PowerForecaster

BackgroundIn today’s society, commercial physical activity apps (eg, Fitbit and Strava) are ubiquitous and hold considerable potential to increase physical activity behavior. Many commercial physical activity apps incorporate social components, in particular app-specific communities (allowing users to interact with other app users) or the capacity to connect to existing social networking platforms (eg, Facebook or Instagram). There is a growing need to gain greater insights into whether commercial physical activity apps and specific components of these apps (social components) are beneficial in facilitating physical activity.ObjectiveThis study aimed to examine the relationship between the use of commercial physical activity apps and engagement in physical activity. The social components of commercial physical activity apps (app-specific communities and existing social networking platforms) were also explored. This involved isolating specific features (eg, sharing, providing, and receiving encouragement, comparisons, and competitions) of app-specific communities and existing social networking platforms that were most valuable in facilitating physical activity.MethodsA cross-sectional web-based survey was conducted. Participants were 1432 adults (mean age 34.1 years, 1256/1432, 88.00% female) who completed measures assessing physical activity, the use of commercial physical activity apps, and engagement with app-specific communities and existing social networking platforms.ResultsOverall, 53.14% (761/1432) of the sample reported engaging with a commercial physical activity app. The most commonly used apps were Fitbit (171/761, 22.5%), Strava (130/761, 17.1%), and Garmin (102/761, 13.4%). The use of physical activity apps was significantly associated with physical activity. Notably, the use of app-specific communities and existing social networking platforms facilitated significantly greater engagement in physical activity. The features of app-specific communities that were most beneficial in promoting engagement in physical activity were providing encouragement to a partner, receiving encouragement from close friends and family, and engaging in competitions with members of public app-specific communities. In relation to existing social networking platforms, sharing physical activity posts predicted engagement in physical activity.ConclusionsThe findings indicate that app-specific communities and existing social networking platforms are components of apps that are fundamental in facilitating physical activity. They further suggest that commercial physical activity apps afford high population level reach and hold great potential to promote engagement in physical activity, an important public health consideration.

Objective“Active couch potato” describes an individual who is sufficiently physically active yet highly sedentary. Cell phones promote activities understood as sedentary behaviors (e.g. watching videos). Research demonstrates that cell phone use is positively associated with sedentary behavior. Although sedentary behavior typically displaces physical activity, no relationship between cell phone use and physical activity has been found. Thus, it is possible that some sufficiently active individuals are also high-frequency cell phone users and therefore highly sedentary. In other words, cell phone use may predict being an “active couch potato” among active people. Testing this hypothesis was the purpose of this study. “Active couch potatoes” are of concern as the negative effects of excessive sedentary behavior are independent of the benefits of physical activity.MethodsCollege students (228) completed validated surveys assessing physical activity, sedentary behavior, and cell phone use. Using a previously validated method, participants were rated as sufficiently active or not based upon their physical activity survey score. Participants who were not sufficiently active were excluded from further analysis resulting in a final sample of 171. These sufficiently active individuals were categorized as “active couch potatoes” if they were also highly sedentary (i.e. sitting for eight or more hours/day). Logistic regression determined if cell phone use predicted being categorized as an “active couch potato.”ResultsCell phone use was a significant, positive predictor of being an “active couch potato.” With each additional hour of daily cell phone use, the odds of becoming an “active couch potato” increased by 11.4% (Wald = 5.934, P = 0.015, Exp(B) = 1.114). On average, active couch potatoes used their cell phone 1.7 hours more each day than their sufficiently active but not overly sedentary peers.ConclusionIncreased cell phone use was a significant predictor of being an “active couch potato.” Explanations and implications are discussed.

In this work, we propose a new paradigm called power emulation, which exploits hardware acceleration to drastically speedup power estimation. Power emulation is based on the observation that most power estimation tools typically perform the following sequence of operations: simulating the circuit to obtain value traces or statistics for the inputs of its constituent components, evaluating power models for each circuit component based on the input values seen during simulation, and aggregating the power consumption of individual components to compute the circuit's power consumption. We further recognize that the steps involved in power estimation (power model evaluation, aggregation) can themselves be thought of as synthesizable functions and implemented as hardware circuits. Thus, any given design can be enhanced by adding to it .power estimation hardware., and the resulting power model enhanced circuit can be mapped onto a hardware prototyping platform. While drastic speedups in power estimation (orders of magnitude) are possible using this approach, a significant challenge arises due to the increase in circuit size as a result of adding power estimation hardware. We propose a systematic methodology to reduce the size of the power model enhanced circuit through a suite of techniques, including power model reuse across different circuit components, regulating the granularity of components for power modeling, exploiting inter-component power correlations, resource sharing for power model computations, and the use of block memories for efficient storage within power models. We demonstrate the benefits of the proposed power emulation paradigm by applying it to register-transfer level (RTL) power estimation for industrial designs, resulting in speedups from around 10X to over 500X compared to state-of-the-art commercial power estimation tools.

There is evidence of a positive relationship between cellular telephone use and sedentary behavior but not physical activity in college-aged individuals (18-29 years).These relationships have not been tested in individuals older than college age (≥ 30 years old).Testing these relationships in older individuals is warranted as cell phone use is inversely associated with age.Methods: A sample of adults aged 30-63 years (N = 69, 50.5 ± 8.2 years old) wore a physical activity monitor (accelerometer) for seven days and completed validated surveys assessing daily cell phone use, physical activity, and sedentary behavior.Results: Cell phone use (̅ = 125.2± 146.8 minutes per day) was inversely associated with age (r = -0.3,p = 0.005).Cell use was not associated with objectively-or subjectively-measured physical activity or sedentary behavior (r ≤ 0.1, p ≥ 0.3).Tertile splits were performed to establish groups of low, moderate, and high cell phone users.There were no significant (F ≤ 2.0, p ≥ 0.12 for all) differences in physical activity or sedentary behavior between groups.Conclusion: Unlike what has been reported in college-aged individuals, cell use was not associated with sedentary behavior in adults older than college age.

Various power estimation methods with high-level hardware power model, i.e. power emulation, had been proposed to overcome the slow speed of traditional power estimation in software environment. But precise speed comparison between gate-level simulation and high-level power emulation was conducted rarely. In this paper, we propose an architecture of power emulation which can compare precise speed gain against the traditional power simulation. Experimental results show that the average power error is 0.29%, the power emulation analyzes power 20,206 times faster than the traditional power simulation.

Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a smartphone application by executing some parts of the application on a remote server. Supporting such capability is not easy for smartphone application developers due to (1) correctness: some code, e.g., that for GPS, gravity, and other sensors, can run only on the smartphone so that developers have to identify which parts of the application cannot be offloaded; (2) effectiveness: the reduced execution time must be greater than the network delay caused by computation offloading so that developers need to calculate which parts are worth offloading; (3) adaptability: smartphone applications often face changes of user requirements and runtime environments so that developers need to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, solutions applicable for legacy applications will be much more valuable. In this paper, we present a tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability. For a given Android application, DPartner first analyzes its bytecode for discovering the parts worth offloading, then rewrites the bytecode to implement a special program structure supporting on-demand offloading, and finally generates two artifacts to be deployed onto an Android phone and the server, respectively. We evaluated DPartner on three real-world Android applications, demonstrating the reduction of execution time by 46%-97% and battery power consumption by 27%-83%.

BackgroundPsoriasis is a common, chronic, immune-mediated skin disorder, which is characterized by clearly demarcated areas of erythematous plaques with overlying silvery scales appearing on the skin [1]. Previous studies have...

Background: Smartphone applications and wearable activity trackers have become popular tools in recent years in managing chronic diseases such as diabetes. More recently, studies have focused on connecting patient-generated health data from mobile devices directly to health care providers and educators. However, not much is known regarding the patient experience in using these mobile devices for diabetes management, particularly the implications of allowing educators direct access to patients’ diet and exercise data. Objective: The objective of this study was to identify patients’ perceived benefits and concerns about using a smartphone application and wristband activity tracker to monitor diet and physical activity, as well as the perceived benefits and concerns of allowing educators access to such data. Methods: We conducted a qualitative, descriptive study as an axillary study to a clinical trial testing a connected interface to link patient self-monitoring diet and physical activity to a nationally used electronic diabetes education system. Our axillary study examined 13 type 2 diabetes patients’ views on perceived benefits and concerns about using a smartphone application and wristband activity tracker to monitor diet and physical activity for three months. A focus group interview was administered to obtain general and specific understanding of the use of smartphone applications and activity trackers during the study period. The central interview questions guiding the discussion included “What did you think about the UP24 wristband and app?”, “What are your thoughts about the connection of UP24 data with Chronicle, the Web-based diabetes education system, so that your diabetes educators can see your behavior?”, and “Has knowing that someone else has access to your diet and exercise data affected your behavior and self-monitoring?” The interviewer also asked specific questions to gain deeper understanding of the following topics: (1) the app and wristband features used to record and monitor diet and physical activity, (2) materials used for intervention orientation, (3) additional data (eg, weight and blood glucose) that participants would like to share with educators, and (4) suggestions for improvement in diabetes self-management and communication with educators and physicians. The focus group sessions were audio-recorded and transcribed. Transcribed data were analyzed to identify key themes based on interpretive coding procedures. Results: We identified 11 key themes under three major categories and described these themes with illustrative quotations. The three major categories of themes covered (1) self-monitoring themes: varied experience and self-monitoring patterns and adherence exist among patients using the wearable tracker and its companion smartphone application; (2) themes related to sharing self-monitoring of diet and physical activity data with diabetes educators: sharing self-monitoring diet influences patient self-monitoring adherence and dietary and activity changes, and communication with educators; and (3) research study-related themes: technical barriers, utilization of manuals and tutorial videos in beginning use of the connected health tools, and desired features on combining lifestyle data with glucose data and caregiver access. Conclusions: Connected technology aiming to incorporate patient-generated health lifestyle data into clinical workflow should consider patient perspectives in terms of their experience and motivation for generating and sharing such data and technical barriers in using such tools.

Today's smartphone application markets host an ever increasing number of applications. The sheer number of applications makes their review a daunting task. We propose AppsPlayground for Android, a framework that automates the analysis smartphone applications. AppsPlayground integrates multiple components comprising different detection and automatic exploration techniques for this purpose. We evaluated the system using multiple large scale and small scale experiments involving real benign and malicious applications. Our evaluation shows that AppsPlayground is quite effective at automatically detecting privacy leaks and malicious functionality in applications.

The purpose of this study was to examine the potential differences in knowledge and behaviors related to active living between two schools with contrasting socioeconomic status (SES) and racial characteristics. The sample involved 523 adolescents from two public middle schools in a southern US state including 243 Black (46.5%), 223 White (42.6%), and 67 others (12.8%). Physical activity (PA) and sedentary behavior (SB) were measured using the validated Youth Activity Profile (YAP), while knowledge of physical activity and fitness (PAF knowledge) was measured using the grade-appropriate PE Metrics written test. Socio-demographic characteristics were self-reported or obtained via secondary government database. The medium SES school showed higher PA outside of school (ΔM = 0.27, d = 0.26), lower SB (ΔM = - 0.35, d = - 0.45), and higher PAF knowledge (ΔM = 8.3%, d = 0.54) than the low SES school. Statistically significant differences were observed for after-school PA, evening PA, weekend PA, TV viewing, video games, and cell phone use (d = - 0.38 to 0.39). Group difference by race was also found with White participants demonstrating higher PA outside of school, lower SB, and higher PAK knowledge than Black participants (d = - 0.43 to 0.57). The lower SES school (mostly Black) showed less favorable PA, SB, and knowledge results than the higher SES school. SES is a significant school-level characteristic influencing the determinants (i.e., knowledge and behaviors) of the active living habit in youth.

“It is frustrating to not have control even though I know it’s not legal!”: A mixed-methods investigation on applications to prevent mobile phone use while driving

Idiopathic musculoskeletal pain, musculoskeletal pain syndromes, and use of electronic devices in adolescents with asthma

Introduction: Existing evidence supports a positive relationship between smartphone use and sedentary behavior (i.e., high smartphone use is associated with high sitting). While evidence supports that smartphone use during physical activity reduces intensity, the relationship between daily smartphone use and daily physical activity is equivocal. Prior research assessing these relationships has relied on self-report survey instruments. The purpose of this research was to assess the relationship between minutes of smartphone use and physical activity (steps) using objective measures. Methods: College-aged individuals (N = 50) completed a brief survey assessing daily screen time (min) and daily steps for the previous 10-days. This data was obtained from participants’ smartphone applications (e.g., “Health” and “Screen Time” apps) and the relationship was assessed. Results: There was a near moderate effect size for a negative correlation between average smartphone use and steps (r = -0.25). Mixed model regression indicated that this relationship was significant (F = 3.65, p = 0.00019). Conclusions: The results differ from prior research which has not reported a significant relationship between smartphone use and physical activity when using self-reported survey instruments. This difference highlights the need for additional research employing objective measures when assessing relationships between smartphone use and health behaviors.

Background Well-documented health benefits are associated with 150 min/week of moderate-to-vigorous physical activity (PA). However, a majority of older adults do not follow this guideline and calls to address this disparity with technology have been made (1). Recommendations from the wider PA literature suggest the incorporation of mobile and wireless technology, i.e. mobile health (mHealth), into PA interventions (2). This study aims to identify and synthesise the evidence base on the experiences of adults using smartphone applications for the promotion of PA. Methods A systematic search of CINAHL, Embase, ERIC, Medline and PsycINFO was conducted in October 2017. Primary qualitative studies with extractable data on the experiences of adults using mHealth for the promotion of PA were included. Data were analysed in NVivo using a meta-ethnographic approach. Results The initial search yielded 4,420 articles. After screening, fifteen articles were included, of which three included young adults, two included young and middle-aged adults, six included middle-aged and older adults and three included young, middle-aged and older adults, while one study did not report age. Because findings were not stratified by age, data were analysed collectively. Our inductive findings included the idea that end-users value the experience of personalisation offered by smartphone applications. Furthermore, mHealth raised awareness of individuals’ PA level, supporting them to strategise ways of incorporating PA into their routines. Finally, negative experiences were not uncommon, though were largely caused by poor design and technical faults (e.g. battery life/ inaccurate data). Conclusion Our review provides evidence of the paucity of studies focused on the experiences of older adults using mHealth for the promotion of PA. Given recommendations for PA interventions to be tailored, particularly across the lifespan (2), and the positive findings noted in the current study, further primary qualitative studies exploring the perspectives of older adults are warranted.

In today's rapidly evolving digital landscape, children encounter unprecedented opportunities for learning, creativity, and connection, but also face challenges and risks that demand careful and informed navigation.As children increasingly engage with online environments, it is imperative to empower healthcare professionals, teachers and parents with the knowledge and skills needed to confidently support children in managing risks safely and effectively.Amid this complexity, the enduring power of movement and physical activity remains a vital cornerstone of healthy child development.This talk highlights potential negative consequences of digital engagement and underscores the importance of nurturing movement as a universal language through which children explore, learn, and grow.In an age dominated by digital noise, physical activity offers children essential opportunities to develop cognitive, emotional, and social skills in an engaging, natural, and holistic manner.Prioritising physical activity and skill-building through movement goes far beyond enhancing physical strength, it develops intelligence, courage, kindness, and self-confidence.These qualities enable children to process experiences, build resilience, and cultivate a grounded sense of self, all of which are critical not only for success offline but also for navigating the digital world with awareness and safety.This presentation will highlight the need to integrate movement and physical activity into contemporary strategies aimed at supporting children's holistic development in the digital age.By embracing both digital literacy and the irreplaceable benefits of active play, we can equip children to thrive as well-rounded, empowered individuals prepared to meet the challenges and possibilities of our interconnected world.