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Acrylic acid is a monomer that has been responsible for a number of severe explosions worldwide as a result of thermal runaway. The present work was intended to lead to an improved understanding of the kinetics of the thermal polymerization and Michael addition reaction (MAR) of acrylic acid. Sealed cell differential scanning calorimetry was carried out, and the kinetics of the thermal polymerization of acrylic acid were assessed on the basis of the model-free Friedman method. In addition, microcalorimetry using a Thermal Activity Monitor IV apparatus was conducted to determine the kinetic parameters for the MAR. The rate constant for the MAR was found to be k (s−1) = 3.45 × 105 × exp (− 9.48 × 103/T (K)), while the activation energy was 78.8 kJ mol−1. The progress of the MAR was fitted with an n-order reaction model, and the reaction order as well as the rate constant was determined to be linearly proportional to temperature. By employing a modified n-order reaction model in which the reaction order was a linear function of temperature, we obtained a reaction rate equation for the MAR that closely reproduced the experimental results over a wide temperature range.

The COVID-19 pandemic has impacted multiple aspects of daily living, including behaviors associated with occupation, transportation, and health. It is unclear how these changes to daily living have impacted physical activity and sedentary behavior. In this study, we add to the growing body of research on the health impact of the COVID-19 pandemic by examining longitudinal changes in objectively measured daily physical activity and sedentary behavior among overweight or obese young adults participating in an ongoing weight loss trial in San Diego, California. Data were collected from 315 overweight or obese (BMI: range 25.0-39.9 kg/m ) participants aged from 18 to 35 years between November 1, 2019, and October 30, 2020, by using the Fitbit Charge 3 (Fitbit LLC). After conducting strict filtering to find valid data on consistent wear (>10 hours per day for ≥250 days), data from 97 participants were analyzed to detect multiple structural changes in time series of physical activity and sedentary behavior. An algorithm was designed to detect multiple structural changes. This allowed for the automatic identification and dating of these changes in linear regression models with CIs. The number of breakpoints in regression models was estimated by using the Bayesian information criterion and residual sum of squares; the optimal segmentation corresponded to the lowest Bayesian information criterion and residual sum of squares. To quantify the changes in each outcome during the periods identified, linear mixed effects analyses were conducted. In terms of key demographic characteristics, the 97 participants included in our analyses did not differ from the 210 participants who were excluded. After the initiation of the shelter-in-place order in California on March 19, 2021, there were significant decreases in step counts (-2872 steps per day; 95% CI -2734 to -3010), light physical activity times (-41.9 minutes; 95% CI -39.5 to -44.3), and moderate-to-vigorous physical activity times (-12.2 minutes; 95% CI -10.6 to -13.8), as well as significant increases in sedentary behavior times (+52.8 minutes; 95% CI 47.0-58.5). The decreases were greater than the expected declines observed during winter holidays, and as of October 30, 2020, they have not returned to the levels observed prior to the initiation of shelter-in-place orders. Among overweight or obese young adults, physical activity times decreased and sedentary behavior times increased concurrently with the implementation of COVID-19 mitigation strategies. The health conditions associated with a sedentary lifestyle may be additional, unintended results of the COVID-19 pandemic.

BACKGROUND: Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors. METHODS: 21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson’s correlation. RESULTS: All consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly. CONCLUSIONS: In free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.

Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.

The integration of body-worn sensors with mobile devices presents a tremendous opportunity to improve just-in-time behavioral interventions by enhancing bidirectional communication between investigators and their participants. This approach can be used to deliver supportive feedback at critical moments to optimize the attainment of health behavior goals. The goals of this systematic review were to summarize data on the content characteristics of feedback messaging used in diet and physical activity (PA) interventions and to develop a practical framework for designing just-in-time feedback for behavioral interventions. Interventions that included just-in-time feedback on PA, sedentary behavior, or dietary intake were eligible for inclusion. Feedback content and efficacy data were synthesized descriptively. The review included 31 studies (15/31, 48%, targeting PA or sedentary behavior only; 13/31, 42%, targeting diet and PA; and 3/31, 10%, targeting diet only). All studies used just-in-time feedback, 30 (97%, 30/31) used personalized feedback, and 24 (78%, 24/31) used goal-oriented feedback, but only 5 (16%, 5/31) used actionable feedback. Of the 9 studies that tested the efficacy of providing feedback to promote behavior change, 4 reported significant improvements in health behavior. In 3 of these 4 studies, feedback was continuously available, goal-oriented, or actionable. Feedback that was continuously available, personalized, and actionable relative to a known behavioral objective was prominent in intervention studies with significant behavior change outcomes. Future research should determine whether all or some of these characteristics are needed to optimize the effect of feedback in just-in-time interventions.

Abstract Background Chiari-like malformation (CM) and syringomyelia (SM) in dogs are associated with signs of neuropathic pain and itch, which are difficult to quantify reliably. Hypothesis/Objectives Develop and validate the Chiari-like malformation and syringomyelia evaluation (CHASE) questionnaire to quantify owners’ assessments of clinical signs of CM/SM in their dogs. Animals Twenty client-owned healthy dogs and 30 CM/SM affected dogs. Methods The CHASE questionnaire was developed using an iterative sampling design, reliability and normal scores were established in healthy dogs. A double-blinded, randomized, crossover study design was conducted using pregabalin and placebo, with CHASE as the primary outcome. Physical and neurologic examinations, activity monitors, quantitative sensory testing (QST), and quality of life assessments (QOL) were performed alongside the CHASE questionnaire at each time point. Results Baseline CHASE scores were significantly higher in CM/SM dogs compared with healthy dogs (15.3 ± 4.8 vs. 1.4 ± 1.8; P < .01), with score ranges of 6-24 and 0-6, respectively. The CHASE scores in CM/SM dogs were moderately repeatable from screening (15.1 ± 4.4) to baseline (15.1 ± 4.7) and decreased significantly with pregabalin compared with baseline and placebo (P < 0.001 and P < 0.03). The questionnaire had strong internal consistency (α = 0.89) and differentiated pregabalin from placebo with 63% sensitivity and 76% specificity (area under the curve = 0.67). No significant differences in QOL, QST, or activity levels were identified. Conclusions and clinical importance We validated the use of the CHASE questionnaire to quantify clinical signs associated with CM/SM and distinguish treatment outcomes when other assessments failed.

Over the last 20 years, the Kirk R. Smith research group at the University of California Berkeley—in collaboration with Electronically Monitored Ecosystems, Berkeley Air Monitoring Group, and other academic institutions—has developed a suite of relatively inexpensive, rugged, battery-operated, microchip-based devices to quantify parameters related to household air pollution. These devices include two generations of particle monitors; data-logging temperature sensors to assess time of use of household energy devices; a time-activity monitoring system using ultrasound; and a CO2-based tracer-decay system to assess ventilation rates. Development of each system involved numerous iterations of custom hardware, software, and data processing and visualization routines along with both lab and field validation. The devices have been used in hundreds of studies globally and have greatly enhanced our understanding of heterogeneous household air pollution (HAP) concentrations and exposures and factors influencing them.

Background Reduced physical activity (PA) was the strongest predictor of all-cause mortality in patients with chronic obstructive pulmonary disease (COPD). This scoping review aimed to map the evidence on the current landscape of physical activity, barriers and facilitators, and assessment tools across COPD patients. Methods Arksey and O’Malley’s scoping review methodology framework guided the conduct of this review. An electronic search was conducted on five English databases (PubMed, Cochrane Library, PsycINFO, CINAHL and Web of Science) and three Chinese databases (CNKI, CQVIP and WAN-FANG) in January 2022. Two authors independently screened the literature, extracted the studies characteristics. Results The initial search yielded 4389 results, of which 1954 were duplicates. Of the remaining 135 articles, 42 studies met the inclusion criteria. Among the reviewed articles, there were 14 (33.3%) cross-sectional study, 9 (21.4%) cohort study, 4 (9.5%) longitudinal study, 3 qualitative study, 12 (28.7%) randomized control trials. The main barriers identified were older age, women, lung function, comorbidities, COPD symptoms (fear of breathlessness and injury, severe fatigue, anxiety and depression), GOLD stage, frequency of exacerbation, oxygen use, lack of motivation and environment-related (e.g., season and weather). Twelve studies have evaluated the effects of physical exercise (e.g., walking training, pulmonary rehabilitation (PR), pedometer, self-efficacy enhancing intervention and behavioral modification intervention) on PA and showed significant positive effects on the prognosis of patients. However, in real life it is difficult to maintain PA in people with COPD. Conclusions Changing PA behavior in patients with COPD requires multidisciplinary collaboration. Future studies need to identify the best instruments to measure physical activity in clinical practice. Future studies should focus on the effects of different types, time and intensity of PA in people with COPD and conduct randomized, adequately-powered, controlled trials to evaluate the long-term effectiveness of behavioral change interventions in PA.

The proliferation and popularity of wearable activity trackers (eg, Fitbit, Jawbone, Misfit) may present an opportunity to integrate such technology into physical activity interventions. While several systematic reviews have reported intervention effects of using wearable activity trackers on adults' physical activity levels, none to date have focused specifically on children and adolescents. The aim of this review was to examine the effectiveness of wearable activity trackers as a tool for increasing children's and adolescents' physical activity levels. We also examined the feasibility of using such technology in younger populations (age range 5-19 years). We conducted a systematic search of 5 electronic databases, reference lists, and personal archives to identify articles published up until August 2016 that met the inclusion criteria. Articles were included if they (1) specifically examined the use of a wearable device within an intervention or a feasibility study; (2) included participants aged 5-19 years old; (3) had a measure of physical activity as an outcome variable for intervention studies; (4) reported process data concerning the feasibility of the device in feasibility studies; and (5) were published in English. Data were analyzed in August 2016. In total, we identified and analyzed 5 studies (3 intervention, 2 feasibility). Intervention delivery ranged from 19 days to 3 months, with only 1 study using a randomized controlled trial design. Wearable activity trackers were typically combined with other intervention approaches such as goal setting and researcher feedback. While intervention effects were generally positive, the reported differences were largely nonsignificant. The feasibility studies indicated that monitor comfort and design and feedback features were important factors to children and adolescents. There is a paucity of research concerning the effectiveness and feasibility of wearable activity trackers as a tool for increasing children's and adolescents' physical activity levels. While there are some preliminary data to suggest these devices may have the potential to increase activity levels through self-monitoring and goal setting in the short term, more research is needed to establish longer-term effects on behavior.

Background Obesity is a growing global health concern that may lead to cardiovascular disease, type II diabetes, and cancer. Several systematic reviews have shown that technology is successful in combating obesity through increased physical activity, but there is no known review on interventions that use an electronic activity monitor system (EAMS). EAMSs are defined as a wearable device that objectively measures lifestyle physical activity and can provide feedback, beyond the display of basic activity count information, via the monitor display or through a partnering application to elicit continual self-monitoring of activity behavior. These devices improve upon standard pedometers because they have the ability to provide visual feedback on activity progression, verbal encouragement, and social comparison. This systematic review aimed to synthesize the efficacy and feasibility results of EAMSs within published physical activity interventions. Methods Electronic databases and journal references were searched for relevant articles. Data sources included CINAHL, Cochrane CENTRAL, Medline Ovid, PsycINFO, and clinicaltrials.gov. Out of the 1,574 retrieved, 11 articles met the inclusion criteria. These articles were reviewed for quality and content based on a risk of bias tool and intervention components. Results Most articles were determined to be of medium quality while two were of low quality, and one of high quality. Significant pre-post improvements in the EAMS group were found in five of nine studies for physical activity and in four of five studies for weight. One found a significant increase in physical activity and two studies found significant weight loss in the intervention group compared with the comparator group. The EAMS interventions appear to be feasible with most studies reporting continual wear of the device during waking hours and a higher retention rate of participants in the EAMS groups. Conclusion These studies provide preliminary evidence suggesting that EAMS can increase physical activity and decrease weight significantly, but their efficacy compared to other interventions has not yet been demonstrated. More high-quality randomized controlled trials are needed to evaluate the overall effect of EAMS, examine which EAMS features are most effective, and determine which populations are most receptive to an EAMS.