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. 2023 Dec 20;11(1):e2059. doi: 10.1002/nop2.2059

Latent classes of health‐promoting lifestyle in breast cancer patients undergoing chemotherapy in China: A cross‐sectional survey

Meixuan Song 1, Qiuyao He 2, Juan Yang 3, Jinyu Zhang 1,✉
PMCID: PMC10733708  PMID: 38268274

Abstract

Aim

To identify the latent classes of a health‐promoting lifestyle and examine the associations of latent class profile with individual characteristics of breast cancer undergoing chemotherapy in China in order to provide some insights and recommendations for targeted and individualized health education of health‐promoting lifestyle.

Design

A descriptive cross‐sectional survey design was used for this work.

Methods

A total of 197 patients with breast cancer undergoing chemotherapy recruited from the Breast Cancer Outpatient Chemotherapy Clinic of a Grade 3A hospital were surveyed. Health‐promoting lifestyle was measured using the Health Promotion Lifestyle Profile‐II: Chinese Version Short (HPLP‐IICR). Latent class analysis was used to examine respondents' health‐promoting lifestyle patterns. Associations between the latent class membership and individual characteristics were examined using multinomial logistic regression.

Results

Four latent classes were identified: Class 1–Good Nutrition and Poor Physical Activity, Class 2–Poor Health Responsibility and Nutrition, Class 3–Active Health‐Promoting Lifestyle, and Class 4–Medium Spiritual Growth and Poor Other Dimensions. Younger respondents and respondents with a higher score in anxiety and depression were more likely to be classified in Class 4 rather than Class 1 or 3. Respondents with low exercise self‐efficacy were more likely to be classified in Class 4 than the others. Respondents in Class 4 had more chemotherapy symptom severity and interference, and cases of menopause were fewer in Class 4 rather than Class 3. Those in Class 4 were more likely to have been diagnosed with cancer within 3 months than those in Class 1.

Keywords: breast cancer, chemotherapy, health‐promoting lifestyle, latent class analysis, nursing

1. INTRODUCTION

Breast cancer is the most common cancer diagnosed in women globally, with an estimated 2.3 million new cases (11.7%) and 684,996 deaths (6.9%) in 2020 (Sung, Ferlay, et al., 2021). In China, breast cancer accounts for 19.9% of all female malignant tumours, making it the most prevalent cancer in women (Cao et al., 2021). Chemotherapy as a primary adjunctive strategy improves survival in patients with breast cancer; however, it also produces many distressing symptoms including fatigue, nausea, and vomiting (Cai et al., 2022; John et al., 2021), which result in poor adherence to treatment (Nugent et al., 2020).

Health‐promoting lifestyle is a multidimensional pattern of self‐initiated actions and perceptions that serves to maintain or enhance one's level of wellness, self‐actualization, and fulfilment (Walker et al., 1987). It encompasses behaviours supporting a healthy lifestyle, such as proper nutrition, regular exercise, avoidance of destructive behaviours, and control of emotions and feelings.

A growing body of evidence suggests that having a lifestyle which incorporates healthy behaviours is significantly negatively correlated with experiencing chemotherapy‐related distressing symptoms (Mazzoni et al., 2023; Montagnese et al., 2020), which can thereby improve patients' quality of life (Solans et al., 2020). Conversely, leading a poor health‐promoting lifestyle can lead to diabetes, heart disease, obesity, and other complications that can reduce overall survival rate and increase mortality in patients with breast cancer (Freisling et al., 2020; Ligibel et al., 2019). Jia et al. (2022) showed that the advantages of health behaviours also included significant self‐perceived improvements in attractiveness, immunity, lymphedema, and mental well‐being, whereas unhealthy behaviours also tended to support the progression of breast cancer as well as increase the risk of its recurrence. There is, therefore, a consensus that regular engagement in behaviours that promote a healthy lifestyle is critical for both chemotherapy tolerance and the prognosis of disease (Campbell et al., 2023).

Previous studies have found that patients with breast cancer receiving chemotherapy are less likely to exhibit a health‐promoting lifestyle, tending to reduce the amount and intensity of physical activity than those who have completed their treatment (Cannioto et al., 2021; Elshahat et al., 2021; Salerno et al., 2021). A systematic review of 25 studies showed that most patients undergoing chemotherapy switched to sweeter or saltier eating habits due to changes experienced in their taste and smell (van den Berg et al., 2017). Similar result has been found in Chinese study (Tang et al., 2022). However, some researchers have had contrary findings. Michalczyk et al. (2022) found that patients with breast cancer manifested a greater resistance to stress during their chemotherapy treatment and were more likely to exhibit positive lifestyle changes. In Tang's survey of 284 patients with breast cancer undergoing chemotherapy in China, the average dietary attitude score was more than 70% of the total possible score, suggesting that patients had a relatively positive attitude towards seeking a scientific diet during chemotherapy (Tang et al., 2023).

The reasons for these reported differences may be related to the heterogeneity of patients with breast cancer undergoing chemotherapy, including areas of cognition, disease status, and personality traits, which can affect health‐promoting lifestyle (Tabrizi, 2015). The statistical methods used in studies on health‐promoting lifestyle in existing literature are often variable‐centred approaches (Nelson et al., 2023; Tabrizi, 2015). It is common practice to evaluate one's level of health‐promoting lifestyle based on the average scores of health‐promoting lifestyle measures with the assumption that study samples come from a homogeneous population (Sung, Yu, et al., 2021). On the other hand, the health‐promoting lifestyle is a multidimensional variable with dimensions such as spiritual growth, physical activity, health management, nutrition, and health responsibility, each of which is measured using multiple items (Teng et al., 2010). Thus, even though patients with breast cancer may report the same scores, they may exhibit different patterns of health‐promoting lifestyle, and these different patterns can in turn provide some useful suggestions for targeted interventions. Therefore, the studies focus on the subgroups or classes of health‐promoting lifestyle considering the heterogeneity of population are needed for healthcare providers to better understand the profile of health‐promoting lifestyle and design targeted intervention. However, the patterns of health‐promoting lifestyle in patients with breast cancer undergoing chemotherapy have not been clearly defined in available literature.

Latent class analysis is a commonly used person‐centred statistical approach which seeks to identify unobserved subgroups of individuals who share common characteristics on a set of indicators (MacInnis et al., 2004; Miranda et al., 2019). In latent class analysis, the optimal number of classes is determined based on formal statistical procedures and the model results are more interpretable to be stated in terms of probabilities. Latent class analysis has been used and validated in many studies, such as patterns of sexual health (Yuan et al., 2020), social support (Cai et al., 2021) in patients with breast cancer and latent classes of parents' caregiving ability (Wang et al., 2022).

Engaging in a health‐promoting lifestyle is a fairly complex phenomenon, so Pender et al. (2002) developed a Health Promotion Model to comprehensively recognize the determinants of health‐promoting lifestyle. The included concepts of model are individual characteristics and experiences (including personal factors and prior related behaviours) and behaviour‐specific cognitions and affects (including perceived benefits and barriers, perceived self‐efficacy, activity‐related affect, interpersonal influences, and situational influences), which have direct or indirect effects to an individual's health behaviour and are the basis for understanding individuals' complex biological–psychological processes to exhibit health behaviours. Of these, personal factors (including physiological, psychological and socio‐cultural factors), perceived benefits and barriers, and perceived self‐efficacy are the best predictors (Pender et al., 2002). A study showed that age, body mass index (BMI), educational level, and levels of anxiety and depression could affect the health behaviours of patients with breast cancer (Di Meglio et al., 2021). Phillips et al. (2020) found that serious disease‐/treatment‐related symptoms were impediment to physical activity in patients with breast cancer. Hamed Bieyabanie and Mirghafourvand (2020) pointed out that the higher the level of self‐efficacy, the better the health behaviours. However, the traditional variable‐centred analytical technique is not sufficient to identify risk factors for different subgroups of health‐promoting lifestyle.

The aim of the current study was to use latent class analysis to identify the latent classes of health‐promoting lifestyle in women with breast cancer undergoing chemotherapy in China and to explore the associations of each defined latent class with individual characteristics of those in each group in order to define the patterns of health‐promoting lifestyle and offer recommendations regarding the planning and implementation of targeted and individualized health‐promoting lifestyle interventions.

2. METHODS

2.1. Design

The current study was a descriptive, cross‐sectional study.

2.2. Sample and setting

Total enumeration sampling was used to recruit patients at the Breast Cancer Outpatient Chemotherapy Clinic of a tertiary hospital in Shanghai, China. A tertiary hospital in China is a general hospital with a bed capacity exceeding 501, providing specialist health services and serving as medical hubs for multiple regions, and is further subdivided into 3A (largest, most specialized/comprehensive), 3B and 3C. The hospital included in this study was Grade 3A hospital. Inclusion criteria included the following: (a) All patients were females aged 18 years or older; (b) patients were diagnosed with breast cancer by pathology and were receiving post‐operative chemotherapy; and (c) signed a written informed consent form. Exclusion criteria included the following: (a) patients with an additional cancer diagnosis or severe complications; (b) patients with metastasis; or (c) their expected survival time was less than 1 year. We used Dziak's formula as a reference to determine sample size for latent class analysis (Dziak et al., 2014). According to the equation n=m80w2/w2, when we set a medium effect size w = 0.30, m80w2 was a constant estimated at 16.2 if including five dimensions with a power of 0.80, the estimated sample size was 180; according to the requirement of the multinomial logistic regression model, the minimum sample size should be 5–10 times the number of independent variables, which was 95–190. Ultimately, the recommended sample size was at least 180.

With the consent from the Head Nurse of the Breast Cancer Outpatient Chemotherapy Clinic, six trained researchers handed out study leaflets with a brief description of the study to patients with breast cancer coming for chemotherapy. A total of 327 patients were recruited from 1 July 2020 to 31 August 2020; 40 patients refused to participate because they were not interested in the study, 15 patients were expected to survive less than 1 year, and 72 patients had additional cancer diagnosis or severe complications. The remaining 200 patients were given questionnaires. Of the 200 questionnaires collected, one patient voluntarily requested to interrupt the investigation due to chemotherapy‐related symptoms and was excluded, and two patients with missing important information were not analysed. Ultimately, the data of 197 women with breast cancer could be analysed in the study, exceeding the required sample size, so we discontinued recruitment.

2.3. Measures

2.3.1. Health‐promoting lifestyle measure

Health Promotion Lifestyle Profile‐II: Chinese Version Short (HPLP‐IICR) was used to evaluate the health‐promoting lifestyle of patients with breast cancer undergoing chemotherapy in China, which was compiled by Teng et al. (2010). The HPLP‐IICR consists of five dimensions (30 items): spiritual growth (6 items), physical activity (6 items), health management (9 items), nutrition (5 items), and health responsibility (4 items). All items are scored on a Likert scale ranging from 1 to 4 (1 = Never, 2 = Sometimes, 3 = Often, 4 = Always). A higher total score reflects a healthier lifestyle. According to the previous study (Zhou et al., 2022), the performance in five dimensions was divided into three grades: poor (item mean score ≤ 2), fair (2 < item mean score ≤ 3), and good (3 < item mean score ≤ 4). Teng et al. (2010) demonstrated that the HPLP‐IICR has good reliability and validity, with a Cronbach's α of 0.90 and a 5‐factor HPLP‐IICR explaining 53% of the variance in healthy lifestyles. The Cronbach's α for the current study was 0.89.

2.3.2. Demographic and health characteristics

Information on age, BMI, marital status, education level, menstrual status, employment status, area of residence, medical insurance, and time of diagnosis was collected by questionnaire.

2.3.3. Psychological factors measure

Hospital Anxiety and Depression Scale‐Chinese version (HADS‐C) was used to assess psychological factors. The HADS was compiled by Zigmond and Snaith (1983) and revised to create a Chinese version by Leung et al. (1993). It consists of 14 items that assess emotional distress, seven of which screen for anxiety and the remaining seven for depression. All items are scored on a Likert scale ranging from 0 (Not at all) to 3 (Most of the time). A higher total score indicates greater mental distress. The HADS‐C has been proven to have adequate reliability (Cronbach's α = 0.93) and construct validity (𝑥2 = 265.50, df = 76, CFI = 0.96, RMSEA = 0.06, and SRMR = 0.03) across cancer patients (Li et al., 2016). The Cronbach's α in the current study was 0.89.

2.3.4. Symptom severity and interference measure

M.D. Anderson Symptom Inventory (MDASI), compiled by Cleeland et al. (2000) was used to assess symptom severity and interference. Wang et al. (2004) developed the Chinese version of the MDASI (MDASI‐C), comprising of 13 core symptom severity items and 6 symptom interference items. All items are rated using a Likert scale ranging from 0 (Not present/Did not interfere) to 10 (As bad as you can imagine/Interferes completely). The higher the score, the higher the symptom severity and interference. The MDASI‐C has been verified with Cronbach's α values of 0.86 and 0.84, respectively, for severity and interference, and the correlation coefficients were moderately high for MDASI‐C item/the MOS 36‐Item Short Form Health Survey (SF‐36) domain pairs (Wang et al., 2004). The Cronbach's α values for the current study were 0.87 and 0.81, respectively.

2.3.5. Self‐efficacy measure

Self‐efficacy was assessed using the Exercise Self‐Efficacy Scale (ESE) which was developed by Bandura and revised into Chinese by Lee et al. (2009). The Exercise Self‐Efficacy Scale‐Chinese version (ESE‐C) consists of nine items, with all items scored on a Likert scale ranging from 0 (Not very confident) to 10 (Very confident). The higher the total score, the higher the respondent's exercise self‐efficacy. A previous study from Taiwan (Lee et al., 2009) demonstrated that ESE‐C has been proven to have adequate reliability (Cronbach's α = 0.75) and construct validity (𝑥2 = 45, df = 27, NFI = 0.90, and RMSEA = 0.06). In the current study, it was 0.86.

2.3.6. The selection and measurement of predictors

Based on the framework of the Health Promotion Model, possible predictors were categorized into 2 groups: (a) individual characteristics and experiences (personal factors): demographic and health characteristics and psychological factors (e.g., HADS‐C); and (b) behaviour‐specific cognitions and affects: perceived barriers to action (e.g., MDASI‐C) and perceived self‐efficacy (e.g., ESE‐C), as shown in Figure 1.

FIGURE 1.

FIGURE 1

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Latent class analysis model with predictive factors.

2.4. Data collection

The on‐site survey was conducted once the study objectives had been explained to respondents, and they had given their informed signed consent to take part. Each respondent was given 20–30 min to complete the questionnaire independently by paper and pencil. Before beginning the formal survey, 12 samples were pre‐surveyed in the target hospital to evaluate the quality of the questionnaire. The survey conformed to the requirements for data collection in the cross‐sectional study, including training for researchers and methods of information collection that could not be changed once established. The whole study process was conducted anonymously.

2.5. Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and was approved by a regional ethical review board in China (2020LCSY016). Respondents were informed about the purpose and procedures of the study and signed written informed consent forms prior to starting the study. They were told that they could withdraw from the study at any time and their rights would not be affected. In the whole investigation design, the unnecessary discussion of cancer sensitivity issues was reduced and patients' information was kept confidential.

2.6. Data analysis

Data were analysed using SPSS version 21.0 (IBM Corporation) and Mplus Version 7.4. For each variable in our study, descriptive analysis was performed including mean, standard deviation, frequency, and percentage.

Latent class analysis was performed in Mplus and was used to identify possible unobserved latent classes in patients with breast cancer undergoing chemotherapy in China using the five measurable dimensions of health‐promoting lifestyle. In the current study, a series of unconditional latent class analysis models were used which differed according to the number of the latent subgroup (i.e., k; 1 through 5), specifically spiritual growth, physical activity, health management, nutrition, and health responsibility. We then compared the frequencies of the observed response pattern as well as the hypothesized patterns to identify which best represented the heterogeneity of response patterns of health‐promoting lifestyle across a given number of the latent subgroups.

The process of model fit was determined using several fit indicators, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted BIC (aBIC). The lower the value, the better the comparative model fit of the k‐class model to the (k‐1)‐class model (Vrieze, 2012).

Following the selection of the best‐fitting number of classes, the Lo–Mendell–Rubin likelihood ratio (LMR LR) was used to evaluate model, which is the most sensitive indicator of classification in a latent class. The adjusted LMR LR (ALMR LR) test and the bootstrap likelihood ratio test (BLRT) were also used for model fit comparison, in which significant p‐values (p < 0.05) indicate a significant improvement in model fit in the k‐class model compared with the (k‐1)‐class model, thus rejecting the (k‐1)‐class model in favour of a model with at least k classes (Asparouhov & Muthén, 2014). A measure of entropy was calculated to assess the relative accuracy of the respondent classifications, with entropy ≤0.60 being considered poor (Asparouhov & Muthén, 2014).

This was followed by a further analysis using multinomial logistic regression in SPSS to examine the association of class membership with demographic, anxiety and depression, symptom severity and interference, and exercise self‐efficacy.

3. RESULTS

3.1. Demographic characteristics and scales

The descriptive characteristics of the study samples are shown in Table 1. A total of 197 respondents were enrolled in the study, with an average age of 53.01 ± 11.56 years and an average BMI of 22.74 ± 3.18 kg/m2. In total, 43.7% (n = 86) of the respondents had received a college education or above, 94.4% (n = 186) were married, and 65.0% (n = 128) were unemployed. Additionally, the mean scores of HPLP‐IICR among respondents were 84.66 ± 13.93, HADS were 8.93 ± 6.80, MDASI were 49.18 ± 30.79, and ESE were 32.49 ± 19.70.

TABLE 1.

Descriptive characteristics of the respondents.

Characteristics Mean (SD) Range Number Per cent
Age (years) 53.01(11.56) 24–82
BMI (kg/m2) 22.74 (3.18) 15.4–34.3
Education
<College 106 53.8%
College or above 86 43.7%
Marital status
Married 186 94.4%
Single 8 4.1%
Divorced or widowed 3 1.5%
Current employment
Employed 67 34.0%
Unemployed 128 65.0%
Area of residence
City or town 169 85.8%
Village 24 12.2%
Menstrual status
Premenopausal 73 37.1%
Postmenopausal 121 61.4%
Medical insurance
No 15 7.6%
Yes 177 89.8%
Time since diagnosis
<3 months 72 36.5%
3–6 months 46 23.4%
>6 months 78 39.6%
Total HPLP‐IICR score 84.66 (13.93) 33–120
Spiritual growth 19.16 (4.32) 8–24
Physical activity 12.42 (4.66) 6–24
Health management 27.31 (5.62) 9–36
Nutrition 15.54 (3.14) 5–20
Health responsibility 10.22 (3.19) 4–16
Total HADS score 8.93 (6.80) 0–33
Anxiety 5.03 (3.73) 0–18
Depression 3.90 (3.62) 0–17
Total MDASI score 49.18 (30.79) 0–139
Symptom severity 33.64 (22.29) 0–104
Symptom interference 15.54 (11.27) 0–44
Total ESE score 32.49 (19.70) 0–90
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Abbreviation: BMI, body mass index; ESE, Exercise Self‐Efficacy Scale; HADS, Hospital Anxiety and Depression Scale; HPLP‐IICR, Health Promotion Lifestyle Profile‐II: Chinese Version Short; MDASI, M.D. Anderson Symptom Inventory; SD, standard deviation.

3.2. Latent class analysis of health‐promoting lifestyle

The fitting indexes for the latent class analysis models with the various numbers of classes are presented in Table 2. The AIC, BIC, and aBIC of the single‐class model were the largest, indicating that this model fit was the worst. The LMR LR and ALMR LR results favoured the 4‐class model over the 3‐class model (p < 0.05). In comparing the 4‐class and 5‐class models, all the model fit indices were smaller for the 4‐class model and all three LR tests were not rejected the 4‐class model. Furthermore, the quality of the latent class classification for the 4‐class model was adequate (Entropy = 0.783). After consideration of the results, the 4‐class model was chosen as having the best fit.

TABLE 2.

Latent class model fit comparison.

Model AIC BIC aBIC LMR LR, p‐value ALMR LR, p‐value BLRT, p‐value Entropy
1C 1889.336 1922.168 1890.489 – – – –
2C 1794.275 1863.222 1796.695 <0.0001 <0.0001 <0.0001 0.720
3C 1787.514 1892.576 1791.202 0.0994 0.1043 <0.0001 0.714
4C 1780.882 1922.059 1785.837 0.0178 0.0188 <0.0001 0.783
5C 1789.307 1966.600 1795.531 0.4868 0.4943 0.6000 0.772
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Abbreviations: AIC, Akaike information criterion; aBIC, adjusted Bayesian information criterion; ALMR LR, adjusted Lo–Mendell–Rubin likelihood ratio; BIC, Bayesian information criterion; BLRT, bootstrap likelihood ratio test; LMR LR, Lo–Mendell–Rubin likelihood ratio.

The proposed classes were defined based on the item response probabilities conditional on class classification. The profiles of each class classification are shown in Table 3. The response probability graph of “good” in five dimensions of four latent classes is shown in Figure 2. All respondents (100%) in Class 1 reported having good nutrition habits but measured poorly in the physical activity dimension. Therefore, Class 1 was called the “Good Nutrition and Poor Physical Activity.” No respondents (0%) in Class 2 showed good performance in health responsibility and nutrition behaviour dimensions, so Class 2 was called the “Poor Health Responsibility and Nutrition.” In Class 3, most of the respondents showed good performance in all five dimensions of health‐promoting lifestyle, especially in spiritual growth (87.5%), health management (92.9%), and nutrition (81.3%). Thus, Class 3 was called the “Active Health‐Promoting Lifestyle.” In Class 4, with the exception of 52.1% who performed well specifically in the dimension of spiritual growth, everyone performed fairly or poorly in all dimensions of health‐promoting lifestyle. Therefore, Class 4 was called the “Medium Spiritual Growth and Poor Other Dimensions.”

TABLE 3.

Health‐promoting lifestyle measure by latent class: selected results of the 4‐class latent class analysis model.

Health‐promoting lifestyle Conditional probability by class
Good nutrition and poor physical activity, (n = 74, 37.6%) Poor health responsibility and nutrition, (n = 23, 11.7%) Active health‐promoting lifestyle, (n = 35, 17.8%) Medium spiritual growth and poor other dimensions, (n = 65, 33.0%)
Spiritual growth
Poor 0.082 0.143 0.000 0.143
Fair 0.353 0.534 0.125 0.345
Good 0.566 0.323 0.875 0.512
Physical activity
Poor 0.549 0.169 0.217 1.000
Fair 0.451 0.576 0.304 0.000
Good 0.000 0.255 0.479 0.000
Health management
Poor 0.000 0.000 0.025 0.356
Fair 0.363 0.565 0.046 0.473
Good 0.637 0.435 0.929 0.171
Nutrition
Poor 0.000 0.000 0.000 0.195
Fair 0.000 1.000 0.187 0.750
Good 1.000 0.000 0.813 0.055
Health responsibility
Poor 0.193 0.725 0.128 0.583
Fair 0.628 0.275 0.229 0.359
Good 0.179 0.000 0.643 0.059
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Abbreviation: n, number of subjects by class.

FIGURE 2.

FIGURE 2

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Conditional probability of health‐promoting lifestyle latent class.

3.3. Multinomial logistic regression model of factors influencing latent classes of health‐promoting lifestyle

Table 4 shows a summary of the results of the multinomial logistic regression model comparing Class 4 against the other classes. Younger respondents and those with higher anxiety and depression had significantly higher odds of being classified in Class 4 than in Class 1 (odds ratio [OR] = 1.033 and 0.939, respectively) or Class 3 (OR = 1.041 and 0.864, respectively). Those with low exercise self‐efficacy were more likely to be in Class 4 than in other classes (OR = 1.019, 1.046, and 1.062, respectively). Those who experienced more severe symptoms and interference from chemotherapy had a significant increase in their likelihood of being in Class 4 rather than Class 3 (OR = 0.972 and 0.960, respectively). However, postmenopausal respondents had significantly less likelihood on being in Class 4 rather than Class 3 (OR = 4.000, 95% C.I. [1.460, 10.956]). Respondents in Class 4 were more likely to have been diagnosed within 3 months of the data collection rather than over 6 months in contrast to those in Class 1 (OR = 0.328, 95% C.I. [0.149, 0.723]). Other factors without significant effects on class classification are detailed in Table 4.

TABLE 4.

Selected results of multinomial logistic regression: prediction of patterns of health‐promoting lifestyle.

Predictors Good nutrition and poor physical activity vs. Medium spiritual growth and poor other dimensions Poor health responsibility and nutrition vs. Medium spiritual growth and poor other dimensions Active health‐promoting lifestyle vs. Medium spiritual growth and poor other dimensions
Mean (SD) OR 95% C.I. Mean (SD) OR 95% C.I. Mean (SD) OR 95% C.I.
LL UL LL UL LL UL
Age 54.595 (12.252) 1.033 1.003 1.064 51.304 (12.615) 1.007 0.966 1.050 55.629 (10.140) 1.041 1.004 1.081
BMI 22.920 (3.092) 1.050 0.943 1.170 21.888 (2.983) 0.940 0.800 1.105 23.435 (2.841) 1.103 0.969 1.256
ESE 30.973 (19.013) 1.019 1.000 1.039 39.957 (14.889) 1.046 1.018 1.075 45.086 (20.809) 1.062 1.035 1.089
HADS 8.338 (5.996) 0.939 0.893 0.988 8.826 (5.254) 0.950 0.885 1.021 5.829 (5.421) 0.864 0.797 0.936
MDASI‐SS 33.757 (21.550) 0.993 0.978 1.008 37.391 (17.338) 1.000 0.979 1.022 24.400 (21.999) 0.972 0.952 0.992
MDASI‐SI 17.000 (11.867) 1.001 0.973 1.031 12.565 (7.310) 0.964 0.921 1.010 12.057 (9.962) 0.960 0.922 0.999
n (%) OR 95% C.I. n (%) OR 95% C.I. n (%) OR 95% C.I.
LL UL LL UL LL UL
Education
<College 38 (51.4) 0.844 0.430 1.657 12 (52.2) 0.848 0.326 2.206 20 (57.1) 1.296 0.543 3.093
College or above 35 (47.3) – – – 11 (47.8) – – – 12 (37.5) – – –
Current employment
Employed 21 (28.4) 0.543 0.267 1.102 11 (47.8) 1.256 0.483 3.270 8 (22.9) 0.422 0.166 1.074
Unemployed 53 (71.6) – – – 12 (52.2) – – – 26 (74.3) – – –
Residence area
Village 6 (8.1) 0.364 0.129 1.022 1 (4.3) 0.182 0.022 1.476 4 (11.4) 0.552 0.165 1.849
City or town 66 (89.2) – – – 22 (95.7) – – – 29 (82.9) – – –
Menstrual status
Postmenopausal 48 (64.9) 1.582 0.800 3.131 10 (43.5) 0.779 0.291 2.088 28 (80.0) 4.000 1.460 10.956
Premenopausal 26 (35.1) – – – 11 (4s7.8) – – – 6 (17.1) – – –
Medical insurance
Yes 67 (90.5) 0.836 0.255 2.736 22 (95.7) 0.424 0.048 3.729 32 (91.4) 0.583 0.111 3.062
No 6 (8.1) – – – 1 (4.3) – – – 2 (5.7) – – –
Time since diagnosis
<3 months 18 (24.3) 0.328 0.149 0.723 8 (34.8) 0.875 0.265 2.885 14 (40.0) 0.613 0.246 1.526
3–6 months 20 (27.0) 1.061 0.426 2.639 9 (39.1) 2.864 0.809 10.142 6 (17.1) 0.764 0.231 2.523
>6 months 36 (48.6) – – – 6 (26.1) – – – 15 (42.9) – – –
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Abbreviation: BMI, body mass index; ESE, Exercise Self‐Efficacy Scale; HADS, Hospital Anxiety and Depression Scale; LL, lower limit; MDASI‐SI, M.D. Anderson Symptom Inventory‐Symptom Interference; MDASI‐SS, M.D. Anderson Symptom Inventory‐Symptom Severity; n, total number; OR, odds ratio; SD, standard deviation; UL, upper limit; 95% C.I., 95% confidence interval of OR.

Bold values are Statistically significant at 0.05.

4. DISCUSSION

The present study explores the profiles of health‐promoting lifestyle in patients with breast cancer undergoing chemotherapy and the influencing factors of different subtypes. Latent class analysis results identified four latent classes of health‐promoting lifestyle: Class 1–Good Nutrition and Poor Physical Activity (n = 74, 37.6%), Class 2–Poor Health Responsibility and Nutrition (n = 23, 11.7%), Class 3–Active Health‐Promoting Lifestyle (n = 35, 17.8%), and Class 4–Medium Spiritual Growth and Poor Other Dimensions (n = 65, 33.0%). The group with the least healthy lifestyle was often used as a reference group (Dong et al., 2022). In our study, Class 4 performed worse than other classes in all dimensions of health‐promoting lifestyle and had a larger number of respondents, so Class 4 was chosen as a reference.

Most of those in Class 4 (Medium Spiritual Growth and Poor Other Dimensions) showed fair or poor levels on all dimensions of health‐promoting lifestyle with the exception of spiritual growth, and in particular, all respondents scored poorly on the physical activity dimension. Compared with the other classes, then, it is clear that those in Class 4 had the strongest need for health‐promoting lifestyle intervention, with priority placed on the physical activity.

Multiple logistic regression analysis showed that respondents with higher HADS scores were more likely to be classified into Class 4 rather than Class 1 or 3. Kelly et al. (2020) have previously demonstrated that breast cancer survivors with higher depression scores had lower HPLP‐II composite scores. It has been suggested that depressive symptoms may have a negative impact on participation in a healthy lifestyle (Shelby et al., 2020). In our study, those in Class 4 were younger than those in Class 1 or 3. Studies have shown that younger patients have higher levels of anxiety, fear, and depression in response to a cancer diagnosis and chemotherapy (Baudry et al., 2022; Berhili et al., 2019). Therefore, counselling and treatment for depressive symptoms should be considered before implementing health‐promoting lifestyle intervention for younger patients in Class 4 to achieve better intervention effects overall. Younger patients with breast cancer have also been shown to need more support from family and society than older patients (Katapodi et al., 2018; Lee & Kim, 2018) because they must endure complicated treatment during a busier phase of life which can include responsibilities such as childbirth, parenting, and household chores as well as working (Ahmad et al., 2015; Hwang & Knobf, 2022), which then leads to fewer health behaviours.

It is worth noting that those in Classes 2 and 4 in our study were not significantly different in many important aspects (e.g., age, HADS, and MDASI), except that the exercise self‐efficacy in Class 2 was significantly higher than in Class 4. Our study found that those in Class 2 had a better performance in the physical activity and health management dimensions than those in Class 4. Previous study has showed that self‐efficacy is a predictor of health‐promoting lifestyle (Skiba et al., 2022). It influences action by affecting perceived barriers to health‐heightening behaviours and statue of responsibility for following a programme in practice. Patients with higher self‐efficacy would energetically acquire knowledge to convert high‐risk behaviours to health‐promoting lifestyle. Thus, one way for those in Class 4 to improve their health behaviours would be to strengthen their exercise self‐efficacy.

Class 1 (Good Nutrition and Poor Physical Activity) was the largest subgroup in our study, accounting for 37.6% of the sample. All respondents in this class attached importance to healthy diet but ignored physical activity. Compared with Class 4, those in Class 1 were likely to have been diagnosed more than 6 months previously. Shi et al. (2020) have also shown that fruit/vegetable intake was characterized by an increasing trend with the prolongation of diagnosis time, and most of patients who diagnosed more than 6 months consumed more healthy foods than others. Similar to those in Class 4, chemotherapy‐induced symptoms and symptom interference were severe in Class 1. Previous researches have noted that fatigue and other chemotherapy side effects tend to make exercise more difficult for patients with breast cancer (Jung et al., 2020; Kim et al., 2020) and reduce their participation in health‐promoting lifestyle (Kelly et al., 2020). This would suggest, then, that before encouraging patients with breast cancer in Class 4 or 1 to increase their physical activity, it is important and necessary to determine whether symptom management is needed to reduce their chemotherapy‐induced symptoms which may be interfering in their ability to engage in a healthy lifestyle.

In previous studies which did not classify their samples into subclasses, nutrition has generally scored high in the five dimensions of health‐promoting lifestyle (Moghaddam Tabrizi et al., 2020; Sadeghi et al., 2016), minimizing the importance of outcome‐based health‐promoting lifestyle interventions focusing too much on the nutritional dimension. However, in our study, these respondents in Class 2 (Poor Health Responsibility and Nutrition) scored only fairly or even poorly in the nutrition dimension. Even though these respondents accounted for only 11.7% of the full study sample, it is nonetheless important to design targeted interventions for these patients. Burris et al. (2012) showed that health behaviours regarding nutrition have been associated with the beliefs that healthy diet can reduce the risk of breast cancer recurrence (r = 0.18). Thus, strengthening the beliefs is as important as increasing nutritional knowledge and skills if the nutritional performance of patients in Class 2 is to be improved. In our study, those in Class 2 performed quite poorly on the health responsibility dimension. They rarely discuss health issues with health professionals nor do they voluntarily report symptoms or signs of suffering, as described by the HPLP‐II scale. The reason may be that these younger patients (the patients in Class 2 were younger and had no significant difference from those in Class 4) tend to seek out information about their disease by other means such as the Internet (Corter et al., 2019) or by speaking with fellow patients (Lazard et al., 2021; Williams et al., 2020). Most patients with breast cancer in China receive chemotherapy in an outpatient department, where they have limited contact time with health professionals busy working with other outpatients, limiting communication time. Therefore, focusing on gaining the trust of younger patients, developing various forms of medical consultation services, and strengthening the connection between hospital and community in health services are key to improving the health responsibility of patients in Class 2.

Those in Class 3 (Active Health‐Promoting Lifestyle) performed well in all dimensions of health‐promoting lifestyle, but the number of members in this class was relatively small, accounting for less than one‐fifth (17.8%) of the entire study sample. Compared to Class 4, those in Class 3 were older with lower HADS scores and higher exercise self‐efficacy. Feng et al. (2019) revealed that approximately 77.2% of the health behaviours in the Chinese elderly have the characteristics of “modified,” with a positive tendency. They tend to be more motivated to eat healthier, exercise more frequently, and live healthier in response to their diseased state. This is consistent with the finding that improvement in health‐promoting lifestyle is associated with patients' ageing (Song et al., 2023). In our study, the main characteristics of Class 3 that differed from the other latent classes were that there more of the respondents were postmenopausal including artificial menopause due to chemotherapy and natural menopause due to age with lower reported symptom severity and interference. Previous research has also demonstrated that menopausal women believe that diet and exercise are important lifestyle components (Pilewska‐Kozak et al., 2020). This can be attributed to the fact that menopause increases women's risk for various complications, which then causes them to focus on living a healthy lifestyle.

Physical immobility is a huge challenge for patients with breast cancer undergoing chemotherapy in all countries (Schmitz et al., 2019). A lower level of physical activity will lead to a reduction in energy expenditure and weight gain, which would increase one's risk of complications (Ee et al., 2023; Ligibel et al., 2022). The European Society for Clinical Nutrition and Metabolism and the European Partnership for Action Against Cancer both suggest that patients with breast cancer undergoing chemotherapy should engage in moderate‐intensity training (i.e., 50% to 75% of baseline maximum heart rate or aerobic capacity) three times a week, exercising for 10 to 60 min each time, either supervised or home‐based (Arends et al., 2017). In our study, no latent class had good physical activity scores. Even Class 3, which is signified by fewer reported symptoms and lower depression scores, more than half of the respondents nonetheless performed fairly or poorly on the physical activity dimension. Therefore, in addition to chemotherapy‐induced symptoms and mood problems, it is needed to explore in further studies what other factors may hinder patients from increasing physical activity. Our findings point to the importance of determining and considering the causes of low physical activity in patients with breast cancer undergoing chemotherapy in each latent class and to take targeted measures to improve physical activity levels.

4.1. Clinical implementation

Distinguishing and characterizing different health‐promoting lifestyle subgroups can drive precision care to the ground and allow a middle way for developing personalized patient care in a cost‐effective, manageable, and actionable manner. Our findings can help nurses gain insight into which patients and behaviours are the focus of health education and contribute to developing health management that targets multiple health behaviours and is tailored according to the characteristics corresponding to specific sub‐populations. For example, since younger patients were mainly associated with Class 4, health interventions targeting this particular age group should focus more on increasing levels of exercise, health management, nutrition, and health responsibility. Previous finding has already suggested that addressing multiple health behaviours in tailored interventions may result in greater health benefits (Nudelman & Yakubovich, 2022).

4.2. Limitations

Our study has several limitations. First, the study is a cross‐sectional design. Respondents' exercise and dietary habits before diagnosis and chemotherapy were not available, which could have an influence on the health‐promoting lifestyle of patients with breast cancer undergoing chemotherapy. Second, the study used only a single sample of patients with breast cancer from one hospital in China. Therefore, our results may not be generalized and should be confirmed in future studies. Third, there may be possible bias due to the self‐reporting nature of the test scales. Fourth, the latent classes of health‐promoting lifestyle in this study were not compared with the other population, which did not allow for an in‐depth exploration of the unique aspects of the behaviours in patients with breast cancer. Future research is needed to further understand potential predictors and examine the trajectory of health‐promoting lifestyle patterns in patients with breast cancer with chemotherapy time.

5. CONCLUSIONS

The current study identified four distinct latent classes with regard to health‐promoting lifestyle measure using of latent class analysis in patients with breast cancer and further examined the predictors of each latent class. Our findings provide useful implications for nurses to better understand the profile of health‐promoting lifestyle in patients with breast cancer and provide additional evidence for nurses to refer to when identifying at‐risk groups and developing person‐centred intervention programmes.

AUTHOR CONTRIBUTIONS

Meixuan Song collected the data, designed the article, and completed drafting the manuscript or revising; Jinyu Zhang made substantial contributions to conception, analysis and interpretation of data, and criticism for important intellectual content; Qiuyao He and Juan Yang revised it critically for important intellectual content. All authors have fully participated in this work and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

FUNDING INFORMATION

This work was supported by the special project of Shanghai Municipal Health Commission for clinical research in the health industry, Grant/Award Number: No. 202040487.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

ETHICS STATEMENT

Ethics approval and consent to participate: The questionnaire and methodology for this study were approved by a regional ethical review board in China (Ethics approval number: 2020LCSY016).

DATA STATISTICS

The statistics were checked prior to submission by an expert statistician.

CODE AVAILABILITY

Not applicable.

CONSENT TO PARTICIPATE

Informed consent was obtained from all individual participants included in the study.

CONSENT FOR PUBLICATION

Patients signed informed consent regarding publishing their data.

ACKNOWLEDGEMENTS

We gratefully acknowledge the support of the women who participated in this study and Grade 3A hospital.

Song, M. , He, Q. , Yang, J. , & Zhang, J. (2024). Latent classes of health‐promoting lifestyle in breast cancer patients undergoing chemotherapy in China: A cross‐sectional survey. Nursing Open, 11, e2059. 10.1002/nop2.2059

DATA AVAILABILITY STATEMENT

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

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