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Anxiety, depression and pain in cancer patients | NDT – Dove Medical Press




Cancer is the leading cause for disease-related deaths in adolescents and young adults (AYAs).1 AYA cancer patients, defined as age 15–39 years by the National Cancer Institute,2 face a crucial period in their lives due to the inevitable changes in their physiology, social situation, and psychological well-being.1 In China, the prevalence of cancer has been estimated to be around 87.56 per million person-years among AYAs.3 A cancer diagnosis can lead to psychological distress, and AYA patients with cancer are specifically vulnerable to psychological distress due to the intersection of disease and young age.4 Consequently, AYA cancer patients are more likely to report treatment disengagement, lower quality of life, and even suicidality.5,6

Psychological disturbances are common in AYA cancer patients.1,7,8 A meta-analysis indicated that approximately 32% of cancer patients experience some type of psychological distress during active treatment,9 and a review reported a significant association between younger age and greater psychological distress during cancer trajectory.10 In China, a study found that about 75% of the AYA cancer patients reported anxiety, and 90% of them reported depression.11 Emotional disturbances can compromise cancer patients’ emotional and role functioning,12 lower their quality of life, and may lead to higher risk of mortality.13–15

As a complex symptom, pain is prevalent in cancer population.16,17 A recent cross-sectional study found that 66% of the cancer patients reported moderate-to-severe pain,18 while another study showed that 42.1% of the adult cancer patients reported pain.12 Previous studies have found that age, gender, genetic predisposition, and cognitive and/or emotional process around pain could significantly affect one’s perceived pain level.19,20 Inappropriate management of pain could result in negative consequences for patients’ emotions and cognitive function, and their quality of life.21

Anxiety, depression, and pain are prevalent, and highly interactive with each other in AYA cancer patients.22 Individuals with comorbid anxiety and depression tend to respond more slowly to treatment and have a higher probability of suicide and recurrences.23 Moreover, pain often co-occurs with anxiety and depression, and together leads to considerable social and economic burden.24 The pain related to the oncological process may be accompanied by anxiety about medical procedures and hospitalization, separation anxiety and psychological stress.22 The presence of pain is also associated with more depressive symptoms and worse outcome, such as lower quality of life, poorer work performance, and higher health service utilization.19

In a randomized controlled trial, patients with increased pain were more likely to report higher levels of anxiety, fatigue, and depression.25 On the other hand, fatigue, anxiety and depression also have significant influence on pain in patients with cancer.25 A previous study showed that compared to non-depressed patients with pain, patients with comorbid depression and pain tend to experience more pain-related complaints, more severe pain symptoms, and greater impairments.26 Subsequently, even though anxiety, depression, and pain are prevalent, and highly interactive with each other, the interaction amongst these conditions has not been fully investigated. To the best of our knowledge, no study has examined the comorbidity of anxiety, depression and pain in cancer population from the perspective of network analysis.

Network analysis is an alternative novel approach to examine the comorbidity of two or more disorders.27 In network analysis, it is believed that comorbidity occurs when symptoms from different disorders are directly linked to each other.28 It estimates the unique associations between each pair of measured symptoms while controlling for all other symptoms and shrinking potentially spurious associations to zero.29 In this study, 1) network analysis was applied to assess the connectivity between anxiety, depression and pain symptoms in a Chinese AYA cancer sample; 2) the prevalence of anxiety, depression and pain in AYAs was also investigated.



This study’s participants were consecutively seen at the outpatient units of Southern Medical University Nanfang Hospital and Guangdong Provincial People’s Hospital. Participants were recruited from 1 January 2018 to 30 November 2018. To be eligible, patients needed to: 1) have a cancer diagnosis within the last 6 months; 2) have ages between 15 and 39 years; 3) be able to understand Chinese. Those with disturbances of consciousness were excluded.

Study Procedure

This study was performed in accordance with the Helsinki standard and the study protocol was approved by the Nanfang Hospital Ethics Committee (ref No: NFEC-2018-038) and Guangdong Provincial People’s Hospital Research Ethics Committee (ref No. 2018295H(R1)). All participants were approached in the waiting room by trained research assistants. For those patients (adults), showing an interest in participating; were asked to provide a written informed consent form. Simultaneously, approval was also required from a parent/legal guardian for participants under the age of 18 years. All recruited participants were asked to complete a personal information collection form and a set of scales after which they were sent back to the research assistants immediately. The recruitment and assessment procedure was supervised by a licensed psychiatrist.


Basic Demographic and Clinical Data

Participants’ basic demographic and clinical data (such as, gender, age, cancer site, comorbidities, and family cancer history) were collected using a specially designed case record form.

Patient Health Questionnaire (PHQ-9)

The PHQ is a self-rating scale that measures depression-related symptoms of patients. PHQ contains 9 items, each item uses a four-level score of 0 to 3, with a total score of 0 to 27. A total score of 5 or more indicates depressive symptoms.30 The Chinese PHQ-9 showed good psychometrics properties,31 and the internal consistency of PHQ-9 was 0.802 in Chinese AYA cancer patients.32

Generalized Anxiety Disorder (GAD-7)

The GAD is a 7-item self-report scale used to assess an individual’s anxiety symptoms. Response options available are rated from 0 to 3, and a total score of 5 or more indicates anxiety symptoms.33 The Chinese GAD-7 showed satisfactory psychometrics properties,34 and the Cronbach’s alpha of GAD-7 was 0.883 among Chinese AYA cancer patients.32

McGill Pain Questionnaire-Visual Analogue Scale (MPQ-VAS)

The McGill Pain Questionnaire-Visual Analogue Scale (MPQ-VAS) is one of the most widely used tests for the measurement of pain,35 and has been validated in the Chinese language in 2013.36 The score for VAS ranges from 0 (no pain) to 100 (worst possible pain), and a total score of 40 or more indicates that the patient is currently suffering from pain.36

Sample Size Estimation

The sample size (N) was calculated with the formula: 37 where Z is the statistic of the significance test, alpha is the significance level, P is the prevalence, and d is the allowable error. In this study, alpha was set at 0.05, Zα was set at 1.96, and d was 0.1P. Based on a previous finding that about 75% of the AYA cancer patients reported anxiety, and 90% of them reported depression in China.11 We, therefore, assume the prevalence as 75% and to enable further analyses, we increased the expected sample size by 50%. Finally, at least 192 participants were recruited in this study.

Statistical Analysis

Network Analysis

First, we estimated the network using the R “bootnet” and “qgraph” packages,38 with “EBICglasso” (ie, the Extended Bayesian Information Criterion combined with the Graphical Least Absolute Shrinkage and Selection Operator method) as the default method.29 A network is a graphical representation of variables (nodes), and their correlations are depicted as edges.39 In the network, thicker and more saturated edges represent stronger correlations, green lines represent positive correlations, and red lines represent negative correlations.

Secondly, to quantify the importance of each node in the network, we computed the centrality indices of Strength, Betweenness, and Closeness. Higher centrality index values are representative of greater importance within the network, and symptoms with high centrality measures might be important as potential targets for further treatment interventions.40 Additionally, to identify the bridge symptoms that connect different communities, we computed the bridge centrality (Bridge Strength, Bridge Betweenness, and Bridge Closeness). Recent studies have recommended that both Betweenness, Bridge Betweenness, Closeness, and Bridge Closeness indices might not be robust in psychological networks.41 Therefore, in the subsequent network analysis, we mainly focused on Strength42 and Bridge Strength.43,44 Strength means the sum of the absolute value of a node’s correlations with other nodes in the structure, while Bridge Strength refers to the sum of absolute edge weight values of all intercommunity edges.43,44 Centrality plots were created to represent these indices.

Thirdly, to examine the stability and accuracy of the networks,29 a case-dropping bootstrap procedure was performed to compute the correlation stability coefficient (CS-C). The CS-C is required to be above 0.25.29 Also, a non-parametric bootstrapping method was used to estimate the accuracy of edge-weights by computing confidence intervals (CIs). This means that larger CIs indicated poorer precision in the estimation of edges, while narrower CIs indicated a more trustworthy network.42 Additionally, to examine the possible influence of age and gender on cancer patient’s emotional disturbances, the network model and the local structure indexes were re-estimated, after controlling for age and gender.


Patient Characteristics

Demographic and clinical characteristics of the included participants are reported in Table 1. The study sample consisted of 218 patients with complete data on the 17 variables (7 anxiety nodes, 9 depression nodes, and 1 pain node) used to construct the network. Comparison of patients with complete data vs the rest of the recruited sample (n = 31) revealed no significant differences in sex and age, which indicates that the subgroup of participants included in the network analysis can be considered to be representative of the overall sample. The mean age of the AYA patients was 30.22 years (SD = 4.82). In total, 38.07% (95% CI = 31.58–44.57%) of the participants reported depression, 30.73% (95% CI = 24.56–36.91%) reported anxiety, and 14.22% (95% CI = 9.55–18.89%) reported current pain.

Table 1 Demographic and Clinical Characteristics of the Study Sample

Network Analysis

Figure 1 presents the association network. The generated network illustrated that the anxiety, depression and pain community were well connected (with no isolated node). Out of 136 edges, 87 (63.97%) of them were estimated to be non-zero. This model and edge weight matrix revealed that the edges between “excessive worry” and “trouble relaxing” (GAD3 – GAD4, edge weight = 0.3031),“nervousness” and “uncontrollable worry” (GAD1 – GAD2, edge weight = 0.2676), and “anhedonia” and “sad mood” (PHQ1 – PHQ2, edge weight = 0.2671) were the strongest positive edges in the model.

Figure 1 Network structure of anxiety, pain and depression.

Abbreviations: PHQ, Patient Health Questionnaire; GAD, General Anxiety Disorder.

Notes: In the diagram, orange nodes represent anxiety symptoms, light blue nodes represent depression symptoms, and grey node represents pain symptom. Nodes with stronger correlations are closer to each other. The thickness of an edge indicates the strength of the correlation. Green lines represent positive connections.

Three important bridges between anxiety and depression communities emerged to be: “afraid” and “guilty” (GAD7 – PHQ6, edge weight = 0.1965), “restlessness” and “concentration difficulties” (GAD5 – PHQ7, edge weight = 0.1784), and “uncontrollable worry” and “appetite change” (GAD2 – PHQ5, edge weight = 0.1548). Additionally, there were three bridges between pain and depression: “pain” – “guilty” (PHQ6) (edge weight = 0.0894), “pain” – “motor change” (PHQ8) (edge weight = 0.0452), and “pain” – “sleep problems” (PHQ3) (edge weight = 0.0326). The only bridge that links the pain and anxiety community was “pain” – “restlessness” (GAD5) (edge weight = 0.0576).

Central Symptoms and Bridging Symptoms

Figure 2 shows the network centrality indices, and Figure 3 shows the network bridge centrality indices. The node “having trouble relaxing” (GAD4, node strength = 1.182), was the most central node within the network, followed by “uncontrollable worry” (GAD2, node strength = 1.165), and “sad mood” (PHQ2, node strength = 1.144). Additionally, “uncontrollable worry” (GAD2, bridge strength = 0.645) was also the strongest bridge symptom in the network that connected the anxiety, depression, and pain clusters, followed by “guilty” (PHQ6, bridge strength = 0.545), and “restlessness” (GAD5, bridge strength = 0.414) (Table 2).

Table 2 Centrality and Bridge Centrality Estimates of Nodes in the Network

Figure 2 Centrality Indices of all symptoms within the network.

Abbreviations: PHQ, Patient Health Questionnaire; GAD, General Anxiety Disorder.

Figure 3 Bridge Centrality Indices of all symptoms within the network.

Abbreviations: PHQ, Patient Health Questionnaire; GAD, General Anxiety Disorder.

Network Stability

Supplementary Figure 1 shows the results of the case-dropping subset bootstrapping test. The CS-coefficient for Strength and Bridge Strength was 0.518, and 0.284, both exceeding the recommended threshold of 0.25. This indicates that the network model is relatively robust. The bootstrapped 95% CIs for the estimated edge-weights were relatively narrow, suggesting that the estimates were accurate (Supplementary Figure 2). The edge weight matrix is presented in Supplementary Table 1. After controlling for age and gender, the network model and local structure indexes were re-estimated. When compared with the original network model, an almost identical network was obtained with respect to magnitude (r = 0.986 [0.971, 0.996]), and strength (r = 0.994 [0.970, 0.998]) of edges.


This was the first study that explored the connectivity between anxiety, depression and pain symptoms in Chinese AYA cancer patients, using a network approach. In this study, we found that the prevalence of depression (38.07%) and anxiety (30.73%) in AYA cancer patients is lower than that in a previous AYA study in China.11 However, the prevalence found in the current study is significantly higher than that in Chinese general cancer population.3 A recent investigation has reported that about 13.9% and 15.1% of the Chinese cancer patients have significant symptoms of depression and anxiety, respectively.3 Although several relevant studies have been conducted in China, generalizations to the larger population cannot be made due to several limitations, such as small sample sizes and single-site study designs. In addition, we found that around 14.22% of the AYA patients reported being in current pain. A previous study revealed that about 81.8% of the AYA cancer survivors reported some pain,45 while another study reported that 36.2% of the AYA cancer survivors reported pain.46 The difference in results could be partially explained by the difference in study sample, patient’s sociocultural background, cancer type, and the use of various measurement tools and cutoff values.

In this study, we found that the three communities (ie, anxiety, depression, and pain) were well connected with each other, particularly anxiety and depression. Previous studies have consistently proved that anxiety and depressive symptoms are highly correlated with each other in different populations,39,47–49 including cancer patients.32 Anxiety and depression often share a high degree of comorbidity amongst cancer patients.7,50 The possible underlying mechanisms behind the anxiety–depression interaction could be the polymorphic variation at the serotonin 1-A receptor gene,51 or the alteration of activation and connectivity of amygdala and ventral cingulate.52

Besides the strong associations between anxiety and depression, several positive edges were identified between pain and anxiety/depression. In the network model, pain was positively linked to “guilty”, “motor change”, “sleep problems”, and “restlessness”. Pain is recognized to have both a sensory dimension (ie intensity) and an affective dimension, such as, unpleasantness, anxiety, sadness, and annoyance.53 It has a profound impact on a patient’s physical, psychological, and social quality of life.54 The network model confirmed results from previous studies that emotional disturbances significantly influence the prognosis and treatment of pain, and vice versa.19,53 For example, studies have proved that depression, anxiety, and fear of cancer progression play important roles in the association between objective pathophysiology and patient’s subjective experience of pain.8 Simultaneously, the presence of pain is associated with worse psychological and health-related outcomes, such as higher depression, poorer work performance, and lower quality of life.19 Meanwhile, our findings are in accordance with previous studies which indicated that insomnia at baseline predicted the development of pain, and anxiety symptoms partially mediated that association between insomnia and pain.55 As suggested by the biopsychosocial models, an increasing number of researchers have delineated psychosocial variables to be important correlates of pain.

In this study, the node “having trouble relaxing” was the most central node within the network, followed by “uncontrollable worry” and “sad mood”. Our findings were consistent with previous similar studies which identified “persistent worrying or anxiety”, and “inability to relax” as core symptoms for the diagnosis of generalized anxiety disorder, and “sad mood” as the hallmark symptom of major depressive disorder.39,48 Also, “uncontrollable worry” was the strongest bridging symptom in the network. Previous studies have indicated that the activation of bridging symptoms is likely to result in the development and maintenance of different disorders.56 Therefore, “uncontrollable worry”, “guilty”, and “restlessness”, which were the key bridging symptoms that linked one cluster to other clusters in the current study, should be considered as potential targets for future intervention. In particular, in this study, “uncontrollable worry” acts as both central symptom and bridging symptom, indicating that alleviating cancer patient’s excessive worries might be helpful in improving the patient’s co-occurring anxiety, depression and pain symptoms.

This study contributes to the literature in several ways. First, we specifically targeted the AYA population, as they have unique biological and psychological needs. However, health care services in this population have not shown any significant improvement in over two decades, and in China, AYA cancer patients are often ignored.32 The current study helped fill the gap between pediatric and adult psycho-oncology. Second, this was the first study that examined the connectivity between anxiety, depression and pain symptoms in Chinese AYA cancer population. Pain and emotional distress (eg, anxiety, and depression) have been considered as the fifth and sixth vital signs for a cancer patient’s well-being, along with signs of blood pressure, heart rate, temperature, and respiration. Thus, investigating the connectivity between emotional distress and pain provided us better insights into the AYA population. Third, this study used sophisticated network analysis approach, which is a novel approach to examine the comorbidity of two or more disorders.

Study Limitations

There are several limitations that should be acknowledged, however. First, due to the cross-sectional design of the study, no causal relationship could be derived. Second, the sample size of the current study was relatively small; thus, our findings may not be generalizable to the entire AYA population in China. In addition, the sample size calculation formula for cross-sectional study might not be applicable to network analysis. Third, cancer pain was assessed by one-item question, and anxiety and depression symptoms were measured by self-rating scales, thus, recall bias and measurement bias may exist. Fourth, certain factors that may influence an individual’s anxiety, depression and pain, such as, family/social support, time since diagnosis, disease severity, and substance use were not examined in this study. A further multicenter, large-sample, longitudinal investigation using validated objective instruments is needed.

Clinical Implications

This is the first network study to assess the bridge symptoms/items that mediate the interaction among different disorders/syndromes (eg, anxiety, and depression) in AYA cancer populations. Examining patient’s anxiety, depression, and pain symptoms as dynamic systems may provide new insights into the maintenance of these psychosomatic problems. As “uncontrollable worry” was the strongest bridge symptom that linked one symptom cluster to other symptom clusters, this finding suggested that interventions aimed at alleviating patients’ worries and fostering a sense of control (eg, Acceptance – commitment therapy and Mindfulness – base stress reduction) might have utility. Further longitudinal studies could also help to better understand the directionality of these bridge pathways.


In conclusion, anxiety, depression, and pain are highly interactive with each other in Chinese adolescent and young adult cancer patients. In the anxiety-depression-pain network model, “having trouble relaxing”, “uncontrollable worry”, and “sad mood” were identified as the most central symptoms, while “uncontrollable worry”, “guilty”, and “restlessness” were the key bridging symptoms that linked one cluster to other clusters. Alleviating AYA cancer patient’s excessive worries might be helpful in improving a patient’s co-occurring anxiety, depression and pain symptoms.

Data Sharing Statement

The dataset supporting the conclusions of this article is included within the article and its additional file.

Ethical Approval and Consent to Participate

This study was performed in accordance with the Helsinki standard, and the study’s protocol was approved by the Nanfang Hospital Ethics Committee (ref No: NFEC-2018-038) and Guangdong Provincial People’s Hospital Research Ethics Committee (ref No. 2018295H(R1)). The informed consent was obtained from all subjects and/or their legal guardian(s).


We are grateful to all the patients who participated in this study.


This study was supported in part by the National Natural Science Foundation of China (72101107); the Clinical Research Program of Nanfang Hospital, Southern Medical University (2021CR014), and the Start-up Funds of Guangdong Provincial People’s Hospital (KY0120211134).


The authors declare that they have no competing interests.


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B.C. pharmacists to renew, issue prescriptions as part of reworked health plan



VANCOUVER — British Columbia is expanding the power of pharmacists to renew and issue some prescriptions as part of a wide-ranging plan to relieve pressure on the province’s struggling health-care system.

Health Minister Adrian Dix said Thursday the five-year health human resources strategy aims to redesign how health staff work, as well as retain, recruit and train workers through 70 action items.

“We intend to work together with every aspect of the health-care system and with patients to develop solutions that will make a career in health care a more sustainable and rewarding opportunity for people,” Dix told a news conference.

The past three years have added demands to an already overburdened system, Dix acknowledged. The COVID-19 pandemic, toxic drug deaths and aging population contributing to exhaustion and burnout among health workers, he said.

The problem isn’t limited to B.C., with the World Health Organization forecasting a global shortage of 15 million health workers by 2030, he added.

Starting Oct. 14, B.C. pharmacists will be able to administer more vaccines and renew prescriptions for up to a two-year period for people with chronic illnesses whose family doctors have retired or left their practices.

Next spring, pharmacists will begin prescribing drugs for minor ailments like urinary tract infections, allergies and indigestion, as well contraception, meaning patients won’t have to visit a doctor first.

The changes bring B.C. in line with guidelines set out by Health Canada and other provinces.

Jamie Wigston, a practising pharmacist and president of the BC Pharmacy Association, said the shift won’t require new training for pharmacists, whose skills have been underused.

“We’ve been trained to do much more than what we’ve been able to do for a long time,” Wigston said.

Empowering pharmacists to renew prescriptions is especially important for patients with mental health and substance use disorders, who need access to medications in a timely manner, he said.

The announcement will also help patients in rural areas who may have community pharmacies, but where a medical clinic or prescriber may be hours away, he added.

The government said renewing prescriptions for patients without family doctors would be at the discretion of the pharmacist. If uncomfortable, the pharmacist could consult with a doctor by phone or send the patient for a medical assessment.

The move comes amid an ongoing crisis in health care that has seen emergency department closures due to staffing shortages and long wait times to see specialists.

About one in five residents don’t have a family doctor.

The plan will also see paramedic training expanded to include pain management and enhanced airway management techniques. Firefighters and other first responders will be equipped to take blood pressure, use medication for life-threatening allergic reactions and prepare patients for transport by ambulance.

The plan does not cover pay for health workers, but Dix said the province is in ongoing talks with Doctors of BC, representing 14,000 physicians in B.C., to create a new compensation model for family practice doctors. Pay and benefits for workers like paramedics would generally be dealt with during bargaining, he added.

Another action item in the plan includes 128 new seats to the University of B.C.’s faculty of medicine and $1.5 million to help establish a previously announced new medical school at Simon Fraser University in Burnaby.

Redesigning the system will include establishing clear workload standards, using technology more efficiently and adopting team-based models of care, the government said during a technical briefing.

It said it’s also working to lower “artificial barriers” to verify international qualifications for nurses, doctors and other health workers, reducing what is typically an 18-month to two-year process.

Expanding employer-based training will also allow health workers to earn and learn at the same time, officials say.

This report by The Canadian Press was first published Sept. 29, 2022.


Amy Smart, The Canadian Press

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Java Burn Reviews – Effective Ingredients for Weight Loss or Bogus Claims – Maple Ridge News



More consumers started consuming Java Burn, and the number is increasing every day. More consumers are pouring product-friendly reviews furnishing relevant information about Java Burn, its ingredients, recommended dosage, price list, complaints, and crucial F&A. Java Burn is a weight loss beverage consisting of green tea extract (300mg), green coffee bean extract (200mg), L-theanine (100mg), vitamin B-12(5mcg), chromium (20mcg), vitamin B6 (1mg) and vitamin D3(20mcg) totaling a 700mg dose. Innovator of this unique formula, John Barban, deems these components will stimulate a state of metabolism called nutritional synergy. This fat-burning supplement dissolves with any beverage without altering its taste and flavor. This morning beverage peps you up all through the day.

Enhance memory power

A healthy diet is crucial for overall well-being, and nutritional synergy fortifies the nutritional value of the foods you eat. The University of Illinois, in a study, found an association between specific nutrients and brain functioning in older individuals. Java Burn supports a weight loss regime without vigorous exercise and a restricted diet and enhances your memory power, intelligence, and brain function associated with global-oriented behavior. This nutritional supplement is organic; the flavorless powder stimulates healthy synergy, a form of metabolism when consumed with a balanced proportion of minerals and vitamins.

Instantly soluble

Java Burn is a weight loss supplement that comes in a tasteless powder form. The product is only available on the official website. A pouch of Java Burn costs $49; its organic ingredients instantly dissipate in coffee or other beverages stimulating metabolism. The product’s creator suggests you put one bag of the supplement in coffee, stir it well and sip. The powder is flavorless, so it does not alter the aroma or taste of brimming coffee. When your body’s metabolism rate increases, it burns the stubborn fat cells rapidly, and you shed that extra body weight.

The components of Java Burn are vegetarian and non-GMO, with no side effects, and a third party examines its effectiveness and safety. A pouch contains thirty sachets; drink one daily with an aromatic brimming cup of coffee.


The critical elements of Java Burn contain green tea and coffee extracts, L-theanine, and chromium. The powder, when intakes in the morning, enhances metabolism speed and efficiency by 500%, and the fat-burning process continues throughout the day. The product reduces the feeling of hunger and optimizes nutrition synergy. All product ingredients are non-GMO, citing they are not genetically modified. Many scientists and environmentalists advocate that GMO products pose serious health threats. Furthermore, the formula does not use fillers, antibiotics, artificial sweeteners/colors, stimulants, or preservatives. Healthy men and women from the age group of twenty-five to sixty-five can enjoy this supplement for weight loss.

Stimulates metabolism

Genetic, junk food habits, and poor lifestyle are significant reasons for obesity; this fat-burning supplement is the easiest way to eliminate the extra pounds. The product naturally stimulates your metabolism process and kicks up fat burning procedures. The product goes well with any type of coffee, regular, espresso, American, and light, medium, or dark roast. The product is manufactured in the US, complying with GMP guidelines. Java Burn effectively burns the stored fat cells, thus supporting the weight loss program. When you consume the supplement mixed with coffee, its effectiveness increases as it is assimilated into the bloodstream. For an optimal result, the product needs to be consumed within two years from the date of manufacture.

2-3 months

When consumed with coffee, Java Burn suppresses hunger, and you need not count your calorie intake. To get the best result out of the product, you need to consume it for two to three months consecutively. It is the last time your body gets acclimatized to the product and starts burning stored fat by increasing metabolism and efficiency. Till now, no side effects have been reported by consumers, and the possibility is almost nil as all ingredients are non –GMO and natural.

Every manufactured sachet undergoes rigorous testing by a third party ensuring quality, effectiveness, and purity. The reviews submitted by thousands of consumers on the official website testify to the efficacy and safety of the supplement. If any health issue arises after consuming the fat-burning supplement, immediately contact a health care professional and stop taking the supplement.

Basal metabolism

The metabolism rate affects the calorie of fat you burn during exercise, sleep or rest. Metabolism, aka metabolic rate, is a biochemical process in living organisms that breaks up nutrients and fat to generate the energy necessary for survival. In simpler terms, the rate at which your body produces energy or burns calories. The human body burns calories in three ways; when the body is at rest (Basal Metabolism) to keep the body running. BMR (basal metabolic rate) is, to a degree, dependent on genetics. Metabolism occurs when you perform daily activities and exercise.


As metabolism is partly genetic, to change it, you need outside influence; people with higher metabolism feel more vigorous. On the other hand, people with poor metabolism feel lethargic; the body resists the fat burning process and the stored fat cells in the belly and thigh do not shed. As the body burns fewer calories, less energy is produced, and the person feels sluggish. Java Burn, coupled with coffee, claims it increases the user’s metabolic rate. Consequently, the mulish stored fat starts to disintegrate. If you achieve a higher metabolic rate, the body will burn more calories at rest and daily errands. Lean people are more active than obese ones as their metabolic rate is higher in the no exercise period than the latter.

Money Back Guarantee

The product is only available at the official portal. It is applicable for any number of orders. One pouch containing thirty sachets costs $49. If you order three pouches, the cost is $34 for each unit, and you get a supply for three months. Six pouches cost $29 per pouch plus the shipping charge. A sixty-day money-back guarantee comes with the supplement, so you can return it if not satisfied within the stipulated time. Due to its ongoing popularity, many counterfeit products torment the market, so purchase them online from the official website.


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Bad flu season predicted for B.C. – Kamloops This Week



After two years of record-low influenza rates, experts are warning the flu will likely be back in full swing this season.

That’s because of a general lifting of pandemic health measures, such as required masking, gathering size limits and travel restrictions, according to pharmacist Kim Myers.

“It definitely increases the spread of germs and colds,” said Myers, who works in the Greater Victoria area.

Health Canada estimates that in a non-pandemic year, about 12,200 Canadians are hospitalized with the flu or flu-like symptoms. Getting an exact number is difficult as only nine of the country’s provinces and territories report hospitalizations to the national flu surveillance system, FluWatch.

Flu hospitalizations dropped during pandemic

of those which do report — Newfoundland and Labrador, Prince Edward Island, Nova Scotia, New Brunswick, Manitoba, Alberta, Yukon, Northwest Territories and Saskatchewan — 5,176 influenza-related hospitalizations were reported during the 2017-2018 season and 3,657 were reported in 2018-2019.

During the 2019-2020 season, half of which occurred within the COVID-19 pandemic, there were 2,493 hospitalizations. That number dropped to zero in 2020-2021, again not including Ontario, Quebec, B.C. or Nunavut.

Myers said it’s hard to tell whether this year’s flu season will be as bad as pre-pandemic years, but that it will almost certainly be worse than the last year or two. She said the awareness the pandemic has raised around the importance of vaccines makes her hopeful more people will get the flu shot this year. Already, Myers said, people coming into her pharmacy are asking when shots will be available.

Possible correlation between COVID-19 and influenza vaccine uptake

B.C. did see a small spike in flu vaccine uptake in the first year of the pandemic. In 2018, 34.6 per cent of people got the shot, followed by 37.2 per cent in 2019 and 42.1 per cent in 2020, according to Statistics Canada. 2021 rates are not yet available.

A 2021 research paper published in medical journal Vaccine found the primary indicator of whether Canadians will get a vaccination is whether they have been vaccinated before, suggesting those who got the COVID vaccine may be more likely to get the influenza one.

More than 87 per cent of British Columbians have received at least one dose of a COVID vaccine as of Sept. 26.

Beginning in early October, B.C. residents will have the option of receiving COVID vaccine boosters and flu shots at the same time. The province said it will have the capacity to vaccinate about 250,000 people per week that way.

Who is most impacted?

For the majority of people, the flu means up to a week of sickness, but for young children, elderly people and the immunocompromised the virus can make it significantly harder for them to fight off infections.

Health Canada said 3,500 deaths are influenza-related each year, although that number is based off a mathematical estimate, rather than actual yearly data.

Myers said the best thing people can do to stop the spread of the virus and protect those most vulnerable to it is to follow many of the same precautions put in place for COVID-19: get vaccinated, wash your hands, wear a mask, stay home if you’re sick and minimize your number of crowded public outings.

“It’s not just for themselves, it’s trying to do it for those around them who are vulnerable and for those who aren’t able to receive vaccines. It’s important that we try and do that to help protect them,” Myers said.

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