Using Point-of-Choice Prompts to Reduce Sedentary Behavior in Sit-Stand Workstation Users

Introduction: Desk-based office workers are at occupational risk for poor health outcomes from excessive time spent sitting. Sit-stand workstations are used to mitigate sitting, but lack of workstation usage has been observed. Point-of-choice (PoC) prompts offer a complementary strategy for office workers to break up their sitting time.Study purpose: The purpose of this study was to examine the preliminary efficacy, preference, and acceptability of a theory-driven (i.e., 40 unique prompts encompassing social cognitive theory; TD-PoC) and an atheoretical basic reminder PoC prompt intervention (R-PoC) on reducing sedentary behavior in office workers with self-reported low sit-stand workstation usage (≤4 h per day).Methods: In a cross-over design, participants (N = 19, 78.9% female, 39.4 ± 10.7 years of age) completed a 5-days no-prompt control condition followed by a random and counterbalanced assignment to one of the TD-PoC and R-PoC active conditions with a 1-week washout period between. Preliminary efficacy was assessed during work hours with the activPAL micro accelerometer. Preference was assessed prior to each active condition and acceptability was assessed following each active condition via questionnaire.Results: The R-PoC prompt condition significantly decreased sitting time (b[se] = −49.0 [20.8], p = 0.03) and increased standing time (b[se] = 49.8 [19.7], p = 0.02) and displayed a significant increase in sit-stand transitions (b[se] = 2.3 [1.1], p = 0.04), relative to no-prompt control. Both the R-PoC and TD-PoC prompt conditions significantly decreased time spent in prolonged sitting bouts at b[se] = −68.1 [27.8], (p = 0.02), (b[se] = −76.7 [27.1], p = 0.008) relative to no-prompt control. Overall, the TD-PoC prompt condition displayed higher preference and acceptability ratings; however, these differences were not significant (p's > 0.05).Conclusion: While the R-PoC prompt condition was slightly more efficacious than the TD-PoC prompt condition, the TD-PoC prompt condition was rated with higher preference and acceptability scores. Large variations between participants in preference, acceptability, and intervention feedback may indicate need for tailored messaging which may facilitate sustained use in the long-term

Computing Components of Everyday Stress Responses: Exploring Conceptual Challenges and New Opportunities

Repeated assessments in everyday life enables collecting ecologically valid data on dynamic, within-persons processes. These methods have widespread utility and application and have been extensively used for the study of stressors and stress responses. Enhanced conceptual sophistication of characterizing intraindividual stress responses in everyday life would help advance the field. This article provides a pragmatic overview of approaches, opportunities, and challenges when intensive ambulatory methods are applied to study everyday stress responses in “real time.” We distinguish between three stress-response components (i.e., reactivity, recovery, and pileup) and focus on several fundamental questions: (a) What is the appropriate stress-free resting state (or “baseline”) for an individual in everyday life? (b) How does one index the magnitude of the initial response to a stressor (reactivity)? (c) Following a stressor, how can recovery be identified (e.g., when the stress response has completed)? and (d) Because stressors may not occur in isolation, how can one capture the temporal clustering of stressors and/or stress responses (pileup)? We also present initial ideas on applying this approach to intervention research. Although we focus on stress responses, these issues may inform many other dynamic intraindividual constructs and behaviors (e.g., physical activity, physiological processes, other subjective states) captured in ambulatory assessment

Hydration Status and Fluid Needs of Division I Female Collegiate Athletes Exercising Indoors and Outdoors

The purpose was to determine differences in acute and chronic hydration status in female student-athletes (n = 40) practicing in moderate, dry conditions (17–25 °C, 30–57% humidity) indoors and outdoors. Body weight and urine samples were recorded before and after exercise as well as fluid intake. Sweat rates expressed as median and interquartile range did not differ, but fluid intake was significantly higher during indoor (0.64 [0.50, 0.83] L/h) vs. outdoor conditions (0.51 [0.43, 0.63] L/h), p = 0.001. Fluid intake compensated for indoor sweat rate but not outdoors. When exercising indoors, 49% of the student-athletes reported urine specific gravity (USG) values >1.020, and 24% of the day after morning samples were scored ≥4 on the color chart rating. The percentages increased to 58% and 31%, respectively, when exercising outdoors (p > 0.05). Thus, fluid intake was higher indoors vs. outdoors but sweat rate did not differ among athletes. Yet, chronic hydration status was impaired in more than 50% of the student-athletes with a discrepancy between USG scores and urine color scores identifying underhydration. This suggest that 24-h fluid intake should be taken into account and that hydration protocols may need to be tailored individually based on urine USG values. Practice location (indoors vs. outdoors) may further complicate hydration protocols

Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18⁻64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings

Social ecological correlates of workplace sedentary behavior

Abstract Background To identify social ecological correlates of objectively measured workplace sedentary behavior. Methods Participants from 24 worksites - across academic, industrial, and government sectors - wore an activPAL-micro accelerometer for 7-days (Jan-Nov 2016). Work time was segmented using daily logs. Sedentary behavior outcomes included time spent sitting, standing, in light intensity physical activity (LPA, stepping cadence 30 min). Outcomes were standardized to an 8 h work day. Two electronic surveys were completed to derive individual (job type and work engagement), cultural (lunch away from the desk, walking at lunch and face-to-face interaction), physical (personal printer and office type) and organizational (sector) factors. Mixed-model analyses with worksite-level clustering were performed to examine multi-level associations. Secondary analyses examined job type and sector as moderators of these associations. All models were adjusted for age, race/ethnicity and gender. Results Participants (N = 478; 72% female; age: 45.0 ± 11.3 years; 77.8% non-Hispanic white) wore the activPAL-micro for 90.2 ± 15.5% of the reported workday. Walking at lunch was positively associated with LPA (5.0 ± 0.5 min/8 h, P < 0.001). Regular face-to-face interaction was negatively associated with prolonged sitting (−11.3 ± 4.8 min/8 h, P < 0.05). Individuals in private offices sat more (20.1 ± 9.1 min/8 h, P < 0.05), stood less (−21.5 ± 8.8 min/8 h, P < 0.05), and engaged in more prolonged sitting (40.9 ± 11.2 min/8 h, P < 0.001) than those in public office space. These associations were further modified by job type and sector. Conclusions Work-specific individual, cultural, physical and organizational factors are associated with workplace sedentary behavior. Associations vary by job type and sector and should be considered in the design of workplace interventions to reduce sedentary behavior. Trial registration Clinical trial No. NCT02566317 ; Registered Sept 22nd 2015