The percentage of adults (aged 16 and over) that responded to the question "How often do you feel lonely?" with "Always or often" or "Some of the time"

Rationale At the beginning of 2018, the Prime Minister highlighted the issue of loneliness, announcing a Minister for Loneliness and committing to develop a national strategy to help tackle loneliness and a national measure for loneliness.

The national strategy, A Connected Society: A Strategy for Tackling Loneliness, was published on 15 October 2018. The commitments made by the Department of Health and Social Care (DHSC) and NHS England in the strategy identify loneliness to be a serious public health concern.

In keeping with the Loneliness Strategy, loneliness is defined here as: “a subjective, unwelcome feeling of lack or loss of companionship. It happens when we have a mismatch between the quantity and quality of social relationships that we have, and those that we want.” This is based on a definition first suggested by Perlman and Peplau in 1981(1).

Loneliness is a feeling that most people will experience at some point in their lives. When people feel lonely most or all of the time, it can have a serious impact on an individual’s well-being and their ability to function in society. Feeling lonely frequently is linked to early deaths and its health impact is thought to be on a par with other public health priorities like obesity or smoking.

Lonely people are more likely to be readmitted to hospital or have a longer stay. There is also evidence that lonely people are more likely to visit a General Practitioner or Accident and Emergency and more likely to enter local authority funded residential care.

At work, higher loneliness among employees is associated with poorer performance on tasks and in a team, while social interaction at work has been linked to increased productivity.

Loneliness can affect anyone of any age and background. It is important to measure loneliness because the evidence on loneliness is currently much more robust and extensive on loneliness in older people, but much less for other age groups including children and young people.

If more people measure loneliness in the same way, we will build a much better evidence base more quickly. That’s why the Prime Minister asked the Office for National Statistics (ONS) to develop national indicators of loneliness for people of all ages, suitable for use on major studies.

When reporting the prevalence of loneliness, ONS advise using the responses from the direct question, “How often do you feel lonely?” The inclusion of the direct loneliness measure in the Public Health Outcomes Framework (PHOF) will help inform and focus future work on loneliness at both a national and local level, providing a focus to support strategic leadership, policy decisions and service commissioning.

In this first set of data on loneliness prevalence at a local authority level, we have merged the two most frequent categories of feeling lonely (often or always and some of the time). This is due to small sample sizes and the limitations of this data will be explained in more detail in the caveats section.

This will be replaced next year by a 2-year pooled dataset which will have large enough sample sizes to report chronic loneliness. Presenting the data this year will help local authorities to work preventatively to tackle chronic loneliness by showing whether a local area has higher than national average levels of loneliness.

(1) Perlman D and Peplau LA (1981) 'Toward a Social Psychology of Loneliness', in Gilmour R and Duck S (eds.), Personal Relationships. 3, Personal Relationships in Disorder, London: Academic Press, pp. 31–56.

Definition of numerator Weighted number of respondents aged 16 and over, with a valid response to the question "How often do you feel lonely" that answered "Always or often" or "Some of the time". Active Lives Adult Survey data is collected November to November.

Definition of denominator Weighted number of respondents aged 16 and over, with a valid response to the question "How often do you feel lonely?".Denominator values in the Download data are unweighted counts. All analyses for this indicator have been weighted to be representative of the population of England.Active Lives Adult Survey data is collected November to November.

Caveats

Due to the sample size at local authority level, the "often or always" category is merged with the next most severe category of loneliness (people who respond as feeling lonely “some of the time”).

Standard practice is to report the two categories separately. However, data from other sources shows a degree of volatility in the ratio between these categories at the local authority (LA) level.

Therefore, there is a risk that when two local authorities are both reported as having 25% of people feeling lonely (often or always combined with some of the time), the actual figures for "often or always" might differ significantly. For example, one LA might have 24% often and always while another has only 3%, which would not be apparent in the combined category.

This could lead to underestimation or overestimation of chronic loneliness levels by local authorities.

Context

Available research shows that social connections are important for our well-being. Having support from family and friends is important for our happiness and health, and is also instrumental to our ability to share information, learn from others, and seize economic opportunities.

In this dataset we can explore data on loneliness and social connections across countries and over time, and review available evidence on how and why social connections and loneliness affect our health and emotional welfare, as well as our material well-being.

Despite the fact that there is a clear link between social connections and well-being, more research is needed to understand causal mechanisms, effect sizes and changes over time.

Researches show here, oversimplified narratives that compare loneliness with smoking, or that claim we are living a ‘loneliness epidemic’, are wrong and unhelpful.

Content

Dr. Vivek Murthy, former Surgeon General of the United States, recently wrote: “Loneliness and weak social connections are associated with a reduction in lifespan similar to that caused by smoking 15 cigarettes a day”.

This ‘15 cigarettes a day’ figure has been reproduced and reported in the news many times, under headlines such as “Loneliness is as lethal as smoking 15 cigarettes per day”.

It is indeed quite a shocking comparison since around 7 million deaths globally are attributed to smoking every year, and back-of-the-envelope calculations published in medical journals say one cigarette reduces your lifespan by 11 minutes.

Here we can dig deeper to try to understand what the data and research tell us about the link between social relations and health. In a nutshell, the reading of the evidence is as follows:

• There is a huge amount of evidence showing individuals who report feelings of loneliness are more likely to have health problems later in their life.
• There is credible theory and explanation of biological mechanisms, whereby isolation can set off unconscious surveillance for social threat, producing cognitive biases, reducing sleep and affecting hormones.
• It’s very likely there is a causal link, but there is no credible experimental evidence that would allow us to have a precise estimate of the magnitude of the causal effect that loneliness has on key metrics of health, such as life expectancy.
• The fact that we struggle to pin down the magnitude of the effect of loneliness on health doesn’t mean we should dismiss the available evidence. But it does show that more research is needed.

Observational studies: A first look at the data

Measuring loneliness

Psychologists and social neuroscientists often refer to loneliness as painful isolation. The emphasis on painful is there to make a clear distinction between solitude – the state of being alone – and subjective loneliness, which is the distressing feeling that comes from unmet expectations of the types of interpersonal relationships we wish to have.

Researchers use several kinds of data to measure solitude and loneliness. The most common source of data are surveys where people are asked about different aspects of their lives, including whether they live alone, how much time they spend with other people in a given window of time (e.g. ‘last week’) or specific context (e.g. ‘at social events, clubs or places of worship’); and whether they experience feelings of loneliness (e.g. ‘I have no-one with whom I can discuss important matters with’). Researchers sometimes study these survey responses separately, but often they also aggregate them in a composite index.

Surveys confirm that people respond differently to questions about subjective loneliness and physical social isolation, which suggests people do understand these as two distinct issues.

The fact that we see such high levels of loneliness, with substantial divergence across countries, explains why this is an important and active research area. Indeed, there are literally hundreds of papers that have used survey data to explore the link between loneliness, solitude, and health. Below is an overview of what these studies find.

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Acknowledgements

https://ourworldindata.org/

Inspiration

• In this dataset we can explore data on loneliness and social connections across countries and over time, and review available evidence on how and why social connections and loneliness affect our health and emotional welfare, as well as our material well-being.

Some references

• https://population.un.org/Household/index.html#/countries/300
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547349/

In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people’s lives are beginning to emerge. To inform policies for the current and/or future pandemics, particularly those involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term effects of lockdown on people’s physical and mental health. An online questionnaire launched on 17 April 2020 was completed by 2,276 people from 66 countries. Focusing on the UK sample (N=382), 10 aggregated variables representing participant’s living environment, physical and mental health were used to train a RandomForest model to predict the week of survey completion. Using an index of importance to identify the best predictor among the 10 variables, self perceived loneliness was identified as the most influential variable. Subsequent statistical analysis showed a significant U-shaped curve for loneliness levels, with a decrease during the 4th and 5th lockdown weeks. The same pattern was replicated in the Greek sample (N = 129). This suggests that for the very first period of time, the adopted lockdown measures affected people’s evaluation of their social support leading to a decreased sense of loneliness.

Percentage of persons aged 15 years and over by frequency with which they feel lonely, by gender, for Canada, regions and provinces.

Percentage of persons aged 15 years and over by frequency with which they feel lonely, by gender and other selected sociodemographic characteristics: age group; immigrant status; visible minority group; Indigenous identity; persons with a disability, difficulty or long-term condition; LGBTQ2+ people; highest certificate, diploma or degree; main activity; and urban and rural areas.

Young adults between the ages of 18-24 are the loneliest age-group in the UK and other Western countries yet little is known about the causes and experiences of their loneliness. In particular, young adults of lower socio-economic status (SES) living in the most deprived areas are loneliest in the United Kingdom. Therefore, the aim of the data collected included exploring the causes and experiences of loneliness in young adults with these qualities and circumstances from their own perspective (part 1). The data also includes exploration of how these young adults conceptualise and experience their neighbourhood and how they impact upon their loneliness and social connectedness (part 2). This is qualitative data collected from forty-eight participants between June and August 2019 from 48 participants. A recruitment agency was employed to access the quota sample required. Participants were living in/recruited from four of the most deprived boroughs of London, UK: Newham (n = 16), Hackney (n = 16), Tower Hamlets (n = 16), and Barking & Dagenham (n = 16). There were two parts to the study. Part 1 included the free association task, in which participants were first presented with a piece of paper that contained a grid of four empty boxes and asked to express what they associated with “the experience of loneliness” by way of images and/or words. They were further instructed to elaborate one image/idea per box. After completion of the free association task, participants were asked to elaborate on the content of each box, in turn, in an interview. This started with “can we talk about what you have put in box 1, please?” Prompts such as “can you tell me more about that?” were used to ensure thoughts and feelings about the experience of loneliness were fully explored and emerged naturalistically without injection of content via researcher questioning. The interviews lasted for an average of 60 min. Most interviews took place at the participants’ homes though some at a local café, park or similar places if home was not an option. As for part 2, which was immediately after part 1, participants were asked to write or draw one place in their neighbourhood where they feel most socially connected and one where they feel most lonely. Beneath each of the two places they were further instructed to write what it is about that place that makes them feel the way they do. Participants were then asked to elaborate on the content of the association they had produced in an interview. This began with “can we talk about what you’ve put in box one (for the most socially connected place), please?” Prompts including “can you tell me more about that?” and “how does that make you feel in this space?” were used to ensure respondents’ thoughts and feelings about their chosen places were fully explored and emerged naturalistically without input from the researcher questioning. The same process ensued for the second box asking about the loneliest place. Each interview lasted between 20 and 30 minutes. The corresponding image/output for each participant for both parts 1 and 2 is presented.

Personal well-being, loneliness and what people in Great Britain felt were important issues; indicators from the Opinions and Lifestyle Survey (OPN).

This survey aims to see if people are feeling more lonely now compared to before the COVID-19 pandemic. The survey also asks if people have been following COVID-19 restrictions in their area and whether or not they would support a second national lockdown in their country if one was deemed necessary.

PurposeSocial restrictions and government-mandated lockdowns implemented worldwide to kerb the SARS-CoV-2 virus disrupted our social interactions, behaviours, and routines. While many studies have examined how the pandemic influenced loneliness and poor mental health, such as depression, almost none have focussed on social anxiety. Further, how the change in social restrictions affected change in mental-health and well-being has not yet been explored.MethodsThis is a longitudinal cohort study in community dwellers who were surveyed across three timepoints in the first six months of the pandemic. We measured loneliness, social anxiety, depression, and social restrictions severity that were objectively coded in a sample from Australia, United States, and United Kingdom (n = 1562) at each time point. Longitudinal data were analysed using a multivariate latent growth curve model.ResultsLoneliness reduced, depression marginally reduced, and social anxiety symptoms increased as social restrictions eased. Specific demographic factors (e.g., younger age, unemployment, lower wealth, and living alone) all influenced loneliness, depression, and social anxiety at baseline. No demographic factors influenced changes for loneliness; we found that those aged over 25 years reduced faster on depression, while those younger than 25 years and unemployed increased faster on social anxiety over time.ConclusionWe found evidence that easing social restrictions brought about additional burden to people who experienced higher social anxiety symptoms. As country-mandated lockdown and social restrictions eased, people are more likely report higher social anxiety as they readjust into their social environment. Mental health practitioners are likely to see higher levels of social anxiety in vulnerable communities even as social restrictions ease.

This article explores the often-hidden experience of unwanted loneliness among youth in an increasingly digital world, where opportunities for connection abound yet true intimacy remains elusive. Using a reflexive ethnographic approach, this study examines how young people in Alicante, Spain, navigate loneliness within both digital and physical spaces. The research integrates multiple methods, including: (1) questionnaires administered to 268 participants, (2) in-depth interviews with 16 young people who later took part in focus groups, (3) UCLA Loneliness Scale assessments with 44 participants before and after the study, and (4) ethnographic field notes. This study focuses on two key dimensions of loneliness: unidentified loneliness (when individuals do not recognize their own isolation) and unacknowledged loneliness (when they feel alone but refuse to verbalize it). Through the life stories of five focal participants, the findings reveal how cultural, social, and gendered dynamics shape youth loneliness, often masking it behind digital interactions. Reflexive ethnography proves essential in engaging deeply with these narratives, shedding light on the contradictions between hyperconnectivity and social disconnection. Ultimately, this research highlights the need for safe, authentic spaces that allow young people to confront and articulate their loneliness without stigma.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

This study examines the relationship between virtual social interaction and people’s social behaviors and psychology using algorithm matching technologies and questionnaire surveys. The focus is on interpersonal communication on virtual social platforms. The findings indicate that engaging in virtual social networking is often accompanied by a high level of loneliness. Users who experience social anxiety in the real world tend to feel more lonely, and this loneliness is exacerbated by presenting an unreal version of oneself and having distrust in virtual social networking. Users with higher anxiety and loneliness levels are more likely to use the algorithm matching function of virtual social networking, engage in false self-presentation, and have less trust in the platform. Since the inherent flaws of virtual social networking cannot be eliminated solely through algorithm matching, a potential solution is to introduce more offline to online social functions for strangers. This exploration of actual matching on social platforms may help reduce users’ loneliness.

Updated to include all season up to and including season 10.

This dataset contains data from the TV series Alone collected and shared by Dan Oehm. As described in Oehm's blog post](https://gradientdescending.com/alone-r-package-datasets-from-the-survival-tv-series/), in the survival TV series ‘Alone,' 10 survivalists are dropped in an extremely remote area and must fend for themselves. They aim to last 100 days in the Artic winter, living off the land through their survival skills, endurance, and mental fortitude.

This package contains four datasets:

• survivalists.csv: A data frame of survivalists across all 9 seasons detailing name and demographics, location and profession, result, days lasted, reasons for tapping out (detailed and categorised), and page URL.

• loadouts.csv: The rules allow each survivalist to take 10 items with them. This dataset includes information on each survivalist's loadout. It has detailed item descriptions and a simplified version for easier aggregation and analysis

• episodes.csv: This dataset contains details of each episode including the title, number of viewers, beginning quote, and IMDb rating. New episodes are added at the end of future seasons.

• seasons.csv: The season summary dataset includes location, latitude and longitude, and other season-level information. It includes the date of drop-off where the information exists.

Acknowledging the Alone dataset

Dan Oehm:

Alone data package: https://github.com/doehm/alone Alone data package blog post: https://gradientdescending.com/alone-r-package-datasets-from-the-survival-tv-series/ Examples of analyses are included in Dan Oehm's blog post.

References

History: https://www.history.com/shows/alone/cast

Wikipedia: https://en.wikipedia.org/wiki/Alone_(TV_series)

Wikipedia (episodes): https://en.wikipedia.org/wiki/List_of_Alone_episodes#Season_1_(2015)_-_Vancouver_Island

The Opinions and Lifestyle Survey is a Face-to-face interview of individuals in households. Data is available for Government Office Regions. The data for these tables is from the Well-Being Module, April, July and October, 2014. They cover a range of subjective well-being indicators that measure the respondents opinions using a score out of 10. The questions analysed here are: Overall, how satisfied are you with your life nowadays? Overall, to what extent do you feel that the things you do in your life are worthwhile? Overall, how happy did you feel yesterday? Overall, how anxious did you feel yesterday? Overall, how satisfied are you with your relationships with family, including spouse/partner? Overall, how satisfied are you with your relationships with friends? Overall, how satisfied are you with your physical health? Overall, how satisfied are you with your mental well-being? Overall, how satisfied are you with the well-being of your child/children? Overall, how satisfied are you with your financial situation? Overall, how satisfied are you with your work situation? Overall, how satisfied are you with your commute to work? How satisfied are you with the the time you spend on your paid work and on other aspects of your life? To what extent do you feel most people can be trusted? To what extent do you feel you have any relatives, friends or neighbours that you can ask for help? On a scale of 0 to 10 how lonely do you feel in your daily life? Overall, how satisfied are you with the local area where you live? How satisfied are you with the public gardens/parks etc. in the local area where you live? To what extent do you feel that you are involved in the local area where you live? To what extent do you feel you belong in the local area where you live? How safe would you feel walking alone in this local area after dark? Overall, how optimistic do you feel about the next 12 months? How satisfied are you with living in this country? How optimistic are you about the future of this country?

This study examines the relationship between virtual social interaction and people’s social behaviors and psychology using algorithm matching technologies and questionnaire surveys. The focus is on interpersonal communication on virtual social platforms. The findings indicate that engaging in virtual social networking is often accompanied by a high level of loneliness. Users who experience social anxiety in the real world tend to feel more lonely, and this loneliness is exacerbated by presenting an unreal version of oneself and having distrust in virtual social networking. Users with higher anxiety and loneliness levels are more likely to use the algorithm matching function of virtual social networking, engage in false self-presentation, and have less trust in the platform. Since the inherent flaws of virtual social networking cannot be eliminated solely through algorithm matching, a potential solution is to introduce more offline to online social functions for strangers. This exploration of actual matching on social platforms may help reduce users’ loneliness.

This study examines the relationship between virtual social interaction and people’s social behaviors and psychology using algorithm matching technologies and questionnaire surveys. The focus is on interpersonal communication on virtual social platforms. The findings indicate that engaging in virtual social networking is often accompanied by a high level of loneliness. Users who experience social anxiety in the real world tend to feel more lonely, and this loneliness is exacerbated by presenting an unreal version of oneself and having distrust in virtual social networking. Users with higher anxiety and loneliness levels are more likely to use the algorithm matching function of virtual social networking, engage in false self-presentation, and have less trust in the platform. Since the inherent flaws of virtual social networking cannot be eliminated solely through algorithm matching, a potential solution is to introduce more offline to online social functions for strangers. This exploration of actual matching on social platforms may help reduce users’ loneliness.

Data come from the follow-up of the main study “Inclusive ageing in place” (IN-AGE), regarding frail older people aged 65 years and over (males and females). The main study was a cross-sectional qualitative survey carried out in 2019 by face-to-face interviews to frail older people without cognitive impairment, and living at home, alone or with a private personal care assistant (PCA), in three Italian Regions: Lombardy (North), Marche (Centre) and Calabria (South). Both peripheral/degraded areas of urban sites and fragile rural locations were included, with regard to social and material vulnerability aspects (e.g. high presence of frail older people living alone, poor provision of services). The follow up was carried out in July-September 2020, and it was aimed to explore and compare effects of lockdown, due to the first wave of the COVID-19 pandemic (February-May 2020), on frail older people living alone at home in Brescia and Ancona, two urban cities located respectively in the Northern and Central Italy. This country was the Western epicenter of the first wave of the pandemic, that differently affected the two cities as for infections, with a more severe impact on the former one. The dataset (41 respondents, vs 48 in the main survey) regards available social networks (family, friends, neighbors), use of communication technology (mobile, smartphone, PC/tablet), and perceived loneliness. Also, fears due to the pandemic were reported. A semi-structured interview was administered by telephone due to social distancing imposed by the pandemic. Participants were asked to report and motivate possible changes due to the pandemic. A simple quantitative analysis (frequency distribution/bivariate analysis) of closed responses was carried out by using Microsoft Excel software 2019. Findings showed that seniors increased mainly telephone (TEL) contacts especially in Ancona. In both cities fears for the infection emerged too, and mainly in Ancona than Brescia several cases of worsened perceived loneliness were detected. Despite the exploratory nature of the study, with a not-representative (small) sample of the target population, and notwithstanding some differences among cities, findings stressed the risk of isolation and loneliness of seniors living alone. This risk was buffered by the use of communication technology during the lockdown, but more interventions allowing a sustainable healthy ageing (HA) in place, enhancing healthy behaviors especially in emergency situations, are needed. The dataset is provided in open format (xlsx) and includes the following: a “numeric” dataset regarding the unlabeled dimensions used for statistics elaboration; a codebook with both the complete variables list and variables labels we used. The dataset was produced within the framework of the IN-AGE project, funded by Fondazione Cariplo, Grant N. 2017-0941.

This study examines the relationship between virtual social interaction and people’s social behaviors and psychology using algorithm matching technologies and questionnaire surveys. The focus is on interpersonal communication on virtual social platforms. The findings indicate that engaging in virtual social networking is often accompanied by a high level of loneliness. Users who experience social anxiety in the real world tend to feel more lonely, and this loneliness is exacerbated by presenting an unreal version of oneself and having distrust in virtual social networking. Users with higher anxiety and loneliness levels are more likely to use the algorithm matching function of virtual social networking, engage in false self-presentation, and have less trust in the platform. Since the inherent flaws of virtual social networking cannot be eliminated solely through algorithm matching, a potential solution is to introduce more offline to online social functions for strangers. This exploration of actual matching on social platforms may help reduce users’ loneliness.

The online overview offers comprehensive metadata on the EVS datasets and variables.

The variable overview of the four EVS waves 1981, 1990, 1999/2000, and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

This overview can be found at: Online Variable Overview.

Moral, religious, societal, political, work, and family values of Europeans.

Themes: Feeling of happiness; state of health; ever felt: very excited or interested, restless, proud, lonely, pleased, bored, depressed, upset because of criticism; when at home: feeling relaxed, anxious, happy, aggressive, secure; respect and love for parents; important child qualities: good manners, politeness and neatness, independance, hard work, honesty, felling of responsibility, patience, imaginantion, tolerance, leadership, self-control, saving money, determination perseverance, religious faith, unselfishness, obedience, loyalty; attitude towards abortion; way of spending leisure time: alone, with family, with friends, in a lively place; frequency of political discussions; opinion leader; volentary engagement in: welfare service for elderly, education, labour unions, polititcal parties, human rights, environment, professional associations, youth work, consumer groups; dislike being with people with different ideas; will to help; characterisation of neighbourhood: people with a ciminal record, of a different race, heavy drinkers, emotionally unstable people, immigrants or foreign workers, left-wing or right-wing extremists, people with large families, students, unmarried mothers, members of minority religious sects or cults; general confidence; young people trust in older people and vice versa; satisfaction with life; freedom of choice and control; satisfaction with financial situation of the household; financial situation in 12 months; important values at work: good pay, not too much pressure, job security, a respected job, good hours, opportunity to use initiative, generous holidays, responsibility, interesting job, a job that meets one´s abilities, pleasant people, chances for promotion, useful job for society, meeting people; look forward to work after weekend; pride in one´s work; exploitation at work; job satisfaction; freedom of decision taking in job; behaviour at paid free days: find extra work, use spare time to study, spend time with family and friends, find additional work to avoid boredom, use spare time for voluntary work, spend time on hobbies, run own business, relaxing; fair payment; preferred management type; attitude towards following instructions at work; satisfaction with home life; sharing attitudes with partner and parents: towards religion, moral standards, social attitudes, polititcal views, sexual attitudes; ideal number of children; child needs a home with father and mother; a woman has to have children to be fulfilled; sex cannot entirely be left to individual choice; marriage as an out-dated institution; woman as a single parent; enjoy sexual freedom; important values for a successful marriage: faithfulness, adequate income, same social background, respect and appreciation, religious beliefs, good housing, agreement on politics, understanding and tolerance, apart from in-laws, happy sexual relationship, sharing household chores, children, taste and interests in common; accepted reasons for divorce; main aim of imprisonment; willingness to fight for the own country; fear of war; expected future changes of values; opinion about scientific advances; interest in politics; political action: signing a petition, joining in boycotts, attending lawful demonstrations, joining unofficial strikes, occupying buildings or factories, damaging things and personal violence; prefence for freedom or equality; self-positioning on a left-right scale; basic kinds of attitudes concerning society; confidence in institutions: churches, armed forces, education system, the press, labour unions, the police, parliament, the civil services, major companies and the justice system; living day to day because of uncertain future; party preference and identification; regularly reading of a daily newspaper; frequency of TV watching; opinion on terrorism; thinking about meaning and purpose of life; feeling that life is meaningless; thoughts about dead; good and evil in everyone; regret having done something; worth risking life for: country, anoth...