Video
Why Purpose and Discipline Promote Psychological Well-BeingWHR and psychological well-being -
All of them were found to be significantly associated with the PGWB scores at follow-up after adjusting for other variables except for social support, which did not reach statistical significance in models with anxiety, self-control and general health.
On average, subjects who had higher scores in questions about being nervous and being depressed at baseline had significantly lower scores for every PGWB assessment than those who had lower scores in these two questions. On the other hand, subjects who had higher scores in the question about social support at baseline had significantly higher scores in some PGWB assessments.
No interaction between baseline psychological variables and work type was found. When the relationship between changes of cardiovascular disease risk profiles and life event were investigated, no significant association was found for changes in BMI, WHR or cholesterol level CHO see Table 5.
Association between cardiovascular risk profile change and baseline life events a. Each column represents a regression model using one of the cardiovascular disease risk variable as the outcome variable. Odds ratio OR of independent variables included in the model were reported.
In each model, it turned out that there was at most one life event variable significantly associated with the score. Interaction between negative life events and work type was found significant, coefficient 5.
The only association between changes of risk profile and life events was present between changes in cholesterol-HDL ratio CHR and experiencing a strongly stressful life event.
Workers who experienced strongly stressful life events at baseline were more likely to have increased the CHR over the period between baseline and follow-up than workers who did not OR 1.
No interaction between strongly stressful life events and other variables was found. Being single at baseline was found to be associated with changes in BMI, WHR and CHO. In the 5-year follow-up investigation of workers at Volvo, Sweden, we found increased cardiovascular risk in blue-collar compared to white-collar workers.
Blue-collar workers were more often smokers, had higher WHR and higher triglycerides. Partially confirming what has been reported in other studies, Volvo blue-collar workers on average reported slightly worse general health and lower self-control, but less anxiety than did white-collar workers [ 8 — 10 ].
During the s, the Volvo Company was successful in implementing a strongly decentralized organization [ 11 ]. Certain tasks were delegated to blue-collar work groups such as increased responsibility for planning and coordination which resulted in a more varied work content and greater overall responsibility.
This situation may not help to explain the worse health experienced by the blue-collar workers as described in other studies [ 12 ]. No change of a biological cardiovascular risk factor was seen as a consequence of work-related life events. In the current study, experience of negative life events was significantly negatively associated with self-control and this association was modified by work type, with a stronger effect in blue-collar workers.
Aro and Hasan [ 8 ] suggested that psychosocial stress is mostly related to indicators of morbidity such as perceived health, bodily symptoms and sickness behaviour.
Furthermore, social support seemed to be a factor which protected, over 5 years, against decreasing psychological well-being in both blue- and white-collar workers. The preventive effect of good social support has been shown in many studies [ 13 — 18 ].
Of those, about one-third were work-related life events mostly mergers and organizational changes. As found in this and other studies, stressful and strongly stressful life events seemed to have lasting consequences in a 5-year perspective [ 19 ].
Strongly stressful events were negatively associated with vitality, negative life events with lower self-control, and work-related events predicted lower general health and anxiety at follow-up independent of type of work.
There are few studies which have investigated the impact of work-related life events on health [ 20 ]. Tennant [ 21 ] showed psychological disorders, especially depression, to be increasingly caused by work-related stressors. In another Swedish study, job—stress factors were not related to coronary risk factors [ 22 ].
We suggest that consideration should be given to intensified psychological support during changes of working conditions so as to prevent adverse health effects.
This may indicate that these complaints are stable over time, and that occupational health staff can estimate the psychological status of the employees without time-consuming questionnaires.
In addition to our findings, the high compliance due to the occupational health setting, the width of data collected and the long-term follow-up add to the strength of this study. While the focus on middle-age men was necessary from a cardiovascular end-point perspective, not including women is a weakness.
As shown in a previous study of Swedish women, low socio-economic status, as assessed by low occupational level, was associated with increased cardiovascular risk and explained by lack of self-control in low status women [ 23 ].
In conclusion, when measuring traditional risk factors for cardiovascular disease, it seems justified to stress the importance of dimensions of psychological well-being and to integrate somatic, social and psychological aspects into a common concept of work-related distress [ 24 — 30 ].
The baseline investigations at Volvo were performed by Siv Thornell, Lisbeth Paffrath and Pia Johannisson. The third step interviews were conducted by Siv Thornell, Annie Jansson, Caroline Karlsson, Inga-Greta Wittlöv and Pia Lindén.
Sandra Ross assisted with the language revision. Baigi A, Fridlund B, Marklund B, Oden A. Cardiovascular mortality focusing on socio-economic influence: the low-risk population of Halland compared to the population of Sweden as a whole.
Public Health ; : — Rosengren A, Hawken S, Ounpuu S et al. Lancet ; : — Rose G, Bengtsson C, Dimberg L, Kumlin L, Eriksson B. Life events, mood, mental strain and cardiovascular risk factors in Swedish middle-aged men.
Occup Med Lond ; 48 : — Casey AL, Masuda M, Holmes MG. Quantitative study of recall of life events. J Psychosom Res ; 11 : — Spielberger CD. State-Trait Anger Expression Inventory; Professional Manual.
Odessa, FL: Psychological Assessment Resources, Simon A, Dimberg L, Levenson J et al. Comparison of cardiovascular risk profile between male employees of two automotives companies in France and Sweden.
Eur J Epidemiol ; 13 : — Dupuy HJ. Psychological General Well-Being PGWB index. In: Wenger NK, Masson ME, Furberg CD, Elinson J, eds. Assessment of Quality of Life in Clinical Trials of Cardiovascular Therapies. USA: Le Jacq Publ Inc.
Aro S, Hasan J. Occupational class, psychosocial stress and morbidity. Ann Clin Res ; 19 : 62 — Siegrist J, Matschinger H, Cremer P, Seidel D. Atherogenic risk in men suffering from occupational stress.
Atherosclerosis ; 69 : — Chandola T. Social inequality in coronary heart disease: a comparison of occupational classifications. Soc Sci Med ; 47 : — Dimberg L.
Overview of ergonomic research and some practical applications in Sweden. In: Bhattacharya A, McGlothlin J, eds. Occupational Ergonomics. New York: Marcel Decker Inc.
Siegrist J, Peter R, Junge A, Cremer P, Seidel D. Low status control, high effort at work and ischemic heart disease: prospective evidence from blue-collar men. Soc Sci Med ; 31 : — Rose G, Sivik T, Delimar N. Gender, psychological well-being and somatic cardiovasular risk factors.
Integr Physiol Behav Sci ; 29 : — Orth-Gomer K, Rosengren A, Wilhelmsen L. Lack of social support and incidence of coronary heart disease in middle-aged Swedish men. Psychosom Med ; 55 : 37 — Rosengren A, Orth-Gomer K, Wedel H, Wilhelmsen L.
Stressful life events, social support, and mortality in men born in Br Med J ; : — Greenwood DC, Muir KR, Packham CJ, Madeley RJ. Coronary heart disease: a review of thee role of psychosocial stress and social support.
J Public Health Med ; 18 : — Tennant C. Life stress, social support and coronary heart disease. Aust N Z J Psychiatry ; 33 : — Eng PM, Rimm EB, Fitzmaurice G, Kawachi I. Social ties and change in social ties in relation to subsequent total and cause-specific mortality and coronary disease incidence in men.
Am J Epidemiol ; : — Gardner RM, Ostrowski TA, Pino RD, Morell JA Jr, Kochevar R. Familiarity and anticipation of negative life events as moderator variables in predicting illness.
J Clin Psychol ; 48 : — Levi L. Occupational stress. Spice of life or kiss of death? Am Psychol ; 45 : — Work-related stress and depressive disorders. J Psychosom Res ; 51 : — Jonsson D, Rosengren A, Dotevall A, Lappas G, Wilhelmsen L. Job control, job demands and social support at work in relation to cardiovascular risk factors in MONICA , Goteborg.
J Cardiovasc Risk ; 6 : — Wamala SP, Mittleman MA, Schenk-Gustafsson K, Orth-Gomer K. Potential explanations for the educational gradient in coronary heart disease: a population-based case-control study of Swedish women.
Am J Public Health ; 89 : — Theorell T. Relationships between critical life events, job stress and cardiovascular illness. Rev Epidemiol Sante Publique ; 35 : 36 — Siegrist J.
Emotions and health in occupational life: new scientific findings and policy implications. Patient Educ Couns ; 25 : — Grayham DA.
Work-related stress: implications for the employer. J R Soc Health ; : 81 — Schmahl FW. Some theoretical remarks regarding the integration of somatic and psychosocial risk factors of coronary artery disease in preventive programmes in occupational medicine.
Int J Occup Med Environ Health ; 11 : — Peter R, Siegrist J. Psychological work environment and the risk of coronary heart disease. Int Arch Occup Environ Health ; 73 Suppl. Work stress and coronary heart disease. Data sources such as Twitter and Reddit have different selection and presentation biases and are generally noisy, with shifting patterns of language use over time.
Analyzing social media data is not without challenges. As data sources, they are relatively new to the scientific community. To realize the potential of social media-based estimation of well-being constructs, it is essential to analyze social media data in a way that maximizes the signal-to-noise ratio.
Despite the literature being relatively nascent, the methods for analyzing social media language to assess psychological traits and states are maturing. See Table 5. The first axis of development — data collection and aggregation strategies — can be categorized into three generations which have produced stepwise increases in prediction accuracies and reductions in the impact of sources of error, such as bots detailed in Table 2 :.
Gen 1: Aggregation of random posts i. Gen 2: Person-level sampling and aggregation of posts, with the potential to correct for sample biases i. Gen 3: Aggregation across a longitudinal cohort design i. The second axis of development — language models — describes how language is analyzed; that is, how numerical well-being estimates are derived from language.
We argue that these have advanced stepwise, which we refer to as Levels for organizational purposes. These iterations improve the accuracy with which the distribution of language use is mapped onto estimates of well-being see Table 3 for a detailed overview.
The Levels have advanced from closed-vocabulary dictionary-based methods to machine learning and large language model methods that ingest the whole vocabulary. Level 1: Closed-vocabulary approaches use word-frequency counts that are derived based on defined or crowdsourced annotation-based dictionaries, such as for sentiment e.
Level 2: Open-vocabulary approaches use data-driven machine learning predictions. Here, words, phrases, or topic features e.
Generations and Levels increase the complexity with which data is processed and analyzed — and typically also, as we detail below, the accuracy of the resultant well-being estimates. The out-of-sample prediction accuracies of the machine learning models demonstrate empirically that these biases can be handled.
The language samples from social media are noisy and can suffer from a variety of biases, and unfamiliar audiences sometimes dismiss social-media-based measurement on these grounds.
We discuss them in relation to selection, sampling, and presentation biases. Selection biases include demographic and sampling biases. Demographic biases — i. Generally, social media platforms differ from the general population; Twitter users, for example, tend to be younger and more educated than the general U.
Sampling biases involve concerns that a few accounts generate the majority of content, [44] including super-posting social bots, and organizational accounts, which in turn have a disproportionate influence on the estimates.
Robust techniques to address these sampling biases, such as person-level aggregation, largely remove the disproportionate impact of super-posting accounts.
As discussed below, machine-learning-based estimates Level 2 reliably converge with non-social-media assessments, such as aggregated survey responses out-of-sample convergence above Pearson r of.
Taken together, despite the widespread prima facie concern about selection, sampling, and presentation biases, the out-of-sample prediction accuracies of the machine learning models demonstrate empirically that these biases can be handled [49] — as we discuss below.
Percentage of adults using each social media platform within each demographic group in the US [50]. Aggregate posts geographically, extract language features, use machine learning to predict outcomes cross-sectionally. Suffers from the disproportionate impact of super-posting accounts e. For longitudinal applications: A new random sample of individuals in every temporal period.
Person-level aggregation [51] and poststratification to adjust the sample towards a more representative sample e. Addresses the impact of super-posting social media users e. With post-stratification: known sample demographics and correction for sample biases.
Increases measurement reliability and external validity. Robust mental health assessments in time and space through social media language analyses. Defined resolution across time and space e. Most words have multiple words senses, which human raters do not anticipate; e.
Dictionaries without weights like LIWC may insufficiently capture differences in valence between words e. Data-driven, bottom-up, unsupervised methods rely on the statistical patterns of word use rather than subjective evaluations.
Produces state-of-the-art representations of text. Takes context into account. Disambiguates word meaning. Semantic biases : transformers models get their representations of text from the structure of the training dataset corpus that is used; this involves the risk of reproducing existing biases in the corpus N.
Liu et al. On social media, bots are accounts that automatically generate content, such as for marketing purposes, political messages, and misinformation fake news.
How bots impact measurement of well-being using social media The content generated by bots should not, of course, influence the assessment of human well-being. While bots compose fewer original tweets than humans, they have been shown to express sentiment and happiness patterns that differ from the human population.
Finally, work has shown that bots exhibit extremely average human-like characteristics, such as estimated age and gender. With modern machine learning systems, bots can be detected and removed. The following methodological review is organized by generations of data aggregation methods Gen 1, 2, and 3 , which we observed to be the primary methodological choice when working with social media data.
But within these generations, the most important distinction in terms of reliability is the transition from dictionary-based word-level Level 1 approaches to those relying on machine learning to train language models Level 2 and beyond.
Initially, a prototypical example of analyzing social media language for population assessments involved simply aggregating posts geographically or temporally — e. for a given day. In this approach, the aggregation of language is carried out based on a naive sampling of posts — without taking into account the people writing them see Fig.
The language analysis was typically done using a Level 1 closed-vocabulary approach — for example, the LIWC positive emotion dictionary was applied to word counts. Later, Level 2 approaches have been used with random samples of tweets, such as open-vocabulary approaches based on machine learning; this includes using modern sentiment systems or predicting county-level Gallup well-being survey outcomes directly using machine learning cross-sectionally.
Example of a Gen 1 Twitter pipeline: A pseudo-random collection of tweets is aggregated directly to the county level. In the U.
This presented some early evidence that using Level 1 closed-vocabulary methods here in the form of LIWC dictionaries can yield unreliable and implausible results. However, applying the LabMT dictionary to geographically aggregated Twitter language can yield unreliable and implausible results.
Some researchers examined spatially high-resolution well-being assessments of neighborhoods in San Diego using the LabMT dictionary [72] see Fig. The estimates were, however, negatively associated with self-reported mental health at the level of census tracts and not at all when controlling for neighborhood factors such as demographic variables.
Other researchers found additional implausible results; using person-to-county-aggregated Twitter data [73] Gen 2 , LabMT estimates of 1, US counties and Gallup-reported county Life Satisfaction have been observed to anti-correlate, which is further discussed below see Fig 5.
Outside in the U. To date, Gen 1 approaches have been applied broadly, in different countries, with different languages. In China, it has been used for assessing positive and negative emotions e. com by bloggers from to Gen 1, Level 1.
The validation involved examining the face validity of the resulting time series by comparing the highs and lows of the index with national events in China. In Turkey, sentiment analysis has been applied to 35 million tweets posted between and by more than 20, individuals Gen 1, Level 1.
In general, applying dictionary-based Level 1 approaches to random Twitter samples Gen 1 has been the most common choice across research groups around the world, but results have generally not been validated in the literature beyond the publication of maps time series. States [78] and C Census tracts.
Level 2 -based estimates , such as those based on Swiss Chocolate — a modern Sentiment system derived through machine learning — yield consistent results. More advanced language analysis approaches, including Level 2 machine learning and Level 3 large language models , have been applied to random Twitter feeds.
For example, random tweets aggregated to the U. However, it is susceptible to many types of noise, such as changing sample composition over time, inconsistent posting patterns, and the disproportionate impact of super-posting accounts e. Measurement accuracies can be increased substantively by improving the sampling and aggregation methods, especially by aggregating tweets first to the person level.
Person-level sampling addresses the disproportionate impact that a small number of highly active accounts can have on geographic estimates. In addition to person-level sampling, demographic person characteristics such as age and gender can be estimated through language, and on their basis, post-stratification weights can be determined, which is similar to the methods used in representative phone surveys see Fig.
This approach shows remarkable improvements in accuracy see Fig. One of the earliest examples of Gen 2 evaluated the predictive accuracy of community-level language as measured with Level 1 dictionaries such as LIWC across 27 health-related outcomes, such as obesity and mentally unhealthy days.
This person-focused aggregation significantly outperformed in terms of out-of-sample predictive accuracy the Gen 1 aggregation methods with an accuracy average Pearson r across all 27 health outcomes of. User-level aggregation. Some researchers have proposed a Level 2 person-centered approach, which first measures word frequencies at the person-level and then averages those frequencies to the county-level, effectively yielding a county language average across users.
Additional work has shown that Gen 2 language estimates show how external validity e. The person-centered approach has also been expanded to consider who uses social media relative to their respective community.
Correction for representativeness. One common limitation with work on social media text is selection bias — the social media sample is not representative of the population from which we would like to infer additional information. When using state-of-the-art machine learning approaches, sociodemographics such as age, gender, income, and education can be estimated for each Twitter user from their social media language, thus allowing for the measurement of the sociodemographic makeup of the sample.
Averaging across genders. Prior work has found demographics like gender and age to impact patterns in language use more than personality and are thus important confounding variables to consider when analyzing language use. Person-level aggregation can down-weight highly active accounts and minimize the influences of bots.
Person-level Gen 2 methods are built on a decade of research using Gen 1 random feed aggregation methods based on the in hindsight obvious intuition that communities are groups of people who produce language rather than a random assortment of tweets.
This intuition has several methodological advantages. First, person-level aggregation treats each person as a single observation, which can down-weight highly active accounts and minimize the influences of bots or organizations. Second, it paves the way for addressing selection biases as one can now weight each person in the sample according to their representativeness in the population.
Furthermore, these methods can be applied to any digital data. Finally, these methods more closely reflect the methodological approaches in demography and public health that survey people and lay the foundation for tracking digital cohorts over time Gen 3.
Example of a Gen 2 Twitter pipeline: Person-level aggregation and post-stratification. Cross-sectional Twitter-based county-level cross-validated prediction performances using Gen 1 direct aggregation of tweets to counties, Gen 2a : person-level aggregation before county aggregation, and Gen 2b : robust post-stratification based on age, gender, income, and education.
Most of the work discussed thus far has been constrained to cross-sectional, between-community analysis, but social media offers high-resolution measurement over time at a level that is not practically feasible with survey-based methods e. This abundance of time-specific psychological signals has motivated much prior work.
In fact, a lot of early work using social media text datasets focused heavily on longitudinal analyses, ranging from predicting stock market indices using sentiment and mood lexicons Gen 1, Level 1 [] to evaluating the temporal diurnal variation of positive and negative affect within individuals expressed in Twitter feeds Gen 1, Level 1.
This early work on longitudinal measurement seemed to fade after one of the most iconic projects, Google Flu Trends Gen 1, Level 1 , [] began to produce strikingly erroneous results. While the CDC traditionally detected flu outbreaks from healthcare provider intake counts; Google sought to detect the flu from something people often do much earlier when they fall sick — google their symptoms.
This came to a head in when its estimates turned out to be nearly double those from the health systems. After the errors of Google Flu Trends were revealed, interest at large subsided, but research within Natural Language Processing began to address this flaw, drawing on machine learning methods Level 2 and 3.
Preliminary results from ongoing research demonstrate the potential of longitudinal digital cohort sampling Fig.
This takes a step beyond user-level sampling while enabling tracking variance in well-being outcomes across time: Changes in well-being are estimated as the aggregate of the within-person changes observed in the sample.
Digital cohort sampling presents several new opportunities. Changes in well-being and mental health can be assessed at both the individual and surrounding group level, opening the door to studying their interaction.
Further, short-term weekly and long-term patterns changes on multi-year time scales can be discovered. Finally, the longitudinal design unlocks quasi-experimental designs, such as difference-in-difference, instrumental variable or regression discontinuity designs.
For example, trends in socioeconomically matched counties can be compared to study the impact of specific events, such as pandemic lockdowns, large-scale unemployment, or natural disasters. Short-term weekly and long-term patterns changes on multi-year time scales can be discovered.
The choice of spatiotemporal resolution. Social media data is particularly suitable for longitudinal designs since many people frequently engage with social media.
For example, in the U. population are feasible e. The higher resolution can provide economists and policymakers with more fine-grained, reliable information that can be used for evaluating the impact of policies within a quasi-experimental framework.
Enabling data linkage. The principled and stabilized estimation of county-level time series opens the door for social-media-based measurements to be integrated with the larger ecosystem of datasets designed to capture health and well-being. Example of a Gen 3 Twitter pipeline: longitudinal digital cohorts compose spatial units.
The number of measurement data points produced as a function of different choices of temporal and spatial resolution in digital cohort design studies Gen 3. Studies employing digital cohorts have only recently emerged i. For example, some researchers Gen 3, Level 1 use Reddit forum data to identify and follow more than 1.
Albeit utilizing coarse-grained temporal resolutions i. The field is on the verge of combining Gen 3 sampling and aggregation with Level 3 contextualized embedding-based language analyses Gen 3, Level 3 , which will provide state-of-the-art resolutions and accuracies.
The digital cohort approach comes with the advantages of the person-level approaches, as well as increased methodological design control and temporal stability of estimates, including improved measurement resolution across time and space e.
As such, it unlocks the control needed for quasi-experimental designs. However, disadvantages include higher complexity in collecting and analyzing person-level time series data including the need for higher security and data warehousing.
It may also be challenging to collect enough data for higher spatiotemporal resolutions e. Regarding the question of self-presentation biases, while they can lead keyword-based dictionary methods astray Level 1; as discussed in the section Addressing Social Media Biases , research indicates that these biases have less impact on machine learning algorithms fit to representative samples Level 2 that consider the entire vocabulary to learn language associations, rather than just considering pre-selected keywords out of context Fig.
Machine-learning-based social media estimates can show strong agreement with assessments from extra-linguistic sources, such as survey responses, and demonstrate that, at least to machine-learning models, language use is robustly related to well-being.
Person-level approaches Gen 2 take large steps towards addressing the problems of the potential influence of social media bots. The person-level aggregation facilitates the reliable identification and removal of bots from the dataset.
This reduces their influence on the estimates. Lastly, the digital cohort design Gen 3 overcomes the shortcomings of data aggregation strategies that rely on random samples of tweets from changing samples of users. This approach opens the door to the toolkit of quasi-experimental methods and to meaningful data linkage with other fine-grained population monitoring efforts in population health.
Regional semantic variation. One challenge of using language across geographic regions and time periods is that words and their various senses vary with location and time.
Geographic and temporal predictions pose different difficulties: Geographically, some words express subcultural differences e. Some words are also used in ways that are temporally dependent e. While Level 3 approaches contextual word embeddings can typically disambiguate word senses, there are also examples where Level 2 methods data-driven topics have been successfully used to model regional lexical variation.
Semantic drift over time. Words in natural languages are also subject to drifts in meaning over time as they adapt to the requirements of people and their surroundings. An uncertain future of Twitter under Musk. The accessibility of social media data may change across platforms.
For example, after buying and taking over Twitter at the end of , Elon Musk is changing how Twitter operates. Future access to Twitter interfaces APIs presents the biggest risk to Twitter for research, as these may only become accessible subject to very high fees.
There are also potentially unknown changes in the sample composition of Twitter post-November , as users may be leaving Twitter in protest and entering it in accordance with perceived political preference.
In addition, changes in user interface features e. A history of undocumented platform changes. This is a new twist on prior observations that the language composition of Twitter has changed discontinuously in ways that Twitter has historically not documented and only careful analysis could reveal.
To some extent, such inconsistencies can be addressed by identifying and removing time series of particular words, but also through the more careful initial aggregation of language into users. Methods relying on the random aggregation of tweets may be particularly exposed to these inconsistencies, while the use of person-level and cohort designs Gen 2 and 3 that rely on well-characterized samples of specific users may likely prove to be more robust.
Data beyond social media. A common concern for well-being assessments derived from social media language analyses is that people may fall silent on social media or migrate to other social media platforms. It is hard to imagine that social media usage will disappear, although there will be challenges with gathering data while preserving privacy.
In addition, work suggests that other forms of communication may also be used. This is particularly easy to collect at scale without consenting individual subjects. Measurement Beyond English.
Beyond these difficulties within the same language, more research is needed in cross-cultural and cross-language comparisons. Most research on social media and well-being is carried out on single-language data, predominantly in English. Synonyms Body shape ; Gluteal—femoral region ; Hour glass.
Definition The waist-to-hip ratio WHR is an anthropometric measure of body shape. Introduction Almost 25 years ago, Devendra Singh proposed that the distribution of body fat in women evolved via sexual selection as an honest cue of youth, health, and fertility.
Waist-to-hip psychologgical, WHR and psychological well-being known as psycholkgical ratio, is the circumference of the Breakfast for improved bone health divided by psychologica, circumference of the psychologicwl. People who carry more weight WHR and psychological well-being their middle than their hips may WHR and psychological well-being psychologocal a higher risk of developing certain health conditions. This article explains how to calculate WHR and includes a chart to help people understand their results. It also looks at how WHR ratio affects health, how a person can improve their ratio, and what else they should consider. To find out their WHR, a person needs to measure both the circumference of their waist and their hips. Circumference means the distance around something.WHR and psychological well-being -
Definition The waist-to-hip ratio WHR is an anthropometric measure of body shape. Introduction Almost 25 years ago, Devendra Singh proposed that the distribution of body fat in women evolved via sexual selection as an honest cue of youth, health, and fertility.
Sexual Dimorphism in Human Body Composition In comparison to nonhuman primates, differences between men and women in overall body size are not striking. pdf 6. Author Botha, Elizabeth Maria. Metadata Show full item record. Abstract The aim of this study was to explore, from different perspectives, whether obesity related variables are associated with facets of psychological well-being, with a vision to future enhancement of health and the quality of life of people in the African context.
This study was undertaken from the perspective of positive psychology and focused on the metabolic syndrome and obesity as biological facets. This research was conducted as part of the multidisciplinary POWIRS Profiles of Obese Women with Insulin Resistance Syndrome project. The thesis consists of 3 articles: I Childhood relationships and bio-psycho-.
gocia1 well-being in African women, 2 Psychological well-being and rhe metabolic syndrome in African and Caucasian women, and 3 Psychological well-being and the absence of obesity in African and Caucasian women. In this study psychological well-being was conceptualized and operationalized by means of the General Health Questionnaire GHQ ; Sense of Coherence Scale SOC ; Affectometer 2 AFM short form ; Fortitude Questionnaire FORQ ; Cognitive Appraisa1 Questionnaire CAQ ; Psychological Well-being Scales SPWB ; Quality of Childhood Relationship Questionnaire QCR ; Satisfaction with Life Scale SWLS and the Jarel Spiritual Well-Being Scale SWS-H.
These scales were chosen to include hedonic as well as eudaimonic psychological well-being facets, but also an index of psychological symptoms.
As far as possible, scales with acceptable psychometric properties as described in international as well as South African context were selected. The first article focused on whether African women with a recalled higher level of quality of childhood relationships mould differ significantly with regard to biological, psychological and social well-being from women with a recalled lower level of quality of childhood relationships.
Body mass index BMI was used as objective measure of obesity to operationalize physical health. Talking to the doctor about weight and any associated health risks is always the best way to get a more complete picture. Want to lose those excess pounds?
This study may offer some encouragement, after finding that the effects of being overweight may have been…. Metabolic syndrome is a condition that includes various health issues. It is linked to obesity, cardiovascular disease, high blood pressure, and type….
Find out what the average American woman weighs and obesity rates are for women globally. We also look at how weight can be measured and controlled…. To find their ideal weight, an individual must look at a number of factors, including gender and activity level. Learn how to find your healthy weight.
Body fat scales can be an easy way to track body composition, but research debates their accuracy. Here, learn about body fat scales and the best…. My podcast changed me Can 'biological race' explain disparities in health?
Why Parkinson's research is zooming in on the gut Tools General Health Drugs A-Z Health Hubs Health Tools Find a Doctor BMI Calculators and Charts Blood Pressure Chart: Ranges and Guide Breast Cancer: Self-Examination Guide Sleep Calculator Quizzes RA Myths vs Facts Type 2 Diabetes: Managing Blood Sugar Ankylosing Spondylitis Pain: Fact or Fiction Connect About Medical News Today Who We Are Our Editorial Process Content Integrity Conscious Language Newsletters Sign Up Follow Us.
Medical News Today. Health Conditions Health Products Discover Tools Connect. Why is the hip-waist ratio important? Medically reviewed by Daniel Bubnis, M. How to calculate waist-to-hip ratio What is a healthy ratio?
Impact on health How to improve the ratio Considerations Conclusion Waist-to-hip ratio, also known as waist-hip ratio, is the circumference of the waist divided by the circumference of the hips.
How to calculate waist-to-hip ratio. Share on Pinterest Waist circumference should be measured just above the belly button. What is a healthy ratio? Share on Pinterest The hips should be measured at the widest part of the hips. Impact on health. How to improve the ratio.
WHR and psychological well-being To analyse psychlogical relationship between psychologgical WHR and psychological well-being, social support, Pomegranate Gift Baskets well-being and cardiovascular risk factors in blue- and white-collar Swedish automotive psychologiccal. Methods Baseline psycholigical regarding life events, WHR and psychological well-being support, depressed mood Oats for energy mental strain psychologicsl smoking habits. Follow-up questionnaire after 5 years included the Psychological General Well-being Inventory to assess various health variables. At baseline and follow-up, anthropometric data were obtained. Blood pressure, blood glucose and serum lipids were measured and smoking habits were surveyed. Results The blue-collar workers showed a profile indicating increased cardiovascular risk with a higher proportion of smokers, a higher waist to hip ratio and higher triglycerides. They also reported themselves to have worse general health and less emotional self-control, but were less anxious than the white-collar workers.
0 thoughts on “WHR and psychological well-being”