---
title: "Health behaviour abilities in patients with haematological malignancies: latent profiles and influ"
id: "bmj-open-3-health-behaviour-abilities-profiles-in-patients-with-haematological"
canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-3-health-behaviour-abilities-profiles-in-patients-with-haematological"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "BMJ Open"
source_url: "http://bmjopen.bmj.com/cgi/content/short/16/7/e110342?rss=1"
published_at: "2026-07-21T12:12:52.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Health behaviour abilities in patients with haematological malignancies: latent profiles and influ
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-3-health-behaviour-abilities-profiles-in-patients-with-haematological
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** BMJ Open
- **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/7/e110342?rss=1)
- **Published At:** 2026-07-21T12:12:52.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This cross-sectional study of 486 hospitalised patients with **haematological malignancies** in three provincial hospitals in Hunan Province, China, assessed self-rated **health behaviour abilities** and their determinants using the Self-rated Abilities for Health Practices Scale and other validated instruments. - The mean overall health behaviour abilities score was 47.96 (SD 23.29) on a 0–112 scale, indicating a low average level. - Latent profile analysis (LPA) of four subscales (nutrition, exercise, psychological adjustment, health responsibility) identified three distinct profiles: an ‘exercise-weak survival profile’ (41%), an ‘exercise-deficient developmental profile’ (45%) and a ‘balanced-robust profile’ (14%), demonstrating marked population heterogeneity. - Multivariable multinomial logistic regression found several factors associated with profile membership, including age, education level, **social support**, **financial toxicity** and **psychological distress**. - Data collection used validated scales: SOC-13 for sense of coherence, Perceived Social Support Scale, the De Souza financial toxicity measure, PHQ-4 for psychological distress, and the Self-rated Abilities for Health Practices Scale (Cronbach’s α overall 0.975). - Strengths include a person-centred LPA approach and sampling across multiple primary diagnoses; limitations include online self-report questionnaires, cross-sectional design precluding causal inference, and recruitment from hospitals in a single city limiting generalisability. - Authors recommend targeted, profile-informed interventions by clinical healthcare providers to improve self-management and long-term outcomes in HM patients. - Data are available from the corresponding author upon reasonable request.
## Clinical Analysis & Structured Key Points
Skip to main content Intended for healthcare professionals Log In Basket Search for this keyword Advanced search Latest content Archive For authors About Browse by collection You are here Home Archive Volume 16, Issue 7 Email alerts Article Text Article info Citation Tools Share Rapid Responses Article metrics Alerts PDF Oncology Original research Health behaviour abilities profiles in patients with haematological malignancies and its influencing factors: a cross-sectional survey http://orcid.org/0000-0003-0439-4169Guiyuan Ma1,2, Nannan Long1,3, Ping Mao1, Yuanyuan Li4, Fang Li5, http://orcid.org/0009-0006-7551-4868Chengyuan Li1,3 Correspondence to Dr Chengyuan Li; lichengyuan_xy3@163.com Abstract Objectives To identify the latent profiles of health behaviour abilities among patients with haematological malignancies and to explore the underlying population heterogeneity and influencing factors. Design A cross-sectional study conducted between October and December 2024. Setting Data were collected from three provincial hospitals in China. Participants A total of 486 hospitalised patients were recruited. Outcome measures Data were collected using a demographic questionnaire, the Sense of Coherence Scale-13, the Perceived Social Support Scale and the Self-rated Abilities for Health Practices Scale. Latent profile analysis was performed to identify distinct classifications of health behaviour abilities and multivariate logistic regression was employed to determine the influencing factors. Results The mean score of health behaviour abilities among the participants was 47.96 (SD 23.29). Three profiles were identified, showcasing substantial population heterogeneity: ‘exercise-weak survival profile’ (41%), ‘exercise-deficient developmental profile’ (45%) and ‘balanced-robust profile’ (14%). Multivariable regression revealed that age, education level, social support, financial toxicity and psychological distress were influencing factors determining profile membership. Conclusions Health behaviour abilities in patients with haematological malignancies were at a low level with three distinct latent profiles. Clinical healthcare providers should implement targeted interventions based on the characteristics of patients’ health behaviour abilities to enhance their self-management capabilities. Data availability statement Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author on reasonable request. https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/. https://doi.org/10.1136/bmjopen-2025-110342 Request Permissions If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways. Request permissions STRENGTHS AND LIMITATIONS OF THIS STUDY The use of latent profile analysis combined with multinomial logistic regression provides a robust, person-centred methodological approach. Sampling across multiple distinct primary diagnoses ensures a methodologically comprehensive representation of haematological malignancies. Data collection relied entirely on online self-reported questionnaires, introducing inherent response and recall biases. Due to the cross-sectional design, causal relationships between these factors and health behaviour abilities cannot be inferred. Recruitment was restricted to hospitals within a single city, potentially limiting the generalisability of the findings. Introduction Haematological malignancies (HMs) comprise a heterogeneous group of aggressive neoplasms originating from the haematopoietic system,1 primarily affecting the blood, bone marrow and lymph nodes. These malignancies encompass primary or secondary disorders such as leukaemia, lymphoma and multiple myeloma, and are clinically characterised by high malignancy, rapid progression and substantial therapeutic challenges. Globally, HMs rank as the fourth most prevalent class of cancer.1 According to global cancer statistics in 2022, approximately 20 million new cancer cases and 9.7 million cancer-related deaths occurred worldwide; among these, HMs accounted for 6.5% of new diagnoses and 7.1% of mortality, indicating an escalating global burden that severely threatens patient health.2 Although breakthroughs in targeted therapy and immunotherapy have significantly prolonged survival rates, patients with HMs still face multiple challenges, including financial toxicity driven by exorbitant drug costs,3 prolonged treatment cycles and psychological distress associated with disease relapse.4 In this context, proactive self-care through health behaviours such as medication adherence, symptom monitoring and nutritional management can effectively optimise health outcomes and prevent adverse events.5 According to the Health Action Process Approach theory, health behaviours are heavily governed by an individual’s psychological drive and coping planning.6 In clinical oncology settings, these underlying motivational dynamics are directly manifested through a patient’s health behaviour abilities, which reflect an individual’s self-rated capability to implement specific actions (eg, treatment adherence or symptom management) when encountering distinct obstacles. Crucially, robust health behaviour abilities are well-documented as a critical determinant of superior quality of life and reduced symptom burden in cancer survivors.7 Patients with higher abilities beliefs demonstrate greater adherence to physical exercise and self-care regimens, directly mitigating cancer-related fatigue and enhancing physical functioning.8 For HM patients undergoing intensive treatments like stem cell transplantation, strong health behaviour abilities are essential to overcome severe treatment-induced physical deconditioning, accelerate haematological recovery and preserve muscle mass,9 serving as a fundamental psychological cornerstone for long-term survivorship.10 The health behaviour abilities are associated with multiple factors, involving four dimensions: patient characteristics, disease treatment, family environment and social context. Within the dimension of patient characteristics, attributes such as educational level11 and sense of coherence (SOC)12 are relevant. In the disease treatment dimension, key variables include disease duration,13 14 comorbidities,14 etc. The family environment dimension encompasses monthly household income,15 place of residence and other factors. The social dimension involves social support,16 insurance type and similar elements. Exploring the influencing factors of health behaviour abilities in haematological tumour patients can provide evidence for constructing targeted intervention strategies, thereby enhancing patients’ long-term health management efficacy. However, current research on health behaviour abilities across diverse cancer populations, including HMs, still faces significant limitations: on one hand, most existing studies rely heavily on aggregate scale scores to assess overall abilities levels, thereby failing to fully capture the heterogeneous characteristics within specific populations.6 For example, Hsia et al6 evaluated health-promoting lifestyle in female breast cancer survivors, while Pitt et al17 studied health behaviour abilities in broad paediatric oncology groups; both lines of research, however, overlook the unique disease-specific trajectories inherent to HMs. On the other hand, systematic research examining the comprehensive influencing factors of health behaviour abilities remains fragmented and largely focused on non-haematological subgroups.6 For instance, Brouwer-Goossensen et al18 investigated the determinants of efficacy changes specifically in brain tumour patients. Consequently, the extent to which these findings apply to HM populations remains unclear, highlighting a pronounced scarcity of disease-specific empirical evidence for this distinct patient group. To address such intra-population heterogeneity, an increasing number of published studies have recently used latent profile analysis (LPA) to capture the distinct patterns of health-promoting behaviours and behavioural abilities across various oncological settings. For instance, in gastrointestinal cancer survivorship, recent multicentre evidence successfully identified potential health-promoting behavioural typologies in patients following oesophageal cancer surgery, uncovering boundary conditions driven by symptom distress.19 Similarly, investigator-led studies on patients with colorectal cancer applied this person-centred approach to uncover heterogeneous patterns of health-promoting lifestyles, demonstrating that sociodemographic factors heavily dictated sub-optimal profile memberships.20 Within the specific domain of HMs, emerging evidence has also confirmed that patients undergoing intensive chemotherapy exhibit substantial inter-individual variations in their self-care and behavioural capabilities.21 Building on these methodological trends, LPA22 can identify latent profiles based on multidimensional indicators and reveal the categorical characteristics within groups and their specific influencing factors. Therefore, this study intends to use the LPA method to deeply explore different categories of health behaviour abilities among HM patients and their population characteristics, and systematically analyse the influencing factors. The research results will provide a theoretical basis for clinical nurses to develop precise and individualised intervention strategies, which is of great practical significance for improving the health behaviour management and long-term prognosis of HM patients. Materials and methods Participants This study employed a cross-sectional design. Between October and December 2024, patients hospitalised in the haematology-oncology departments of three provincial hospitals in Hunan Province, China, were recruited using convenience sampling. The inclusion criteria were (1) pathologically diagnosed with HMs; (2) currently hospitalised and undergoing treatment; (3) aged ≥18 years; (4) possessed adequate communication and cognitive skills to complete the survey. The exclusion criteria were (1) cognitive impairment or diagnosed with mental disorders; and (2) complicated with diseases of vital organs such as the heart, brain and kidneys. Measurements The selection and structural framework of the measurement variables in this study were systematically guided by the Social Ecological Model.23 This theoretical approach conceptualises health behaviour abilities as a multifaceted outcome driven by multi-layered systemic influences. Consequently, the evaluated variables were structured into four hierarchical ecosystems: the intrapersonal level (such as age, gender, SOC), the microsystem level (such as residence, monthly family income), the exosystem level (such as clinical diagnosis, treatment stage) and the macrosystem level (such as social support, insurance). These measured dimensions, along with their specific variables and instruments, are elaborated below and summarised in table 1. VIEW INLINE VIEW POPUP Table 1 Included variables General information questionnaire It includes 14 variables: age, gender, marital status, educational level, occupation, residence, monthly family income, clinical diagnosis, chronic diseases, disease course, treatment stage, relapse, insurance and complications. Sense of coherence The Sense of Coherence Scale-13 (SOC-13), designed and developed by Antonovsky12 and translated by Bao and Liu.24 The scale consists of 13 items across three dimensions: the comprehensibility dimension (five items), the manageability dimension (four items) and the meaningfulness dimension (four items). A 7-point Likert scale is employed for scoring, ranging from ‘never’ (coded as 1 point) to ‘frequently’ (coded as 7 points). Total scores on the scale range from 13 to 91, with higher scores indicating a higher level of SOC. Scores between 13 and 63 indicate a low level, 64 and 79 a moderate level and 80 and 91 a high level. The scale demonstrates a Cronbach’s α of 0.780. Social support We measured perceived social support using the 12-item Perceived Social Support Scale, originally developed by Zimet et al25 and translated by Jiang et al.26 The tool assesses the degree of support an individual perceives from family, friends and significant others. Each item is rated on a 7-point scale (1=strongly disagree to 7=strongly agree), yielding a total score between 12 and 84, the higher the score, the more social support the person perceives. According to the total score, support levels are categorised into three tiers: low (≤36), moderate (37–60) and high (61–84). In our study, the scale achieved a Cronbach’s α of 0.90. Financial toxicity Developed by De Souza et al3 and translated by Yu et al,27 this scale is primarily used to assess the perceived financial toxicity of research participants over the past 7 days. The scale comprises three dimensions: active economic expenditure, passive economic resources and psychosocial responses, with a total of 11 items. A 5-point Likert scale is employed, ranging from ‘not at all’ to ‘very much’, scored 0–4. Items 1, 6, 7 and 11 are positively scored, while the remaining items are reverse-scored. The total score ranges from 0 to 44. A total score >26 indicates no financial toxicity, below 26 indicates low financial toxicity, below 14 indicates medium financial toxicity and 0 indicates high financial toxicity. The scale demonstrates a Cronbach’s α of 0.893 Psychological distress Psychological distress was measured using the Patient Health Questionnaire-4 (PHQ-4) developed by Kroenke et al.28 The Chinese version of the PHQ-4 was adapted from the validated Chinese versions of the PHQ-9 and Generalised Anxiety Disorder-7 and has demonstrated good psychometric properties in Chinese populations.29 It evaluates patients’ emotional states over the past 2 weeks. Each item is rated on a 4-point scale: ‘not at all’ (0), ‘several days’ (1), ‘more than half the days’ (2) and ‘nearly every day’ (3), with total scores ranging from 0 to 12. Scores are interpreted as follows: 0–2 indicates no psychological distress, 3–5 indicates low psychological distress, 6–8 indicates medium psychological distress and 9–12 indicates high psychological distress. The scale demonstrated a Cronbach’s α of 0.896. Health behaviour abilities The Self-rated Abilities for Health Practices Scale7 developed by Becker et al and translated by Hu and Zhou,30 was used to measure participants’ abilities for health behaviours. The scale encompasses 28 items across four dimensions: nutrition, exercise, psychological adjustment and health responsibility. Each item is scored on a 5-point Likert scale ranging from ‘almost no confidence’ (0) to ‘absolute confidence’ (4), yielding a total score range of 0–112. Higher scores indicate greater abilities in performing health behaviours. The overall Cronbach’s α for the scale was 0.975, with subscale coefficients ranging from 0.902 to 0.953. Data collection and quality control After obtaining the consent of the hospital’s nursing department, data were collected by researchers and two trained research assistants. Before the investigation, a unified guideline was used to explain the purpose, significance, content and precautions of this study to the research subjects. All research subjects were required to independently complete the questionnaire anonymously within the prescribed time, and the patients filled it out by themselves. For the research subjects who had difficulty filling in the form, the investigators filled it out orally and based on the patients’ responses on their behalf. After filling out the questionnaire, check it on the spot. If any errors or omissions are found, return it promptly for filling and correction. A total of 500 questionnaires were distributed in this study, and 492 questionnaires were retrieved. Among them, 23 questionnaires were filled out by the researcher on behalf of others. After double-person verification, invalid questionnaires were excluded (four were answered regularly and two had an answering time of less than 5 min), and 486 valid questionnaires were retrieved, with an effective recovery rate of 98.78%. Statistical method Potential profile analysis was conducted using Mplus V.8.0 software. Taking the scores of the four dimensions of the Health Behaviour Abilities Rating Scale (nutrition, exercise, psychological adjustment and health responsibility) as explicit variables, the exploration started from one category and went up to four categories for analysis. The fitting indices of each item were compared and the best category model was selected in combination with practical significance, and the potential profile was drawn. The specific fitting indicators of the evaluation model are as follows: (1) information evaluation indicators: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the sample corrected BIC (adjusted Bayesian Information Criterion, aBIC); the smaller the statistical value, the better the model fit; (2) classification evaluation index: Entropy reflects the classification accuracy, with a value range of 0–1. The closer it is to 1, the more accurate the model classification is. (3) Likelihood ratio test: The Lo-Mendel-Rubin (LMR) and Bootstrap-based strapped likelihood ratio test (BLRT) were used as the comparison statistics of the model. If p<0.05, it indicates that the model of this category is superior to that of the previous category. The above evaluation indicators are for reference only. When determining the best model, the interpretability of each category should also be taken into consideration. Data were statistically analysed using SPSS V.28.0 software. Measurement data were described by x±s and M (Q1, Q3), and count data were described by frequency and rate. χ2 test or rank-sum test was used to compare the general information and scores of each scale of patients with different health behaviour abilities categories, and indicators with statistically significant differences between categories were screened out. And logistic regression was used to analyse the predictive indicators of different categories. A two-tailed p value <0.05 was considered statistically significant. Sample size For sample size estimation, the events-per-variable criterion for multivariable logistic regression analysis was applied, which dictates a minimum ratio of 10–15 cases per independent variable. Given that 18 independent variables were examined in the current study, and accommodating an anticipated 20% invalid questionnaire rate, the minimum required sample size was determined to be 324 cases. Ultimately, a larger sample of 486 participants was achieved, satisfying the statistical power requirements. Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination of this research. Results General information A total of 486 patients completed the survey, among whom approximately half (49.6%) were aged between 40 and 59. The other demographic characteristics are shown in table 2. The average score of health behaviours abilities in HMs patients was 47.96±23.29 points. The average scores of the four dimensions of nutr
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