA). Categorical variables were presented as frequencies and percentages. The normality of continuous variables was assessed using the Shapiro–Wilk test. Normally distributed continuous variables were presented as mean ± standard deviation (SD).

Independent-samples t-tests and one-way ANOVA were used for univariate comparisons of decision fatigue scores across demographic and clinical subgroups. Pearson correlation analysis was performed to examine bivariate associations between decision fatigue and continuous psychosocial variables. The adequacy of treating education, monthly household income, and disease duration as ordinal predictors was assessed by comparing the ordinal-coded models with corresponding categorical models using nested-model partial F -tests. Multiple linear regression analysis was conducted to identify factors independently associated with decision fatigue.

Assumptions of multiple linear regression were evaluated systematically: linearity was assessed via scatter plots of standardized residuals versus predicted values; homoscedasticity was examined using the Breusch–Pagan test; independence of residuals was verified with the Durbin–Watson statistic; normality of residuals was checked via normal Q–Q plots and the Shapiro–Wilk test; influential observations were identified using Cook’s distance; and multicollinearity was evaluated using variance inflation factors (VIF<5,D-W=2.032,Cook’s distance<0.05). A P - value < 0.05 was regarded as statistically significant.

Results

General Information of Patients with Hematologic Malignancies

A total of 333 questionnaires were distributed. After excluding 11 invalid questionnaires defined as > 5% missing critical data (eg, demographics, primary outcome variables), 322 valid questionnaires were collected, yielding an effective recovery rate of 96.69%. The general information of 322 patients with hematologic malignancies is presented in Table 1 , including demographic characteristics (age, gender, educational level, etc.) and clinical characteristics (disease type, disease duration, recurrence status, etc). Table 1 General Information and Univariate Analysis of DF in Patients with Hematologic Malignancies ( n = 322) Variable Category n (%) Decision Fatigue Score ( χ±s ) Statistic P- Value Age 18- 67 (20.8) 12.28±4.94 F =2.630 0.074 45- 154 (50.4) 13.23±5.33 >60 101 (28.2) 14.08±4.46 Gender Male 170 (52.8) 11.80±4.74 t =−5.975 <0.001 Female 152 (47.2) 14.98±4.79 Educational Level Illiterate 52 (16.1) 15.56±4.36 F =7.792 <0.001 Primary School 76 (23.6) 13.93±4.77 Junior High School 96 (29.8) 13.26±4.98 High School 46 (14.3) 13.02±4.55 College and above 52 (16.1) 10.44±5.22 Place of Residence Urban 125 (38.8) 12.29±5.28 t =−2.919 0.004 Rural 197 (61.2) 13.94±4.74 Marital Status Unmarried 23 (7.1) 13.17±4.80 F =2.581 0.770 Married 277 (86.0) 13.13±5.09 Widowed or Divorced 22 (6.8) 15.64±3.73 Employment Status Employed 83 (25.8) 11.84±5.20 t =−3.113 0.002 Unemployed 239 (74.2) 13.81±4.86 Type of Medical Insurance Provincial Insurance 5 (1.6) 10.40±3.50 F =5.505 0.004 Municipal Employee Insurance 75 (23.3) 11.83±5.34 Urban and Rural Resident Insurance 242 (75.2) 13.82±4.84 Monthly Household Income <5000 120 (37.3) 14.07±4.74 F =4.596 0.011 5000–10,000 124 (38.5) 13.44±4.62 >10,000 78 (24.2) 11.90±5.76 Caregiver Presence Yes 211 (65.5) 14.71±4.80 t =7.526 <0.001 No 111 (34.5) 10.62±4.29 Disease Type Leukemia 157 (48.8) 13.62±5.31 F =1.147 0.319 Lymphoma 95 (29.5) 12.65±4.62 Multiple Myeloma 70 (21.7) 13.47±4.83 Disease Duration <6 months 82 (25.5) 15.43±4.52 F =7.517 <0.001 6 months–1 year 68 (21.1) 12.99±4.99 1–5 years 128 (39.8) 12.63±5.01 ≥5 years 44 (13.7) 11.80±4.90 Disease Recurrence Yes 80 (24.8) 15.85±5.15 t =5.470 <0.001 No 242 (75.2) 12.46±4.68 Comorbid Chronic Disease Yes 139 (43.2) 13.37±5.35 t =0.227 0.821 No 183 (56.8) 13.25±4.76 Treatment Type Single Modality 194 (60.2) 13.52±4.98 F =1.850 0.159 Two Modalities 112 (34.8) 12.70±4.85 Three or More Modalities 16 (5.0) 14.94±6.21

Univariate Analysis of Decision Fatigue in Patients with Hematologic Malignancies

Univariate analysis showed that gender, educational level, place of residence, employment status, type of medical insurance, monthly household income, caregiver availability, disease duration, and disease recurrence were significantly associated with DF among patients with hematologic malignancies ( P < 0.05). The results are presented in Table 1 .

Correlation Analysis of Decision Fatigue with Decision Preparation, Decision Self-Efficacy, Anxiety, Depression, and Sleep Quality

The mean total DF score among patients with hematologic malignancies was 13.30 ± 5.02. The mean total decision preparation score was 30.94 ± 7.84, the decision self-efficacy score was 24.09 ± 8.50, the anxiety score was 7.06 ± 4.06, the depression score was 7.16 ± 4.31, and the PSQI was 8.72 ± 4.33.

Pearson correlation analysis showed that DF was negatively correlated with decision preparation and decision self-efficacy ( r = −0.711, −0.778, P < 0.001), whereas it was positively correlated with anxiety, depression, and the PSQI ( r = 0.254, 0.259, 0.617, P < 0.001).

Multiple Linear Regression Analysis of Factors Associated with Decision Fatigue in Patients with Hematologic Malignancies

The total DF score was treated as the dependent variable. Variables with P < 0.05 in univariate or correlation analyses were initially selected for inclusion in the multivariable model. This variable selection strategy was chosen to prioritize robust bivariate associations in this understudied population, and was further guided by the conceptual framework of decision fatigue antecedents proposed by Pignatiello et al, 7 which classifies correlates into decision-related, self-regulatory, and contextual domains. We acknowledge that reliance on unadjusted p-values alone may omit clinically relevant confounders and produce unstable estimates, and this limitation is addressed in the Limitations section.

Binary categorical variables were coded as dichotomous indicators, while nominal categorical variables with more than two categories were represented using dummy variables with clinically meaningful reference groups ( Table S1 ). Education level, monthly household income, and disease duration were entered as ordinal predictors. Nested-model partial F -tests showed that modelling these variables categorically did not significantly improve model fit compared with ordinal coding for education level ( F = 0.962, P = 0.411), monthly household income ( F = 0.647, P = 0.422), or disease duration ( F = 1.411, P = 0.245). Therefore, the ordinal specification was retained in the final model.

The results indicated that female gender, caregiver presence, shorter disease duration, disease recurrence, lower decision preparation, lower decision self-efficacy, and poorer sleep quality were independently associated with higher DF levels among patients with hematologic malignancies ( Table 2 ). Table 2 Multiple Linear Regression Analysis of Factors Associated with Decision Fatigue in Patients with Hematologic Malignancies ( n = 322) Variable Unstandardized Coefficient Standard Error Standardized Coefficient t P VIF Constant 22.044 1.463 – 15.072 <0.001 Gender −0.872 0.345 −0.087 −2.524 0.012 1.231 Education Level 0.151 0.164 0.039 0.923 0.357 1.849 Place of Residence 0.626 0.443 0.061 1.414 0.158 1.929 Occupation 0.423 0.449 0.037 0.942 0.347 1.598 Provincial Insurance 0.289 1.361 0.007 0.212 0.832 1.172 Urban and Rural Resident Insurance 0.206 0.488 0.018 0.422 0.673 1.844 Monthly Household Income −0.251 0.264 −0.039 −0.953 0.341 1.719 Caregiver Availability 0.822 0.371 0.078 2.215 0.028 1.287 Disease Duration −0.430 0.201 −0.068 −2.143 0.033 1.054 Disease Recurrence 0.817 0.388 0.070 2.107 0.036 1.161 Decision Preparation −0.149 0.033 −0.233 −4.529 <0.001 2.744 Decision Self-Efficacy −0.263 0.033 −0.444 −7.959 <0.001 3.241 Anxiety −0.069 0.077 −0.056 −0.895 0.371 4.039 Depression 0.085 0.074 0.073 1.151 0.251 4.227 Sleep Quality 0.234 0.046 0.202 5.112 <0.001 1.631 Notes : D-W=2.032,R 2 = 0.706, adjusted R 2 = 0.690, F = 48.902, P < 0.05.

Discussion

Moderate Level of Decision Fatigue Among Patients with Hematologic Malignancies

This study showed that the total DF score among patients with hematologic malignancies was 13.30 ± 5.02, which, relative to the scale median of 13.5 of the 0–27 Decision Fatigue Scale. As no clinically validated severity thresholds have been established for this scale in hematologic malignancy populations, no categorical severity classification was assigned in the present study. This value is lower than those reported in several studies of patients with solid tumors. 9 , 10 , 28 This discrepancy might be attributable to the fact that hematologic malignancies primarily involve hematopoietic tissues and immune cells, which are non-solid tissues, and that treatment is mainly medical and pharmacological rather than surgical. 29 Compared with patients with gastrointestinal and breast cancers, fewer surgery-related complex decisions are required, which might lead to relatively lower levels of DF. Nevertheless, multiple variable factors that complicate decision-making remain during the treatment process. Differences in the efficacy and cost of novel targeted drugs, CAR-T cell therapy, and hematopoietic stem cell transplantation, together with the issues of donor matching and potential adverse effects, increase the diversity of decision-making. Decisions pertaining to infection prevention and transfusion support are characterized by multiple steps, which may be a contributing factor to decision fatigue in this population. Therefore, DF among patients with hematologic malignancies presents at a moderate level. Consequently, there is a need to develop patient-centered decision support strategies tailored to this population. Rather than simply providing extensive medical information—which may induce information overload and exacerbate cognitive fatigue—clinicians should consider evidence-based approaches aligned with decision science principles. These include structured patient decision aids with visual risk communication, value-clarification exercises to help patients align choices with personal goals, shared decision-making consultations, and delivery of information in manageable staged chunks across multiple visits. However, the efficacy of these strategies for reducing decision fatigue in hematologic malignancies has not been directly evaluated in interventional studies, and their application should be accompanied by ongoing assessment of patient cognitive burden.

Analysis of Factors Influencing Decision Fatigue in Patients with Hematologic Malignancies

Gender

Gender was independently associated with higher DF scores: female patients exhibited higher levels of DF than male patients ( P < 0.05), consistent with previous findings. 30 , 31 The underlying mechanisms for this sex difference were not directly examined in this study. One plausible hypothesis is that cyclical hormonal fluctuations may affect emotional regulation and stress sensitivity in women, 32 potentially amplifying cognitive load during decision-making. An alternative hypothesis relates to traditional gender roles, which often assign greater household and caregiving responsibilities to women, 33 thereby increasing overall decision burden. These interpretations remain speculative and require direct empirical testing. These everyday decision-making demands continuously consume energy and exacerbate DF. Therefore, Healthcare professionals should enhance psychological assessments for female patients and implement customized psychological support sessions. These initiatives should focus on addressing emotional distress during treatment decision - making, thereby reducing energy depletion and alleviating DF.

Caregiver Presence

Caregiver presence was independently associated with higher DF scores. Compared with patients who had no caregiver during hospitalization, those accompanied by caregivers had higher levels of DF. In previous studies, both domestic and international, caregivers of patients with hematologic malignancies bore a heavy caregiving burden, 34–37 experienced moderate-to-severe psychological distress, 38 and often exhibited negative emotions such as anxiety. 39 Based on the theory of interpersonal interdependence, 40 we hypothesize that caregiver distress may be transmitted to patients, who may additionally balance their own treatment preferences against caregiver expectations, thereby increasing decision-related cognitive load. However, caregiver burden, relationship quality, decisional involvement, and caregiver psychological status were not directly measured in this study, so this interpretation remains a plausible but untested hypothesis. Therefore, healthcare providers should pay particular attention to patients with caregivers. On the basis of the extent of caregiving burden, both patients and caregivers should receive psychological counseling and caregiver education to promote a more scientific understanding of the disease, reduce uncertainty and anxiety, and prevent additional decision-making burdens on patients.

Disease Duration

Similar to gender and caregiver presence, disease duration was associated with DF. Patients with shorter disease durations exhibited higher levels of DF, consistent with Zhu et al 28 A possible reason for this finding is that sudden onset of disease may brings strong psychological shock. The transition in social roles makes patients worry about the impact of the disease on their body, life, and work. In addition, limited knowledge about disease symptoms, progression, treatment options, and prognosis, combined with exposure to multiple treatment recommendations within a short time, leads to confusion and uncertainty. Consequently, patients might experience DF when making medical decisions. 41 As the disease duration extends, patients may gradually adapt to living with their illness and develop a more comprehensive understanding of their condition. This growing familiarity and acceptance can contribute to a more rational perspective on treatment options, thereby potentially alleviating decision pressure and fatigue. Healthcare providers should pay particular attention to patients with short disease durations, especially newly diagnosed ones. Through concise materials such as illustrated manuals and short videos, healthcare providers should give patients key information to bolster disease knowledge, and information on treatment options and prognosis, to help them quickly understand their condition and key decision points.

Disease Recurrence

In this study, 24.8% of patients with hematologic malignancies experienced disease recurrence, and those with recurrence exhibited higher levels of DF. This finding is consistent with Kang et al 9 Disease recurrence indicates disease progression, decreased treatment tolerance, and increased therapeutic difficulty. It necessitates reassessment and adjustment of treatment plans, which increases treatment risk and cost. At the same time, recurrence severely affects the psychological state of patients with hematologic malignancies, intensifying negative emotions such as anxiety, depression, and fear. 42 These psychological burdens may compromise patients’ psychological resilience, potentially predisposing them to decision fatigue and helplessness when confronted with new treatment options. It is recommended that healthcare providers develop individualized treatment plans for patients with recurrent disease, offer psychological counseling, and organize sharing sessions featuring successful recovery cases. These strategies can help patients adjust their mindset more effectively and engage rationally in subsequent treatment decisions.

Decision Preparation

The decision preparation score of patients with hematologic malignancies was 30.94 ± 7.84, which was lower than that of patients with breast cancer 11 but higher than that of patients with esophageal cancer. 28 These differences might relate to variations in cancer types, incidence rates, and levels of public attention, leading to differences in public awareness of diseases. We also found that patients’ decision preparation was negatively correlated with DF, indicating that lower levels of decision preparation were associated with higher levels of DF, consistent with previous results. 11 , 28 A higher level of decision preparation can improve the timeliness and accuracy of decision-making, 43 whereas a decision support system can enhance decision preparation. 44 However, in clinical practice, healthcare providers often show a significant deficiency in providing decision support. A survey indicated that approximately 82% of medical personnel have not received training related to decision support, and 66% of clinical practitioners have not effectively applied decision support systems in their work. 45 Therefore, hospitals should implement targeted training programs on decision support for healthcare providers and ensure their supervised use. By improving patients’ readiness for treatment decision-making, decision efficiency could be enhanced, and DF could be reduced.

Decision Self-Efficacy

The decision self-efficacy score of our patients with hematologic malignancies was 24.09 ± 8.50, which is lower than the norm for Chinese cancer patients (31.25 ± 7.89) reported in a national multicenter study 46 and lower than scores for patients with non-small cell lung cancer (28.67 ± 8.12). 47 This indicates a moderate level of decision self-efficacy with significant room for improvement among patients with hematologic malignancies. We also found that patients’ decision self-efficacy negatively correlated with DF ( r = −0.778, P < 0.001). The lower the level of decision self-efficacy, the higher the level of DF, consistent with Mei et al. 10 Patients with low decision self-efficacy tend to lack confidence during the decision-making process, hesitate, and repeatedly weigh pros and cons, which easily leads to anxiety and self-doubt. This behavior makes the decision-making process more complicated and increases fatigue. 48 Therefore, healthcare providers should take measures to enhance patients’ self-efficacy. They might, for example, share successful cases and provide recognition and encouragement to patients who actively participate in decision-making and achieve phased decision goals. Such interventions might help patients gradually build confidence and improve their decision self-efficacy.

Sleep Quality

The PSQI score of our patients with hematologic malignancies was 8.72 ± 4.33 (with a score > 7 indicating sleep disturbance), indicating that sleep disorders were common among these patients and that their sleep quality requires improvement. We also found that the PSQI score was positively correlated with DF. The higher the PSQI score, the poorer the sleep quality, and the higher the level of DF. Sleep duration is closely related to DF. 11 , 49 Patients with sleep disorders experience elevated cortisol levels and reduced immune function, which negatively affect quality of life, mood, cognitive function, and overall health. 50 Prolonged poor sleep quality may also lead to memory decline and slower information processing, 28 making it difficult for individuals to quickly, comprehensively, and accurately integrate and analyze multidimensional information when making decisions. This circumstance may increase decision-making difficulty and further exacerbate DF. In the future, healthcare professionals should provide targeted sleep health education for patients, guide them in establishing regular sleep routines, and offer relaxation and sleep-promoting techniques. For patients with severe sleep disorders, multidisciplinary collaboration should be sought to both improve sleep quality and restore the psychological resources necessary for decision-making.

Interpretation of Attenuated Bivariate Associations

Notably, several variables that showed significant bivariate associations with DF—including education level, place of residence, employment status, type of medical insurance, monthly household income, anxiety, and depression—were no longer statistically significant in the adjusted multivariable model. This attenuation suggests that the crude associations of these sociodemographic and psychological variables with DF may be largely explained by their overlap with decision preparation, decision self-efficacy, and sleep quality.

In particular, anxiety and depression showed significant positive correlations with DF in bivariate analyses but became non-significant after adjustment for decision self-efficacy and sleep quality. This finding indicates that the association between emotional distress and decision fatigue may be partially mediated by reduced decision-making confidence and impaired sleep, rather than reflecting an independent effect. For clinical practice, this suggests that interventions targeting sleep and decision self-efficacy may also mitigate the impact of anxiety and depression on decision fatigue, and should be prioritized alongside general psychological support.

Conclusion

This study investigates DF among adult patients with hematologic malignancies, revealing a DF level slightly below the DFS median (13.30 ± 5.02 vs 13.5). It identified seven key factors independently associated with DF: female gender, presence of a caregiver, shorter disease duration, disease recurrence, lower decision preparedness, lower decision self-efficacy, and poor sleep quality. The study’s innovations include: (1) the counterintuitive finding that caregiver presence increases DF, which differs from the previous assumption that caregivers alleviate decision burden—this highlights the need to address caregivers’ psychological distress to avoid transferring decision pressure to patients; (2) demonstrating a stronger correlation between sleep quality and DF (r = 0.617) than between psychological symptoms (anxiety/depression) and DF, emphasizing sleep intervention as a priority for reducing DF; and (3) identifying a statistically significant association between disease recurrence and higher DF, although the magnitude of the adjusted effect was modest and residual confounding by disease severity and treatment intensity cannot be excluded.

Several limitations of this study should be acknowledged. First, the cross-sectional design cannot establish causal relationships between associated factors and DF; reverse causation and residual confounding remain plausible, and future longitudinal studies are needed to track DF trajectories across treatment cycles. Second, the convenience sample from a single tertiary hospital limits generalizability; multicenter studies across different care settings and regions are required to enhance external validity. Third, all outcomes were measured via self-reported scales, which may be subject to response bias. Fourth, assisted questionnaire administration for participants with comprehension difficulties may introduce interviewer bias, although standardized procedures were implemented to minimize this risk. Fifth, the study included heterogeneous hematologic malignancies (leukemia, lymphoma, multiple myeloma), and detailed information on treatment phase, treatment intensity, prognosis, current decision context, and the number or complexity of decisions faced by patients was not collected. These factors may influence decision fatigue and should be systematically examined in future research.

Despite these limitations, this study provides a foundation for understanding DF in patients with hematologic malignancies. Healthcare professionals should develop targeted interventions: female patients should be provided with gender-specific psychological counseling; joint counseling should be offered for patients with caregivers; concise disease and treatment education should be provided to newly diagnosed patients; and sleep hygiene guidance should be given to patients with poor sleep. These measures could alleviate DF, improve decision-making quality, and ultimately optimize treatment outcomes.

Data Sharing Statement

Data are available from the corresponding author upon reasonable request.

Ethics Approval

This study was conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki and its subsequent amendments. Ethical approval was obtained from the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University. (Approval number: SL-YX2025-039).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare no conflicts of interest.

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