Journals of Gerontology: Medical Sciences cite as: J Gerontol A Biol Sci Med Sci, 2020, Vol. 75, No. 9, 1754–1762 doi:10.1093/gerona/glaa016 Advance Access publication January 14, 2020 Research Article Accelerometer-Measured Sedentary and Physical Activity Time and Their Correlates in European Older Adults: The SITLESS Study Maria Giné-Garriga, PhD,1,2,* Oriol Sansano-Nadal, MS,1 Mark A. Tully, PhD,3 Paolo Caserotti, PhD,4 Laura Coll-Planas, PhD,5 Dietrich Rothenbacher, MD, MPH,6, Dhayana Dallmeier, PhD, MD,6,7 Michael Denkinger, MD,8 Jason J. Wilson, PhD,3 Carme Martin-Borràs, PhD,1,2 Mathias Skjødt, MS,4 Kelly Ferri, MS,1 Ana Claudia Farche, MS,1 Emma McIntosh, PhD,9 Nicole E. Blackburn, PhD,3 Antoni Salvà, PhD,5 and Marta Roqué-i-Figuls, MPH,5 on behalf of the SITLESS Group 1 Department of Sport Sciences, Faculty of Psychology, Education and Sport Sciences Blanquerna, Universitat Ramon Llull, Barcelona, Spain. 2Department of Physical Therapy, Faculty of Health Sciences Blanquerna, Universitat Ramon Llull, Barcelona, Spain. 3Institute of Mental Health Sciences, School of Health Sciences, Ulster University, Newtownabbey, UK. 4Department of Sports Science and Clinical Biomechanics, Center for Active and Healthy Ageing (CAHA), University of Southern Denmark, Odense, Denmark. 5Fundació Salut i Envelliment, Universitat Autònoma de Barcelona, Spain. 6Institute of Epidemiology and Medical Biometry, Ulm University, Germany. 7 Department of Epidemiology, Boston University School of Public Health, Massachusetts. 8Agaplesion Bethesda Clinic, Geriatric Research Unit Ulm University and Geriatric Center Ulm, Germany. 9Department of Health Economics, University of Glasgow, UK. *Address correspondence to: Maria Giné-Garriga, PhD, Faculty of Psychology, Education and Sport Sciences Blanquerna, Universitat Ramon Llull, C/Císter 34, 08022 Barcelona, Spain. E-mail: mariagg@blanquerna.url.edu Received: September 26, 2019; Editorial Decision Date: January 10, 2020 Decision Editor: Anne Newman, MD, MPH Abstract Background: Sedentary behavior (SB) and physical activity (PA) are important determinants of health in older adults. This study aimed to describe the composition of accelerometer-measured SB and PA in older adults, to explore self-reported context-specific SB, and to assess sociodemographic and functional correlates of engaging in higher levels of SB in participants of a multicenter study including four European countries. Method: One thousand three hundred and sixty community-dwelling older adults from the SITLESS study (61.8% women; 75.3 ± 6.3 years) completed a self-reported SB questionnaire and wore an ActiGraph accelerometer for 7 days. Accelerometer-determined compositional descriptive statistics were calculated. A fixed-effects regression analysis was conducted to assess the sociodemographic (country, age, sex, civil status, education, and medications) and functional (body mass index and gait speed) correlates. Results: Older adults spent 78.8% of waking time in SB, 18.6% in light-intensity PA, and 2.6% in moderate-to-vigorous PA. Accelerometry showed that women engaged in more light-intensity PA and walking and men engaged in higher amounts of moderate-to-vigorous PA. Watching television and reading accounted for 47.2% of waking time. Older age, being a man, single, taking more medications, being obese and overweight, and having a slower gait speed were statistically significant correlates of more sedentary time. Conclusions: The high amount of SB of our participants justifies the need to develop and evaluate interventions to reduce sitting time. A clinically relevant change in gait speed can decrease almost 0.45 percentage points of sedentary time. The distribution of context-specific sedentary activities by country and sex showed minor differences, albeit worth noting. Keywords: Compositional analysis, Sedentary behavior, Physical activity, Sociodemographic correlates © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. 1754 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Journals of Gerontology: MEDICAL SCIENCES, 2020, Vol. 75, No. 9 1755 Physical activity (PA) and sedentary behavior (SB) (any waking ac- Different types of SB occur in a variety of situations for different tivity in a sitting, reclining, or lying posture where energy expend- purposes, including leisure, household, occupation, and transporta- iture is <1.5 metabolic equivalents) (1) are important determinants tion (24). Most health-based studies in older adults have focused on of health and quality of life in older adults (2,3). Diseases associated total sedentary time (9,25,26). However, knowing in which context with prolonged SB cost the UK public health system £0.8 billion SB is accumulated (eg, watching television, reading a book, sitting in the 2016–2017 financial year (4). Prolonged SB throughout the in any transport mode) as well as sex-related differences might be day increases the risk of poor health, even in people who are mod- of use in targeting the best-suited strategies to decrease overall SB erately physically active (5,6). However, some evidence suggests the time. Also, being able to identify how SB and PA (LPA and MVPA) observed risks of SB may not be completely independent of total PA are interrelated and distributed across countries, age categories and levels (7), as it is assumed that the reduction in PA on the one hand is other demographic and health factors is needed to identify the char- accompanied by concomitant increases in SB on the other hand (8). acteristics of older adults that could be targeted by preventive inter- Older adults spend most of their waking day sitting (9), placing them vention efforts and aging research and by cross-European policies at increased risk for various detrimental health outcomes, among and guidelines (27–29). them all-cause mortality, metabolic syndrome, obesity, and cognitive Accordingly, the goals of this study were to (a) describe the com- health (10–12). position of accelerometer-measured SB and PA time in a cohort of Most of the past research has relied on self-reported assess- community-dwelling older adults from four European countries ments of the time spent in sedentary activities. Both objective and from the SITLESS study; (b) explore the context in which self- self-reported measures have strengths and limitations. Self-reported reported SB occurs in both men and women and across countries; tools can be subject to response bias (eg, recall bias) (13). For ex- and (c) assess the correlates of SB according to country or residence, ample, Harvey and colleagues (9) found that the self-reported SB age, sex, civil status, education level, number of current medications, of older adults averaged 5.3 h/d, well below values recorded using body mass index (BMI), and gait speed. accelerometry, which averaged 9.4 h/d. Objective measures such as accelerometers are unable to capture how the sedentary time is spent. Method Self-report instruments to assess PA have mainly focused on moderate-to-vigorous PA (MVPA) as these activities are more re- The SITLESS study is a multicenter pragmatic three-armed parallel gimented and therefore easier to remember (14), and in line with randomized controlled trial. Community-dwelling older adults aged international and national PA guidelines (15). However, evidence is 65 years or older, with a score on the Short Physical Performance accumulating that older adults spend much more of their time in Battery (SPPB) of four or above (30), who were insufficiently active lower-intensity PA. In a recent study, objectively measured MVPA and/or reported high levels of SB (31), were recruited in study cen- accounted for just 2% of the day in a large sample of older adults ters in Denmark, Spain, United Kingdom, and Germany according from the Netherlands. In contrast, the time spent in light-intensity PA to their existing primary prevention pathways. Spain and the United (LPA) was 33% and time spent sedentary was 65% of their day (16). Kingdom had 85.5% and 58.6% recruitment through primary To date, efforts to increase PA in the population have also mainly health care professionals, respectively. In Denmark, the largest re- focused on increasing MVPA (17). As accumulating evidence is sug- cruitment pathway was through existing preventive home visits gesting that a higher level of LPA is associated with health benefits (83.2%). In Germany, participants were mostly reached through in- (18–20), it would appear logical that inactive adults should initially vitation letters (76.8%). The study protocol can be found elsewhere be encouraged to reduce SB and engage in any intensity of PA. (32). The present article uses data from the preintervention baseline For a more detailed understanding of the relationship between assessments. PA and SB, it is necessary to know how the time spent in both be- A total of 1,360 community-dwelling older adults (61.8% haviors is distributed across the day, and whether sex-related differ- women; 75.3 ± 6.3 years old) were analyzed at baseline. The ences are worth exploring. The pattern of waking activity is made study design was approved by the Ethics and Research Committee up of periods of SB, LPA, and MVPA interspersed throughout the of each intervention site: The Regional Committees on Health day (21). SB is tightly linked in a zero-sum time-use relationship Research Ethics for Southern Denmark (Denmark), the Ethics and with overall PA (22); standing up from a chair results in increased Research Committee of Ramon Llull University (Spain), the Office PA, albeit typically of a low intensity. The evidence linking SB to for Research Ethics Committees in Northern Ireland (ORECNI; poor health therefore suggests that health-related benefits may be United Kingdom), and the Ethical Review Board of Ulm University acquired displacing prolonged sitting time with LPA throughout the (Germany). Participation was voluntary, and all participants signed day. It is widely thought that a greater understanding of the role of informed consent before the start of the study. each PA and SB component in this age group is necessary to inform appropriate strategies to modify both behaviors, and should be as- Outcome Measures sessed in a holistic way (23). Compositional analysis provides a new Personal information regarding age, sex, civil status, educational method to deal directly with the compositional nature of movement background, medical conditions, and number of current medications behavior. The amount of time spent on a behavior is meaningful only was collected by means of a structured interview in the study centers. in light of the time spent on other behaviors and not on its own (21). Weight and height were objectively measured by a trained researcher In comparison to more traditional methods, compositional analysis using a TANITA BC 420 and a SECA 213 portable stadiometer, re- eliminates collinearity problems and deals with the codependence spectively, to derive the participants’ BMI. Gait speed was obtained between time spent in different movement behaviors (21). Even if from a 4-m walk test. Participants were asked to walk at their normal the information contained in the movement behavior composition pace, and speed was calculated as distance in meters divided by time is relative and thus scale invariant, it can be normalized to any sum in seconds. Participants self-reported the number of hours spent sit- (such as 100 for percentages) without loss of information. ting on a weekday and on a weekend day in different contexts using 1756 Journals of Gerontology: MEDICAL SCIENCES, 2020, Vol. 75, No. 9 the Sedentary Behavior Questionnaire (SBQ). Reliability and validity To assess the covariates related to accelerometer-derived sed- of the SBQ had been validated among overweight adults in a pre- entary time, a linear regression analysis was conducted with vious study (33); intraclass correlation coefficients were acceptable covariables: country of residence, age, sex, civil status (single vs. for all items and the total scale, and significant associations were other—in a relationship, widowed or, separated), education (pri- found with the sitting time question of the International Physical mary vs. secondary vs. post-secondary), number of medications Activity Questionnaire and BMI (33). Context-specific SBs included currently taken, category of BMI (obese when BMI is ≥30 kg/m2 in the SBQ were as follows: watching television, playing computer vs. overweight when BMI is 25–29.9 kg/m2 vs. normal weight when or video games, sitting listening to music or radio, sitting and talking BMI is 18.5–24.9 kg/m2), and gait speed. The fixed-effects regression on the phone, doing paperwork or computer work, sitting reading a model included country as a cluster factor to take into account any book or magazine, playing a musical instrument, doing artwork or potential correlation between participants in the same country, by crafts, and sitting and driving in a car, bus, or train. setting up a diagonal covariance matrix structure for the residuals. Participants wore an ActiGraph wGT3X-BT triaxial accel- The results were reported as unstandardized regression coefficients erometer (ActiGraph, LLC, Pensacola, FL) on their dominant hip with 95% confidence intervals. during waking hours for seven consecutive days, removing it only Statistical test significance was assessed at the usual 5% signifi- for water-based activities such as bathing or swimming and to sleep cance level. For the statistical analyses, STATA V13 software was during the night. Participants recorded wear time in an activity used. diary. The devices were initialized to collect data at 30 Hz. To be included in the analysis, an accelerometer record needed to contain at least four valid days (including at least one weekend day), with Results a valid day defined as containing at least 600 minutes (10 h/d) of Of the overall SITLESS participants (n = 1,360), mean age was wear time as in previous studies (34). Nonwear time was defined 75.3 (SD 6.3) years (range from 72.8 years in United Kingdom to using a two-window system: a 90-minute window for checking for 77.4 years in Denmark) and 840 (61.8%) were women (Table 1). consecutive zero counts and another 30-minute upstream and down- Half of the sample were married (52.6%), whereas 27.0% were wid- stream window for checking for more than 2 minutes of nonzero owed. Participants (53.2%) reported having completed secondary counts (35). Due to some participants wearing the ActiGraph during education, with German participants having the highest proportion nighttime sleeping, a maximum daily wear time threshold was set at at 71.6%, whereas Spanish participants had the lowest one (30.8%). 19 hours using a pragmatic choice based on participants’ diaries and Participants from Germany and Spain reported the highest number sleep time duration recommendations for older adults (36). For par- of medical conditions (3.5 [SD 2.1] and 3.4 [SD 2.2], respectively), ticipants meeting the selected threshold for maximum wear time the and current medications ranged from 0 to 19 across all participants activity diary was used to determine whether the wear time by the (mean 4.5). Gait speed was the slowest in German and Danish par- software was similar to the activity diaries. For relevant participants, ticipants (1.0 [SD 0.2] m/s in both sites). Mean BMI was 28.9 (SD a log diary was used to determine daily wear time when awake. 5.2) kg/m2. Self-reported SB did not differ between weekdays versus SB was defined as <100 counts per minute (CPM), LPA as 100– weekend days among the overall sample. The self-reported average 2019 CPM, and MVPA as ≥2,020 CPM (37) on the vertical axis. mean hours per day in SB was 7.75 (SD 2.9). Daily step counts were also extracted. Values were normalized to the total wear time. Raw accelerometry data were analyzed using Composition of Accelerometer-Measured SB and PA ActiLife v6.13.3 software with the normal filter and summarized Overall, participants spent 78.8% of daily awake time in SB, 18.6% into 10-second epochs, as have been recommended for estimation of in LPA, and 2.6% in MVPA (375 participants with less than 1%). SB in clinical older adult populations (38). Values were normalized Participants in Denmark showed the highest percentage of daily against total wear time and the proportions of daily time spent in SB, awake time in SB (81%), followed by participants from Spain LPA, and MVPA are presented. (79.2%), Germany (78.4%), and the United Kingdom (76.5%). LPA ranged from 17.2% of waking time among participants from Data Analysis Denmark to 19.8% among participants in the United Kingdom. Baseline cross-sectional characteristics were presented descriptively MVPA accounted for less than 4% of waking time across all four as mean and SD for continuous variables or number and percentage sites (Denmark 1.8%; Spain 2.3%; Germany 2.7%; and United for categorical variables. Kingdom 3.7%). Accelerometry showed minimal differences by sex Analyses followed the guide to compositional data analysis for (data not in table), with women engaging in more LPA (19.7% [SD SB, PA, and sleep research published by Chastin and colleagues 5.6] vs. 16.6% [SD 5.5]) and walking (5,099 steps/day [SD 2,436.1] (21). Accelerometer-determined compositional descriptive statistics vs. 4,987 steps/day [SD 2,839.7]) and men engaging in higher including compositional geometric means for central tendency and amounts of MVPA (2.8% [SD 2.6] vs. 2.5% [SD 2.1]). variation matrices for dispersion were calculated among the overall Participants in the United Kingdom took the highest mean study sample and also for each country’s sample separately. Log- number of daily steps with 5,839 (SD 2,985), with participants from ratio plots with the three behaviors (SB, LPA, and MVPA) were gen- Denmark taking the lowest (mean 4,420 [SD 2,410]). erated to show the distribution of the sample compositions using the Figure 1 shows the sample composition of time spent in SB, LPA, CoDaPack software 2.02.21 (39). and MVPA for the whole sample, by means of a matrix of ternary The composition of daily sedentary time according to sedentary plots with the three behaviors represented at a time. Ternary plots activity was obtained crossing the context-specific distribution of can be understood as the scatterplots of compositions. The overlap of self-reported sedentary time using the SBQ, with the percentage of points toward the SB corner indicates the highest data concentration daily sedentary time assessed with accelerometry. Descriptive results in this behavior. The dispersion structure is represented by 99% and were presented for the overall sample, by country, and by sex. 95% normal-based probability regions around the compositional Journals of Gerontology: MEDICAL SCIENCES, 2020, Vol. 75, No. 9 1757 Table 1. Sociodemographic and Movement Behavior Characteristics of the Study Sample Overall Denmark Spain United Kingdom Germany (n = 1,360) (n = 338) (n = 356) (n = 321) (n = 345) Age, y, mean (SD) 75.3 (6.3) 77.4 (5.7) 76.0 (6.5) 72.8 (5.7) 74.8 (6.2) Sex, n (%) women 840 (61.8) 197 (58.3) 273 (76.7) 172 (53.6) 198 (57.4) Civil status, n (%) Single 117 (8.9) 43 (13.0) 28 (8.2) 19 (6.0) 27 (8.3) Married/stable relationship 690 (52.6) 149 (45.2) 173 (50.9) 186 (58.5) 182 (56.0) Widow/widower 354 (27.0) 104 (31.5) 111 (32.6) 71 (22.3) 68 (20.9) Divorced 147 (11.2) 34 (10.3) 28 (8.2) 42 (13.2) 43 (13.2) Unknown 5 (0.4) 0 (0.0) 0 (0.0) 0 (0.0) 5 (1.5) Education, n (%) I do not know how to read or write 5 (0.4) 0 (0.0) 5 (1.5) 0 (0.0) 0 (0.0) I know how to read and write 36 (2.7) 1 (0.3) 34 (10.0) 0 (0.0) 1 (0.3) Primary education 279 (20.8) 97 (28.9) 151 (44.3) 20 (6.3) 11 (3.2) Secondary education 712 (53.2) 195 (58.0) 105 (30.8) 167 (52.2) 245 (71.6) University 303 (22.6) 42 (12.5) 45 (13.2) 132 (41.3) 84 (24.6) Unknown 3 (0.2) 1 (0.3) 1 (0.3) 1 (0.3) 0 (0.0) Number of self-reported medical 2.9 (2.1) 2.9 (1.8) 3.4 (2.2) 1.9 (1.7) 3.5 (2.1) conditions: mean (SD) Number of current medications, mean 4.5 (0–19) 4.0 (0–14) 4.0 (0–17) 4.9 (0–19) 4.4 (0–16) (range) BMI, mean (SD) 28.9 (5.2) 27.4 (5.0) 29.8 (4.9) 29.0 (5.1) 29.3 (5.6) BMI categories, n (%) Underweight and normal 301 (22.3) 111 (33.0) 54 (15.3) 68 (21.3) 68 (19.7) Overweight 555 (41.1) 136 (40.5) 140 (39.8) 131 (41.1) 148 (42.9) Obese 496 (36.7) 89 (26.5) 158 (44.9) 120 (37.6) 129 (37.4) Gait speed, mean (SD) 1.1 (0.3) 1.0 (0.2) 1.1 (0.3) 1.2 (0.2) 1.0 (0.2) Self-report SB, h/d, mean (SD) 7d 7.75 (2.9) 7.93 (2.7) 7.46 (3.3) 7.86 (2.8) 7.72 (2.5) Weekday 7.82 (3.0) 7.85 (2.8) 7.59 (3.6) 8.08 (3.1) 7.78 (2.6) Weekend day 7.54 (3.0) 8.13 (2.9) 7.05 (3.2) 7.30 (2.8) 7.62 (2.7) Accelerometry a n = 1,266 n = 326 n = 313 n = 310 n = 317 % daily sedentary time 78.8 (7.0) 81.0 (6.6) 79.2 (6.6) 76.5 (6.5) 78.4 (7.4) % daily LPA time 18.6 (5.8) 17.2 (5.6) 18.5 (5.9) 19.8 (5.2) 18.9 (6.1) % daily MVPA time 2.6 (2.3) 1.8 (1.8) 2.3 (1.9) 3.7 (2.6) 2.7 (2.2) Number daily steps, mean (SD) 5,056.0 (2,596.9) 4,420.1 (2,409.7) 5,225.7 (2,302.8) 5,838.5 (2,985,3) 4,777.1 (2,439.5) MVPA daily counts, mean (SD) 2,945.3 (386.8) 2,915.3 (352.4) 2,844.7 (335.8) 3,062.3 (388.0) 2,961.1 (433.6) Daily wear time, h, mean (SD) 14.4 (1.1) 14.5 (1.1) 14.3 (1.2) 14.3 (1.1) 14.3 (1.2) Notes: BMI = body mass index; LPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity. an of participants with valid accelerometry data. proportional. For example, the variance of log(SB/LPA) ranges from 0.138 to 0.206, which reflects the highest (proportional) relation- ship or codependence (not correlation in the usual sense) between two behaviors. On the other end, it can be observed that the highest log-ratio variances all involve MVPA, which shows that time spent in MVPA is the least codependent on the other behaviors (is inde- pendent of LPA and SB). Self-report Context-Specific Sedentary Behavior Table 2 shows the descriptive statistics of the proportion of time Figure 1. Ternary plots of the sample composition of time spent in sedentary spent in context-specific SBs by participants of the four European behavior (X), light physical activity (Y), and moderate-to-vigorous physical countries and by sex. Watching television and reading were the activity (Z) for the whole sample (A) and for the whole sample centered (B). context-specific SBs that accounted for most of the daily time in the four countries (34.8% and 12.4% respectively; ie, 47.2% of daily center. These reflect that the highest variability is found in the direc- waking hours). Distribution of context-specific SBs was broadly tion of MVPA, with some variability toward the LPA. similar across countries. The highest proportion of daily sedentary The variability of the data is summarized in the variation matrix time was spent watching television, and Spain showed the highest (Supplementary File 1) containing all pair-wise log-ratio variances. percentage with 40.1% (SD 17.8), followed by Denmark (34.2% A value close to zero implies that the time spent in the two behav- [SD 14.0]), Germany (33.8% [SD 15.1]), and the United Kingdom iors involved in the ratio (arranged by rows and columns) is highly showing the lowest (31.7% [SD 12.9]). Distribution of activities 1758 Journals of Gerontology: MEDICAL SCIENCES, 2020, Vol. 75, No. 9 Table 2. Compositional Descriptive Statistics of the Percentage of Daily Time Spent in Context-Specific Sedentary Behaviors by Country Overall Overalla Denmark United Kingdom Germany Overall Men Women (n = 1,230) (n = 325) Spain (n = 284) (n = 309) (n = 312) (n = 474) (n = 756) Daily % Watching television 34.8 (15.3) 34.2 (14.0) 40.1 (17.8) 31.7 (12.9) 33.8 (15.1) 34.6 (15.4) 35.0 (15.2) Playing computer games 3.6 (7.2) 3.6 (7.4) 3.6 (7.3) 2.7 (5.8) 4.4 (7.8) 2.9 (6.6) 4.1 (7.5) Sitting listening to music/radio 5.7 (7.9) 6.2 (8.5) 6.8 (9.3) 5.3 (6.8) 4.6 (6.6) 6.3 (8.0) 5.4 (7.8) Sitting and talking on the phone 4.2 (4.1) 3.7 (3.2) 4.6 (5.3) 4.2 (3.6) 4.3 (4.0) 3.4 (3.3) 4.7 (4.4) Doing paperwork 6.6 (9.3) 7.5 (8.8) 3.7 (7.4) 7.4 (9.4) 7.4 (10.7) 9.9 (11.5) 4.6 (6.9) Sitting reading 12.4 (9.2) 14.0 (9.8) 9.9 (8.8) 10.3 (7.6) 15.1 (9.3) 12.6 (9.6) 12.3 (9.0) Playing a musical instrument 0.4 (2.0) 0.5 (2.6) 0.2 (1.7) 0.5 (2.0) 0.4 (1.6) 0.6 (2.4) 0.3 (1.8) Doing artwork or crafts 3.4 (7.4) 2.8 (7.1) 5.0 (8.5) 3.5 (8.0) 2.2 (5.5) 1.3 (4.5) 4.6 (8.5) Sitting and driving in a car 7.7 (6.5) 8.3 (6.4) 5.5 (5.2) 10.6 (7.4) 6.2 (5.3) 8.9 (7.3) 6.9 (5.7) Note: an of participants with valid accelerometry and Sedentary Behavior Questionnaire data. Table 3. Mixed Linear Regression Analysis of Correlates of Sedentary Timea per Country SITLESS Overall Sample (n = 1,360) Β 95% CI p Value Constant 89.85 (86.39, 93.32) <.001 Site (Denmark = reference) Spain −0.22 (−1.25, −0.81) .671 United Kingdom −3.14 (−4.18, −2.10) <.001 Germany −2.95 (−3.97, −1.93) <.001 Age (y) 0.23 (0.17, 0.29) <.001 Sex Men (women = reference) 2.78 (2.05, 3.52) <.001 Civil status Single (not single = reference) 1.89 (0.67, 3.12) .002 Education level (primary = reference) Secondary 0.83 (−0.11, 1.77) .084 Higher 1.54 (0.39, 2.69) .009 Number of current medications 0.36 (0.24, 0.47) <.001 BMI (kg/m2) (obese = reference) Under or normal weight −2.77 (−3.74, −1.80) <.001 Figure 2. Distribution of daily time spent in context-specific sedentary Overweight −1.98 (−2.79, −1.17) <.001 behaviors by sex and country. Gait speed (m/s) −4.29 (−5.83, −2.75) <.001 by sex was similar, with some differences in activities such as doing Notes: BMI = body mass index; CI = confidence interval; SE = unstandard- paperwork (9.9% [SD 11.5] in men and 4.6% [SD 6.9] in women) ized regression coefficients and standard errors. Likelihood test for the cluster and doing artwork or crafts (1.3% [SD 4.5] in men and 4.6% [SD component, p = .0098. 8.5] in women; Figure 2). a Expressed as percentage of total wear time. Correlates of Sedentary Time time. Compared with obese participants (obese = reference; β = 1), Table 3 displays correlates of sedentary time according to the those with who were overweight (β = −1.98) and normal weight country of residence, age, sex, civil status, education, medication, (β = −2.77) were less sedentary. gait speed, and BMI. Most of the variables had a weak association The model accounted for within-country correlation of parti- with daily sedentary time. Participants who were male (women = ref- cipants in the same site, which is not negligible as shown by the erence; β = 2.78), those who declared themselves to be single (eg, significant likelihood test comparing this model against a standard had never been married or living with a stable relationship; not regression model with no clustering (p = .0098). single = reference; β = 1.89), and those with upper-secondary educa- tion (education level, primary = reference; β = 1.54) were more likely to be sedentary. Discussion Sedentary time increased with age and number of medications In 1,360 participants of a multicenter study including four European taken, and it was also higher in participants with slower gait speed. countries (the SITLESS study), we show that participants spent For each 10-year increase in age over 65 years (the minimum to 78.8% of daily awake time in SB, 18.6% in LPA, and 2.6% in be included in the study), sedentary time increased two percentage MVPA. Accelerometry showed minimal differences by sex with points (2.3%). A clinically relevant change in gait speed of 0.1 m/s women engaging in more LPA and walking and men engaging in (40) translated into a change of 0.43 percentage points of sedentary higher amounts of MVPA. Notably, we found that correlates of SB Journals of Gerontology: MEDICAL SCIENCES, 2020, Vol. 75, No. 9 1759 were broadly similar in the four included countries except for a few Our study showed that the highest proportion of daily sed- minor differences, an observation that enables a relative general pre- entary time was spent watching television (34.8%) and reading ventative strategy applicable to older adults in similar socioeconomic (12.4%), with a similar percentage among men and women. Time living conditions, irrespective of the country. spent watching television has been related to increased odds for A recent study in a cohort of Dutch older adults showed that multimorbidity (51), and the risk for multiple chronic conditions participants spent on average 65% of total wear time sedentary, has been previously investigated to be higher for those spending 33% performing LPA, and 2% MVPA using the same tri-axial ac- more time watching television (52). Being physically active (ie, celerometer worn at the hip (16). The mean age in van Ballegooijen spending ≥ 30 min/d of MVPA for at least 5 d/wk) has not only been and colleagues’ (16) study was younger compared with our study beneficial for having reduced risk of multiple morbidities, but also (70.7 [SD 8] years vs. 75.3 [SD 6.3] years), and this might partly helped to attenuate or eliminate the negative role of watching tele- explain the differences. Several studies have reported higher pro- vision (51). However, mean MVPA in our older adults accounted portions of time spent being sedentary in older age groups using for less than 2% of waking hours, highlighting the difficulty of ac- hip-worn accelerometers (41–43). However, the difference noted quiring such benefits in an older adult population and stressing the in daily time in SB and LPA in both studies might also be related importance of reducing SB with LPA. Detrimental associations of SB to a lower health status reported by the SITLESS participants. In while watching television with various health outcomes (53) may be our study, women engaged in more LPA and walking and men en- due to the continuous nature of television viewing and its linked un- gaged in higher amounts of MVPA, whereas daily SB was similar. healthy behaviors such as eating snacks or smoking. Following our Similarly, in a recent study, combined categories of SB and PA in- first step approach, breaking television time by simple means (eg, dicated that men were more often high sedentary and high phys- standing up during advertisement breaks and leaving remote control ically active, whereas women were more often low sedentary on TV which would require standing up to switch program) should and low physically active (16). These differences could be partly be a key strategy to reduce the health impact of SB among older explained by traditional gender roles, where women may be in adults, and when done with a partner one could act as a reminder charge of household chores and thus engaging in LPA, and men to the other. being more sedentary at home and engaging in more organized and Distribution of activities by sex, in general, showed some dif- regimented PA. ferences such as doing paperwork being more common in men and In our study, as in previous literature (21), the highest log-ratio doing artwork or crafts being more common in women. Previous variances all involved MVPA, which shows that time spent in MVPA research has provided some insight into the type and context of SB is the least codependent with the other behaviors and might be un- and has shown that sitting activities, which older adults typically en- reliable. As shown in previous studies (44), environmental-, social-, gage in, include watching television, reading, eating meals, using the and individual-level determinants for sedentary time are distinct computer and transport (54). A recent study showed that many sed- from those linked to the adoption and maintenance of MVPA. As entary activities are embedded in older adults’ lives as part of their a result, novel intervention strategies that focus on reducing SB and daily routines, meaning that they might be difficult to change (55). increasing any intensity of PA by leveraging the surrounding envir- A small number of qualitative studies have begun to explore factors onment (eg, home) as well as individual-level cues and habits of SB that influence older adults’ SB. These studies suggest that older adults should be designed and tested (45). enjoy and recognize the physical, social, and mental benefits of some The percentage of daily time spent in SB in older adults is con- sitting community-based activities (eg, doing arts, crafts, and puz- cerning. A better understanding of the distribution of movement zles) (56,57), but view excessive sitting as unhealthy. However, older behavior across the awake-time span will benefit and inform the adults tend to report that many community activities are not only development of cost-effective public health interventions. Reducing sedentary (56), but also lack the availability of information about sitting could potentially improve older adults’ subjectively and ob- community-based resources that lead them to sit more (57). A third jectively measured health status (46,47) and well-being (48). Thus, step in the approach to engaging older adults in healthier lifestyles focusing on ways to reduce sitting with nonsitting activities (eg, could be offering information about community-based activities doing some activities that are usually completed in a seated position using each country’s existing primary prevention pathways (eg, pri- by standing up) may be a promising first step to address sedentary mary health care professionals in Spain and the United Kingdom or time among older adults. preventive home visits in Denmark), ideally thought to engage both Because LPA appears to makeup a larger proportion of the day men and women according to their preferences, and searching for compared with MVPA in older adults, replacing SB with LPA seems alternatives to perform such activities in a nonsitting position. to be the second step to designing successful strategies to enhance It would also be interesting to know when these context-specific movement without immediately increasing MVPA, which might not SB activities take place during the day. One study using time-lapse be feasible for some older adults. This may eventually lead to a pro- cameras suggested that older adults often sit most in the afternoon gression to higher-intensity activities, if this is safe and appropriate and evening (compared with the morning), and when they are alone for the individual. Thus, efforts that target SB as a means to increase at home (58). Following our second and third-step approach, prom- LPA is a new behavioral leverage-point that could help us increase ising strategies may be to reduce television time mainly in the after- overall PA and induce health benefits within the population (45). noon by first, supporting older adults to go out more and engage A recent study using isotemporal substitution regression modeling in community-based activities in local facilities and other resources to assess the relationship of replacing the amount of time spent in (eg, community groups). Supporting older adults to remain socially one activity for another showed that replacing 60 min/d of SB with active will not only support them to reduce SB, but might help en- 10 min/d of MVPA and 50 min/d of LPA was associated with signifi- hance new social connections and reduce social isolation, which is cant improvements in physical function (49). However, there is little associated with poor health (59). evidence to guide SB limiting strategies or LPA promotion activities In our study, being older, a man, single, taking more medica- for older adults (50). tions per day, and being obese and overweight were important 1760 Journals of Gerontology: MEDICAL SCIENCES, 2020, Vol. 75, No. 9 correlates of higher levels of SB time. Recent studies showed that Supplementary Material men, those who were older, and those with higher BMI were also Supplementary data are available at The Journals of Gerontology, found to be more sedentary (16,28,41,42,60). A previous study Series A: Biological Sciences and Medical Sciences online. among Japanese older adults revealed living alone was signifi- cantly associated with prolonged television viewing time (61). Living with a partner was associated with more activity in par- Funding ticipants younger than 80 years and in those with BMI ≥ 27 kg/ This work was supported by the European Union program Horizon 2020 m2 in another study (28). The results from this and previous (H2020-Grant 634270). studies suggest the need for opportunities of focused intervention, highlighting the need to engage older adults in group-based com- munity activities with those sharing a similar profile to themselves Acknowledgments (eg, single or overweight). The named authors present the study on behalf of the SITLESS Group: Laura SB was also higher in participants with slower gait speed in our Coll-Planas (PI), Maria Giné-Garriga, Àlex Domingo, Marta Roqué, Antoni study, similar to that reported in another study (16). SB shows a ten- Salvà, Míriam Guerra, Carme Martín-Borràs, Javier Jerez-Roig, Oriol Sansano- dency to increase with frailty-related outcomes in the current litera- Nadal, Marta Santiago, Kelly Ferry, Manel Font, Ana Claudia Farche, Paolo ture such as age, loneliness, and mobility restrictions (62). Slowing Caserotti, Mathias Skjødt, Mark A. Tully, Frank Kee, Jason J. Wilson, Nicole gait may reflect both damaged systems and a high-energy cost of E. Blackburn, Ilona McMullan, Michael Denkinger, Dietrich Rothenbacher, walking (63–65). Gait speed, age, and sex may offer the clinician Katharina Wirth, Dhayana Dallmeier, Emma McIntosh, Manuela Deidda, tools for assessing expected survival to contribute to tailoring goals Guillaume Lefebvre, and Denise González. of care in older adults (66). German and Danish participants showed the slowest gait speed and German participants reported the highest Conflict of Interest number of medical conditions, probably due to the most common re- cruitment pathway in Germany that used invitation letters sent from None reported. health professionals targeting participants with major health needs. Once again, our fourth step is that these outcomes should be borne References in mind when designing and prioritizing health-related interventions 1. Tremblay MS, Aubert S, Barnes JD, et al.; SBRN Terminology Consensus for older adults. Project Participants. Sedentary Behavior Research Network (SBRN)— This study has not only several strengths but also limita- Terminology Consensus Project process and outcome. Int J Behav Nutr tions. It is the first study providing a comprehensive description Phys Act. 2017;14:75. doi:10.1186/s12966-017-0525-8 of the composition of accelerometer-measured SB and PA time in 2. Kim Y, Lee E. 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