Prevalence of poor functional capacity in patients undergoing prehabilitation before major surgery: A systematic review and meta-analysis

Abstract

Introduction and objectives

Prehabilitation aims to enhance physiological reserve prior to major surgery; however, the proportion of patients with impaired functional capacity who actually receive these interventions remains unclear. Identifying this subgroup is crucial, as patients with reduced functional capacity are those most likely to benefit from prehabilitation. This systematic review and meta-analysis aimed to estimate the prevalence of poor functional capacity among adult patients undergoing prehabilitation before major surgery.

Patients or Materials and methods

A systematic review was conducted according to PRISMA 2020 guidelines and was prospectively registered in PROSPERO (CRD420251026613). MEDLINE, Embase, Scopus, Web of Science, SciELO, and Biblioteca Virtual de Salud were searched from inception to September 2025. Studies including adults undergoing prehabilitation prior to major surgery with reported functional capacity data were eligible. Risk of bias was assessed using the Joanna Briggs Institute checklist for prevalence studies. A random-effects meta-analysis was performed to estimate pooled prevalence, with heterogeneity explored through subgroup, sensitivity, and meta-regression analyses.

Results

Seven studies comprising 306 participants were included. Functional capacity was assessed with cardiopulmonary exercise testing in four studies and the six-minute walk test in three. The pooled prevalence of poor functional capacity among patients undergoing prehabilitation was 36% (95% CI: 27–46%), with substantial heterogeneity (I² = 63%). A higher prevalence was observed in patients undergoing oncological surgery (41%; 95% CI: 30–53%) than in those undergoing non-oncological surgery (23%; 95% CI: 13–35%). Sensitivity analyses confirmed the robustness of the findings.

Conclusions

More than one-third of patients receiving prehabilitation before major surgery have poor functional capacity, though considerable variability exists across studies. Standardized, systematic assessment of functional capacity may improve identification of high-risk patients and optimize the targeting and effectiveness of prehabilitation programs in perioperative care.

Resumen

Introducción y objetivos

El objetivo de la prehabilitación es potenciar la reserva fisiológica previa a la cirugía mayor; sin embargo, la proporción de pacientes con deterioro de la capacidad funcional que reciben realmente dichas intervenciones sigue siendo incierta. Es esencial identificar este subgrupo, dado que los pacientes con capacidad funcional reducida tienen mayor probabilidad de beneficiarse de la prehabilitación. El objetivo de esta revisión sistemática y metaanálisis es calcular la prevalencia de la mala capacidad funcional entre los pacientes adultos que reciben prehabilitación antes de la cirugía mayor.

Pacientes o materiales y métodos

Se realizó una revisión sistemática conforme a las guías PRISMA 2020, que fue prospectivamente registrada en PROSPERO (CRD420251026613). Se realizó una búsqueda en MEDLINE, Embase, Scopus, Web of Science, SciELO y Biblioteca Virtual de Salud desde su inicio hasta septiembre de 2025, siendo elegibles los estudios que incluyeron adultos que recibieron prehabilitación previamente a la cirugía mayor, con datos reportados sobre capacidad funcional. Se evaluó el riesgo de sesgo utilizando la lista de revisión del Instituto Joanna Briggs para estudios de prevalencia. Se realizó un metaanálisis de efectos aleatorios para calcular la prevalencia agrupada, explorándose la heterogeneidad mediante análisis de subgrupo, sensibilidad y metaregresión.

Resultados

Se incluyeron siete estudios, que abarcaron 306 participantes. Se evaluó la capacidad funcional con prueba de ejercicio cardiopulmonar en cuatro estudios, y la prueba de marcha de seis minutos en tres. La prevalencia agrupada de la mala capacidad funcional entre los pacientes que recibieron prehabilitación fue del 36% (95% IC: 27–46%), con heterogeneidad sustancial (I² = 63%). Se observó una prevalencia más alta en los pacientes sometidos a cirugía oncológica (41%; 95% IC: 30–53%), en comparación con los pacientes de cirugía no oncológica (23%; 95% IC: 13–35%). Los análisis de sensibilidad confirmaron la robustez de los hallazgos.

Conclusiones

Más de un tercio de los pacientes que recibieron prehabilitación antes de la cirugía mayor tuvo mala capacidad funcional, aunque existe variabilidad considerable entre los estudios. La evaluación estandarizada y sistemática de la capacidad funcional puede mejorar la identificación de los pacientes de alto riesgo, así como optimizar la focalización y efectividad de los programas de prehabilitación en el cuidado perioperatorio.

Introduction

Prehabilitation is a perioperative intervention designed to enhance patients’ physiological reserve before surgery. It typically comprises three core components: physical activity, nutritional optimization, and psychological support, aimed at reducing anxiety and stress.¹ While this multimodal approach is considered the gold standard for maximizing outcomes, prehabilitation can also be defined by the implementation of any one of these components individually (unimodal approach).¹ However, the synergy of addressing multiple risk factors simultaneously is generally recognized as the most effective strategy for improving functional capacity. By addressing these modifiable factors prior to surgery, prehabilitation seeks to prepare patients to better tolerate surgical stress and accelerate postoperative recovery.²

The physiological response to surgery depends largely on an individual's physiological reserve, which varies across patients and is influenced by factors such as age, comorbidities, muscle mass, and nutritional status, including serum albumin levels.³ Some of these determinants are modifiable, and their optimization has become a central focus of modern perioperative care, prompting changes in management strategies aimed at enhancing recovery and minimizing postoperative complications.³

Functional capacity is a key indicator of overall physiological status and reflects an individual's ability to perform activities of daily living. In clinical and surgical settings, it can be assessed using objective tools such as cardiopulmonary exercise testing (CPET) or walking tests, as well as subjective methods including clinical interviews or perceived exertion scales.⁴

Assessment of functional capacity or exercise tolerance plays a fundamental role in perioperative risk stratification. Patients with poor functional capacity experience higher rates of perioperative complications and mortality. Early identification of this high-risk group allows for appropriate referral for additional cardiovascular evaluation, and implementation of prehabilitation programs. Optimizing the preoperative condition of surgical patients extends beyond reducing surgical invasiveness alone. Improving physical, nutritional, and psychological functional status is essential to mitigate adverse postoperative outcomes and enhance recovery trajectories.⁵ Evidence, although limited, suggests that patients with poor functional capacity may derive the greatest benefit from prehabilitation interventions.⁶

Despite this, baseline functional capacity and related patient characteristics are infrequently reported in prehabilitation studies. This lack of data limits the understanding of the true prevalence of patients with poor functional capacity undergoing prehabilitation prior to major surgery, even though these programs were originally conceived to target this vulnerable population.⁶ Therefore, this systematic review aims to determine the prevalence of patients with poor functional capacity receiving prehabilitation before major surgery.

Methods

This systematic review adheres to the PRISMA 2020 reporting guidelines.⁷ The protocol was prospectively registered in the PROSPERO database (CRD420251026613).

Eligibility criteria

Patient selection

We included primary research studies focusing on adults (≥18 years) enrolled in prehabilitation programs prior to major elective surgery, regardless of baseline functional capacity. Manuscripts with insufficient data to estimate prevalences were also excluded.

Definition of prehabilitation interventions

Prehabilitation is defined as a proactive approach aimed at enhancing a patient's physiological and psychological resilience before a major surgical stressor.

In this study, we included both multimodal and unimodal (nutritional or physical) programs. This decision is based on evidence showing that even single-domain interventions can drive significant improvements in baseline functional status.⁸

Assessment of functional capacity

Functional capacity was assessed using two validated diagnostic tools.

First, the 6-Minute Walk Test (6MWT) was employed, a submaximal exercise test that measures the maximum distance an individual can walk on a flat surface within six minutes. This test is a robust indicator of physical fitness for performing activities of daily living and reflects the integrated function of the cardiovascular, pulmonary, and muscular systems. In this context, reduced functional capacity was defined as a walking distance of less than 400 meters, a threshold that identifies patients with low physiological reserve and an increased risk of postoperative adverse events.⁹

Second, cardiopulmonary exercise testing (CPET), considered the gold standard for the objective assessment of aerobic reserve, was used. CPET allows evaluation of gas exchange and ventilatory response under incremental physical stress. Reduced functional capacity was defined as a peak oxygen consumption (VO₂peak) of less than 15 ml/kg/min, a level below which patients demonstrate limited tolerance to the metabolic stress imposed by major surgery, thereby increasing the likelihood of cardiopulmonary complications.¹⁰

Types of studies included

The study design prioritized in this review was the cross-sectional design. However, methodological guidance for systematic reviews of prevalence indicates that other study designs, including cohort studies and randomized or non-randomized clinical trials, may also contribute valid prevalence data when information is extracted exclusively from baseline assessments, prior to any intervention or follow-up, and when such data are not influenced by the study intervention or longitudinal design. Accordingly, this review included cross-sectional studies, cohort studies, and interventional studies, reporting prevalence data on poor functional capacity from baseline measurements only.^11–13

Studies were eligible regardless of language, year of publication, or publication status, including original articles, conference abstracts, and preprints. Excluded documents comprised individual case reports, non-original content such as expert commentaries, editorials, Letters to the Editor, and opinion pieces.

Information sources

A comprehensive search was conducted across several databases: MEDLINE (via PubMed), Embase, Scopus, the Biblioteca Virtual de Salud, SciELO, and Web of Science. In addition, manual searches of the reference lists of included articles were performed, and a gray literature search (including preprint servers) was conducted to maximize coverage. All sources were last searched on September 17, 2025.

Search strategy

The search strategy was developed a priori and adapted for each database. It combined controlled vocabulary terms and free-text keywords related to major surgery, prehabilitation, and physical fitness. Boolean operators (AND, OR) were used to combine concepts. No restrictions were applied regarding study design or publication status. The complete electronic search strategies for all databases, including the full MEDLINE (via PubMed) strategy, are provided in Supplementary Appendix 1.

Study selection process

Rayyan–Intelligent Systematic Review software facilitated the selection process.¹⁴ Two reviewers (DP-V and AH-T) independently eliminated duplicates. Then, titles and abstracts were screened for relevance to the review question. Manuscripts that passed the initial screening were assessed in full text. Studies meeting the eligibility criteria were selected, and a citation tracking search was also performed. Two authors (DP-V and AH-T) made inclusion decisions independently, and disagreements were resolved by a third reviewer (LF-G).

Data extraction and synthesis process

Data were extracted independently by two reviewers (DP-V and AH-T) using a standardized data extraction form based on the Joanna Briggs Institute (JBI) data extraction form for prevalence studies.¹² Data were recorded in a Microsoft Excel spreadsheet specifically designed for this review. Any discrepancies between reviewers were resolved through discussion and consensus, with consultation of a third reviewer when necessary (LF-G). When relevant data were missing or unclear, attempts were made to contact the study authors for clarification. No automation tools were used in the data extraction process.

Data list

Data were sought for the prevalence of poor functional capacity among patients undergoing prehabilitation prior to elective major surgery. Point prevalence data were extracted from the population of patients who received a prehabilitation program, either unimodal or multimodal, before major elective surgery, with the denominator defined as the total number of patients undergoing prehabilitation and the numerator as the number of patients classified as having poor functional capacity at baseline. Poor functional capacity was defined according to the criteria used in each study, including specific cut-off points derived from validated functional capacity assessment tools, such as a VO₂peak < 15 mL/kg/min or a 6-min walk test distance < 400 m. All reported definitions, measurement instruments, and assessment time points used to define poor functional capacity were considered for prevalence estimation. Prevalence 95% CI were either collected directly or calculated using the Wilson score method.¹⁵

Additional data extracted included study design, year of publication, country and setting, sample size, participant characteristics (age and sex), type of surgery (oncologic or non-oncologic), functional capacity assessment instrument, cut-off points used to define poor functional capacity, type of prehabilitation program (physical/unimodal or multimodal), duration of prehabilitation, and follow-up period.

Risk of bias assessment

Although the included studies used different designs, including randomized controlled trials and non-randomized studies, the risk of bias assessment focused specifically on the validity of the baseline data used to estimate the prevalence of poor functional capacity. Therefore, in accordance with methodological guidance for systematic reviews of prevalence, the Joanna Briggs Institute (JBI) checklist for studies reporting prevalence data was considered the most appropriate tool to assess potential bias in prevalence reporting across different study designs.^11–13,16

This instrument evaluates nine aspects¹¹: (D1) adequacy of the sampling frame, (D2) recruitment method, (D3) sample size, (D4) study subjects and setting, (D5) coverage, (D6) diagnostic methods, (D7) reliability and standardization of measurements, (D8) statistical analysis, and (D9) response rate. Two reviewers (DP-V and AH-T) performed independent evaluations, marking each item as “yes,” “no,” “unclear,” or “not applicable.” Any discrepancies were discussed and resolved by consensus. The percentage of affirmative responses determined overall RoB, classifying studies as “high” risk (≤49%), “moderate” risk (50–69%), or “low” risk (≥70%). RoB summary plots across domains were generated using the ROBVIS R package v.0.3.0.900.¹⁷

Synthesis methods

Given the topic's clinical relevance and the objective of estimating a pooled point prevalence of poor functional capacity in prehabilitated surgical patients, a meta-analysis was performed using a random-effects model with restricted maximum likelihood estimation. To stabilize the variance of proportions, prevalence estimates were transformed using the Freeman–Tukey double arcsine method prior to pooling. Following the meta-analysis, pooled estimates were back-transformed to prevalence values to facilitate clinical interpretation.

To assess heterogeneity, Cochran's Q statistic, τ₂, and the I² were employed. Cochran's Q test was considered statistically significant at a p-value < 0.05, while I² values were interpreted as indicators of the magnitude of between-study heterogeneity. Between-study variance (τ²) was estimated using the REML approach. To explore potential sources of heterogeneity, univariable meta-regression analyses were conducted using the following covariates: RoB, sample size, type of surgery, type of prehabilitation program, method used to assess functional capacity, and duration of prehabilitation.

Sensitivity analyses included subgroup analyses, leave-one-out meta-analysis, and robustness assessments using alternative variance estimators, including the Sidik–Jonkman method and Knapp–Hartung adjustments with truncation. All statistical analyzes were performed using Stata version 18 (StataCorp LLC, College Station, TX).

Publication bias assessment

Formal methods for assessing publication bias (e.g., Egger's test, Begg's test, and funnel plot asymmetry) were developed for comparative effect estimates and rely on assumptions that are not applicable to proportional meta-analyses. In prevalence studies, publication is not driven by statistically “positive” results, as estimates are primarily influenced by population characteristics and measurement variability. In addition, there is no empirical evidence supporting the validity of these methods for proportional data.¹⁸ Accordingly, formal statistical tests and funnel plot analyses will not be conducted, and potential publication bias will be evaluated qualitatively.

Certainty of evidence rating

This assessment was not performed because there is no specific instrument for evaluating the certainty of evidence in prevalence systematic reviews.

Results

Study selection

A total of 423 studies were retrieved from the information sources; 257 were excluded as duplicates, and 166 articles were screened by title and abstract. Of these, 140 studies were excluded, and 26 full-text manuscripts were assessed for eligibility. Nineteen were excluded for not meeting the inclusion criteria. Ultimately, 7 primary studies comprising 306 participants were included (Fig. 1).^19–25

Fig. 1.

PRISMA Flow diagram for studies selection.

Study characteristics

The included articles were published between 2016 and 2024. Four studies were conducted in the Netherlands,^19–22 and one each in Australia,²⁵ Ireland,²³ and New Zealand²⁴ (Table 1).

Table 1.

Characteristics of the included studies.

Author year	Country	Study design	Population	Number of cases (n)	Age, n (SD)	Male, n (%)	Setting
Huang 2016	Australia	Retrospective cohort study	26	14	67.7 (±9.6)	22 (84.6%)	Ambulatory
Loughney 2024	Ireland	Randomized clinical trial	36	12	62.8 (±9.2)	27 (75%)	Ambulatory
de Klerk 2021	Netherlands	Retrospective cohort study	76	34	75.01 (±9.2)	39 (51.3%)	Ambulatory
ten Cate 2024	Netherlands	Prospective cohort study	101	28	69.7 (±12.7)	52 (51.5%)	Ambulatory
Franssen 2022	Netherlands	Non-randomized interventional study	11	7	74 (±7.4)	6 (54.5%)	Ambulatory
Woodfield 2022	New Zealand	Randomized clinical trial	28	6	66.5 (±13.5)	20 (71.4%)	Ambulatory
Berkel 2022	Netherlands	Randomized clinical trial	28	7	74 (±7)	16 (57.1%)	Ambulatory

Population (n) refers to the total number of patients who underwent prehabilitation. The number of cases indicates patients classified as having poor functional capacity at baseline. Age is reported as mean ± standard deviation (SD) unless otherwise specified; values in parentheses indicate measures of dispersion (SD, interquartile range, or range) as reported in the original studies. Sex distribution is presented as number and percentage of male participants.

Of the included studies, 43% were randomized controlled trials, 29% were retrospective cohort studies, 14% were non-randomized interventional studies, and 14% were prospective cohort studies. All studies (100%) were conducted in outpatient settings.

Most studies (57%) assessed functional capacity using cardiopulmonary exercise testing (CPET), whereas the remaining studies used the six-minute walk test (6MWT). Most of the included articles (71%) involved patients undergoing oncological surgical procedures,^{19,21–23,25} whereas 29% involved non-oncological surgeries.^20,24 Regarding the type of prehabilitation, 57% of the manuscripts used physical exercise alone, and 43% employed multimodal strategies (Supplementary Appendix 2).

Overall, 60% of participants were male, with ages ranging from 62 to 75 years.

Risk of bias in studies

Fig. 2 shows the risk of bias (RoB) assessment for each included study. A total of 29% of studies were judged to have moderate RoB,^22,23 and 71% to have low RoB^{19–21,24,25} (Fig. 2a).

Fig. 2.

Risk of bias of the articles included. (a) Individual assessment; (b) Assessment for each domain.

The domains with the highest risk of bias were sample adequacy (100%)^19–25 and sample size estimation (71%),^{19,21–23,25} primarily due to inadequate sampling frames and lack of information supporting appropriate sample size estimation. High RoB was observed in 14% of the articles, in the domains of recruitment method and response rate, related to non-probabilistic sampling in Franssen's study and low response rates in Loughney's study (Fig. 2b).

Results of syntheses

Based on the 7 included studies, a pooled prevalence of poor functional capacity of 36% was estimated (95% CI: 27%–46%), with substantial heterogeneity (τ² = 0.04; I² = 63.5%; p = 0.01) (Fig. 3). Heterogeneity was explored through subgroup analyses based on the functional capacity assessment tool, type of surgical procedure, and type of prehabilitation. Significant between-subgroup differences were observed only when stratified by surgical procedure, while substantial heterogeneity persisted within subgroups (Supplementary Appendix 3).

Fig. 3.

Forest plot meta-analysis of pooled prevalence.

Functional capacity assessment tool

In the CPET subgroup, the pooled prevalence was 38% (95% CI: 20–59), with substantial heterogeneity. In the 6MWT subgroup, the pooled prevalence was 35% (95% CI: 25–46), with moderate heterogeneity (Supplementary Appendix 4).

Surgical procedure

In oncological surgery, pooled prevalence was 41% (95% CI, 30–53) with moderate heterogeneity, versus 23% (95% CI, 13–35) with low heterogeneity in non-oncological surgery (Supplementary Appendix 3).

Type of prehabilitation

For participants receiving physical exercise only, the pooled prevalence was 33% (95% CI: 20–47), with moderate heterogeneity. For those undergoing multimodal prehabilitation, the pooled prevalence was 41% (95% CI: 24–60), with substantial heterogeneity (Supplementary Appendix 5).

In the meta-regression analyses, univariable models assessing sample size, risk of bias, type of functional assessment, type of prehabilitation, and duration of prehabilitation showed no statistically significant associations with the outcome. When the type of surgical procedure was included in the model, a τ² of 0.04 and an I² of 56% were observed, with 27% of the between-study variance accounted for by this variable; however, this association did not reach statistical significance (p = 0.08).

Sensitivity analyses

Assuming an I² of 10%, the pooled prevalence was 35% (95% CI: 29–41) (Supplementary Appendix 6). In the leave-one-out analysis, the pooled prevalence ranged from 33% to 38%, with no significant changes (p < 0.05) (Supplementary Appendix 7). Using the Sidik–Jonkman and Knapp–Hartung truncated methods, the pooled prevalence was estimated to be 36% (95% CI: 24–50%).

Discussion

In this meta-analysis, a pooled prevalence of 36% of poor functional capacity was identified among patients undergoing prehabilitation prior to major surgery. The interpretation of this estimate requires caution, as its magnitude is influenced by the clinical context, the characteristics of the surgical populations included, and the criteria used to define functional impairment. In the absence of a standardized reference framework for patient selection in prehabilitation programs, this proportion cannot be considered inherently low or high, but rather should be interpreted within the heterogeneity of the included studies.

The inclusion criteria inherent to prehabilitation programs may shape the functional profile of enrolled patients. In routine clinical practice, such programs typically require clinical stability, an adequate preoperative time window, and the ability to actively participate in structured interventions, which may limit the participation of patients with severe functional impairment, advanced frailty, or logistical constraints.²⁶ Moreover, referral to prehabilitation is not consistently based on standardized functional assessment tools and may instead rely on surgeon or multidisciplinary team judgment. Consequently, the included studies may represent a functionally selected population, potentially underestimating the overall burden of functional impairment among all candidates for major surgery.²⁶

The physiological rationale supporting prehabilitation is well established. Structured exercise, particularly multimodal programs combining aerobic and resistance training, improves cardiorespiratory fitness, muscular strength, and skeletal muscle mass, while nutritional and psychological components further promote an anabolic environment and treatment adherence. Consistent with these mechanisms, prior meta-analyses have demonstrated that prehabilitation reduces postoperative complications, shortens hospital stay, and accelerates return to baseline functional status, supporting its role as a clinically meaningful strategy for preoperative optimization in patients undergoing major surgery.^27–31 However, in this review, a predominance of unimodal interventions, mainly exercise-based programs, was observed, highlighting the importance of early identification of eligible patients and the availability of a sufficient preoperative period to enable the implementation of more comprehensive programs.⁶

In our review, most studies included patients scheduled for oncologic surgery. This context illustrates the complexity of integrating prehabilitation into oncologic care pathways, where clinical urgency and limited preoperative windows may influence program design and patient inclusion.

Notwithstanding these contextual challenges, evidence supports the effectiveness of prehabilitation in improving preoperative functional capacity. A meta-analysis of 27 randomized clinical trials including nearly 2000 patients demonstrated significant improvements in both 6MWD and VO₂Peak in patients undergoing oncological resections.³² Notably, patients with poor functional capacity have been shown to derive the greatest absolute gains from multimodal prehabilitation, with improvements nearly twice those observed in patients without poor functional capacity, underscoring the clinical relevance of identifying this subgroup preoperatively. In quantitative terms, structured preoperative exercise programs have demonstrated mean increases of approximately 2–3 mL/kg/min in patients with poor functional capacity at baseline, compared with average increases of approximately 1–2 mL/kg/min in those without poor functional capacity. These findings indicate that, while patients with poor functional capacity may experience greater absolute gains, those without poor functional capacity can also benefit from prehabilitation through further enhancement of their preoperative physiological reserve.^29,30 In this context, the 36% prevalence of poor functional capacity identified in our review acquires particular clinical relevance, as it suggests that a substantial proportion of patients currently enrolled in prehabilitation programs may represent precisely the subgroup with the greatest potential for preoperative functional optimization, regardless of whether this proportion is considered high or low in absolute terms.

Importantly, substantial heterogeneity persisted across studies, even after subgroup analyses and metaregression. None of the explored covariates significantly explained this variability, suggesting that commonly reported study level characteristics may be insufficient to account for differences in baseline functional capacity. This finding underscores the methodological challenges in synthesizing evidence in this field and highlights the need for more standardized definitions and assessment approaches. Several factors may account for this unexplained variability, including the diversity of assessment tools used to define poor functional capacity ranging from the 6-minute walk test to cardiopulmonary exercise testing and metabolic equivalent estimation the heterogeneity of surgical populations included, and the absence of standardized cutoff values across studies, which may have introduced substantial classification variability. Collectively, these limitations suggest that aggregate study level moderators are unlikely to fully explain between study differences, and that individual patient-level data analyses may be necessary to identify the true determinants of baseline functional capacity in prehabilitation populations.

Taken together, patient selection for prehabilitation should move beyond a single functional metric and instead reflect a multidimensional assessment of surgical risk and modifiable vulnerability. A broader framework that integrates physiological, nutritional, functional, and psychosocial domains may allow for more precise identification of candidates who could benefit from targeted preoperative optimization. Future studies should aim to establish standardized, risk-based referral algorithms and determine which combinations of patient characteristics yield the greatest clinical benefit, thereby refining the role of prehabilitation within perioperative care pathways.

Limitations

In addition to the limitations already acknowledged, several other constraints should be considered when interpreting the findings of this review. First, the limited number of studies included in the quantitative synthesis reduced the statistical power of subgroup analyses and meta-regression models. Second, moderate to substantial between-study heterogeneity persisted despite exploratory analyses, suggesting the presence of unmeasured or inadequately captured sources of variability. Third, considerable heterogeneity was observed in the definitions and cut-off points used to classify poor functional capacity across studies, which may have affected the comparability of results and the pooled prevalence estimates.

Furthermore, several included studies were not primarily designed to estimate prevalence, but rather reported functional capacity as mean values without stratification into clinically relevant categories, limiting the accurate identification of impaired subgroups. In addition, most studies enrolled relatively small samples, which restricts generalizability and may compromise the robustness of the pooled estimates. Collectively, these limitations warrant cautious interpretation of the results.

Conclusions

In conclusion, this systematic review and meta-analysis identified a pooled prevalence of 36% of poor baseline functional capacity among patients enrolled in prehabilitation programs prior to major surgery. This estimate should be interpreted within the context of study heterogeneity, variability in definitions of functional impairment, and differences in patient selection across surgical populations. The observed prevalence does not necessarily indicate inappropriate selection, but rather reflects the diversity of current clinical practices and referral pathways.

Given the substantial heterogeneity and the lack of significant study-level effect modifiers, our findings highlight the need for greater standardization in the assessment and reporting of baseline functional capacity in prehabilitation research. Functional evaluation should be considered an important component of perioperative risk stratification and patient prioritization, integrated within a broader multidimensional assessment rather than used as an exclusive criterion for program enrollment. Future research should aim to develop and validate risk-based referral frameworks that more precisely identify patients most likely to benefit from preoperative optimization.

Authorship contributions

D. Peñaherrera-Vásquez: Conceptualization; search and screening; data extraction; drafting and critical revision; final approval.

A. Heredia-Tituaña: Methodological design; support in search and selection; data interpretation; critical revision; final approval.

J. Rivadeneira: Project supervision; bias and validity assessment; synthesis of results; technical review; final approval.

T. Fajardo-Loaiza: Support in search and selection; data extraction; narrative/structural review; final approval.

L. Fuenmayor-González: Corresponding author; project coordination; methodological oversight; final manuscript editing; final approval.

Ethical considerations

This systematic review was conducted using data extracted solely from previously published studies. No individual patient data or identifiable information were utilized. Therefore, approval from an institutional ethics committee was not required, in accordance with the principles of the Declaration of Helsinki.

Informed consent

As no human subjects or patient-level data were involved, informed consent was not required for this study.

Use of artificial intelligence

The authors declare that generative artificial intelligence tools were not used for the literature search, data extraction, data analysis, or manuscript writing. All stages of the review process were performed manually by the authors.

Funding

The authors declare that they received no external funding or financial support for the development of this systematic review.

Conflict of interests

The authors declare that they have no conflicts of interest related to the development, analysis, or publication of this manuscript.