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Article

Association of Self-Reported and Device-Measured Sedentary Behaviour and Physical Activity with Health-Related Quality of Life among European Older Adults

by
Oriol Sansano-Nadal
1,*,
Maria Giné-Garriga
1,2,
Beatriz Rodríguez-Roca
3,
Myriam Guerra-Balic
1,
Kelly Ferri
1,
Jason J. Wilson
4,5,
Paolo Caserotti
6,
Pia Øllgaard Olsen
6,
Nicole E. Blackburn
7,
Dietrich Rothenbacher
8,
Dhayana Dallmeier
9,10,
Marta Roqué-Fíguls
11,
Emma McIntosh
12 and
Carme Martín-Borràs
1,2
1
Department of Physical Activity and Sport Sciences, Faculty of Psychology, Education and Sport Sciences (FPCEE) Blanquerna, Ramon Llull University, Císter 34, 08022 Barcelona, Spain
2
Department of Physical Therapy, Faculty of Health Sciences (FCS) Blanquerna, Ramon Llull University, Padilla 326-332, 08025 Barcelona, Spain
3
Department of Physiatry and Nursing, Faculty of Health Sciences, University of Zaragoza, Domingo Miral, 50009 Zaragoza, Spain
4
Sport and Exercise Sciences Research Institute, School of Sport, Ulster University, Newtownabbey BT37 0QB, UK
5
Institute of Mental Health Sciences, School of Health Sciences, Ulster University, Newtownabbey BT37 0QB, UK
6
Center for Active and Healthy Ageing (CAHA), Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
7
Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Newtownabbey BT37 0QB, UK
8
Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtztr 22, 89081 Ulm, Germany
9
Research Unit on Aging, Agaplesion Bethesda Clinic, Zollernring 26, 89073 Ulm, Germany
10
Department of Epidemiology, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
11
Fundació Salut i Envelliment (Foundation on Health and Ageing)—UAB, Universitat Autònoma de Barcelona, Sant Antoni Maria Claret 171, 08041 Barcelona, Spain
12
Health Economics and Health Technology Assessment (HEHTA), Institute of Health and Wellbeing (IHW), University of Glasgow, Glasgow G12 8QQ, UK
 
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*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(24), 13252; https://doi.org/10.3390/ijerph182413252
Submission received: 23 September 2021 / Revised: 11 December 2021 / Accepted: 14 December 2021 / Published: 16 December 2021
(This article belongs to the Special Issue Physical Activity for Health)
Review Reports Versions Notes

Abstract

:
Human movement behaviours such as physical activity (PA) and sedentary behaviour (SB) during waking time have a significant impact on health-related quality of life (HRQoL) in older adults. In this study, we aimed to analyse the association between self-reported and device-measured SB and PA with HRQoL in a cohort of community-dwelling older adults from four European countries. A subsample of 1193 participants from the SITLESS trial (61% women and 75.1 ± 6.2 years old) were included in the analysis. The association between self-reported and objective measures of SB and PA with HRQoL were quantified using Spearman’s Rho coefficients. The strength of the associations between self-reported and device-measured PA and SB with self-rated HRQoL (mental composite score, MCS; physical composite score, PCS) were assessed through multivariate multiple regression analysis. Self-reported and device-measured PA and SB levels showed significant but poor associations with PCS (p < 0.05). The association with MCS was only significant but poor with self-reported light PA (LPA) and moderate-to-vigorous PA (MVPA). In conclusion, the findings of this study suggest that both self-reported and device-measured PA of all intensities were positively and significantly associated, while SB was negatively and significantly associated with the PCS of the SF-12.

1. Introduction

Human movement behaviours such as physical activity (PA) and sedentary behaviour (SB) have a significant impact on health and quality of life (QoL) in older adults [1,2]. Health-related quality of life (HRQoL) in the older adult population is gaining the attention of researchers and policy makers since life expectancy worldwide, especially in European countries, is increasing. In 2015, the older adult population (≥65 years old) was 17.4% worldwide [3]; this percentage is expected to increase, reaching 33% by 2060 [4].
Low levels of SB, defined as any waking behaviour with an energy expenditure ≤1.5 Metabolic Equivalent Task (MET) while in a sitting, reclining or lying posture [5], and regular PA, have been associated with better-perceived quality of life in older adults [6,7,8,9,10]. More active and less sedentary older adults have shown better self-rated health in several studies [11,12]. Based on these findings, another recent study has suggested that higher levels of PA—and therefore, a better perception of HRQoL—were significantly associated with successful ageing measured with the Successful Ageing Scale for older adults [13]. Successful ageing is considered a complex construct cluster of factors such as QoL, life satisfaction and well-being, which includes movement behaviours [14]. Higher levels of PA predicted further maintenance of functional status including functional capacity, increased muscle mass and strength, which had also been related to HRQoL in this population [15]. Another study suggested that higher amounts of PA could improve cognitive and physical functions, leading to a positive effect on successful ageing in older adults [16]. High levels of SB had also been associated with HRQoL among older adults. Kim and colleagues showed that higher SB levels as older adults age were associated with poorer HRQoL [17]. A recent overview of systematic reviews showed that movement behaviours, including SB and PA levels, influenced heath status among adults [18]. SB and PA and their relationship to HRQoL have been widely studied in younger age groups (e.g., adolescents, adults) [19,20]. However, scientific literature on the older adult population is limited [21,22], and even more scarce using device-based measures of PA and SB in large samples [23].
Therefore, this study aimed to further investigate the associations between self-reported SB and PA and device-measured SB and PA with HRQoL in a cohort of community-dwelling older adults from four European countries.

2. Materials and Methods

2.1. Participants

This study used a cross-sectional design using baseline data from the SITLESS study. Briefly, the SITLESS study is a multi-centre, pragmatic, three-armed, randomised controlled trial. The purpose of the study was to determine whether an exercise referral scheme, enhanced by self-management strategies to reduce SB, could increase PA levels and improve health in the long term in community-dwelling European older adults (≥65 years old) from Spain, Germany, Denmark and United Kingdom (U.K.; in Northern Ireland). Included participants were insufficiently active (according to general guidelines) and/or reported being highly sedentary (>6 h in SB) [24]. The SITLESS study was approved by the Ethics and Research Committee of each institution. Participation was voluntary and all participants signed informed consent before the first assessment. Out of 1360 community-dwelling older adults, a subsample of 1193 participants who fully completed the SF-12, the SBQ, the modified IPAQ and returned valid accelerometer data from the hip-worn ActiGraph were analysed for this cross-sectional study.

2.2. Data Collection

Personal information including age, sex and educational background was collected with an individual interview and the number of current medications was obtained through the healthcare electronic registry. All demographic characteristics were collected during each assessment across all sites between 2016 and 2017. Weight and height were measured using a TANITA BC 420 and a SECA 213 portable stadiometer to obtain the participants’ body mass index (BMI). Participants were asked to self-report their daily sedentary time using the SBQ [25] and their total time spent walking (LPA; 3.3 METs per minute), in moderate PA (MPA; 4 METs per minute) and in vigorous PA (VPA; 8 METs per minute) using the modified IPAQ [26]. Both questionnaires are valid to assess SB [27] and PA [28]. Some limitations such as recall bias and poor correlation with objective measures are likely with self-reported measures of both behaviours in older adults [27,29,30,31,32]. However, they have been widely used in large-scale studies due to their low administration cost. Moreover, the SBQ provides relevant information regarding context of behaviour [33]. Self-rated HRQoL was assessed using the 12-Item Short-Form Health Survey (SF-12) to obtain the physical and mental composite scores [34]. The SF-12 questionnaire has been used as an important tool to describe self-reported perception of HRQoL [35]. The SF-12 scoring method proposed by Ware et al. (1996) assumes that each item (from an 8-dimension profile including physical functioning, role limitations due to physical problems, bodily pain, general health, vitality, limitations due to emotional problems and mental health) contributes to both the physical component score (PCS) or the mental component score (MCS), and that these two measures are uncorrelated [34]. To overcome the aforementioned limitations of self-reported measures, participants were also asked to wear an ActiGraph wGT3X-BT triaxial accelerometer (ActiGraph, LLC, Pensacola, FL, USA) on their dominant hip during waking hours for seven consecutive days. Participants were asked to remove the device only for water-based activities (e.g., bathing or swimming) and during sleep time. Wear time and non-wear time was calculated using the Choi 2011 algorithm [36]. A small number of participants wore the device continuously (i.e., no removal during sleep). To reduce conflation of sleep and SB time, a pragmatic maximum daily wear time threshold of 19 h was used. For relevant participants, their activity diary was used to record on/off times. The accelerometers were initialised to collect data at 30 Hz using the normal filter setting. A valid accelerometer dataset contained at least four valid days (including at least one weekend day), with a valid day defined as containing at least 600 min of wear time to be included in the analysis as in previous studies [37]. SB was defined as <100 counts per minute (CPM), LPA as 100–2019 CPM and MVPA as ≥2020 CPM on the vertical axis [38]. Raw accelerometry data were analysed using ActiLife v6.13.3 software summarised into 10 s epochs, as has been recommended for estimation of SB in clinical older adult populations [39].

2.3. Statistical Analysis

Demographic characteristics of the sample size were presented descriptively as mean and standard deviation (SD) for continuous variables and percentage for categorical variables, separately by country and overall. The association between self-reported and objective measures of SB and PA and HRQoL were quantified using non-parametric Spearman’s Rho coefficients after all variables were examined for normality using the Kolmogorov–Smirnov test. Correlations were interpreted as follows: coefficient value between +1 and −1, perfect positive/negative linear relationship or correlation; between +0.8 and −0.9, very strong positive/negative linear relationship or correlation; between +0.6 and −0.7, moderate positive/negative linear relationship or correlation; between +0.3 and −0.5, fair positive/negative linear relationship or correlation; between +0.1 and −0.2, poor positive/negative linear relationship or correlation; 0, non-linear relationship or correlation [40]. The strength of the association between self-reported time spent in PA and SB (model a) and device-measured daily time spent in PA and SB (model b) and self-rated HRQoL (SF-12: MCS and PCS) were assessed through multivariate multiple regression analysis. Model c and model d were adjusted for covariates: country (Spain, Germany, Denmark and U.K.), age (years), sex (male/female), BMI categories (≤24.9 underweight and normal, 25.0–29.9 overweight, ≥30 obese), educational background (never attended school, primary education, secondary education, university, unknown) and number of current medications. Each independent variable was investigated for collinearity using the variance inflation factor (VIF; collinearity: VIF > 4), which identifies correlation between independent variables and the strength of that correlation. All statistical analyses were performed using IBM SPSS Statistics 26 (SPSS, Inc., an IBM Company, Chicago, IL, USA) and the significance level was set at p < 0.05.

3. Results

Out of the 1360 participants in the SITLESS trial, 167 participants were excluded as they did not meet the pre-specified ActiGraph wear time criteria or did not complete the self-reported questionnaires. A final subsample of 1193 participants (75.1 ± 6.2 years old, 61% women) returned valid accelerometer data, completed the SF-12, the SBQ and the IPAQ.
Descriptive characteristics of the sample are presented in Table 1. Approximately 75% of the overall sample was overweight or obese (mean BMI = 28.8 ± 5.2 and ranged from 16.7 kg/m2 to 51.5 kg/m2). Women BMI ranged from 16.7 kg/m2 to 51.5 kg/m2 (mean women BMI = 28.9 ± 5.8) and men BMI ranged from 19.8 kg/m2 to 45.8 kg/m2 (mean men BMI = 28.7 ± 4.2). Of all participants, 54.1% reported having completed secondary education while 3.1% of the overall sample reported that they never attended school. The number of current medications ranged from 0 to 19 with a mean of 4.5 ± 3.2. Some 70% of the participants reported good to excellent general health. Nevertheless, a small percentage (3.9%) reported poor general health status with the SF-12 survey. The PCS across all participants (45.0 ± 9.1) ranged from 15.6 to 65.7. The MCS across all participants (51.9 ± 8.9) ranged from 18.4 to 71.1.
Descriptive characteristics of self-reported and device-measured SB and PA levels are shown in Table 1. No significant differences (p = 0.082) were found between countries regarding the self-reported average mean hours per day of SB (overall mean average 7.7 ± 2.8 h/d). Self-reported LPA differed (p < 0.05 using Bonferroni’s test) between Spanish (1.0 ± 1.3 h/d) and U.K. participants (1.0 ± 1.0 h/d) versus Danish (1.3 ± 1.6 h/d) and German participants (1.3 ± 1.4 h/d). U.K. participants showed significant differences (p < 0.05 using Bonferroni’s test) against other countries’ participants regarding MVPA levels (overall 0.7 ± 1.1 h/d). Device-measured SB was 78.8% of daily awake time out of 14.4 h of mean daily wear time in the overall sample. Danish participants showed the highest proportion of device-measured daily SB (81.0%). Device-measured daily LPA was 18.6% and MVPA was 2.6% in the overall sample. U.K. participants showed the highest levels on both PA intensities (19.8% and 3.7%, respectively).
Table 2 displays the association between self-reported and device-measured levels of PA and SB with PCS as well as MCS. PA and SB levels, both device-measured and self-reported, showed a poor-to-fair significant association (p ≤ 0.05 across all PA and SB variables) with PCS. It is important to note that all SB-related measures (both device-based assessment and self-reported) showed a poor negative significant association with PCS in the model (e.g., the less sedentary an individual was, the better PCS was perceived) (p < 0.05). Nevertheless, when the association between self-reported and device-measured levels of PA and SB with MCS was analysed, significant associations were only found between self-reported LPA and MVPA (p < 0.05), but no significant associations were found between device-measured daily hours in LPA and MVPA or daily hours in SB with MCS.
Table 3 shows the multivariate multiple regression models for PCS and MCS, unadjusted (models a and b) and adjusted for relevant covariates (models c and d). The full model a for the PCS adjusted by self-reported PA and SB time predicted 19% of the total variance (p < 0.001). The effect modification was significant (p < 0.05) in all explanatory self-reported variables except for LPA time (IPAQ). The same model for the MCS predicted 0.07% of the total variance (p < 0.001) with a significant effect modification in self-reported daily sedentary time (SBQ) (p < 0.05). No collinearity was identified between each independent variable of this model (VIF ranges from 1.02 to 1.15). The full model b for PCS adjusted by device-measured SB and PA (LPA and MVPA) predicted 14% of the total variance (p < 0.001) with a significant effect modification (p < 0.05) in device-measured LPA, MVPA and SB. On the other hand, the full model for MCS adjusted for device-measured SB and PA (LPA and MVPA) predicted 0.1% of the total variance (p < 0.001) with a significant effect modification (p < 0.05) in MVPA and sedentary time. No collinearity was identified between each independent variable of this model (VIF ranges from 1.10 to 1.47). The model c for PCS and MCS adjusted by self-reported PA and SB, and for covariates predicted 25% and 0.8% of the total variance (p < 0.001), respectively. The model d for the same dependent variables was adjusted by device-measured SB and PA, and for covariates predicted 29% of the total variance for PCS and 0.08% for MCS (p < 0.001).

4. Discussion

This study aimed to analyse the association between self-reported and device-measured SB and PA with HRQoL in a cohort of community-dwelling older adults from four European countries, assessing possible differences between the PCS and the MCS of the SF-12 questionnaire. Our results showed poor-to-fair significant associations between self-reported and device-measured SB and PA with the PCS. When assessing these associations with MCS, poor significant associations were found with self-reported LPA and MVPA. Our multivariate multiple regression models adjusted by self-reported and device-measured PA and SB predicted between 19% and 14% of the variance in the PCS by both self-reported and device-measured SB and PA. For MCS, the same models, self-reported and device-measured PA and SB, predicted between 0.07% and 0.1% of the variance.
A recent study that analysed self-reported PA levels with HRQoL using the SF-12 found that more active participants reported higher levels of HRQoL as well as significantly higher MCS (p < 0.01) [8]. This significant association of self-reported PA with MCS differs from our findings, and could be potentially explained due to a difference in the age of the sample and the use of a different questionnaire which assessed several types of PA and different intensities using METs [8]. Another recent study found that self-reported physical inactivity was significantly associated with diminished HRQoL, including physical and mental health composite scores [41]. In our study, device-measured PA levels were only significantly associated with PCS. A recent study that aimed to assess the association of device-measured PA levels of different intensities (light, moderate and MVPA) in women with fibromyalgia found that all PA intensity levels were positively associated with HRQoL [42]. Our study found a higher association with device-measured MVPA and PCS than in the al-Ándalus project [42]. This higher association found in our study could be explained due to a worse baseline functional state in the al-Ándalus project’s population (mean PCS 29.5 ± 6.9 versus PCS 45.0 ± 9.1 units), which may prevent their participants from engaging in higher-intensity PA. Davis and colleagues (2014) found that higher levels of device-based MVPA and greater SB were associated with greater and worse Short Physical Performance Battery scores, respectively, in adults >70 years old [43]. Previous research has suggested that engaging in MVPA could contribute to better physical function performance in older adults, which is related to some of the eight dimensions assessed in the SF-12 to assess PCS, such as physical functioning or limitations due to physical problems [44,45,46]. However, taking into account the cross-sectional nature of these data, such associations could reflect bi-directional causation, where a better physical function is needed to engage in most types of MVPA.
Previous scientific literature suggests that PA and SB are independently associated with HRQoL, and that results regarding SB are still mostly inconsistent [47]. In our study, we found a significant, negative and poor association between self-reported SB and PCS. López-Torres and colleagues (2019) also concluded that higher self-reported SB levels were significantly associated with poorer HRQoL. Likewise, Wilson and colleagues [10] found that daily sitting time was negatively and significantly associated with HRQoL. Similarly, other recent studies found significant associations between device-measured SB and HRQoL [42,48,49,50,51]. However, previous studies suggested that device-measured PA levels tend to be more valid and reliable than self-reported [38,52]. Therefore, the lower associations between self-reported SB and PA levels with PCS compared to device-measured SB and PA levels could be explained by the accuracy in measuring both behaviours. Nevertheless, having analysed the associations between SB and PA between MCS, only significant but poor associations were found with self-reported PA levels.
The model used to predict MCS from the al-Ándalus project had higher predictive capability (R2 = 0.19) compared to our results in both SB and PA models with the MCS [42]. However, the PCS models with both self-reported and device-measured SB and PA showed higher prediction of the total variance compared to the al-Ándalus project (R2 = 0.04). As previously stated, the different regression coefficients between studies could be explained by the different sample characteristics (e.g., the sample with women suffering from fibromyalgia included in the al-Ándalus project reported lower PCS than older adults from the SITLESS study) or the instrument used to assess HRQoL. Our results may not be clinically significant due to some of the correlations being poor. However, these findings may provide insight when designing strategies and interventions aimed at improving both physical and mental components of overall quality of life. Furthermore, we must suggest that public resources should be allocated to strategies aimed at reducing SB and increasing PA levels to improve quality of life of older adults.

Strengths and Limitations

Some limitations of the present cross-sectional study are worth noting. It is difficult to determine associations between physical behaviours and HRQoL due to the large number of factors related to HRQoL [53]. The strengths of this study include the device-measured SB and PA of a large heterogeneous sample of community-dwelling older adults from four European countries as well as their association with HRQoL in both the physical and mental components.

5. Conclusions

In conclusion, the findings of this study suggest that both self-reported or device-measured PA intensity levels were positively and statistically significant associated with PCS of the SF-12. On the other hand, SB was negatively and statistically significant associated with the PCS of the SF-12. These findings indicate the importance of increasing PA and reducing SB levels if the physical function component related to HRQoL is to be improved. Only self-reported LPA and MVPA were statistically significant with MCS. In general, some of the associations we found were very poor, but our results support the accumulating research of the benefits of increasing PA levels and reducing sitting time for better HRQoL among older adults. Further longitudinal studies are necessary to confirm the associations between the PCS and self-rated health and further explore associations between the MCS.

Author Contributions

Conceptualisation, O.S.-N., M.G.-G. and C.M.-B.; methodology, O.S.-N., M.G.-G. and C.M.-B.; formal analysis, O.S.-N.; prepared device-based data, J.J.W.; investigation, O.S.-N., M.G.-G., B.R.-R., M.G.-B., K.F., J.J.W., P.C., P.Ø.O., N.E.B., D.R., D.D., M.R.-F., E.M. and C.M.-B.; writing—original draft preparation, O.S.-N., M.G.-G. and C.M.-B.; writing—review and editing, O.S.-N., M.G.-G., B.R.-R., M.G.-B., K.F., J.J.W., P.C., P.Ø.O., N.E.B., D.R., D.D., M.R.-F., E.M. and C.M.-B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the European Union Horizon 2020 programme (grant number H2020-Grant 634270).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics and Research Committee of each intervention site: the Ethics and Research Committee of Ramon Llull University (Fundació Blanquerna, Spain), the Regional Committees on Health Research Ethics for Southern Denmark (University of Southern Denmark, Denmark), Office for Research Ethics Committees in Northern Ireland (ORECNI) (Queen’s University of Belfast) and the Ethical Review Board of Ulm University (Ulm, Germany).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

The authors wish to express their appreciation for all those who volunteered for this study. Consortium members of the participating organisations of the SITLESS project: Antoni Salvà Casanovas, Àlex Domingo, Marta Roqué and Laura Coll-Planas—Health and Ageing Foundation of the Autonomous University of Barcelona, Barcelona, Spain; Maria Giné-Garriga, Miriam Guerra-Balic, Carme Martin-Borràs, Javier Jerez-Roig, Guillermo R. Oviedo, Marta Santiago-Carrés, Oriol Sansano-Nadal, Manel Farré-Font and Kelly Ferri—Faculty of Psychology, Education and Sport Sciences Blanquerna, Ramon Llull University, Barcelona, Spain; Emma McIntosh and Manuela Deidda—Health Economics and Health Technology Assessment, University of Glasgow, Glasgow, United Kingdom; Dietrich Rothenbacher, Michael Denkinger, Katharina Wirth, Dhayana Dallmeier and Jochen Klenk—Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany; Frank Kee—Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, United Kingdom; Mark A. Tully, Jason J. Wilson, Nicole E. Blackburn and Ilona McMullan—School of Health Sciences, Ulster University, Northern Ireland, United Kingdom; Paolo Caserotti and Mathias Skjødt—Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; Guillaume Lefebvre—SIEL, Sport Initiative et Loisir Bleu association, Straßbourg, France; Denise González—SIEL, Sport Initiative et Loisir Bleu Association, Barcelona, Spain.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. López-Torres, O.; del Pozo-Cruz, B.; Maroto-Sánchez, B.; Vila-Maldonado, S.; Gómez-Cabello, A.; Martín-García, M.; González-Agüero, A.; Gusi, N.; Espino, L.; Casajús, J.A.; et al. Does fitness attenuate the relationship between changes in sitting time and health-related quality of life over time in community-dwelling older adults? Evidence from the EXERNET multicenter longitudinal study. Qual. Life Res. 2019, 28, 3259–3266. [Google Scholar] [CrossRef] [PubMed]
  2. 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. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. He, W.; Goodkind, D.; Kowal, P. An Aging World: 2015. Int. Popul. Rep. 2016, 16, 95. [Google Scholar]
  4. Federal Office of Statistics of Germany. German Population by 2060; Federal Statistical Office: Wiesbaden, Germany, 2015. [Google Scholar]
  5. 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.; On Behalf of Sbrn Terminology Consensus Project Participants. Sedentary Behavior Research Network (SBRN)—Terminology Consensus Project process and outcome. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 75. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Boberska, M.; Szczuka, Z.; Kruk, M.; Knoll, N.; Keller, J.; Hohl, D.H.; Luszczynska, A. Sedentary behaviours and health-related quality of life. A systematic review and meta-analysis. Health Psychol. Rev. 2018, 12, 195–210. [Google Scholar] [CrossRef]
  7. Kim, J.; Im, J.S.; Choi, Y.H. Objectively measured sedentary behavior and moderate-tovigorous physical activity on the health-related quality of life in US adults: The National Health and Nutrition Examination Survey 2003–2006. Qual. Life Res. 2017, 26, 1315–1326. [Google Scholar] [CrossRef]
  8. Su, T.T.; Azzani, M.; Adewale, A.P.; Thangiah, N.; Zainol, R.; Majid, H. Physical activity and health-related quality of life among low-income adults in metropolitan kuala lumpur. J. Epidemiol. 2019, 29, 43–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Withall, J.; Stathi, A.; Davis, M.; Coulson, J.; Thompson, J.L.; Fox, K.R. Objective indicators of physical activity and sedentary time and associations with subjective well-being in adults aged 70 and over. Int. J. Environ. Res. Public Health 2014, 11, 643–656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Wilson, J.J.; Blackburn, N.E.; O’Reilly, R.; Kee, F.; Caserotti, P.; Tully, M.A. Association of objective sedentary behaviour and self-rated health in English older adults. BMC Res. Notes 2019, 12, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Liposcki, D.B.; da Silva Nagata, I.F.; Silvano, G.A.; Zanella, K.; Schneider, R.H. Influence of a Pilates exercise program on the quality of life of sedentary elderly people: A randomized clinical trial. J. Bodyw. Mov. Ther. 2019, 23, 390–393. [Google Scholar] [CrossRef]
  12. Tew, G.A.; Howsam, J.; Hardy, M.; Bissell, L. Adapted yoga to improve physical function and health-related quality of life in physically-inactive older adults: A randomised controlled pilot trial. BMC Geriatr. 2017, 17, 131. [Google Scholar] [CrossRef]
  13. Choi, M.; Lee, M.; Jung, D.; Lee, M.-J. Physical activity, quality of life and successful ageing among community-dwelling older adults. Int. Nurs. Rev. 2017, 64, 396–404. [Google Scholar] [CrossRef] [PubMed]
  14. Ziółkowski, A.; Błachnio, A.; Pąchalska, M. An evaluation of life satisfaction and health—Quality of life of senior citizens. Ann. Agric. Environ. Med. 2015, 22, 147–151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Cunningham, C.; O’ Sullivan, R.; Caserotti, P.; Tully, M.A. Consequences of physical inactivity in older adults: A systematic review of reviews and meta-analyses. Scand. J. Med. Sci. Sports 2020, 30, 816–827. [Google Scholar] [CrossRef]
  16. Lin, Y.-H.; Chen, Y.-C.; Tseng, Y.-C.; Tsai, S.-T. Physical activity and successful aging among middle-aged and older adults: A systematic review and meta-analysis of cohort studies. Aging 2020, 12, 7704–7716. [Google Scholar] [CrossRef]
  17. Kim, Y.; Lee, E. The association between elderly people’s sedentary behaviors and their health-related quality of life: Focusing on comparing the young-old and the old-old. Health Qual. Life Outcomes 2019, 17, 1–9. [Google Scholar] [CrossRef] [Green Version]
  18. Saunders, T.J.; McIsaac, T.; Douillette, K.; Gaulton, N.; Hunter, S.; Rhodes, R.E.; Prince, S.A.; Carson, V.; Chaput, J.P.; Chastin, S.; et al. Sedentary behaviour and health in adults: An overview of systematic reviews. Appl. Physiol. Nutr. Metab. 2020, 45, S197–S217. [Google Scholar] [CrossRef]
  19. Palomino-Devia, C.; Reyes-Oyola, F.A.; Sánchez-Oliver, A. Levels of physical activity, health-related quality of life, physical self-concept and body-mass index among Colombian students. Biomedica 2018, 38, 224–231. [Google Scholar] [CrossRef]
  20. Ge, Y.; Xin, S.; Luan, D.; Zou, Z.; Liu, M.; Bai, X.; Gao, Q. Association of physical activity, sedentary time, and sleep duration on the health-related quality of life of college students in Northeast China. Health Qual. Life Outcomes 2019, 17, 1–8. [Google Scholar] [CrossRef] [Green Version]
  21. Heesch, K.C.; Hill, R.L.; Aguilar-Farias, N.; Van Uffelen, J.G.Z.; Pavey, T. Validity of objective methods for measuring sedentary behaviour in older adults: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2018, 15, 1–17. [Google Scholar] [CrossRef] [PubMed]
  22. Chigateri, N.G.; Kerse, N.; Wheeler, L.; MacDonald, B.; Klenk, J. Validation of an accelerometer for measurement of activity in frail older people. Gait Posture 2018, 66, 114–117. [Google Scholar] [CrossRef] [PubMed]
  23. Matthews, C.E.; Hagstromer, M.; Pober, D.M.; Bowles, H.R. Best practices for using physical activity monitors in population-based research. Med. Sci. Sports Exerc. 2012, 44, S68–S76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Giné-Garriga, M.; Coll-Planas, L.; Guerra, M.; Domingo, À.; Roqué, M.; Caserotti, P.; Denkinger, M.; Rothenbacher, D.; Tully, M.A.; Kee, F.; et al. The SITLESS project: Exercise referral schemes enhanced by self-management strategies to battle sedentary behaviour in older adults: Study protocol for a randomised controlled trial. Trials 2017, 18, 1–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Rosenberg, D.E.; Norman, G.J.; Wagner, N.; Patrick, K.; Calfas, K.J.; Sallis, J.F. Reliability and Validity of the Sedentary Behavior Questionnaire (SBQ) for Adults. J. Phys. Act. Health 2010, 7, 697–705. [Google Scholar] [CrossRef]
  26. Hurtig-Wennlf, A.; Hagstrmer, M.; Olsson, L.A. The International Physical Activity Questionnaire modified for the elderly: Aspects of validity and feasibility. Public Health Nutr. 2010, 13, 1847–1854. [Google Scholar] [CrossRef]
  27. Sansano-Nadal, O.; Wilson, J.J.; Martín-Borràs, C.; Brønd, J.C.; Skjødt, M.; Caserotti, P.; Figuls, M.R.I.; Blackburn, N.E.; Klenk, J.; Rothenbacher, D.; et al. Validity of the Sedentary Behavior Questionnaire in European Older Adults Using English, Spanish, German and Danish Versions. Meas. Phys. Educ. Exerc. Sci. 2021, 1–14. [Google Scholar] [CrossRef]
  28. Lines, R.L.; Ntoumanis, N.; Thøgersen-Ntoumani, C.; McVeigh, J.A.; Ducker, K.J.; Fletcher, D.; Gucciardi, D.F. Cross-sectional and longitudinal comparisons of self-reported and device-assessed physical activity and sedentary behaviour. J. Sci. Med. Sport 2020, 23, 831–835. [Google Scholar] [CrossRef] [PubMed]
  29. Urda, J.L.; Larouere, B.; Verba, S.D.; Lynn, J.S. Comparison of subjective and objective measures of office workers’ sedentary time. Prev. Med. Rep. 2017, 8, 163–168. [Google Scholar] [CrossRef]
  30. Gilbert, A.L.; Lee, J.; Ma, M.; Semanik, P.A.; DiPietro, L.; Dunlop, D.D.; Chang, R.W. Comparison of subjective and objective measures of sedentary behavior using the Yale Physical Activity Survey and accelerometry in patients with rheumatoid arthritis. J. Phys. Act. Health 2016, 13, 371–376. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Aguilar-Farías, N.; Brown, W.; Olds, T.S.; Peeters, G.G. Validity of self-report methods for measuring sedentary behaviour in older adults. J. Sci. Med. Sport 2014, 18, 662–666. [Google Scholar] [CrossRef]
  32. Hart, T.L.; Ainsworth, B.E.; Tudor-Locke, C. Objective and subjective measures of sedentary behavior and physical activity. Med. Sci. Sports Exerc. 2011, 43, 449–456. [Google Scholar] [CrossRef]
  33. Ainsworth, B.; Cahalin, L.; Buman, M.; Ross, R. The Current State of Physical Activity Assessment Tools. Prog. Cardiovasc. Dis. 2015, 57, 387–395. [Google Scholar] [CrossRef]
  34. Ware, J.E.; Kosinski, M.; Keller, S.D. A 12-Item Short-Form Health Survey: Construction of Scales and Preliminary Tests of Reliability and Validity. Med. Care 1996, 34, 220–233. [Google Scholar] [CrossRef] [Green Version]
  35. Thiel, D.M.; Al Sayah, F.; Vallance, J.K.; Johnson, S.T.; Johnson, J.A. Association between Physical Activity and Health-Related Quality of Life in Adults with Type 2 Diabetes. Can. J. Diabetes 2017, 41, 58–63. [Google Scholar] [CrossRef] [Green Version]
  36. Choi, L.; Liu, Z.; Matthews, C.E.; Buchowski, M.S. Validation of accelerometer wear and nonwear time classification algorithm. Med. Sci. Sports Exerc. 2011, 43, 357–364. [Google Scholar] [CrossRef] [Green Version]
  37. Migueles, J.H.; Cadenas-Sanchez, C.; Ekelund, U.; Delisle Nyström, C.; Mora-Gonzalez, J.; Löf, M.; Labayen, I.; Ruiz, J.R.; Ortega, F.B. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports Med. 2017, 47, 1821–1845. [Google Scholar] [CrossRef] [PubMed]
  38. 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. [Google Scholar] [CrossRef] [PubMed]
  39. Byrom, B.; Stratton, G.; Mc Carthy, M.; Muehlhausen, W. Objective measurement of sedentary behaviour using accelerometers. Int. J. Obes. 2016, 40, 1809–1812. [Google Scholar] [CrossRef] [Green Version]
  40. Akoglu, H. User’s guide to correlation coefficients. Turk. J. Emerg. Med. 2018, 18, 91–93. [Google Scholar] [CrossRef] [PubMed]
  41. Wardoku, R.; Blair, C.; Demmer, R.; Prizment, A. Association between physical inactivity and health-related quality of life in adults with coronary heart disease. Maturitas 2019, 128, 36–42. [Google Scholar] [CrossRef]
  42. Gavilán-Carrera, B.; Segura-Jiménez, V.; Estévez-López, F.; Álvarez-Gallardo, I.C.; Soriano-Maldonado, A.; Borges-Cosic, M.; Herrador-Colmenero, M.; Acosta-Manzano, P.; Delgado-Fernández, M. Association of objectively measured physical activity and sedentary time with health-related quality of life in women with fibromyalgia: The al-Ándalus project. J. Sport Health Sci. 2019, 8, 258–266. [Google Scholar] [CrossRef]
  43. Davis, M.G.; Fox, K.R.; Stathi, A.; Trayers, T.; Thompson, J.L.; Cooper, A.R. Objectively Measured Sedentary Time and its Association With Physical Function in Older Adults. J. Aging Phys. Act. 2014, 22, 474–481. [Google Scholar] [CrossRef] [PubMed]
  44. Lai, T.-F.; Liao, Y.; Lin, C.-Y.; Huang, W.-C.; Hsueh, M.-C.; Chan, D.-C. Moderate-to-vigorous physical activity duration is more important than timing for physical function in older adults. Sci. Rep. 2020, 10, 1–6. [Google Scholar] [CrossRef]
  45. Keevil, V.L.; Cooper, A.J.M.; Wijndaele, K.; Luben, R.; Wareham, N.J.; Brage, S.; Khaw, K.T. Objective Sedentary Time, Moderate-to-Vigorous Physical Activity, and Physical Capability in a British Cohort. Med. Sci. Sports Exerc. 2016, 48, 421–429. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Gebel, K.; Ding, D.; Bauman, A.E. Volume and intensity of physical activity in a large population-based cohort of middle-aged and older Australians: Prospective relationships with weight gain, and physical function. Prev. Med. 2014, 60, 131–133. [Google Scholar] [CrossRef] [PubMed]
  47. Bampton, E.A.; Johnson, S.T.; Vallance, J.K. Profiles of resistance training behavior and sedentary time among older adults: Associations with health-related quality of life and psychosocial health. Prev. Med. Rep. 2015, 2, 773–776. [Google Scholar] [CrossRef] [Green Version]
  48. Ge, L.; Ong, R.; Yap, C.W.; Heng, B.H. Effects of chronic diseases on health-related quality of life and self-rated health among three adult age groups. Nurs. Health Sci. 2019, 21, 214–222. [Google Scholar] [CrossRef]
  49. Davies, C.A.; Vandelanotte, C.; Duncan, M.J.; van Uffelen, J.G.Z. Associations of physical activity and screen-time on health related quality of life in adults. Prev. Med. 2012, 55, 46–49. [Google Scholar] [CrossRef]
  50. Rosenberg, D.E.; Bellettiere, J.; Gardiner, P.A.; Villarreal, V.N.; Crist, K.; Kerr, J. Independent Associations between Sedentary Behaviors and Mental, Cognitive, Physical, and Functional Health among Older Adults in Retirement Communities. J. Gerontol. Ser. A Boil. Sci. Med. Sci. 2015, 71, 78–83. [Google Scholar] [CrossRef] [Green Version]
  51. Cheak-Zamora, N.C.; Wyrwich, K.W.; McBride, T.D. Reliability and validity of the SF-12v2 in the medical expenditure panel survey. Qual. Life Res. 2009, 18, 727–735. [Google Scholar] [CrossRef]
  52. Prince, S.A.; Adamo, K.B.; Hamel, M.E.; Hardt, J.; Gorber, S.C.; Tremblay, M. A comparison of direct versus self-report measures for assessing physical activity in adults: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2008, 5, 1–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Theofilou, P. Quality of Life: Definition and Measurement. Eur. J. Psychol. 2013, 9, 150–162. [Google Scholar] [CrossRef]
Table
Table 1. Descriptive variables of the sample.
Table 1. Descriptive variables of the sample.
Overall
(n = 1193)		Spain
(n = 263)		Germany
(n = 304)		Denmark
(n = 318)		U.K.
(n = 308)
Age (years), mean (SD)75.2 (6.2)75.9 (6.4)74.7 (6.1)77.3 (5.7)72.6 (5.5)
Sex (women), n (%)733 (61.4)207 (78.7)172 (56.6)185 (58.2)169 (54.9)
BMI, mean (SD)28.8 (5.2)28.9 (5.0)29.2 (5.6)27.3 (5.0)28.9 (5.0)
BMI categories, n (%)
    Underweight and normal (≤24.9 kg/m2)274 (23.0)39 (14.8)61 (20.1)106 (33.4)68 (22.1)
    Overweight (25 to 29.9 kg/m2)488 (40.9)102 (38.8)131 (43.1)129 (40.7)126 (40.9)
    Obese (≥30 kg/m2)430 (36.1)122 (46.4112 (36.8)82 (25.9)114 (37.0)
Educational background, n (%)
    Never attended school37 (3.1)35 (13.4)1 (0.3)1 (0.3)-
    Primary education232 (19.5)112 (42.9)9 (3.0)93 (29.3)18 (5.9)
    Secondary education642 (54.1)84 (32.2)217 (71.9)182 (57.4)159 (51.8)
    University272 (22.9)29 (11.1)74 (24.5)40 (12.6)129 (42.0)
    Unknown4 (0.4)3 (0.4) 3 (0.3)2 (0.3)2 (0.3)
Number of current medications, mean (range)4.5 (0–19)4.8 (0–16)4.3 (0–16)3.9 (0–14)4.8 (0–19)
Self-reported general health (SF-12), n (%)
    Excellent35 (2.9)1 (0.4)1 (0.3)16 (5.0)17 (5.5)
    Very good216 (18.1)19 (7.2)24 (7.9)69 (21.7)104 (33.8)
    Good584 (49.0)116 (44.1)160 (52.6)177 (55.7)131 (42.5
    Fair311 (26.1)106 (40.3)107 (35.2)51 (16.0)47 (15.3)
    Poor47 (3.9)21 (8.0)12 (3.9)5 (1.6)9 (2.9)
Physical component (SF-12), mean (SD)45.0 (9.1)42.1 (9.4)43.6 (8.7)46.1 (8.6)47.6 (8.9)
Mental component (SF-12), mean (SD)51.9 (8.9)49.7 (9.1)50.1 (9.3)54.1 (8.1)53.2 (8.2)
Self-reported SB (h/d), mean (SD)
    Daily sedentary time7.7 (2.8)7.4 (3.2)7.6 (2.4)7.9 (2.7)7.9 (2.8)
IPAQ (h/d), mean (SD)
    LPA1.9 (1.3)1.0 (1.0)1.3 (1.4)1.3 (1.6)1.0 (1.0)
    MVPA0.6 (1.1)1.1 (1.1)0.6 (0.9)0.5 (0.8)1.1 (1.2)
Accelerometry (h/d), mean (SD)
    Daily sedentary time11.3 (1.3)11.3 (1.3)11.2 (1.3)11.7 (1.1)10.9 (1.1)
    Daily LPA2.7 (0.9)2.6 (0.8)2.7 (0.9)2.5 (0.9)2.8 (0.8)
    Daily MVPA0.4 (0.3)0.3 (0.3)0.4 (0.3)0.2 (0.2)0.5 (0.4)
Total wear time (h/d), mean (SD)14.4 (1.2)14.2 (1.2)14.3 (1.2)14.5 (1.1)14.3 (1.0)
BMI: body mass index; IPAQ: international physical activity questionnaire; LPA: light physical activity; MVPA: moderate-vigorous physical activity.
Table
Table 2. Relationship between self-reported and device-measured SB and PA, and the physical and mental component scores of the SF-12 (HRQoL). Spearman’s Rho coefficients and p-values.
Table 2. Relationship between self-reported and device-measured SB and PA, and the physical and mental component scores of the SF-12 (HRQoL). Spearman’s Rho coefficients and p-values.
Physical Component SF-12Mental Component SF-12
Spearman’s Rhop-ValueSpearman’s Rhop-Value
Self-reported SB (SBQ)
Daily sedentary time−0.078 **0.0070.0330.251
Self-reported PA (IPAQ)
    LPA0.091 **0.0020.058 *0.046
    MVPA0.204 **<0.0010.072 *0.013
Accelerometry
    Daily Sedentary Time−0.263 **<0.0010.0120.674
    Daily LPA0.168 **<0.001−0.0290.319
    Daily MVPA0.419 **<0.0010.0510.081
SBQ: sedentary behaviour questionnaire; IPAQ: international physical activity questionnaire; MVPA: moderate-vigorous physical activity; LPA: light physical activity. ** p ≤ 0.001; * p ≤ 0.05.
Table
Table 3. Multivariate multiple regression models for PCS and MCS adjusted by self-reported and device-measured PA and SB levels.
Table 3. Multivariate multiple regression models for PCS and MCS adjusted by self-reported and device-measured PA and SB levels.
PCSMCS
R2Non St. Betap-ValueR2Non St. Betap-Value
Model a0.19 <0.0010.007 <0.001
LPA (IPAQ) 0.1980.322 0.3160.105
MVPA (IPAQ) 0.7350.002 0.0710.763
Daily sedentary time (SBQ) −0.3130.001 −0.2000.028
Model b0.148 <0.0010.016 <0.001
LPA (ActiGraph) 0.7930.019 0.1610.647
MVPA (ActiGraph) 10.210<0.001 2.5640.003
Sedentary time (ActiGraph) 0.6370.005 0.880<0.001
Model c0.258-<0.0010.081-<0.001
LPA (IPAQ) −0.0470.797 0.2970.133
MVPA (IPAQ) 0.4120.063 −0.0780.748
Daily sedentary time (SBQ) −0.2440.004 −0.2200.017
Country (Denmark as reference)
    Spain −2.2840.002 −3.464<0.001
    UK 1.1630.103 −0.9190.237
    Germany −2.705<0.001 −4.334<0.001
Age (years) −0.197<0.001 0.0700.120
Sex (women as reference)
    Men 1.5890.002 2.101<0.001
BMI categories (obese as reference)
    Underweight and normal 2.666<0.001 −1.5150.038
    Overweight 3.226<0.001 −0.0700.908
Educational background (university as reference)
    Never attended school and primary −0.6500.418 −1.7780.042
    Secondary 0.0450.766 −0.2250.173
Number of current medications −0.976<0.001 −0.2130.010
Model d0.293-<0.0010.082-<0.001
LPA (ActiGraph) 0.3890.244 0.4930.187
MVPA (ActiGraph) 6.434<0.001 1.8250.011
Sedentary time (ActiGraph) 0.2840.189 0.1090.059
Country (Denmark as reference)
    Spain −2.728<0.001 −3.373<0.001
    UK 0.1520.831 −1.0560.184
    Germany −3.183<0.001 −4.178<0.001
Age (years) −0.0880.040 0.1010.034
Sex (women as reference)
    Men 1.3680.007 2.123<0.001
BMI categories (obese as reference)
    Underweight and normal 1.6230.015 −1.6630.026
    Overweight 2.511<0.001 −0.2450.058
Educational background (university as reference)
    Never attended school and primary −0.4920.531 −1.6670.058
    Secondary 0.0860.559 −0.2180.186
Number of current medications −0.832<0.001 −0.1800.035
Abbreviations: PCS, physical composite score; MCS, mental composite score; R2, r square; Non St Beta., non-standardised beta coefficient; SBQ, sedentary behaviour questionnaire; IPAQ, international physical activity questionnaire; LPA, light physical activity; MVPA, moderate to vigorous physical activity. a PCS and MCS adjusted by self-reported PA (LPA and MVPA) and SB. b PCS and MCS adjusted by device-measured PA (LPA and MVPA) and SB. c PCS and MCS adjusted by self-reported PA (LPA and MVPA), SB and covariates. d PCS and MCS adjusted by device-measured PA (LPA and MVPA), SB and covariates.
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Sansano-Nadal, O.; Giné-Garriga, M.; Rodríguez-Roca, B.; Guerra-Balic, M.; Ferri, K.; Wilson, J.J.; Caserotti, P.; Olsen, P.Ø.; Blackburn, N.E.; Rothenbacher, D.; et al. Association of Self-Reported and Device-Measured Sedentary Behaviour and Physical Activity with Health-Related Quality of Life among European Older Adults. Int. J. Environ. Res. Public Health 2021, 18, 13252. https://doi.org/10.3390/ijerph182413252

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Sansano-Nadal O, Giné-Garriga M, Rodríguez-Roca B, Guerra-Balic M, Ferri K, Wilson JJ, Caserotti P, Olsen PØ, Blackburn NE, Rothenbacher D, et al. Association of Self-Reported and Device-Measured Sedentary Behaviour and Physical Activity with Health-Related Quality of Life among European Older Adults. International Journal of Environmental Research and Public Health. 2021; 18(24):13252. https://doi.org/10.3390/ijerph182413252

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Sansano-Nadal, Oriol, Maria Giné-Garriga, Beatriz Rodríguez-Roca, Myriam Guerra-Balic, Kelly Ferri, Jason J. Wilson, Paolo Caserotti, Pia Øllgaard Olsen, Nicole E. Blackburn, Dietrich Rothenbacher, and et al. 2021. "Association of Self-Reported and Device-Measured Sedentary Behaviour and Physical Activity with Health-Related Quality of Life among European Older Adults" International Journal of Environmental Research and Public Health 18, no. 24: 13252. https://doi.org/10.3390/ijerph182413252

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