- Original Paper
- Open Access
Examining the relationships between individual’s time use and activity participations with their health indicators
© The Author(s) 2017
- Received: 15 October 2016
- Accepted: 21 April 2017
- Published: 4 May 2017
Using a three-week household activity-travel survey, this paper explores the relationship between individuals’ self-reported physical, mental and social health conditions and their time allocation for different types of in-home and out-of-home time activities.
A path model is developed to investigate the roles of activity-travel time use on the self-reported health conditions, while the socio-demographics and residential environment characteristics are also considered.
The model results reveal heterogeneous impacts of different types of activities and intensities on individual’s self-reported health conditions. This study, however, did not find evidence of positive relationship between cycling and walking and self-reported physical health condition, which has been found in many developed countries. Presumably this is because in developing countries like Indonesia the individuals who walk and cycle are likely to be a part of economically disadvantaged groups who have less awareness to their own health conditions.
Beside activity and travel time use factors, age and working status were found significantly affecting the self-reported health conditions, regardless of respondents’ gender and income. Neighbourhood characteristics, such as population density, are also found positively correlated to self-reported respondents’ physical, social and mental health conditions.
- Day-to-day variability
- In-home and out-of-home activity participations
- Physical mental and social health indicators
In the last decade, there has been a surge in the studies that investigate the positive health effects from the use of physically active travel modes such as walking and cycling. These active travel modes have gained attentions from both transport/urban planners and public health experts because they are considered as of low-emissions, space efficient, and can positively contribute to the travellers’ physical health [1–4]. Hallal et al.  estimated that today, globally, 30% of adults were lack of physical activities. Together with an energy-dense diet, insufficient physical activities would lead to obesity epidemic, a condition that is associated with cardiovascular diseases, type 2 diabetes, cancer and impaired mental health [6–9].
Based on this believe, many governments are promoting cycling and walking as an alternative to reduce the health risk that comes out from sedimentary lifestyle (e.g. [10, 11]). The physical health impacts from walking and cycling have now started to be taken into account in various governments’ cost-benefit analysis processes. Various methods were proposed to measure and compare the health impacts of active travel. Typical health impacts models that were used to assess the impact of these physically active transport activities are based on hypothetical scenarios with mostly linear correlation assumption on changes in the amount of active travel and the health gains (e.g. [12, 13]). Systematic reviews on the impacts of physically active travel modes to the travellers’ health conditions can be seen at Mueller et al.  and Wanner et al. , for example. Most of previous studies agree to each other that higher participations in physically active travel activities, such as walking and cycling, correlate with a better physical health condition in general. The expected impacts, however, vary across different socio-demographic groups, and some are contradicting with each other. For example, some studies such as Vogel et al.  highlighted the benefit of physically active travel for older people since the activity is expected to substantially reduce the absolute risk for chronic degenerative disease. Some studies (e.g. [15–22]) also argued that the risk would outweigh the detriments of traffic incidents and air pollution exposure. However, Mueller et al.  also pointed out that the argument that older people benefit differently from the same physically active exposure than younger people remains inconclusive. Woodcock et al. [17, 23] argue that older people would benefit a larger mortality risk reduction from physically active participation compared to younger people, whereas Götschi et al.  explained that the impacts of physically active travel is also a function of the current conditions of the travellers. 30 min cycling would have relatively smaller impacts to the travellers who have been cycling everyday compared to the ones who hardly cycle at all in their live. Mueller et al.  concluded that the benefits of physically active travel participation “are sensitive to the contextual setting and population parameters. Health impact estimations depend on baseline prevalence of active travel participation, baseline exposure to health pathways and the general status of the population.”
Moving to the context of the analysis, most previous studies measured the health effects of active travel based on one day (or “average day”) observation of a given individual’s travel or aggregate, cross-section, based observations. Observing a physically active travel on the given day for a given individual does not necessarily mean he/she has a physically active travel routine on a day-to-day basis (thus difficult to measure his/her baseline exposure to a healthy lifestyle). Furthermore, many studies put a lot of emphasise on a particular indicator of active travel, i.e. frequency or amount of walking and cycling, as individual’s main travel mode on their main trip leg. However, in reality, most travellers are multimodal travellers and, in many cases, the last-mile and short trips modes have a higher frequency and intensity than a regular, medium-long distance, routine trips. Furthermore, since individual needs and desires are not constant from day-to-day, an individual’s activity-travel pattern is neither totally repetitious nor random every day [24–29]. The repetitiveness of mode choices highly correlates with the accessibilities of the activity locations, whereas different types of activity have different patterns of repetition. Moreover, most of the previous studies tend to overlook the benefits and/or dis-benefits that the individuals gain from and the trade-off between their daily in-home and out-of-home activities. This can lead to an over or under estimated benefits of physical active travel to individual’s well-being and also to their physical health conditions, e.g. trade-off between an active travel and an active training in a gym. Therefore, it is important to quantify the benefits that an individual gains from both physically activity travel and their day-to-day in-home and out-of-home activities in order to better understand the real value of physically active travel behaviours; not only to physical health, but also to individual’s social and mental health.
To contribute to these research gaps, using a 3-weeks travel diary which was collected in the Bandung Metropolitan Area, Indonesia, this study investigates the relationships between travellers’ physically active travel participation with the type and intensity of their daily activities and their self-assessed physical, mental and social health conditions. This study not only investigates the impacts of day-to-day variability of different types of in-home and out-of-home activities to individual’s health related aspects, but also reveals such behaviours, correlations, and trends in the context of developing country, i.e. Indonesia, which, to authors’ knowledge, has never been studied in the past.
In the next section, the study area is described and so as the used datasets. It is followed by a section which presents the descriptive analyses on individual’s different time use allocation on different day towards different type of in-home and out-of-home activities. Section 4 describes the estimation results on the influence of one’s activity participations with ones’ self-reported physical, mental, social health conditions. Conclusions will be presented in Section 5.
2.1 The Bandung metropolitan area (BMA)
The Bandung Metropolitan Area (BMA) is the capital of the Province of West Java and is approximately 200 km or two to three hours’ drive south of Jakarta. With its conurbation, BMA population is about 7.89 million people, in a 3382.89 square km size area and is the second largest metropolitan area in Indonesia after the Jakarta Metropolitan Area. As a typical city in developing countries, the BMA has a very relaxed or unplanned mixed and monocentric land use, congested road networks, and poor public transport networks and services [30, 31]. Road congestion and the low performance of public transport/paratransit encourage the BMA’s travellers to use motorcycles to reduce their travel costs and time [32–35]. At the same time, they usually have more choices within a closer range in which to conduct their activities along their travel routes, due to the highly mixed land use configurations.
2.2 The 2013 BMA dataset
The BMA dataset contains household, physical activity and lifestyle, individual’s subjective characteristics, time-use and activity diary, and subjective well-being data. The survey involved 732 individuals and 191 households from all over BMA for 21 consecutive days. The household data section contains household composition, individual’s perception about how far his/her accommodation from city centre, public and transportation facilities, and build environment variables. Time-use and activity diary survey captured twenty-three in-home and out-of-home activity classifications, travel duration and mode characteristics, and multitasking activities for adults, young adults and children above 7 years old. In this study, time-use activity participation was classified into several groups of mandatory and discretionary activities.
In this study, the mandatory activities are categorised into: (1) in-home and (2) out-of-home mandatory activities. Out-of-home mandatory activities were defined as activities to meet other individuals or materials with high degree temporal and spatial fixity at a location outside home base such as working at workplace locations, studying at school, having business meeting and dropping off/picking up children to/from school locations. Activities undertaken at home for fulfilling individual’s basic needs such as sleeping, eating and personal care is defined as in-home mandatory activities which have high degree of temporal and spatial fixity .
Discretionary activities were categorised into (1) maintenance and (2) leisure activities. Discretionary activities for satisfying household and personal physiological and biological needs  were defined as maintenance activities. This includes in-home maintenance activities, such as housekeeping and nursing activities, as well as out-of-home maintenance activities such as grocery shopping, health treatment activities and other service activities (such as going to bank, post office) . As leisure activities were discretionary activities undertaken within individual’s available time either in-home or out-of-home for satisfying cultural and physiological needs . This includes entertainment (such as watching TV, listening music/radio, reading magazines/newspapers and accessing the Internet), social and family activities (such as meeting with family members and friends, visiting relatives/friends and undertaking sport, leisure, and voluntary activities and going on holiday). Multi-tasking activities is defined as concurrent activities contained primary and secondary activities [39, 40] for satisfying different needs and desires at the same time.
Health survey questions
1. Body weight and height
2. Vigorous activities as part of the individual’s work/school activities and around the home environment
3. Moderate activities as part of the individual’s work/school activities and around the home environment
4. Walking as part of the individual’s work/school activities (excluding travel activities)
5. Travel activities using a motorised mode/s
6. Non-motorised transport activities (cycling and walking)
7. Activities performed sitting down
Physical activities in leisure time as part of health promoting activities
1. Objective and subjective measurement of walking
2. Objective and subjective measurement of vigorous active exercise with and without bodily collision, such as soccer, basketball, running, playing tennis/badminton, fast cycling, aerobics, swimming, etc.
3. Objective and subjective measurement of moderate active exercise with and without bodily collision, such as light cycling, light swimming, light tennis/badminton, etc.
Social and communication activities with family members and other people
1. Social and communication activities with other people, such as voluntary and organisational activities, attending events and socialising at events
2. Social and communication activities with other family members
Lifestyle and health habits
Eight types of habits: eating breakfast, enough sleep, eating balanced meals, smoking, drinking alcohol, working less than nine hours each day, under stress/pressure circumstances
Health-related quality of life
1. Subjective measurement of an individual’s health and comparison with the previous year
2. Physical functioning
3. Limitations on role functioning due to physical health
4. Bodily pain
5. General health
6. Mental health
7. Limitations on role functioning due to emotional problems
8. Social functioning
Profiles of the samples used in the study
Socio-demographic and Travel Activity Characteristics
Trip engagements and travel time spent across different days of the week:
Number of trips/day from Monday-Thursday
Number of trips/day on Friday
Number of trips/day on weekends
Number of trip chains/day from Monday-Thursday
Number of trip chains/day on Friday
Number of trip chains/day on weekends
Percentage of days using motorized mode from Monday-Thursday
Percentage of days using motorized mode on Friday
Percentage of days using motorized mode on weekends
Percentage of days using public transport mode from Monday-Thursday
Percentage of days using public transport mode on Friday
Percentage of days using public transport mode on weekends
Percentage of days using non-motorized mode from Monday-Thursday
Percentage of days using non-motorized mode on Friday
Percentage of days using non-motorized mode on weekends
Total travel time spent from Monday-Thursday (minutes)
Total travel time spent on Friday (minutes)
Total travel time spent on weekends (minutes)
Time spent for different activities on different days of the week:
Time spent for in-home mandatory activities from Monday-Thursday (minutes)
Time spent for in-home mandatory activities on Friday (minutes)
Time spent for in-home mandatory activities on weekends (minutes)
Time spent for in-home leisure and maintenance activities from Monday-Thursday (minutes)
Time spent for in-home leisure and maintenance activities on Friday (minutes)
Time spent for in-home leisure and maintenance activities on weekends (minutes)
Time spent for working/school activities from Monday-Thursday (minutes)
Time spent for working/school activities on Friday (minutes)
Time spent for working/school activities on weekends (minutes)
Time spent for out-of-home leisure and maintenance activities from Monday-Thursday (minutes)
Time spent for out-of-home leisure and maintenance activities on Friday (minutes)
Time spent for out-of-home leisure and maintenance activities on weekends (minutes)
Average factor score of physical health
−0.734 (0.95) †
Average factor score of mental health
Average factor score of social health
−0.106 (0.96) †
2.3 Descriptive analysis
The travel and activity participation distribution across the observed period can be seen at Table 2. It is shown in the table that individuals engage with different amounts of activity and trips on different day of the weeks. Whilst Friday, on average, is the busiest day in terms of activity engagement, travel time and number of trips and trip chaining, it also has the highest proportion of non-motorised mode trips. Consistent with case studies in developed countries (e.g., ), it was found that in Bandung individuals only spent around 4–5% of their time on travel. The data on Table 2 shows that in-home activities such as sleeping, in-home preparation and eating activities take more than 75% of individuals’ daily time expenditure. This highlights the importance of understanding the physical activity intensity whilst one is at home and the trade-off between in-home and out-of-home activities since in-home mandatory and working/school activities act as an anchor and will limit the individual’s ability to do other activities within his/her time-space on a given day [46–49].
Using a multiday household activity-travel survey (2013 BMA dataset), this paper explored the relationship between individuals’ self-reported health conditions and their activity-travel time use. The self-reported health conditions are hypothesized to be affected by individuals’ activity participation pattern and usual travel mode use, described by the observed activity-travel time use in 21 consecutive days. General descriptive analysis shows clear day-to-day variability of individuals’ activity-travel time use. On average, individuals only spent around 4–5% of their time on travel. In-home activities such as sleeping, in-home preparation and eating activities take more than 75% of individuals’ daily time expenditure, highlighting the importance of understanding the physical activity intensity that is caused by in-home activities as well. The self-reported health conditions show, however, more confounding relationships. The trends of in-home and out-of-home activity time use may be affected by the fact that self-reported health groups consist individuals with different socio-demographics and residential environment.
The model results reveal that individuals’ activity participation and socio-demographic characteristics significantly affects their self-reported health conditions, while self-reported travel time spent does not show any significant effects on the self-reported health conditions after controlling for the endogeneity (some effects are offset by the time use trade-offs between activities and travel). Surprisingly, income shows no significant correlation with self-reported physical health condition, although part of income effect may be absorbed in the effect of motorised travel time. This study, however, did not find evidence of positive relationship between cycling and walking and self-reported physical health condition, which has been found in many developed countries. Presumably because in developing countries like Indonesia the individuals who walk and cycle are likely to be a part of economically disadvantaged and less educated groups who may have low appreciation and less awareness to oneself condition [52, 53].
Beside activity and travel time use factors, age and working status were found significantly affect the self-reported health conditions, regardless of respondents’ gender and income. Population density also found positively correlates to self-reported respondents’ health conditions, indicating benefits such as better health care in urbanised areas on individuals’ health conditions in general.
However, one should also be aware of several limitations of this study. The self-reported health condition is likely to be as well related to one’s well-beings, past habit, etc. and not a detailed measured medical report. Self-reported scores are reference-dependent, meaning that individuals with almost the same actual (true) health condition may judge their own health conditions very differently. Twenty-one consecutive days activity-travel participation may be enough to represent their current habit but may not describe their past habit. Thus, the self-reported health condition may also differ significantly from the true health condition due to individual’s cognitive limits, that individual do not realize their health condition, and behavioural failure, that individuals themselves do know the negative effect of certain habit (e.g. smoking) on their health but do not admit it. The mismatch between self-reported health and true health may affect the estimated variable effects. It is also important that this study reveals the correlations between activity participations and individual self-reported health conditions, not necessarily the causal relationships. Thus, further interpretation need to be done with great care. Further investigation on how individual self-reported health conditions and physically active travel and activities influenced by individual attitudes and psychological characteristics would be one of plausible research directions of this study.
This intensity assumption is different than activity intensity that is used in the context of physical activity and health discipline. Whilst it is acknowledged that the activity intensity is a function of various internal and external factors that influence the amount of energy used by the body per minute of activity, since the analysis is based on self-reported activity participation for 24 h for three-weeks, there is no way we can collect such detail metabolic intensity data for this particular analysis.
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