Abstract
Few studies on older populations consider several energy balance-related behaviors together. This cross-sectional study compared subjectively and objectively measured physical activity (PA) and sedentary behavior (SB) patterns between older adults with and without a healthy diet. We recruited 127 community-dwelling older Taiwanese adults (69.9 ± 5.0 years); data were collected during April and September 2018. Objectively measured total PA, moderate-to-vigorous PA, light PA, step count, total sedentary time, duration of sedentary bouts, number of sedentary bouts, and number of sedentary breaks were assessed using activity monitors. Subjectively measured PA and SB were measured using the International Physical Activity Questionnaire and Sedentary Behavior Questionnaire for Older Adults. Chi-square tests and independent sample t-tests were performed. For subjective measures, older adults without a healthy diet spent significantly less total leisure time on PA and more leisure sitting time than those with a healthy diet. For objective measures, older adults without a healthy diet spent less time on light PA and had a higher total sedentary time, duration of sedentary bouts, times of sedentary bouts, and times of sedentary breaks than those with a healthy diet. Regardless of the use of objective or subjective measurements, older adults without a healthy diet engaged in a more inactive and sedentary lifestyle. These findings have implications for health promotion practitioners in designing tailored interventions.
Keywords: accelerometer, questionnaire, physical activity, sedentary bouts, sedentary break, elderly
1. Introduction
Energy balance-related behaviors (EBRBs) are behaviors that may influence the energy balance and include dietary behavior, sedentary behavior (SB), and physical activity [1,2]. Healthy dietary behavior, physical activity (PA), and reduced sedentary time decrease the risks of mortality, non-communicable diseases, and geriatric syndrome in older adults [3,4,5]. Despite the fact that dietary behavior, physical activity, and sedentary behavior play significant roles in older adults’ health, only a few studies on the elderly population have considered these three EBRBs together [6]. In particular, for older adults, it is important to further compare physical activity and sedentary behavior patterns of those with and without a healthy diet, because physical activity and sedentary behavior may play important roles in influencing functional ability [7,8]. This information is critical for designers of behavioral change interventions and health-promotion practitioners in designing tailored interventions for older adults without a healthy diet.
However, there is a major concern regarding the measurement methods in the existing studies [9,10,11]. Few studies have examined physical activity and sedentary behavior patterns using both subjective and objective measures [9]. Using objective measures such as accelerometers can help to precisely measure activity intensity (sedentary, light, moderate, and vigorous), activity patterns (long sedentary bouts and break times), and step counts [10]. Despite the limitation of recall methods, using subjective measures can reflect the awareness of participants’ physical activity and sedentary behaviors and provide information on the types, frequency, and duration of PA, which are all important health indicators [11]. Therefore, we used both objective and subjective measures to collect data on older adults’ awareness of and actual physically active and sedentary lifestyles. In addition, although several studies examined the association between physical activity and dietary behavior [12,13], few compared the sedentary behavior patterns of older adults with and without a healthy dietary behavior [14]. We hypothesized that older adults without a healthy diet may engage in less physical activity and more sedentary time than those with a healthy diet. To fill these research gaps, this study aimed to compare the patterns of physical activity and sedentary behavior between older adults with and without a healthy diet using objective and subjective measures.
2. Materials and Methods
2.1. Participants
The present cross-sectional study was conducted in Taipei city, Taiwan, during April and September 2018. The inclusion criteria of this study were being community-dwelling older adults and aged 60 years or older. Those who were unable to walk independently (were using any walking assistance devices) were excluded from the present study [15]. The participants were recruited through local advertisements and announcement in the community. The participants contacted the study recruiters if they were interested in participation. In total, 170 men and women aged over 60 years were enrolled. First, every participant completed an informed consent form and questionnaire about socio-demographic information, healthy behavior, health condition, physical activity, and sedentary behavior. Furthermore, the participants wore an accelerometer on their waist for seven consecutive days. A convenience-store gift certificate (worth 7 USD) was given after they completed the questionnaire and wore the accelerometer for the purposes of this study. Incomplete questionnaire and accelerometer data were excluded (n = 43). For the data analysis, 127 participants were included in the study.
2.2. Research Ethics
Our study was conducted following the Declaration of Helsinki of 1975 and its subsequent revisions. Before participation in the study, each participant provided written informed consent. We also obtained ethical approval from the Research Ethics Committee of the University (REC number: 201711HM003).
2.3. Subjectively Measured Physical Activity and Sedentary Behavior
2.3.1. Leisure-Time Physical Activity
We used the Taiwanese version of the International Physical Activity Questionnaire-Long Version (IPAQ-LV) to measure leisure-time physical activity. The IPAQ-LV had acceptable validity (Intraclass Correlation Coefficient [ICC] = 0.79) [16]. We collected data on the time spent on different types of physical activity including physical activities of vigorous and moderate intensities and walking for the previous seven days.
2.3.2. Leisure-Time Sedentary Behavior
Sedentary behavior was assessed using the 10-item Sedentary Behavior Questionnaire for Older Adults, which was validated in previous studies [17]. The questionnaire covered various types of sedentary behavior including TV viewing, computer using, reading, socializing, sitting in an automobile, eating, working (or volunteering), napping, hobbies (e.g., chess), and other sedentary behaviors (e.g., craft or class). Each item demonstrated reasonable test–retest reliability and validity and assessed the overall sedentary time. The participants were asked to recall how much time they spent on each sedentary behavior and how many days they performed these behaviors in the previous seven days. In accordance with the measures of leisure-time physical activity, three items (eating, napping, and working time) were excluded because eating and working are not leisure-time sedentary behavior, and nap is not a waking behavior [17]. The remaining seven items were summed as the variable total time spent on leisure-time sedentary behavior.
2.4. Objectively Measured Physical Activity and Sedentary Behavior
In this study, we used ActiGraph, which is an activity monitor (wGT3X-BT) that counts the steps and measures the time spent on total, light, moderate-to-vigorous physical activity, and sitting, as well as sedentary patterns including the duration of a sedentary bout, times of sedentary bouts, and sedentary breaks. We also used the ActiLife software (version 6.0, Pensacola, FL, USA) to analyze the accelerometer data. Triaxial accelerometer models have demonstrated high intra-monitor reliability and have been validated with acceptable criteria [18]. The accelerometer recorded data for seven days (five weekdays and two weekend days). However, sleeping time was not calculated as sedentary time but was classified as unfitted time. Non-wear time was defined as 60 min or more of continuous unbroken 0 counts with a tolerance of up to 2 min of limited movement. Participants with at least three valid days of accelerometer data and at least one weekend day were included in this study. A period of 600 min (10 h) or more of monitor wear time was categorized as a valid day.
Accelerometer counts can distinguish sedentary behavior from physical activity. Here, ≤99 counts/min was regarded as sedentary time, and ≥100 counts/minute as physical activity. For physical activity, light-intensity physical activity (LPA) was defined as PA between 100 and 2019 counts/min, and moderate-to-vigorous-intensity physical activity (MVPA) as greater than or equal to 2020 counts/min [19]. The MVPA bouts as periods of MVPA lasting at least 10 consecutive minutes with a one-minute allowance below the MVPA threshold were included in the analysis [18]. In this study, LPA, MVPA, total PA (LPA + MVPA), and daily step counts were included as exposure variables. Following previous studies [20,21], total sedentary time, number and duration of ≥30 min sedentary bouts were calculated for the analysis. The drop time of a sedentary bout was set at two minutes when the data were analyzed.
2.5. Healthy Dietary Behavior
Participants were asked to report whether they had a healthy dietary behavior based on the requirements of six key nutrient groups daily, according to the current Taiwanese dietary guidelines [22]. The six nutrient groups include the following: (1) miscellaneous, (2) milk and dairy product, (3) vegetables, (4) fruits, (5) soybean/fish/meat/egg, and (6) nuts, seeds, oil, and fat. The healthy dietary behavior was categorized as “yes” or “no” in the present study.
2.6. Statistical Analyses
All analyses were conducted using SPSS 22.0 for Windows (SPSS Inc., Chicago, IL, USA). General characteristics were evaluated using a chi-square test, and independent sample t-tests were performed to compare the differences of time spent in physical activity and sedentary behavior between older adults with and without a healthy diet. For all analyses, the results are shown as means with standard deviations (M ± SD). Finally, significance was set at p = 0.05.
3. Results
Table 1 provides the results for the 127 older adults with valid data for the analysis. The mean age of those with or without a healthy diet was 69.8 ± 4.7 and 70.4 ± 5.3 years, respectively. Moreover, no differences were evident between the groups for socio-demographic factors and health conditions.
Table 1.
Characteristics of the participants.
| Variables | Healthy Diet (n = 94) | No Healthy Diet (n = 33) | p |
|---|---|---|---|
| Age, M ± SD | 69.8 ± 4.9 | 70.4 ± 5.3 | 0.558 |
| Gender (%) | 0.291 | ||
| Male | 30.9% | 21.2% | |
| Female | 69.1% | 78.8% | |
| BMI (kg/m2), M ± SD | 24.2 ± 3.3 | 24.0 ± 3.6 | 0.752 |
| Marital Status (%) | 0.353 | ||
| Married | 63.8% | 72.7% | |
| Unmarried | 36.2% | 27.3% | |
| Living status (%) | 0.127 | ||
| Living with others | 91.5% | 81.8% | |
| Living alone | 8.5% | 18.2% | |
| Education level (%) | 0.110 | ||
| University | 25.5% | 12.1% | |
| Up to high school | 74.5% | 87.9% | |
| Employment (%) | 0.229 | ||
| Yes | 4.3% | 0.0% | |
| No | 95.7% | 100.0% | |
| Self-rated health (%) | 0.149 | ||
| Good | 34.8% | 21.2% | |
| Poor | 65.2% | 78.8% | |
| Smoking habit (%) | 0.872 | ||
| Yes | 5.3% | 6.1% | |
| No | 94.7% | 93.9% | |
| Alcohol consumption (%) | 0.071 | ||
| Yes | 5.3% | 15.2% | |
| No | 94.7% | 84.8% | |
| Depression (%) | 0.595 | ||
| Yes | 16.0% | 12.1% | |
| No | 84.0% | 87.9% | |
| Diabetes (%) | 0.153 | ||
| Yes | 16.0% | 27.3% | |
| No | 84.0% | 72.7% | |
| Hypertension (%) | 0.676 | ||
| Yes | 38.3% | 42.4% | |
| No | 61.7% | 57.6% | |
| High blood lipid (%) | 0.956 | ||
| Yes | 29.8% | 30.3% | |
| No | 70.2% | 69.7% |
BMI: body mass index; M: mean; SD: standard deviation. The general characteristics were evaluated using a chi-square analysis.
Table 2 shows subjectively and objectively measured time of intensity-specific physical activity and sedentary behavior patterns. For subjective measures, compared with those with a healthy diet, older adults without a healthy diet spent significantly less time on total leisure-time PA (60.59 ± 65.61 versus 35.09 ± 41.67, t = 2.09, p = 0.039) and leisure-time walking (39.26 ± 45.06 versus 16.04 ± 23.52, t = 3.75, p < 0.001). No differences were found for the leisure-time MVPA of the two groups (21.33 ± 37.42 versus 19.05 ± 36.74, t = 0.30, p = 0.763). Also, older adults without a healthy diet spent significantly more time than those with a healthy diet on leisure sitting time (7.47 ± 3.20 versus 8.83 ± 2.56, t = −2.20, p = 0.029). Regarding the objective measures of physical activity, the results showed that older adults with a healthy diet had a significantly higher LPA than those without a healthy diet (301.21 ± 74.94 versus 266.81 ± 89.42, t = 2.15, p = 0.033). In addition, older adults without a healthy diet had significantly higher total sedentary time (9.87 ± 1.24 versus 10.56 ± 1.21, t = −2.76, p = 0.007), sedentary bout duration (190.95 ± 73.91 versus 257.35 ± 115.24, t = −3.09, p = 0.004), sedentary bout times (4.43 ± 1.54 versus 5.66 ± 2.30, t = −2.85, p = 0.007), and sedentary break times (4.25 ± 1.54 versus 5.47 ± 2.28, t = −2.84, p = 0.007) than older adults with a healthy diet.
Table 2.
Subjectively and objectively measured physical activity and sedentary behavior patterns of older adults with and without a healthy diet.
| Variables | Healthy Diet (n = 94) | No Healthy Diet (n = 33) | t | df | p | |
|---|---|---|---|---|---|---|
| M ± SD | M ± SD | |||||
| Subjective Measurement |
Total LTPA, min/week | 60.59 ± 65.61 | 35.09 ± 41.67 | 2.09 | 125 | 0.039 * |
| Leisure-time walking, min/week | 39.26 ± 45.06 | 16.04 ± 23.52 | 3.75 | 106.67 | <0.001 * | |
| Leisure-time MVPA, min/week | 21.33 ± 37.42 | 19.05 ± 36.74 | 0.30 | 125 | 0.763 | |
| Leisure sitting time, h/day | 7.47 ± 3.20 | 8.83 ± 2.56 | −2.20 | 125 | 0.029 * | |
| Objective Measurement |
Total PA, min/day | 326.29 ± 77.60 | 289.30 ± 95.66 | 2.00 | 47.62 | 0.051 |
| LPA, min/day | 301.21 ± 74.94 | 266.81 ± 89.42 | 2.15 | 125 | 0.033 * | |
| MVPA, min/day | 25.08 ± 24.49 | 22.49 ± 18.91 | 0.55 | 125 | 0.582 | |
| Steps, counts/day | 7730.47 ± 3372.53 | 6674.12 ± 3371.51 | 1.55 | 125 | 0.124 | |
| Total sedentary time, h/day | 9.87 ± 1.24 | 10.56 ± 1.21 | −2.76 | 125 | 0.007 * | |
| Sedentary bout duration, min/day | 190.95 ± 73.91 | 257.35 ± 115.24 | −3.09 | 41.61 | 0.004 * | |
| Sedentary bout times | 4.43 ± 1.54 | 5.66 ± 2.30 | −2.85 | 42.46 | 0.007 * | |
| Sedentary break times | 4.25 ± 1.54 | 5.47 ± 2.28 | −2.84 | 42.61 | 0.007 * |
* p < 0.05; LTPA: leisure-time physical activity; PA: physical activity; LPA: Light physical activity; MVPA: Moderate-to-vigorous physical activity; M: mean; SD: standard deviation; df: degree of freedom. Independent sample t-tests were performed to compare the differences of time spent in physical activity and sedentary behavior between older adults with and without a healthy diet.
4. Discussions
This is the first study comparing the patterns of physical activity and sedentary behavior of older adults with and without a healthy diet using accelerometers and questionnaire-based measures. The main findings of this study are that older adults without a healthy diet tend to engage in more inactive and sedentary lifestyles than those with a healthy diet, regardless objective or subjective measurements. These findings may have great implications for behavioral change designers and health-promotion practitioners and indicate an urgent need to design interventions or programs to promote physical activity and reduce the sedentary behavior of older adults without a healthy diet.
An interesting finding when comparing the time spent on objectively measured physical activity between older adults with and without a healthy dietary behavior was that the time spent on total PA, MVPA, and daily step counts did not significantly differ between the two groups. However, the healthy-diet group engaged in more LPA than the unhealthy-diet group. Given that many previous studies emphasized the health benefits of engaging in LPA [23,24,25], our results may suggest that encouraging older adults without a healthy diet to accumulate more LPA could be an effective strategy to obtain optimal health benefits. In addition, when comparing the objectively measured sedentary behavior patterns of the two groups, older adults without a healthy diet engaged in higher total sedentary time, more prolonged sedentary bouts, and a longer duration of sedentary bouts than those with a healthy diet. Possibly, older adults without a healthy diet were also more likely to have a sedentary lifestyle. Since EBRBs contribute to negative impacts on older adults’ health [3,4,5], our results using objective measures may highlight the importance of decreasing the total sedentary time and 30 min sedentary bouts in older adults without a healthy diet.
Noteworthy is that when using subjective measures of leisure-time physical activity and sedentary behavior, older adults with a healthy diet engaged in significantly more leisure-time physical activity and less sedentary behavior. Questionnaire-measured physical activity and sedentary behavior were also associated with health indicators in older adults [26,27]. A possible explanation for this result is that older adults with a healthy diet may be more likely to perceive themselves as having a healthy lifestyle. This may also suggest the importance of increasing the awareness of physical activity and sedentary behavior levels among older adults without a healthy diet.
There are several limitations to this study that must be acknowledged. The cross-sectional study design cannot make strong causal inferences of the association of PA and sedentary behavior with diet quality. Possibly, if participants do not have a healthy diet, they may also have lower physical activity and spend more time on sedentary behavior. Moreover, because of a potential selection bias, the results of this study should be interpreted with caution; for instance, healthier older adults were more likely to participate in this study than unhealthy ones. Therefore, our results could not be generalized to the entire Taiwanese older population. The current study used a single question for health diet distribution and did not examine specific dietary constituents. While leisure-time physical activity and leisure sitting time are widely used and validated instruments [28,29], self-reported measurements may reflect personal biases. Finally, several potential confounders such as the basal resting metabolic rate of the participants and research duration (i.e., season’s influence) were not considered in the present study. Future studies on this issue should control for these confounders in the analyses.
In conclusion, using objective or subjective measurements, we found that older adults without a healthy diet tend to engage in more inactive and sedentary lifestyles than older adults with a healthy diet. These findings support the hypothesis of the present study and have great implications for designers of behavioral changes and health-promotion practitioners in planning tailored interventions.
Acknowledgments
This work was supported by the “Higher Education Sprout Project” of the National Taiwan Normal University and Ministry of Education, Taiwan, R.O.C.
Author Contributions
Conceptualization M.-C.H. and Y.L.; methodology R.R., H.-Y.C.C., and C.-H.C.; software M.-C.H., R.R., and Y.-H.H.; formal analysis M.-C.H., R.R., and Y.-H.H.; investigation M.-C.H. and R.R.; resources M.-C.H. and Y.L.; writing—original draft preparation M.-C.H., R.R., Y.-H.H., H.-Y.C.C., C.-H.C., J.-H.P., and Y.L.; writing—review and editing M.-C.H., R.R., Y.-H.H., H.-Y.C.C., C.-H.C., J.-H.P., and Y.L.; supervision J.-H.P. and Y.L.; project administration M.-C.H., J.-H.P., and Y.L.; funding acquisition M.-C.H., J.-H.P., and Y.L.
Funding
Ming-Chun Hsueh received a personal grant from the Ministry of Science and Technology of Taiwan (MOST 106-2410-H-003 -144 -MY2). The Ministry of Science and Technology of Taiwan was not involved in the study design, data collection, analysis, interpretation, and writing of the manuscript. This work was supported by a Global Research Network program through the Ministry of Education of the Republic of Korea and National Research Foundation of Korea (NRF-Project number: NRF-2017S1A2A2038558).
Conflicts of Interest
The authors declare no conflict of interest.
References
- 1.Timmermans M., Mackenbach J.D., Charreire H., Bardos H., Compernolle S., De Bourdeaudhuij I., Oppert J.M., Rutter H., McKee M., Lakerveld J. Exploring the mediating role of energy balance-related behaviours in the association between sleep duration and obesity in European adults. The SPOTLIGHT project. Prev. Med. 2017;100:25–32. doi: 10.1016/j.ypmed.2017.03.021. [DOI] [PubMed] [Google Scholar]
- 2.Kremers S.P., Visscher T.L., Seidell J.C., van Mechelen W., Brug J. Cognitive determinants of energy balance-related behaviours: Measurement issues. Sports Med. 2005;35:923–933. doi: 10.2165/00007256-200535110-00001. [DOI] [PubMed] [Google Scholar]
- 3.Nelson M.E., Rejeski W.J., Blair S.N., Duncan P.W., Judge J.O., King A.C., Macera C.A., Castaneda-Sceppa C. Physical activity and public health in older adults: Recommendation from the American College of Sports Medicine and the American Heart Association. Med. Sci. Sports Exerc. 2007;39:1435–1445. doi: 10.1249/mss.0b013e3180616aa2. [DOI] [PubMed] [Google Scholar]
- 4.De Rezende L.F., Rey-Lopez J.P., Matsudo V.K., do Carmo Luiz O. Sedentary behavior and health outcomes among older adults: A systematic review. BMC Public Health. 2014;14:333. doi: 10.1186/1471-2458-14-333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Milte C.M., McNaughton S.A. Dietary patterns and successful ageing: A systematic review. Eur. J. Nutr. 2016;55:423–450. doi: 10.1007/s00394-015-1123-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Alley S.J., Duncan M.J., Schoeppe S., Rebar A.L., Vandelanotte C. 8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: A comparison of younger and older Queensland adults. PLoS ONE. 2017;12 doi: 10.1371/journal.pone.0172510. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Manas A., Del Pozo-Cruz B., Garcia-Garcia F.J., Guadalupe-Grau A., Ara I. Role of objectively measured sedentary behaviour in physical performance, frailty and mortality among older adults: A short systematic review. Eur. J. Sport Sci. 2017;17:940–953. doi: 10.1080/17461391.2017.1327983. [DOI] [PubMed] [Google Scholar]
- 8.Kehler D.S. The impact of sedentary and physical activity behaviour on frailty in middle-aged and older adults. Appl. Physiol. Nutr. Metab. 2018;43:638. doi: 10.1139/apnm-2018-0092. [DOI] [PubMed] [Google Scholar]
- 9.Celis-Morales C.A., Perez-Bravo F., Ibañez L., Salas C., Bailey M.E.S., Gill J.M.R. Objective vs. self-reported physical activity and sedentary time: Effects of measurement method on relationships with risk biomarkers. PLoS ONE. 2012;7 doi: 10.1371/journal.pone.0036345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kelly L.A., McMillan D.G., Anderson A., Fippinger M., Fillerup G., Rider J. Validity of actigraphs uniaxial and triaxial accelerometers for assessment of physical activity in adults in laboratory conditions. BMC Med. Phys. 2013;13:5. doi: 10.1186/1756-6649-13-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Van Poppel M.N., Chinapaw M.J., Mokkink L.B., van Mechelen W., Terwee C.B. Physical activity questionnaires for adults: A systematic review of measurement properties. Sports Med. 2010;40:565–600. doi: 10.2165/11531930-000000000-00000. [DOI] [PubMed] [Google Scholar]
- 12.Stefan L., Petrinovic L., Sporis G., Vrgoc G. Frequency of Dietary Intake and Physical Activity in Older Adults: A Cross-Sectional Study. Nutrients. 2018;10:1960. doi: 10.3390/nu10121960. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.McCormack L., Wey H., Meendering J., Specker B. Differences in Physical Activity and Diet Patterns between Non-Rural and Rural Adults. Nutrients. 2018;10:1601. doi: 10.3390/nu10111601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Jezewska-Zychowicz M., Gębski J., Guzek D., Świątkowska M., Stangierska D., Plichta M., Wasilewska M. The associations between dietary patterns and sedentary behaviors in polish adults (Lifestyle study) Nutrients. 2018;10:1004. doi: 10.3390/nu10081004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Storti K.L., Pettee K.K., Brach J.S., Talkowski J.B., Richardson C.R., Kriska A.M. Gait speed and step-count monitor accuracy in community-dwelling older adults. Med. Sci. Sports Exerc. 2008;40:59–64. doi: 10.1249/mss.0b013e318158b504. [DOI] [PubMed] [Google Scholar]
- 16.Macfarlane D.J., Lee C.C., Ho E.Y., Chan K.L., Chan D.T. Reliability and validity of the Chinese version of IPAQ (short, last 7 days) J. Sci. Med. Sport. 2007;10:45–51. doi: 10.1016/j.jsams.2006.05.003. [DOI] [PubMed] [Google Scholar]
- 17.Ku P.W., Sun W.J., Chen L.J. Development of the Sedentary Behavior Questionnaire for the Elderly. Sports Exerc. Res. 2016;18:41–55. doi: 10.5297/ser.1801.004. [DOI] [Google Scholar]
- 18.Troiano R.P., Berrigan D., Dodd K.W., Mâsse L.C., Tilert T., McDowell M. Physical activity in the United States measured by accelerometer. Med. Sci. Sports Exerc. 2008;40:181–188. doi: 10.1249/mss.0b013e31815a51b3. [DOI] [PubMed] [Google Scholar]
- 19.Troiano R.P. Large-scale applications of accelerometers: New frontiers and new questions. Med. Sci. Sports Exerc. 2007;39:1501. doi: 10.1097/mss.0b013e318150d42e. [DOI] [PubMed] [Google Scholar]
- 20.Liao Y., Hsu H.H., Shibata A., Ishii K., Koohsari M.J., Oka K. Associations of total amount and patterns of objectively measured sedentary behavior with performance-based physical function. Prev. Med. Rep. 2018;12:128–134. doi: 10.1016/j.pmedr.2018.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Tremblay M.S., Aubert S., Barnes J.D., Saunders T.J., Carson V., Latimer-Cheung A.E., Chastin S.F.M., Altenburg T.M., Chinapaw M.J.M. Sedentary Behavior Research Network (SBRN)—Terminology Consensus Project process and outcome. Int. J. Behav. Nutr. Phys. Act. 2017;14:75. doi: 10.1186/s12966-017-0525-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Health Promotion Administration . Dietary Guidelines. 2nd ed. Health Promotion Administration, Ministry of Health and Welfare; Taipei, Taiwan: 2018. pp. 1–26. [Google Scholar]
- 23.Chastin S.F.M., De Craemer M., De Cocker K., Powell L., Van Cauwenberg J., Dall P., Hamer M., Stamatakis E. How does light-intensity physical activity associate with adult cardiometabolic health and mortality? Systematic review with meta-analysis of experimental and observational studies. Br. J. Sports Med. 2019;53:370–376. doi: 10.1136/bjsports-2017-097563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Chang Y.K., Chu I.H., Liu J.H., Wu C.H., Chu C.H., Yang K.T., Chen A.G. Exercise Modality Is Differentially Associated with Neurocognition in Older Adults. Neural Plast. 2017;2017 doi: 10.1155/2017/3480413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Amagasa S., Machida M., Fukushima N., Kikuchi H., Takamiya T., Odagiri Y., Inoue S. Is objectively measured light-intensity physical activity associated with health outcomes after adjustment for moderate-to-vigorous physical activity in adults? A systematic review. Int. J. Behav. Nutr. Phys. Act. 2018;15:65. doi: 10.1186/s12966-018-0695-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Gennuso K.P., Gangnon R.E., Matthews C.E., Thraen-Borowski K.M., Colbert L.H. Sedentary behavior, physical activity, and markers of health in older adults. Med. Sci. Sports Exerc. 2013;45:1493–1500. doi: 10.1249/MSS.0b013e318288a1e5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Chang S.H., Fang I.Y., Hsueh M.C., Liao Y. Combined associations of television viewing and physical activity with overweight/obesity in Taiwanese elderly adults. S. Afr. J. Res. Sport Phys. 2016;38:27–35. [Google Scholar]
- 28.Hsueh M.C., Liao Y., Chang S.H. Associations of Total and Domain-Specific Sedentary Time with Type 2 Diabetes in Taiwanese Older Adults. J. Epidemiol. 2016;26:348–354. doi: 10.2188/jea.JE20150095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Liou Y.M., Jwo C.J., Yao K.G., Chiang L.C., Huang L.H. Selection of appropriate Chinese terms to represent intensity and types of physical activity terms for use in the Taiwan version of IPAQ. J. Nurs. Res. 2008;16:252–263. doi: 10.1097/01.JNR.0000387313.20386.0a. [DOI] [PubMed] [Google Scholar]