Wearable Technology Reduces Prolonged Bouts of Sedentary Behavior : Translational Journal of the American College of Sports Medicine

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Original Investigation

Wearable Technology Reduces Prolonged Bouts of Sedentary Behavior

Ellingson, Laura D.; Meyer, Jacob D.; Cook, Dane B.

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Translational Journal of the ACSM: April 15, 2016 - Volume 1 - Issue 2 - p 10-17
doi: 10.1249/TJX.0000000000000001
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Wearable technology is a rapidly growing industry with 1 in 10 adults now reporting using a fitness tracker to monitor and improve health (7). These devices are capable of tracking an array of health-related variables including movement, sleep, HR, temperature, and posture and thus have the potential for providing feedback to promote healthy behaviors (3,10). Further, there is evidence demonstrating the benefits of using wearable technology to promote increases in physical activity (3,10), suggesting that interventions employing these technologies could be used to deliver scalable behavior-change interventions to a variety of populations.

To date, little is known about the influence of wearable technology on sedentary time. Sedentary time, defined as any waking behavior characterized by a sitting or reclining posture and an energy expenditure of ≤1.5 METs (44), is a distinct risk factor for many chronic health conditions, even when controlling for participation in moderate-to-vigorous physical activity (MVPA) (4,18,19). American adults are now accumulating as much as 9–10 h of sedentary time per day (28). High rates of sedentary behavior, especially prolonged, uninterrupted bouts, are associated with increased risk for cardiovascular and metabolic health conditions and an increased rate of all-cause mortality (4,18). There is also emerging evidence that sedentary behavior is associated with negative mental health consequences including increased depressive symptoms, lower levels of emotional well-being (20,24), and higher rates of incidence for depression and anxiety disorders (46,47). Thus, there is growing interest in interventions targeting sedentary behaviors with an emphasis on those with potential for application beyond the laboratory to positively impact public health (40).

Reducing sedentary time is challenging, in part, because of the environment in which we live. An examination of determinants related to sedentary behavior indicated that excessive screen time at home combined with prolonged periods of sitting in schools and the workplace and time spent in vehicles results in the accumulation of large amounts of sedentary time for many adults (40). Further, evidence has demonstrated that sedentary behavior is often habitual (14), meaning that it is performed automatically as part of many activities of daily living (e.g., eating and working). As such, people are often unaware of how long they spend being sedentary. For example, several studies found that adults underestimated the amount of sedentary time they accumulate throughout the day by 2–4 h (12,31). Thus, use of technology to monitor and provide feedback regarding sedentary behavior could prove particularly beneficial.

The purpose of this pilot study was to determine the efficacy of using real-time feedback provided by wearable technology to reduce sedentary time throughout the day and under naturalistic conditions during a college semester. Feedback was provided in the form of a brief, salient vibration when sedentary time exceeded 30 min. The secondary aims of this pilot intervention were to determine the impact of reducing sedentary time on physical activity and mood. We hypothesized that participants receiving this feedback from a wearable device would decrease the amount of sedentary time they accumulated in bouts longer than 30 min and would have concomitant improvements in physical activity and mood compared with baseline and a minimal education control (MEC) condition (refer to audio, Wearable Technology to Monitor Sedentary Behavior, https://links.lww.com/TJACSM/A3).



Thirty male and female college students were recruited from a large midwestern university using posted flyers and word of mouth. Inclusion criteria were 1) being full-time students, 2) between the ages of 18 and 26 yr, 3) absence of mobility limitations, and 3) self-reporting more than 3 h of daily leisure time sedentary behavior. Leisure time sedentary behavior was defined as that accumulated outside of class, work, and transportation. In order to improve generalizability to the broader university population, participants were recruited using stratified sampling to enroll an equal number of males and females and no more than two students from a single major.


This was a single-blind study. Participants and study personnel delivering instructions were aware of the group assignment. However, data entry and quality checking of accelerometer data at the single-subject level were conducted by study personnel who were blind to the group assignment. Data were collected in two cohorts: one during the fall semester (n = 16) and one during the spring semester (n = 14). For both cohorts, participants were enrolled within the first 3 wk of the semester and finished at least 2 wk before finals. The protocol involved three laboratory visits (baseline, week 2, and week 10) and was divided into three phases: a 1-wk baseline, a 5-wk intervention, and a 4-wk follow-up (see Fig. 1 for an overview of the study). The institutional review board approved all procedures, and all participants read and signed an informed consent document before participation. To characterize the sample resting HR, blood pressure, height (m), and weight (kg) were assessed at baseline. Participants then completed additional baseline assessments including questionnaires (detailed below) and received a set of monitors to objectively measure sedentary (activPAL3™ VT (AP)) and physical activity behaviors (ActiGraph GT3X+ (AG)) over the subsequent 7 d under free-living conditions.

Figure 1:
Timeline of procedures associated with participation in the 10-wk intervention.

Participants returned to the laboratory 1 wk after their baseline assessment, completed 7-d recalls of physical activity and sedentary time, and viewed their baseline sedentary data using the proprietary graphical output from the AP software. Because young adults may be unaware of the health implications of sedentary time, participants were also provided with minimal education regarding the risks of a sedentary lifestyle (e.g., increased risk for cardiometabolic disease). After this, they were randomly assigned (using a random number generator) to one of two groups: 1) sedentary feedback (SF) or 2) a MEC condition. To reduce participant burden, during the 5-wk intervention period, all participants wore AG and AP monitors every other week for a total of 3 wk, as shown in Figure 1.


After random assignment, participants in the SF group were informed that they would receive real-time feedback every other week over the subsequent 5 wk (3 wk total) in the form of a small vibration provided by the AP monitors when sedentary time exceeded 30 min. They were then given information about developing new habits based on the habit theory of behavior change (32) to aid in reducing sedentary time. Briefly, the habit theory suggests that long-term behavior change is most successful when salient cues that provoke a new or modified routine are established, which over time becomes associated with a perceived or actual reward, reinforcing the desired behavior. In line with this, participants were instructed to use feedback from the AP to increase their awareness of their sedentary time and to note the location and activity when the vibration occurred on a daily log sheet provided for them (potential cues to break up sedentary time). They were encouraged to use this information in order to anticipate and move before the vibration alert (establishing a new routine) in whatever way was feasible at the time of the alert (e.g., stand up or take a walk) or as soon as possible for situations when breaking up sitting time was not practical (e.g., during class or while driving). Lastly, they were encouraged to take note of how breaking up sedentary time affected how they felt (e.g., energy levels and ability to focus while studying) in order to increase awareness of potential rewards associated with this new behavior. This informational component of the intervention was standardized among participants and was brief, lasting approximately 10 to 15 min. They were then issued AP and AG monitors and received this feedback from the AP during waking hours every other week (weeks 2, 4, and 6 of the protocol). After each week of wearing the monitors, participants returned to the laboratory to drop them off for processing and to pick up another set for the subsequent week of wear time.


After random assignment, participants in the MEC group were also given AG and AP monitors, which they wore every other week over the subsequent 5 wk (weeks 2, 4, and 6), but they did not receive SF or further information about behavior change for the remainder of the study.

For both groups, contact with the study staff was restricted to picking up and dropping off sets of monitors during the 5-wk intervention period. This was done to increase ecological validity by maintaining regular daily activities during the course of the intervention. Follow-up assessments occurred 4 wk postintervention after a period of no contact with the study personnel. The follow-up visit was immediately preceded by the final 7-d sedentary and physical activity monitoring assessments using the AG and AP as well as reassessments of the questionnaires given at baseline and several additional follow-up questions assessing awareness of sedentary behaviors. Participants in the SF group did not receive feedback during this final week of the study in order to determine whether new enduring habits were formed around breaking up sedentary behaviors in the absence of feedback.


Participant characteristics

Demographic characteristics including the age, gender, ethnicity, year in school, and major were assessed using a study-specific questionnaire developed for this purpose. HR and blood pressure were measured using an automated blood pressure monitor (Omron M2 HEM-7121-E, Hoofddorp, the Netherlands), and height and weight were measured using a standard stadiometer and calibrated balance scale. Body mass index was then calculated using the standard equation (kg·m−2).

Sedentary behaviors

As noted above, AP (Physical Activity Technologies, Glasgow, UK) was used to both assess and intervene sedentary time objectively. This device is a small (35 × 35 × 7 mm), light (15 g), capacitive, triaxial accelerometer that records information in selected epochs. This device classifies time spent in sedentary, upright, and stepping activities. It is also designed to provide real-time feedback to the wearer regarding his or her sedentary behavior. The device is capable of making a small vibration when sedentary time has exceeded a predefined duration (e.g., 30 min). This monitor is a version of the AP, a device that has been validated for measuring free-living sedentary behaviors (30). Before use in the study, we conducted extensive pilot testing under controlled and free-living conditions to ensure that the vibration occurred in response to prolonged sitting and at appropriate time intervals. The AP performed as expected in these conditions, providing a vibratory signal at 30-min intervals, when seated or lying down, both in the laboratory and under free-living conditions. The AP was initialized and data were downloaded using PAL Technologies' proprietary software. Participants wore the AP on the midline of the right thigh, one third of the way between the hip and the knee. To attach this monitor, it was first placed in a Grafco latex finger cot (Graham-Field Health Products, Atlanta, GA) and then attached to the thigh using a Tegaderm™ Film transparent dressing (3M, St. Paul, MN). Participants were instructed to wear the monitor during waking hours, unless bathing or swimming. Primary outcome variables from the AP included average minutes of sedentary time per day and minutes of sedentary time accumulated in bouts of at least 30 min. Participants were also given a daily log to accompany both monitors to record AP and AG time on/off, waking hours (time out of/into bed), and the activities they were doing when the vibrotactile feedback occurred (e.g., watching TV; SF group only).

The Sedentary Behavior Questionnaire (SBQ) was used to assess self-reported sedentary behaviors performed on weekdays and weekends by asking participants to note the time they spent in commonly performed sedentary activities (e.g., watching television or computer use) over the last week. Test–retest reliability for the SBQ was demonstrated to be moderate to excellent (intraclass correlation (ICC) range = 0.51–0.93), and correlations between the SBQ and the objective measures of sedentary time were small (r = 0.01–0.26) but comparable with other self-reported measures of sedentary behavior (43). The primary outcome was the average hours of sedentary time per day.

Physical activity

AG (ActiGraph, LLC, Fort Walton Beach, FL) was used to objectively measure and classify physical activity behaviors into different intensities (e.g., light, moderate, and vigorous). The device is a small (46 × 33 × 19 mm), light (19 g), triaxial accelerometer that records at 30 Hz. The AG records accelerations as activity counts, which provides an indication of the intensity of bodily movement along three axes. The AG is a reliable device, with a mean intrainstrument coefficient of variability of 4.1% and a mean interinstrument coefficient of variability of 4.9% (12). In free-living conditions, the device has an intraclass correlation coefficient of 0.98 to 0.99 (30). This monitor does not provide any feedback to the individual regarding their physical activity over each 7-d data collection period. The AG was also initialized and downloaded using the ActiGraph's proprietary software on the same computer as the AP in order to ensure that the time stamps matched for purposes of integrating monitor data during postprocessing, as detailed below. Participants were instructed to wear the monitor on their right hip attached by an elastic belt during all waking hours, unless bathing or swimming. The primary outcome variables were the average minutes per day in light-, moderate-, and vigorous-intensity activities, as well as minutes of MVPA accumulated in 10-min bouts to assess changes in activity that would count toward meeting the current physical activity recommendations (48).

The International Physical Activity Questionnaire was used to assess self-reported physical activity in different domains of life (e.g., recreation or transportation) over the last 7 d (8). This questionnaire has been validated and has demonstrated good test–retest reliability (generally above 0.70) and validity (criterion, fair to moderate agreement with Computer Science and Applications, Inc. accelerometers) (15).


The mood state was assessed using POMS, which measured mood over the course of the last week (38). The POMS has six subscales, including tension, depression, anger, vigor, fatigue, and confusion as well as providing a summary score for total mood disturbance. The POMS has demonstrated acceptable levels of reliability and validity for use with the general adult population (38,39). Internal consistency reliability coefficients (α) range from 0.63 to 0.96 with test–retest reliability estimates of 0.65 to 0.74 (38). Construct validity has been demonstrated by significant positive correlations between the POMS subscales and the Visual Analog Mood Scales ranging from r = 0.54 for anger to r = 0.70 for depression (39).

Sedentary awareness

An experimenter-created questionnaire was used at follow-up to assess perceptions of sedentary behavior. Participants were asked to indicate their level of agreement from 1 (“strongly agree”) to 5 (“strongly disagree”) with each of the following statements: “I am more aware of the time I spend sitting,” “I think it is important to limit the amount of time I spend sitting during the day,” and “I plan to limit my sitting when I can.”

Data Processing

Physical activity and sedentary behavior

In healthy adults, 3–4 d of monitor wearing has been shown to capture about 80% of the interindividual differences in moderate–vigorous activity levels (37). Thus, inclusion criteria for both AP and AG were a minimum of 10 h·d−1 of wear time, for at least 4 d, including one weekend day. Sixty minutes or more of no movement (zero acceleration in all three axes) was considered non-wear time and excluded from further processing.

Data from the AG were processed using the validated Sojourns method, which uses an artificial neural network to identify bouts of activity based on rapid acceleration/deceleration and assigns MET values to each bout (29,35). Postural data from the AP (sitting, standing, and stepping) were subsequently integrated into the AG data to correct for misclassifications of sedentary and light-intensity activity. The AP better distinguishes sedentary activity from light-intensity activity (30,36) and was used as the primary classification tool during sedentary/light periods where the AP and AG were not in agreement. For example, if the AG designated a bout of time as “sedentary” and the AP indicated that the participant was standing, this bout of activity was reclassified as light-intensity activity.

Statistical analyses

To determine the sample size necessary for this project, a power analysis was conducted using G*Power 3.1.3 (22) for an F-test, powering on the ability to detect a group–time interaction—predicting a 30 min·d−1 decrease in sedentary time accumulated in prolonged bouts (>30 min) for the SF condition and no change in the MEC condition. Estimates of effect size (ES) were based on the pilot data showing that college students on the campus where this intervention was to take place accumulated an average of 300 ± 50 min·d−1 of sedentary time in prolonged bouts. Because there are currently no specific recommendations for healthy adults regarding sedentary time, this study aimed to assess the influence of a 10% change (30 min) in prolonged sedentary time for the intervention group. To achieve 80% power, with α = 0.05, a moderate ES [(300–270)/50, d = 0.60], and an estimated moderate correlation between measures, the necessary sample size was set to 28 or 14 per group.

IBM SPSS Statistics (Version 22.0) was used for all data analyses. Demographics and baseline characteristics of the two groups were compared with independent-samples t-tests and chi-square analyses (gender and year in school). Group (two: SF vs MEC) × time (three: baseline, postintervention, and follow-up) repeated-measures ANOVA was used to test our primary hypotheses related to changes in objectively measured sedentary behaviors across the intervention and between groups. Simple effects analyses were used to further examine significant main effects. Secondary outcomes related to physical activity, self-reported sedentary time, and mood were assessed using group (two: SF vs MEC) × time (two: baseline and follow-up) repeated-measures ANOVA. α was set to 0.05 for all analyses. ES calculations (Cohen's d) were also used to describe the magnitude of differences in sedentary behaviors and mood between groups and over the course of the intervention (13). Questions regarding awareness of sedentary behavior postintervention were examined descriptively, calculating the frequency of responses indicating agreement with each of the three statements noted above.


Thirty college-aged men and women from 22 different majors enrolled in the study. All were full-time students during the intervention and follow-up periods. One participant in the MEC group did not complete the study because of scheduling difficulties, and one participant (also in MEC) had AP malfunctions during baseline testing; both of these individuals were in the fall cohort. Analyses were limited to those with complete data only; therefore, data are presented from 28 participants (SF = 15; MEC = 13; for participant characteristics and baseline mood, see Table 1).

Participant Characteristics.

Sedentary Behavior

There were no significant within- or between-group differences in total minutes of objectively measured sedentary behavior over the intervention (P > 0.05). However, as illustrated in Figure 2, participants significantly decreased the amount of sedentary time spent in prolonged bouts (>30 min) and increased the amount of time spent in shorter bouts (<30 min) (main effect of time; decreased prolonged bouts: F2,52 = 7.45, P = 0.001; increased shorter bouts: F2,52 = 4.21, P = 0.02). Although there were no significant between-group differences in prolonged or shorter bouts of sedentary time (P > 0.05) across the intervention, ES calculations demonstrated moderate group differences with respect to both decreasing prolonged bouts (d = 0.50) and increasing time spent in shorter bouts (d = 0.65) from baseline to follow-up.

Figure 2:
Changes in prolonged (30+ min) and short (<30 min) bouts and total minutes of sedentary behavior in SF and MEC groups over the intervention and follow-up periods. * P < 0.05.

Follow-up simple effects analyses for each group, independently, demonstrated that the SF group showed moderate to large and significant decreases in prolonged bouts from baseline to postintervention (Δ −68.43 ± 102.08 min; F1,14 = 6.74, P = 0.021; d = 0.72) and from baseline to follow-up (Δ −80.85 ± 95.93 min; F1,14 = 10.66, P = 0.006; d = 0.83) and a significant and moderate increase in shorter bouts from baseline to postintervention (Δ +38.61 ± 63.88 min; F1,14 = 5.48, P = 0.035; d = 0.56) and from baseline to follow-up (Δ +48.96 ± 78.19 min; F1,14 = 5.882, P = 0.029; d = 0.65). In contrast to this, the MEC group showed small, nonsignificant decreases in time spent in prolonged bouts from baseline to the end of the intervention (Δ 25.25 ± 88.02 min; F1,12 = 0.99, P = 0.342; d = 0.36) and from baseline to follow-up (Δ −39.64 ± 70.06 min; F1,12 = 3.84, P = 0.076; d = 0.44). Nonsignificant results were also seen for increases in shorter bouts from baseline to postintervention (Δ 2.70 ± 27.81 min; F1,12 = 0.11, P = 0.743 ; d = 0.04) and from baseline to follow-up (Δ +9.5 ± 42.07 min; F1,12 = 0.61, P = 0.459; d = 0.14).

There was also a main effect of time for self-reported sedentary behavior, measured with the total score on the SBQ (F1,27 = 4.38, P = 0.04). Across both groups, participants reported significantly decreasing their time spent in sedentary behaviors from baseline to follow-up. There were no group differences in self-reported sedentary time at baseline or follow-up (P > 0.05) as shown in Table 2.

Table 2:
Physical Activity and Sedentary Time in Minutes per Day (Mean ± SD).

Physical Activity

There were no significant differences in physical activity between groups at baseline or follow-up, and physical activity did not change significantly over the course of the intervention for either group (P > 0.05, see Table 2).


For mood, a repeated-measures multivariate ANOVA incorporating the six subscales on the POMS demonstrated a significant group–time interaction (F1,27 = 4.17, P = 0.05). As illustrated in Figure 3, participants in SF showed small improvements in mood, whereas participants in MEC showed small declines in mood, with the exception of vigor, over the 10-wk study. ES calculations demonstrated that the magnitude of the group differences across the subscales was small to moderate and most pronounced for tension, depression, and confusion (d = 0.58–0.64).

Figure 3:
Changes in mood states over the course of the intervention in SF and MEC groups and ES calculations (Cohen's d) showing group differences over the intervention period. Values are shown as means and SE.

Sedentary Awareness

Measures examining awareness of sedentary time were scored using a five-point Likert scale ranging from strongly agree (1) to strongly disagree (5). Postintervention, all participants in SF and 12 of 13 in MEC agreed that they were more aware of their sedentary behavior since beginning the intervention (SF: 1.3 ± 0.50; MEC: 1.54 ± 0.67). Further, 14 of 15 in SF and all participants in MEC agreed that reducing sedentary behavior was important (SF: 1.56 ± 0.63; MEC: 1.38 ± 0.51), and 13 of 15 in SF and all participants in MEC agreed that they planned to limit their sedentary time going forward (SF: 1.69 ± 0.70; MEC: 1.62 ± 0.51).


Our results demonstrate that salient feedback from a wearable device was effective for influencing sedentary behavior and improving mood under naturalistic conditions in college students. Although minimal education regarding the negative consequences of sedentary time resulted in small decreases in total sedentary time and sedentary time accumulated in prolonged bouts, participants receiving real-time feedback significantly reduced the amount of sedentary time accumulated in prolonged bouts by more than an hour over the intervention period. Moreover, the improvements in prolonged sedentary behavior in the SF group were maintained 4 wk postintervention in the absence of feedback, suggesting that feedback from a wearable device was useful in creating healthier habits in our sample. Evidence for the development of habits is particularly noteworthy because research has demonstrated that once formed, habits are likely to persist even when conscious motivation decreases (26).

Our data add to the evidence demonstrating that sedentary behavior is amenable to change via intervention. To date, a variety of strategies to reduce sedentary behaviors have been assessed ranging from relatively traditional educational interventions (25) to environmental changes such as the installation of sit–stand desks (1,42). Across studies, the efficacy of intervention strategies is highly variable. However, a recent review by Gardner et al. (27) demonstrated that strategies involving self-monitoring, problem solving, and restructuring the environment appeared to be most effective for reducing sedentary time.

In line with this, a number of studies have incorporated the use of prompts to remind individuals to move periodically during typically sedentary periods of the day (2,6,21,41,45). The majority of these used computer prompts that occurred at regular intervals to break up sedentary behavior in the workplace (21,41,45) and demonstrated that these prompts were effective for changing workplace behaviors. However, there is some evidence that targeting sedentary behavior in a single domain (e.g., workplace) can lead to compensatory increases in sedentary time in other domains (1) and consequently may not result in net benefits with respect to health outcomes. As such, to decrease the likelihood of compensation for decreases in sedentary behavior in one domain with increases in another, in the present study, we employed salient prompts that occurred throughout all waking hours (i.e., across different domains).

To our knowledge, there are two sedentary interventions that have employed a wearable device that provides feedback to the wearer in response to his or her own behaviors throughout daily life (2,6). Both studies used the Gruve Solutions™ monitor (Gruve Technologies, Anoka, MN). Similar to the device used in this study, the Gruve monitor vibrates when an individual accumulates a particular amount of sedentary time. Barwais et al. (2) used this device to target sedentary behavior in healthy adults across a 4-wk intervention. Results demonstrated that self-reported sedentary time significantly decreased over the intervention, but there was not an objective measure of sedentary behavior. More recently, Biddle et al. (6) also employed the Gruve monitor as part of a year-long intervention to reduce sedentary time in adults at risk for developing type II diabetes. Results showed that hours of objectively measured sedentary time did not decrease significantly from baseline. However, this study did not examine sedentary time with respect to bout length, and consequently, the impact of wearable technology on prolonged sedentary behavior was undetermined. Further, both studies included an alert after 60 min of sedentary time, which may be too infrequent to promote the development of new habits. Our study adds to this small body of research demonstrating that a prompt after 30 min of sedentary time was effective for reducing prolonged bouts of sedentary behavior and that this interval may be effective for promoting the development of new habits.

The health implications of accumulating sedentary time in prolonged bouts are becoming increasingly apparent. Prolonged sitting is associated with a host of negative health consequences ranging from increases in low back pain (5) to impairments in vascular and cardiometabolic function (11,34). Moreover, there is recent evidence that breaking up longer periods of sitting has demonstrable short-term health benefits, primarily for cardiometabolic outcomes (4). We extend upon this literature with data suggesting that breaking up prolonged periods of sedentary behavior without significantly decreasing total sedentary time may have mood-related benefits. Although our sample consisted of young adults with an absence of psychopathology, participants assigned to receive feedback demonstrated reductions in prolonged bouts of sedentary behavior and moderate improvements in mood compared with participants who did not receive feedback or significantly reduce their prolonged sedentary time.

Previous research has demonstrated a link between sedentary time and mental health (46,47), and a small number of interventions suggest that changing sedentary time may influence mental health-related outcomes in generally healthy populations. For example, Pronk et al. (42) introduced sit-to-stand workstations into an office setting and found that those who reported standing more had improvements in mood over a 7-wk period in comparison with a control group who did not have access to modified workstations. In the intervention conducted by Barwais et al. (2) described above, measures of mood and well-being significantly improved concomitantly with decreases in self-reported sedentary time. More recently, Endrighi et al. (20) employed an alternative approach and conducted an intervention to increase sedentary time over a 2-wk period in healthy active adults. Results demonstrated concomitant decrements in mood associated with the increased sedentary time.

Our study adds to this evidence by showing that interventions designed to alter accumulation patterns of sedentary time (i.e., replacing long bouts with multiple short bouts although not changing the total time) hold promise for the improvement in mood in healthy adults. This also highlights the potential independence of the contribution of prolonged sedentary time to mood as improvements occurred in the absence of alterations in MVPA or total sedentary time. These encouraging results coupled with the large amount of time that inpatients with mental illness spend sitting (23) indicate that future work aimed at altering sedentary time in individuals with mental health disorders is warranted.

Because the public health implications of movement-based behaviors are increasingly apparent, translation of behavior change interventions from research to practice is needed with scalable interventions being particularly powerful. In recognition of this, although this was a pilot study, it was conducted with the goal of maximizing generalizability including a focus on both external and ecological validity. Because college students are highly sedentary (16) and college is a time of transition where individuals engage in behaviors and develop habits that impact their current and future health status (17), this population may benefit from developing healthy habits surrounding reducing sedentary time. Further, although our sample size was small, we used broad inclusion criteria, enrolling both genders as well as individuals from a variety of majors across campus to help further generalize our findings.

We also chose a method of implementation that prioritized ecological validity to increase the potential for scalability. The current intervention was designed to necessitate minimal contact with study staff with the relevant interactions largely occurring between the participants and the device. In addition, minimal guidance was provided regarding how to specifically respond to the feedback from the monitor; participants were encouraged to respond to the alert in a way that was feasible for them given their surroundings and present activity. Our results suggested that the majority of participants chose to replace longer bouts of sedentary time with more frequent, shorter bouts resulting in minimal changes in MVPA or total sedentary time. Further, upon visual inspection of data at the individual levels, the breaks in sedentary time frequently involved periods of standing, potentially explaining the absence of significant changes in physical activity behaviors. Coupled with the positive effect on mood, the present data demonstrate that this minimal contact intervention resulted in decreases in prolonged bouts of sedentary behavior and positive mental health effects, outcomes with important relevance for public health.

Wearable devices are increasingly prevalent. Although research has been conducted examining their validity and the inclusion of different features related to the potential for behavior change (e.g., goal setting and social support) (33,50) and a growing body of literature has demonstrated their use for promoting behavior change (3,10,49), few studies have assessed their value for the improvement in sedentary behaviors (2,6). Our study adds to this by demonstrating that a wearable device is capable of effectively targeting reductions in bouts of prolonged sedentary time, a set of behaviors with significant health implications. Further research to clarify the potential of tracking devices to alter different aspects of sedentary behavior patterns in healthy individuals and those with chronic conditions will enhance the applicability of these devices in both research and public health settings.

Our project had a number of limitations. The sample included a relatively small number of healthy college-aged men and women. Consequently, the results may not generalize to older or younger populations or those with chronic physical or mental health conditions. Further, the duration of the intervention and follow-up periods was relatively short, and it is unknown whether participants maintained their new habits of breaking up sedentary time or reverted back to older patterns. Also, given that participants were educated regarding the negative health consequences of sedentary time, responses to questions asked regarding sedentary awareness may have been subject to demand characteristics and thus may be artificially inflated. In addition, the device used in our intervention, the AP, is a research-grade device that is not suitable for use by the general public. However, a growing number of commercially available devices and apps exist, which include features designed to increase awareness of sedentary time and consequently could be used to provide similar information to users (9). Lastly, although ES calculations suggest meaningful differences between groups for prolonged and shorter bouts of sedentary behavior over the intervention, the lack of significance necessitates cautious interpretation regarding the efficacy of wearable technology for decreasing sedentary time. As such, the additional cost associated with providing monitoring devices over in-person education regarding the negative effects of sedentary behavior should be carefully considered in future application research.

This study demonstrates that increasing awareness of sedentary behaviors using wearable technology may be a viable approach to address growing health concerns surrounding accumulation of large amounts of prolonged sedentary time. Furthermore, the improvement in mood indicates that reducing prolonged sedentary time might be an area of high public health importance because of the prevalence of clinical and subclinical mental health issues in the general population. Due to the market for commercially available wearable devices growing, this technology may be beneficial to the improvement in behaviors like sedentary time that have previously been challenging to monitor and change because of their pervasiveness in daily life. Future research employing commercially available devices with sedentary alert functions in larger samples of varying ages and health statuses will be an important step toward determining the broader use and translatability of this type of intervention for promotion of health and well-being.

This work was funded by the American College of Sports Medicine's Paffenbarger–Blair Fund for Epidemiological Research on Physical Activity. Jacob Meyer was supported by a National Research Service Award from the Health Resources and Services Administration T32HP10010 to the University of Wisconsin Department of Family Medicine and Community Health.

The contents do not represent the views of the US Department of Veterans Affairs or the United States Government.

The authors have no conflicts of interest. The findings of the current study do not constitute endorsement by the American College of Sports Medicine.


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