Comparing the *** selected regions regarding the smoking prevalence , Myanmar is leading the ranking (***** percent) and is followed by Serbia with ***** percent. At the other end of the spectrum is Ghana with **** percent, indicating a difference of ***** percentage points to Myanmar. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The global number of smokers in was forecast to continuously increase between 2024 and 2029 by in total **** million individuals (+**** percent). After the ******** consecutive increasing year, the number of smokers is estimated to reach *** billion individuals and therefore a new peak in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco, be it on a daily or non-daily basis.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smokers in countries like Caribbean and Africa.

The smoking prevalence in the United States was forecast to continuously decrease between 2024 and 2029 by in total two percentage points. After the eighth consecutive decreasing year, the smoking prevalence is estimated to reach 19.93 percent and therefore a new minimum in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smoking prevalence in countries like Canada and Mexico.

The information is from the "National Health Interview Survey" of the Ministry of Health and Welfare, which collects information on smoking behavior from the public through telephone interviews. For more information, please visit the "Tobacco Hazard Prevention Information Website" of the National Health Administration (http://tobacco.hpa.gov.tw/).The definition of "daily smoking rate" is the ratio of individuals who have smoked more than 100 cigarettes from the past to present and have used tobacco daily in the last 30 days. The formula for calculation is: Number of respondents aged 15 and above who answered "smoked more than 100 cigarettes so far" and "used tobacco daily in the last 30 days" / Number of valid completed interviews of individuals aged 15 and above * 100%.

This repository contains 523 human feedback messages sent to daily smokers and vapers who were preparing to quit smoking/vaping with a virtual coach.

Study

Daily smokers and vapers recruited through the online crowdsourcing platform Prolific interacted with the text-based virtual coach Kai in up to five sessions between 1 February and 19 March 2024. The sessions were 3-5 days apart. In each session, participants were assigned a new preparatory activity for quitting smoking (e.g., listing reasons for quitting smoking, envisioning one's desired future self after quitting smoking, doing a breathing exercise). Between sessions, participants had a 20% chance of receiving a feedback message from one of two human coaches. More information on the study can be found in the Open Science Framework (OSF) pre-registration: https://doi.org/10.17605/OSF.IO/78CNR. The implementation of the virtual coach Kai can be found here: https://doi.org/10.5281/zenodo.11102861.

Feedback messages

All feedback messages were written by one of two Master's students in psychology. The two human coaches were directed to craft messages incorporating feedback, argument, and either a suggestion or reinforcement. They were also instructed to connect with individuals by referencing aspects of their lives, express empathy toward those with low confidence, and provide reinforcement when people were motivated.

When writing the feedback, the human coaches had access to data on people's baseline smoking and physical activity behavior (i.e., smoking/vaping frequency, weekly exercise amount, existence of previous quit attempts of at least 24 hours, and the number of such quit attempts in the last year), introduction texts from the first session with the virtual coach, previous preparatory activity (i.e., activity formulation, effort spent on the activity and experience with it, return likelihood), current state (i.e., self-efficacy, perceived importance of preparing for quitting, human feedback appreciation), and new activity formulation. Notably, the human coaches only had access to anonymized versions of the introduction texts and activity experience responses (e.g., names were removed). Except for the free-text responses describing participants' experiences with their previous activity and their introduction texts, all of this information is provided together with the feedback messages. For the previous and new activities, we just provide the titles and not also the entire formulations that the human coaches had access to.

Before sending the messages to participants on Prolific, we added a greeting (i.e., "Best wishes, Karina & Goda on behalf of the Perfect Fit Smoking Cessation Team"), a disclaimer that the messages were not medical advice, and a link to confirm having read the message at the end. We also added "This is your feedback message from your human coaches Karina and Goda for preparing to quit [smoking/vaping]:" at the start of the message.

The human coaches approved publishing these feedback messages.

Additional data from the study

Additional data from the study such as participants' free-text descriptions of their experiences with their activities and their introductions from the first session with the virtual coach will also be published and linked to the OSF pre-registration of the study.

In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).

This is a development key figure, see questions and answers on kolada.se for more information. Percentage of people who smoke daily. Two different surveys with comparable results are included in the statistics. Uppsala, Sörmland, Västmanland, Värmland and Örebro (the so-called. The CDUST region) has Life & Health (LV) as a data source, with a sample of inhabitants 18-84 years, which refers to year T. The rest of the country that has the source of the Public Health Agency (HLV), and with a sample of residents aged 16-84, refers to the years T-3 to T. The Public Health Agency, Health on Equal Conditions (HLV) and Life & Health Data are available according to gender breakdown.

Tobacco smoking is one of the largest preventable causes of death and disease in Australia. In 2017-18, 13.8% of adults aged 18 years and over were daily smokers (2.6 million people), down from 14.5% in 2014-15. The decrease is a continuation of the trend over the past two decades, in 1995, 23.8% of adults were daily smokers.

Additionally the proportion of adults who have never smoked is increasing over time, from 49.4% in 2007-08 to 52.6% in 2014-15 and 55.7% in 2017-18.

Indonesia ID: Smoking Prevalence: Total: % of Adults: Aged 15+ data was reported at 39.400 % in 2016. This records an increase from the previous number of 39.000 % for 2015. Indonesia ID: Smoking Prevalence: Total: % of Adults: Aged 15+ data is updated yearly, averaging 37.600 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 39.400 % in 2016 and a record low of 32.900 % in 2000. Indonesia ID: Smoking Prevalence: Total: % of Adults: Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank: Health Statistics. Prevalence of smoking is the percentage of men and women ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Tobacco smoking is one of the largest preventable causes of death and disease in Australia. In 2017-18, 13.8% of adults aged 18 years and over were daily smokers (2.6 million people), down from …Show full descriptionTobacco smoking is one of the largest preventable causes of death and disease in Australia. In 2017-18, 13.8% of adults aged 18 years and over were daily smokers (2.6 million people), down from 14.5% in 2014-15. The decrease is a continuation of the trend over the past two decades, in 1995, 23.8% of adults were daily smokers. Additionally the proportion of adults who have never smoked is increasing over time, from 49.4% in 2007-08 to 52.6% in 2014-15 and 55.7% in 2017-18.

This dataset presents the footprint of the age-standardised percentage of adults who are daily smokers. A current daily smoker was defined as a person who smokes one or more cigarettes, roll-your-own cigarettes, cigars or pipes at least once a day. Chewing tobacco, electronic cigarettes (and similar) and the smoking of non-tobacco products were excluded. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Age-standardisation is a method of removing the influence of age when comparing populations with different age structures - either different populations at the same time or the same population at different times. For this data the Australian estimated resident population of people aged 18 and over as at 30 June 2001 has been used as the standard population. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data.

This is an uncertainty number, for more information see Uncertainty in data on kolada.se. The percentage of people aged 16 to 84 who smoke daily is an excise estimate from a sample survey. This means that the estimate is not necessarily representative of the population as a whole. However, with the point estimate ± the uncertainty number, one can most likely say that the true mean is within the range. Data is available according to gender breakdown.

This is a development key figure, see questions and answers on kolada.se for more information. Percentage (%) of population aged 16-84 who smoke daily. The results are taken from the national survey Health on equal terms (Public Health Agency) and the question; Do you smoke every day? The data refers to year T-2 to year T. Data is available according to gender breakdown.

Background: People who smoke and who face challenges trying to quit or wish to continue to smoke may benefit by switching from traditional cigarettes to noncombustible nicotine delivery alternatives, such as heated tobacco products (HTPs) and electronic cigarettes (ECs). HTPs and ECs are being increasingly used to quit smoking, but there are limited data about their effectiveness.

Objective: We conducted the first randomized controlled trial comparing quit rates between HTPs and ECs among people who smoke and do not intend to quit.

Methods: We conducted a 12-week randomized noninferiority switching trial to compare effectiveness, tolerability, and product satisfaction between HTPs (IQOS 2.4 Plus) and refillable ECs (JustFog Q16) among people who do not intend to quit. The cessation intervention included motivational counseling. The primary endpoint of the study was the carbon monoxide-confirmed continuous abstinence rate from week 4 to week 12 (CAR weeks 4-12). The secondary endpoints included the continuous self-reported ≥50% reduction in cigarette consumption rate (continuous reduction rate) from week 4 to week 12 (CRR weeks 4-12) and 7-day point prevalence of smoking abstinence.

Results: A total of 211 participants completed the study. High quit rates (CAR weeks 4-12) of 39.1% (43/110) and 30.8% (33/107) were observed for IQOS-HTP and JustFog-EC, respectively. The between-group difference for the CAR weeks 4-12 was not significant (P=.20). The CRR weeks 4-12 values for IQOS-HTP and JustFog-EC were 46.4% (51/110) and 39.3% (42/107), respectively, and the between-group difference was not significant (P=.24). At week 12, the 7-day point prevalence of smoking abstinence values for IQOS-HTP and JustFog-EC were 54.5% (60/110) and 41.1% (44/107), respectively. The most frequent adverse events were cough and reduced physical fitness. Both study products elicited a moderately pleasant user experience, and the between-group difference was not significant. A clinically relevant improvement in exercise tolerance was observed after switching to the combustion-free products under investigation. Risk perception for conventional cigarettes was consistently higher than that for the combustion-free study products under investigation.

Conclusions: Switching to HTPs elicited a marked reduction in cigarette consumption among people who smoke and do not intend to quit, which was comparable to refillable ECs. User experience and risk perception were similar between the HTPs and ECs under investigation. HTPs may be a useful addition to the arsenal of reduced-risk alternatives for tobacco cigarettes and may contribute to smoking cessation. However, longer follow-up studies are required to confirm significant and prolonged abstinence from smoking and to determine whether our results can be generalized outside smoking cessation services offering high levels of support.

Comparison between the 2010 National Health Interview Survey (NHIS) and the 2011 National Health and Wellness Survey (NHWS).Note. Presented are row percentages (summing to 100% across columns), and in brackets are 95% confidence intervals for the row percentages. Data in this table are based on two questions in the NHIS: “Have you smoked at least 100 cigarettes in your entire life?” and “Do you now smoke cigarettes every day, some days, or not at all?”†Source: Schiller et al., 2012 [15]. Race/ethnicity was recoded from variables (HISPAN_I; RACERPI2) to create mutually exclusive groups, summing to total adults. Percentages were from Table XV. Confidence intervals were manually calculated based on the standard errors as noted in Table XV; they could not be determined for non-Hispanic Asian and non-Hispanic Other, as standard errors were not available for these subgroups.‡Current smokers have smoked at least 100 cigarettes in their lifetime and still currently smoke. Every day smokers are current smokers who smoke every day, while some day smokers are current smokers who smoke on some days.‡‡Former smokers are persons who have smoked at least 100 cigarettes in their lifetime but currently do not smoke at all.‡‡‡Nonsmokers are persons who have never smoked at least 100 cigarettes in their lifetime.§Current smokers defined as those who responded, “Yes, I smoke” or, “Yes, but I am trying to quit.”§§Former smokers defined as those who responded, “No, I quit smoking” or, “No, I am in the process of quitting.”§§§Nonsmokers defined as those who responded, “No” to the question, “Have you ever smoked cigarettes?”

This is the analysis code underlying the paper "Attitudes Toward a Virtual Smoking Cessation Coach: Relationship and Willingness to Continue" by Nele Albers, Mark A. Neerincx, Nadyne L. Aretz, Mahira Ali, Arsen Ekinci and Willem-Paul Brinkman. In this paper, we conduct a mixed-methods analysis of people's relationship and willingness to continue working with the text-based virtual coach Sam.

Data:

Our analysis is based on the data collected in an online experiment in which more than 500 daily smokers interacted with the text-based virtual coach (i.e., a conversational agent) in up to 5 sessions spread over at least 9 days. In each session, Sam proposed a new preparatory activity for quitting smoking or becoming more physically active, with the latter possibly aiding the former. After the 5 sessions, participants filled in a post-questionnaire in which they answered 6 questions about their attitude toward Sam by means of a rating on a scale from -5 to 5 and a free-text response to the follow-up question "Why do you think so?" The questions were adapted based on the ones by Provoost et al. (2020). Our paper focuses on people's responses to the questions "Do you prefer continuing or stopping to work with the conversational agent Sam?" (-5: "Definitely prefer stopping", 5: "Definitely prefer continuing") and "How would you characterize your relationship with the conversational agent Sam?" (-5: "Complete stranger", 5: "Close friend"). The complete dataset can be found here: https://doi.org/10.4121/19934783.v1. Notably, this dataset contains only data used to study the acceptance of the virtual coach Sam. Further data from the same study has been published in separate repositories (e.g., https://doi.org/10.4121/20284131.v2).

Virtual coach:

The implementation of the virtual coach is available here: https://doi.org/10.5281/zenodo.6319356.

Analysis:

Our mixed-methods analysis is based on the publicly available Bachelor's theses by Nadyne L. Aretz and Mahira Ali:

• Reasons for wanting to continue or stop using a virtual coach for quitting smoking and increasing physical activity: http://resolver.tudelft.nl/uuid:cb4e9c2e-27fb-4074-95f0-307d4d964be9.
• Relationship with Sam: http://resolver.tudelft.nl/uuid:a79c34d7-ab6f-4385-b566-c0c3302a580e.

In this repository, we provide all code needed to reproduce the analyses reported in our paper. We thereby largely rely on Docker. Please refer to the README-file for more information.

In case of questions about the analysis or data, please contact Nele Albers (n.albers@tudelft.nl).

Context

Cannabis Strains

Content

strain name: Given name of strain

type of strain: indica, sativa, hybrid

rating: user ratings averaged

effects: different effects optained

taste: taste of smoke

description: backround, etc

Acknowledgements

leafly.com

Inspiration

Marijuana may get a bad rep in the media as far as the decriminalization debate goes, but its health benefits can no longer go unnoticed. With various studies linking long-term marijuana use to positive, health-related effects, there are more than just a few reasons to smoke some weed every day.

A study done by the Boston Medical Center and the Boston University of Medicine, examined 589 drug users—more than 8 out of 10 of whom were pot smokers. It determined that “weed aficionados” were no more likely to visit the doctor than non-drug users. If an increased risk of contracting ailments is what’s preventing you from smoking more weed, it looks like you’re in the clear!

One of the greatest medicinal benefits of marijuana is its pain relieving qualities, which make it especially effective for treating chronic pain. From menstruation cramps to nerve pain, as little as three puffs of bud a day can help provide the same relief as synthetic painkillers. Marijuana relieves pain by “changing the way the nerves function,” says Mark Ware, MD and assistant professor of anesthesia and family medicine at McGill University.

Studies have found that patients suffering from arthritis could benefit from marijuana use. This is because naturally occurring chemicals in cannabis work to activate pathways in the body that help fight off joint inflammation.

The global smoking prevalence in was forecast to continuously decrease between 2024 and 2029 by in total *** percentage points. After the ****** consecutive decreasing year, the smoking prevalence is estimated to reach ***** percent and therefore a new minimum in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smoking prevalence in countries like North America and Caribbean.

These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues. The National Health Interview Survey (NHIS) is conducted annually and sponsored by the National Center for Health Statistics (NCHS), which is part of the U.S. Public Health Service. The purpose of the NHIS is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive across the United States population through the collection and analysis of data on a broad range of health topics. The redesigned NHIS questionnaire introduced in 1997 (see National Health Interview Survey, 1997 [ICPSR 2954]) consists of a core that remains largely unchanged from year to year, plus an assortment of supplements varying from year to year. The 2010 NHIS Core consists of three modules: Family, Sample Adult, and Sample Child. The datasets derived from these modules include Household Level, Family Level, Person Level, Injury/Poison Episode Level, Injury/Poison Verbatim Level, Sample Adult Level, and Sample Child level. The 2010 NHIS supplements consist of stand alone datasets for Cancer Level and Quality of Life data derived from the Sample Adult core and Disability Questions Tests 2010 Level derived from the Family core questionnaire. Additional supplementary questions can be found in the Sample Child dataset on the topics of cancer, immunization, mental health, and mental health services and in the Sample Adult dataset on the topics of epilepsy, immunization, and occupational health. Part 1, Household Level, contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit. Parts 2-5 are based on the Family Core questionnaire. Part 2, Family Level, provides information on all family members with respect to family size, family structure, health status, limitation of daily activities, cognitive impairment, health conditions, doctor visits, hospital stays, health care access and utilization, employment, income, participation in government assistance programs, and basic demographic information. Part 3, Person Level, includes information on sex, age, race, marital status, education, family income, major activities, health status, health care costs, activity limits, and employment status. Parts 4 and 5, Injury/Poisoning Episode Level and Injury/Poisoning Verbatim Level, consist of questions about injuries and poisonings that resulted in medical consultations for any family members and contains information about the external cause and nature of the injury or poisoning episode and what the person was doing at the time of the injury or poisoning episode, in addition to the date and place of occurrence. A randomly-selected adult in each family was interviewed for Part 6, Sample Adult Level, regarding specific health issues, the relation between employment and health, health status, health care and doctor visits, limitation of daily activities, immunizations, and behaviors such as smoking, alcohol consumption, and physical activity. Demographic information, including occupation and industry, also was collected. The respondents to Part 6 also completed Part 7, Cancer Level, which consists of a set of supplemental questions about diet and nutrition, physical activity, tobacco, cancer screening, genetic testing, family history, and survivorship. Part 8, Sample Child Level, provides information from an adult in the household on medical conditions of one child in the household, such as developmental or intellectual disabilities, respiratory problems, seizures, allergies, and use of special equipment like hearing aids, braces, or wheelchairs. Parts 9 through 13 comprise the additional Supplements and Paradata for the 2010 NHIS. Part 9, Disability Questions Tests 2010 Level

Context

This dataset is open-source data collected from ScienceDirect under a Creative Commons license. This dataset had collected some information of residents in Mexico, Peru and Colombia about their lifestyle. The original file type is off so I transformed it into a CSV file then did some clean processes and transformations.

Content

id: unique id for each row

Gender: sex - male or female

Age: age

Height: height

Weight: weight

family_history_with_overweight: Has a family member suffered or suffers f from overweight? - yes or no

FAVC: Frequent consumption of high caloric food - yes or no

FCVC: Frequency of consumption of vegetables - Never, Sometimes, Always

NCP: Number of main meals - 1, 2, 3, 4

CAEC: Consumption of food between meals - No, Sometimes, Frequently, Always

SMOKE: Do you smoke - yes o no

CH2O: Consumption of water daily - Less than a litter, between 1 and 2 l, more than 2 l

SCC: Calories consumption monitoring - yes or no

FAF: Physical activity frequency - 0, 1 to 2, 2 to 4, 4 to 5

TUE: Time using technology devices - 0 to 2, 3 to 5, >5

CALC: Consumption of alcohol - no, sometimes, frequently, always

MTRANS: Transportation used - automobile, motorbike, bike, public_transportation, walking

NObeyesdad: Type of obesity - insufficient_weight, normal_weight, overweight-level_i, overweight-level_ii, obesity_type_i, obesity_type_ii, obesity_type_iii BMI: Body mass index