European Journal of Cardiovascular Medicine

Postoperative complications remain a major contributor to morbidity, prolonged hospitalization, unplanned intensive care admissions, and mortality worldwide. Surgical site infections, deep vein thrombosis (DVT), respiratory distress, and cardiac arrhythmias are among the most common adverse events in the early postoperative period, often developing within hours to days after surgery. The timely recognition and treatment of these complications are critical for preventing clinical deterioration, reducing hospital resource utilization, and improving patient outcomes. However, in standard ward settings, vital signs are typically recorded intermittently—every four to six hours in most institutions—leaving long periods during which patient deterioration may go unnoticed. This “blind window” can delay recognition of subtle physiological changes that precede clinical instability. Continuous physiological monitoring is standard practice in critical care units, where it is associated with improved detection of deterioration and reduced adverse outcomes. Extending such monitoring to general surgical wards has traditionally been limited by the cost, complexity, and impracticality of wired bedside monitors, as well as by concerns about patient comfort and mobility. Recent advances in wearable sensor technology have addressed many of these barriers[1,2]. Lightweight, wireless, multiparameter wearable devices can now continuously capture vital signs such as heart rate, respiratory rate, peripheral oxygen saturation (SpO₂), and skin temperature, while allowing patients to remain mobile. Data can be transmitted in real time to clinical staff and integrated with early warning score systems, enabling earlier detection of clinical deterioration[3,4]. Several pilot studies and small-scale trials have demonstrated the feasibility of wearable continuous monitoring in hospitalized surgical patients, showing earlier detection of abnormal vital signs and potential improvements in escalation of care. For example, patch-based systems have identified changes in respiratory rate and oxygen saturation several hours before overt deterioration, while wrist-worn photoplethysmography devices have shown clinically acceptable accuracy for heart rate and respiratory rate measurements in postoperative patients[5,6]. However, despite promising feasibility data, the evidence for measurable impact on hard clinical outcomes—such as complication detection rates, time to intervention, length of stay, ICU transfers, and readmissions—remains limited and heterogeneous. Many studies have been single-center, non-randomized, or underpowered for outcome measures, and few have provided quantitative estimates of the “lead time” between physiological change detection and clinical recognition.

This gap in evidence highlights the need for robust, controlled studies evaluating not only the accuracy and feasibility of wearable monitoring devices, but also their real-world clinical effectiveness in improving postoperative surveillance and outcomes. A particular focus should be on high-risk surgical populations, where even small gains in detection speed could significantly influence prognosis and resource use[7,8].

The present prospective, randomized controlled trial was designed to address this evidence gap. We aimed to investigate whether continuous multiparameter wearable monitoring, compared with standard intermittent vital sign measurement, improves early detection of postoperative complications in adult surgical patients. We also sought to quantify the time advantage conferred by wearable monitoring for detecting deviations in vital signs, assess its impact on length of stay and ICU transfer rates, and examine the patterns of pre-diagnostic physiological change.

Study Design and Setting

This prospective, randomized controlled trial was conducted over a 12-month period in the surgical wards of a tertiary care teaching hospital. The objective was to evaluate the efficacy of wearable health-monitoring devices in detecting early postoperative complications compared to standard intermittent monitoring. The study adhered to the principles of the Declaration of Helsinki, and ethical clearance was obtained from the Institutional Ethics Committee. Written informed consent was obtained from all participants before enrollment.

Participants

Patients aged 18 years and older who underwent major elective or emergency surgery, including abdominal, thoracic, or orthopedic procedures, and who were expected to remain hospitalized for at least three days postoperatively, were considered eligible. Exclusion criteria included pre-existing severe arrhythmias, advanced heart failure, chronic respiratory failure, use of implantable monitoring devices, dermatological conditions that would prevent wearable sensor application, and cognitive impairment interfering with adherence to the study protocol. Of the 240 patients screened, 40 were excluded—28 for not meeting inclusion criteria and 12 who declined participation—resulting in a final sample of 200 patients.

Randomization and Group Allocation

Eligible participants were randomly assigned in a 1:1 ratio to either the wearable monitoring group or the standard intermittent monitoring group using a computer-generated randomization sequence. Allocation concealment was maintained with sealed opaque envelopes that were opened only after baseline data collection. One hundred patients were allocated to each group.

Intervention: Wearable Monitoring Devices

In the intervention arm, patients were fitted within two hours post-surgery with multiparameter wearable sensors, including either patch-style or wrist-worn devices. These devices continuously measured heart rate via photoplethysmography, respiratory rate via impedance or waveform analysis, peripheral oxygen saturation (SpO₂), skin temperature via integrated thermistors, and physical activity via accelerometers. All data were transmitted in real time to a secure monitoring dashboard at the nursing station. Automated alerts were generated when readings exceeded predefined thresholds, prompting immediate bedside clinical review and intervention.

Control: Standard Monitoring

The control group received standard postoperative ward care, which involved manual measurement of vital signs every four to six hours using conventional bedside equipment. Abnormalities detected during these scheduled checks or in response to patient symptoms triggered standard diagnostic evaluation and management.

Outcome Measures

The primary outcome was the rate of detection of early postoperative complications, defined as clinically confirmed surgical site infection, deep vein thrombosis (DVT), respiratory distress, or cardiac arrhythmia occurring during the inpatient postoperative period. Secondary outcomes included time from first physiological change to clinical recognition, magnitude of vital sign changes preceding diagnosis, length of hospital stay, unplanned ICU transfer, readmission within 30 days of discharge, and in-hospital mortality.

Data Collection and Verification

For patients in the wearable group, device-generated data were time-stamped and archived automatically. Alerts and subsequent clinical actions were logged in real time by nursing staff. In both groups, complications were confirmed using standard diagnostic procedures such as Doppler ultrasound for DVT, microbiological cultures for surgical site infections, arterial blood gases for respiratory distress, and ECG for arrhythmias. Two independent senior clinicians, blinded to group allocation, verified all diagnoses.

Statistical Analysis

Continuous variables were reported as mean ± standard deviation and compared between groups using the Student’s t-test. Categorical variables were expressed as percentages and compared using the chi-square tes54t or Fisher’s exact test when appropriate. Differences in time to detection between groups were analyzed using independent t-tests. A two-sided p-value of less than 0.05 was considered statistically significant. Data analysis was performed using SPSS software

Figure 1 : CONSORT-style flow diagram

Figure 1. CONSORT-style flow diagram illustrating participant screening, eligibility assessment, randomization, group allocation, and analysis. Of 240 patients assessed for eligibility, 40 were excluded (28 did not meet inclusion criteria and 12 declined to participate). Two hundred patients were randomized equally into the wearable monitoring group (n=100) and the standard monitoring group (n=100), with all participants receiving their allocated intervention and being included in the final analysis

Participant Flow and Baseline Characteristics

Of the 240 patients assessed for eligibility, 40 were excluded (28 did not meet inclusion criteria, 12 declined participation). The remaining 200 patients were randomized into two equal groups: Wearable Monitoring (n=100) and Standard Intermittent Monitoring (n=100).

Both groups were well matched in terms of age, sex distribution, BMI, prevalence of comorbidities, and surgical profile. This comparability reduces the risk of baseline confounding and allows for direct attribution of outcome differences to the intervention.

Table 1 – Participant Flow and Baseline Demographics (n=200)

Values are mean ± standard deviation (SD) or percentage. BMI – Body Mass Index. p-values calculated using Student’s t-test or χ² test as appropriate.

No statistically significant baseline differences were observed, supporting internal validity of comparative outcome analysis.

Figure 2 – Baseline Characteristics and Participant Flow

figure 2 grouped bar chart, line plots for trends, and pie charts for sex and surgery distribution

Complication Detection Rates

Postoperative complications were actively monitored in both groups. The wearable group recorded significantly higher early detection rates for several key complications.

Table 2 – Complication Detection Rates (n=200)

Detection rate = number of patients diagnosed with complication ÷ total patients in group × 100. p-values by χ² test.

Wearable monitoring nearly doubled overall complication detection, with statistically significant increases in infection, DVT, and respiratory distress identification.

Time to Detection and Intervention

The time from the first abnormal physiological change to formal clinical recognition was significantly shorter in the wearable group across all complication categories.

Table 3 – Mean Time from Symptom Onset to Detection and Intervention (n=200)

Time measured from earliest abnormal wearable reading to confirmed diagnosis. Negative mean difference indicates earlier detection in wearable group.

Continuous monitoring provided a clinically meaningful early detection advantage of 7–11 hours, enabling prompt intervention.

Vital Sign Deviations Before Clinical Recognition

Analysis of physiological parameters showed that the wearable group experienced measurable deviations in heart rate, respiratory rate, oxygen saturation, and skin temperature hours before clinicians recognized the problem.

Table 4 – Average Vital Sign Changes Detected Before Clinical Recognition (n=200)

Baseline defined as average of first 6 hours post-surgery. SpO₂ – Peripheral oxygen saturation.

Wearable devices identified abnormal trends several hours before conventional detection, offering a window for preventive care.

Clinical Outcomes and Resource Utilization

Early detection translated into better patient outcomes and reduced healthcare resource use.

Table 5 – Clinical Outcomes (n=200)

ICU – Intensive Care Unit. Readmission calculated within 30 days post-discharge.

Continuous wearable monitoring was associated with shorter hospital stays, fewer ICU transfers, and lower readmission rates, though mortality differences were not statistically significant.

This prospective randomized controlled trial demonstrated that continuous wearable health-monitoring devices significantly improved the early detection of postoperative complications compared with standard intermittent monitoring. The intervention group exhibited nearly double the complication detection rate and a 7–11 hour earlier identification of adverse events, which translated into shorter hospital stays, fewer ICU transfers, and reduced readmission rates. These findings highlight the potential of wearable technology as an adjunct to routine postoperative care, offering real-time physiological surveillance and enabling timely interventions[9,10].

The superiority of wearable monitoring in detecting complications early can be attributed to its capacity for continuous, multiparameter data acquisition. Unlike standard monitoring, which is inherently limited by the intervals between vital sign checks, wearables can capture transient or subtle physiological changes that may precede clinical deterioration. For instance, in our study, elevated heart rate, increased respiratory rate, reduced SpO₂, and marginal increases in skin temperature were consistently observed several hours before overt symptoms or clinical recognition. These early trends provided a valuable window for intervention, potentially preventing escalation to more severe states[11,12].

Our findings align with previous research showing that continuous remote monitoring can lead to earlier intervention and improved outcomes in hospitalized patients. Studies evaluating devices such as patch-based biosensors and wrist-worn photoplethysmography systems have similarly reported earlier detection of sepsis, arrhythmias, and hypoxemia compared with routine monitoring schedules. Importantly, the technology used in our study did not rely solely on alert thresholds but also enabled clinicians to review longitudinal data trends, which is consistent with evidence that trend-based monitoring can reduce false positives and increase clinical confidence in wearable-derived alerts[13,14].

Beyond clinical detection, the present study suggests potential economic and operational benefits. Reduced ICU transfers and shorter hospital stays imply lower resource utilization, which could offset the initial costs of wearable deployment. While cost-effectiveness analysis was not formally undertaken, the decreased length of stay in the intervention group indicates a probable downstream economic benefit. Furthermore, the enhanced detection of complications such as DVT and respiratory distress could prevent costly readmissions and long-term morbidity[15-17].

Nevertheless, this study has several limitations that must be acknowledged. First, the trial was conducted in a single tertiary care center, which may limit generalizability to other settings, particularly those with lower nurse-to-patient ratios or different postoperative care protocols. Second, while all complications were confirmed by blinded senior clinicians, it is possible that the increased detection rate in the wearable group reflects heightened vigilance triggered by frequent alerts, rather than solely earlier physiological changes. Third, the study did not assess patient comfort or compliance with wearable devices, factors that could influence adoption in routine practice[18-20].

Table 6 – Comparative Analysis of Original Studies on Continuous/Wearable Postoperative Monitoring

HPB – Hepatopancreatobiliary surgery; CMVS – Continuous Monitoring of Vital Signs; PPG – Photoplethysmography; LOS – Length of Stay; SAE – Serious Adverse Event; ICU – Intensive Care Unit.

Future research should focus on multicenter trials to validate these findings across diverse patient populations and healthcare environments. Integrating wearable monitoring systems with advanced predictive algorithms and early warning scores may further enhance their predictive accuracy. Additionally, cost-effectiveness analyses and qualitative assessments of patient and staff acceptance will be essential to guide large-scale implementation.

Continuous wearable monitoring demonstrated clear advantages over standard intermittent monitoring in detecting postoperative complications, allowing earlier interventions and improving short-term outcomes. The integration of such technology into standard postoperative care pathways holds promise for enhancing patient safety, optimizing hospital resources, and potentially transforming postoperative surveillance practices.

Acknowledgments:

The authors would like to thank all of the study participants and the administration of Department of General Surgery, SRM Medical College Hospital and Research Centre, Tamilnadu, India  for granting permission to carry out the research work.

Conflicts of interest: There are no conflicts of interest.

Ethical statement:

Institutional ethical committee accepted this study. The study was approved by the institutional human ethics committee, Department of General Surgery, SRM Medical College Hospital and Research Centre,Chennai. Informed written consent was obtained from all the study participants and only those participants willing to sign the informed consent were included in the study. The risks and benefits involved in the study and the voluntary nature of participation were explained to the participants before obtaining consent. The confidentiality of the study participants was maintained.

Funding: Nil.

Authors’ contributions:

Dr.Reegan Jose Mathias: Conceptualization, Formal analysis, Project administration, Writing‑original draft, Validation, Investigation. Dr.Karthick Govindarajan : Conceptualization, Writing‑review and editing, Formal analysis, Validation, Investigation, Visualization. Dr.Najeem Fazil M: Conceptualization, Methodology, Writing‑review and editing, Validation, Resources.  All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. All authors have read and agreed to the published version of the manuscript.

DATA AVAILABILITY:

All datasets generated or analysed during this study are included in the manuscript.

INFORMED CONSENT: Written informed consent was obtained from the participants before enrolling in the study

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