Consumption Strategies Research Articles

Work- & family-related stress & the risk of hazardous alcohol use: role of sex and social support

Abstract Background There is a lack of longitudinal studies investigating the effect of individual stress factors on alcohol use behaviour, and how sex and social support may mitigate the effect of these stressors. We aimed to determine the association of work- and family-related stressors with developing hazardous alcohol use over time among habitual drinkers, and to examine the role of sex and social support in these associations. Methods A sample of 4,046 individuals, working and living in Stockholm, age 25-55 years, habitual drinkers, who answered the PART study (Swedish acronym for Mental Health, Work and Social Relations) questionnaire regarding work- and family-related stress (exposures) in 1998-2000 (wave1) and 2001-2003 (wave2) were followed until 2010 (wave3) regarding hazardous alcohol use (outcome) measured using Alcohol-Use-Disorder-Identification-Test (AUDIT) score ( > =8 in men, > =7 in women). Weights for selective attrition were calculated, and crude and multivariate (adjusting for sociodemographic, health- and previous stress-related factors) logistic regression models, yielding Odds Ratios (OR) with 95% Confidence Intervals (CI), were used to estimate the exposure-outcome associations. Analyses were stratified for sex and social support in general and at work. Results Job strain (a combination of high demand and low control) was not statistically significantly associated with hazardous alcohol use (OR, CI: 1.3, 0.9-1.9). Among those reporting any family-related stress, a significantly higher risk was observed (OR, CI: 1.5, 1.1-2.1), particularly among women (OR, CI: 1.7, 1.02-2.7) and those with low social support (OR, CI: 2.1, 1.2-3.6). These associations became non-significant after adjusting for previous family-related stress. Conclusions Public health measures aiming to prevent transitions from habitual to hazardous alcohol use should give special attention to individuals with family-related stress, especially among women and those with low social support. Key messages • While job stress was not significantly associated, those experiencing family-related stress had a higher risk of hazardous alcohol use, especially among women and those without social support. • Individuals experiencing family-related stress, particularly women and those lacking social support, require special attention in public health prevention strategies for risky alcohol consumption.

Trainee Teachers’ Shift towards Sustainable Actions in Their Daily Routine

The argument made in this study is that sustainability is a system of beliefs that extends beyond policy and the classroom to transform actions and societies into pro-sustainable behaviours. Therefore, it is crucial to equip trainee teachers to embrace their role as catalysts for change in driving Sustainable development goals, sustainable thinking, systems thinking, wise consumption, sustainable competencies, and sustainable action in their daily lives. The lack of practice-led research on incorporating Education for Sustainable Development (ESD) into trainee teachers’ training programmes is noted in the literature. This qualitative practice-led research explored trainee mathematics and technology teachers’ (TMTTs) experiences learning about sustainability. Kolb’s experiential theory framed this study theoretically. The data were collated from twenty purposively selected TMTTs via semi-structured interviews and reflective diaries. All ethical protocols were observed. The analysis highlighted the core experiences TMTTs gained in learning about sustainability, transformative learning, design thinking, wise consumption, agency, and sustainable teaching strategies. Theoretically, the findings emphasise the importance of using an ESD perspective and experiential learning methods to integrate sustainability education into mathematics and technology teaching. The results suggest that when TMTTs are encouraged to actively engage with sustainability issues in their communities, informed decisions are made about their future roles as teachers, the teaching strategies they intend to use, and the type of learning they aim to foster in their learners.

Tree holes as a source of water for primate species in an Amazonian Forest fragment, northern Brazil

Abstract. Water is an essential nutrient for living beings and is fundamental to metabolic processes. Under free-living conditions, primate individuals can use different strategies, skills, and resources to access water. Here, we report on observations of water consumption in Ateles chamek and Sapajus apella; describe the environmental conditions in which such events were observed, as well as the behavior of the individuals; and compare these observations with similar records in neotropical primates. Water consumption was observed during primate surveys in a forest fragment of approximately 52 ha bordered by the Jaru River, located southwest of the Brazilian Amazon, Vale do Paraíso municipality, state of Rondônia, Brazil. To access water, individuals of A. chamek used their tails, whereas S. apella used a leaf as a tool. Our observations suggest that tree holes may be important water sources for primates in forest fragments and that individuals of different species use different strategies to collect water from tree holes. Access and consumption strategies are directly associated with different cognitive skills and behaviors, which may include using tools, as in the case of capuchins. As water consumption records are limited, these findings highlight the need for continuous reporting to better understand water acquisition. Such reports are especially needed in the context of fragmented and degraded habitats, where water availability is affected by edge effects and the reduction in both fleshy fruits and moisture, which are important for primate species.

Future pathways for green hydrogen: Analyzing the nexus of renewable energy consumption and hydrogen development in Chinese cities

Hydrogen energy offers a promising pathway for achieving zero-carbon urban energy consumption. This study quantitatively analyzes the patterns of hydrogen energy development and its linked to electricity consumption in China's urban areas. It investigates the spatial relationships between hydrogen energy expansion, the potential for green hydrogen production, and overall energy use. Utilizing a coupling coordination degree (CCD) model and a panel vector autoregressive (PVAR) model, the research explores the dynamic interactions between urban hydrogen energy development and the transition to renewable energy sources. In line with Chinese government policy, the study also conducts a scenario analysis of green hydrogen production by 2030, proposing a consumption strategy that balances environmental sustainability with economic growth. The key conclusions are as follows: (1) There is a spatial mismatch between the locations where hydrogen energy is being developed and where the potential for green hydrogen production is greatest. Cities with higher electricity consumption demonstrate stronger synergies in the coordinated development of hydrogen and renewable energy. (2) The development of hydrogen energy significantly accelerates the transition to renewable energy consumption in urban areas, with this impact being more substantial than that of the utilization capacity alone. (3) Hydrogen production in the Plan Scenario is significantly higher than in the Pessimistic Scenario, underscoring the critical importance of implementing renewable hydrogen projects in provinces with high production potential. Future trends in green hydrogen consumption at the provincial level indicate that addressing the challenges of efficient inter-provincial transportation of hydrogen energy remains essential.

A machine learning and linear programming aided approach to wine ranking and selection

PurposeThis paper aims to mitigate the subjective nature of wine rating by introducing statistical and optimization tools for analysis, providing a unique approach not found in existing literature.Design/methodology/approachThe research uses an unsupervised machine learning algorithm, k-means, to cluster wines based on their chemical characteristics, followed by the application of the PROMETHEE II multicriteria decision-making model to rank the wines based on their sensorial characteristics and selling price. Lastly, a linear programming model is used to optimize the selection of wines under different scenarios and constraints.FindingsThe study presents a method to rank wines based on both chemical and sensorial characteristics, providing a more comprehensive assessment than traditional subjective ratings. Clustering wines based on their characteristics and ranking them according to sensorial characteristics provides the user/consumer with meaningful information to be used in an optimization model for wine selection.Practical implicationsThe proposed framework has practical implications for wine enthusiasts, makers, tasters and retailers, offering a systematic approach to ranking and selecting/recommending wines based on both objective and subjective criteria. This approach can influence pricing, consumption and marketing strategies within the wine industry, leading to more informed and precise decision-making.Originality/valueThe research introduces a novel framework that combines machine learning, decision-making models and linear programming for wine ranking and selection, addressing the limitations of subjective ratings and providing a more objective approach.

Numerical and Experimental Study on the Performance of Photovoltaic—Trombe Wall in Hot Summer and Warm Winter Regions: Energy Efficiency Matching and Application Potential

Enhancing the energy efficiency of building envelopes is one of the key strategies for energy conservation and reducing consumption in buildings. This study employs numerical research methods to explore the impact of crucial factors such as solar cell coverage, air channel height, indoor relative humidity, and indoor wind speed on the power generation performance and thermal comfort of a photovoltaic (PV)—Trombe wall. The dynamic changes in optical and thermal performance and energy efficiency matching mechanisms of this system are also discussed in hot summer and warm winter regions. The research findings indicate that the periods of good thermal comfort and power generation efficiency for humans are from 9:00 to 17:00 in winter. In summer, these periods are from 5:00 to 8:00 as well as from 17:00 to 20:00. When the system height is 2 m, the electricity price for power supplied by the PV—Trombe wall system is 25% lower than the residential price, with an annual energy generation of 322.5 kWh/m2 of solar panel, which can save USD 6.35 in costs. Moreover, an experiment is conducted to investigate the thermoelectric correlation by constructing a traditional Trombe wall and an external PV—Trombe wall. When the coverage reached 52.08%, the overall system efficiency was maximized. At a coverage of 78.12%, the system’s thermal efficiency was at its lowest, while the maximum power generation was 510.3 W. It can be seen that the PV—Trombe wall possesses good economic benefits and energy saving as well as emission reduction potential in hot summers and warm winters regions, and the smooth implementation of related works will effectively promote its applications and promotions.

The Impact of Smart Materials Usage in Buildings Envelope: an Approach to Investigate Energy Consumption Versus The Aesthetics Forming The City Image in New Agglomerations

Abstract The notion of creating New Agglomerations often demonstrates the challenge between maintaining city’s identity and the necessity to adopt contemporary building materials. The main target in the current era is to monitor the degree of sustainable strategies adopted while defining the potential materials used whether in the construction process or for final building envelopes. This study aims to evaluate the implications of energy-saving potential of smart glazing materials in building envelopes through a comparative analysis creating the emergent city image created by building façade systems. A thorough review presents various types of smart glazing materials performance versus traditional glazing systems, in terms of heating and cooling results. The research presents a case study of actual buildings deploying smart glazing systems to provide real-world evidence of their energy-saving potential. The results showed that smart glazing materials can significantly reduce energy consumption compared to traditional glazing systems. The results also indicated that the performance of smart glazing materials varies with different types of materials, and the specific energy-saving potential depends on factors such as building orientation, glazing area, and climate conditions. This study provides insights related to emergent design decisions for new agglomerations in terms of energy-efficient building envelopes and energy consumption strategies.

A carbon integrated energy pricing strategy based on non-cooperative game for energy hub in seaport energy system

Seaports are significant energy consumers and sources of carbon emissions within the maritime industry. To address this problem, an energy hub (EH) can be established as an intensive energy interconnection center. This paper proposes a carbon emission flow (CEF) model based on the energy hub in seaport, which monitors the carbon emissions generated by the internal energy conversion unit operating within the hub. Additionally, a carbon integrated energy pricing strategy based on non-cooperative game theory is proposed. Based on duality theory and gradient descent method, an iterative algorithm is designed to seek the global optimal solution satisfying the Nash equilibrium point. The aim of the strategy is to encourage seaport energy consumers to adjust their energy consumption strategy according to the optimal benefits and reduce carbon emissions. A comparative study of three different strategy cases are conducted to evaluate the effectiveness of the proposed strategy. Numerical simulation results show the reduction of the total energy consumption and carbon emissions of the system, which is conducive to realizing the low-carbon operation of the seaport energy system.

One‐Pot Biocatalytic Conversion of Chemically Inert Hydrocarbons into Chiral Amino Acids through Internal Cofactor and H2O2 Recycling

Chemically inert hydrocarbons are the primary feedstocks used in the petrochemical industry and can be converted into more intricate and valuable chemicals. However, two major challenges impede this conversion process: selective activation of C‐H bonds in hydrocarbons and systematic functionalization required to synthesize complex structures. To address these issues, we developed a multi‐enzyme cascade conversion system based on internal cofactor and H2O2 recycling to achieve the one‐pot deep conversion from heptane to chiral (S)‐2‐aminoheptanoic acid under mild conditions. First, a hydrogen‐borrowing‐cycle‐based NADH regeneration method and H2O2in situ generation and consumption strategy were applied to realize selective C‐H bond oxyfunctionalization, converting heptane into 2‐hydroxyheptanoic acid. Integrating subsequent reductive amination driven by the second hydrogen‐borrowing cycle, (S)‐2‐aminoheptanoic acid was finally accumulated at 4.57 mM with eep > 99%. Hexane, octane, 2‐methylheptane, and butylbenzene were also successfully converted into the corresponding chiral amino acids with eep > 99%. Overall, the conversion system employed internal cofactor and H2O2 recycling, with O2 as the oxidant and ammonium as the amination reagent to fulfill the enzymatic conversion from chemically inert hydrocarbons into chiral amino acids under environmentally friendly conditions, which is a highly challenging transformation in traditional organic synthesis.

One-Pot Biocatalytic Conversion of Chemically Inert Hydrocarbons into Chiral Amino Acids through Internal Cofactor and H2O2 Recycling.

Chemically inert hydrocarbons are the primary feedstocks used in the petrochemical industry and can be converted into more intricate and valuable chemicals. However, two major challenges impede this conversion process: selective activation of C-H bonds in hydrocarbons and systematic functionalization required to synthesize complex structures. To address these issues, we developed a multi-enzyme cascade conversion system based on internal cofactor and H2O2 recycling to achieve the one-pot deep conversion from heptane to chiral (S)-2-aminoheptanoic acid under mild conditions. First, a hydrogen-borrowing-cycle-based NADH regeneration method and H2O2in situ generation and consumption strategy were applied to realize selective C-H bond oxyfunctionalization, converting heptane into 2-hydroxyheptanoic acid. Integrating subsequent reductive amination driven by the second hydrogen-borrowing cycle, (S)-2-aminoheptanoic acid was finally accumulated at 4.57 mM with eep > 99%. Hexane, octane, 2-methylheptane, and butylbenzene were also successfully converted into the corresponding chiral amino acids with eep > 99%. Overall, the conversion system employed internal cofactor and H2O2 recycling, with O2 as the oxidant and ammonium as the amination reagent to fulfill the enzymatic conversion from chemically inert hydrocarbons into chiral amino acids under environmentally friendly conditions, which is a highly challenging transformation in traditional organic synthesis.

Life-cycle planning with CEV model and time-inconsistent preferences

In this paper, we investigate an optimization problem for a wage earner seeking to maximize expected utilities until retirement by choosing optimal consumption, investment, and life insurance purchase strategies. The constant elasticity of variance (CEV) model is adopted to describe the price process of the risky asset. Additionally, we assume that the wage earner has time-inconsistent preferences. This makes the wage earner discount her payoff by a non-constant discount rate. Applying the dynamic programming principle, we have derived the Hamilton-Jacobi-Bellman (HJB) equation corresponding to the optimization problem. Furthermore, we present semi-analytical expressions for optimal strategies and value functions in three cases: the benchmark model with time-consistent preferences, the naive and sophisticated wage earners with time-inconsistent preferences. Finally, illustrations of the optimal solutions and some economic insights are provided in the numerical examples.

Optimal consumption and investment in pooled annuity funds with and without fund managers

In this paper, we study two schemes of pooled annuity funds: pools with and without fund managers. In the pool without managers, there are no artificial rules or management cost and we introduce Nash equilibrium to depict the simultaneous non-cooperative game among participants. Given others' strategies, participants choose optimal consumption and investment strategies to maximize their own utility. In the pool with managers, the fund manager makes artificial rules for consumption and investment, ensuring cooperation among participants. The manager then determines optimal consumption withdrawal and investment strategies to maximize the total utility of all participants. By using variational methods and recursive algorithms, semi-analytical solutions in both pools are obtained. Due to prisoner's dilemma, larger pools without managers induce more intense competition in consumption and reduce utility. Thus, there exists an optimal pool size and only homogeneous participants form the pool. Meanwhile, larger pools with managers diversify investment and longevity risks more sufficiently and the subsidy effect among participants is mitigated. Therefore, the perfect pool with infinite participants is recommended as the optimal pool. Moreover, the preference between the two optimal pools depends on risk aversion degree, management cost and flexibility in selecting the strategies.

Temporal variability in biochemical composition and morphometric parameters of cultivated Mediterranean mussels (Mytilus galloprovincialis) in Çanakkale, Türkiye

In recent years, there has been a growing interest in bivalve and gastropod species (Mollusca phylum) due to their high nutritional value compared to terrestrial organisms. Bivalve species, in particular, hold significant potential for both aquaculture and fishing industries worldwide. Among shellfish, mussels play a crucial role in aquatic ecosystems. Parameters such as meat yield and condition index are essential for determining consumption strategies of bivalve species. Thus, this study focused on monthly collections of Mediterranean mussels (Mytilus galloprovincialis) from Çanakkale Gelibolu Ilgardere Mediterranean Mussel Cultivation Farmers between March 2019 and February 2020. During this period, analyses were conducted for condition index, meat yield, shell component index, as well as protein, carbohydrate, lipid, and ash content. The temperature variations in the study area aligned with seasonal changes maximum value and minimal value were recorded as 8.7 °C in March and 27.3 °C in July. Salinity values ranged from 15.2 ‰ in November and January to 24.01 ‰ in December. The length of mussels ranged from 52.54 mm to 68.53 mm, with total weights ranging from 11.18 g to 25.37 g. The length-weight relationship of Mediterranean mussels was determined as Weight = 0.0002 * Length2.806. Condition index, meat yield, and shell component index exhibited significant variations across months (P < 0.05). There was no relationship between these parameters and environmental factors. Fluctuations in protein content were observed, with the lowest recorded in May (41.88 %) and the highest in November (61.19 %). Lipid, carbohydrate, and ash content ranged between 12.49 % and 18.59 %, 9.69 % and 34.31%, and 6.95 % and 18.06 %, respectively. Overall, it can be concluded that aside from the summer months, the harvesting and consumption of cultivated mussels are more suitable in terms of meat quality and biochemical profile.

Dynamic optimal hedging with futures in portfolio context

In this paper, we investigate the optimal hedging strategy in a continuous time framework that is more adequate for commodities. We consider the consumption-investment problem where all asset prices follow mean-reverting jump-diffusion processes. The optimal investment and consumption strategies are derived in closed form. The framework is used to address one of the major risk factors faced by commodity producers. We show that a commodity producer will be better off hedging his/her futures contracts by simultaneously investing in foreign exchange products to minimize the adverse impacts of the jump risk prevalent in commodity prices.

Delayed effects of alcohol consumption on the association between serum BDNF levels and post-traumatic stress disorder development over two-years

BackgroundsThis study aimed to examine the individual and combined effects of serum BDNF (sBDNF) levels and alcohol consumption status, assessed shortly after a physical injury, on the development of post-traumatic stress disorder (PTSD) over two years. MethodsParticipants were consecutively recruited from a trauma center and followed prospectively for two years. At baseline, sBDNF levels and alcohol consumption history were assessed. A range of socio-demographic and clinical covariates were also collected. PTSD diagnosis during follow-up (3, 6, 12, and 24 months post-injury) was established using the Clinician-Administered PTSD Scale for DSM-5. Binary and multinomial logistic regression analyses were employed to investigate the relationships between sBDNF levels, alcohol consumption status, and PTSD onset. ResultsOut of 923 participants analyzed, 112 (12.1%) developed PTSD at some point during the study, with prevalence rates of 8.8% at 3 months, 7.6% at 6 months, 4.8% at 12 months, and 3.7% at 24 months. The study found no individual associations between sBDNF levels or alcohol consumption status and PTSD development. However, lower sBDNF levels significantly predicted PTSD in individuals who consumed alcohol, a relationship not observed in non-drinkers, with significant interaction terms. This pattern was consistent at later follow-up points from 12 to 24 months, but not at earlier assessments at 3 and 6 months. LimitationsThe study's reliance on participants from a single trauma center with moderate to severe injuries may limit the generalizability of the findings. ConclusionsA significant interaction between sBDNF levels and alcohol consumption in relation to PTSD development was observed, particularly in the long term. These findings highlight the necessity of considering both sBDNF levels and alcohol consumption in strategies aimed at preventing PTSD among individuals with physical injuries, underscoring the need for tailored approaches based on these factors.

Investigation on Properties of Pervious Concrete Containing Co-Sintering Lightweight Aggregate from Dredged Sediment and Rice Husks

The utilization of dredged sediment (DS) as a transformative material in building applications presents an ideal consumption strategy. This study endeavors to create a novel ceramsite lightweight aggregate (LWA) through the co-sintering of DS and rice husks (RHs), further integrating this LWA into the construction of pervious concrete. Results revealed that the optimum production procedure for the DS-based LWA incorporated a 21% RH addition, a sintering temperature of 1100 °C, and a sintering duration of 21 min. Notably, the optimal ceramsite LWA, denoted as SDC-H, exhibited a cylinder compressive strength of 28.02 MPa and an adsorption efficiency for Pb2+ of 94.33%. Comprehensive analysis (encompassing bulk density, cylinder compressive strength, water absorption, and the leaching concentrations of heavy metals) confirmed that SDC-H impacted the specification threshold of high-strength light aggregate derived from solid waste (T/CSTM 00548-2022). Substituting 50% of SDC-H led to a diminution in the mechanical properties but an improvement in the dynamic adsorption capacity of the innovative pervious concrete, registering a mechanical strength of 26.25 MPa and a cumulative adsorption capacity for Pb2+ of 285 mg/g. These performances of pervious concrete containing 50% SDC-H might correlate with the evolution of an interconnected and open-pore structure.

Enhancing sustainability of urban waste management through data envelopment analysis for municipal solid waste composting in Tehran, Iran

The critical necessity of decreasing energy consumption and environmental effects is vital in various facets of urban life, particularly in urban waste management. This research aimed to evaluate the energy flow, CExD, and Life Cycle of a composting system for Municipal Solid Waste (MSW) in Tehran province, Iran. The initial phase entailed assessing the energy flow and effectiveness of the composting system, while the subsequent phase focused on enhancing energy efficiency through the application of Data Envelopment Analysis (DEA) to determine an optimal energy consumption strategy for conserving energy and reducing environmental impacts in Tehran's MSW composting system. Over a 90-day period, the analysis of energy usage revealed an energy input and output of 417.30 GJ (8500 t MSW)−1, with 227.02 GJ (8500 t MSW)−1 linked to transportation and 190.28 GJ (8500 t MSW)−1 related to composting activities. The overall energy consumption in the composting system was determined to be 6424.77 GJ (8500 t MSW)−1. By applying DEA optimization, energy savings of 14.45 GJ (8500 t MSW)−1 were accomplished in the composting process, with a significant reduction in electricity usage contributing to these savings. The environmental impact of the energy consumption patterns identified by DEA was notably lower compared to current energy practices. The most notable decrease in impact was observed in the Photochemical Oxidation category. By following the energy usage pattern recommended by DEA, the Global Warming Potential impact reduced by 500 kg CO2 eq. per 8500 t MSW. Additionally, the optimization of energy use through DEA led to a decrease in fossil energy resource depletion by around 4 GJ (8500 t MSW)−1 in the composting process.

Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling

Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes. For manufacturing a single product, predicting energy demand can be decisive in determining parametric control and other factors. Previously analytical models have been largely used to model machining requirements and energy demand. However, these models largely depend on parameterization and do not facilitate the integration of external sub-systems. Therefore, in this paper, an artificial intelligence-based power reduction strategy is developed and implemented on single material (Inconel 718), four control parameters (cutting speed, feed rate, depth of cut and flow rate) and two sub-systems (minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL)). The paper employs four machine learning algorithms,’ K-Nearest Neighbor’, ‘Gaussian Regression’, ‘Decision Tree’, and ‘Logistic Regression’, to evaluate their functionality in predicting power consumption (Pc) of CNC machining systems using a real experimental data set. As per evaluation based on five performance metrics (R-square, MSE, RMSE, MAE, and MedAE), ‘Decision Tree’ has achieved the most accurate power consumption predictions. The comparative results highlight ‘Decision Tree’ as the most better predictor with the optimal max_depth of 2 showing Pc MQL R2 of 0.915 and Pc NF-MQL R2 of 0.931.

Legendre transform dual-asymptotic solution for optimal investment, consumption and life insurance strategy under the HLSV model

Legendre transform dual-asymptotic solution for optimal investment, consumption and life insurance strategy under the HLSV model

Optimal portfolio and insurance strategy with biometric risks, habit formation and smooth ambiguity

This paper studies the optimal consumption, investment, health insurance and life insurance strategy for a wage earner with smooth ambiguity, habit formation and biometric risks. The individual can invest in the financial market composed of a risk-free asset and a risky asset whose unknown market price results in ambiguity. The habit formation depends on historical consumption and satisfies an ordinary differential equation. Moreover, the biometric risks, which consist of health shock risk and mortality risk, can impact the individual's income and health state. The individual can purchase health insurance and life insurance to respectively deal with health shock risk and mortality risk, and aims at maximizing the total expected utility of consumption, legacy and terminal wealth. Using the dynamic programming technique, we derive the corresponding Hamilton-Jacobi-Bellman equation in the states of health and critical illness respectively, prove the verification theorem and obtain closed-form solutions for the optimal strategies. Finally, numerical experiments are carried out to illustrate the impact of risk aversion, ambiguity aversion, health shock and habit formation on the optimal strategy. The results reveal that the wage earner with different utility functions and different health states will show different behaviors in consumption, investment and insurance purchase.