Using periodicity intensity to detect long term behaviour change

This paper introduces a new way to analyse and visualize quantified-self or lifelog data captured from any lifelogging device over an extended period of time. The mechanism works on the raw, unstructured lifelog data by detecting periodicities, those repeating patters that occur within our lifestyles at different frequencies including daily, weekly, seasonal, etc. Focusing on the 24 hour cycle, we calculate the strength of the 24-hour periodicity at 24-hour intervals over an extended period of a lifelog. Changes in this strength of the 24-hour cycle can illustrate changes or shifts in underlying human behavior. We have performed this analysis on several lifelog datasets of durations from several weeks to almost a decade, from recordings of training distances to sleep data. In this paper we use 24 hour accelerometer data to illustrate the technique, showing how changes in human behavior can be identified

Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities

BACKGROUND: The accuracy of step detection in consumer-based wearable activity monitors in older adults with varied ambulatory abilities is not known. METHODS: We assessed the validity of two hip-worn (Fitbit One and Omron HJ-112) and two wrist-worn (Fitbit Flex and Jawbone UP) activity monitors in 99 older adults of varying ambulatory abilities and also included the validity results from the ankle-worn StepWatch as a comparison device. Nonimpaired, impaired (Short Physical Performance Battery Score < 9), cane-using, or walker-using older adults (62 and older) ambulated at a self-selected pace for 100 m wearing all activity monitors simultaneously. The criterion measure was directly observed steps. Intraclass correlation coefficients (ICC), mean percent error and mean absolute percent error, equivalency, and Bland-Altman plots were used to assess accuracy. RESULTS: Nonimpaired adults steps were underestimated by 4.4% for StepWatch (ICC = 0.87), 2.6% for Fitbit One (ICC = 0.80), 4.5% for Omron HJ-112 (ICC = 0.72), 26.9% for Fitbit Flex (ICC = 0.15), and 2.9% for Jawbone UP (ICC = 0.55). Impaired adults steps were underestimated by 3.5% for StepWatch (ICC = 0.91), 1.7% for Fitbit One (ICC = 0.96), 3.2% for Omron HJ-112 (ICC = 0.89), 16.3% for Fitbit Flex (ICC = 0.25), and 8.4% for Jawbone UP (ICC = 0.50). Cane-user and walker-user steps were underestimated by StepWatch by 1.8% (ICC = 0.98) and 1.3% (ICC = 0.99), respectively, where all other monitors underestimated steps by >11.5% (ICCs < 0.05). CONCLUSIONS: StepWatch, Omron HJ-112, Fitbit One, and Jawbone UP appeared accurate at measuring steps in older adults with nonimpaired and impaired ambulation during a self-paced walking test. StepWatch also appeared accurate at measuring steps in cane-users

Behavioral periodicity detection from 24h waveform wrist accelerometry

Develop a framework for identifying meaningful periodicities (i.e., repeating patterns) and the strength of these periodicities from 24h waveform wrist accelerometr

Behavioral periodicity detection from 24h wrist accelerometry and associations with cardiometabolic risk and health-related quality of life

Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data. GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline. Periodograms were constructed to determine patterns emergent from the accelerometer data. Periodicity strength was calculated using circular autocorrelations for time-lagged windows. The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants. Periodicity strength was most consistently associated with LDL-cholesterol (’s = 0.40–0.79, ’s < 0.05) and triglycerides (’s = 0.68–0.86, ’s < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms. Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes

Assessing the Pragmatic Nature of Mobile Health Interventions Promoting Physical Activity: Systematic Review and Meta-analysis

Background: Mobile health (mHealth) apps can promote physical activity; however, the pragmatic nature (ie, how well research translates into real-world settings) of these studies is unknown. The impact of study design choices, for example, intervention duration, on intervention effect sizes is also understudied. Objective: This review and meta-analysis aims to describe the pragmatic nature of recent mHealth interventions for promoting physical activity and examine the associations between study effect size and pragmatic study design choices. Methods: The PubMed, Scopus, Web of Science, and PsycINFO databases were searched until April 2020. Studies were eligible if they incorporated apps as the primary intervention, were conducted in health promotion or preventive care settings, included a device-based physical activity outcome, and used randomized study designs. Studies were assessed using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) and Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) frameworks. Study effect sizes were summarized using random effect models, and meta-regression was used to examine treatment effect heterogeneity by study characteristics. Results: Overall, 3555 participants were included across 22 interventions, with sample sizes ranging from 27 to 833 (mean 161.6, SD 193.9, median 93) participants. The study populations’ mean age ranged from 10.6 to 61.5 (mean 39.6, SD 6.5) years, and the proportion of males included across all studies was 42.8% (1521/3555). Additionally, intervention lengths varied from 2 weeks to 6 months (mean 60.9, SD 34.9 days). The primary app- or device-based physical activity outcome differed among interventions: most interventions (17/22, 77%) used activity monitors or fitness trackers, whereas the rest (5/22, 23%) used app-based accelerometry measures. Data reporting across the RE-AIM framework was low (5.64/31, 18%) and varied within specific dimensions (Reach=44%; Effectiveness=52%; Adoption=3%; Implementation=10%; Maintenance=12.4%). PRECIS-2 results indicated that most study designs (14/22, 63%) were equally explanatory and pragmatic, with an overall PRECIS-2 score across all interventions of 2.93/5 (SD 0.54). The most pragmatic dimension was flexibility (adherence), with an average score of 3.73 (SD 0.92), whereas follow-up, organization, and flexibility (delivery) appeared more explanatory with means of 2.18 (SD 0.75), 2.36 (SD 1.07), and 2.41 (SD 0.72), respectively. An overall positive treatment effect was observed (Cohen d=0.29, 95% CI 0.13-0.46). Meta-regression analyses revealed that more pragmatic studies (−0.81, 95% CI −1.36 to −0.25) were associated with smaller increases in physical activity. Treatment effect sizes were homogenous across study duration, participants’ age and gender, and RE-AIM scores

No Significant Differences in Muscle Growth and Strength Development When Consuming Soy and Whey Protein Supplements Matched for Leucine Following a 12 Week Resistance Training Program in Men and Women: A Randomized Trial

There are conflicting reports regarding the efficacy of plant versus animal-derived protein to support muscle and strength development with resistance training. The purpose of this study was to determine whether soy and whey protein supplements matched for leucine would comparably support strength increases and muscle growth following 12 weeks of resistance training. Sixty-one untrained young men (n = 19) and women (n = 42) (18–35 year) enrolled in this study, and 48 completed the trial (17 men, 31 women). All participants engaged in supervised resistance training 3×/week and consumed 19 grams of whey protein isolate or 26 grams of soy protein isolate, both containing 2 g (grams) of leucine. Multi-level modeling indicated that total body mass (0.68 kg; 95% CI: 0.08, 1.29 kg; p \u3c 0.001), lean body mass (1.54 kg; 95% CI: 0.94, 2.15 kg; p \u3c 0.001), and peak torque of leg extensors (40.27 Nm; 95% CI: 28.98, 51.57 Nm, p \u3c 0.001) and flexors (20.44 Nm; 95% CI: 12.10, 28.79 Nm; p \u3c 0.001) increased in both groups. Vastus lateralis muscle thickness tended to increase, but this did not reach statistical significance (0.12 cm; 95% CI: −0.01, 0.26 cm; p = 0.08). No differences between groups were observed (p \u3e 0.05). These data indicate that increases in lean mass and strength in untrained participants are comparable when strength training and supplementing with soy or whey matched for leucine

Moderators and Mediators of Exercise-Induced Objective Sleep Improvements in Midlife and Older Adults With Sleep Complaints

Objective: Exercise can improve sleep quality, but for whom and by what means remains unclear. We examined moderators and mediators of objective sleep improvements in a 12-month randomized controlled trial among underactive midlife and older adults reporting mild/moderate sleep complaints. Methods: Participants (N ϭ 66, 67% women, 55-79 years) were randomized to moderate-intensity exercise or health education control. Putative moderators were gender, age, physical function, selfreported global sleep quality, and physical activity levels. Putative mediators were changes in BMI, depressive symptoms, and physical function at 6 months. Initially less active individuals with higher initial physical function and poorer sleep quality improved the most. Affective, functional, and metabolic mediators specific to sleep architecture parameters were suggested. These results indicate strategies to more efficiently treat poor sleep through exercise in older adults

Determining who responds better to a computer vs. human-delivered physical activity intervention: Results from the community health advice by telephone (CHAT) trial

Background Little research has explored who responds better to an automated vs. human advisor for health behaviors in general, and for physical activity (PA) promotion in particular. The purpose of this study was to explore baseline factors (i.e., demographics, motivation, interpersonal style, and external resources) that moderate intervention efficacy delivered by either a human or automated advisor. Methods Data were from the CHAT Trial, a 12-month randomized controlled trial to increase PA among underactive older adults (full trial N = 218) via a human advisor or automated interactive voice response advisor. Trial results indicated significant increases in PA in both interventions by 12 months that were maintained at 18-months. Regression was used to explore moderation of the two interventions. Results Results indicated amotivation (i.e., lack of intent in PA) moderated 12-month PA (d = 0.55, p \u3c 0.01) and private self-consciousness (i.e., tendency to attune to one’s own inner thoughts and emotions) moderated 18-month PA (d = 0.34, p \u3c 0.05) but a variety of other factors (e.g., demographics) did not (p \u3e 0.12). Conclusions Results provide preliminary evidence for generating hypotheses about pathways for supporting later clinical decision-making with regard to the use of either human- vs. computer-delivered interventions for PA promotion

Physical activity and sedentary time are related to clinically relevant health outcomes among adults with obstructive lung disease

The purpose of the current study was to determine the association between sedentary time and physical activity with clinically relevant health outcomes among adults with impaired spirometry and those with or without self-reported obstructive lung disease (asthma or COPD).Data from participants of the Canadian Longitudinal Study on Aging were used for analysis (n = 4156). Lung function was assessed using spirometry. Adults were said to have impaired spirometry if their Forced Expiratory Volume in 1\ua0s wa

Effects of Changing Work Environments on Employer Support for Physical Activity During COVID-19

COVID-19 dramatically accelerated evolving changes in the way we define the “work environment” in the United States. In response to COVID-19, many employers have offered increased flexibility for where employees work, including remote (an employee’s workstation is at home) and hybrid work (an employee works both at the employer worksite and remotely, on predetermined schedules). Accordingly, worksite physical activity (PA) and sedentary behaviors (SB) such as extended sitting time (ST) may have changed.1,2 However, little is known about whether these work arrangements are associated with changes in employer support for PA. Interviews were conducted to assess this gap in understanding. Because little is known about employer support for equity with respect to PA and SB, this study sought to identify potential strategies to assure equity in PA opportunities across work environments