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CONVERSATIONS ABOUT SITTING Eat well. Eat moderately. Exercise regularly. These are messages we hear and repeat as doctors. But how often do we advocate sitting less in our health promotion information and lifestyle advice? Sedentary behaviour is defined as any waking behaviour with low energy expenditure equivalent to ≤1.5 metabolic equivalents (METs).1 Less than 1.5 MET is thought to be the amount of energy we use when resting. Not surprisingly, sitting is the commonest sedentary behaviour. The UK 2011 Start Active, Stay Active document2 advises avoiding lengthy periods of sitting. It should, therefore, perhaps be entering conversations in the consultation room. Yet are we facilitating this and are we equipped to? Do we know the detrimental effects of the inconspicuous pastime of sitting? Even if being sedentary is detrimental, will advice from the primary care team change this behaviour?

OBJECTIVE: This review explored whether cardiorespiratory fitness or physical activity act as either confounders or effect modifiers of the relationship between adiposity markers and all-cause mortality in older adults. METHODS: Systematic searches were carried out to identify observational studies that examined the association of adiposity markers (BMI, waist circumference, and waist-hip ratio) with all-cause mortality in adults aged ≥ 60 which took into account cardiorespiratory fitness or physical activity. Data from each included study was analyzed to produce a graphical representation of this relationship. RESULTS: Fourteen of the fifteen identified studies found that increasing BMI had a non-positive association with all-cause mortality, with persistence of the obesity paradox despite adjustment for physical activity or cardiorespiratory fitness. Physical activity measurement methods were all subjective and often unvalidated. The two studies stratifying for cardiorespiratory fitness did not find that fitness had a significant impact on the relationship between excess adiposity and mortality but found that overweight and fit people had better survival than normal-weight unfit people, CONCLUSIONS: The predominant use of poor physical activity measurement suggests that studies are currently not adequately accounting for possible physical activity confounding. More studies are needed for addressing the modification of the relationship between adiposity markers and mortality by cardiorespiratory fitness.

INTRODUCTION: Compensatory behaviours may be one of the reasons for the limited success of sedentary time interventions in older adults, but this possibility remains unexplored. Activity compensation is the idea that if we change activity levels at one time we compensate for them at a later time to maintain a set point. We aimed to assess, among adults aged ≥60 years, whether sedentary time and time spent in prolonged sedentary bouts (≥30 mins) on one day were associated with sedentary time and time spent in prolonged sedentary bouts (≥30 mins) on the following day. We also sought to determine whether these associations varied by sociodemographic and comorbid factors. METHODS: Sedentary time was assessed for seven days using hip-worn accelerometers (ActiGraph GT1M) for 3459 adults who participated in the EPIC-Norfolk Study between 2004 and 2011. We assessed day-to-day associations in total and prolonged bouts of sedentary time using multi-level regressions. We included interaction terms to determine whether associations varied by age, sex, smoking, body mass index, social class, retirement, education and comorbid factors (stroke, diabetes, myocardial infarction and cancer). RESULTS: Participants (mean age = 70.3, SD = 6.8 years) accumulated 540 sedentary mins/day (SD = 80.1). On any given day, every 60 minutes spent in sedentary time was associated with 9.9 extra sedentary minutes on the following day (95% CI 9.0, 10.2). This association was greater in non-retired compared to retired participants (non-retired 2.57 extra minutes, p = 0.024) and in current compared to former and never-smokers (5.26 extra mins for current vs former; 5.52 extra mins for current vs never, p = 0.023 and 0.017, respectively). On any given day, every 60 minutes spent in prolonged bouts was associated with 7.8 extra minutes in these bouts the following day (95% CI 7.6, 8.4). This association was greater in older individuals (0.18 extra minutes/year of age, 95% CI 0.061, 0.29), and for retired versus non-retired (retired 2.74 extra minutes, 95% CI 0.21, 5.74). CONCLUSION: Older adults did not display day-to-day compensation. Instead, individuals demonstrate a large stable component of day-to-day time spent sedentary and in prolonged bouts with a small but important capacity for positive variation. Therefore older adults appear to be largely habitual in their sedentary behaviour. Strategies to augment these patterns may be possible, given they may differ by age, smoking, and working status.

BACKGROUND: High sedentary time, low physical activity (PA), and low physical fitness place older adults at increased risk of chronic diseases, functional decline, and premature mortality. Mobile health (mHealth) apps, apps that run on mobile platforms, may help promote active living. OBJECTIVE: We aimed to quantify the effect of mHealth app interventions on sedentary time, PA, and fitness in older adults. METHODS: We systematically searched five electronic databases for trials investigating the effects of mHealth app interventions on sedentary time, PA, and fitness among community-dwelling older adults aged 55 years and older. We calculated pooled standardized mean differences (SMDs) in these outcomes between the intervention and control groups after the intervention period. We performed a Cochrane risk of bias assessment and Grading of Recommendations, Assessment, Development, and Evaluation certainty assessment. RESULTS: Overall, six trials (486 participants, 66.7% [324/486] women; age mean 68 [SD 6] years) were included (five of these trials were included in the meta-analysis). mHealth app interventions may be associated with decreases in sedentary time (SMD=-0.49; 95% CI -1.02 to 0.03), increases in PA (506 steps/day; 95% CI -80 to 1092), and increases in fitness (SMD=0.31; 95% CI -0.09 to 0.70) in trials of 3 months or shorter and with increases in PA (753 steps/day; 95% CI -147 to 1652) in trials of 6 months or longer. Risk of bias was low for all but one study. The quality of evidence was moderate for PA and sedentary time and low for fitness. CONCLUSIONS: mHealth app interventions have the potential to promote changes in sedentary time and PA over the short term, but the results did not achieve statistical significance, possibly because studies were underpowered by small participant numbers. We highlight a need for larger trials with longer follow-up to clarify if apps deliver sustained clinically important effects.

UNLABELLED: To develop healthy ageing interventions, longitudinal associations between objectively assessed physical behaviours and physical function need to be better understood. We assessed associations between accelerometer-assessed total physical activity (PA), moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary time and prolonged sedentary bout time, and clinically assessed physical function (grip strength, usual walking speed (UWS), chair stand speed) at two time-points in 3188 participants (≥ 60 years) of the EPIC-Norfolk study. Bidirectional associations were assessed using multivariable linear regression. Over an average of 6.1 years, baseline physical behaviours (greater total PA, MVPA and LPA, and less sedentary time) were associated with better subsequent walking and chair stand speed. Better baseline physical function was associated with better follow-up physical behaviours. There were no bidirectional associations between changes in physical behaviours and grip strength. Improvements in UWS were associated with improvements in all physical behaviours. Improvements in chair stand speed were associated with improvements in total PA, MVPA, and sedentary bout time. Improvements in physical behaviours were associated with improvements in UWS (3.1 cm/s/yr per 100 cpm/yr total PA, 3.6 cm/s/yr per hr/day/yr MVPA, 2.5 cm/s/yr per hr/day/yr LPA, - 2.9 cm/s/yr per hour/day/yr sedentary time, and - 1.6 cm/s/yr per hr/day/yr prolonged sedentary bout time). Only improvements in total PA, MVPA and sedentary bout time were associated with improvements in chair stand speed. In conclusion, we found bidirectional associations between changes in some physical behaviours and physical function and between baseline physical behaviours and subsequent physical function, highlighting the importance of considering the full range of physical behaviours to promote healthy ageing. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10433-022-00733-y.

IntroductionCompensatory behaviours may be one of the reasons for the limited success of sedentary time interventions in older adults, but this possibility remains unexplored. Activity compensation is the idea that if we change activity levels at one time we compensate for them at a later time to maintain a set point. We aimed to assess, among adults aged ≥60 years, whether sedentary time and time spent in prolonged sedentary bouts (≥30 mins) on one day were associated with sedentary time and time spent in prolonged sedentary bouts (≥30 mins) on the following day. We also sought to determine whether these associations varied by sociodemographic and comorbid factors.MethodsSedentary time was assessed for seven days using hip-worn accelerometers (ActiGraph GT1M) for 3459 adults who participated in the EPIC-Norfolk Study between 2004 and 2011. We assessed day-to-day associations in total and prolonged bouts of sedentary time using multi-level regressions. We included interaction terms to determine whether associations varied by age, sex, smoking, body mass index, social class, retirement, education and comorbid factors (stroke, diabetes, myocardial infarction and cancer).ResultsParticipants (mean age = 70.3, SD = 6.8 years) accumulated 540 sedentary mins/day (SD = 80.1). On any given day, every 60 minutes spent in sedentary time was associated with 9.9 extra sedentary minutes on the following day (95% CI 9.0, 10.2). This association was greater in non-retired compared to retired participants (non-retired 2.57 extra minutes, p = 0.024) and in current compared to former and never-smokers (5.26 extra mins for current vs former; 5.52 extra mins for current vs never, p = 0.023 and 0.017, respectively). On any given day, every 60 minutes spent in prolonged bouts was associated with 7.8 extra minutes in these bouts the following day (95% CI 7.6, 8.4). This association was greater in older individuals (0.18 extra minutes/year of age, 95% CI 0.061, 0.29), and for retired versus non-retired (retired 2.74 extra minutes, 95% CI 0.21, 5.74).ConclusionOlder adults did not display day-to-day compensation. Instead, individuals demonstrate a large stable component of day-to-day time spent sedentary and in prolonged bouts with a small but important capacity for positive variation. Therefore older adults appear to be largely habitual in their sedentary behaviour. Strategies to augment these patterns may be possible, given they may differ by age, smoking, and working status.

Background High sedentary time, low physical activity (PA), and low physical fitness place older adults at increased risk of chronic diseases, functional decline, and premature mortality. Mobile health (mHealth) apps, apps that run on mobile platforms, may help promote active living. Objective We aimed to quantify the effect of mHealth app interventions on sedentary time, PA, and fitness in older adults. Methods We systematically searched five electronic databases for trials investigating the effects of mHealth app interventions on sedentary time, PA, and fitness among community-dwelling older adults aged 55 years and older. We calculated pooled standardized mean differences (SMDs) in these outcomes between the intervention and control groups after the intervention period. We performed a Cochrane risk of bias assessment and Grading of Recommendations, Assessment, Development, and Evaluation certainty assessment. Results Overall, six trials (486 participants, 66.7% [324/486] women; age mean 68 [SD 6] years) were included (five of these trials were included in the meta-analysis). mHealth app interventions may be associated with decreases in sedentary time (SMD=−0.49; 95% CI −1.02 to 0.03), increases in PA (506 steps/day; 95% CI −80 to 1092), and increases in fitness (SMD=0.31; 95% CI −0.09 to 0.70) in trials of 3 months or shorter and with increases in PA (753 steps/day; 95% CI −147 to 1652) in trials of 6 months or longer. Risk of bias was low for all but one study. The quality of evidence was moderate for PA and sedentary time and low for fitness. Conclusions mHealth app interventions have the potential to promote changes in sedentary time and PA over the short term, but the results did not achieve statistical significance, possibly because studies were underpowered by small participant numbers. We highlight a need for larger trials with longer follow-up to clarify if apps deliver sustained clinically important effects.