Matching Items (29)

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Validation of a Smartphone App for the Assessment of Sedentary and Active Behaviors

Description

Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus,

Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors.

Objective: This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA).
Methods: Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute).

Results: Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA.

Conclusions: Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors.

Contributors

Agent

Created

Date Created
2017-08

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Simplifying Self-Tracking through the Utilization of Wearable Technology

Description

The purpose of this project is to understand how wearable technology can improve a person's practice of self-tracking, or monitoring one's data. Self-tracking is regularly recording information about one's different life patterns (such as diet, activities, or sleep). Some technology

The purpose of this project is to understand how wearable technology can improve a person's practice of self-tracking, or monitoring one's data. Self-tracking is regularly recording information about one's different life patterns (such as diet, activities, or sleep). Some technology that helps users record personal data are seen today as devices (FitBit, Smart Watches) or as applications (MyFitnessPal). Data is collected for the user to observe certain habits that he/she would like to improve upon. Their personal data that is collected and this helps keep the person self-tracking. This data can be converted to show personal behavioral patterns which a person analyzes so that they can make changes that lead to a healthier lifestyle. People self-track in order to analyze their behavior patterns, so that they can make changes to those patterns that lead to a healthier lifestyle. However, some people are not motivated to continue self-tracking, or use their data to make positive behavioral changes. To better understand this problem, we are conducting four co-design sessions with four users who have shown varying levels of self-tracking. Sessions' activities included: storyboarding, reviewing existing user interfaces, generating feedback on prototypes and discussion into thoughts and feelings about the prototype and self-tracking in general. Current findings highlight the importance of customization and simplicity within the application. We are developing an Apple Watch prototype application for self-tracking that incorporates features tailored to those needs in order to better motivate users to track and improve their well-being. Our main goal is to gain a better understanding of our participants and their need and usage with self-tracking. More information can be found on our website at ani6gup.me/CareTrack.

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Agent

Created

Date Created
2017-12

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Determining Who Responds Better to a Computer- VS. Human-Delivered Physical Activity Intervention: Results From the Community Health Advice by Telephone (CHAT) Trial

Description

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

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 < 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 < 0.05) but a variety of other factors (e.g., demographics) did not (p > 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.

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Agent

Created

Date Created
2013-09-22

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Harnessing Different Motivational Frames Via Mobile Phones to Promote Daily Physical Activity and Reduce Sedentary Behavior in Aging Adults

Description

Mobile devices are a promising channel for delivering just-in-time guidance and support for improving key daily health behaviors. Despite an explosion of mobile phone applications aimed at physical activity and other health behaviors, few have been based on theoretically derived

Mobile devices are a promising channel for delivering just-in-time guidance and support for improving key daily health behaviors. Despite an explosion of mobile phone applications aimed at physical activity and other health behaviors, few have been based on theoretically derived constructs and empirical evidence. Eighty adults ages 45 years and older who were insufficiently physically active, engaged in prolonged daily sitting, and were new to smartphone technology, participated in iterative design development and feasibility testing of three daily activity smartphone applications based on motivational frames drawn from behavioral science theory and evidence. An “analytically” framed custom application focused on personalized goal setting, self-monitoring, and active problem solving around barriers to behavior change. A “socially” framed custom application focused on social comparisons, norms, and support.

An “affectively” framed custom application focused on operant conditioning principles of reinforcement scheduling and emotional transference to an avatar, whose movements and behaviors reflected the physical activity and sedentary levels of the user. To explore the applications' initial efficacy in changing regular physical activity and leisure-time sitting, behavioral changes were assessed across eight weeks in 68 participants using the CHAMPS physical activity questionnaire and the Australian sedentary behavior questionnaire. User acceptability of and satisfaction with the applications was explored via a post-intervention user survey. The results indicated that the three applications were sufficiently robust to significantly improve regular moderate-to-vigorous intensity physical activity and decrease leisure-time sitting during the 8-week behavioral adoption period. Acceptability of the applications was confirmed in the post-intervention surveys for this sample of midlife and older adults new to smartphone technology. Preliminary data exploring sustained use of the applications across a longer time period yielded promising results. The results support further systematic investigation of the efficacy of the applications for changing these key health-promoting behaviors.

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Agent

Created

Date Created
2013-04-25

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An Adaptive Physical Activity Intervention for Overweight Adults: A Randomized Controlled Trial

Description

Background: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback

Background: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.

Objective: To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.

Methods: Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9±9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.

Results: A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001) and a group by study phase interaction was observed (p = .017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74).

Conclusions: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a “behavior change technology” that could be incorporated into mHealth technologies and scaled to reach large populations.

Contributors

Agent

Created

Date Created
2013-12-09

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Liberating Science: Accelerating the Innovation of Health Technology

Description

Translating research has been a goal of the Department of Health and Human Services since 1999. Through two years of iteration and interview with our community members, we have collected insights into the barriers to accomplishing this goal. Liberating Science

Translating research has been a goal of the Department of Health and Human Services since 1999. Through two years of iteration and interview with our community members, we have collected insights into the barriers to accomplishing this goal. Liberating Science is a think-tank of researchers and scientists who seek to create a more transparent process to accelerate innovation starting with behavioral health research.

Contributors

Agent

Created

Date Created
2014-05

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Designing a Facebook Group to Promote Physical Activity Among Adults

Description

Background: Physical inactivity is a major cause of obesity, hypertension, cardiovascular disease, and diabetes and has become a major public health problem. Physical inactivity is detrimental to one's health, but it has also created a significant healthcare burden. Within the

Background: Physical inactivity is a major cause of obesity, hypertension, cardiovascular disease, and diabetes and has become a major public health problem. Physical inactivity is detrimental to one's health, but it has also created a significant healthcare burden. Within the past decade, many health-based interventions have been implemented to encourage physically inactive individuals to adopt a more active lifestyle. These health-based interventions have used social media websites, particularly Facebook, to establish social support between the participants of those interventions. There is currently limited research on this topic. This study aims to add to that literature by exploring strategies to encourage participants of health-based interventions to interact with a Facebook group. Purpose: An exercise and nutrition-based intervention called Athletes for Life (AFL) has been using a Facebook page over the past 2.5 years to establish social support between participants of the program, among other functions. The level of interaction that participants had with the Facebook page has declined over the past year. The objective of this study is to redesign and refine the AFL Facebook page so that it is more appealing and interactive to AFL participants. Methods: Redesigning and refining the AFL Facebook page were achieved through three strategies. The first strategy was to recruit approximately twenty participants to the new AFL Facebook group. The next strategy was to select a participant to become the group champion who would post encouraging content on the Facebook group wall. The final strategy was to maintain the consistency with which participants liked and viewed posts on the group wall. Results: The results of this study showed nine participants joined the group and these participants had a combined total of 62 likes and 110 views on the group wall over an eleven-week period. Participants interacted with the content posted by the Facebook group administrators on a consistent basis, but only one participant posted a recipe to the group wall. Measuring the level of interaction for each individual post was significant because it illustrated that the level of interaction participants had with posts depended on the identity of the posts' author. Conclusions: Future research should test the effectiveness of a Facebook group page for promoting physical activity and implementing the suggestions from study participants to increase Facebook usage.

Contributors

Agent

Created

Date Created
2016-05

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Assessing Neighborhood Walkability in South Phoenix

Description

As the prevalence of childhood obesity in the United States rises, opportunities for children to be physically active become more vital. One opportunity for physical activity involves children walking to and from school. However, children that live in areas with

As the prevalence of childhood obesity in the United States rises, opportunities for children to be physically active become more vital. One opportunity for physical activity involves children walking to and from school. However, children that live in areas with a pedestrian-unfriendly built environment and a low degree of walkability are less likely to be physically active and more likely to be overweight. The purpose of this study was to study walking routes from schools in low-income neighborhoods in Southwestern United States to a local community center. Walking routes from the three study schools (South Mountain High School, Percy Julian Middle School, and Rose Linda Elementary School) were determined by distance, popularity, and the presence of a major thoroughfare. Segments and intersections, which formed the routes, were randomly selected from each school's buffer region. The walking routes as a whole, along with the segments and intersections, were audited and scored using built environment assessments tools: MAPS, PEQI and Walkability Checklist. These scores were utilized to develop interactive mapping tools to visualize the quality of the routes, segments and intersections and identify areas for improvement. Results showed that the routes from Percy Julian to the Kroc Center were, overall, rated higher than routes from the other two schools. The highest scoring route, from the seven routes studied, was route 2 from Percy Julian to the Kroc Center along Broadway Road. South Mountain High School was overall the worst starting point for walking to the Kroc Center as those three walking routes were graded as the least walkable. Possible areas for improvement include installing traffic calming features along major thoroughfares and reducing the perceived risk to pedestrian safety by beautifying the community by planting greenery. Future directions include studying the built environment in South Phoenix communities that surround the Kroc Center.

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Agent

Created

Date Created
2015-05

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Validation of the ACT24 Physical Activity Recall for Sedentary and Active Behaviors

Description

Tools that accurately assess physical activity and sedentary behaviors have broad implications relative to understanding the association of adverse health outcomes and these behaviors. Given the ease of distribution and inexpensive nature of self-report tools, they are the most widely

Tools that accurately assess physical activity and sedentary behaviors have broad implications relative to understanding the association of adverse health outcomes and these behaviors. Given the ease of distribution and inexpensive nature of self-report tools, they are the most widely used means to assess human behavior in large-scale populations. The purpose of this study was to validate the ACT24 online self-report recall for measures of sedentary and active behavior against criterion measure. Participants of a larger study were asked to complete the ACT24 recall on a random day in three different weeks during which they were wearing the criterion device. A total of 16 recalls were completed that were used to assess ACT24 measures of sedentary, active, and MVPA behavior. Four different comparisons afforded this analysis: criterion sitting time to ACT24 sedentary time, criterion standing time to ACT24 active behavior, criterion stepping time to ACT24 active behavior, and criterion stepping of 3.0+METs to ACT24 MVPA. Results for the comparisons made between ACT24 sedentary time versus criterion sitting time and ACT24 active time to criterion active time showed little systematic differences at the group level, but the limits of agreement were relatively wide. The comparisons made between ACT24 active time to criterion stepping time and ACT24 MVPA to criterion stepping time at 3.0+ METs both showed a positive systematic difference. Increased incidence of physical activity was correlated with more difference between the measures, likely due to an underestimation of criterion active time measurement. These results are important in the preliminary validity analysis of ACT24 measures of active and sedentary time. Future directions include implementing validation protocols in larger and more diverse samples.

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Agent

Created

Date Created
2015-05

SeeSick: A Mobile Application for Tracking and Visualizing the Spread of Illnesses in Real Time

Description

With the population size growing rapidly at Arizona State University, students are more likely to get sick and miss school when living on campus. The purpose of this project was to design a mobile web application called, SeeSick, that would

With the population size growing rapidly at Arizona State University, students are more likely to get sick and miss school when living on campus. The purpose of this project was to design a mobile web application called, SeeSick, that would visualize the spread of illness on the ASU Tempe campus. This application would provide students with information that could help prevent the spread of illness and allow them to take actionable steps for staying healthy. To accomplish the design and testing of this application, research was conducted on how technology is currently used by students when they are sick, how to design an effective user interface for ASU students, how to physically visualize the spread of the flu on an app, and if an application like this would be useful. The visualizations are created from a user input form and from Twitter data scraping and are displayed on a heat map of the Tempe campus. 126 students were surveyed before the development of the application and once the application was functional, 87 students were interviewed for user testing. Through trial-and-error design and testing, the application was analyzed to determine if it would be used and change behavior. The design of SeeSick successfully provided users with a way to visualize the spread of symptoms on campus and presented them personalized feedback about their symptoms. 62% of students interviewed found the application to be useful and 84% of participants found it easy to use. However, 57% of students said their behavior would not change while using SeeSick. Of the students who tested the application, SeeSick was found to be useful, easy to use, but would not cause behavior change. The current version supports the goal to create a mobile application that tracks the spread of the flu on campus, however it was not tested enough to determine if it would change behavior. With further development and larger testing groups, SeeSick could be improved to not only track the spread of illness on a hyper-local level, but also create actionable steps to prevent the spread of illness.

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Agent

Created

Date Created
2014-12