Patterns of Accelerometer-Measured Physical Activity and Health Outcomes in Adults: A Systematic Review

Physical activity volume, frequency, and intensity: Associations with hypertension and obesity over 21 years in Australian women

BACKGROUND

Optimal patterns of accrual of recommended levels of physical activity (PA) for prevention of hypertension and obesity are not known. The overall aim of this study was to investigate whether different patterns of accumulation of PA are differentially associated with hypertension and obesity in Australian women over 21 years. Specifically, we investigated whether, for the same weekly volume of PA, the number of sessions (frequency) and vigorousness of PA (intensity) were associated with a reduction in the occurrence of hypertension and obesity in women.

METHODS

Data from the 1973-1978 and 1946-1951 cohorts of the Australian Longitudinal Study on Women's Health were analyzed (n = 20,588; 12%-16% with a Bachelor's or higher degree). Self-reported PA, hypertension, height, and weight were collected using mail surveys every 3 years from 1998/2000 to 2019/2021. Generalized Estimating Equation models with a 3-year lag model were used to investigate the association of PA volume (metabolic equivalent min/week) (none; 33-499; 500-999; ≥1000, weekly frequency (none; 1-2 times; 3-4 times; 5-7 times; ≥8 times), and the proportion of vigorous PA to total volume of PA (none; 0%; 1%-33%; 34%-66%; 67%-100%) with odds of hypertension and obesity from 2000 to 2021.

RESULTS

The cumulative incidence of hypertension was 6% in the 1973-1978 and 23% in the 1946-1951 cohort; 27% of women in the 1973-1978; and 15% in the 1946-1951 cohort developed obesity over the period. Overall, a higher volume of PA was associated with reduced odds of hypertension and obesity. When the volume of PA was considered, the odds of hypertension did not vary according to the frequency or intensity of PA. However, increased proportion of vigorous PA to the total volume of PA was associated with a small additional reduction in the risk of obesity.

CONCLUSION

PA volume appears to be more important than the pattern of accumulation for the prevention of hypertension and obesity. Incorporating more sessions, particularly of vigorous-intensity PA, may provide extra benefits for the prevention of obesity.

Interplay between physical activity volume and intensity with modeled life expectancy in women and men: A prospective cohort analysis

BACKGROUND

There is a lack of research examining the interplay between objectively measured physical activity volume and intensity with life expectancy.

METHODS

Individuals from UK Biobank with wrist-worn accelerometer data were included. The average acceleration and intensity gradient were extracted to describe the physical activity volume and intensity profile. Mortality data were obtained from national registries. Adjusted life expectancies were estimated using parametric flexible survival models.

RESULTS

40,953 (57.1%) women (median age = 61.9 years) and 30,820 (42.9%) men (63.1 years) were included. Over a median follow-up of 6.9 years, there were 1719 (2.4%) deaths (733 in women; 986 in men). At 60 years, life expectancy was progressively longer for higher physical activity volume and intensity profiles, reaching 95.6 years in women and 94.5 years in men at the 90th centile for both volume and intensity, corresponding to 3.4 (95% confidence interval (95%CI): 2.4-4.4) additional years in women and 4.6 (95%CI: 3.6-5.6) additional years in men compared to those at the 10th centiles. An additional 10-min or 30-min daily brisk walk was associated with 0.9 (95%CI: 0.5-1.3) and 1.4 (95%CI: 0.9-1.9) years longer life expectancy, respectively, in inactive women; and 1.4 (95%CI: 1.0-1.8) and 2.5 (95%CI: 1.9-3.1) years in inactive men.

CONCLUSION

Higher physical activity volumes were associated with longer life expectancy, with a higher physical activity intensity profile further adding to a longer life. Adding as little as a 10-min brisk walk to daily activity patterns may result in a meaningful benefit to life expectancy.

Physical Behavior Profiles Among Older Adults and Their Associations With Physical Capacity and Life-Space Mobility

We identified data-driven multidimensional physical activity (PA) profiles using several novel accelerometer-derived metrics. Participants aged 75, 80, and 85 (n = 441) wore triaxial accelerometers for 3-7 days. PA profiles were formed with k-means cluster analysis based on PA minutes, intensity, fragmentation, sit-to-stand transitions, and gait bouts for men and women. Associations with physical capacity and life-space mobility were examined using age-adjusted general linear models. Three profiles emerged: "Exercisers" and "actives" accumulated relatively high PA minutes, with actives engaging in lighter intensity PA. "Inactives" had the highest activity fragmentation and lowest PA volume, intensity, and gait bouts. Inactives showed lower scores in physical capacity and life-space mobility compared with exercisers and actives. Exercisers and actives had similar physical capacity and life-space mobility, except female exercisers had higher walking speed in the 6-min walk test. Our findings demonstrate the importance of assessing PA as multidimensional behavior rather than focusing on a single metric.

Cross-sectional associations between patterns and composition of upright and stepping events with physical function: insights from The Maastricht Study

INTRODUCTION

Age-related declines in physical functioning have significant implications for health in later life. Physical activity (PA) volume is associated with physical function, but the importance of the pattern in which PA is accumulated is unclear. This study investigates associations between accelerometer-determined daily PA patterns, including composition and temporal distribution (burstiness) of upright and stepping events, with physical function.

METHODS

Data was from participants who wore an activPAL3 accelerometer as part of The Maastricht Study. Exposures included a suite of metrics describing the composition and the temporal distribution (burstiness) of upright and sedentary behaviour. Physical function outcomes included the six-minute walk test (6MWT), timed chair-stand test (TCST), grip strength (GS), and SF-36 physical functioning sub-scale (SF-36pf). Multivariable linear regression models were used to assess associations, adjusting for covariates including overall PA volume (daily step count).

RESULTS

Participants(n = 6085) had 6 or 7 days of valid data. Upright and stepping event metrics were associated with physical function outcomes, even after adjusting PA volume. Higher sedentary burstiness was associated with better function (6MWT, TCST, and SF-36pf), as was duration and step volume of stepping events (6MWT, TCST, GS, and SF-36pf), step-weighted cadence (6MWT, TCST, and SF-36pf). Number of stepping events was associated with poorer function (6MWT, GS, and SF-36pf), as was upright event burstiness (SF-36pf). Associations varied according to sex.

CONCLUSION

Our study reveals that diverse patterns of physical activity accumulation exhibit distinct associations with various measures of physical function, irrespective of the overall volume. Subsequent investigations should employ longitudinal and experimental studies to examine how changing patterns of physical activity may affect physical function, and other health outcomes.

Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach

BACKGROUND

Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity.

METHODS

To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models.

RESULTS

We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group.

CONCLUSIONS

Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.

Cross-sectional associations between temporal patterns and composition of upright and stepping events with physical function in midlife: Insights from the 1970 British Cohort Study

INTRODUCTION

Age-related decline in physical functioning has significant implications for health in later life but declines begin earlier in midlife. Physical activity (PA) volume is associated with physical function, but the importance of the pattern in which PA is accumulated is unclear. This study investigates associations between patterns of PA accumulation, including the composition, variation, and temporal distribution of upright and stepping events, with physical function in midlife.

METHODS

Participants (n = 4378) from the 1970 British Cohort Study wore an activPAL3 accelerometer on the thigh for 7 consecutive days. Exposure measures included a suite of metrics describing the frequency, duration, and composition of upright events, as well as the duration and volume (total steps) of stepping events. In addition, patterns of accumulation of upright and sedentary events were examined including how fragmented/transient they were (upright-to-sedentary transition probability [USTP]) and their burstiness (the tendency for events to be clustered together followed by longer interevent times). Physical function outcomes included grip strength (GS), balance, and SF-36 physical functioning subscale (SF-36pf). Cross-sectional analyses included multivariable linear regression models to assess associations, adjusting for covariates including overall PA volume (mean daily step count).

RESULTS

Higher upright event burstiness was associated with higher GS, and higher USTP was associated with lower GS. Duration and step volume of stepping events were positively associated with SF-36pf in females. Step-weighted cadence was positively associated with SF-36pf and balance. Contradictory findings were also present (e.g., more transient stepping events were associated with better GS) particularly for GS in males. Inconsistencies between sexes were observed across some associations.

CONCLUSION

Our study reveals that diverse patterns of PA accumulation exhibit distinct associations with various measures of physical function in midlife, irrespective of the overall volume. Contradictory findings and inconsistency between sexes warrant further investigation. Patterns of PA accumulation, in addition to volume, should be considered in future PA research. Longitudinal studies are required to determine whether a given volume of activity accumulated in different patterns, impacts associations between PA and health outcomes.

Association of 'Tortoise' and 'Hare' movement behavior patterns with cardiometabolic health, body composition, and functional fitness in older adults

We investigated the association of movement behavior patterns with cardiometabolic health, body composition, and functional fitness in older adults. A total of 242 older adults participated of this cross-sectional study. Sedentary time, light physical activity (LPA) and moderate-vigorous physical activity (MVPA), steps/day, and step cadence were assessed by accelerometry. The movement behavior patterns were derived by principal component analysis. Cardiometabolic health was defined by a metabolic syndrome score (cMetS). Body composition was determined by appendicular lean mass/body mass index (ALM/BMI). Functional fitness was assessed by a composite z-score from the Senior Fitness Test battery. Two patterns were identified: 'Tortoise' (low sedentary time, high LPA and steps/day) and 'Hare' (high MVPA, steps/day, and step cadence). 'Tortoise' and 'Hare' were associated with better cMetS. 'Hare' was positively associated with ALM/BMI and Functional Fitness. While 'Tortoise' and 'Hare' were associated with better cMetS, only 'Hare' was associated with better ALM/BMI and functional fitness.

Associations Between Intersecting Sociodemographic Characteristics and Device-Measured Physical Activity Among Children and Adolescents Living in the United States

BACKGROUND

Despite robust evidence demonstrating sociodemographic characteristics may underly some of the disparities in physical activity observed among children and adolescents, the often-overlooked nexus of potential interactions between these characteristics warrants further exploration. This study explored the intersectionality of gender, race/ethnicity, parental education, and household income in relation to device-measured physical activity volume and intensity in a nationally representative sample of US children and adolescents.

METHODS

Cross-sectional data from 3 cycles of the US National Health and Nutrition Survey (2011-2012; 2012 National Youth Fitness Survey; and 2013-2014) were used. A total of 6116 participants (49% female) between 3 and 17 years of age wore an accelerometer on their nondominant wrist for 7 days. Monitor-independent movement summary units were used to represent physical activity volume and intensity. A Social Jeopardy Index was created to represent increasing levels of intersecting social disadvantages based on combinations of gender, race/ethnicity, parental education, and household income-to-poverty ratio tertiles. Generalized linear regression models were computed.

RESULTS

The results showed social disadvantages become increasingly evident among children and adolescents during the most intense 60 minutes of daily physical activity (B = -48.69 [9.94] SE, P < .001), but disparities in total volume were not observed (B = 34.01 [44.96] SE, P = .45).

CONCLUSIONS

Findings suggest that patterns of physical activity behavior may differ based on intersecting sociodemographic characteristics-more socially disadvantaged children and adolescents appear to accumulate activity at lighter intensities. Collecting contextual information about device-measured physical activity represents an important next step for gaining insight into these sociodemographic differences.

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

BACKGROUND

Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders.

METHODS

Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20-65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003-2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data.

RESULTS

Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as "Early-morning movers" and the profile characterized as "Highest activity" both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the "Lowest activity" profile. No significant differences were observed for the "Least sedentary movers" profile compared to the "Lowest activity" profile.

CONCLUSIONS

Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.

Unravelling upright events: a descriptive epidemiology of the behavioural composition and temporal distribution of upright events in participants from the 1970 British Cohort Study

BACKGROUND

Continued proliferation of accelerometers in physical activity research has opened new avenues for understanding activity behaviours beyond simple aggregate measures of frequency and duration. This study explores the standing and stepping composition, and the temporal distribution, of upright events, and investigates their associations with sociodemographic and health factors.

METHODS

Participants from the 1970 British Cohort Study wore activPAL3 accelerometers for seven days. Event-based analysis was used to extract a time series of upright, standing, and stepping events. Derived metrics included daily number of upright and stepping events, total upright and stepping time, the burstiness of upright events and burstiness of sedentary events (burstiness refers to the pattern of how physical activity and sedentary behaviour are distributed throughout a given time period), within-event stepping proportion, within-event step count, and stepping cadence. Generalized linear regression models, adjusted for total step count, were employed to explore associations between derived metrics and sociodemographic and health-related factors.

RESULTS

A total of 4527 participants, provided 30992 valid days (≥ 10 h of waking wear) and 1.64 million upright events. Upright event composition and temporal distribution varied across a range of sociodemographic and health-related factors. Females had more upright events than males (4.39 [3.41,5.38] n), spent more time upright, and exhibited burstier patterns of upright events (0.05 [0.04,0.05] Bn). Individuals with higher BMI had fewer upright events and a lower daily step count, but their temporal distribution of upright events was less bursty (overweight -0.02 [-0.02,-0.01] Bn; obese -0.03 [-0.04,-0.02] Bn), and upright events had a higher step count. People in active occupations were upright for longer, displayed burstier patterns of upright events (standing 0.04 [0.03,0.05] Bn; physical work 0.05 [0.04,0.05] Bn; heavy manual 0.06 [0.04,0.07] Bn), with more variable durations and shorter, slower paced stepping events compared with sedentary occupations.

CONCLUSIONS

This study has revealed novel phenotypes of standing and sitting that go beyond simple aggregate measures of total steps, step event duration or time between events. People with the same volume of stepping and frequency of gaps between upright events can accumulate their steps in very different ways. These differences and associations with population sub-groups, which persisted after adjustment for total stepping volume, may have important relations with functional and health outcomes. The findings lay the groundwork for future studies to investigate how different sitting and standing phenotypes can add to our understanding of the relationship between physical activity and health.

Day-to-day variability in accelerometer-measured physical activity in mid-aged Australian adults

PURPOSE

The aim was to use accelerometer data to describe day-to-day variability in physical activity in a single week, according to sociodemographic variables, in mid-aged Australian adults.

METHODS

Data were from participants in the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) study who took part in a 2014 sub-study (N = 612; Mean age 60.6 [SD 6.9; range 48-73]). Participants wore a triaxial accelerometer (ActiGraph wGT3X-BT) on their non-dominant wrist for seven days, and data were expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g). These were, used to estimate daily acceleration (during waking hours) and daily time spent in moderate-vigorous physical activity (MVPA, defined as ≥ 100mg). Coefficient of variation (calculated as [standard deviation/mean of acceleration and MVPA across the seven measurement days] * 100%) was used to describe day-to-day variability.

RESULTS

Average values for both acceleration (24.1-24.8 mg/day) and MVPA (75.9-79.7 mins/day) were consistent across days of the week, suggesting little day-to-day variability (at the group level). However, over seven days, average individual day-to-day variability in acceleration was 18.8% (SD 9.3%; range 3.4-87.7%) and in MVPA was 35.4% (SD 15.6%; range 7.3-124.6%), indicating considerable day-to-day variability in some participants. While blue collar workers had the highest average acceleration (28.6 mg/day) and MVPA (102.5 mins/day), their day-to-day variability was low (18.3% for acceleration and 31.9% for MVPA). In contrast, variability in acceleration was highest in men, those in professional occupations and those with high income; and variability in MVPA was higher in men than in women.

CONCLUSION

Results show group-level estimates of average acceleration and MVPA in a single week conceal considerable day-to-day variation in how mid-age Australians accumulate their acceleration and MVPA on a daily basis. Overall, there was no clear relationship between overall volume of activity and variability. Future studies with larger sample sizes and longitudinal data are needed to build on the findings from this study and increase the generalisability of these findings to other population groups.

Absolute intensity thresholds for tri-axial wrist and waist accelerometer-measured movement behaviors in adults

AIM

This study was aimed to: (1) compare raw triaxial acceleration data from GENEActiv (GA) and ActiGraph GT3X+ (AG) placed on the non-dominant wrist; (2) compare AG placed on the non-dominant and dominant wrist, and waist; (3) derive brand- and placement-specific absolute intensity thresholds for inactive and sedentary time, and physical activity intensity in adults.

METHODS

Eighty-six adults (44 men; 34.6 ± 10.8 years) performed nine activities while simultaneously wearing GA and AG on wrist and waist. Acceleration (in gravitational equivalent units; mg) was compared with oxygen uptake (measured with indirect calorimetry).

RESULTS

Increases in acceleration mirrored increases in intensity of activities, regardless of device brand and placement. Differences in acceleration between GA and AG worn at the non-dominant wrist were small but tended to be high at lower intensity activities. Thresholds for differentiating inactivity (<1.5 MET) from activity (≥1.5 MET) ranged from 25 mg (AG non-dominant wrist; sensitivity 93%, specificity 95%) to 40 mg (AG waist; sensitivity 78%, specificity 100%). For moderate intensity (≥3 METs), thresholds ranged from 65 mg (AG waist; sensitivity 96%, specificity 94%) to 92 mg (GA non-dominant; sensitivity 93%, specificity 98%); vigorous intensity (≥6 METs) thresholds ranged from 190 mg (AG waist; sensitivity 82%, specificity 92%) to 283 mg (GA non-dominant; sensitivity 93%, specificity 98%).

CONCLUSION

Raw triaxial acceleration outputs from two widely used accelerometer brands may have limited comparability in low intensity activities. Thresholds derived in this study can be utilized in adults to reasonably classify movement behaviors into categories of intensity.

Defining and reporting activity patterns: a modified Delphi study

BACKGROUND

Despite significant interest in assessing activity patterns in different populations, there has been no consensus concerning the definition and operationalisation of this term. This has limited the comparability, interpretability, and synthesis of study findings to date. The aim of this study was to establish a consensus regarding the way in which activity patterns and activity pattern components are defined and reported.

METHODS

The activity patterns literature was searched to identify experts to be invited to participate and to develop a proposed definition of activity patterns and activity pattern components. A three-round modified Delphi survey was conducted online (November 2021 to May 2022). In Round 1, participants were asked to rate their agreement with a proposed activity patterns definition, which also included six activity pattern components (e.g., activity intensity, activity bout, transitions), six examples of activity patterns (e.g., frequency of postural transitions in discrete time periods) and eight items for reporting activity patterns in future research (n = 21 items). Open-ended questions enabled participants to provide further comments and suggestions for additional items. Consensus was defined a priori as ≥ 80% participants rating their agreement with an item. In Round 2, participants were asked to rate their agreement with 25 items (13 original items, eight amended, and four new). In Round 3, participants rated their agreement with 10 items (five original items, four amended, and one new).

RESULTS

Twenty experts in activity patterns research participated in Round 1, with response rates of 80% and 60% in Rounds 2 and 3, respectively. The proposed activity pattern definition, all activity pattern components definitions, four of the six activity pattern examples, and 10 items in the activity patterns reporting framework achieved consensus. The removal of one activity component item between Rounds 1 and 2 achieved consensus.

CONCLUSION

This modified Delphi study achieved consensus for defining and reporting activity patterns for the first time. This consensus definition enables standardisation of activity patterns terminology, which is important given the significant interest in quantifying how individuals accumulate their physical activity and sedentary behaviour across the lifespan to inform the development of future public health guidelines and interventions efforts.

Physical Activity and Depression and Anxiety Disorders: A Systematic Review of Reviews and Assessment of Causality

Introduction

Globally, depressive and anxiety disorders are the leading contributors to mental ill health. Physical activity reduces symptoms of depression and anxiety and has been proposed as an adjunct treatment therapy for depression and anxiety. Prospective studies suggest that physical activity may reduce the incidence of depression and anxiety. We conducted a systematic review of reviews with the aim to provide a comprehensive overview of available epidemiologic evidence on the strength of the association between physical activity and incident cases of depression and anxiety and to assess the likelihood of these associations being causal.

Methods

We searched Embase and PubMed databases for systematic reviews published between January 1, 2000 and March 19, 2020 that reported findings on the strength of association between physical activity and incidence of depression and anxiety. We updated this search to October 15, 2022. Two reviewers independently assessed the methodologic quality of the included reviews using the Assessment of Multiple Systematic Reviews rating scale. We carried out a narrative synthesis of the evidence. We used the Bradford Hill criteria to assess the likelihood of associations being causal.

Results

The initial search yielded 770 articles, of which 4 remained for data extraction. Two of the included reviews were scored as high quality, and 2 were scored as low quality. From the 2 included reviews that reported pooled estimates, people with high physical activity levels were found to have a decreased risk of incident depression (adjusted RR=0.83, 95% CI=0.76, 0.90) and reduced odds of developing anxiety (adjusted OR=0.74,95% CI=0.62, 0.88) when compared with those with low physical activity levels. We assessed physical activity to be probably causally related to both depression and anxiety.

Discussion

Our evidence is drawn from systematic reviews of observational data. Further high-quality studies, such as randomized control trials, would help to strengthen the evidence base of the associations between physical activity and depression and anxiety. Nonetheless, our findings provide empirical support for the consideration of physical activity in strategies for the prevention of mental ill health.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

BACKGROUND

Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours.

METHODS

The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss.

RESULTS

123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent.

CONCLUSION

Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

Accelerometer-measured physical activity in mid-age Australian adults

Background

Raw data from accelerometers can provide valuable insights into specific attributes of physical activity, such as time spent in intensity-specific activity. The aim of this study was to describe physical activity assessed with raw data from triaxial wrist-worn accelerometers in mid-age Australian adults.

Methods

Data were from 700 mid-age adults living in Brisbane, Australia (mean age: 60.4; SD:7.1 years). Data from a non-dominant wrist worn triaxial accelerometer (Actigraph wGT3X-BT), expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g), were used to estimate time spent in moderate-vigorous intensity physical activity (MVPA; >100 mg) using different bout criteria (non-bouted, 1-, 5-, and 10-min bouts), and the proportion of participants who spent an average of at least one minute per day in vigorous physical activity.

Results

Mean acceleration was 23.2 mg (SD: 7.5) and did not vary by gender (men: 22.4; women: 23.7; p-value: 0.073) or education (p-value: 0.375). On average, mean acceleration was 10% (2.5 mg) lower per decade of age from age 55y. The median durations in non-bouted, 1-min, 5-min and 10-min MVPA bouts were, respectively, 68 (25th -75th : 45–99), 26 (25th -75th : 12–46), 10 (25th -75th : 3–24) and 8 (25th -75th : 0–19) min/day. Around one third of the sample did at least one minute per day in vigorous intensity activities.

Conclusion

This population-based cohort provided a detailed description of physical activity based on raw data from accelerometers in mid-age adults in Australia. Such data can be used to investigate how different patterns and intensities of physical activity vary across the day/week and influence health outcomes.