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The 24-Hour Activity Cycle: A New Paradigm for Physical Activity


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Medicine & Science in Sports & Exercise: March 2019 - Volume 51 - Issue 3 - p 454-464
doi: 10.1249/MSS.0000000000001811
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During the past decade, evidence has continued to accumulate that a person’s behaviors during both sleep and awake time have important consequences for health and quality of life. Robust evidence on the health benefits of optimal patterns of sleep and moderate-to-vigorous physical activity (MVPA) has led to specific, time-based public health guidelines for both of these activities in adults (1,2). Less evidence is available about optimal patterns of sedentary behavior and light-intensity physical activity (LIPA), so only general recommendations are possible to guide time spent in these behaviors (3). In this article, we refer to these four categories of behavior as “activities.” In this context, “activity” is a term referring to any behavior that consumes time during the day (as used in time-use surveys), as opposed to a physically active endeavor (4).

Current public health recommendations in adults deal separately with sleep, sedentary behavior, LIPA, and MVPA over a 24-h sleep and wake cycle. This reflects that fact that much of the data published to date deals with the relationship of specific health outcomes to time spent in only one activity during a daily 24-h cycle. Studies generally have not considered the potential interrelationships of activities with each other. However, some evidence suggests that time spent in each activity can modify the health-related influence of time spent in any of the other activities. For example, increasing time spent in MVPA significantly reduces the negative cardiometabolic health consequences of sedentary behavior (5). In youth, it seems as though the relationships between MVPA and sedentary behavior might have a different effect on health compared to the same relationship in adults (6). Several comprehensive 24-h guidelines for youth and early childhood have been published in recent years (7,8).

The development of device-based measurement tools and the need for more comprehensive behavior recommendations have spurred the creation of a new paradigm of daily activities connected through time and physiology in a 24-h cycle (9). This new paradigm—the 24-h Activity Cycle (24-HAC) model—is intended to help characterize an optimal 24-h pattern of physical activity for health and quality of life and aid in creating future public health (or individualized) recommendations. Because of the volume and novelty of research in this area in adults, this review will focus on this age group. Although the youth are not the focus of this review. it is important to acknowledge the application of 24-h cycle research to youth and youth guidelines (10). The discussion that follows defines the 24-HAC, reviews the relationship of each activity in the cycle to disease prevention, outlines recent findings with behavioral synergies, and describes innovations from investigators who are already examining activity on a 24-h scale.

This article also discusses measurement of the activities of the 24-HAC and the analytical and statistical challenges in analyzing data based upon the model. The potential usefulness of this model is described by reviewing selected research findings that aided in the creation of the model and discussing future applications of the 24-HAC model. This article reviews the most recent evidence on sleep, sedentary behavior, physical activity, and health in the context of the 24-HAC, with a focus on measurement and health promotion challenges and opportunities. In addition, consideration has been given to bring together studies representing a complete interdisciplinary research cycle based on available tools.


In April 2016, the Stanford Center on Longevity hosted a workshop entitled “Wearable Devices & the 24-h Activity Cycle: A Framework for Developing Daily Activity Recommendations,” which convened researchers and industry leaders to examine a new hypothesis, namely, that health recommendations would be more effective if they could include all established health-related activities (sleep, sedentary behavior, LIPA, and MVPA) experienced in a daily cycle. This was a shift in paradigm from traditional activity recommendations, which have been created separately by individual research fields (11).

The Stanford workshop stimulated a symposium at the 2017 American College of Sports Medicine Annual Meeting entitled “Wake up! Optimizing Physical Activity, Sedentary Behavior, and Sleep for Better Health.” The symposium presented the 24-HAC and the science supporting its development. In collaboration, Stanford conference organizers and American College of Sports Medicine symposium speakers have refined the 24-HAC model, collated literature relevant to the various aspects of the 24-HAC and presented it in the form of this brief review.

Introducing Component Activities of the 24-HAC and Related Public Health Guidelines

The four basic activities of the 24-HAC model are sleep, sedentary behavior, LIPA, and MVPA. Although this article does not subdivide these activities, as seen in Table 1, LIPA may be divided into very light and light activities, and MVPA divided into moderate-intensity and vigorous-intensity activities. Thus, the 24-HAC is a comprehensive model of daily activity. Other health-related behaviors also occur within the 24 h (e.g., diet, smoking). However, the 24-HAC focuses on activities that are physical behaviors, primarily time-based, and can be assessed with wearable technologies, as seen in Figure 1. Definitions for these activities and the evidence of health risks or benefits are included in Table 1. Evidence is limited, however, on the ideal daily balance of each of these health-promoting activities to maintain optimal disease prevention, weight maintenance, and physical function and performance. The 24-HAC is intended to create new opportunities to better examine daily activities, their interrelationships, and how they may synergistically contribute to optimal health and well-being.

Definitions, MET levels, and evidence for the association with health outcomes for activities of the 24-HAC.
Defining the activities (blue) and the research purposes (orange) for creating a 24-HAC model for research.


Human sleep is a naturally recurring and easily reversible state that is characterized by reduced or absent consciousness, perceptual disengagement, immobility, and the adoption of a characteristic sleeping posture. Regulation of the sleep–wake system includes homeostatic and circadian components (15) and is modified by physiologic (16) and environmental factors (17). The sleep–wake regulatory system is comprised of two independent systems (sleep and wake) that originate in the midbrain and project throughout the brain and body. Reviews of this neurobiological system are described elsewhere (16) but, in summary, sleep is regulated by a set of sleep–wake switches that are at least partially controlled by the neurotransmitter orexin (18). The circadian system is comprised of a central clock as well as many peripheral clocks that work to maintain regular rhythms (19). A model of sleep regulation that combines the sleep–wake and circadian processes has been proposed by Borbely (20).

Sleep physiology affects health through total sleep time and sleep quality (Table 2). Insufficient sleep is related to weight gain and obesity (21), cardiometabolic disease (22), mortality (23), and other negative health outcomes (17). About one third of the US population reports averaging <7 h of sleep per day, below the recommended amount of ≥7 h·d−1 (1,24). Insufficient sleep also is related to deficits in cognitive function, which can impair physical function (12). In addition, poor sleep quality (defined in Table 2) is prevalent in the population and contributes to the sleep–health relationship (17). Lastly, sleep exists in a social-ecological context, such that a number of factors are known to be associated with the increased likelihood of insufficient or poor-quality sleep (17).

Terms and descriptions of variables used to measure sleep.

Sedentary Behavior

Sedentary behavior (Table 1) is related to health outcomes independently of MVPA (13,25,26) and has an unknown relationship with sleep. Several studies show time recalled in sedentary behaviors, such as sitting and TV viewing, increase the risk for all-cause mortality. A meta-analysis of six prospective studies showed higher amounts of daily total sitting time was associated with greater risk of all-cause mortality (27) and a meta-analysis of 10 prospective studies showed prolonged TV viewing time might increase the risk of all-cause mortality (28). Similarly, studies using accelerometers to estimate sedentary time show adults participating in less sedentary time (6 h·d−1 compared with 10 h·d−1) have lower risks of mortality (29).

Investigators have examined whether participation in MVPA can eliminate the health risks of sedentary behavior, and the answer may be yes, though very large amounts of MVPA (e.g., greatly exceeding guidelines) may be needed, and only in highly sedentary individuals. Limited evidence exists on the physiologic mechanisms of sedentary behaviors (30), but two reviews (5,27) show the risks of sitting on mortality are attenuated when participants are physically active. A meta-analysis of high levels of sitting (>8 h·d−1) indicates that risks are attenuated when people engage in weekly moderate-intensity activity of 60 to 75 min·d−1 (5). However, sleep and LIPA were not considered in finding this balance. Results from these studies may provide a starting point from which to consider developing public health guidance for sedentary behavior that take MVPA into account.

LIPA and Moderate-to-Vigorous Intensity Physical Activity

Light-intensity physical activity is relatively undefined and usually considered as “other” movement not described as sleep, sedentary behavior, or MVPA. Housework, shopping, cooking, easy gardening, and standing around are examples of activities usually categorized as light intensity. Participation in LIPA makes up much of individuals’ daily activity profiles (31).

Several prospective cohort studies show participation in LIPA is associated with improved survival or decreased mortality. An accelerometer-based study from the National Health and Nutrition Examination Survey 2003 to 2006 showed that adults participating in 5 or more h·d−1 of LIPA, compared with those participating in less than 3 h·d−1, had about a 20% lower risk of mortality, with most of the risk reduction coming from the groups who are not participating in large amounts of MVPA (29). A prospective study of older adults showed, through self-report, that replacing sedentary time with light-intensity activities such as household chores, gardening, and walking were associated with longevity (32). Finally, a study of a representative sample of Swedish adults showed that replacing sedentary time with accelerometer-measured LIPA was associated with decreased mortality (33).

Hundreds of studies have confirmed the relationship between MVPA and health and have linked MVPA to improvements in a wide range of conditions, including heart disease, cancer, obesity, and diabetes (13,34). In recent years, this research has expanded to include strong associations with brain health, including reduction in dementia and Alzheimer’s disease, as well as improvements in mood disorders like depression. Newer analyses have shown that MVPA is associated with reductions in the rate of 13 different cancers (35).

Public Health Guidelines for Sleep and Physical Activity

Guidelines exist for healthy sleep targets for adults. The American Academy of Sleep Medicine and Sleep Research Society jointly recommend at least 7 h·d−1 of sleep for healthy adults (1). National guidelines do not exist for sleep quality, but considering known risk factors for sleep disorders, healthy sleep should consist of a sleep latency that is not too long (typically fewer than 30 min), fewer than four awakenings during the night, and sleep that feels restorative without leading to sleepiness during the day (36).

The 2008 Physical Activity Guidelines for Americans provides recommendations for the amount and intensity of physical activity needed to obtain substantial health benefits (2). For adult aerobic activity, the guidelines recommend a weekly dose of at least 150 min of moderate-intensity physical activity, 75 min of vigorous-intensity physical activity, or an equivalent combination. The evidence supporting the Guidelines is taken largely from studies in which participants recalled their patterns of MVPA. At the time of the 2008 guidelines, evidence for recommendations dealing with sedentary behavior and light-intensity activity was insufficient. In the decade since the publication of the 2008 guidelines, a substantial amount of research has been published, and the 2018 Physical Activity Guidelines Advisory Committee Scientific Report (13) reflects these new findings, including scientific evidence on physical activity and sleep, sedentary behavior, and LIPA. The Committee’s report is being used as the basis for the Physical Activity Guidelines for Americans, second edition.

Introducing the 24-HAC

The 24-HAC uses a holistic approach to integrate the four health-related activities described above (i.e., sleep, sedentary behavior, LIPA, and MVPA) into a paradigm that comprehensively describes the activities of daily life for optimum health (11) (Fig. 1). The 24-HAC is dynamic. Changes in time spent in one activity will influence time spent in at least one other activity. For example, reductions in sedentary behavior (e.g., watching TV), may lead to increases in LIPA (e.g., taking a walk) and/or extensions in sleep duration (e.g., going to bed earlier). The model also assumes interrelationships among the activities. For example, participation in MVPA may promote better sleep (37), which may in turn lead to better alertness during the day and more activity (11). Several studies demonstrate these relationships. For example, in a 16-wk study in which participants logged daily activities, temporal associations were observed between physical activity and improved sleep quality during the following night (38). In another study, greater sleep efficiency among older women was associated with increased activity counts and MVPA the following day (39).

A key goal for the 24-HAC model is to provide an integrated paradigm for unifying current time- and quality-based recommendations for sleep and MVPA with emerging evidence around sedentary behavior and LIPA.

Challenges and Considerations in Using the 24-HAC in Practice and Research

The 24-HAC model combines sleep, sedentary behavior, LIPA and MVPA into a new, comprehensive paradigm for describing the complex patterns of daily activities. Combining these activity types requires applying research methods and behavioral approaches from a variety of sources to understand their independent yet interdependent associations. The following sections discuss five areas that illustrate the challenges and considerations of using the 24-HAC: 1) measuring the 24-HAC; 2) analytic methods applied to the 24-HAC; 3) associations between activities in the 24-HAC; 4) a combined approach to improve the 24-HAC activities through behavioral interventions; and 5) an example of how research could be enhanced with the 24-HAC approach.

Measuring Activities of the 24-HAC

The strength of the 24-HAC measurement model is its ability to classify and link activities into health-related categories that are mutually exclusive. The model depends on the ability to accurately measure the daily human activity cycle for days at a time. Historically, the main source of information on how people in free-living environments spent their time has been self-report and this tool was unable to achieve the level of detail in data collection necessary to support a 24-h model of activity. This has changed with widespread use of device-based measures (i.e., wearables). Data collected from wearable sensors, particularly from accelerometers and heart rate sensors, provide useful information for documenting time spent in each of the 24-HAC activities. In practice though, measuring the 24-HAC is challenging because individuals are unable to accurately self-monitor both sedentary time and MVPA with the same consumer wearable because of limitations built into the devices (40) and investigators are unable to comprehensively assess the complete 24-HAC with available research-grade wearables (41,42). Suggestions of additional needs are listed in Figure 2.

Example research needs and directions for the 24-HAC Model.

The rapidity with which wearable technologies are developing is exciting although it presents an additional challenge for measuring the 24-HAC. The speed of technology development in wearable devices and the algorithms and software to support the devices is developing so rapidly that much of the validation research by scientists is outdated by the time the publishing cycle has been completed. In the future, physical activity epidemiology research as it pertains to the 24-HAC may be largely influenced by the ability to accurately measure the activities that make up the 24-HAC (10); therefore, a discussion of measurement challenges for each of the 24-HAC activities follows.

Sleep measurement

The most accepted measure of sleep is polysomnography, a combination of electroencephalography, electromyography, electrooculography, electrocardiography, oximetry, and measures of respiration (43). Polysomnography assesses sleep at the level of cortical activity and can be used to discern brainwave-defined “sleep stages.” Although polysomnography is an indirect measure of sleep, it is considered the gold standard even with its important limitations. Most notably, it is typically expensive and burdensome, and is usually conducted in a laboratory setting. Because of this, polysomnography can interfere with sleep and because it is rarely recorded over several nights, is not well suited to reflect habitual sleep behavior (44). Therefore, measurement outside of the laboratory is conducted using wearable devices, which estimate sleep time through actigraphy, using movement-detection apparatus to assess patterns of mobility and immobility to estimate sleep and wake time. Although other measurement paradigms exist for habitual sleep, movement is still the most well characterized and most frequently used.

Actigraphy was developed as an alternative to polysomnography and is often validated with polysomnography. Using research-grade wearable devices with algorithms developed by sleep researchers, high rates of agreement (about 85% to 90%, in diverse samples) are observed between wearable devices and polysomnography (45). Commercial wearables for behavioral monitoring have typically not shown the same agreement with polysomnography (41). Caution therefore should be taken with these approaches. Guidelines for the conduct of actigraphy have been recently published by the Society of Behavioral Sleep Medicine (44). Given that actigraphy is the primary measurement tool for sleep research in the field and is also used to measure the other 24-HAC activities, combining these measurements into a 24-h model may be achievable.

Sedentary behavior measurement

Sedentary behavior measurement should be consistent with the definition of sedentary behavior provided in Table 1, as the definition includes both a sitting or lying posture and low levels of energy expenditure (46). However, most studies of sedentary behavior rely on devices placed in body locations where they measure body movement, but not posture (e.g., a hip accelerometer) (47). Despite less than optimal measurement, strong associations between sedentary time and health outcomes are observed (13,27) To date, two validated devices accurately measure body posture, but neither have been used in a large epidemiological study (48,49). Obtaining accurate 24-HAC measurement for posture, and therefore true sedentary behavior, from most wearables remains a challenge. The promise of the 24-HAC is that as other activities, such as LIPA, are measured more accurately with devices, the associated algorithms will be able to more accurately isolate sedentary time as defined in Table 1.

LIPA measurement

Time spent in LIPA is challenging to measure by self-report, and few consumer wearables measure or report LIPA (31,50). LIPA can be measured with accelerometry, with LIPA defined as above the threshold for sedentary behavior and below the threshold for MVPA. Other sophisticated methods have been proposed for measuring LIPA (51), but devices used in research studies often do not measure posture, making the classification of sedentary behavior versus LIPA prone to classification error (41,47). Additionally, many consumer device manufacturers combine all activity into their movement score, making it difficult to distinguish between LIPA and MVPA (51,52). To accurately measure LIPA in the 24-HAC, efforts will be needed to standardize the hardware, software, and algorithms of device-based measures.

MVPA measurement

Device-based measurement of MVPA is associated with several health outcomes (13,53). Many consumer wearable devices (e.g., Fitbit, Apple Watch) focus on quantifying MVPA. Algorithms for wearable devices from accelerometry and heart rate have been validated on numerous devices since the 1990s (54). A simple model for consumers using wearable devices is to measure MVPA and use those numbers to create a goal for meeting physical activity guidelines (e.g., the rings on the Apple watch).

Despite the extensive validation, the substantial health outcome-related research, and translation to research and consumer applications, a number of challenges remain in measuring MVPA in the 24-HAC model. First, the measurement of MVPA lacks consistency and precision. Although research devices usually try to build algorithms and scoring around the accepted 3 MET value as the threshold for moderate-intensity physical activity, device-based metrics of this intensity level vary across devices and consumer devices may use a proprietary metric (e.g., Nike Fuel). Other challenges with MVPA measurement include body placement (48) and difficulties with specific activities such as cycling and swimming (55) whose movement patterns do not reflect their intensity. Additionally, validation of these devices outside of the laboratory is challenging because of a lack of gold-standard comparison (41,42).

As the interest in activities across the 24-h grow, and if a unified 24-HAC becomes recognized, it will be important to consider validity of a combination of device-based and self-reported methods for understanding the full composition of activities (both context and actual activities performed). The level of validity will vary by the application and type of research question; however, it should be noted that current conceptions of most devices and self-report tools do not consider the integrated nature of these behaviors, and steps will be required to fully leverage their capabilities within this new 24-HAC context.

Applying Optimal Analytic Methods for the 24-HAC

The challenges inherent in balancing different time-based components and their relationships to health mean that new analytic methods for physical activity epidemiology research need to be developed to make full use of the 24-HAC. Traditional regression techniques are inadequate because the components of the 24-HAC add up to the 24 h·d−1, so the durations of the components are fully interdependent. In response to the increasing availability of 24-h data and interest in examining the components together, methods from other fields of research have been applied to data from the 24-HAC.

Isotemporal substitution modeling, used to model the substitution of food components, such as macronutrient or food groups while holding total energy intake constant (56), is a promising method. For physical activity, this method uses linear regression to estimate the effect of substituting one type of activity with the same time amount of another activity type. The estimates are obtained by comparing models with all activity types included to those with one activity type removed. Mekary et al. (57) applied the isotemporal substitution approach to physical activity data from the Nurses Health Study. The approach also has been applied to numerous other data sets using both reported, as well as device-based measures of activities (29). In addition, isotemporal substitution models have examined the effects of substituting unhealthy behavior (such as sedentary time) with healthier alternatives (sleep or physical activity) during the 24-h cycle (9).

Most studies using the isotemporal substitution technique are limited by accurate measurement of some but not all of the 24-HAC activities. In addition, research often only captures behaviors of primary interest (e.g., physical activity researchers capture waking hours, sleep researchers capture sleeping hours). To address these limitations, 24-h accelerometer-based protocols are emerging, particularly with wrist-worn protocols (58), and these devices show promise for improving inclusive measurement across the 24 h as well as participant compliance. However, it is rare and challenging for researchers to apply methods and algorithms that can accurately quantify sleep, sedentary, and more active behaviors within a continuous recording of accelerometer data, as described above.

A second analytic method being applied to 24-HAC research is compositional analysis. This method is derived from research based on topics such as geology and the composition of drugs and is used to determine an optimal contribution from each ingredient (59,60). For the 24-HAC, the “ingredients” are activities, and the health outcome is disease prevention. Several studies have used this approach (60,61), and Canadian 24-h Movement Guidelines for Children and Youth (7) were created using this type of analysis (62). A difference between isotemporal substitution models and compositional analysis is the assumption about how the covariates and outcomes are related: isotemporal substitution assumes linear relationships, whereas compositional analysis assumes nonlinear relationships and transforms the covariates into composite variables of a whole. Both rely on traditional forms of regression analyses.

Given that 24-HAC research is still relatively new and unexplored, other approaches to 24-HAC data analyses, such as machine learning and functional data analysis, are expected to emerge in the coming years.

Understanding the Interrelationships among Sleep, Sedentary Behavior, LIPA, and MVPA

The relationship between sleep and MVPA is well established. Moderate-to-vigorous physical activity is associated with greater ease in falling asleep, greater depth of sleep, greater morning alertness, and better perceived sleep quality (37). Laboratory studies and randomized trials of exercise show modest but consistent improvements in sleep. These effects are observed for total sleep time, slow wave sleep, sleep onset latency, and overall sleep quality, with the greatest benefits among those with poorer baseline sleep quality and older adults (63). Additional relationships, such as how LIPA affects health outcomes separately from MVPA or sedentary behavior, are largely unexplored.

Surprisingly little research has explored whether sedentary behavior is linked to sleep quality independent of MVPA. In the American Time Use Survey, common sedentary pursuits (i.e., work commute time, TV viewing) were associated with shortened sleep duration (64). In youth, video gaming and computer use have been linked to shortened sleep duration (65). Delayed sleep timing also appears to be associated with more self-reported minutes of sedentary time and lower levels of free-living physical activity (66,67). Individuals with delayed sleep timing patterns report less routinized exercise patterns and more difficulty making time for exercise. Excess sitting time (particularly TV viewing) is associated with poor sleep quality and obstructive sleep apnea risk (68).

Using the 24-HAC in Health Promotion Interventions

The 24-HAC has significant potential for use in health promotion interventions, although continued work is needed to identify the best strategies to apply it in these approaches. To accomplish this, innovative research designs will be necessary to parse out the unique and joint effects of changes in behavioral activities across the 24 h (69). For example, effective strategies to optimize changes in multiple behavioral activities are needed, such as perhaps identifying key motivating behavioral activities among the 24-HAC. Sequential, multiple assignment randomized trials can be conducted to clarify when and for whom intervention strategies that target sleep, sedentary time, LIPA, or MVPA may be most influential and effective.

Because the 24-HAC entails complex behaviors that are influenced by diverse factors, effective interventions will need to reflect a solid understanding of the multilevel individual, social, and environmental determinants that operate across the 24 h. For example, experimental evidence suggests neighborhood characteristics may contribute to obesity and diabetes (70). The social (e.g., social connectivity, proximity to others) and built (e.g., crime, street connectivity) environments are key predictors of physical activity (70). Taken together, these results suggest that the environment (both social and physical features) plays a role in health across the 24 h. For sleep, this includes the microenvironment (i.e., bedroom), where established sleep hygiene recommendations exist for optimal sleep conditions. However, macroenvironmental features beyond the bedroom, such as ambient noise and temperature, actual and perceived crime, and social capital, also should be accounted for. Validated scales and objective measures of these metrics within the sleep context have not been fully developed. Furthermore, only a few studies have described favorable environments to reduce sedentary behavior, such as the use of height-adjustable desks in the workplace (71) and TV and electronics restrictions in the home (72). Collectively, a broader contextual view of health behaviors that accounts for their interrelationships and dependence on environmental factors is needed to harness the interplay of the behaviors across the 24 h and inform health promotion interventions.

Using the 24-HAC in Research

The 24-HAC has great potential to inform research. This potential is illustrated by an example of its use in research on the role of physical activity in fall prevention in older adults through at least four pathways (Fig. 3). Strong evidence demonstrates that fall prevention exercise programs substantially reduce risk of falls and fall injuries in older adults (13). The traditional framework of fall prevention emphasizes the beneficial physiologic effects of exercise on fall risk factors—in particular exercise effects on muscle weakness and impaired balance (pathway 1 in Fig. 3). The 24-HAC model posits that physical activity categories are inter-related, as shown in the dashed box in Figure 3, and thereby emphasizes the possibility of additional mechanisms. The separate arrows in the figure support the plausibility of these mechanisms.

Possible mechanisms by which fall prevention exercise programs reduce risk of falls, based upon the 24-HAC paradigm. Activities of the paradigm are within the dashed box and possible mechanisms of fall prevention are labeled 1, 2, 3, and 4. Pathway 1 represents the traditional framework that physiologic effects of exercise modify fall risk factors, for example, balance training improves balance. Modeling fall prevention with the 24-HAC paradigm creates the possibility of pathways 2, 3, and 4. These pathways derive from the inter-relatedness of activity categories, with the model proposing that fall prevention exercise program could also reduce falls by pathways involving less sedentary behavior (during nonexercise time) and improved sleep quality. See text for discussion of evidence that pathways 2, 3, and 4 are plausible.

Pathway 2 occurs if the increased activity from participation in fall prevention exercise programs also causes a shift in time spent in sedentary behavior outside of exercise. National Health and Nutrition Examination Survey accelerometer data show sufficiently active older adults engage in less sedentary behavior (73), and a meta-analysis reported higher fall risk in more sedentary older adults (74).

Pathway 3 occurs if the increased activity of fall prevention exercise improves sleep quality. A randomized trial illustrates research on the beneficial effects of exercise on sleep quality (75), and fewer sleep problems are associated with fewer falls in older adults (76).

Pathway 4 occurs if fall prevention exercise reduces polypharmacy and use of sedative drugs, as it is well documented that polypharmacy and sedatives are associated with fall risk in older adults (14). Evidence exists that higher amounts of physical activity are associated with lower rates of polypharmacy (77), and higher sleep quality should decrease use of sedative drugs, as these drugs are commonly prescribed to improve sleep quality (78). Figure 3 also shows why it is potentially important to study and understand pathways 2, 3, and 4. Fall prevention exercise programs historically have emphasized strength training and balance training (14). To the extent that MVPA activates pathways 2, 3, and 4, research could lead to more emphasis on MVPA in fall prevention.

Potential limitations of the 24-HAC

The application of the 24-HAC model to future public health guidelines may have limitations. The 24-HAC model brings together several scientific disciplines in different stages of development. For example, sedentary physiology is a less well-established discipline than is physical activity research. To advance the 24-HAC, it will be important to continue to advance the science on sedentary and LIPA. In addition, the 24-HAC model because it combines different constructs (as compared with guidelines such as for physical activity or sleep) may be challenging to communicate clear recommendations. In the future, communications experts and researchers should consider working together to develop strategies to communicate the 24-HAC model to a variety of audiences to include policy makers and health professionals.

Next steps for research with the 24-HAC

Device-based measures (i.e., wearable devices), improved data collection methods, increased data access, novel applied analytical techniques, and additional intervention approaches are forming a unique research environment for examining the relationships between activity and health. In the previous sections we discussed several issues in the use of wearable devices relevant to assess the activities of the 24-HAC model. Below are comments on the general issue of assessing intensity are relevant broadly to all research with wearable devices.

First, accurate measurement of the 24-HAC components is still elusive despite the promise of combining motion and heart rate measurement in one wrist-worn device. The traditional threshold analysis of accelerometer data has challenges. The health benefits of physical activity are likely to follow a more continuous pattern; therefore, intensity effects might be better modeled as a continuous function. The same is true for duration. Research on how to best model health effects from activity intensity and duration is a priority for future studies in the 24-HAC.

From a measurement perspective, the number of large databases being created using accelerometers worn for the full 24 h·d−1 is increasing, and with wrist measurement, compliance is improved. However, this promising development for 24-HAC research cannot yet be realized because of a few key issues in processing large datasets with 24-h wear. For example, the timing of some activities is difficult to determine because of the similar signals that are associated with inactivity (sleep, sedentary behavior, and nonwear). Including accurate heart rate measurement in these large studies will go a long way to address the deficits in analytics with an accelerometer alone. In addition, improvement in the output from the sensors to identify sedentary behaviors (based on posture) as compared to quiet but active behaviors is a key to improving 24-HAC research. Lastly, the creation of methods to assess health-related activities with all types of sensors, whether they were built for commercial use (and therefore ease data collection issues), or research use (which may be more of a burden to participants), and independent of the current model of software and hardware will create a more open research environment.

Finally, assuming that accurate measurement of the 24-HAC is close at hand, the true utility of the 24-HAC can be realized. Using assessment of the 24-HAC in epidemiology research can lead to more specific recommendations, which in turn will inform interventions (10). In the future, these recommendations and interventions could also be personalized based on the specific activity level of the individual. This is the true value in creating a new paradigm for activity, sedentary behavior and sleep research.


The 24-HAC model is proposed as a paradigm for research, intervention, and public health recommendations. The 24-HAC model provides a paradigm for extending research into how time should be divided among its four component activities so as to improve health. Thus, the model has the potential to extend public health guidance, beyond separate MVPA and sleep guidelines, to integrated guidance for sleep, sedentary behavior, LIPA, and MVPA. However, Figure 1 illustrates a research feedback loop that can be imagined with the 24-HAC. Future iterations of the 24-HAC could include metrics that occur as part of a daily cycle and affect disease outcomes in a population. Examples of these disciplines might include nutrition, circadian rhythms, timing of food intake and activity, alertness, stress, fatigue, and a range of other related state indicators. The 24-HAC may improve the understanding of pathophysiological mechanisms underlying the role of sleep, sedentary, and active behaviors and their combined role in the disease process. This model represents a new paradigm, but it is rooted in science developed in separate academic disciplines. After initially optimizing the 24-HAC for health outcomes, one could imagine a roadmap to better health that could be specific to populations, and even potentially to individuals. The promise of the 24-HAC model lies in an interdisciplinary approach to sleep, sedentary behavior, and physical activity research for optimal health in a way that directly addresses people’s daily activity patterns. In the future, the 24-HAC may be used to educate audiences, including researchers, policymakers, and health professionals; inform public health guidelines on how people can best distribute their activity across the 24-h cycle for optimal health and quality of life, and develop strategies to help individuals achieve this balance.

The authors would like to acknowledge and thank Dr. John Staudenmeyer for his oral presentation at the International Conference on Ambulatory Monitoring of Physical Activity and Movement in Bethesda, MD, 2017. Additionally, the authors also thank Dr. Charles Matthews for his effort in organizing the 2017 ACSM symposium. M. A. G. is supported by a grant from the National Institute on Minority Health and Health Disparities (R01MD011600) and the Department of Defense (W81XWH-17-0088). The results of this review are presented clearly, honestly and without fabrication, falsification or inappropriate data manipulation. This review does not constitute endorsement by ACSM.

The findings and conclusions of this report are those of the authors and do not necessarily represent the official position of the National Institutes of Health or the Centers for Disease Control and Prevention.


1. Watson NF, Badr MS, Belenky G, et al. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med. 2015;11(6):591–2.
2. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116(9):1081–93.
3. Tremblay MS, Aubert S, Barnes JD, et al. Sedentary behavior research network (SBRN)—terminology consensus project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75–91.
4. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126–31.
5. Ekelund U, Steene-Johannessen J, Brown WJ, et al. Physical activity attenuates the detrimental association of sitting time with mortality: a harmonised meta-analysis of data from more than one million men and women. Lancet. 2016;388(1051): 1302–10.
6. Janz KF, Boros P, Letuchy EM, Kwon S, Burns TL, Levy SM. Physical activity, not sedentary time, predicts dual-energy X-ray absorptiometry-measured adiposity age 5 to 19 years. Med Sci Sports Exerc. 2017;49(10):2071–7.
7. Tremblay MS, Carson V, Chaput JP, et al. Canadian 24-hour movement guidelines for children and youth: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):311–27.
8. Okely AD, Ghersi D, Hesketh KD, et al. A collaborative approach to adopting/adapting guidelines—the Australian 24-hour movement guidelines for the early years (birth to 5 years): an integration of physical activity, sedentary behavior, and sleep. BMC Public Health. 2017;17(S5):869.
9. Buman MP, Winkler EA, Kurka JM, et al. Reallocating time to sleep, sedentary Behaviors, or active Behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005–2006. Am J Epidemiol. 2014;179(3):323–34.
10. Chaput JP, Carson V, Gray CE, Tremblay MS. Importance of all movement behaviors in a 24 hour period for overall health. Int J Environ Res Public Health. 2014;11:12575–81.
11. Smith K, Rosenberger M. Keeping seniors active - a 24-hour approach. 2017; [cited 2017 Jul 22 ] Available from:
12. Goel N, Basner M, Dinges DF. Phenotyping of neurobehavioral vulnerability to circadian phase during sleep loss. Methods Enzymol. 2015;552:285–308.
13. 2018 Physical Activity Guidelines Advisory Committee, 2018 Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC; 2018.
14. Drootin M. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. 2011;59(1):148–57.
15. Borbély AA, Tobler I. Manifestations and functional implications of sleep homeostasis. Handb Clin Neurol. 2011;98:205–13.
16. Saper CB, Fuller PM. Wake–sleep circuitry: an overview. Curr Opin Neurobiol. 2017;44:186–92.
17. Grandner MA. Sleep, health, and society. Sleep Med Clin. 2017;12(1):1–22.
18. Sakurai T, Pandi-Pamural SR, Monte JM. Orexin and Sleep: Molecular, Functional and Clinical Aspects. 2015; pp. 181–202.
19. Albrecht U. Timing to perfection: the biology of central and peripheral circadian clocks. Neuron. 2012;74(2):246–60.
20. Borbély AA. A two process model of sleep regulation. Hum Neurobiol. 1982;1(3):195–204.
21. Grandner MA. Sleep and obesity risk in adults: possible mechanisms; contextual factors; and implications for research, intervention, and policy. Sleep Health. 2017;3(5):393–400.
22. Grandner MA, Seixas A, Shetty S, Shenoy S. Sleep duration and diabetes risk: population trends and potential mechanisms. Curr Diab Rep. 2016;16(11):106.
23. Grandner MA, Hale L, Moore M, Patel NP. Mortality associated with short sleep duration: the evidence, the possible mechanisms, and the future. Sleep Med Rev. 2010;14(3):191–203.
24. Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of healthy sleep duration among adults — United States, 2014. MMWR Morb Mortal Wkly Rep. 2016;65(6): 137–41.
25. Katzmarzyk PT, Church TS, Craig CL, Bouchard C. Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc. 2009;41(5):998–1005.
26. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996-2011. Am J Prev Med. 2011;41(2):207–15.
27. Chau JY, Grunseit AC, Chey T, et al. Daily sitting time and all-cause mortality: a meta-analysis. PLoS One. 2013;8(11):e80000.
28. Sun JW, Zhao LG, Yang Y, Ma X, Wang YY, Xiang YB. Association between television viewing time and all-cause mortality: a meta-analysis of cohort studies. Am J Epidemiol. 2015;182(11):908–16.
29. Matthews CE, Keadle SK, Troiano RP, et al. Accelerometer-measured dose–response for physical activity, sedentary time, and mortality in US adults. Am J Clin Nutr. 2016;104(5):1424–32.
30. Hamilton MT, Hamilton DG, Zderic TW. Exercise physiology versus inactivity physiology: an essential concept for understanding lipoprotein lipase regulation. Exerc Sport Sci Rev. 2004;32(4):161–6.
31. Loprinzi PD. Light-intensity physical activity and all-cause mortality. Am J Health Promot. 2017;31(4):340–2.
32. Matthews CE, Moore SC, Sampson J, et al. Mortality benefits for replacing sitting time with different physical activities. Med Sci Sports Exerc. 2015;47(9):1833–40.
33. Dohrn IM, Sjöström M, Kwak L, Oja P, Hagströmer M. Accelerometer-measured sedentary time and physical activity—a 15 year follow-up of mortality in a Swedish population-based cohort. J Sci Med Sport. 2018;21:702–7.
34. Committee PAGA. Physical Activity Guidelines Advisory Committee report [Internet]. Washington, DC: US Dep Heal Hum Serv; 2008.
35. Moore SC, Lee IM, Weiderpass E, et al. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. JAMA Intern Med. 2016;176(6):816.
36. Taylor D, Gehrman P, Dautovich N, Lichstein K, McCrae C. Handbook of Insomnia. Tarporley: Springer Healthcare Ltd.; 2014.
37. Buman MP, Youngstedt SD. Physical activity, sleep, and Biobehavioral synergies for health. Sleep and Affect Elsevier. 2015;321–37.
38. Dzierzewski JM, Buman MP, Giacobbi PR, et al. Exercise and sleep in community-dwelling older adults: evidence for a reciprocal relationship. J Sleep Res. 2014;23(1):61–8.
39. Lambiase MJ, Gabriel KP, Kuller LH, Matthews KA. Temporal relationships between physical activity and sleep in older women. Med Sci Sports Exerc. 2013;45(12):2362–8.
40. Sanders JP, Loveday A, Pearson N, et al. Devices for self-monitoring sedentary time or physical activity: a scoping review. J Med Internet Res. 2016;18(5):e90.
41. Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016;48(3):457–65.
42. Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. Int J Behav Nutr Phys Act. 2015;12(1):42.
43. Keenan S. An overview of polysomnorgraphy. In: Barkoukis T, Avidan A, editors. Review of Sleep Medicine. Philadelphia: Butterworth Heinemann Elsevier; 2007. pp. 143–67.
44. Ancoli-Israel S, Martin JL, Blackwell T, et al. The SBSM guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med. 2015;13(1 Suppl):S4–38.
45. Marino M, Li Y, Rueschman MN, et al. Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep. 2013;36(11):1747–55.
46. Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38(3):105–13.
47. Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43(8):1561–7.
48. Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013;45(5):964–75.
49. Lyden K, Kozey Keadle SL, Staudenmayer JW, Freedson PS. Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc. 2012;44(11):2243–52.
50. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12(1):159.
51. Buman MP, Hekler EB, Haskell WL, et al. Objective light-intensity physical activity associations with rated health in older adults. Am J Epidemiol. 2010;172(10):1155–65.
52. Healy GN, Dunstan DW, Salmon JJ, et al. Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose. Diabetes Care. 2007;30(6):1384–9.
53. Fishman EI, Steeves JA, Zipunnikov V, et al. Association between objectively measured physical activity and mortality in NHANES. Med Sci Sports Exerc. 2016;48(7):1303–11.
54. Haskell WL, Yee MC, Evans A, Irby PJ. Simultaneous measurement of heart rate and body motion to quantitate physical activity. Med Sci Sports Exerc. 1993;25(1):109–15.
55. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA accelerometer. Med Sci Sports Exerc. 2011;43(6):1085–93.
56. Willett W, Stampfer MJ. Total energy intake: implications for epidemiologic analyses. Am J Epidemiol. 1986;124(1):17–27.
57. Mekary RA, Lucas M, Pan A, et al. Isotemporal substitution analysis for physical activity, television watching, and risk of depression. Am J Epidemiol. 2013;178(3):474–83.
58. Doherty A, Jackson D, Hammerla N, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLoS One. 2017;12(2):e0169649.
59. Aitchison J. The statistical analysis of compositional data. J R Stat Soc Ser B. 1982;44:139–77.
60. Chastin SF, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS One. 2015;10(10):e0139984.
61. Winkler EA, Chastin S, Eakin EG, et al. Cardiometabolic impact of changing sitting, standing, and stepping in the workplace. Med Sci Sports Exerc. 2018;50(3):516–24.
62. Carson V, Tremblay MS, Chaput JP, Chastin SF. Associations between sleep duration, sedentary time, physical activity, and health indicators among Canadian children and youth using compositional analyses. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S294–302.
63. Buman MP, King AC. Exercise as a treatment to enhance sleep. Am J Lifestyle Med. 2010;4(6):500–14.
64. Basner M, Fomberstein KM, Razavi FM, et al. American time use survey: sleep time and its relationship to waking activities. Sleep. 2007;30(9):1085–95.
65. Foti KE, Eaton DK, Lowry R, McKnight-Ely LR. Sufficient sleep, physical activity, and sedentary behaviors. Am J Prev Med. 2011;41(6):596–602.
66. Shechter A, St-Onge M-P. Delayed sleep timing is associated with low levels of free-living physical activity in normal sleeping adults. Sleep Med. 2014;15(12):1586–9.
67. Baron KG, Reid KJ, Zee PC. Exercise to improve sleep in insomnia: exploration of the bidirectional effects. J Clin Sleep Med. 2013;9(8):819–24.
68. Buman MP, Kline CE, Youngstedt SD, Phillips B, Tulio de Mello M, Hirshkowitz M. Sitting and television viewing: novel risk factors for sleep disturbance and apnea risk? Results from the 2013 National Sleep Foundation sleep in America poll. Chest. 2015;147(3):728–34.
69. Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007;32(5):S112–8.
70. King AC, Sallis JF, Frank LD, et al. Aging in neighborhoods differing in walkability and income: associations with physical activity and obesity in older adults. Soc Sci Med. 2011;73(10):1525–33.
71. Alkhajah TA, Reeves MM, Eakin EG, Winkler EA, Owen N, Healy GN. Sit–stand workstations: a pilot intervention to reduce office sitting time. Am J Prev Med. 2012;43(3):298–303.
72. Otten JJ, Jones KE, Littenberg B, Harvey-Berino J. Effects of television viewing reduction on energy intake and expenditure in overweight and obese adults. Arch Intern Med. 2009;169(22):2109.
73. Gennuso KP, Gangnon RE, Matthews CE, Thraen-Borowski KM, Colbert LH. Sedentary behavior, physical activity, and markers of health in older adults. Med Sci Sports Exerc. 2013;45(8):1493–500.
74. Thibaud M, Bloch F, Tournoux-Facon C, et al. Impact of physical activity and sedentary behaviour on fall risks in older people: a systematic review and meta-analysis of observational studies. Eur Rev Aging Phys Act. 2012;9(1):5–15.
75. King AC, Oman RF, Brassington GS, Bliwise DL, Haskell WL. Moderate-intensity exercise and self-rated quality of sleep in older adults: a randomized controlled trial. JAMA. 1997;277(1): 32–7.
76. Min Y, Nadpara PA, Slattum PW. The association between sleep problems, sleep medication use, and falls in community-dwelling older adults: results from the health and retirement study 2010. J Aging Res. 2016;2016:1–10.
77. Volaklis KA, Thorand B, Peters A, et al. Physical activity, muscular strength, and polypharmacy among older multimorbid persons: results from the KORA-age study. Scand J Med Sci Sports. 2018;28(2):604–12.
78. Glass J, Lanctôt KL, Herrmann N, Sproule BA, Busto UE. Sedative hypnotics in older people with insomnia: meta-analysis of risks and benefits. BMJ. 2005;331(7526):1169–73.


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