Qiu FX, Li H. Impact of multi-dimensional monitoring combined with wearable devices on early physical recovery in postoperative liver cancer patients. World J Gastrointest Surg 2026; 18(6): 117962 [DOI: 10.4240/wjgs.117962]
Corresponding Author of This Article
Han Li, PhD, School of Economics and Management, Huzhou University, No. 759 Erhuan East Road, Huzhou 313000, Zhejiang Province, China. wwtgzy@126.com
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Oncology
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Qiu FX, Li H. Impact of multi-dimensional monitoring combined with wearable devices on early physical recovery in postoperative liver cancer patients. World J Gastrointest Surg 2026; 18(6): 117962 [DOI: 10.4240/wjgs.117962]
Author contributions: Qiu FX collected and curated the clinical data, wearable device monitoring data, and drafted the initial manuscript; Li H performed the statistical analyses, interpreted the results, revised the manuscript critically for important intellectual content; Qiu FX and Li H conceived and designed the study; and all authors read and approved the final version of the manuscript.
AI contribution statement: During the preparation of this manuscript, DeepL was used for English translation and language polishing, while no other AI tools including ChatGPT and Grammarly were utilized; none of the main text of the manuscript (Abstract, Introduction, Materials and Methods, Results, Discussion, and Conclusion) was AI-generated, and no AI tools were applied for data analysis, writing assistance, study design or result interpretation, nor were any images in the manuscript generated by AI.
Supported by Huzhou City Science and Technology Program Project, No. 2025YZ30.
Institutional review board statement: This study was approved by the Medical Ethics Committee of the School of Economics and Management, Huzhou University, approval No. 202510-01.
Informed consent statement: All research participants or their legal guardians provided written informed consent prior to study registration.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: No other data available.
Corresponding author: Han Li, PhD, School of Economics and Management, Huzhou University, No. 759 Erhuan East Road, Huzhou 313000, Zhejiang Province, China. wwtgzy@126.com
Received: January 16, 2026 Revised: February 3, 2026 Accepted: March 9, 2026 Published online: June 27, 2026 Processing time: 159 Days and 0.9 Hours
Abstract
BACKGROUND
Postoperative physical recovery is challenging for patients undergoing hepatectomy for hepatocellular carcinoma. Traditional assessment methods have limitations in guiding personalized rehabilitation.
AIM
To evaluate the impact of a multidimensional monitoring system combined with wearable device-guided intervention on early physical recovery.
METHODS
A single-center retrospective cohort study enrolled 305 consecutive patients undergoing radical hepatectomy between June 2020 and June 2024. Participants were allocated to observation group (multidimensional monitoring plus wearable devices, n = 121) or control group (traditional management, n = 184). Primary outcomes included six-minute walk test distance, Borg score, liver function indicators, Quality of Life (QoL) Scale for Liver Cancer score, and complication rates. Statistical analyses comprised independent samples t-tests, χ2 tests, and repeated measures analysis of variance.
RESULTS
At one month postoperatively, the observation group demonstrated significantly greater six-minute walk test distances (492.71 ± 53.14 m vs 441.63 ± 61.02 m, P < 0.001) and lower Borg scores (0.76 ± 0.52 vs 1.13 ± 0.63, P < 0.001) compared to controls. Repeated-measures analysis of variance revealed significant group, time, and interaction effects for liver enzymes (all P < 0.001). The observation group exhibited superior QoL scores (124.49 ± 11.90 vs 116.40 ± 12.63, P < 0.001), reduced hospital stay (P < 0.001), and lower complication rates (9.92% vs 19.02%, P = 0.031). Daily steps and moderate-to-vigorous physical activity duration positively correlated with QoL (r = 0.51 and r = 0.40, respectively, both P < 0.001).
CONCLUSION
Multi-dimensional monitoring with wearable devices significantly enhances early physical recovery, liver function, QoL, and reduces postoperative complications.
Core Tip: This study demonstrates that a multi-dimensional monitoring system integrating wearable device data with guided rehabilitation significantly enhances recovery after liver cancer surgery. Patients receiving this intervention showed markedly better physical capacity, faster liver function recovery, improved quality of life, and fewer complications compared to conventional care. Objectively measured physical activity levels correlated strongly with patient-reported outcomes. These findings support the clinical value of technology-enabled, data-driven rehabilitation programs to optimize postoperative recovery in surgical oncology.
Citation: Qiu FX, Li H. Impact of multi-dimensional monitoring combined with wearable devices on early physical recovery in postoperative liver cancer patients. World J Gastrointest Surg 2026; 18(6): 117962
Hepatocellular carcinoma (HCC), as the predominant histological subtype of liver cancer, ranks among the leading malignant tumours globally in terms of both incidence and mortality[1]. According to epidemiological estimates, there are approximately 747000 HCC cases worldwide, an increase of 70% compared to 1990[2]. Among them, 480000 deaths were attributed to HCC, posing a significant threat to global health and the economy[2,3]. Surgical resection remains the primary clinical treatment for early-to-mid-stage HCC[4]. With advances in minimally invasive techniques such as laparoscopy and robotic-assisted surgery, surgical safety and precision have significantly improved, leading to effective enhancement in patients’ short-term postoperative survival rates[5,6]. However, patients with liver cancer often present with underlying chronic liver disease[7]. The surgical trauma and stress response can readily lead to a series of issues in the early postoperative period, such as physical exhaustion and compromised immune function[8]. Therefore, how to accurately assess and effectively promote early physical recovery in patients following HCC surgery has become a critical clinical issue requiring urgent resolution.
Early physical recovery constitutes a pivotal component of the overall rehabilitation process for patients following HCC surgery[9,10]. At its core, this represents the body’s post-traumatic restoration of multiple systems - including energy metabolism, muscular function, and cardiopulmonary reserve - through coordinated regulation via the neuroendocrine-immune network[11]. In traditional clinical management, healthcare professionals predominantly rely on patients’ subjective complaints, bedside physical examinations, and routine laboratory indicators to assess physical condition[12]. Such evaluation methods exhibit significant limitations: On the one hand, subjective indicators are susceptible to influence from patients' cognitive abilities and emotional states, lacking objectivity; on the other hand, conventional objective indicators are predominantly static test results, struggling to reflect real-time changes in a patient’s functional capacity or energy expenditure. Consequently, clinical interventions often lag behind the progression of the patient's condition, preventing the implementation of personalized, precision management. Multiple studies have confirmed that early postoperative physical recovery is significantly associated with the prognosis of liver cancer patients, fully highlighting the need to optimize the existing assessment and intervention models[13,14].
Multi-dimensional monitoring enables a comprehensive assessment of a patient's rehabilitation progress by integrating data from multiple levels, including physiological indicators, functional status, and psychological state[15]. With the rapid advancement of mobile healthcare technology, smart wearable devices have emerged as the ideal medium for achieving multidimensional monitoring, owing to their portability and real-time capabilities[16]. Based on this, the present study employed a retrospective design utilizing hospital electronic medical systems and nursing follow-up records to systematically analyze the impact of multidimensional monitoring combined with information-enabled wearable device interventions on early physical recovery in patients following HCC surgery. By comparing physical recovery indicators, hospitalization duration, and complication rates between patients under traditional management and those receiving the combined intervention, this study aims to clarify the clinical value of the integrated approach. It provides clinical evidence and practical guidance for optimizing postoperative rehabilitation management strategies and establishing a precision rehabilitation system for HCC patients.
MATERIALS AND METHODS
Study design
This study is a single-center retrospective cohort study. Patients who underwent radical hepatectomy at School of Economics and Management, Zibo Medical Security Center from June 2020 to June 2024 and were pathologically diagnosed with HCC were continuously included. All demographic information, clinical data, laboratory results and nursing follow-up records were exported through the hospital’s electronic medical record system and the “Postoperative Cloud Nursing Platform for Liver Cancer”. After strict screening, a total of 305 patients were included. This study strictly adheres to all the principles of the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of School of Economics and Management, Zibo Medical Security Center Hospital.
Inclusion and exclusion criteria
Inclusion criteria: (1) The postoperative pathological diagnosis clearly indicates primary HCC[17]; (2) Patients without surgical contraindications who have undergone laparoscopic or robot-assisted laparoscopic radical resection of liver cancer; (3) Age: 18-75 years old, preoperative Eastern Cooperative Oncology Group physical condition score: 0-1; and (4) Complete clinical data, including preoperative baseline data, surgical records, postoperative monitoring records, nursing follow-up files, and monitoring data from information wearable devices (if applicable).
Exclusion criteria: (1) Combined with other malignant tumors or severe failure of organs such as the heart, lungs, and kidneys; (2) There was sarcopenia, severe malnutrition [serum albumin (ALB) < 25 g/L] or active infection before the operation; (3) If severe complications occur after the operation (such as massive hemorrhage, liver failure, biliary fistula with infection, etc.), a second operation is required; and (4) Cognitive dysfunction or mental illness makes it impossible to cooperate with the assessment.
Grouping criteria and management plan
According to different postoperative management models, the patients were divided into the observation group (n = 121) and the control group (n = 184). The intervention group adopted multi-dimensional monitoring combined with information-based wearable devices for postoperative management, while the control group adopted the traditional postoperative management mode. This disparity in grouping stems from genuine clinical practice - as an emerging technology, the digital management model was progressively implemented within departments. Its adoption was influenced by non-human factors such as equipment availability, individual patient preferences, and acceptance levels, thereby naturally forming the two comparative cohorts in this study.
Control group: A standardised postoperative rehabilitation management protocol for HCC was implemented. Postoperative monitoring centred on bedside assessments, encompassing daily vital signs measurement, hepatic and renal function testing, and regular ward rounds by healthcare personnel to ascertain subjective well-being. Rehabilitation guidance comprised generic recommendations: Bed rest with encouraged turning and limb mobilization on days 1-3 post-surgery; short-distance ambulation permitted on days 4-7 based on patient-reported tolerance, without quantified training targets or individualized regimens. Post-discharge follow-up was routine, with patients returning for review at 1 week, 1 month, and 3 months post-surgery. Telephone follow-ups were conducted during this period to inquire about symptom changes, with no continuous activity monitoring undertaken.
Observation group: The core of the intervention plan for the observation group is the closed-loop management of “monitoring - feedback - intervention”. The patient is equipped with a smart bracelet to continuously monitor their average daily steps, moderate to high-intensity activities, and sleep quality. At the same time, report the degree of fatigue, pain score and appetite status daily or weekly through the mobile application. These data are synchronized to the hospital management platform, where the system presets personalized activity goals and alarm thresholds. When an alarm is triggered or the system recognizes a delay in rehabilitation, the dedicated follow-up nurse will immediately initiate structured and stepwise proactive intervention: (1) Multi-dimensional monitoring: Patients wear smart wristbands (Huawei Band, supporting real-time monitoring of heart rate, blood oxygen, sleep and activity levels) upon returning to their ward post-surgery. Data is synchronised via internet of things to the medical workstation. This is combined with daily liver and kidney function tests, inflammatory markers, and subjective indicators such as pain and fatigue to establish a monitoring system integrating ‘objective data and subjective experience’; and (2) Targeted training: Rehabilitation therapists develop stepwise training programmes based on wearable device data and individual patient conditions. In the early bed rest phase (6-24 hours after surgery), passive intervention in bed was carried out: Rehabilitation therapists assisted patients in performing ankle flexion and extension exercises (1 set per hour, 15 times per set) and passive knee flexion and extension exercises (holding for 3 seconds each time, 10 times per set, 1 set every 2 hours), and guided patients to perform abdominal breathing training (5 minutes each time, 1 time every 3 hours) to avoid increasing the burden on the liver. Bedside core muscle training commences 1-2 days post-surgery (ankle pumping: 10 sets per hour, 15 repetitions per set; quadriceps contraction: 5-second holds, 20 repetitions per set, 3 sets daily). Postoperative days 3-5: Bedside sit-ups, standing balance training (5-10 minutes per session, 3-4 times daily), and short-distance walking (initially 50 m per session, gradually increasing to 100-150 m per session based on heart rate response). From days 6-7 post-surgery until discharge, commence endurance training (walking back and forth along the ward corridor, 15-20 minutes per session, maintaining heart rate at 50%-60% of maximum heart rate); post-discharge, activity levels are remotely monitored via wearable devices. Rehabilitation therapists adjust training plans weekly based on data, push training videos via WeChat, and conduct monthly in-person follow-ups to assess training efficacy.
Data collection and observation indicators
All data were retrospectively collected from the hospital’s electronic medical record system (for demographic information, clinical data, laboratory results, and surgical records) and nursing archives (for rehabilitation records, subjective assessment scores, wearable device data, and complication documentation) by two independent researchers, with cross-verification to ensure data accuracy. The observation indicators included: (1) Baseline characteristics: Demographic data (age, gender, body mass index), clinical data (preoperative hypertension, diabetes, cirrhosis, Child-Pugh classification, tumor diameter, tumor number, Eastern Cooperative Oncology Group performance status), and surgical data (surgery duration, Pringle time); (2) Time-dependent functional and physiological indicators (measured at admission, 1 week postoperatively, and 1 month postoperatively): 6-minute walk test[18] (6MWT, conducted in a 30-meter corridor in accordance with American Thoracic Society standards, recording the maximum distance walked in 6 minutes), Borg scale[19] (scored from 0 to 10, with higher scores indicating greater perceived exertion), and liver function indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALB, measured using an automatic biochemical analyzer in the hospital’s clinical laboratory]; (3) 1-month postoperative wearable device-derived indicators: Average daily steps, average daily moderate to high-intensity activity time, average daily sleep duration (extracted from the nursing management platform’s wearable device data archives); (4) Quality of life (QoL) scores (measured at admission and 1 month postoperatively): Assessed using the QoL Scale for Liver Cancer V2.0[20], which includes symptom, physical, social, and psychological domains, with a total score ranging from 0 to 220 (higher scores indicating better QoL); and (5) Postoperative complications: Recorded within 1 month postoperatively, including incisional infection, abdominal effusion, pulmonary infection, and biliary fistula (diagnosed based on clinical symptoms, imaging findings, and laboratory results), with the total complication rate calculated as the proportion of patients with at least one complication.
Statistical analysis
Statistical analyses were performed using SPSS software (Version 26.0; IBM Corp., Armonk, NY, United States). Continuous variables were expressed as mean ± SD and compared using Student’s t-test or Mann-Whitney U test as appropriate, while categorical variables were presented as frequencies with percentages and analyzed using χ2 or Fisher’s exact tests. For repeated measures data, repeated measures analysis of variance was employed with Mauchly’s test of sphericity applied to assess the sphericity assumption; when this assumption was violated, the Greenhouse-Geisser correction was used to adjust the degrees of freedom. Post-hoc pairwise comparisons with Bonferroni adjustment were conducted to examine differences at specific time points when significant main or interaction effects were detected. Correlations between continuous variables were evaluated using Pearson’s correlation coefficient. A two-tailed P value less than 0.05 was considered statistically significant for all analyses.
RESULTS
Baseline characteristics of the study population
The baseline characteristics of the 305 patients included in this study are summarized in Table 1. No significant differences were observed between the observation group (n = 121) and the control group (n = 184) in terms of age, gender, body mass index, prevalence of hypertension and diabetes, tumor diameter, surgery duration, Pringle time, Child-Pugh classification, cirrhosis status, tumor number, and Eastern Cooperative Oncology Group performance status (all P > 0.05). These findings indicate that the two groups were well-balanced and comparable at baseline.
Table 1 Comparison of baseline characteristics between two groups, n (%).
Repeated measures analysis of variance of the 6MWT distances revealed significant main effects for group (F = 108.87, P < 0.001), time (F = 576.03, P < 0.001), and group-by-time interaction (F = 36.73, P < 0.001). Although both groups showed a decline in 6MWT distance at one week post-surgery, the observation group demonstrated a significantly higher 6MWT distance compared to the control group at both one week (410.52 ± 54.71 m vs 337.85 ± 63.28 m, P < 0.001) and one month (492.71 ± 53.14 m vs 441.63 ± 61.02 m, P < 0.001) (Table 2, Figure 1A).
Figure 1 Longitudinal changes in six-minute walk test and Borg scores.cP < 0.001. A: For six-minute walk test; B: For Borg scores. 6MWT: Six-minute walk test.
Table 2 Longitudinal changes in six-minute walk test distance between groups, mean ± SD.
Analysis of the Borg scale scores also indicated significant effects for group (F = 18.05, P < 0.001), time (F = 591.45, P < 0.001), and interaction (F = 10.29, P < 0.001). At one week post-surgery, the observation group reported lower Borg scores than the control group (3.12 ± 1.18 vs 3.43 ± 1.35, P < 0.05). By one month, the observation group continued to exhibit significantly lower perceived exertion (0.76 ± 0.52 vs 1.13 ± 0.63, P < 0.001) (Table 3, Figure 1B).
Table 3 Longitudinal changes in Borg perceived exertion scale between groups, mean ± SD.
Repeated measures analysis of variance for liver enzymes indicated that the observation group had significantly lower ALT and AST levels at both one week and one month post-surgery compared to the control group (P < 0.001) (Tables 4 and 5, Figure 2A and B). ALB levels were also significantly higher in the observation group at one month post-surgery (37.89 ± 6.51 g/L vs 35.73 ± 6.15 g/L, P < 0.01) (Table 6, Figure 2C).
The time to first ambulation did not differ significantly between the two groups (P = 0.201) (Figure 3A). However, the mean postoperative hospital stay was significantly shorter in the observation group compared to the control group (11.14 ± 2.03 days vs 12.52 ± 2.45 days, P < 0.001) (Figure 3B).
Figure 3 Comparison between the first ambulation hours and hospital stay days.
A: First ambulation hours; B: Hospital stay days.
QoL outcomes
At one month post-surgery, the observation group showed significantly better scores in the physical domain (35.94 ± 6.71 vs 32.58 ± 6.31, P < 0.001), psychological domain (35.85 ± 6.53 vs 33.14 ± 6.46, P < 0.001), and total QoL score (124.49 ± 11.90 vs 116.40 ± 12.63, P < 0.001) compared to the control group (Table 7).
Table 7 Comparison of quality-of-life scores at admission and one month after surgery, mean ± SD.
Correlation between wearable device monitoring indicators and QoL
Significant positive correlations were observed between the average daily step count at one month and the total QoL score [r = 0.51, 95% confidence interval (CI): 0.36-0.63, P < 0.001, Figure 4A], as well as between the duration of moderate-to-vigorous physical activity and QoL (r = 0.40, 95%CI: 0.24-0.54, P < 0.001, Figure 4B). In contrast, no significant correlation was found between average nightly sleep duration and QoL (r = 0.11, 95%CI: -0.078 to 0.28, P = 0.246, Figure 4C).
Figure 4 Correlation between monitoring indicators of wearable devices and quality of life.
A: Daily steps; B: Moderate to high-intensity activities; C: Duration of sleep.
Association between intervention and QoL, and postoperative complications
To quantify the specific effect of the intervention on QoL, multivariate linear regression analysis was performed with the total QoL score at one month postoperatively as the dependent variable and grouping as the independent variable, adjusted for potential confounding factors. The results showed that, compared with the control group, the observation group had a significant increase in the total QoL score at one month postoperatively (crude model: Β = 8.09, 95%CI: 5.25-10.92, P < 0.001; adjusted for age, gender and body mass index: Β = 7.93, 95%CI: 5.11-10.75, P < 0.001; fully adjusted model: Β = 8.18, 95%CI: 5.35-11.02, P < 0.001) (Table 8). As presented in Table 9, the overall incidence of postoperative complications within 3 months was significantly lower in the observation group (9.92%, 12/121) than in the control group (19.02%, 35/184) (χ2 = 4.64, P = 0.031). This suggested that the multidimensional monitoring intervention contributed to reducing the overall postoperative complication burden.
Table 8 Multivariable linear regression analysis of factors associated with 1-month quality-of-life total score.
HCC remains a global health burden, and surgical resection is the cornerstone of curative treatment for early-to-mid-stage disease[21]. However, postoperative recovery is often complicated by underlying chronic liver disease and surgical stress, highlighting the need for optimized rehabilitation strategies[22]. The present study investigated the impact of multi-dimensional monitoring combined with wearable devices on early physical recovery in postoperative patients, specifically those undergoing radical hepatectomy for HCC. Our findings demonstrate that patients managed with this integrated approach exhibited significantly improved physical recovery, as evidenced by enhanced performance in the 6MWT, reduced perceived exertion, and better liver function recovery compared to those under traditional management. These results align with and extend previous research, highlighting the potential of digital health technologies to transform postoperative rehabilitation.
A key finding of this study is the superior trajectory of physical function recovery in the observation group, as evidenced by the 6MWT results. While both groups experienced a decline in 6MWT distance at 1 week postoperatively due to surgical trauma, the observation group maintained significantly higher distances at both 1 week and 1 month, with a more robust recovery trajectory confirmed by the significant group-time interaction effect. This improvement is clinically significant, as the 6MWT is a well-established predictor of postoperative outcomes in cancer patients[23]. The observed benefits are consistent with those reported by Liang et al[10], who found that short-term exercise-based prehabilitation programs improved functional capacity in liver cancer surgery patients. In addition, Kaibori et al[24] also reported that perioperative rehabilitation exercises can help improve the prognosis of patients and recommended that patients undergoing hepatectomy receive exercise rehabilitation treatment as early as possible, which is consistent with the results of this study. Our study uniquely integrates real-time wearable device data with personalized rehabilitation plans, offering a more dynamic and responsive approach to postoperative care. Similar findings were reported in the study by Kim et al[25], whose results demonstrated that exercise prescriptions based on wearable devices could significantly improve postoperative physical function in patients with HCC. Unlike traditional rehabilitation that relies on general guidance without quantified goals, wearable device-based interventions can tailor step-by-step training plans for patients based on multi-dimensional data, ensuring safety and effectiveness. The lower Borg scores in the observation group further support this, as reduced perceived exertion indicates improved exercise tolerance - a critical factor in sustaining long-term physical activity adherence.
The accelerated normalization of liver enzymes (ALT, AST) and higher ALB levels in the observation group point towards a tangible biological benefit of the intervention. This is consistent with Ren et al[8], who reported that enhanced recovery after surgery programs improve liver function by reducing surgical stress and promoting early mobilization. Our study extends this finding by demonstrating that multidimensional monitoring - integrating wearable device data with laboratory indicators - enables precise titration of rehabilitation intensity to avoid hepatic overload. Surgical trauma induces a systemic inflammatory response and hepatic ischemia-reperfusion injury, which manifests as a transient rise in transaminases[26]. The attenuated peak and faster decline of ALT and AST in our intervention group suggest a mitigated hepatic stress response. This could be attributed to several factors. First, the early, controlled mobilization may have improved systemic and portal venous flow, potentially reducing hepatic congestion and promoting the clearance of inflammatory mediators[27]. Second, the intervention may have indirectly reduced the metabolic burden on the liver by preventing complications and improving overall physiological reserve[28]. The higher ALB level at one month further supports better synthetic function and nutritional status, potentially linked to improved appetite and reduced catabolism from earlier functional recovery[12]. While few studies have directly linked physical activity interventions to postoperative liver function recovery, our results provide compelling evidence for this association.
The significant reduction in postoperative hospital stay and complication rates further validates the clinical utility of our intervention. The observation group had a 47.8% lower overall complication rate (9.92% vs 19.02%) and a shorter hospital stay, which aligns with Izmailova et al[15], who proposed that digital health models enhance functional status monitoring and enable early detection of complications. The significant reduction in postoperative complications observed in the observation group warrants careful interpretation of the underlying mechanisms. While we hypothesize that the closed-loop “monitoring-feedback-intervention” system played a crucial role by enabling early detection of subtle clinical deterioration (e.g., a sudden decline in daily step count or self-reported fatigue often preceded the clinical manifestation of infections like pneumonia), it must be acknowledged that our retrospective design precludes the provision of direct, case-level evidence for this specific causal pathway. Therefore, alternative or complementary explanations must be considered. The structured, stepwise rehabilitation training itself likely enhanced patients’ cardiopulmonary function and muscular strength, thereby increasing physiological reserve and resilience against postoperative stressors[28]. Furthermore, the increased attention from healthcare providers inherent in the intervention protocol (the “Hawthorne effect”) may have improved overall care quality and patient adherence to preventive measures, such as incentive spirometry and early mobilization[29]. In essence, the complication reduction is likely the result of a synergistic effect of the enhanced physiological reserve from targeted training, increased clinical vigilance from continuous data monitoring, and the positive behavioral impact of increased engagement. Future prospective studies should incorporate detailed tracking of alert triggers and subsequent clinical actions to directly elucidate the contribution of the digital monitoring component. These results are clinically meaningful, as shorter hospital stays reduce healthcare costs and the risk of nosocomial infections, while lower complication rates improve patient outcomes and reduce readmission rates[30,31]. In addition, the significantly improved QoL scores and the strong positive correlations between objective activity data and QoL scores reinforce the patient-centered benefit of our intervention. Average daily steps and moderate-to-vigorous activity duration were significantly correlated with total QoL scores, whereas sleep duration showed no significant correlation. In an observational study, Bade et al[32] reported that the average daily step count and activity level of patients were significantly associated with QoL. It is worth noting that Xiong et al[33] found through a study involving 558 patients that the average daily step count was associated with the QoL in a U-shaped curve, meaning that patients with an average daily step count ranging from 7000 to 12000 had the highest QoL. Possibly limited by the sample size, this nonlinear correlation was not observed in this study. The lack of correlation between sleep duration and QoL may be due to the multidimensional nature of sleep quality - while we monitored duration, other factors such as sleep fragmentation or depth (not captured in this study) may have a greater impact[34]. The observation group’s superior QoL in physical and psychological domains further highlights the holistic benefits of the intervention, as improved physical function often translates to reduced anxiety and depression.
From a clinical practice perspective, our intervention offers a scalable and cost-effective model for postoperative HCC management. The use of affordable wearable devices and existing digital platforms (WeChat, cloud nursing platform) minimizes barriers to implementation, making it feasible for widespread adoption in both tertiary and community hospitals. The stepwise training program, with clear protocols for each postoperative phase, can be integrated into standard nursing workflows without requiring additional specialized personnel. Moreover, the remote monitoring component ensures continuity of care post-discharge, addressing the gap in traditional follow-up that relies on infrequent in-person visits. This is particularly relevant for HCC patients, who require long-term rehabilitation and surveillance to prevent recurrence. By empowering patients to actively participate in their recovery through real-time data access, the intervention also enhances patient engagement - a key driver of rehabilitation success. From the perspective of health economics, this integrated intervention model shows significant potential for cost-effectiveness, which is mainly reflected in three aspects. First, in terms of hospital stay duration, the observation group had a mean postoperative hospital stay of 11.14 days, which was 1.38 days shorter than that of the control group (12.52 days). The reduction in hospital stay directly reduces the consumption of medical resources such as bed occupancy, nursing care and examination, and effectively cuts down the average daily hospitalization cost per patient. Second, the intervention reduced the overall complication rate by 47.8% (9.92% vs 19.02%). The decrease in complications such as ascites and biliary fistula avoids additional medical expenses caused by the diagnosis and treatment of complications, as well as the economic burden of patients caused by prolonged hospitalization due to complications. Third, the multivariate linear regression results confirmed that the intervention significantly improved the total QoL Scale for Liver Cancer V2.0 score of patients by 8.09 points at one month postoperatively. This improvement in QoL is accompanied by a reduction in the risk of readmission and the need for subsequent rehabilitation interventions, which further reduces long-term medical costs. In summary, although the intervention increases the initial input of wearable devices and cloud platform maintenance, the cost can be effectively offset by the reduction of hospitalization days and complication-related expenses, while bringing significant patient-reported QoL benefits.
Despite the promising results, this study has several limitations. First, its retrospective and single-center nature introduces potential selection bias and limits the generalizability of the findings. Although baseline characteristics were well-balanced, unmeasured confounding factors (e.g., subtle differences in surgical technique, socio-economic status) could have influenced the outcomes. Second, the use of a specific brand of wearable device may affect the reproducibility of the step-count and heart-rate zone algorithms with other devices. Third, the follow-up period was limited to 1 month, which is a critical limitation. For HCC patients, postoperative rehabilitation is not only about early physical function recovery but also about preparing for subsequent adjuvant therapies (e.g., transarterial chemoembolization, targeted therapy) and improving long-term tumor-survival outcomes. The 1-month follow-up only reflects the short-term efficacy of the intervention in enhancing physical capacity, improving liver function, and reducing complications, but it cannot confirm whether these beneficial effects can be sustained for 3 months or 6 months postoperatively. Furthermore, this study failed to explore the impact of the wearable-guided rehabilitation model on patients' tolerance to adjuvant therapies and long-term QoL, which are key factors related to the ultimate prognosis of HCC patients. Finally, the reliance on a specific consumer-grade wearable device (Huawei Band) represents a methodological limitation. While such devices offer practical advantages for continuous monitoring, their algorithms for step counting, heart rate monitoring, and classification of moderate-to-vigorous physical activity are proprietary and may vary across different brands and models. This variability could influence the precision of activity goal setting and the consistency of the intervention if different devices were used. More importantly, we did not perform a formal validation of the device’s measurements against a criterion standard (e.g., research-grade accelerometers or direct observation) in our specific postoperative population. Consequently, the observed correlations between device-derived metrics (e.g., daily steps) and clinical outcomes might be, in part, specific to the algorithms of the device used. This limits the generalizability of our findings and suggests that the absolute values of activity metrics should be interpreted with caution. Future studies should incorporate device validation protocols to ensure measurement accuracy and to facilitate the development of standardized, device-agnostic activity prescriptions for postoperative rehabilitation. Future prospective, multicenter randomized controlled trials with extended follow-up periods (3-6 months) are urgently needed. These studies should focus on verifying the long-term sustainability of intervention effects, exploring the correlation between rehabilitation adherence and adjuvant therapy tolerance, and evaluating the impact of this model on long-term survival and QoL to fully clarify its clinical value.
CONCLUSION
In conclusion, this study provides robust evidence that a multi-dimensional monitoring strategy combined with wearable device-guided intervention significantly enhances early physical recovery, liver function restoration, QoL, and key clinical outcomes in patients after HCC surgery. It establishes the value of objective digital biomarkers in guiding postoperative rehabilitation and underscores that consistent, monitored activity progression is more critical than the mere achievement of initial mobility milestones. This model presents a viable and effective approach for implementing precision rehabilitation in surgical oncology, paving the way for more personalized and proactive patient care in the future.
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