Association between Daily Step Counts and Frailty Trajectory Classes in Lung Cancer Patients Undergoing Radiotherapy: A Prospective Longitudinal Study

Association between Daily Step Counts and Frailty Trajectory Classes in Lung Cancer Patients Undergoing Radiotherapy: A Prospective Longitudinal Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Daily Step Counts and Frailty Trajectory Classes in Lung Cancer Patients Undergoing Radiotherapy: A Prospective Longitudinal Study Jiang Zhang, Jiang Wu, Song Li, Xiaoyan Wu, Lingyun Ran, Xijuan Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8999549/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose To characterize frailty-trajectory categories in patients receiving radiotherapy for lung cancer, identify factors associated with trajectory membership, and examine the association between daily step counts during radiotherapy and frailty trajectories. Methods In this prospective longitudinal study, daily step counts were collected using the WeChat Step Mini Program from the start to the end of radiotherapy. Frailty was assessed using the Frailty Phenotype Scale at 3 time points: before radiotherapy, at the end of radiotherapy, and 1 month after radiotherapy. A latent class growth model (LCGM) was used to identify distinct frailty-trajectory classes. Multinomial logistic regression was performed to examine factors associated with class membership and to evaluate the association between step counts and frailty-trajectory class. Results Among 327 patients, LCGM identified 3 frailty trajectories: prefrailty declining (56.58%), frailty stable increasing (27.83%), and severe frailty increasing (15.60%). Factors associated with frailty-trajectory membership included older age, poorer nutritional status, more advanced disease stage, poorer sleep status, and oral intake impairment. Lower daily step counts during radiotherapy were associated with membership in trajectories characterized by persistent frailty worsening. Conclusions Frailty trajectories from before radiotherapy to 1 month after radiotherapy were heterogeneous. Step-count gradients were observed around 3000, 6000, and 9000 steps/day, suggesting exploratory cut points corresponding to different frailty-risk intervals and providing a quantitative reference for early risk identification and individualized activity recommendations. Continuous step monitoring combined with routine frailty screening may help identify high-risk patients early and support multidimensional assessment and proactive management during radiotherapy care. Implications for Cancer Survivors: Daily step counts provide a scalable, real-world metric to support early risk stratification and guide targeted frailty screening and management during early survivorship after lung cancer radiotherapy. Lung cancer Radiotherapy Frailty Latent class growth model Longitudinal study Daily step count Figures Figure 1 Figure 2 Background Lung cancer is one of the most prevalent malignancies worldwide. According to the latest data from the International Agency for Research on Cancer (IARC), there were 2.5 million new cases and 1.8 million deaths globally in 2022. 1 In China, lung cancer remains the most common cancer, with 1.0606 million new cases and 733,300 deaths reported in 2022 alone, thereby posing a serious threat to human health. 2 Frailty is a clinical syndrome characterized by decreased physiological reserve and multisystem dysfunction, leading to increased vulnerability and reduced stress resistance. 3 It has become a major concern in elderly cancer patients because of its significant impact on prognosis. 4 Lung cancer patients are particularly susceptible to physical or functional frailty due to disease burden, psychological distress, and treatment-related adverse effects. Reportedly, physical or functional impaired lung cancer patients have prevalence rates ranging from 18% to 45% within populations affected by the disease. 5 Frailty stands as a well-recognized risk factor that precipitates a host of unfavorable clinical outcomes, encompassing an accelerated decline in functional capacity, the progression of underlying diseases, a heightened incidence of complications, an elevated mortality rate, and a diminished quality of life. 6 , 7 Studies have revealed that frailty is not a static condition but rather a dynamic and reversible one, and can be improved or aggravated with external intervention or time, and its development trajectory reveals obvious heterogeneity due to individual differences in patients. 8 In a prospective longitudinal study Du J et al 9 used both the FRAIL scale and the Frailty Index (FI) to identify frailty trajectory classes and their determinants in a cohort of 2268 older Chinese adults. The results reveal that the trajectory categories based on the FRAIL scale include the no-frailty group (58.8%), the increasing-frailty group (17.0%), the worsened-frailty group (12.2%), and the improved-frailty group (12.0%). The trajectory categories based on FI are divided into low-stable groups (81.4%), medium-stable groups (8.3%), and low-rapid groups (10.4%). Although different tools are divided in different ways, their influential factors are largely consistent. Radiotherapy (RT) is one of the main treatment methods for lung cancer patients. Patients with lung cancer undergoing radiotherapy are at high risk of frailty, and their frailty has obvious population heterogeneity. 10 , 11 Moreover, the reported researches on frailty status in lung cancer patients receiving radiotherapy is mainly limited to single-time-point status evaluation and its associated factors. Additionally, few longitudinal studies have been conducted on post-habit formation trajectory analysis of frail status before and after radiation therapy. Besides, daily step counts, as a quantifiable and interventional indicator, can reflect the level of daily physical activity of patients. In recent years, with the development of mobile internet and smart wearable technology, the use of daily activity steps to monitor patients’ health status has become an efficient and feasible means of assessment and intervention. Previous studies have shown that low activity levels may aggravate muscle atrophy and metabolic disorders, thereby accelerating the progression of frailty. 12 However, the exact association between daily step counts and frailty trajectory classes is still unclear. This gap hampers early frailty detection, precise care, and the design of tailored exercise plans. To address this gap, this study employed a prospective longitudinal design to systematically evaluate the frailty status of patients with lung cancer before radiotherapy, at the end of radiotherapy and 1 month after radiotherapy. LCGM was used to explore the dynamic change trajectory categories and influencing factors of frailty. Meanwhile, the relationship between the number of daily activity steps of patients during radiotherapy and the category of frailty trajectory was analyzed. It may aim to provide a theoretical basis for dynamic frailty assessment and precise nursing intervention for patients with lung cancer undergoing radiotherapy, and also lay a foundation for the construction of early warning model of frailty and the formulation of scientific exercise intervention strategies in the future. Methods Study Design and Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki and is reported in compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Frailty was assessed at three time points: T1 (1 day before radiotherapy), T2 (at the end of radiotherapy), and T3 (1 month after radiotherapy). Daily step counts were continuously recorded throughout the radiotherapy period (from the first to the last treatment day) using the WeChat Step Mini-program. The study protocol was approved by the Ethics Committee of Yunnan Cancer Hospital (approval No. SLKYLX2023 031) and was registered at the Chinese Clinical Trial Registry (ChiCTR2400081213). All participants provided written informed consent before enrollment and were informed of their right to withdraw at any time without any adverse consequences. The study was conducted in accordance with institutional and national ethical standards and complied with the Declaration of Helsinki (2013 revision), the CIOMS ethical guidelines, and ICMJE recommendations. Study participants This study was a prospective longitudinal observational study conducted in a tertiary cancer hospital in Yunnan Province, China from March 2023 to November 2024. The subjects were patients with lung cancer with radical thoracic radiotherapy who were continuously enrolled in the radiotherapy department. Eligible participants were required to meet the following criteria: (1) Age ≥ 18 years old. (2) Pathological or cytological diagnosis of lung cancer, including non-small cell lung cancer (NSCLC) or limited-stage small cell lung cancer (SCLC).(3) The radiotherapy target area is limited to the chest: the GTV / CTV / PTV in the radiotherapy planning system is located in the primary lung tumor and / or mediastinal and hilar lymph node regions.(4) The course record or doctor's advice is clearly marked as radical radiotherapy and meets any of the following dosimetric criteria: a) conventional radiotherapy or intensity modulated / volume modulated intensity modulated radiotherapy: prescription dose 60–70 Gy / 30–35 times. b) Postoperative adjuvant thoracic radiotherapy (radical / reduce the risk of recurrence): prescription dose 50–60 Gy / 25–30 times. Exclusion criteria were as follows: (1) Lack of complete dosimetric data or clinical follow-up information, (2) Treatment intention is palliative and the doctor 's advice, the course of disease clearly records palliative or the prescription dose does not meet the above-mentioned radical criteria. (3) the main irradiation site is outside the chest such as bone, brain and other metastases as the main irradiation area.(4) Previous history of radical dose chest radiotherapy. (5) During radiotherapy, the steps of WeChat Step Mini-program were not updated for more than 3 days. Sample size consideration There is no uniform sample size determination standard for latent class and trajectory analysis. Referring to previous research experience, in order to ensure the stability of model selection especially on the basis of BIC and parameter estimation, this study sets the target sample size as n > 200. At the same time, the stability and identifiability of the model depend on the minimum class size to a certain extent. The existing methodological recommendations point out that the minimum sample size of a single latent class should usually reach 30–50 cases, and the proportion of this class should not be less than 5% − 10% 13, 14 of the total sample size. In this study, the minimum latent class accounts for about 15.6% (about 51 / 327) of the total sample size, which is higher than the above recommended threshold, thus supporting the robustness of model parameter estimation and the reliability of model convergence to a certain extent. Data collection The study used a structured questionnaire, which was performed by the main researchers and trained nursing graduate students. At the baseline (T1) stage, the researchers explained the purpose and process of the study and obtained written consent, collected demographic and clinical data, and completed the Fried frailty phenotype assessment. In order to facilitate motion monitoring, researchers record the contact phone and add WeChat with each other. During radiotherapy, the researchers continuously collected daily step data through the WeChat Step Mini-program, and performed weekly summary and verification. Frailty assessment was repeated at T1, T2 and T3. Before follow-up, the research team calculated the next time point on the basis of the previous survey date and confirmed the arrangement by telephone. The questionnaire was completed on-site in paper form and the integrity was checked immediately. For those who could not fill in independently, the researchers asked orally in a neutral tone and recorded them truthfully. A total of 400 cases were included in the baseline. At T2, 39 cases were excluded due to the inability to accurately record the number of daily activity steps during radiotherapy, 10 cases were actively withdrawn, and the remaining 351 cases were excluded. 24 cases were lost to follow-up at T3. The final analysis sample was 327 cases, with an overall retention rate of 81.75% (Fig. 1 ). Outcome measurements General information sheet demographic information including age, gender, and medical payment method was collected by face-to-face communication between researchers and patients. Disease-related information was obtained from the department's radiotherapy information system, hospital information system (HIS) and medical record system. The patients’ information includes lung cancer pathological type, TNM stage, complications, the course of disease and the previous treatment history. Radiotherapy-related information including GTV / CTV / PTV, prescription dose, the number of splits, and treatment intention comes from the radiotherapy planning system and doctor's advice and course record, and is used to determine the inclusion criteria for radical radiotherapy. Frailty phenotype (FFP): i n this study, the FFP was used to evaluate the frailty status. 15 FFP included five indicators: unintentional weight loss, subjective fatigue / lack of energy, decreased grip strength, decreased walking speed, and decreased physical activity. All indicators were assigned to two categories (existence = 1 point, non-existence = 0 point), and the total score range was 0–5 points. According to the standard threshold, 0 is divided into robust (non-frail / robust), 1–2 is divided into prefrailty, ≥ 3 is divided into frailty. It should be pointed out that FFP belongs to the clinical syndrome index, rather than a single-dimensional psychological measurement scale, so internal consistency indicators such as Cronbach 's α is not conceptually applicable. In contrast, FFP has been widely validated in a variety of populations including patients with chronic diseases and tumors, with good construct validity and prognosis correlation. 11 , 16 Previous studies have also shown that FFP has a stable association with adverse outcomes such as death, hospitalization and functional decline. In this study, we used standardized FFP criteria to ensure methodological consistency and facilitate comparison with previous validation studies. The Nutrition Risk Screening-2002 (NRS-2002) it consists of three components including nutritional status (0–3 points), disease severity (0–3 points), and age ≥ 70 years old (0–1 point). A total score ≥ 3 indicates nutritional risk. 17 T1 (1 day before radiotherapy) was evaluated face-to-face by the researchers according to the content of the scale. Daily activity steps during radiotherapy after the researchers added WeChat with the patients, the daily activity steps during radiotherapy were collected via their WeChat Step Mini-program, and they summarized and recorded data weekly to ensure the timeliness and integrity of the data. Data analysis Statistical analyses were performed using IBM SPSS Statistics version 28.0 (IBM Corp) and Mplus 8.7 (Muthén & Muthén). Trajectory figures were finalized using Adobe Illustrator 2020 (Adobe Inc). Continuous variables were summarized as mean (SD) for approximately normally distributed data and median (IQR) for nonnormally distributed data; categorical variables were summarized as No. (%). Normality wassessed using skewness, kurtosis, and the Kolmogorov-Smirnov test. Between-group comparisons for continuous variables were conducted using the independent-samples t test or the Mann-Whitney U test, as appropriate. Categorical variables were compared using the χ² test or Fisher exact test. For comparisons among 3 or more groups with nonnormally distributed continuous variables, the Kruskal-Wallis test was used. Frailty was assessed at 3 time points: T1 (1 day before radiotherapy), T2 (end of radiotherapy), and T3 (1 month after radiotherapy). Because the frailty score was nonnormally distributed, within-participant changes across T1 to T3 were evaluated using the Friedman test, followed by post hoc pairwise comparisons with Bonferroni adjustment. To evaluate potential selection bias related to step-count availability, baseline characteristics (eg, age, disease stage, NRS-2002 score, and baseline frailty score) were compared between participants included vs excluded from step-count analyses using the corresponding tests described above. Latent trajectory/class modeling was conducted in Mplus, starting with a 1-class model and incrementally increasing the number of classes. Model selection considered statistical fit and clinical interpretability, including Akaike information criterion, Bayesian information criterion, sample-size adjusted Bayesian information criterion, entropy, the Lo-Mendell-Rubin adjusted likelihood ratio test, and the bootstrap likelihood ratio test comparing k vs k − 1 class models (P < .05 indicating improved fit) 181920 . The final model identified 3 frailty trajectory groups: prefrailty declining (56.6%), frailty stable increasing (27.8%), and severe frailty increasing (15.6%). After selecting the optimal class solution, growth mixture modeling was used to evaluate the robustness and stability of the identified trajectory structure. Associations between average daily step counts during radiotherapy and frailty trajectory group membership were examined using multivariable multinomial logistic regression (unordered outcome). Key baseline covariates related to frailty and physical activity (eg, age, disease stage, NRS-2002 score, and treatment history) were included to control for confounding, informed by prior literature and univariable analyses (P < .05). Results are reported as adjusted odds ratios with 95% CIs. Daily step counts were collected from the first to the last day of radiotherapy, with radiotherapy dates verified in the radiotherapy information system. A valid step day was defined as a successful WeChat step upload with confirmation that the participant carried the mobile phone that day; days with 0 steps or no upload were considered invalid. Participants were required to have at least 25 valid days during radiotherapy. Participants with 3 or more consecutive missing days were excluded. When the missing proportion was less than 20%, missing values were imputed using the mean steps for the corresponding week; participants with more than 20% missing step days (valid-day proportion < 80%) were excluded. The median valid-day recording rate during radiotherapy was 97.0% (IQR, 93.0%–100%), indicating high data completeness. All tests were 2-sided, and P < .05 was considered statistically significant. Results General and clinical characteristics of the lung cancer survivors A total of 327 lung cancer patients who had received radiotherapy successfully completed the study. Among them, 272 patients (83.2%) were male, while 55 patients (16.8%) were female. Histological examination showed that squamous cell carcinoma was present in 101 patients, accounting for 30.9% of the total; adenocarcinoma was identified in 124 patients (37.9%); small cell carcinoma was diagnosed in 83 patients (25.4%); and other types of lung cancer were found in 19 patients (5.8%). Regarding the Tumor Node Metastasis （TNM）staging, 170 patients (52.0%) were at Stage II and below, 157 patients (48.0%) were at stage III, (Table 1). Daily activity steps during radiotherapy The daily activity steps during radiotherapy were extracted by WeChat Step Mini-program (Table 2). Frailty prevalence and overall trend across radiotherapy phases Frailty total and domain scores were nonnormally distributed at all time points. Friedman’s test revealed a significant change across the three assessments ( P < 0.001), and Bonferroni-corrected pairwise comparisons revealed significant differences between every pair of time points (all P < 0.001) (Table 3). Model selection and robustness analysis Although Fried's frailty score is an ordinal variable ranging from 0 to 5 with a non-normal distribution, LCGM are relatively insensitive to the normality assumption in practice. Modeling it as an approximate continuous index can be used to identify the overall trajectory heterogeneity. We successively fitted models with 1 - 6 classes in Mplus 8.7 (Table 4 and Figure 2). Overall, as the number of categories increased, AIC/BIC/aBIC continued to decrease until the 5-class model, where it rebounded for the 6-class model. This suggests that further increasing the number of categories may lead to overfitting. Comparing the 3-class and 4-class models, although the 4-class model showed a continued decrease in information criteria and LMR/BLRT still indicated statistical improvement, its classification certainty decreased (Entropy: 0.969 to 0.929). Moreover, the proportion of newly added subcategories was relatively small (approximately 13.8%), and the trajectory patterns showed significant overlap with adjacent categories, limiting clinical interpretability and operational feasibility. Therefore, based on the marginal improvement, category proportion, classification accuracy and parsimony principle of the fitting index, we finally selected three types of models as the optimal solution, and named them as the prefrailty declining group, the frailty stable increasing group, and the severe frailty increasing group (Table 4). Categories of frailty trajectories in patients with lung cancer undergoing radiotherapy Among patients with lung cancer undergoing radiotherapy, three different frailty trajectories were identified in this study (Figure 2). At baseline (before radiotherapy), the frailty level of C3 was the lowest (intercept = 0.789; n = 185, 56. 6 %), C1 was in the middle level (intercept = 2. 446; n = 91,27.8 %), while C2 had the highest baseline frailty (intercept = 3.385; n = 51, 15.6 %). Over time, the C3 exhibited a downward trend (slope = −0.267), indicating an improvement in frailty severity, the C1 showed a slight increase (slope = 0.021), indicating a gradual worsening of frailty, while the C2 demonstrated a marked rise (slope = 0.497), indicating persistent deterioration in frailty. Based on the baseline level and change rate of each category, the three trajectories are respectively named: the prefrailty declining group (C3), the frailty stable increasing group (C1), and the severe frailty increasing group (C2). Univariate analysis of frailty trajectory classes in lung cancer patients receiving radiotherapy Univariate analysis revealed that the distribution of frailty trajectory classes differed significantly across age, occupation, medical expense payment method, disease stage, sleep status, disease duration, nutritional score, and prior medical treatment (all P < 0.05) (Table 5). Multinomial logistic regression analysis and conceptual causal framework The 3-category frailty trajectory class was modeled as the dependent variable (1 = frailty stable increasing group, 2 = severe frailty increasing group, and 3 = prefrailty declining group). Multinomial logistic regression was performed including variables that were statistically significant in univariable analyses. Independent variables were coded as follows: age, ≤60 years = 1 and >60 years = 2. Occupation, manual labor = 1, nonmanual labor = 2, and unemployed/retired labor = 3. Medical payment, employee insurance = 1 and resident insurance = 2. Disease stage, Stage II and below = 1, stage III = 2. Sleep status, normal = 1, and abnormal = 2. Disease duration, ≤12 months = 1, and >12 months = 2. Nutritional score, <3 = 1, and ≥3 = 2. Medical treatment, surgery =1, surgery/chemotherapy/radiotherapy plus immunotherapy/targeted therapy = 2, chemotherapy = 3, and targeted/immunotherapy/other = 4. WeChat step count≤3000=1,3001-6000=2,6001-9000=3,9001-12000=4, and >12000=5). Average daily steps during radiotherapy showed the strongest association with frailty trajectory membership (Tables 6–7). In Model I, which included step count only, step count explained 51.9% of the variation in trajectory class (adjusted pseudo-R²; Table 6). Using >12 000 steps/day as the reference, lower step counts were associated with less favorable trajectories. In the comparison of C1 vs C3, 3000–5999 steps/day (OR, 20.497; P<.001) and 6000–8999 steps/day (OR, 8.319; P<.001) were associated with higher odds of belonging to C1. In the comparison of C2 vs C3, <3000 steps/day (OR, 64.167; P<.001) and 3000–5999 steps/day (OR, 37.065; P<.001) showed stronger associations with membership in C2. After adding demographic and clinical covariates (Model II), the model’s explanatory power increased to 69.1% (adjusted pseudo-R²; Table 7). Step count remained independently associated with trajectory membership, with ≤5999 steps/day continuing to be significantly associated with C2 vs C3 (eg, <3000 steps/day: OR, 23.928; P=.001; 3000–5999 steps/day: OR, 22.574; P=.001). Discussion To our knowledge, this is the first study to use the Latent Class Growth Model (LCGM) to identify frailty trajectory categories in lung cancer patients undergoing radiotherapy, and to explore the association between daily activity steps and frailty trajectory categories during radiotherapy in a prospective follow-up framework. The study revealed three distinct categories of frailty trajectories: the prefrailty declining group (56.6%), the frailty stable increasing group (27.8%), and the severe frailty increasing group (15.6%). Compared with the results reported by Chen et al 21 regarding acquired frailty trajectories in critically ill patients, the subjects in this study may exhibit more persistent fluctuations in frailty and greater individual heterogeneity throughout treatment-related burdens and recovery processes. The initial frailty scores for the frailty stable increasing group and the severe frailty increasing group were moderate and severe respectively, exhibiting varying degrees of increase from pre-radiotherapy to 1 month post-radiotherapy. This trend may be associated with adverse reactions to radiotherapy, such as inadequate nutritional intake, compromised immune function, fatigue, coughing, and excessive sputum production. 22 – 24 These factors may cumulatively weaken patients' physical reserves through multi-system overload. Moreover, the acute inflammatory response and chronic fibrotic changes resulting from radiation-induced pulmonary injury may further constrain pulmonary function and physical endurance, 25–28 thereby affecting recovery and sustaining frailty levels within a higher range for an extended period. These results further suggest that frailty more likely reflects the cumulative manifestation of multi-system functional impairment, rather than being attributable to the decline of a single organ system. Different frailty trajectory categories also exhibit certain clinical distinctions: the frailty stable increasing group and the severe frailty increasing group still maintained a high frailty score one month after radiotherapy (> 3 points). This suggests that routine frailty assessment and risk stratification before radiotherapy may aid in identifying individuals requiring prioritized follow-up and support. For patients in the pre-frailty stage (< 2 points), previous studies suggested that exercise-related interventions are associated with slowing or reversing frailty progression. 29 – 31 Thus, early identification and activity management theoretically hold potential preventive significance. In contrast, patients with severe frailty often present with higher disease burden and functional limitations. Their management may require multidisciplinary support encompassing rehabilitation, nutrition, and nursing care to achieve symptom relief and improved quality of life. 9 , 32 – 34 Overall, frailty trajectory classification may partially reflect variations in patients' physiological resilience and provide insights for subsequent exploration of stratified management strategies. In the analysis of influencing factors, Model I demonstrated a strong statistical association (51.9%) between daily activity steps during radiotherapy and trajectory category assignment. While Model II, incorporating demographic and disease-related factors, further enhanced explanatory power to 69.1%. The associations between age, disease stage, nutritional risk, and oral feeding restrictions with the frailty trajectory category largely concurred with previous studies. 11 , 35 – 37 Notably, within the multivariate model, daily activity steps during radiotherapy remained significantly and independently associated with trajectory category. This finding suggests that step count variations may reflect dynamic fluctuations in patients' functional tolerance and physical reserve throughout treatment. From a functional status assessment perspective, daily step count serves not only as an external indicator of activity levels but also potentially as a composite behavioral signal of physical reserve and metabolic homeostasis. Consequently, it has potential application value for further investigation in frailty assessment and follow-up. The distribution of daily steps during radiotherapy between different trajectories showed a certain stratification: compared with the prefrailty declining group, the lower step level was related to the higher probability of being classified into the frailty stable increasing group or the severe frailty increasing group. When comparing between the two types of poor trajectories, a lower step level is also associated with a higher probability of being classified into the severe frailty increasing group. It should be emphasized that the 3000, 6000, and 9000 steps reported in this study represent an explanatory range and risk gradient intended to describe stratification within the sample. They do not denote fixed biological thresholds or cut-off points directly applicable to clinical decision-making. The results of this study suggest that step monitoring during radiotherapy, as a readily obtainable indicator of daily behavior, may aid in identifying individuals at high risk of frailty or capturing early signals of frailty progression. Notably, these range values are markedly lower than the World Health Organization (WHO) recommended activity standards for healthy adults (approximately 10,000 steps per day). 38 It also reflects the special physiological burden of patients with lung cancer during the treatment period. Future management strategies should be grounded in individualized activity targets, employing wearable devices or mobile applications such as WeChat Step Mini-program to monitor step counts in real time. This enables dynamic assessment of frailty risk during clinical follow-ups, facilitating comprehensive interventions encompassing exercise prescriptions and nutritional support. 39 – 41 The implementation of such digital continuous monitoring tools may represent a pivotal opportunity for transitioning frailty management towards precision rehabilitation models. Conclusion In this prospective longitudinal study, we employed LCGM to identify three distinct frailty trajectory categories among lung cancer patients undergoing radiotherapy, and assessed the association between daily step counts during radiotherapy and frailty trajectory categories. Results revealed that lower activity levels correlated with adverse frailty trajectory categories. The step ranges and stratification intervals corresponding to approximately 3000, 6000, and 9000 steps exhibited a discernible risk gradient within the sample. This provides reference for dynamic monitoring of frailty risk during radiotherapy and facilitates stratified analysis in subsequent studies. This study suggests that daily activity steps, as a simple, non-invasive and sustainable daily behavior index, have potential feasibility and indicative significance in frailty monitoring and follow-up evaluation during radiotherapy. However, the above findings mainly reflect statistical associations, which cannot be used to infer causality, nor do they constitute sufficient verification of clinical screening tools due to this study is an observational design. In the future, it is still necessary to conduct external verification in a multi-center, larger sample cohort, and further evaluate through longer-term longitudinal studies and intervention studies: whether step-based continuous monitoring and stratified support strategies can improve clinical outcomes such as functional recovery and quality of life. Limitations This prospective cohort study revealed the dynamic trajectory of frailty in patients with lung cancer undergoing radiotherapy, highlighting the potential value of daily step counts as a dynamic indicator of physical recovery capacity. However, the following limitations should be fully considered when interpreting the results. Firstly, the study subjects were sourced from a single tertiary cancer hospital in Yunnan Province, which may limit the extrapolation of the results among different populations, medical institutions or regions. Future prospective cohort studies should be conducted across multiple centers and regions to validate the reproducibility and external validity of the present findings. Secondly, although Model II explained about 69.1% of the variation, there may still be potential influencing factors that are not included in the analysis, such as mental state, fatigue perception or social support level. Incorporating psychosocial and behavioral variables into future research will help to understand the multidimensional mechanisms of frailty more systematically. Declarations Consent to participate Informed consent was obtained from all individual participants included in the study. Conflict of Interest Disclosures The authors declare no competing interests Funding/Support The authors sincerely thank all patients with lung cancer who participated in this study for their cooperation and valuable contributions. At the same time, thanks to the clinical staffs who supported the patient care and research data collection process. This study was supported by the Scientific Research Fund of the Yunnan Provincial Department of Education (Grant Nos. 2026J0359, 2026J0335); the Research Fund of the Chinese Nursing Association (Grant No. ZHKYQ202312); the Joint Special Program of the Yunnan Provincial Department of Science and Technology and Kunming Medical University (Grant No. 2024XKTDPY15); the 2025 Yunnan Province Health Sector Medical Discipline Leader Program—Specialized Nursing(Grant No. D-2025044); and the 2025 Nursing Special Fund of Kunming Medical University(Grant Nos. 2025KYHLZXSK15, 2025KYHLZXZK09). The sponsors did not participate in research design, data collection, data analysis, result interpretation or paper writing. Author Contribution **J Z, X J Z** conceived the research idea, developed the study framework and plan, and wrote the manuscript. **X Y W, J W** participated in data collection. **S L** , **L Y R** provided valuable feedback an contributed to the final draft. 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Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56:M146–56. Kahlon S, Pederson J, Majumdar SR, Belga S, Lau D, Fradette M, et al. Association between frailty and 30-day outcomes after discharge from hospital. CMAJ. 2015;187:799–804. Trestini I, Sperduti I, Sposito M, et al. Evaluation of nutritional status in non-small-cell lung cancer: screening, assessment and correlation with treatment outcome. ESMO Open. 2020;5(3):e000689. Yang Y, Tian X, Zhou H, et al. A score prediction model for predicting the heterogeneity symptom trajectories among lung cancer patients during perioperative period: a longitudinal observational study. Ann Med. 2025;57(1):2479588. Willis M, Jozkowski KN. Correction to: Sexual Consent Perceptions of a Fictional Vignette: A Latent Growth Curve Model. Arch Sex Behav. 2023;52:2701. Green MJ. Latent class analysis was accurate but sensitive in data simulations. J Clin Epidemiol. 2014;67(10):1157–62. Chen XX, Xiong J, Chen JX, et al. Trajectory and determinants of intensive care unit-acquired weakness in critical illness: A multicentre, prospective, longitudinal study. Nurs Crit Care. 2025;30(4):e13209. Zhang J, Zhao XJ, Yang BK, Yu SP, Fei YY, Wu J. Characteristics of Symptom Clusters and Sentinel Symptoms in Lung Cancer Patients Before and at the Completion of Radiotherapy. Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2025;47:931–8. Zhang J, Zhao X, Zhang G, Wu J, Guan Q, Tian Z et al. Network analysis of core symptom changes in lung cancer survivors: a longitudinal study. J Cancer Surviv. 2025. Chen JL, Pan CK, Lin LC, Huang YS, Huang TH, Yang SJ, et al. Combination of ataxia telangiectasia and Rad3-related inhibition with ablative radiotherapy remodels the tumor microenvironment and enhances immunotherapy response in lung cancer. Cancer Immunol Immunother. 2024;74:8. Lu X, Wang J, Zhang T, et al. Comprehensive Pneumonitis Profile of Thoracic Radiotherapy Followed by Immune Checkpoint Inhibitor and Risk Factors for Radiation Recall Pneumonitis in Lung Cancer. Front Immunol. 2022;13:918787. Ji W, Jiang T, Chen Z, Chen G, Zhang Y, Du S. V(15(Gy)) as a predictor of asymptomatic radiation pneumonitis in patients with lung cancer: A retrospective dosimetric analysis. Precis Radiat Oncol. 2025;9:185–91. Lee JH, Cha S, Ko EJ, Kim W, Kim SS, Song SY, et al. Clinical effect of pulmonary rehabilitation during radiotherapy in lung cancer: A randomized controlled trial. Lung Cancer. 2025;204:108546. Liang G, Chang R, Zhang Q, Luo Y, Peng Y, Luo B. Segmental bronchi radiation dose affected the progression of radiation pneumonitis in lung cancer patients. Discov Oncol. 2025;16:793. Guan Y, Hu Z, Wang Q, Ou R, Xue H, Du W, et al. Effectiveness of interventions to improve frailty among community-dwelled older adults: A systematic review. Arch Gerontol Geriatr. 2025;137:105946. Lin S, Wang F, Huang M, Chen J, Jiang X, Li Q, et al. Multidomain intervention for delaying aging in community-dwelling older adults (MIDA): study design and protocol. Ann Med. 2025;57:2496409. Dent E, Daly RM, Hoogendijk EO, Scott D. Exercise to Prevent and Manage Frailty and Fragility Fractures. Curr Osteoporos Rep. 2023;21:205–15. Dent E, Hanlon P, Sim M, Jylhävä J, Liu Z, Vetrano DL, et al. Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology. Ageing Res Rev. 2023;91:102082. Roller-Wirnsberger R, Lindner S, Liew A, O'Caoimh R, Koula ML, Moody D, et al. European Collaborative and Interprofessional Capability Framework for Prevention and Management of Frailty-a consensus process supported by the Joint Action for Frailty Prevention (ADVANTAGE) and the European Geriatric Medicine Society (EuGMS). Aging Clin Exp Res. 2020;32:561–70. Gabrovec B, Antoniadou E, Soleymani D, Kadalska E, Carriazo AM, Samaniego LL, et al. Need for comprehensive management of frailty at an individual level: European perspective from the advantage joint action on frailty. J Rehabil Med. 2020;52:jrm00075. Yu Y, Zhang C, Dong Y, Rao H. Unravelling the trajectory of frailty and its influencing factors in elderly patients with coronary heart disease after percutaneous coronary intervention: protocol for a cohort study in China. BMJ Open. 2025;15:e089528. Shen X, Qi X, Fu X. Pre-radiotherapy frailty and associated determinants in elderly patients with thoracic tumors. Transl Cancer Res. 2025;14:3642–53. Jeon M, Jang H, Lim A, Kim S. Frailty and its associated factors among older adults with cancer undergoing chemotherapy as outpatients: A cross-sectional study. Eur J Oncol Nurs. 2022;60:102192. Segar ML, Marques MM, Palmeira AL, Okely AD. Everything counts in sending the right message: science-based messaging implications from the 2020 WHO guidelines on physical activity and sedentary behaviour. Int J Behav Nutr Phys Act. 2020;17:135. Guo Y, Miao X, Hu J, Chen L, Chen Y, Zhao K, et al. Summary of best evidence for prevention and management of frailty. Age Ageing. 2024;53:afae011. [pii]. Pérez-Zepeda MU, Martínez-Velilla N, Kehler DS, Izquierdo M, Rockwood K, Theou O. The impact of an exercise intervention on frailty levels in hospitalised older adults: secondary analysis of a randomised controlled trial. Age Ageing. 2022;51:afac028. [pii]. Mohd Suffian NI, SN' A, Abu Saad H, Chan YM, Ibrahim Z, Omar N, et al. Frailty Intervention through Nutrition Education and Exercise (FINE). A Health Promotion Intervention to Prevent Frailty and Improve Frailty Status among Pre-Frail Elderly-A Study Protocol of a Cluster Randomized Controlled Trial. Nutrients. 2020;12:2758. Tables Table 1 to 7 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 01 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8999549","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620115733,"identity":"3e773131-4715-40a4-ba13-d30518f9f9fb","order_by":0,"name":"Jiang Zhang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Kunming Medical University， Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Zhang","suffix":""},{"id":620115734,"identity":"ab9435f8-631b-42dd-b550-183dec5c9421","order_by":1,"name":"Jiang Wu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Kunming Medical University， Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Wu","suffix":""},{"id":620115736,"identity":"6d978ccf-1057-4eb9-a669-d4b16ffbf9c9","order_by":2,"name":"Song Li","email":"","orcid":"","institution":"The Third Affiliated Hospital of Kunming Medical University， Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Li","suffix":""},{"id":620115737,"identity":"1442cb32-baa1-4a2c-b304-bc65b8ae4a2b","order_by":3,"name":"Xiaoyan Wu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Kunming Medical University， Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Wu","suffix":""},{"id":620115740,"identity":"db4546fb-7bf5-413e-aada-c479dcae3aaa","order_by":4,"name":"Lingyun Ran","email":"","orcid":"","institution":"Nursing School of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Ran","suffix":""},{"id":620115742,"identity":"c02cc25c-5df9-4eb2-9abb-3461a910bc89","order_by":5,"name":"Xijuan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACfmb+Bwc+GLDZyTMcPkCcFsn2HsaDMwr4kg0bjyUQp8XgzBnmwzwf5BgbDp8xINJlN3IPHJxhYMbM2Hbm4403DHZyug0EdDDOyEsA+iWNj53n7GbLOQzJxmYHCGhhlkgwANpyjJlxxtlt0jwMBxK3EdLCBtRymMfgP2PD/TfPiNPCw3MGpIWNseHAGTbitEiwtyUAHcaWbNhwzNhyjgERfrE/zHz4w4c/4Kh8eONNhZ0cQS2oVvIQGzVIWkjVMQpGwSgYBSMCAADn40m2IOYTTwAAAABJRU5ErkJggg==","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xijuan","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-03-01 06:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8999549/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8999549/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107479668,"identity":"118bb0d2-d662-40ad-b85a-03ae6ec26ebe","added_by":"auto","created_at":"2026-04-22 01:42:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51932,"visible":true,"origin":"","legend":"\u003cp\u003eA flow chart about data collection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8999549/v1/c622daad74d0852147a99a67.png"},{"id":106728173,"identity":"d79a5054-7e0a-419d-a85e-f462db43ed7e","added_by":"auto","created_at":"2026-04-12 18:42:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124879,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectory of the development of frailty in patients receiving lung cancer radiotherapy\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8999549/v1/01eba2b74514d711ad76cfb6.jpeg"},{"id":107480763,"identity":"cf709ce1-f452-4534-8b68-e3bce38e4a1c","added_by":"auto","created_at":"2026-04-22 02:13:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":564582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999549/v1/11ed1691-260c-4c98-a3a6-350753fb651c.pdf"},{"id":106703494,"identity":"9b327ffc-8f71-4d33-8368-d174d7634ce3","added_by":"auto","created_at":"2026-04-12 07:42:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43985,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8999549/v1/d67b8e2af81dc22b762ad032.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Daily Step Counts and Frailty Trajectory Classes in Lung Cancer Patients Undergoing Radiotherapy: A Prospective Longitudinal Study","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer is one of the most prevalent malignancies worldwide. According to the latest data from the International Agency for Research on Cancer (IARC), there were 2.5\u0026nbsp;million new cases and 1.8\u0026nbsp;million deaths globally in 2022.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In China, lung cancer remains the most common cancer, with 1.0606\u0026nbsp;million new cases and 733,300 deaths reported in 2022 alone, thereby posing a serious threat to human health.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFrailty is a clinical syndrome characterized by decreased physiological reserve and multisystem dysfunction, leading to increased vulnerability and reduced stress resistance.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e It has become a major concern in elderly cancer patients because of its significant impact on prognosis.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Lung cancer patients are particularly susceptible to physical or functional frailty due to disease burden, psychological distress, and treatment-related adverse effects. Reportedly, physical or functional impaired lung cancer patients have prevalence rates ranging from 18% to 45% within populations affected by the disease.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Frailty stands as a well-recognized risk factor that precipitates a host of unfavorable clinical outcomes, encompassing an accelerated decline in functional capacity, the progression of underlying diseases, a heightened incidence of complications, an elevated mortality rate, and a diminished quality of life.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStudies have revealed that frailty is not a static condition but rather a dynamic and reversible one, and can be improved or aggravated with external intervention or time, and its development trajectory reveals obvious heterogeneity due to individual differences in patients.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In a prospective longitudinal study Du J et al\u003csup\u003e9\u003c/sup\u003e used both the FRAIL scale and the Frailty Index (FI) to identify frailty trajectory classes and their determinants in a cohort of 2268 older Chinese adults. The results reveal that the trajectory categories based on the FRAIL scale include the no-frailty group (58.8%), the increasing-frailty group (17.0%), the worsened-frailty group (12.2%), and the improved-frailty group (12.0%). The trajectory categories based on FI are divided into low-stable groups (81.4%), medium-stable groups (8.3%), and low-rapid groups (10.4%). Although different tools are divided in different ways, their influential factors are largely consistent. Radiotherapy (RT) is one of the main treatment methods for lung cancer patients. Patients with lung cancer undergoing radiotherapy are at high risk of frailty, and their frailty has obvious population heterogeneity.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Moreover, the reported researches on frailty status in lung cancer patients receiving radiotherapy is mainly limited to single-time-point status evaluation and its associated factors. Additionally, few longitudinal studies have been conducted on post-habit formation trajectory analysis of frail status before and after radiation therapy. Besides, daily step counts, as a quantifiable and interventional indicator, can reflect the level of daily physical activity of patients. In recent years, with the development of mobile internet and smart wearable technology, the use of daily activity steps to monitor patients\u0026rsquo; health status has become an efficient and feasible means of assessment and intervention. Previous studies have shown that low activity levels may aggravate muscle atrophy and metabolic disorders, thereby accelerating the progression of frailty.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, the exact association between daily step counts and frailty trajectory classes is still unclear. This gap hampers early frailty detection, precise care, and the design of tailored exercise plans.\u003c/p\u003e \u003cp\u003eTo address this gap, this study employed a prospective longitudinal design to systematically evaluate the frailty status of patients with lung cancer before radiotherapy, at the end of radiotherapy and 1 month after radiotherapy. LCGM was used to explore the dynamic change trajectory categories and influencing factors of frailty. Meanwhile, the relationship between the number of daily activity steps of patients during radiotherapy and the category of frailty trajectory was analyzed. It may aim to provide a theoretical basis for dynamic frailty assessment and precise nursing intervention for patients with lung cancer undergoing radiotherapy, and also lay a foundation for the construction of early warning model of frailty and the formulation of scientific exercise intervention strategies in the future.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Ethical Considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and is reported in compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Frailty was assessed at three time points: T1 (1 day before radiotherapy), T2 (at the end of radiotherapy), and T3 (1 month after radiotherapy). Daily step counts were continuously recorded throughout the radiotherapy period (from the first to the last treatment day) using the WeChat Step Mini-program. The study protocol was approved by the Ethics Committee of Yunnan Cancer Hospital (approval No. SLKYLX2023 031) and was registered at the Chinese Clinical Trial Registry (ChiCTR2400081213). All participants provided written informed consent before enrollment and were informed of their right to withdraw at any time without any adverse consequences. The study was conducted in accordance with institutional and national ethical standards and complied with the Declaration of Helsinki (2013 revision), the CIOMS ethical guidelines, and ICMJE recommendations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eThis study was a prospective longitudinal observational study conducted in a tertiary cancer hospital in Yunnan Province, China from March 2023 to November 2024. The subjects were patients with lung cancer with radical thoracic radiotherapy who were continuously enrolled in the radiotherapy department. Eligible participants were required to meet the following criteria: (1) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old. (2) Pathological or cytological diagnosis of lung cancer, including non-small cell lung cancer (NSCLC) or limited-stage small cell lung cancer (SCLC).(3) The radiotherapy target area is limited to the chest: the GTV / CTV / PTV in the radiotherapy planning system is located in the primary lung tumor and / or mediastinal and hilar lymph node regions.(4) The course record or doctor's advice is clearly marked as radical radiotherapy and meets any of the following dosimetric criteria: a) conventional radiotherapy or intensity modulated / volume modulated intensity modulated radiotherapy: prescription dose 60\u0026ndash;70 Gy / 30\u0026ndash;35 times. b) Postoperative adjuvant thoracic radiotherapy (radical / reduce the risk of recurrence): prescription dose 50\u0026ndash;60 Gy / 25\u0026ndash;30 times. Exclusion criteria were as follows: (1) Lack of complete dosimetric data or clinical follow-up information, (2) Treatment intention is palliative and the doctor 's advice, the course of disease clearly records palliative or the prescription dose does not meet the above-mentioned radical criteria. (3) the main irradiation site is outside the chest such as bone, brain and other metastases as the main irradiation area.(4) Previous history of radical dose chest radiotherapy. (5) During radiotherapy, the steps of WeChat Step Mini-program were not updated for more than 3 days.\u003c/p\u003e\n\u003ch3\u003eSample size consideration\u003c/h3\u003e\n\u003cp\u003eThere is no uniform sample size determination standard for latent class and trajectory analysis. Referring to previous research experience, in order to ensure the stability of model selection especially on the basis of BIC and parameter estimation, this study sets the target sample size as n\u0026thinsp;\u0026gt;\u0026thinsp;200. At the same time, the stability and identifiability of the model depend on the minimum class size to a certain extent. The existing methodological recommendations point out that the minimum sample size of a single latent class should usually reach 30\u0026ndash;50 cases, and the proportion of this class should not be less than 5% \u0026minus;\u0026thinsp;10% \u003csup\u003e13, 14\u003c/sup\u003e of the total sample size. In this study, the minimum latent class accounts for about 15.6% (about 51 / 327) of the total sample size, which is higher than the above recommended threshold, thus supporting the robustness of model parameter estimation and the reliability of model convergence to a certain extent.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe study used a structured questionnaire, which was performed by the main researchers and trained nursing graduate students. At the baseline (T1) stage, the researchers explained the purpose and process of the study and obtained written consent, collected demographic and clinical data, and completed the Fried frailty phenotype assessment. In order to facilitate motion monitoring, researchers record the contact phone and add WeChat with each other.\u003c/p\u003e \u003cp\u003eDuring radiotherapy, the researchers continuously collected daily step data through the WeChat Step Mini-program, and performed weekly summary and verification. Frailty assessment was repeated at T1, T2 and T3. Before follow-up, the research team calculated the next time point on the basis of the previous survey date and confirmed the arrangement by telephone. The questionnaire was completed on-site in paper form and the integrity was checked immediately. For those who could not fill in independently, the researchers asked orally in a neutral tone and recorded them truthfully. A total of 400 cases were included in the baseline. At T2, 39 cases were excluded due to the inability to accurately record the number of daily activity steps during radiotherapy, 10 cases were actively withdrawn, and the remaining 351 cases were excluded. 24 cases were lost to follow-up at T3. The final analysis sample was 327 cases, with an overall retention rate of 81.75% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOutcome measurements\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eGeneral information sheet\u003c/strong\u003e \u003cp\u003edemographic information including age, gender, and medical payment method was collected by face-to-face communication between researchers and patients. Disease-related information was obtained from the department's radiotherapy information system, hospital information system (HIS) and medical record system. The patients\u0026rsquo; information includes lung cancer pathological type, TNM stage, complications, the course of disease and the previous treatment history. Radiotherapy-related information including GTV / CTV / PTV, prescription dose, the number of splits, and treatment intention comes from the radiotherapy planning system and doctor's advice and course record, and is used to determine the inclusion criteria for radical radiotherapy.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFrailty phenotype (FFP): i\u003c/em\u003en this study, the FFP was used to evaluate the frailty status.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e FFP included five indicators: unintentional weight loss, subjective fatigue / lack of energy, decreased grip strength, decreased walking speed, and decreased physical activity. All indicators were assigned to two categories (existence\u0026thinsp;=\u0026thinsp;1 point, non-existence\u0026thinsp;=\u0026thinsp;0 point), and the total score range was 0\u0026ndash;5 points. According to the standard threshold, 0 is divided into robust (non-frail / robust), 1\u0026ndash;2 is divided into prefrailty, \u0026ge; 3 is divided into frailty. It should be pointed out that FFP belongs to the clinical syndrome index, rather than a single-dimensional psychological measurement scale, so internal consistency indicators such as Cronbach 's α is not conceptually applicable. In contrast, FFP has been widely validated in a variety of populations including patients with chronic diseases and tumors, with good construct validity and prognosis correlation.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Previous studies have also shown that FFP has a stable association with adverse outcomes such as death, hospitalization and functional decline. In this study, we used standardized FFP criteria to ensure methodological consistency and facilitate comparison with previous validation studies.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThe Nutrition Risk Screening-2002 (NRS-2002)\u003c/strong\u003e \u003cp\u003eit consists of three components including nutritional status (0\u0026ndash;3 points), disease severity (0\u0026ndash;3 points), and age\u0026thinsp;\u0026ge;\u0026thinsp;70 years old (0\u0026ndash;1 point). A total score\u0026thinsp;\u0026ge;\u0026thinsp;3 indicates nutritional risk.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e T1 (1 day before radiotherapy) was evaluated face-to-face by the researchers according to the content of the scale.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDaily activity steps during radiotherapy\u003c/strong\u003e \u003cp\u003eafter the researchers added WeChat with the patients, the daily activity steps during radiotherapy were collected via their WeChat Step Mini-program, and they summarized and recorded data weekly to ensure the timeliness and integrity of the data.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 28.0 (IBM Corp) and Mplus 8.7 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n). Trajectory figures were finalized using Adobe Illustrator 2020 (Adobe Inc).\u003c/p\u003e \u003cp\u003eContinuous variables were summarized as mean (SD) for approximately normally distributed data and median (IQR) for nonnormally distributed data; categorical variables were summarized as No. (%). Normality wassessed using skewness, kurtosis, and the Kolmogorov-Smirnov test. Between-group comparisons for continuous variables were conducted using the independent-samples t test or the Mann-Whitney U test, as appropriate. Categorical variables were compared using the χ\u0026sup2; test or Fisher exact test. For comparisons among 3 or more groups with nonnormally distributed continuous variables, the Kruskal-Wallis test was used.\u003c/p\u003e \u003cp\u003eFrailty was assessed at 3 time points: T1 (1 day before radiotherapy), T2 (end of radiotherapy), and T3 (1 month after radiotherapy). Because the frailty score was nonnormally distributed, within-participant changes across T1 to T3 were evaluated using the Friedman test, followed by post hoc pairwise comparisons with Bonferroni adjustment.\u003c/p\u003e \u003cp\u003eTo evaluate potential selection bias related to step-count availability, baseline characteristics (eg, age, disease stage, NRS-2002 score, and baseline frailty score) were compared between participants included vs excluded from step-count analyses using the corresponding tests described above.\u003c/p\u003e \u003cp\u003eLatent trajectory/class modeling was conducted in Mplus, starting with a 1-class model and incrementally increasing the number of classes. Model selection considered statistical fit and clinical interpretability, including Akaike information criterion, Bayesian information criterion, sample-size adjusted Bayesian information criterion, entropy, the Lo-Mendell-Rubin adjusted likelihood ratio test, and the bootstrap likelihood ratio test comparing k vs k\u0026thinsp;\u0026minus;\u0026thinsp;1 class models (P \u0026lt; .05 indicating improved fit) \u003csup\u003e181920\u003c/sup\u003e. The final model identified 3 frailty trajectory groups: prefrailty declining (56.6%), frailty stable increasing (27.8%), and severe frailty increasing (15.6%). After selecting the optimal class solution, growth mixture modeling was used to evaluate the robustness and stability of the identified trajectory structure.\u003c/p\u003e \u003cp\u003eAssociations between average daily step counts during radiotherapy and frailty trajectory group membership were examined using multivariable multinomial logistic regression (unordered outcome). Key baseline covariates related to frailty and physical activity (eg, age, disease stage, NRS-2002 score, and treatment history) were included to control for confounding, informed by prior literature and univariable analyses (P \u0026lt; .05). Results are reported as adjusted odds ratios with 95% CIs.\u003c/p\u003e \u003cp\u003eDaily step counts were collected from the first to the last day of radiotherapy, with radiotherapy dates verified in the radiotherapy information system. A valid step day was defined as a successful WeChat step upload with confirmation that the participant carried the mobile phone that day; days with 0 steps or no upload were considered invalid. Participants were required to have at least 25 valid days during radiotherapy. Participants with 3 or more consecutive missing days were excluded. When the missing proportion was less than 20%, missing values were imputed using the mean steps for the corresponding week; participants with more than 20% missing step days (valid-day proportion\u0026thinsp;\u0026lt;\u0026thinsp;80%) were excluded. The median valid-day recording rate during radiotherapy was 97.0% (IQR, 93.0%\u0026ndash;100%), indicating high data completeness. All tests were 2-sided, and P \u0026lt; .05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral and clinical characteristics of the lung cancer survivors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 327 lung cancer patients who had received radiotherapy successfully completed the study. Among them, 272 patients (83.2%) were male, while 55 patients (16.8%) were female. Histological examination showed that squamous cell carcinoma was present in 101 patients, accounting for 30.9% of the total; adenocarcinoma was identified in 124 patients (37.9%); small cell carcinoma was diagnosed in 83 patients (25.4%); and other types of lung cancer were found in 19 patients (5.8%). Regarding the Tumor Node Metastasis （TNM）staging, 170 patients (52.0%) were at Stage II and below, 157 patients (48.0%) were at stage III, (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDaily activity steps during radiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe daily activity steps during radiotherapy were extracted by WeChat Step Mini-program (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrailty prevalence and overall trend across radiotherapy phases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty total and domain scores were nonnormally distributed at all time points. Friedman\u0026rsquo;s test revealed a significant change across the three assessments (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), and Bonferroni-corrected pairwise comparisons revealed significant differences between every pair of time points (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel selection and robustness analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough Fried\u0026apos;s frailty score is an ordinal variable ranging from 0 to 5 with a non-normal distribution, LCGM are relatively insensitive to the normality assumption in practice. Modeling it as an approximate continuous index can be used to identify the overall trajectory heterogeneity. We successively fitted models with 1 - 6 classes in Mplus 8.7 (Table 4 and Figure 2). Overall, as the number of categories increased, AIC/BIC/aBIC continued to decrease until the 5-class model, where it rebounded for the 6-class model. This suggests that further increasing the number of categories may lead to overfitting. Comparing the 3-class and 4-class models, although the 4-class model showed a continued decrease in information criteria and LMR/BLRT still indicated statistical improvement, its classification certainty decreased (Entropy: 0.969 to 0.929). Moreover, the proportion of newly added subcategories was relatively small (approximately 13.8%), and the trajectory patterns showed significant overlap with adjacent categories, limiting clinical interpretability and operational feasibility. Therefore, based on the marginal improvement, category proportion, classification accuracy and parsimony principle of the fitting index, we finally selected three types of models as the optimal solution, and named them as the prefrailty declining group, the frailty stable increasing group, and the severe frailty increasing group (Table 4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCategories of frailty trajectories in patients with lung cancer undergoing radiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong patients with lung cancer undergoing radiotherapy, three different frailty trajectories were identified in this study (Figure 2). At baseline (before radiotherapy), the frailty level of C3 was the lowest (intercept = 0.789; n = 185, 56. 6 %), C1 was in the middle level (intercept = 2. 446; n = 91,27.8 %), while C2 had the highest baseline frailty (intercept = 3.385; n = 51, 15.6 %). Over time, the C3 exhibited a downward trend (slope = \u0026minus;0.267), indicating an improvement in frailty severity, the C1 showed a slight increase (slope = 0.021), indicating a gradual worsening of frailty, while the C2 demonstrated a marked rise (slope = 0.497), indicating persistent deterioration in frailty. Based on the baseline level and change rate of each category, the three trajectories are respectively named: the prefrailty declining group (C3), the frailty stable increasing group (C1), and the severe frailty increasing group (C2). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis of frailty trajectory classes in lung cancer patients receiving radiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis revealed that the distribution of frailty trajectory classes differed significantly across age, occupation, medical expense payment method, disease stage, sleep status, disease duration, nutritional score, and prior medical treatment (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultinomial logistic regression analysis and conceptual causal framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3-category frailty trajectory class was modeled as the dependent variable (1 = frailty stable increasing group, 2 = severe frailty increasing group, and 3 = prefrailty declining group). Multinomial logistic regression was performed including variables that were statistically significant in univariable analyses.\u003c/p\u003e\n\u003cp\u003eIndependent variables were coded as follows: age, \u0026le;60 years = 1 and \u0026gt;60 years = 2. Occupation, manual labor = 1, nonmanual labor = 2, and unemployed/retired labor = 3. Medical payment, employee insurance = 1 and resident insurance = 2. Disease stage, Stage II and below = 1, stage III = 2. Sleep status, normal = 1, and abnormal = 2. Disease duration, \u0026le;12 months = 1, and \u0026gt;12 months = 2. Nutritional score, \u0026lt;3 = 1, and \u0026ge;3 = 2. Medical treatment, surgery =1, surgery/chemotherapy/radiotherapy plus immunotherapy/targeted therapy = 2, chemotherapy = 3, and targeted/immunotherapy/other = 4. WeChat step count\u0026le;3000=1,3001-6000=2,6001-9000=3,9001-12000=4, and \u0026gt;12000=5).\u003c/p\u003e\n\u003cp\u003eAverage daily steps during radiotherapy showed the strongest association with frailty trajectory membership (Tables 6\u0026ndash;7). In Model I, which included step count only, step count explained 51.9% of the variation in trajectory class (adjusted pseudo-R\u0026sup2;; Table 6). Using \u0026gt;12 000 steps/day as the reference, lower step counts were associated with less favorable trajectories. In the comparison of C1 vs C3, 3000\u0026ndash;5999 steps/day (OR, 20.497; P\u0026lt;.001) and 6000\u0026ndash;8999 steps/day (OR, 8.319; P\u0026lt;.001) were associated with higher odds of belonging to C1. In the comparison of C2 vs C3, \u0026lt;3000 steps/day (OR, 64.167; P\u0026lt;.001) and 3000\u0026ndash;5999 steps/day (OR, 37.065; P\u0026lt;.001) showed stronger associations with membership in C2.\u003c/p\u003e\n\u003cp\u003eAfter adding demographic and clinical covariates (Model II), the model\u0026rsquo;s explanatory power increased to 69.1% (adjusted pseudo-R\u0026sup2;; Table 7). Step count remained independently associated with trajectory membership, with \u0026le;5999 steps/day continuing to be significantly associated with C2 vs C3 (eg, \u0026lt;3000 steps/day: OR, 23.928; P=.001; 3000\u0026ndash;5999 steps/day: OR, 22.574; P=.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to use the Latent Class Growth Model (LCGM) to identify frailty trajectory categories in lung cancer patients undergoing radiotherapy, and to explore the association between daily activity steps and frailty trajectory categories during radiotherapy in a prospective follow-up framework. The study revealed three distinct categories of frailty trajectories: the prefrailty declining group (56.6%), the frailty stable increasing group (27.8%), and the severe frailty increasing group (15.6%). Compared with the results reported by Chen et al\u003csup\u003e21\u003c/sup\u003e regarding acquired frailty trajectories in critically ill patients, the subjects in this study may exhibit more persistent fluctuations in frailty and greater individual heterogeneity throughout treatment-related burdens and recovery processes. The initial frailty scores for the frailty stable increasing group and the severe frailty increasing group were moderate and severe respectively, exhibiting varying degrees of increase from pre-radiotherapy to 1 month post-radiotherapy. This trend may be associated with adverse reactions to radiotherapy, such as inadequate nutritional intake, compromised immune function, fatigue, coughing, and excessive sputum production.\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e These factors may cumulatively weaken patients' physical reserves through multi-system overload. Moreover, the acute inflammatory response and chronic fibrotic changes resulting from radiation-induced pulmonary injury may further constrain pulmonary function and physical endurance,\u003csup\u003e25\u0026ndash;28\u003c/sup\u003e thereby affecting recovery and sustaining frailty levels within a higher range for an extended period. These results further suggest that frailty more likely reflects the cumulative manifestation of multi-system functional impairment, rather than being attributable to the decline of a single organ system.\u003c/p\u003e \u003cp\u003eDifferent frailty trajectory categories also exhibit certain clinical distinctions: the frailty stable increasing group and the severe frailty increasing group still maintained a high frailty score one month after radiotherapy (\u0026gt;\u0026thinsp;3 points). This suggests that routine frailty assessment and risk stratification before radiotherapy may aid in identifying individuals requiring prioritized follow-up and support. For patients in the pre-frailty stage (\u0026lt;\u0026thinsp;2 points), previous studies suggested that exercise-related interventions are associated with slowing or reversing frailty progression.\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Thus, early identification and activity management theoretically hold potential preventive significance. In contrast, patients with severe frailty often present with higher disease burden and functional limitations. Their management may require multidisciplinary support encompassing rehabilitation, nutrition, and nursing care to achieve symptom relief and improved quality of life.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Overall, frailty trajectory classification may partially reflect variations in patients' physiological resilience and provide insights for subsequent exploration of stratified management strategies.\u003c/p\u003e \u003cp\u003eIn the analysis of influencing factors, Model I demonstrated a strong statistical association (51.9%) between daily activity steps during radiotherapy and trajectory category assignment. While Model II, incorporating demographic and disease-related factors, further enhanced explanatory power to 69.1%. The associations between age, disease stage, nutritional risk, and oral feeding restrictions with the frailty trajectory category largely concurred with previous studies.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Notably, within the multivariate model, daily activity steps during radiotherapy remained significantly and independently associated with trajectory category. This finding suggests that step count variations may reflect dynamic fluctuations in patients' functional tolerance and physical reserve throughout treatment. From a functional status assessment perspective, daily step count serves not only as an external indicator of activity levels but also potentially as a composite behavioral signal of physical reserve and metabolic homeostasis. Consequently, it has potential application value for further investigation in frailty assessment and follow-up.\u003c/p\u003e \u003cp\u003eThe distribution of daily steps during radiotherapy between different trajectories showed a certain stratification: compared with the prefrailty declining group, the lower step level was related to the higher probability of being classified into the frailty stable increasing group or the severe frailty increasing group. When comparing between the two types of poor trajectories, a lower step level is also associated with a higher probability of being classified into the severe frailty increasing group. It should be emphasized that the 3000, 6000, and 9000 steps reported in this study represent an explanatory range and risk gradient intended to describe stratification within the sample. They do not denote fixed biological thresholds or cut-off points directly applicable to clinical decision-making. The results of this study suggest that step monitoring during radiotherapy, as a readily obtainable indicator of daily behavior, may aid in identifying individuals at high risk of frailty or capturing early signals of frailty progression. Notably, these range values are markedly lower than the World Health Organization (WHO) recommended activity standards for healthy adults (approximately 10,000 steps per day).\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e It also reflects the special physiological burden of patients with lung cancer during the treatment period. Future management strategies should be grounded in individualized activity targets, employing wearable devices or mobile applications such as WeChat Step Mini-program to monitor step counts in real time. This enables dynamic assessment of frailty risk during clinical follow-ups, facilitating comprehensive interventions encompassing exercise prescriptions and nutritional support.\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e The implementation of such digital continuous monitoring tools may represent a pivotal opportunity for transitioning frailty management towards precision rehabilitation models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this prospective longitudinal study, we employed LCGM to identify three distinct frailty trajectory categories among lung cancer patients undergoing radiotherapy, and assessed the association between daily step counts during radiotherapy and frailty trajectory categories. Results revealed that lower activity levels correlated with adverse frailty trajectory categories. The step ranges and stratification intervals corresponding to approximately 3000, 6000, and 9000 steps exhibited a discernible risk gradient within the sample. This provides reference for dynamic monitoring of frailty risk during radiotherapy and facilitates stratified analysis in subsequent studies.\u003c/p\u003e \u003cp\u003eThis study suggests that daily activity steps, as a simple, non-invasive and sustainable daily behavior index, have potential feasibility and indicative significance in frailty monitoring and follow-up evaluation during radiotherapy. However, the above findings mainly reflect statistical associations, which cannot be used to infer causality, nor do they constitute sufficient verification of clinical screening tools due to this study is an observational design. In the future, it is still necessary to conduct external verification in a multi-center, larger sample cohort, and further evaluate through longer-term longitudinal studies and intervention studies: whether step-based continuous monitoring and stratified support strategies can improve clinical outcomes such as functional recovery and quality of life.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis prospective cohort study revealed the dynamic trajectory of frailty in patients with lung cancer undergoing radiotherapy, highlighting the potential value of daily step counts as a dynamic indicator of physical recovery capacity. However, the following limitations should be fully considered when interpreting the results.\u003c/p\u003e \u003cp\u003eFirstly, the study subjects were sourced from a single tertiary cancer hospital in Yunnan Province, which may limit the extrapolation of the results among different populations, medical institutions or regions. Future prospective cohort studies should be conducted across multiple centers and regions to validate the reproducibility and external validity of the present findings.\u003c/p\u003e \u003cp\u003eSecondly, although Model II explained about 69.1% of the variation, there may still be potential influencing factors that are not included in the analysis, such as mental state, fatigue perception or social support level. Incorporating psychosocial and behavioral variables into future research will help to understand the multidimensional mechanisms of frailty more systematically.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest Disclosures\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding/Support\u003c/h2\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe authors sincerely thank all patients with lung cancer who participated in this study for their cooperation and valuable contributions. At the same time, thanks to the clinical staffs who supported the patient care and research data collection process. This study was supported by the Scientific Research Fund of the Yunnan Provincial Department of Education (Grant Nos. 2026J0359, 2026J0335); the Research Fund of the Chinese Nursing Association (Grant No. ZHKYQ202312); the Joint Special Program of the Yunnan Provincial Department of Science and Technology and Kunming Medical University (Grant No. 2024XKTDPY15); the 2025 Yunnan Province Health Sector Medical Discipline Leader Program\u0026mdash;Specialized Nursing(Grant No. D-2025044); and the 2025 Nursing Special Fund of Kunming Medical University(Grant Nos. 2025KYHLZXSK15, 2025KYHLZXZK09). The sponsors did not participate in research design, data collection, data analysis, result interpretation or paper writing.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**J Z, X J Z** conceived the research idea, developed the study framework and plan, and wrote the manuscript. **X Y W, J W** participated in data collection. **S L** , **L Y R** provided valuable feedback an contributed to the final draft. All the authors contributed to and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the first author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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Age Ageing. 2022;51:afac028. [pii].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohd Suffian NI, SN' A, Abu Saad H, Chan YM, Ibrahim Z, Omar N, et al. Frailty Intervention through Nutrition Education and Exercise (FINE). A Health Promotion Intervention to Prevent Frailty and Improve Frailty Status among Pre-Frail Elderly-A Study Protocol of a Cluster Randomized Controlled Trial. Nutrients. 2020;12:2758.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-survivorship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcsu","sideBox":"Learn more about [Journal of Cancer Survivorship](https://www.springer.com/journal/11764)","snPcode":"11764","submissionUrl":"https://submission.nature.com/new-submission/11764/3","title":"Journal of Cancer Survivorship","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lung cancer, Radiotherapy, Frailty, Latent class growth model, Longitudinal study, Daily step count","lastPublishedDoi":"10.21203/rs.3.rs-8999549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8999549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo characterize frailty-trajectory categories in patients receiving radiotherapy for lung cancer, identify factors associated with trajectory membership, and examine the association between daily step counts during radiotherapy and frailty trajectories.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective longitudinal study, daily step counts were collected using the WeChat Step Mini Program from the start to the end of radiotherapy. Frailty was assessed using the Frailty Phenotype Scale at 3 time points: before radiotherapy, at the end of radiotherapy, and 1 month after radiotherapy. A latent class growth model (LCGM) was used to identify distinct frailty-trajectory classes. Multinomial logistic regression was performed to examine factors associated with class membership and to evaluate the association between step counts and frailty-trajectory class.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 327 patients, LCGM identified 3 frailty trajectories: prefrailty declining (56.58%), frailty stable increasing (27.83%), and severe frailty increasing (15.60%). Factors associated with frailty-trajectory membership included older age, poorer nutritional status, more advanced disease stage, poorer sleep status, and oral intake impairment. Lower daily step counts during radiotherapy were associated with membership in trajectories characterized by persistent frailty worsening.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFrailty trajectories from before radiotherapy to 1 month after radiotherapy were heterogeneous. Step-count gradients were observed around 3000, 6000, and 9000 steps/day, suggesting exploratory cut points corresponding to different frailty-risk intervals and providing a quantitative reference for early risk identification and individualized activity recommendations. Continuous step monitoring combined with routine frailty screening may help identify high-risk patients early and support multidimensional assessment and proactive management during radiotherapy care.\u003c/p\u003e\u003ch2\u003eImplications for Cancer Survivors:\u003c/h2\u003e \u003cp\u003eDaily step counts provide a scalable, real-world metric to support early risk stratification and guide targeted frailty screening and management during early survivorship after lung cancer radiotherapy.\u003c/p\u003e","manuscriptTitle":"Association between Daily Step Counts and Frailty Trajectory Classes in Lung Cancer Patients Undergoing Radiotherapy: A Prospective Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-12 07:42:34","doi":"10.21203/rs.3.rs-8999549/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"13583504819186444707562954770222292898","date":"2026-05-17T15:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T15:12:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T10:26:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T10:24:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Survivorship","date":"2026-03-01T06:10:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-survivorship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcsu","sideBox":"Learn more about [Journal of Cancer Survivorship](https://www.springer.com/journal/11764)","snPcode":"11764","submissionUrl":"https://submission.nature.com/new-submission/11764/3","title":"Journal of Cancer Survivorship","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b0e09d87-fbaf-4c55-bd4c-f137ec9af29e","owner":[],"postedDate":"April 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"13583504819186444707562954770222292898","date":"2026-05-17T15:24:10+00:00","index":48,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-12T07:42:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-12 07:42:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8999549","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8999549","identity":"rs-8999549","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}