This paper mainly focuses on developing a joint cloud and wireless resource allocation algorithm based on evolutionary game (JRA-EG) considering mobile terminals' energy consumption and time delay as well as monetary cost in mobile edge computing environment. The mobile terminals (MTs) in different service areas form different populations. In addition, the competition among populations of MTs in different service areas is to share the limited amount of bandwidth and computation resources in the accessible service providers (SPs). We analyse the stability property of the evolutionary game model and obtain the evolutionary equilibrium (EE). EE is acquired by replicator dynamics approach. Simulation results show that our JRA-EG algorithm can converge to the EE rapidly. Compared with the existing algorithms, the algorithms can save more energy and have less time delay when input data size becomes larger.

The prevalence of multimorbidity is increasing in aging populations globally. Multimorbidity involves various noncommunicable disease (NCD) combinations that extend beyond individual conditions. Identifying how multimorbidity patterns (MPs) configure is crucial for understanding the role of NCD patterns in health prognosis. This study identified MPs and examined their associations with sociodemographic and economic factors in 23,452 participants aged ≥ 60 years from the Lifelines cohort in northern Netherlands (baseline: 2007–2013; follow-up: 2011–2019). Complete data on 14 NCDs at two time points were analyzed, with multimorbidity defined as ≥ 2 NCDs. Latent class and factor analyses identified clusters of NCDs, stratified into MPs based on multimorbidity presence. Multinomial logistic regression assessed the relationships between MPs and sociodemographic and economic traits. Multimorbidity prevalence was 55% at baseline. Five MPs, consistent across assessments, were identified. The ‘Vascular’ MP included the fewest NCDs (2–4), while the ‘Complex-Treatment Spectrum’ had the most (5–11). Adjusted analyses revealed that lower education, not having a partner, and lower income significantly increased the relative-risk of belonging to high-risk MPs, such as ‘Metabolic Risk,’ ‘Major CVD-Vascular Conditions,’ and ‘Complex-Treatment Spectrum’, compared to participants without multimorbidity. These MPs reflect profiles with distinct risk factors and prognoses. Multimorbidity manifests as stable patterns in this population. MPs derived from latent class analysis were more interpretable and consistent over time compared to correlation-based approaches. Income disparities influence MP profiles, highlighting the need for tailored interventions. Longitudinal studies are recommended to explore NCD contributions to MP dynamics and inform strategies addressing health and social inequities.

There is currently no consensus on the number and domains of lifestyle factors to incorporate in research examining lifestyle combinations. A common approach in this field involves creating unweighted lifestyle scores by summing healthy/unhealthy lifestyle scores. However, further exploration and comparison are needed to advance beyond simple summation and investigate more nuanced lifestyle patterns. Latent class analysis was performed to identify lifestyle patterns among 112,842 participants aged 18 or older from the Dutch Lifelines cohort (baseline data, collected from 2007 to 2013). Ten lifestyle factors were selected based on the six pillars of Lifestyle Medicine: smoking habits, binge drinking, daily alcohol intake, diet quality, ultra-processed food consumption, long-term stress, physical (in)activity, sleeping, TV watching time and social connections. Lifestyle factors were assessed using validated self-report questionnaires. We identified five lifestyle patterns: “Healthy but physically inactive” (8.6% of the total population, class 1), “Unhealthy but no substance use” (8.5%, class 2), “Healthy in a balanced way” (37.2%, class 3), “Unhealthy but light drinking and never smoked” (31.6%, class 4) and “Unhealthy” (14.2%, class 5). Socio-demographic characteristics including age distribution, sex, education level, income and employment status differed significantly (nominal p < 0.05) across lifestyle patterns. Multiple comparison analysis showed that healthy lifestyle scores differed within lifestyle pattern pairs. Proportions of unhealthy lifestyle scores are comparable between the “Healthy but physically inactive” and the “Healthy in a balanced way” patterns as well as the “Unhealthy but no substance use” and “Unhealthy but light drinking and never smoked” patterns. The five identified lifestyle patterns exhibit distinct, non-random clustering of behaviours, each linked to specific socio-demographic characteristics. Understanding these clustering tendencies can help identify target populations and uncover barriers to unhealthy behaviours, aiding the development of tailored health interventions. The overlap in the distribution of unhealthy lifestyle scores between two lifestyle patterns suggests that the latter may provide a more comprehensive perspective on habitual behaviours.

Background: Lifestyle factors often interact in complex ways when influencing chronic disease risk. We aimed to examine the prospective associations between empirically derived real-life lifestyle patterns (LPs) and the incidence of major chronic diseases, and to explore the linearity of the relationships between lifestyle summation scores and disease risk. Methods: We included adults free of cardiovascular diseases (CVDs), cancer, chronic respiratory diseases (CRDs), or type 2 diabetes (T2D) at baseline (2006–2013) from the Dutch Lifelines cohort. LPs and lifestyle summation scores were derived from baseline self-reported data on diet, physical activity, substance use, sleep, stress, and social connectedness, each categorised as healthy, moderately healthy, or unhealthy. Fine–Gray sub-hazard regression models assessed associations between LPs and disease incidence, with natural spline functions used to evaluate linearity in summation scores. Results: Among 114,919 T2D-free, 131,248 cancer-free, 91,777 CRD-free, and 77,645 CVD-free participants, we observed 3114 T2D, 4685 cancer, 4133 CRDs, and 2850 CVD incident cases (median follow-up time: 8 years). Compared to the “Unhealthy” pattern, both the “Healthy-in-a-balanced-way” and “Healthy-but-physically-inactive” patterns were broadly significantly protective. The “Unhealthy-but-no-substance-use” pattern was associated with increased T2D risk (Sub-Hazard Ratio (SHR) = 1.27, 95% Confidence Interval (CI): 1.11–1.47) but reduced cancer risk (SHR = 0.85, 95%CI: 0.74–0.97). The “Unhealthy-but-light-drinking-and-never-smoked” pattern was protective for T2D (SHR = 0.89, 95%CI: 0.79–0.99). Linear associations were observed between lifestyle summation scores and disease risk, except for “healthy lifestyle” scores with T2D and “unhealthy lifestyle” scores with CRDs (non-linear p-value < 0.05). Conclusions: There are potential protective effects of healthy lifestyles on T2D, cancer, CRDs, and CVDs. However, the “Unhealthy but no substance use” demonstrated increased risk on T2D, protective effect on cancer and no significant effect on CRDs or CVDs. The relationship between combined lifestyle factors and NCD risk is complex and partly non-linear, showing diminishing benefits beyond certain thresholds, especially T2D and CRDs.

Multimorbidity is common in older adults, and certain combinations of chronic conditions may confer higher mortality risk. Unhealthy lifestyle behaviours are also linked to shorter life expectancy. This study examined whether lifestyle patterns (LPs) modify the association between multimorbidity configurations (MCs) and mortality. We analysed data from 20,853 adults aged 60 and older in the Northern Netherlands Lifelines cohort, followed for a mean of 12 years. Five LPs were previously identified via latent class analysis, based on diet, physical activity, substance use, sleep, social connection, and stress. Multimorbidity was defined both as a disease count (≥2 non-communicable diseases [NCDs]) and as five latent MCs reflecting distinct NCD combinations. All-cause mortality was estimated using Kaplan-Meier plots and Cox models, reporting hazard ratios (HRs) with 95% confidence intervals (CIs), stratified by LPs. Compared to having no NCDs, mortality risk differed across MCs, with associations varying by LPs. Among participants with a ‘Healthy in a balanced way’ lifestyle, the ‘Complex-Treatment’ (HR 3.18, 95%CI: 2.24-4.51) and ‘CVD-&-Vascular’ (HR 2.35, 95%CI: 1.97-2.79) configurations showed the highest risks. In the ‘Unhealthy but no substance use’ group, mortality risk across MCs was more heterogeneous, with larger effect sizes. In contrast, multimorbidity defined by disease count showed limited variation in effect sizes across other LPs. LPs shape the mortality risk associated with multimorbidity. Risk variability across MCs was more pronounced in healthier lifestyles. These findings support the value of considering specific multimorbidity profiles—beyond disease count—for prognostic assessment and targeted interventions in older adults.