The effect of maternal risk factors during pregnancy on children's motor development at 5-6 years

BACKGROUND AND AIMS

Maternal diet and health may influence a child's later neurodevelopment. We investigated the effect of maternal diet, adiposity, gestational diabetes mellitus (GDM), and depressive/anxiety symptoms during pregnancy on the child's motor outcome at 5-6 years.

METHODS

The motor performance of 159 children of women with overweight or obesity (pre-pregnancy body mass index 25-29.9 kg/m2 and ≥30 kg/m2, respectively) was assessed by the Movement Assessment Battery for Children - Second Edition (Movement ABC-2, total scores and subscales of manual dexterity, aiming and catching, balance) at 5-6 years. Higher percentiles denoted better motor performance with ≤15th percentiles for total scores being used as a cut-off for developmental coordination disorder (DCD). Diet (dietary patterns from three-day food diaries and fish consumption from a frequency questionnaire), adiposity (air displacement plethysmography), depression and anxiety symptoms (Edinburgh Postnatal Depression scale and the SCL-90/anxiety subscale, respectively) were assessed in early and late pregnancy. GDM was diagnosed with an oral glucose tolerance test at early or mid-pregnancy. Logistic and general regression models were used to analyse the associations.

RESULTS

The mean percentiles for total scores of the Movement ABC-2 were 47.5 (SD 28.3), and 14.3 % of the children had DCD. A healthier maternal dietary pattern in early pregnancy associated with better motor performance in the child at 5-6 years (adj.mean difference = 9.80, 95%CI = 0.66-19.0). Higher maternal body fat mass both in early and late pregnancy (adj.OR = 1.07, 95%CI = 1.01-1.13, and adj.OR = 1.08, 95%CI = 1.02-1.14) and fat percentage in late pregnancy (adj.OR = 1.12, 95%CI = 1.09-1.24) were associated with higher odds for DCD. Increasing maternal depressive symptoms were associated with lower odds for impaired aiming/catching (early/late pregnancy adj.OR = 0.78, 95%CI = 0.65-0.93, adj.OR = 0.82, 95%CI = 0.70-0.96). GDM was not associated with the motor performance.

CONCLUSIONS

A healthier dietary pattern during pregnancy favoured children's motor development, while it was compromised by higher maternal adiposity. Promoting an overall healthy diet throughout pregnancy might support the motor development in children born to mothers with overweight or obesity. Our findings indicating that maternal depressive symptoms during pregnancy might associate with better motor performance in the child will require further research for confirmation.

CLINICALTRIALS

GOV IDENTIFIER

NCT01922791.

Comparison of subjectively and objectively measured sleep-wake patterns among patients with primary brain tumors

Background

The sleep diary and wrist-worn actigraphy are widely used to assess sleep disturbances in patients with primary brain tumors (PwPBT) in both clinical and research settings. However, their comparability has not been systematically examined. This study aimed to compare the sleep-wake patterns measured using the subjectively measured Consensus Sleep Diary (CSD) and the objectively measured ActiGraph (AG) actigraphy among PwPBT.

Methods

Sleep-wake patterns were assessed through CSD and AG over 14 consecutive nights across 2 occasions among 30 PwPBT. AG data were processed with AG proprietary and open-source GGIR (GGIR-based approach without the aid of sleep log algorithms), both with and without the assistance of CSD. Thirteen sleep parameters covering sleep-wake times, sleep disruptions, sleep durations, and sleep efficiency were compared using equivalency testing, mean absolute percent error (MAPE), and intra-class correlation. The estimated sleep parameters were correlated with perceived sleep quality and compared across the different sleep measures.

Results

Significant between-measure equivalency was claimed for sleep-wake time parameters (P ≤ .05), with acceptable MAPEs (<10%). Sleep disruption parameters such as wake-after-sleep-onset were not statistically equivalent, with a large MAPE (≥10%) between the measures. Sleep efficiency was equivalent, though varied depending on how sleep efficiency was calculated. For most sleep parameters, ICCs were low and unacceptable (<0.50) suggesting incomparability between the measures. Lastly, CSD-derived sleep parameters exhibited a stronger correlation with perceived sleep quality compared to actigraphy measures.

Conclusions

The findings suggest the incomparability of sleep parameters estimated from different measures. Both subjective and objective measures are recommended to better describe sleep health among PwPBT.

Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.

Does Wrist-Worn Accelerometer Wear Compliance Wane over a Free-Living Assessment Period? An NHANES Analysis

PURPOSE

Accelerometers are used to objectively measure physical behaviors in free-living environments, typically for seven consecutive days or more. We examined whether participants experience "wear fatigue," a decline in wear time day over day, during typical assessment period acquired in a nationally representative sample of 6- to 80-yr-olds in the United States.

METHODS

Participants were instructed to wear an ActiGraph GT3X+ on their nondominant wrist continuously for seven consecutive days. Participants with seven complete days of recorded data, regardless of wear status, were included in the analyses ( N = 13,649). Wear was scored with the sleep, wake, and nonwear algorithm.

RESULTS

Participants averaged 1248 ± 3.6 min·d -1 (mean ± SE) of wear over the assessment, but wear time linearly decreased from day 1 (1295 ± 3.2 min) to day 7 (1170 ± 5.3 min), resulting in a wear fatigue of -18.1 ± 0.7 min·d -1 ( β ± SE). Wear fatigue did not differ by sex but varied by age-group-highest in adolescents (-26.8 ± 2.4 min·d -1 ) and lowest in older adults (-9.3 ± 0.9 min·d -1 ). Wear was lower in evening (1800-2359 h) and early morning (0000-0559 h) compared with the middle of the day and on weekend days compared with weekdays. We verified similar wear fatigue (-23.5 ± 0.7 min·d -1 ) in a separate sample ( N = 14,631) with hip-worn devices and different wear scoring. Applying minimum wear criteria of ≥10 h·d -1 for ≥4 d reduced wear fatigue to -5.3 and -18.7 min·d -1 for the wrist and hip, respectively.

CONCLUSIONS

Patterns of wear suggest noncompliance may disproportionately affect estimates of sleep and sedentary behavior, particularly for adolescents. Further study is needed to determine the effect of wear fatigue on longer assessments.

Weekday and weekend physical activity patterns and their correlates among young adults

BACKGROUND

Accelerometers enable assessment of within and between day variation in physical activity. The main aim was to examine weekday and weekend physical activity patterns among young adults. Additionally, correlates of the physical activity patterns were examined.

METHODS

Overall 325 adults (mean age 26.0 years, standard deviation 0.03) from the Special Turku Coronary Risk Factor Intervention Project used a wrist-worn ActiGraph accelerometer continuously for 1 week. Physical activity patterns over weekdays and weekends were identified by using the group-based trajectory modeling. Adolescent leisure time physical activity (LTPA) and sociodemographic characteristics (sex, marital and family status, education, work status, occupation, and health consciousness) were examined as possible correlates of physical activity patterns using multinomial regression analysis.

RESULTS

Five patterns were identified: consistently low activity (45%), active on weekday evenings and weekends (32%), consistently moderate activity (11%), active on weekdays (7%), and consistently high activity (5%). Low adolescent LTPA was associated with consistently low activity pattern in young adulthood. Women were more likely than men to belong in the more physically active groups (all other groups except active on weekdays, odds ratios between 2.26 and 6.17). Those in the active on weekdays group had lower education, were more often in the working life and in manual occupations than those in the consistently low activity group.

CONCLUSIONS

Marked heterogeneity in physical activity patterns across the week was observed among young adults. Especially history of physical activity, sex, education, work status, and occupation were associated with different physical activity patterns.

Modifications of 24-h movement behaviors to prevent obesity in retirement: a natural experiment using compositional data analysis

BACKGROUND

Retirement often leads to a more passive lifestyle and may therefore lead to weight gain. This study aims to investigate longitudinal associations between changes in 24-h movement behaviors and BMI and waist circumference in relation to the transition from work to retirement.

METHODS

The study population included 213 retiring public sector workers (mean age 63.5 years, standard deviation 1.1) from the Finnish Retirement and Aging study. Before and after retirement participants wore an Axivity accelerometer on their thigh and filled in a daily log for at least four days to measure daily time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Also, their body mass index (BMI) and waist circumference were measured repeatedly. Compositional linear regression analysis and isotemporal substitution analysis were used to study associations between one-year changes in 24-h movement behaviors and concurrent changes in BMI and waist circumference.

RESULTS

An increase in MVPA in relation to sleep, SED and LPA was associated with a decreasing BMI (β = -0.60, p = 0.04) and waist circumference (β = -2.14, p = 0.05) over one year from before retirement to after retirement. In contrast, increasing sleep in relation to SED, LPA and MVPA was associated with an increasing BMI (β = 1.34, p = 0.02). Reallocating 60 min from MVPA to SED or sleep was estimated to increase BMI by on average 0.8-0.9 kg/m2 and waist circumference by 3.0 cm during one year.

CONCLUSIONS

During the transition from work to retirement, increasing MVPA was associated with a slight decrease in BMI and waist circumference, whereas increasing sleep was associated with an increasing BMI. Common life transitions, like retirement, should be considered when giving recommendations and guidance for physical activity and sleep.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

Replacing sedentary time with physical activity and sleep: Associations with cardiometabolic health markers in adults

This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 h of movement and non-movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4585 members of the Northern Finland Birth Cohort 1966, who wore a hip-worn accelerometer at the age of 46 years for 14 consecutive days. Time spent in sedentary behaviors, light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) were determined from the accelerometer and combined with self-reported sleep duration to obtain the 24-h time use. CRF was estimated from the peak heart rate in a submaximal step test. An isotemporal substitution paradigm was used to examine how sedentary time and substituting sedentary time with an equal amount of LPA, MVPA, or sleep were associated with adiposity markers, blood lipid levels, and fasting glucose and insulin. Sedentary time was independently and adversely associated with the markers of cardiometabolic health, even after adjustment for CRF, but not in partition models including LPA, MVPA, sleep, and CRF. Substituting 60, 45, 30, and 15 min/day of sedentary time with LPA or MVPA was associated with 0.2%-13.7% favorable differences in the cardiometabolic health markers after accounting for LPA, MVPA, sleep, CRF, and other confounders. After adjustment for movement and non-movement behaviors within the 24-h cycle, reallocating additional time to both LPA and MVPA was beneficially associated with markers of cardiometabolic health in middle-aged adults regardless of their CRF level.

Comparison of Oura Smart Ring Against ActiGraph Accelerometer for Measurement of Physical Activity and Sedentary Time in a Free-Living Context

Smart rings, such as the Oura ring, might have potential in health monitoring. To be able to identify optimal devices for healthcare settings, validity studies are needed. The aim of this study was to compare the Oura smart ring estimates of steps and sedentary time with data from the ActiGraph accelerometer in a free-living context. A cross-sectional observational study design was used. A convenience sample of healthy adults (n = 42) participated in the study and wore an Oura smart ring and an ActiGraph accelerometer on the non-dominant hand continuously for 1 week. The participants completed a background questionnaire and filled out a daily log about their sleeping times and times when they did not wear the devices. The median age of the participants (n = 42) was 32 years (range, 18-46 years). In total, 191 (61% of the potential) days were compared. The Oura ring overestimated the step counts compared with the ActiGraph. The mean difference was 1416 steps (95% confidence interval, 739-2093 steps). Daily sedentary time was also overestimated by the ring; the mean difference was 17 minutes (95% confidence interval, -2 to 37 minutes). The use of the ring in nursing interventions needs to be considered.

Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey

INTRODUCTION

Estimating physical activity, sedentary behavior, and sleep from wrist-worn accelerometer data requires reliable detection of sensor nonwear and sensor wear during both sleep and wake.

PURPOSE

This study aimed to develop an algorithm that simultaneously identifies sensor wake-wear, sleep-wear, and nonwear in 24-h wrist accelerometer data collected with or without filtering.

METHODS

Using sensor data labeled with polysomnography ( n = 21) and directly observed wake-wear data ( n = 31) from healthy adults, and nonwear data from sensors left at various locations in a home ( n = 20), we developed an algorithm to detect nonwear, sleep-wear, and wake-wear for "idle sleep mode" (ISM) filtered data collected in the 2011-2014 National Health and Nutrition Examination Survey. The algorithm was then extended to process original raw data collected from devices without ISM filtering. Both algorithms were further validated using a polysomnography-based sleep and wake-wear data set ( n = 22) and diary-based wake-wear and nonwear labels from healthy adults ( n = 23). Classification performance (F1 scores) was compared with four alternative approaches.

RESULTS

The F1 score of the ISM-based algorithm on the training data set using leave-one-subject-out cross-validation was 0.95 ± 0.13. Validation on the two independent data sets yielded F1 scores of 0.84 ± 0.60 for the data set with sleep-wear and wake-wear and 0.94 ± 0.04 for the data set with wake-wear and nonwear. The F1 score when using original, raw data was 0.96 ± 0.08 for the training data sets and 0.86 ± 0.18 and 0.97 ± 0.04 for the two independent validation data sets. The algorithm performed comparably or better than the alternative approaches on the data sets.

CONCLUSIONS

A novel machine-learning algorithm was designed to recognize wake-wear, sleep-wear, and nonwear in 24-h wrist-worn accelerometer data that are applicable for ISM-filtered data or original raw data.

Daily Physical Activity Patterns and Their Association With Health-Related Physical Fitness Among Aging Workers-The Finnish Retirement and Aging Study

BACKGROUND

This study aimed to identify accelerometer-measured daily physical activity patterns, and to examine how they associate with health-related physical fitness among aging workers.

METHODS

The study population consisted of 263 participants (mean age 62.4 years, SD 1.0) from the Finnish Retirement and Aging study, who used wrist-worn ActiGraph accelerometer for at least 1 week including both workdays and days off. Health-related physical fitness measures included body composition (waist circumference, bioimpedance), cardiorespiratory fitness (bicycle ergometer test), and muscular fitness (push-up and chair rise tests).

RESULTS

Based on the latent class trajectory analysis, 6 trajectories were identified for workdays showing variation in activity level on working hours and on evening hours. Moderate activity during working hours and increase of activity level in the evening was associated with the most favorable health-related fitness in comparison to low activity throughout the workday: waist circumference 90.0 cm (95% confidence interval [CI] 85.5-94.5) versus 99.5 cm (95% CI 96.8-102.3), fat mass 13.9 kg (9.3-18.5) versus 23.8 kg (20.2-27.4), cardiorespiratory fitness 33.4 mL/kg/min (95% CI 31.4-35.3) versus 29.1 mL/kg/min (95% CI 27.8-30.3) (adjusted for age, sex, days off activity, smoking, and alcohol). For the days off, 2 different trajectories were identified, but they differed only in terms of level and not by timing of physical activity.

CONCLUSIONS

A large variation in the workday physical activity patterns was observed among aging workers. Independent of worktime activity, people who were more active in the evenings had more favorable health-related physical fitness than those who were less active throughout the day.

Changes in accelerometer-measured sleep during the transition to retirement: the Finnish Retirement and Aging (FIREA) study

STUDY OBJECTIVES

Retirement is associated with increases in self-reported sleep duration and reductions in sleep difficulties, but these findings need to be confirmed by using more objective measurement tools. This study aimed at examining accelerometer-based sleep before and after retirement and at identifying trajectories of sleep duration around retirement.

METHODS

The study population consisted of 420 participants of the Finnish Retirement and Aging study. Participants' sleep timing, sleep duration, time in bed, and sleep efficiency were measured annually using a wrist-worn triaxial ActiGraph accelerometer on average 3.4 times around retirement. In the analyses, sleep on nights before working days and on nights before days off prior to retirement were separately examined in relation to nights after retirement.

RESULTS

Both in bed and out bed times were delayed after retirement compared with nights before working days. Sleep duration increased on average by 41 min (95% confidence interval [CI] = 35 to 46 min) from nights before working days and decreased by 13 min (95% CI = -20 to -6 min) from nights before days off compared with nights after retirement. By using latent trajectory analysis, three trajectories of sleep duration around retirement were identified: (1) shorter mid-range sleep duration with increase at retirement, (2) longer mid-range sleep duration with increase at retirement, and (3) constantly short sleep duration.

CONCLUSIONS

Accelerometer measurements support previous findings of increased sleep duration after retirement. After retirement, especially out bed times are delayed, thus, closely resembling sleep on pre-retirement nights before non-working days.

Objectively Measured Sedentary Time Before and After Transition to Retirement: The Finnish Retirement and Aging Study

BACKGROUND

Retirement is associated with an increase in self-reported daily sedentary time, but no longitudinal evidence exists on how objectively measured sedentary time changes during retirement transition. The aim of this study was to compare objectively measured daily and hourly sedentary time before and after retirement and examine whether these changes differ by gender and occupational status.

METHODS

The study population consisted of 478 participants (mean age 63.2 years, standard deviation 1.7, 85% women) from the Finnish Retirement and Aging Study. Sedentary time was measured using a wrist-worn triaxial ActiGraph accelerometer before and after transition to retirement with 1 year interval. Preretirement occupational status was categorized as manual and non-manual.

RESULTS

Daily sedentary time was 8 hours 10 minutes in women and 9 hours 49 minutes in men before retirement. Considering all measurement days before and after retirement, daily sedentary time increased in women by 29 minutes (95% confidence interval [CI]: 20 to 38). Especially women retiring from manual occupations showed marked increase in sedentary time (63 minutes, 95% CI: 50 to 77). When only non-working days before retirement were considered, increase in daily sedentary time among women was less marked (16 minutes, 95% CI: 7 to 25). Among men, daily sedentary time did not change in retirement transition (-7 minutes, 95% CI: -26 to 12).

CONCLUSIONS

Objectively measured sedentary time increases among women and remains at high level among men during the retirement transition. Attention should be paid to reduce daily sedentary time in retiring women and men.

Physical Activity across Retirement Transition by Occupation and Mode of Commute

Supplemental digital content is available in the text.

Purpose

Retirement induces changes in the composition of daily physical activity. Our aim was to examine changes in accelerometer-measured physical activity around transition to statutory retirement among men and women by occupational category and by preretirement modes of commuting.

Methods

We included 562 workers (mean [SD] age, 63.3 [1.1] yr; 85% women) from the Finnish Retirement and Aging study. The participants wore an accelerometer on their nondominant wrist for 1 wk before and 1 wk after retirement, with 1 yr between the measurements. We compared mean daily activity counts before and after retirement between manual and nonmanual occupations by gender and by preretirement commuting mode using linear models with generalized estimating equations.

Results

Before retirement, women were more active than men (2550 (95% confidence interval, 2500–2590) vs 2060 (1970–2140) mean daily activity counts), with the most active group being women in manual occupations. After retirement, physical activity decreased by 3.9% among women and increased, albeit nonsignificantly, by 3.1% in men. The decrease was most pronounced among women in manual and increase among men in nonmanual occupations. After retirement, women remained more active than men (2450 (95% confidence interval 2390–2500) vs 2120 (2010–2230) counts). Active commuting, especially cycling, before retirement was associated with higher physical activity both before and after retirement, and these people also maintained their total activity lever better than did those who commuted by public transportation.

Conclusions

Although women in manual occupations decreased and men in nonmanual occupations increased their activity after retirement, women were more active than men both before and after retirement. Those who engaged in active commuting before retirement maintained their activity level also after retirement.

Comparison of Sedentary Time Between Thigh-Worn and Wrist-Worn Accelerometers

Background: The accuracy of wrist-worn accelerometers in identifying sedentary time has been scarcely studied in free-living conditions. The aim of this study was to compare daily sedentary time estimates between a thigh-worn accelerometer, which measured sitting and lying postures, and a wrist-worn accelerometer, which measured low levels of movement. Methods: The study population consisted of 259 participants (Mage = 62.8 years, SD = 0.9) from the Finnish Retirement and Aging Study (FIREA). Participants wore an Axivity AX3 accelerometer on their mid-thigh and an Actigraph wActiSleep-BT accelerometer on their non-dominant wrist simultaneously for a minimum of 4 days in free-living conditions. Two definitions to estimate daily sedentary time were used for data from the wrist-worn accelerometer: 1) the count cutpoint, ≤1853 counts per minute; and 2) the Euclidean Norm Minus One (ENMO) cutpoint, <30 mg. Results: Compared to the thigh-worn accelerometer, daily sedentary time estimate was 63 min (95% confidence interval [CI] = −53 to −73) lower by the count cutpoint and 50 min (95% CI = 34 to 67) lower by the ENMO cutpoint. The limits of agreement in daily sedentary time estimates between the thigh- and cutpoint methods for wrist-worn accelerometers were wide (the count cutpoint: −117 to 243, the ENMO cutpoint: −212 to 313 min). Conclusions: Currently established cutpoint-based methods to estimate sedentary time from wrist-worn accelerometers result in underestimation of daily sedentary time compared to posture-based estimates of thigh-worn accelerometers. Thus, sedentary time estimates obtained from wrist-worn accelerometers using currently available cutpoint-based methods should be interpreted with caution and future work is needed to improve their accuracy.

Alternative Wear-time Estimation Methods Compared to Traditional Diary Logs for Wrist-Worn ActiGraph Accelerometers in Pregnant Women

BACKGROUND

This study sought to compare three sensor-based wear-time estimation methods to conventional diaries for ActiGraph wGT3X-BT accelerometers worn on the non-dominant wrist in early pregnancy.

METHODS

Pregnant women (n= 108) wore ActiGraph wGT3X-BT accelerometers for 7 days and recorded their device on and off times in a diary (criterion). Average daily wear-time estimates from the Troiano and Choi algorithms and the wGT3X-BT accelerometer wear sensor were compared against the diary. The Hibbing 2-regression model was used to estimate time spent in activity (during periods of device wear) for each method. Wear-time and time spent in activity were compared with multiple repeated measures ANOVAs. Bland Altman plots assessed agreement between methods.

RESULTS

Compared to the diary [825.5 minutes (795.1, 856.0)], the Choi [843.0 (95% CI 812.6, 873.5)] and Troiano [839.1 (808.7, 869.6)] algorithms slightly overestimated wear-time, whereas the sensor [774.4 (743.9, 804.9)] underestimated it, although only the sensor differed significantly from the diary (P < .0001). Upon adjustment for average daily wear-time, there were no statistically significant differences between the wear-time methods in regards to minutes per day of moderate to vigorous physical activity (MVPA), vigorous PA, and moderate PA. Bland Altman plots indicated the Troiano and Choi algorithms were similar to the diary and within ≤ 0.5% of each other for wear-time and MVPA.

CONCLUSIONS

The Choi or Troiano algorithms offer a valid and efficient alternative to diaries for the estimation daily wear-time in larger-scale studies of MVPA during pregnancy, and reduce burden for study participants and research staff.

Patterns of Physical Activity and Sedentary Behavior for Older Adults with Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Normal in Hong Kong

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia, and mild cognitive impairment (MCI) is a transitional phase between healthy cognition and dementia. Physical activity (PA) has protective effects on cognitive decline. However, few studies have examined how PA and sedentary behavior is structured throughout the day in older adults across varied cognitive status in Hong Kong.

OBJECTIVE

This study aimed to compare patterns of PA and sedentary behavior among individuals with AD, MCI, or normal cognition living in Hong Kong.

METHODS

Participants in the MrOs and MsOs Hong Kong cohort study and the Hong Kong AD biomarker study (n = 810) wore a wrist-worn accelerometer for 7 days in free-living environment. Patterns of PA in wake time and in-bed time, and detailed analysis of sedentary bouts were compared between groups using analysis of covariance adjusting for covariates.

RESULTS

Participants with MCI and low MoCA only did not differ from their cognitively normal peers in PA and sedentary behavior. Nevertheless, when comparing to the others, participants with AD exhibited significantly lower average daily counts per minute during the day (p < 0.05), and tended to start their activity later in the morning. AD participants spent a larger proportion of time in sedentary behavior (p < 0.05) and had more sedentary bouts≥30 minutes (p < 0.05).

CONCLUSIONS

The pattern of PA and sedentary behavior was different between individuals with AD and the others. Cognitive status may alter the purpose and type of PA intervention for AD individuals.

Daily physical activity patterns among aging workers: the Finnish Retirement and Aging Study (FIREA)

OBJECTIVES

Physical activity is associated with the aging workers' ability to work and predicts working beyond retirement age. To better understand physical activity behaviour in this growing population group, we aimed at characterising 24-hour physical activity patterns among aging workers, and to describe the association between occupational category and total, occupational and leisure-time physical activities.

METHODS

We included 878 workers (mean age 62.4 years, SD 1.1, 85% women) from the Finnish Retirement and Aging Study, who wore an accelerometer on their non-dominant wrist for 1 week. We plotted mean hourly activity counts per minute (CPM) for working days and days off. We also compared mean daily CPM between genders and occupations between working days and days off, and work and leisure time by using repeated measures analysis of variance.

RESULTS

Activity patterns were different between genders, occupations and types of the day. Women (2580, 95% CI 2540 to 2620) had higher daily mean CPM than men (2110, 95% CI 2020 to 2000). Women in manual occupations were more active than women in non-manual occupations during working days. The differences among men were in the same direction but less pronounced than among women. We found no differences in activity levels between occupations during days off and leisure time on working days.

CONCLUSIONS

In aging workers, physical activity differs by gender and occupation during working time, but not during leisure time. As low physical activity is associated with increased risk of early exit from employment, physical activity should be promoted at workplaces, especially among men and people in non-manual occupations.