Latent Class Analysis Research Articles

Associations Among Lifetime Discrimination Typologies and Psychological Health in Black and Hispanic Women After Birth

ObjectiveTo identify lifetime discrimination typologies and examine their associations with psychological health outcomes among Black and Hispanic women after birth. DesignSecondary analysis of the Community and Child Health Network study data. ParticipantsA total of 1,350 Black and 607 Hispanic women. MethodsWe built two latent class models for Black and Hispanic women using eight indicator variables from different life domains of discrimination (childhood, family, work, police, education, housing, health care, and loans). We used bivariate and multiple regression analyses to examine the association among the identified typologies and postpartum depression and perceived stress at 6 months postpartum. ResultsWe selected the three-class model with best fit indices and interpretability: no lifetime discrimination (n = 1,029; 76.22%), high childhood–family racial discrimination (n = 224, 16.59%), and moderate lifetime discrimination (n = 97, 7.19%) among Black women and no lifetime discrimination (n = 493, 81.22%), high childhood–family racial discrimination (n = 93, 15.32%), and high education discrimination (n = 21, 3.46%) among Hispanic women. The adjusted postpartum depression and perceived stress scores were significantly greater in Typologies 2 and 3 than Typology 1 in Black women. The adjusted perceived stress scores were significantly greater in Typologies 2 and 3 than Typology 1 in Hispanic women. ConclusionLifetime discrimination experiences manifested in complex patterns. Women who experienced moderate to high discrimination across all or specific life domains had worse postpartum depression and perceived stress at 6 months after birth. It is crucial to address lifetime discrimination to improve maternal mental health.

Latent heterogeneity of deviant behaviors and associated factors among ethnic minority adolescents: a latent class analysis

BackgroundDeviant behaviors are common during adolescence. Despite the diversity of juvenile delinquency, the patterns of deviant behaviors remain unclear in ethnic minorities. The present study aimed to evaluate the latent heterogeneity of deviant behaviors and associated factors in ethnic minority Yi adolescents.MethodsThe present study recruited a large sample of 1931 ethnic minority Yi adolescents (53.4% females, mean age = 14.7 years, SD 1.10) in five secondary schools in 2022 in Sichuan, China. The participants completed measures on 13 deviant behaviors and demographic characteristics, attitudinal self-control, and psychological distress. Sample heterogeneity of deviant behaviors was analyzed via latent class analysis using class as the cluster variable.ResultsThe data supported three latent classes with measurement invariance by sex. 68.2%, 28.0%, and 3.8% of the sample were in the Normative, Borderline, and Deviant class, with minimal, occasional, and extensive deviant behaviors, respectively. The Deviant class was more prevalent in males (6.5%) than females (1.6%). There were significant class differences in domestic violence, school belonging, self-control, anxiety, and depressive symptoms. Males, domestic violence, low school belonging, and impaired self-control significantly predicted higher odds of the Deviant and Borderline classes compared to the normative class.ConclusionThis study provided the first results on three latent classes of deviant behaviors with distinct profiles in ethnic minority adolescents in rural China. These results have practical implications to formulate targeted interventions to improve the psycho-behavioral functioning of the at-risk adolescents in ethnic minorities.

Falls and patterns of physical activity participation over 18 years in the Australian Longitudinal Study on Women’s Health

ObjectiveTo explore the relationship between long-term physical activity (PA) participation and falls.MethodsParticipants in the Australian Longitudinal Study of Women’s Health born 1946–1951 self-reported amounts of PA every 3 years since...

Socioeconomic Factors Determining Multidimensional Child Poverty Groups in Central America: A Measurement Proposal from the Wellbeing Approach Using a Comprehensive Set of Children’s Rights

This article aims to show that multidimensional child poverty (MCP) is determined by several socioeconomic factors that influence the formation of stratified groups of poor children under five years of age living in Central America. This study advocates for a comprehensive set of children's rights with the purpose of addressing the different facets of child poverty from the perspective of child well-being, in order to estimate the incidence of MCP, by including multiple childhood deprivations and socioeconomic determinants. Child-specific indicators and household deprivation indicators are considered in the estimation of MCP. The study also states that child poverty is a complex concept, which includes the various types of deprivations experienced by children in the Central American societies and their deprivations are considered as the denial of children’s rights. Therefore, the MCP is estimated based on a conditional latent class analysis that includes not only manifest deprivation variables, but also socioeconomic determinants that help to better predict the incidence and probabilities of children being multidimensionally poor according to different poverty strata. The socioeconomic factors that show high risks of MCP are rural areas, indigenous children, young mothers and low levels of education attained by the head of the household, among others. One of the reasons to investigate the MCP for Belize and El Salvador is because there are few studies that address this problem for these countries and this research sheds light on the characteristics of early childhood poverty. The results indicate that the incidence of MCP is 49% in Belize and 76% in El Salvador. The research work concludes that the International Rights of the Child provide the opportunity to implement comprehensive social policies in Central America to eradicate child poverty.

Sensitivity and specificity of International Classification of Diseases algorithms (ICD-9 and ICD-10) used to identify opioid-related overdose cases: A systematic review and an example of estimation using Bayesian latent class models in the absence of gold standards.

This study aimed to summarize validity estimates of International Classification of Diseases (ICD) codes in identifying opioid overdose (OOD) among patient data from emergency rooms, emergency medical services, inpatient, outpatient, administrative, medical claims, and mortality, and estimate the sensitivity and specificity of the algorithms in the absence of a perfect reference standard. We systematically reviewed studies published before December 8, 2023, and identified with Medline and Embase. Studies reporting sufficient details to recreate a 2 × 2 table comparing the ICD algorithms to a reference standard in diagnosing OOD-related events were included. We used Bayesian latent class models (BLCM) to estimate the posterior sensitivity and specificity distributions of five ICD-10 algorithms and of the imperfect coroner's report review (CRR) in detecting prescription opioid-related deaths (POD) using one included study. Of a total of 1990 studies reviewed, three were included. The reported sensitivity estimates of ICD algorithms for OOD were low (range from 25.0% to 56.8%) for ICD-9 in diagnosing non-fatal OOD-related events and moderate (72% to 89%) for ICD-10 in diagnosing POD. The last included study used ICD-9 for non-fatal and fatal and ICD-10 for fatal OOD-related events and showed high sensitivity (i.e. above 97%). The specificity estimates of ICD algorithms were good to excellent in the three included studies. The misclassification-adjusted ICD-10 algorithm sensitivity estimates for POD from BLCM were consistently higher than reported sensitivity estimates that assumed CRR was perfect. Evidence on the performance of ICD algorithms in detecting OOD events is scarce, and the absence of bias correction for imperfect tests leads to an underestimation of the sensitivity of ICD code estimates.

Group of longitudinal adverse event patterns after the fourth dose of COVID-19 vaccination with a latent class analysis.

Vaccination has been implemented as a useful measure to combat the COVID-19 pandemic. However, there is a tendency for individuals to avoid vaccination due to the possibility of adverse events, making it important to investigate the relationship between COVID-19 vaccines and their adverse events. This study explored longitudinal adverse event patterns and factors that influence adverse events following the second to fourth doses of the COVID-19 vaccine through a latent class analysis. Participants were recruited from the Fukushima Prefecture and included individuals who had completed four doses of the COVID-19 mRNA vaccine. This study utilized data from questionnaire surveys and blood collection conducted between September 2021 and November 2022. In the questionnaire, factors such as sex, age, medical history, medication, type of vaccine administered, and adverse events following vaccination were recorded. Additionally, in the blood data, serological tests [IgG(S)] and cellular immune responses (T-spot) were measured. Descriptive statistics, latent class analysis, multivariable logistic regression, and multiple regression analyses were performed to identify the longitudinal adverse event patterns and influencing factors. By analyzing adverse events over time, we identified two distinct groups: those less prone to experiencing adverse events (Group 1) and those more susceptible (Group 2) to latent class analysis. A total of 1,175 participants were included after excluding those without any adverse events. The median age of the participants in Group 1 was 70 years, and in Group 2 it was 51 years. The proportion of female participants was 298 in Group 1 and 353 in Group 2. Patients in Group 2 were significantly younger (p < 0.001) and more likely to be female (p < 0.001) than those in Group 1. Furthermore, the median IgG(S) value after the fourth vaccination was 3,233 AU/mL in Group 1 and 4,059.39 AU/mL in Group 2. The median T-spot value was 15.4 in Group 1 and 28.5 in Group 2. Group 2 showed significantly higher IgG(S) and T-spot values after the fourth vaccination (p < 0.001). Our findings suggest that factors other than age, particularly sex and a history of allergies, significantly influence the likelihood of experiencing adverse events. Groups categorized by latent class analysis for longitudinal adverse events are expected to be valuable for optimizing vaccination strategies and formulating public health measures.

Comparing individual and collective valuation of ecosystem service tradeoffs: A case study from montane forests in southern California, USA

Accounting for the tradeoffs and importance urban, disadvantaged communities place on ecosystem services has implications for the management of nearby forests. Although stated preference valuation approaches are often used, they are based on an individual’s perspective and rarely account for collective or societal values. Thus, alternative methods are needed to capture this dichotomy from urban communities who may not even be aware of these benefits to themselves or society at-large. We explored individual and collective importance of, and tradeoffs for, ecosystem services (ES) and ecosystem disservices (ED) by urban residents living near montane forests in greater Los Angeles, California, USA. Using an online panel survey, individual (I-rationality) versus collective (We-rationality) scenarios, best-worst scaling (BWS) choice experiments, and latent class analyses, we ranked the importance and tradeoffs among ES-ED attributes tonearby residents based on the frequency of visits to montane forests as well as Hispanic ethnicity. Results show statistically significant tradeoffs and differences in importance rankings between individual versus collective valuation scenarios. Under the individual valuation scenario, non-Hispanics highly ranked the high forest density indicator, which has implications for wildfire EDs to montane forests and communities. Gender and income were more influential sociodemographic factors affecting importance for water and recreation-related ES than was education. Our BWS and econometric methods, attributes, and importance rankings can facilitate participatory processes with diverse urban communities and designing more effective policies and management guidelines. This approach canalso more inclusively, and equitably, account for the tradeoffs and values that nearby urban communities place on ES/ED from Wildland-Urban Interface forests.

Adverse and benevolent childhood experiences among adults in the United Kingdom: a latent class analysis

BackgroundAdverse childhood experiences (ACEs) are important factors for population mental and physical health. While considerable public health literature demonstrates the global relevance of ACEs, more recent research shows that benevolent childhood experiences (BCEs) might be important to consider in their direct and mitigating roles for psychological distress and other mental health outcomes. There is little evidence of latent class examinations involving both ACEs and BCEs among adults in western nations. The present study sought to replicate and extend prior literature by: (1) assessing the extent to which past latent class groupings reproduce in present samples, and (2) analyzing the association of latent classes of childhood experiences with psychological distress and suicidal thoughts and behaviours (STBs). We examined psychological distress (i.e., depression, anxiety, post-traumatic stress, general wellbeing) and STBs (i.e., suicidal ideation, self-harm ideation and behaviour, entrapment, and defeat).MethodData were drawn from two nationwide cross-sectional online survey studies in the United Kingdom. The first sample (N = 488) was drawn from a study on suicidal behaviour, and the second sample (N = 447) was from a study concerning risk for interpersonal violence.ResultsResults largely replicated an existing four class solution of childhood experiences: Class 1 (Moderate ACEs/High BCEs; 17.6%), Class 2 (High ACEs/Moderate BCEs; 15.3%), Class 3 (Low ACEs/High BCEs; 48.3%), and Class 4 (Low ACEs/Moderate BCEs; 18.8%). Class 2 (High ACEs/Moderate BCEs) was associated with consistently worse psychological distress and STBs. Classes containing high BCEs (1 and 3) were characterized by generally lower levels of psychological distress and STBs.ConclusionsResults affirm the potential value for jointly considering ACEs and BCEs to understand psychological distress and STBs. ACEs and BCEs may serve foundational roles in theories of suicide. The protective role of BCEs hypothesized in resiliency theory may be supported. Prevention practice and research implications are discussed.

A Case Study of Ridership and Equity Implications of All-Day Massachusetts Bay Transportation Authority Commuter Rail Service

As the COVID-19 pandemic emerged from the acute phase and vaccines became widely available in 2021, transit agencies like the Massachusetts Bay Transportation Authority (MBTA) faced a daunting challenge of drawing riders back into their systems. Despite systemwide staffing shortages, service disruptions, and ridership patterns that have yet to return to 2019 levels, ridership on MBTA’s Commuter Rail has consistently outperformed the agency’s other services in the current COVID recovery era. In April 2021, as part of a multiyear vision to overhaul the system, MBTA switched from a schedule focused on serving traditional peak-period commuters to providing steady, all-day service modeled on more legible “clockface” departures. This study used regression analysis to show this change was consistent with the Commuter Rail outperforming other modes in ridership recovery, generating over 7,000 average daily weekday boardings and over 9,000 average weekend boardings. Latent class analysis demonstrated that this schedule shift primarily benefited riders traveling for a variety of purposes, rather than just traditional office work. This group, which we referred to as general riders, was more likely to be low-income (household income below $75,000), young (25 or younger), and Hispanic. This research demonstrated the ability of all-day service on Commuter Rail to serve MBTA’s key aims of increasing ridership and providing more equitable, accessible service.

A Latent Class Analysis of Online Victim-Offender Overlap among Chinese Youth: Examining Overlap Risks across Online Deviance Types

A Latent Class Analysis of Online Victim-Offender Overlap among Chinese Youth: Examining Overlap Risks across Online Deviance Types

Class Enumeration in Mixture Modeling with Nested Data: A Brief Report

Given the increased focus of educational research on what works for whom and under what circumstances over the last decade, educational researchers are increasingly turning toward mixture models to identify heterogeneous subgroups among students. Such data are inherently nested, as students are nested within classrooms and schools. Yet there has been limited guidance on which specifications are most appropriate for enumerating latent classes when data are nested. This study utilized longitudinal, state-collected student data to demonstrate the impact of different specifications (i.e., ignoring nested data, using a post-hoc adjustment, and a parametric and non-parametric approach) of a latent class model when analyzing nested data. The overarching goal of this study was to provide the implications of four different model specifications commonly used to adjust for clustering in the context of mixture modeling. We highlight factors that may influence researchers’ decisions to employ one approach over another when conducting multilevel mixture modeling. We conclude with a set of recommendations that may be particularly helpful for the use of these methods in educational settings, where nested data is common.

Typologies of sexually motivated abductions: a latent class analysis

ABSTRACT To date, sexually motivated abductions have not received as much attention from researchers in comparison to the acts of abduction and sexual assault alone. Further, the majority of research that is conducted on abductions focuses solely on child victims, creating a gap in the literature. The current study was set forth to identify qualitatively different subgroups of offenders who commit sexually motivated abductions. Using victim, crime, and offender characteristics taken from sexual abduction cases (n = 1288), a latent class analysis was conducted. Results from the latent class analysis revealed that four subgroups of sexually motivated abductors exist: Convenience Opportunist, Strategic Opportunist, Child Opportunist, and Familiar Opportunist. In addition, bivariate analyses were run to test the latent class solution with different characteristics; results suggest that these subgroups of sexually motivated offenders may not be dependent on some victim lifestyle characteristics. These results provide evidence that there are qualitatively different subgroups of offenders who commit sexually motivated abductions, which may be useful for law enforcement when conducting investigations.

Associations Between PTSD and Depressive Symptoms and Victimization Among Female College Students: A Latent Class Analysis.

Research on subgroups of female college students who share similar experiences of childhood maltreatment (CM) and intimate partner violence (IPV) victimization is sparse. The primary aims of the current study are: (a) to identify subgroups of victims related to experience of psychological, physical, and sexual CM and IPV and (b) to test the association between subgroups and depressive and post-traumatic stress disorder (PTSD) symptom severity. Survey data was collected from 327 female students at a public university in the Northeast. Three distinct subgroups, that is, victimization classes, were found using Latent Class Analysis: A lifetime victimization class, comprised of females reporting high rates of CM and IPV across types (19.0% of the sample), a childhood victimization class (26.9%); and a low victimization class (54.1%). Depressive symptom severity was positively associated with lifetime and childhood, relative to low, victimization class membership. PTSD symptom severity was associated with the childhood victimization class relative to both lifetime and low victimization class. Subgroups of institutions of higher education (IHE) females who share CM and IPV victimization experiences are discernable. The co-occurrence of IPV and CM in female college students is not uncommon. PTSD symptom severity may be more related to CM than IPV in IHE female students.

Health and disability – a multi-group latent class analysis of the World Health Organization Disability Assessment Schedule 2.0 among those with mental and physical health conditions

BackgroundThis study aims to identify disability classes among people with schizophrenia spectrum disorder, depression, anxiety or diabetes via the WHODAS 2.0; investigate the invariance of disability patterns among the four diagnostic groups; and examine associations between disability classes and sociodemographic variables.MethodsPatients seeking treatment for schizophrenia spectrum disorder, depression, anxiety or diabetes (n=1076) were recruited. Latent class analysis was used to identify disability classes based on WHODAS 2.0 responses. Measurement invariance was tested using multi-group latent class analysis. Associations between classes and sociodemographic variables were tested via multinomial logistic regression.ResultsA five-class solution was identified; examination of model invariance showed that the partially constrained five-class model was most appropriate, suggesting that class structure was consistent while class membership differed across diagnostic groups. Finally, significant associations were found between class membership and ethnicity, education level, and employment status.ConclusionsThe results show the feasibility of using the WHODAS 2.0 to identify and compare different disability classes among people with mental or physical conditions and their sociodemographic correlates. Establishing a typology of different disability profiles will help guide research and treatment plans that tackle not just clinical but also functional aspects of living with either a chronic psychiatric or physical condition.

Visit probability and accessibility within space–time prism of activity program

Space–time prism (STP) is widely applied to measure individuals’ ability to reach opportunities given the resource limitations. The majority of STP models treat prism-based accessibility as binary measures, wherein all locations within a prism are assumed equally accessible while others are deemed inaccessible. Although a few STP models examined heterogeneous interiors by modeling visit probabilities within the STPs, they primarily focused on trip-level analysis and did not explore the application for accessibility measurement. This study proposes a model framework based on multi-state supernetworks for constructing and estimating the probabilistic STPs of daily activity programs. The estimation is implemented with latent class models to account for individual heterogeneities in travel and activity participation. Based on the probabilistic STPs, we suggest a space–time accessibility measurement incorporating visit probabilities. We validate the model framework using mobility trajectory data collected in the Netherlands and demonstrate that the visit probability model can effectively capture the probabilistic STP interiors and the proposed accessibility measurement can provide a comprehensive evaluation of accessibility in the presence of activity chains.

Meat, plant-based or in-vitro salami: Explaining food product choice of Generation Y and Z in Germany through carnism and the core dimensions of the food-related lifestyle scale

The food industry contributes to negative consequences for the environment through animal husbandry. To provide more transparency to consumers, husbandry labels were introduced. In addition, many vegetarian/vegan meat alternatives are available on the market to reduce meat consumption. A new type of meat substitute is in vitro-meat, which is grown in the laboratory using stem cells. There is a gap in knowledge regarding social attitudes that influence customers to eat meat or meat substitutes. Carnism and the core dimensions of the food-related lifestyle scale were used to explain the perception of meat, plant-based meat, and in-vitro meat. The research gap was explored using a survey on salami. The results can be used in marketing and to understand the factors that contribute to Generation Y and Z ´ s choice to eat meat. Attendants could choose between products with different attributes, such as salami type, environment label, animal welfare label, origin label, and price. The survey shows that type of salami is the most important characteristic for consumer´s choice immediately before animal welfare label. Three segments were formed through latent class analysis that differed in their attitudes and the importance of attributes for their choices. The majority of Generation Y and Z in Germany prefer vegan meat over real meat, and in-vitro meat is more popular than beef or pork meat, although this is still unknown and not yet on the German market. Carnism and core dimensions of food-related lifestyle scale partly explain choice behavior, and are also guiding people choosing in-vitro meat.

Investigating the role of metaverse influencers’ attributes for the next generation of services

PurposeThe purpose of this research is to identify the most important attributes of metaverse influencers and examine their impact on customer engagement and social glue.Design/methodology/approachThree studies (one qualitative and two quantitative) were conducted to understand the phenomenon better. The qualitative study (Study 1) was conducted to identify the antecedents of the theoretical model, which was tested in Study 2 using the covariance-based structural equation modelling (CB-SEM) technique. Study 3 then divided the respondents based on the metaverse influencer attribute preferences.FindingsResults of Study 1 revealed the six most influential attributes of metaverse influencers: physical attractiveness, social attractiveness, perceived credibility, metaverse-influencer fit, intimacy and attitude homophily. Further, Study 2 validated that attractiveness and perceived credibility enhance engagement. Also, the results revealed that intimacy, perceived credibility and homophily enhance social glue. Moreover, parasocial relationships mediate the association between intimacy, attitude homophily, perceived credibility and (engagement and social glue). The conditional indirect effect of physical attractiveness, social attractiveness and metaverse–influencer fit on (engagement and social glue) via parasocial relationships at different high and low levels of self-discrepancy was significant. Finally, Study 3 used latent class analysis to reveal different clusters of metaverse users.Originality/valueThis research enriches our understanding of metaverse influencers, contributing to the influencer marketing literature. It offers actionable insights for marketers by elucidating key influencer attributes, aiding in enhancing engagement and social glue.

Client preferences for the design and delivery of injectable opioid agonist treatment services: Results from a best-worst scaling task.

Clinical trials support injectable opioid agonist treatment (iOAT) for individuals with opioid use disorder (OUD) for whom other pharmacological management approaches are not well-suited. However, despite substantial research indicating that person-centered care improves engagement, retention and health outcomes for individuals with OUD, structural requirements (e.g. drug policies) often dictate how iOAT must be delivered, regardless of client preferences. This study aimed to quantify clients' iOAT delivery preferences to improve client engagement and retention. Cross-sectional preference elicitation survey. Metro Vancouver, British Columbia, Canada. 124 current and former iOAT clients. Participants completed a demographic questionnaire package and an interviewer-led preference elicitation survey (case 2 best-worst scaling task). Latent class analysis was used to identify distinct preference groups and explore demographic differences between preference groups. Most participants (n = 100; 81%) were current iOAT clients. Latent class analysis identified two distinct groups of client preferences: (1) autonomous decision-makers (n = 73; 59%) and (2) shared decision-makers (n = 51; 41%). These groups had different preferences for how medication type and dosage were selected. Both groups prioritized access to take-home medication (i.e. carries), the ability to set their own schedule, receiving iOAT in a space they like and having other services available at iOAT clinics. Compared with shared decision-makers, fewer autonomous decision-makers identified as a cis-male/man and reported flexible preferences. Injectable opioid agonist treatment (iOAT) clients surveyed in Vancouver, Canada, appear to prefer greater autonomy than they currently have in choosing OAT medication type, dosage and treatment schedule.

Patterns of digital health access and use among US adults: a latent class analysis

BackgroundDigital technologies allow users to engage in health-related behaviors associated with positive outcomes. We aimed to identify classes of US adults with distinct digital technologies access and health use patterns and characterize class composition. Data came from Health Information National Trends Survey Wave 5 Cycles 1–4, a nationally representative cross-sectional survey of US adults (N = 13,993). We used latent class analysis to identify digital technologies access and health use patterns based on 32 ternary variables of behaviors and access to requisite technologies and platforms, including the internet, internet-enabled devices, health monitors, and electronic health records (EHRs). We ran a multinomial logistic regression to identify sociodemographic and health correlates of class membership (n = 10,734).ResultsTen classes captured patterns of digital technology access and health use among US adults. This included a digitally isolated, a mobile-dependent, and a super user class, which made up 8.9%, 7.8%, and 13.6% of US adults, respectively, and captured access patterns from only basic cellphones and health monitors to near complete access to web-, mobile-, and EHR-based platforms. Half of US adults belonged to classes that lacked access to EHRs and relied on alternative web-based tools typical of patient portals. The proportion of class members who used digital technologies for health purposes varied from small to large. Older and less educated adults had lower odds of belonging to classes characterized by access or engagement in health behaviors. Hispanic and Asian adults had higher odds of belonging to the mobile-dependent class. Individuals without a regular healthcare provider and those who had not visited a provider in the past year were more likely to belong to classes with limited digital technologies access or health use.DiscussionOnly one third of US adults belonged to classes that had near complete access to digital technologies and whose members engaged in almost all health behaviors examined. Sex, age, and education were associated with membership in classes that lacked access to 1 + digital technologies or exhibited none to limited health uses of such technologies. Results can guide efforts to improve access and health use of digital technologies to maximize associated health benefits and minimize disparities.

Remote working and experiential wellbeing: A latent lifestyle perspective using UK time use survey before and during COVID-19.

Mental health in the UK had deteriorated compared with pre-pandemic trends. Existing studies on heterogenous wellbeing changes associated COVID-19 tend to segment population based on isolated socio-economic and demographic indicators, notably gender, income and ethnicity, while a more holistic and contextual understanding of such heterogeneity among the workforce seems lacking. This study addresses this gap by 1) combining UK time use surveys collected before and during COVID-19, 2) identifying latent lifestyles within three working mode groups (commuter, homeworker and hybrid worker) using latent class model, and 3) quantifying nuanced experiential wellbeing (ExWB) changes across workers of distinct lifestyles. The direction and magnitude of ExWB changes were not uniform across activity types, time of day, and lifestyles. The direction of ExWB change during the daytime activities window varied in accordance with lifestyle classifications. Specifically, ExWB decreased for all homeworkers but increased significantly for certain hybrid workers. Magnitude of ExWB change correlated strongly with lifestyle. To understand the significant heterogeneity in ExWB outcomes, a spatial-temporal conceptualisation of working flexibility is developed to explicate the strong yet complex correlations between wellbeing and lifestyles. The implications to post-pandemic "back-to-work" policies are 1) continued expansion of hybrid working optionality, 2) provide wider support for lifestyle adaptation and transitions.