Psychosocial Predictors of Healthcare Decision-Making Capacity in Older Adults: The Role of Cognitive Functioning and Affective Symptoms

1. Introduction

Decision-making capacity in healthcare is a fundamental component of clinical practice, particularly in situations where individuals may be unable to make informed decisions independently [1]. Healthcare decision-making refers to a cognitive and emotional ability to evaluate information, consider alternatives, and accept or refuse proposed medical interventions [2]. This process encompasses decisions regarding diagnostic procedures, treatment options, and other healthcare interventions, requiring the integration of specific cognitive functions [3].

The assessment of decision-making capacity is especially relevant in later life, as aging is frequently associated with increased health problems and cognitive decline, leading to a higher demand for formal capacity evaluations [4]. Older adults with cognitive impairment tend to make less advantageous decisions and exhibit more dysfunctional decision-making profiles compared to cognitively healthy individuals, given that decision-making relies on multiple cognitive domains, including semantic and episodic memory as well as executive functioning [5].

Evaluating healthcare decision-making capacity in older adults is inherently complex, requiring the consideration of psychological variables and contextual factors [6]. This complexity is heightened in conditions such as mild cognitive impairment and early-stage dementia, where deficits may be subtle yet clinically significant [6]. A single clinical interview is typically insufficient; comprehensive assessments should integrate psychological, functional, and neuropsychological evaluations, alongside medical record reviews and collateral information, to ensure a thorough understanding of the individual’s abilities [7].

A widely accepted framework for assessing decision-making capacity is the four-abilities model proposed by Grisso and Appelbaum [8], which conceptualizes capacity as comprising understanding, appreciation, reasoning, and expression of choice. Understanding refers to the ability to comprehend relevant clinical information, including diagnosis, prognosis, treatment options, and associated risks and benefits [9]. Importantly, individuals must demonstrate meaningful comprehension rather than mere repetition of information [10]. Appreciation involves the ability to apply this information to one’s own situation. Reasoning reflects the capacity to logically manipulate information and weigh alternatives, while expression of choice refers to the ability to communicate a clear and consistent decision [10]. Impairment in any of these domains may compromise autonomy and result in diminished decisional capacity [11].

Effective decision-making has traditionally been conceptualized as a process guided by rational evaluation, leading to choices that maximize personal benefit [12]. However, this perspective has evolved to recognize that decision-making is not solely a cognitive process but rather emerges from the interaction between cognitive, emotional, and motivational factors [1]. In this context, affective states—particularly depression and anxiety—play a critical role in shaping how individuals process information and evaluate alternatives [12,13,14].

Depressive symptomatology has been consistently associated with impairments in decision-making. Older adults experiencing active depression demonstrate less adaptive decision-making profiles compared to those in remission or without depressive symptoms [14]. Depression may negatively impact decisional capacity primarily by affecting appreciation—the ability to recognize and value the consequences of different options—while also influencing understanding and reasoning to a lesser extent [11]. Empirical evidence suggests that individuals with higher levels of depressive symptoms tend to seek less information, underutilize available resources, and show reduced ability to resolve ambiguity, even when decision-support tools are provided [12]. Additionally, avoidance of anxiety-provoking situations may lead to the rejection of potentially beneficial but uncertain options [12].

From a cognitive perspective, non-depressed individuals tend to exhibit a positive processing bias, attending more to favorable information and recalling positive experiences more readily [15]. This bias can have adaptive value by promoting motivation and persistence. In contrast, the absence of such bias and the presence of negative cognitive schemas in depression may impair evaluative processes essential for sound decision-making, reinforcing the need for ethically grounded and methodologically robust capacity assessments [11].

Anxiety disorders, among the most prevalent psychiatric conditions globally, are characterized by excessive fear, persistent worry, and avoidance behaviors [16,17]. In older adults, anxiety is associated with functional impairment, cognitive deficits—particularly in memory—and poorer outcomes in comorbid medical conditions [17]. Moreover, even mild worry symptoms may help predict early cognitive decline in healthy, community-dwelling older adults, underscoring the role of subtle emotional manifestations as relevant indicators of cognitive change [18]. Although anxiety is known to influence everyday decision-making, often by promoting threat avoidance [13], its specific impact on healthcare decision-making capacity remains underexplored, highlighting an important gap in the literature.

Mental health conditions are highly prevalent among older adults, with approximately 14% of individuals aged 60 years and older experiencing a mental disorder, most commonly depression [19,20]. Despite not being a normative aspect of aging, depression is frequently underdiagnosed and undertreated, contributing to adverse outcomes such as functional decline, poorer physical health, and increased mortality [21].

Depression and anxiety frequently co-occur and, although distinct in symptomatology, both are associated with significant impairments in decision-making [5,22]. These impairments are particularly relevant in clinical contexts, as they may compromise individuals’ ability to make autonomous and informed healthcare decisions [11]. Alterations in reward and threat processing have been proposed as underlying mechanisms, influencing how individuals evaluate potential gains and losses [22].

Despite the growing body of research on healthcare decision-making capacity, most studies have predominantly focused on cognitive determinants, while comparatively less attention has been given to psychosocial factors, particularly affective symptoms such as depression and anxiety, and their differential contribution to decisional abilities [11,12]. Evidence suggests that the magnitude of these effects varies depending on symptom severity and population characteristics [11]. Although both depression and anxiety have been associated with impairments in decision-making, the specific mechanisms through which these conditions influence distinct components of decisional capacity in older adults remain insufficiently understood [13,22]. In older adults, although cognitive functioning and emotional well-being are interrelated, change in one domain does not consistently predict change in the other; instead, factors such as education, physical functioning, and self-rated health emerge as key predictors of both cognitive trajectories and depressive symptoms [23]. The existing literature is limited by a relative scarcity of studies examining these associations in populations with varying levels of cognitive functioning [6].

Moreover, few studies have adopted a multidimensional approach that simultaneously considers cognitive and emotional variables when examining predictors of healthcare decision-making capacity. As such, a more integrative perspective is needed to better understand the relative and combined influence of these factors on decisional capacity.

The present study addresses these gaps by examining healthcare decision-making capacity within a multidimensional framework, incorporating cognitive functioning and affective symptomatology (depression and anxiety) in a sample of older adults. By exploring psychosocial predictors of decisional capacity, this study aims to contribute to a more comprehensive understanding of the factors that support or undermine autonomy in healthcare contexts. Ultimately, these findings may inform the development of more accurate assessment practices and targeted interventions to promote person-centered and ethically grounded care in aging populations.

The present study aims to: (1) assess healthcare decision-making capacity, cognitive functioning, and depressive and anxiety symptoms in older adults; (2) examine the relationship between decision-making capacity and depressive and anxiety symptomatology; (3) analyze the association between cognitive functioning and decision-making capacity; and (4) explore psychosocial predictors of healthcare decision-making capacity.

2. Materials and Methods

Participants

A total of 60 community-dwelling older adults aged 60 years and above participated in this study (M = 71.17, SD = 5.96). The majority were female (n = 45, 75%), married (n = 39, 65%), and retired (n = 52, 86.7%). Regarding education, 31.7% (n = 19) had completed secondary education, and most participants resided in predominantly urban areas (n = 38, 63.3%).

Functional status was assessed using the IAFAI (Functional Assessment Inventory for Adults and Older Adults [Inventário de Avaliação Funcional de Adultos e Idosos]), indicating overall low levels of functional impairment. Mean disability scores were 1.24% (SD = 2.17) for basic activities of daily living (ADL), 0.41% (SD = 1.28) for instrumental ADL (family-related), and 0.07% (SD = 0.36) for advanced instrumental ADL. The global functional disability index averaged 1.71% (SD = 2.75). Hypertension was the most frequently reported medical condition (n = 15, 25%).

Instruments

Capacity Assessment Instrument – Health (CAI-Health)

Decision-making capacity was assessed using the Capacity Assessment Instrument – Health (CAI-Health) [3,24]. This instrument includes a clinical vignette (gender-matched) describing a hypothetical case of knee osteoarthritis, followed by a structured interview comprising 19 items assessing four core abilities: understanding, appreciation, reasoning, and expression of choice. Responses are scored on a 3-point scale (0–2), with higher scores indicating better decisional capacity.

Cognitive Functioning

Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA) [25,26], a widely used screening tool with a maximum score of 30 points. It evaluates multiple cognitive domains, including executive functions, visuospatial abilities, memory, attention, language, and orientation. Portuguese normative data adjusted for age and education were considered [27].

Anxiety Symptoms

Anxiety was assessed using the Geriatric Anxiety Inventory (GAI) [28,29], a 20-item self-report instrument specifically developed for older adults. The Portuguese version demonstrates good psychometric properties, with a cut-off score of 10/11 (sensitivity = .900; specificity = .859).

Depressive Symptoms

Depressive symptoms were measured using the Geriatric Depression Scale (GDS-30) [30,31,32], a 30-item dichotomous self-report scale. Scores are categorized as follows: ≤10 (no clinically significant symptoms), 11–20 (mild depression), and >20 (severe depression) [33].

Sociodemographic and Clinical Data

A structured questionnaire was used to collect information on age, gender, education, marital status, residence, and occupational status, as well as self-reported chronic conditions based on national prevalence data [34].

Procedures

This study was conducted within the framework of the project “Assessment of Healthcare Decision-Making Capacity – Development and Validation of the IACTD-CS”, approved by the Ethics Committee of the University of Beira Interior (CE-UBI-Pj-2020-072).

Participants were recruited using community-based strategies, including dissemination of an informational poster via social media and a Microsoft Forms link, as well as through formal partnerships with institutions working directly with older adult populations. These strategies aimed to maximize outreach and facilitate access to potential participants.

Individuals who expressed interest in participating were provided with detailed information regarding the study’s objectives, procedures, and implications. Written informed consent was obtained prior to participation, ensuring adherence to ethical principles of voluntariness, confidentiality, and anonymity.

Data were collected through face-to-face structured interviews conducted by four trained interviewers, with an average duration of approximately 45 minutes. Additional participants were recruited beyond those identified through institutional contacts to ensure adequate sample size.

Inclusion criteria were: (a) age ≥ 60 years; (b) at least 1 year of schooling; (c) Portuguese as a native language; (d) residence in the community; and (e) absence of cognitive impairment, as determined by the MoCA. A total of 74 individuals were initially assessed, of whom 14 were excluded due to evidence of cognitive impairment, resulting in a final sample of 60 participants.

Data Analysis

Data were analyzed using IBM SPSS Statistics (version 30). The final dataset included 60 participants who met all inclusion and exclusion criteria. Descriptive statistics (means, standard deviations, frequencies, and percentages) were computed to characterize the sample and to describe levels of depressive and anxiety symptoms.

The distribution of continuous variables was assessed using the Kolmogorov–Smirnov test, which indicated significant deviations from normality (p < .05). Accordingly, non-parametric analyses were conducted. Spearman’s rank-order correlation coefficients were calculated to examine associations between healthcare decision-making capacity (CAI-Health), depressive symptoms (GDS), anxiety symptoms (GAI), and cognitive performance (MoCA). Correlation strength was interpreted based on conventional thresholds [35].

To examine predictors of healthcare decision-making capacity, a multiple linear regression analysis was performed [36]. The dependent variable was decision-making capacity (CAI-Health), and independent variables included age, education, cognitive performance (MoCA), depressive symptoms (GDS), and anxiety symptoms (GAI), selected based on theoretical and empirical evidence. Education was dummy-coded into three categories (basic, secondary, and higher education). Statistical significance was set at p < .05 for all analyses.

3. Results

Descriptive statistics for the study variables are presented in Table 1. Participants showed a mean score of 27.10 (SD = 4.16) in healthcare decision-making capacity, with scores ranging from 15 to 30. Mean depressive symptomatology was 5.47 (SD = 5.16; range = 0–24), while anxiety symptoms presented a mean of 5.37 (SD = 5.77; range = 0–20). Cognitive performance, assessed with the MoCA, yielded a mean score of 23.65 (SD = 2.84), ranging from 17 to 30.

Spearman correlation analyses (Table 2) indicated that healthcare decision-making capacity was not significantly associated with depressive symptoms (r_s = −.076, p = .565) or anxiety symptoms (r_s = −.007, p = .958). However, a moderate, positive, and statistically significant correlation was found between decision-making capacity and cognitive performance (r_s = .480, p < .001), suggesting that higher cognitive functioning is associated with better decisional capacity.

A strong and statistically significant positive correlation was observed between depressive and anxiety symptoms (r_s = .730, p < .001), indicating substantial co-occurrence of these affective symptoms in the sample. No significant associations were found between cognitive performance and depressive symptoms (r_s = −.108, p = .410) or anxiety symptoms (r_s = −.095, p = .468).

A multiple linear regression analysis was conducted to examine predictors of healthcare decision-making capacity (Table 3). The model, which included age, education, cognitive performance, depressive symptoms, and anxiety symptoms, was statistically significant, F(7, 52) = 6.053, p < .001, explaining 44.9% of the variance (R² = .449).

Within the model, depressive symptomatology (β = −.411, p = .040) and age (β = −.490, p < .001) emerged as significant predictors of decision-making capacity. These findings indicate that higher levels of depressive symptoms and older age are associated with lower healthcare decision-making capacity. In contrast, anxiety symptoms, cognitive performance, and education did not significantly contribute to the model.

4. Discussion

The present study aimed to examine the relationship between healthcare decision-making capacity, cognitive performance, depressive and anxiety symptoms, and sociodemographic variables in cognitively healthy older adults. Overall, the findings support the multifactorial nature of decision-making capacity, highlighting the role of cognitive functioning and depressive symptomatology, while also revealing nuances that contribute to the current literature.

Consistent with prior research (e.g., [1,37]), a significant association was found between healthcare decision-making capacity and cognitive performance. This finding reinforces the well-established notion that decision-making relies on multiple cognitive processes, including working memory, attention, and reasoning. Previous studies have shown that older adults with cognitive impairment are more likely to exhibit difficulties in decision-making compared to cognitively intact individuals [5,14]. The present results extend this evidence by demonstrating that even within a cognitively preserved sample, variability in cognitive functioning remains a relevant factor influencing decisional capacity. These findings underscore the importance of incorporating cognitive assessment into evaluations of decision-making capacity and support interventions aimed at maintaining or enhancing cognitive functioning as a means of promoting autonomy in later life.

Contrary to expectations, no significant associations were observed between healthcare decision-making capacity and depressive or anxiety symptoms. This finding diverges from previous literature suggesting that affective states may impair decision-making processes, particularly through their impact on appreciation and reasoning abilities [11,38]. One possible explanation is that the relatively low levels of depressive and anxiety symptoms observed in this sample may not have reached a threshold sufficient to produce measurable impairments in decisional capacity. Additionally, it is plausible that the influence of affective symptoms operates indirectly, potentially mediated or moderated by cognitive functioning or other psychosocial variables.

Importantly, regression analyses revealed that depressive symptomatology emerged as a significant negative predictor of decision-making capacity when controlling for other variables. This apparent discrepancy between non-significant correlations and significant regression findings suggests the presence of suppression or interaction effects, indicating that the role of depressive symptoms may only become evident when considered within a multivariate framework. This finding aligns with theoretical perspectives emphasizing the complex interplay between cognitive and emotional processes in decision-making [1]. From a cognitive model standpoint, depressive symptomatology may influence decision-making through the activation of negative cognitive schemas, as described in Beck’s cognitive triad [39,40], leading to pessimistic evaluations of available options and reduced motivation to engage in information-seeking behaviors. This interpretation is further supported by empirical findings indicating that individuals with higher depressive symptoms tend to adopt less effective decision-making strategies and show reduced engagement with available resources [12].

In contrast, anxiety symptoms were not significantly associated with decision-making capacity in either correlational or regression analyses. Although previous studies suggest that anxiety may bias decision-making toward threat avoidance and risk aversion [12,13], the lack of significant findings in the present study may again be attributable to the low severity of symptoms in the sample or to the possibility that anxiety exerts more subtle or context-dependent effects that are not captured by global measures of decisional capacity. This finding highlights the need for further research examining the specific mechanisms through which anxiety influences different components of decision-making.

A robust and expected finding was the strong positive association between depressive and anxiety symptoms, reflecting the high comorbidity between these conditions in older adults [41,42,43]. This result reinforces the importance of adopting an integrated clinical perspective when assessing emotional functioning in later life, as co-occurring symptoms may have cumulative or interactive effects on functioning.

Age also emerged as a significant negative predictor of decision-making capacity, suggesting that increasing age is associated with reduced decisional abilities. This finding is consistent with literature indicating age-related declines in cognitive domains critical for decision-making, such as processing speed, executive functioning, and working memory [44,45,46]. Beyond cognitive decline, age-related factors such as reduced functional independence and lower engagement in cognitively stimulating activities may further contribute to diminished decisional capacity [47]. Nevertheless, it is important to emphasize that cognitive aging is heterogeneous, and protective factors such as cognitive reserve and continued engagement in meaningful activities may mitigate these effects [45].

Unexpectedly, education did not show a significant association with decision-making capacity, contrasting with previous findings linking higher literacy levels to better decision-making outcomes [48]. This discrepancy may be explained by the relatively high and homogeneous educational level of the sample, which may have limited variability and reduced the ability to detect significant effects.

Taken together, the findings of this study contribute to the literature by highlighting that depressive symptomatology may play a clinically relevant role in healthcare decision-making capacity, even in cognitively healthy older adults, particularly when considered alongside other variables. The results also reinforce the central role of cognitive functioning while supporting a multidimensional perspective that integrates cognitive and affective factors.

From a clinical standpoint, these findings underscore the importance of incorporating both cognitive and emotional assessments into evaluations of decision-making capacity, in line with existing recommendations [10]. Specifically, assessing depressive symptomatology is crucial to avoid misinterpretations, particularly in cases where transient mood disturbances may affect performance without reflecting a stable cognitive decline. Given that depression is a potentially reversible condition, its identification and treatment should be followed by reassessment of decision-making capacity to determine whether observed impairments persist. This approach supports more dynamic and ethically grounded assessment practices, promoting autonomy, self-determination, and dignity in older adults.

Finally, the present findings highlight the importance of addressing modifiable factors, such as depressive symptoms and cognitive functioning, as potential targets for intervention aimed at preserving decision-making capacity. Future research should adopt longitudinal and multidimensional approaches to further clarify the mechanisms underlying these relationships and to inform the development of targeted interventions that support decision-making in aging populations.

5. Conclusions

The present study contributes to the growing body of literature on healthcare decision-making capacity by examining its associations with cognitive functioning, affective symptomatology, and sociodemographic factors in cognitively healthy older adults. The findings highlight that increasing age and depressive symptomatology are significant factors associated with reduced decision-making capacity, reinforcing the need to consider both cognitive and emotional dimensions when evaluating decisional abilities in later life. In contrast, anxiety symptoms did not emerge as a significant predictor, although their strong association with depressive symptoms confirms the well-established comorbidity between these conditions.

Overall, the results support a multidimensional understanding of healthcare decision-making capacity, emphasizing that it is not solely determined by cognitive functioning but rather emerges from the dynamic interaction between cognitive, emotional, and demographic factors. These findings underscore the importance of adopting integrative and multidisciplinary approaches in both research and clinical practice, particularly in aging populations.

From a clinical and ethical perspective, this study reinforces the development of a systematic assessment of depressive symptoms alongside cognitive evaluation in capacity assessments. Given that depressive symptomatology may transiently impair decisional abilities without reflecting irreversible cognitive decline, its identification is critical to avoid misclassification of individuals as incapable. This highlights the importance of dynamic assessment processes, including reassessment following appropriate psychological or psychiatric intervention, in order to safeguard autonomy, self-determination, and dignity in older adults.

Several limitations should be acknowledged. The relatively small sample size (N = 60) may limit the statistical power and robustness of the findings. Additionally, the homogeneity of the sample, particularly in terms of sociodemographic characteristics, may have constrained variability and reduced the ability to detect associations between variables such as education and decision-making capacity. The use of a non-clinical sample, while informative for understanding normative aging, limits the generalizability of findings to clinical populations, particularly those with neurocognitive or psychiatric disorders.

Furthermore, the exclusion of individuals with cognitive impairment, although methodologically justified, may have resulted in an underrepresentation of depressive and anxiety symptoms, given their higher prevalence in cognitively impaired populations [49]. Consequently, the findings related to affective symptomatology should be interpreted with caution. It is also important to consider that the length and cognitive demands of the evaluation protocol may have influenced participants’ performance, particularly among older individuals.

Despite these limitations, this study offers several important contributions. It addresses a relatively underexplored domain by focusing on psychosocial predictors of healthcare decision-making capacity and adopts an integrative approach that simultaneously considers cognitive, emotional, and sociodemographic variables. By doing so, it advances a more holistic and person-centered understanding of decisional capacity, grounded in both clinical and ethical considerations.

Future research should build upon these findings by employing larger and more diverse samples, allowing for greater statistical power and the exploration of subgroup differences. The inclusion of clinical populations, such as individuals with neurocognitive disorders or affective conditions, would provide a more comprehensive understanding of how these variables interact in contexts of increased vulnerability. Longitudinal designs would also be particularly valuable in clarifying causal relationships and temporal dynamics between cognitive decline, emotional states, and decision-making capacity.

Additionally, the development and use of shorter, psychometrically robust assessment protocols may help reduce participant burden and improve data quality. Further research should also aim to refine theoretical models that integrate cognitive and affective processes in decision-making, contributing to more precise and clinically useful frameworks.

Finally, these findings have practical implications for healthcare practice. Enhancing health literacy among older adults may support more effective decision-making, particularly in complex or unfamiliar clinical situations [50]. Healthcare professionals should adapt communication strategies, ensure adequate time for reflection, and verify patients’ understanding of relevant information to support informed decision-making. Such approaches are essential to promoting autonomy and ensuring ethically sound, person-centered care in aging populations.