Latent Class Modeling Approach Research Articles

Optimizing environmental viral surveillance: bovine serum albumin increases RT-qPCR sensitivity for high pathogenicity avian influenza H5Nx virus detection from dust samples

ABSTRACT The latest outbreaks of high pathogenicity avian influenza viruses (HPAIVs) were the most widespread ever seen in Europe, entailing considerable economic costs and raising public health concerns. Virological surveillance protocols involve reverse transcription quantitative polymerase chain reactions (RT-qPCR) from tracheal and cloacal swabs, which are laborious to perform on a large scale and require special skills. Environmental sampling, and especially dust collection, may represent a relevant alternative, as it is cheap, non-invasive for animals, simpler, and quicker to carry out. The main drawback of dust samples is that they may contain high amounts of organic substances that can inhibit RT-qPCR reactions. Bovine serum albumin (BSA) is a molecule known to facilitate DNA polymerization in the presence of numerous inhibitors, including those from feces, litter, or food. We tested its use on dust samples collected on 107 farms localized in areas affected by epizootics of clade 2.3.4.4b HPAIV H5N1. We used a latent class modeling approach to compare the performance of three detection protocols: (i) BSA addition to the RT-qPCR reaction mix, (ii) dilution of template RNA, and (iii) a control protocol. Our results indicate that BSA addition to the RT-qPCR reaction mix improved the sensitivity of the method, by neutralizing inhibitors’ effect. Indeed, for hemagglutinin (HA) or matrix (M) RNA detection, the sensitivity of the BSA protocol was the highest, followed by that of the control protocol, and that of the dilution protocol. Our results suggest that the use of BSA could be routinely implemented in HPAIV dust monitoring RT-qPCR protocols. IMPORTANCE With the circulation of high pathogenicity avian influenza viruses having intensified considerably in recent years, the European Union is considering the vaccination of farmed birds. A prerequisite for this vaccination is the implementation of drastic surveillance protocols. Environmental sampling is a relevant alternative to animal sampling. However, environmental samples often contain inhibitory compounds in large enough quantities to inhibit RT-qPCR reactions. As bovine serum albumin is a molecule used in many fields to overcome this inhibitory effect, we tested its use on dust samples from poultry farms in areas heavily affected by HPAIV epizootics. Our results show that its use significantly increases the sensitivity of the method.

Modelling the composition of household portfolios: A latent class approach

AbstractWe explore portfolio allocation in Great Britain by introducing a latent class modelling approach using household panel data based on a nationally representative sample of the population, namely the Wealth and Assets Survey. The latent class aspect of the model splits households into four groups, from lowest‐wealth and least‐diversified through to highest‐wealth and most‐diversified, which serves to unveil a more detailed picture of the determinants of portfolio diversification than existing econometric approaches. A pattern of class heterogeneity is revealed that conventional econometric models are unable to identify because the statistical significance and the direction of the effect of some explanatory variables vary across the groups. For example, the effect of labour income on the number of financial assets held influences the level of diversification for the two middle classes, whereas no effect is found for households with the lowest or the highest levels of diversification. Noticeable differences in the magnitude of the effects of pension wealth and occupation are also revealed across the four classes. Such findings demonstrate the importance of accounting for latent heterogeneity when modelling financial behaviour. Ultimately, treating the population as a single homogeneous group may lead to biased parameter estimates, whereby policy based on such models could be inappropriate or erroneous.

Evaluating therapeutic benefits of ubrogepant via latent class models: A post hoc exploratory analysis of the ACHIEVE I and ACHIEVE II trials.

To evaluate an alternative method of defining acute treatment success in migraine by combining multiple indicators into a single dichotomous measure of success. Migraine is characterized by a symptom complex; combining these features as a single endpoint may improve the measurement of treatment effects and better predict patient satisfaction with treatment. We used a confirmatory latent class model (LCM) with two latent classes interpreted as treatment success and treatment failure. Pooled data for placebo and ubrogepant 50 mg from the ACHIEVE I and ACHIEVE II trials and data for ubrogepant 100 mg from ACHIEVE I were used. LCM inputs included pre-dose and 2-h post-dose measures of pain severity (0-3), the presence/absence of associated symptoms (nausea, photophobia, and phonophobia [0 or 1]), and functional disability (0-3). All definitions were validated against satisfaction with study medication (SWSM) at 24 h post-dose; results were compared with 2-hour pain freedom (2hPF). This pooled analysis included 2247 participants. At 2 h post-dose in the ubrogepant 50 and 100 mg dose groups, 53.2% (472/887) and 54.9% (246/448) of participants, respectively, were classified as achieving treatment success using the LCM-based approach, compared to 39.0% (356/912) of participants in the placebo group. The results for treatment success using the 2hPF endpoint were 20.7% (184/887) and 21.5% (96/447) in the ubrogepant 50 and 100 mg dose groups, respectively, compared to 12.7% (116/912) for placebo. Using 24-h SWSM as an external validator, the LCM approach sensitivity and correct classification rates were higher than for 2hPF. The LCM approach led to higher rates of treatment success and greater separation between ubrogepant and placebo and was a more sensitive predictor of treatment satisfaction than the regulatory endpoint of 2hPF.

The role of information heterogeneity in blockchain-based traceability systems: evidence from fresh fruits buyers in China

Blockchain technology is now being piloted to agri-food traceability systems to restore consumers’ confidence for food quality and safety. It is important for the industry to understand what information to be recorded and tracked in blockchain-based fresh produce traceability systems to meet consumers’ preferences for information. Yet little research has focused specifically on consumers’ preferences concerning information attributes traced by this new blockchain technology. This study conducts a best-worst scaling experiment with fresh fruit buyers in China to investigate consumers’ preference and perceived value regarding sixteen information attributes about blockchain-based fresh fruit traceability systems. The results from the analysis of a random parameter logit model reveal that consumers consistently rank testing information as the first-most valuable attribute, followed by production inputs (pesticides and fertilizers), quality certification and grades information attributes, while supplier and logistics information are considered to be the least valuable traceability one. Furthermore, there exist significant heterogeneity in relative value placed on traceable information attributes. The findings identify four different consumer segments by using a latent class modelling approach: (1) sensitivity for authoritative information, (2) preferences for comprehensive information, (3) information preferences equally, and (4) preferences for production inputs information. Preference heterogeneity is mainly explained by risk attitude, risk perception, information concern, traceability cognition, gender and other factors. The findings from this study can provide stakeholders and policymakers with certain insights as well as strategies on information provision and disclosure for fresh produce blockchain-based traceability.

Patterns of computed tomography utilisation in injury management: latent classes approach using linked administrative data in Western Australia

PurposeWhilst computed tomography (CT) imaging has been a vital component of injury management, its increasing use has raised concern regarding ionising radiation exposure. This study aims to identify latent classes (underlying patterns) of CT use over a 3-year period following the incidence of injury and factors predicting the observed patterns.MethodA retrospective observational cohort study was conducted in 21,544 individuals aged 18 + years presenting to emergency departments (ED) of four tertiary public hospitals with new injury in Western Australia. Mixture modelling approach was used to identify latent classes of CT use over a 3-year period post injury.ResultsAmongst injured people with at least one CT scan, three latent classes of CT use were identified including a: temporarily high CT use (46.4%); consistently high CT use (2.6%); and low CT use class (51.1%). Being 65 + years or older, having 3 + comorbidities, history with 3 + hospitalisations and history of CT use before injury were associated with consistently high use of CT. Injury to the head, neck, thorax or abdomen, being admitted to hospital after the injury and arriving to ED by ambulance were predictors for the temporarily high use class. Living in areas of higher socio-economic disadvantage was a unique factor associated with the low CT use class.ConclusionsInstead of assuming a single pattern of CT use for all patients with injury, the advanced latent class modelling approach has provided more nuanced understanding of the underlying patterns of CT use that may be useful for developing targeted interventions.

Investigation of the Policy Effect of High-Speed Metropolitan Rail Service in Seoul Through Latent Class Modeling Analysis of Stated Preference Survey

The sprawl of megacities is increasing the need to improve public transit services to cover longer distances in shorter times than current services. High-speed metropolitan rail services have been introduced to satisfy the needs of passengers and are expected to alleviate the transportation problems caused by urban sprawl. However, the effectiveness of public transit demand inducement policies, which have been employed in various ways in South Korea, has been questioned. Thus, agencies responsible for high-speed metropolitan rail services should also evaluate whether such policies will work properly. The achievement of the policy can be evaluated as sufficiently inducing the demand of the target group, and investigated through the residence information and the current travel behavior. This study investigates the current travel behavior of users to be replaced when a new high-speed metropolitan rail service is introduced and directly analyzes the travel mode shifting effect. While previous studies focused on increasing the applicability to general services by classifying groups based on personal attributes, this study focuses on evaluating detailed policy achievements for specific services. We apply a latent class modeling approach using stated preference survey data for group classification. In particular, by employing the user’s current travel behavior as a class membership attribute, the characteristics of potential passengers of the high-speed metropolitan rail service are analyzed. The estimation results suggest that current travel behavior significantly classifies high-speed rail passengers. In addition, the policy achievement was investigated in detail by region and operation time, and several additional policy directions are presented to satisfy the policy goals.

Investigating college youth travellers’ food choices using latent class modelling: a case study in China

ABSTRACT This study examines college youth travellers’ food choices using a market segmentation-based latent class modelling approach. A D-efficient stated-preference experiment was conducted in four Chinese cities, identifying two tourist market segments with different preferences. Key factors influencing food choices include cost, taste, waiting time, service responsiveness and noise levels. Elasticities, first differences and willingness-to-pay values are estimated, providing insights for effectively targeting the youth tourism market. Results suggest that improving service quality and reducing noise levels can significantly enhance food appeal, while higher quality yields marginal returns. These findings can assist tourism and catering agencies in devising efficient strategies to attract youth travellers.

Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Non-commuting travel is essential for people to meet daily demands and regulate mental health, which is greatly disrupted due to the COVID-19 pandemic. To explore non-commuting intentions during COVID-19 across different groups of residents, this paper uses online survey data in Nanjing and constructs a hybrid latent class choice model that combines sociodemographic characteristics and psychological factors. Results showed that the respondents can be divided into two groups: the “cautious” group versus the “fearless” group. The “cautious” group with lower willingness to travel tend to be older, higher-income, higher-educated, female and full-time employees. Furthermore, the “cautious” group with higher perceived susceptibility is more obedient to government policies. In contrast, the “fearless” group is significantly affected by perceived severity and is more inclined to turn to personal protection against the pandemic. These results suggested that non-commuting trips were influenced not only by individual characteristics but also by psychological factors. Finally, the paper provides implications for the government to formulate COVID-19 management measures for the heterogeneity of different groups.

Automated Mobility-on-Demand Service Improvement Strategy through Latent Class Analysis of Stated Preference Survey

Automated driving technologies have advanced remarkably and are expected to be a part of our lives soon. Because automated driving technology does not require a driver, a significant change in future mobility services is expected. Automated driving technology is closely related to the development of public transit services as it can significantly reduce driver labor costs and provide a more comfortable in-vehicle environment. In particular, the preference for automated mobility-on-demand services that can respond in real time to the dynamic demand through automated driving technology is growing. Previous studies have compared passengers’ preferences for automated mobility-on-demand services and other transportation modes and proposed a way to enable more passengers to use automated mobility-on-demand services. However, as the number of pilot operations increases, future research will focus on ways to improve competitiveness among automated mobility-on-demand services. This study conducts a passenger preference survey based on the characteristics of automated mobility-on-demand services. In particular, changes in the in-vehicle environment and seat selection system, which differ from existing mobility-on-demand services due to automated driving technology, are investigated. The latent class modeling approach is used to classify passengers based on stated preference data collected from the survey. The estimation results show that vehicle type and seat choice system have a significant impact on passengers’ preference for automated mobility-on-demand services. In addition, considering that a high percentage of passengers do not prefer to improve autonomy in seat reservation and the in-vehicle environment, this study suggests that cost-consuming service improvement strategies are not always appropriate.

Differential item functioning across gender with MIMIC modeling: PISA 2018 financial literacy items

The aim of this study is to investigate the presence of DIF over the gender variable with the latent class modeling approach. Data were 953 students from the USA who participated in the PISA 2018 8th-grade financial literacy assessment. Latent class analysis (LCA) approach was used to determine the latent classes and the data fit the three-class model better in line with fit indices. To obtain more information about the characteristics of the emerging classes, uniform and non-uniform DIF sources were determined by using the Multiple Indicator Multiple Causes (MIMIC) model. The findings are very important in terms of contributing to the interpretation of latent classes. According to the results, the gender variable is a potential source of DIF for latent class indicators. Gathering unbiased estimates for the measurement and structural parameters, it is important to include direct effects in the classes. Ignoring these effects can lead to incorrect determination of implicit classess. An example of the application of Multiple Indicator Multiple Causes (MIMIC) model showed in a latent class framework with a stepwise approach with this study.

Fire Eaters: Exploring Consumer Preferences for Labeling Extensive Livestock Products Providing Wildfire Prevention Services

Extensive livestock grazing has proved to be a valuable tool to reduce wildfire risk in Mediterranean landscapes. Meat from herds providing wildfire prevention services exhibit sustainability traits that can appeal to ethical consumers and find a suitable niche in local markets. This study assesses the preferences of a consumer sample in the province of Girona (north-eastern Spain) for different lamb meat labeling options from herds providing wildfire prevention services. The aim is to disentangle consumer profiles, providing evidence for improved product labeling. This may increase the added value and the viability of small farms providing this service. Employing a latent class modeling approach, we explore how meat consumption patterns and socioeconomic features may contribute to explain preferences for different meat labeling options. Our results have identified three consumer profiles: traditional rural consumers relying on trust with producers, younger consumers more akin to new labeling schemes, and urban consumers that support local butchers as a trusted information source. Different labeling mechanisms may work in a complementary way to arrive to different audiences of potential consumers. Geographical indication labels can serve as a good departure point, complemented with information cues on environmental factors related to wildfire protection.

Performance of bovine genital campylobacteriosis diagnostic tests in bulls from Uruguay: a Bayesian latent class model approach.

The sensitivity (Se) and specificity (Sp) of three diagnostic tests for the detection of Campylobacter fetus venerealis (Cfv) using field samples were estimated using a Bayesian latent class model (BLCM), accounting for the absence of a gold standard. The tests included in this study were direct immunofluorescence antibody test (IFAT), polymerase chain reaction (PCR), and real-time PCR (RT-PCR). Twelve farms from two different populations were selected and bull prepuce samples were collected. The IFAT was performed according to the OIE Manual. The conventional PCR was performed as multiplex, targeting the gene nahE for C. fetus species identification and insertion element ISCfe1 for Cfv identification. The RT-PCR was performed as uniplex: one targeting the gene nahE for C. fetus and the other targeting the insertion ISCfe1 (ISC2) for Cfv. Results from the BLCM showed a median Se of 11.7% (Bayesian credibility interval (BCI): 1.93–29.79%), 53.7% (BCI: 23.1–95.0%), and 36.1% (BCI: 14.5–71.7%) for IFAT, PCR, and RT-PCR respectively. The Sp were 94.5% (BCI: 90.1–97.9%), 97.0% (BCI: 92.9–99.3%), and 98.4% (BCI: 95.3–99.7%) for IFAT, PCR, and RT-PCR respectively. The correlation between PCR and RT-PCR was positive and low in samples from both sampled population (0.63% vs 8.47%). These results suggest that diagnostic sensitivity of the studied tests is lower using field samples than using pure Cfv strains.

Investigating commuters’ satisfaction with public transit: A latent class modeling approach

This study explores the factors associated with commute satisfaction of public transit users in the Seoul Metropolitan Area, identifying the critical role of commuters’ attitudes in influencing their commute satisfaction. Using a latent class ordered probit model, the study identifies two latent segments—optimistic multitasker (Class 1) and dissatisfied commuter (Class 2)—assuming that the association between determinants and satisfaction with commute could depend highly on attitudes. A majority of transit commuters (71.8%) belong to Class 1, meaning that most Seoul commuters are satisfied with their commute and use their travel time productively. Moreover, in comparing the two-class profiles, Class 1 commuters tend to be more satisfied with their lifestyle, perceive themselves as healthier, have a positive personality, and are more satisfied with their commute. Such empirical evidence demonstrates the existence of taste heterogeneity in determining commute satisfaction and the role of attitudes in that mechanism.

Supplemental Material for The Evolution of Patients’ Concept of the Alliance and Its Relation to Outcome: A Dynamic Latent-Class Structural Equation Modeling Approach

Supplemental Material for The Evolution of Patients’ Concept of the Alliance and Its Relation to Outcome: A Dynamic Latent-Class Structural Equation Modeling Approach

A comparison of multiple-hurdle approach and latent class modeling approach for detecting insufficient effort responding in the survey data

자기보고식 설문자료에서 다양한 양상으로 나타나는 불성실 응답은 분석 결과의 타당성을 저해하거나 연구 결과에 편향을 야기할 수 있는 중요한 요소이므로, 자료 분석 시 불성실 응답을 보인 사례들을 탐지하여 그 영향력을 제외하는 것은 분석 결과의 타당성을 확보하는 데 있어서 매우 중요하다. 이에 본 연구에서는 자료 내의 불성실 응답 탐지를 위한 주요한 두 접근법인 다중허들 접근법과 잠재계층 모형의 불성실 응답 탐지 성능을 비교하였다. 먼저 표본크기, 불성실 응답자의 비율과 불성실 응답 문항 비율 등의 조건 하에서 두 접근법의 정확성을 비교하기 위한 모의실험 연구를 수행하였다. 또한 대학생들을 대상으로 수집된 실제 온라인 설문자료 분석의 맥락에서 두 접근법을 적용하여 불성실 응답을 제외한 자료 분석을 실시한 결과를 비교함으로써 보다 합리적인 불성실 응답 탐지에 대한 방법론적 시사점에 대해 논의하고자 하였다. 본 연구의 주요 결과를 요약하면 다음과 같다. 첫째, 모의실험 분석 결과 다중허들 접근법은 대체로 잠재계층 모형에 비해 자료 내의 불성실 응답을 과도하게 탐지하는 반면, 잠재계층 모형은 어느 정도 불성실 응답을 정확히 탐지하는 동시에 표본의 손실이 비교적 적은 것으로 나타났다. 둘째, 실제자료 분석에서는 두 접근법을 적용하여 각각 수행한 통계 분석의 모형 적합도는 대체로 유사하였으나, 다중허들 접근법을 적용한 경우에는 표본의 손실이 과도한 경향이 있는 것으로 나타났다. 본 연구의 결과를 바탕으로 설문자료 내 불성실 응답의 탐지에서 다중허들 접근법과 잠재계층 모형의 유용성과 한계에 대해 논의하였다.Insufficient effort responding(IER) is commonly observed in self-report survey data. Since the presence of the IER can threaten the validity of the study by leading to potential bias for analysis results, it is an essential procedure to detect and treat the IER before the main statistical analyses are conducted. The purpose of this study is to explore the accuracy of the two most widely used methods for detecting the IER: the multiple-hurdle approach and the latent class modeling approach. The accuracy of the two methods were compared using simulation study considering various factors such as sample size, proportion of the examinees with IER, and the proportion of the IER items. The main results of this study are as follows. First, the findings in the simulation study showed that the multiple-hurdle approach tended to detect the IER excessively, resulting in the loss of sample size. The latent class modeling approach tended to detect the IER with reasonably high level of accuracy and had an advantage to preserve the sample size. Second, the real data analysis with the datasets that removed the IER cases with each of the two approaches showed slightly higher level of model-data fit for the latent class modeling approach and the loss of sample size was more serious in the multiple-hurdle approach. Based on the results, the advantages and limitations of the two approaches were discussed.

Heterogeneity in flood risk awareness: A longitudinal, latent class model approach

Insights into the heterogeneity of human behaviours and attitudes toward risk require the understanding of the role played by a plurality of factors, such as risk awareness and trust. However, our knowledge of the interplay of these factors is limited, as is our knowledge of the patterns in risk attitudes and behaviours and their evolution over time. This study explores the interplay between attitudes and behaviours related to flood risk awareness and structural flood protection in two communities in the Eastern Italian Alps, and how they changed over time. To this end, a questionnaire was submitted to a total of 420 residents, in the year 2005 (N = 200) and 2018 (N = 220), and then analysed using Latent Class Analysis. No floods were recorded in the area during this period. The results show that there is a group of residents characterized by low risk awareness and high trust in structural flood protection. Such individuals are likely to live in urban – rather than mountain – communities and are characterized by a lack of or limited experience with floods. They are also prone to believe that such events will not happen in the future. In 2005, this group represented less than half of the sample, but its size substantially increased in 2018. This result has strong implications for local risk managers, because this group of residents is less risk aware than the others and they may deserve special attention and targeted messages in flood risk communication campaigns. This and other results are discussed, including the potential development of generalizable models to provide emergency and risk managers with tools to unveil risk awareness patterns and to tailor risk communication actions to citizens attitudes and behaviours. The paper ends with some considerations about the need not only to better understand but also to address diversity in flood risk awareness.

Preferences for shared automated vehicles: A hybrid latent class modeling approach

We model the mode choice between three novel emerging transportation services and identify latent market segments not studied yet in the context of shared automated vehicles, including ridesharing, car sharing and automated transit, using a hybrid choice model. We use a discrete distribution to capture taste heterogeneity of distinct latent classes. Latent variables, socio-demographics and travel habits inform latent class assignment estimated simultaneously with a discrete choice kernel. Respondents chose their preferred mode for going to work in a set of stated preference choice tasks, based on the attributes of their current commutes using a Bayesian D-Efficient design. Users were segmented into two latent classes based on latent factors that capture time style orientation and public transit dislike. The effects of seating designation, not modeled previously in this context, trip cost and travel times in a shared ride, were estimated for the two classes. Users who neither like transit nor ridesharing with strangers are less likely to choose a shared ride if their designated seat is the middle seat, and overall less likely to choose automated transit. Individuals who have more organized time styles demonstrate higher marginal sensitivity to travel times and costs and are more likely to choose automated transit. Value of time analysis reveals that wait time of services that offer a convenient home pickup is valued lower than in-vehicle time. The implications for future adoption of shared automated vehicles is further discussed.

Can Social Connections Improve Dementia Knowledge Among Homebound Community-Dwelling Older African Americans?

Despite the clear and compelling association between social connections and well-being, the underlying mechanisms that help stave off adverse health impacts are not well understood, particularly among older adults in disadvantaged groups. Social relationships in older age may be instrumental for exchanging and gaining knowledge that further influence health and help increase awareness about misconceptions and lifestyle behaviors known to delay or reduce cognitive decline. This study used cross-sectional survey data from 147(aged 58-90 years; 75% female) low-income African American homebound community dwellers to investigate heterogeneity in dementia literacy profiles and its association with social connectedness. Eleven items (false=0, true=1, don’t know=2) from a validated instrument were used to measure dementia literacy (DL). The Lubben’s social network scale was used for a social connectedness construct. We employed a 2-stage latent class modeling approach to examine heterogeneity in DL and estimate the regressions among the derived classes and the predictors (social connectedness, education level and age). A 3-class model produced a reasonable fit and classification (entropy=0.852) of “dementia literacy patterns” labeled as (high-literacy:37%), moderate-literacy:45.2%), low-literacy:17.8%)). Social connectedness was highly predictive of class membership. A high level of social relationships increased the probability of being in the “high-dementia-literacy” class compared to the “low-dementia-literacy” class (OR=2.189, p=0.016). For a unit increase in social connectedness, the odds of being in the “high-dementia-literacy” class compared to the “low-dementia-literacy” class increased by a factor of 2.2. Tailored and focused interventions to reduce social disconnectedness may also help increase dementia awareness and reduce barriers to early diagnosis.

The demand for electrification in Canadian fleets: A latent class modeling approach

A latent class model is estimated with revealed and stated preference data obtained from over 1000 fleet operating entities (FOEs). The model identifies four latent FOE classes,namely battery electric vehicles (BEV) leaning, EV skeptical, EV averse, and internal combustion engine vehicles (ICEV) oriented with class probabilities of 19%, 15%, 27% and 39%, respectively.BEV leaningFOEs seem to place high value on extended trip range and are found to agree with the notion that adopting EVsis a cost-effective decision. These FOEsare also willing to install additional charging infrastructure. Corporate FOEs, relative to their governmental counterparts inICEV orientedclass are found to be more concerned with operating cost than any other cost.Policiesgeared towards investing in public charging infrastructure, launching campaigns to highlight the cost-effectiveness of EVs, incentivising FOEs with climate action plan, and encouraging on-site charging infrastructure investmentscould accelerateEV acquisition among Canadian fleets.

Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system

As a special case of multitasking, travel-based multitasking typically refers to conducting a set of in-vehicle activities while traveling. Travel-based multitasking has an indisputable influence on offering a pleasant travel experience to transit users during their rides, given that they can use their travel time to perform desirable activities and gain benefits in various form. For instance, the in-activities could help the rider free up time from his/her schedule for the day (i.e., a worthwhile use of travel time). In this study, we investigate how the worthwhileness of a travel-based multitasking could be under the influence of: (1) the transit user’s lifestyle, and (2) socio-demographics, and (3) the characteristics of the transit trip. Towards this, we conducted an intercept survey focusing on the transit trips in the Chicago metropolitan area and analyzed it using latent class modeling approach. Per the results, two classes of transit users could be identified: (1) worthwhileness seekers, productively travelers and (2) leisure seekers, occasional worthwhile travelers. The results also suggest travel time, waiting time and walking distance to the transit station, and the set of in-vehicle activities as significant predictors of worthwhile use of travel time. The findings provide insights to policymakers for improving public transit systems in the current form, as well as designing an autonomous mobility system as the future form of public transit.