Diurnal Patterns of Physical Activity in Relation to Activity Induced Energy Expenditure in 52 to 83 Years-Old Adults

Abstract

BackgroundAgeing is associated with a declining physical activity level (PAL) and changes in the diurnal activity pattern. Changes in the activity pattern might help explaining the age-associated reduction of physical activity.ObjectiveThe aims were to investigate diurnal activity patterns within groups of older adults classified by PAL, to investigate diurnal activity patterns within age-groups and to investigate the association between the drop in activity and aerobic fitness.MethodsThirty-one healthy subjects aged between 52 and 83y were recruited for the study. Subjects were divided in sedentary (PAL<1.75), moderately active (1.75<PAL<1.90), and active (1.90<PAL) older adults with energy expenditure measurements obtained with the doubly labelled water technique. Diurnal activity patterns were based on activity counts from an accelerometer during wake time and then divided in four quarters of equal time length. Additionally, aerobic fitness was measured as maximal oxygen uptake.ResultsSubjects had a PAL between 1.43 and 2.34 and an aerobic fitness between 18 and 49 ml/kg/min. Overall, activity patterns showed a peak in the first quarter of wake time (around 10AM) followed by a gradual decline of, on average, 5% per hour. Active subjects reached their peak in the first quarter and remained active until after the third quarter (11% drop each quarter on average). Moderately active and sedentary subjects reached their peak during the second quarter with a decrease during the third quarter (respectively 29% and 17% drop each quarter on average). The drop in physical activity between the first and the second half of the wake time was negatively associated with aerobic fitness (r = -0.39, p<0.05).ConclusionActive older adults maintained a larger amount of body movement for longer during their wake time. Diurnal physical activity declined more in adults ≥66 years old with lower aerobic fitness.

Association between Objectively Measured Physical Activity and Gait Patterns in People with Parkinson’s Disease: Results from a 3-Month Monitoring

“…To our knowledge, only the recent study by Cai et al [ 12 ] calculated a mean curve of variation for step counts calculated for a sample of 21 pwPD, but their data appear not to suggest the existence of a well-defined pattern. However, our results do appear to be partly consistent with those reported in two studies performed on healthy older adults [ 33 , 34 ], both of which observed a marked peak in PA (expressed in terms of either steps or accelerometric counts) approximately at 10. Sartini et al [ 33 ] detected a second peak located approximately at 14-15, while in the study by Valenti et al [ 34 ], activity appears to monotonically decrease from 10 until night.…”

“…However, our results do appear to be partly consistent with those reported in two studies performed on healthy older adults [ 33 , 34 ], both of which observed a marked peak in PA (expressed in terms of either steps or accelerometric counts) approximately at 10. Sartini et al [ 33 ] detected a second peak located approximately at 14-15, while in the study by Valenti et al [ 34 ], activity appears to monotonically decrease from 10 until night. As previously mentioned, in the present study, a second relevant PA peak was found between 18 and 19 and thus later in the day with respect to Sartini et al [ 33 ].…”

“…Such differences can probably be explained by the fact that, in our case, PA was mostly monitored during months characterized by favorable environmental conditions (Cagliari has a mild climate for most of the year) which probably encouraged participants to walk or spend time outdoor in the evening as well. In any case, it must be noted that when PA is analyzed on the basis of the 3 defined time slots, our results appear to be fully consistent also with those of Valenti et al [ 34 ] who also detected a significant reduction in PA in the evening time slots.…”

Physical activity measured with wrist and ankle accelerometers: Age, gender, and BMI effects

“…Circadian rhythms influence PA-related biological processes [ 10 ] and behavioral factors [ 11 ] and research has started to recognize the importance of sleep-awake cycles when exploring gender-related [ 12 ], obesity-related [ 13 ] and age-related [ 14 ] differences of PA. Irregular patterns of sleep-wake cycles are associated with poor mental health [ 15 ], lower PA trends [ 16 ], and higher engagement in occupational than in leisure PA [ 17 ]. Scarce consideration has been given to how PA measures of upper and lower extremities between day and night periods could vary by gender, BMI, and age in healthy individuals, despite the evidence that these demographics factors impact PA mean levels [ 18 ] and PA variability [ 17 ].…”

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“…Concurrently, other factors may interfere with these associations, i.e. the circadian rhythm (Valenti, Bonomi, & Westerterp, 2016). While the ambient temperature and precipitation can be "managed" by staying at home, exercising indoors, or wearing appropriate clothing, we need to think of the unalterable character of the daylight length.…”