Matching Items (49)
Description
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.
ContributorsLi, Vincent (Author) / Turaga, Pavan (Thesis director) / Buman, Matthew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Background
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
ContributorsHuberty, Jennifer (Author) / Ehlers, Diane (Author) / Kurka, Jonathan (Author) / Ainsworth, Barbara (Author) / Buman, Matthew (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-07-30
Validity of the Rapid Eating Assessment for Patients for assessing dietary patterns in NCAA athletes
Description
Background
Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport.
Methods
Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race.
Results
A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns.
Conclusions
REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.
Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport.
Methods
Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race.
Results
A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns.
Conclusions
REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.
ContributorsKurka, Jonathan (Author) / Buman, Matthew (Author) / Ainsworth, Barbara (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-08-15
Description
Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
ContributorsMartin, Logan Rhett (Author) / Buman, Matthew (Thesis director) / Toledo, Meynard John (Committee member) / Kurka, Jonathan (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
This thesis paper examines the effects of increased standing and light physical activity in the workplace on postprandial glucose. Sedentary behavior is detrimental to our health, affecting metabolic risk factors. An easy way to implement change is by decreasing sedentary time in workplaces where sitting is common, such as office workspaces. To consider how postprandial glucose is affected by decreasing sedentary time, participants ate a standardized meal for lunch and were asked to decrease their sitting time by replacing it with standing and light physical activity.
ContributorsChilders, Autumn Skye (Author) / Buman, Matthew (Thesis director) / Sears, Dorothy (Committee member) / Hasanaj, Kristina (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
The purpose of this study was to examine the overall maintenance of behavior during the 12 to 24 month period of the Stand&Move@Work study and the impact of implementation factors (i.e., facilitators, advocate activity, and the amount of strategies used) on behavior change. The design of the study was a cluster randomized trial which was facilitated by researchers for the first 12 months of the study. The primary aim of the study was to examine the maintenance of behavior change (i.e., sitting time) at the 12 month and 24 month marks using objectively measured sedentary behavior (activPAL micro). The secondary aim of the study was to examine the impact of implementation factors (i.e., facilitators, advocate activity, and the amount of strategies used) on behavior change during the 12 through 24 months maintenance period. Participants (N=630) included full-time, caucasian, middle-aged office workers. For the primary aim, descriptive means were used to cluster for observations within-persons and were adjusted for age, gender, race, job-type, and ordering effects.. For the secondary aim, descriptive means adjusted for workplace culture and environment were computed. At the 24 month mark, participants spent 280.67 ± 87.67 min/8hr workday sitting and 161.94 ± 85.87 min/8hr workday standing. The top performing worksites displayed reductions in sitting time which largely translated into standing time by about 30 minutes per 8 hour workday at 24 months. Feasibility findings indicated that implementation strategies do not show differences between the top 25% and bottom 25% performing worksites. This study provides insight to implementation strategies for interventions in the workplace.
ContributorsTong, Alyssa Taylor (Author) / Buman, Matthew (Thesis director) / Larouche, Miranda (Committee member) / Estabrooks, Paul (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Cardiovascular disease attributed to about 800,000 deaths per year and is the leading cause of all-cause mortality in the U.S. Previous studies indicate that reducing sedentary time or increasing physical activity (PA) can independently reduce cardiometabolic risk (CMR). Further, studies have shown that higher levels of moderate-to-vigorous PA can attenuate the negative effects of sedentary behavior on CMR.
In this study, we evaluated the association between sedentary time, light-intensity PA (LPA), and moderate-vigorous PA (MVPA) and CMR biomarkers (high density lipoprotein level, low density lipoprotein level, triglycerides, fasting glucose, total cholesterol, blood pressure, and body mass index). Additionally, we examined if the detrimental association between sedentary time and CMR biomarkers is partially or fully attenuated by MVPA. Baseline objective physical activity and cardiometabolic risk data from a two-arm-cluster randomized trial (Stand&Move@work) were used in this study. Multilevel models clustered by worksite evaluated the fixed effects and interaction between MVPA and sedentary time on CMR. Data from 630 sedentary working adults (from 24 worksites) were included in the analysis. The sample was mainly middle aged (44.6±11.2) females (74%) with race distributions as follows; 70.5% white, 13.8% hispanic, 4.1% black, 5.1% asian, and 2.1% other. Our study showed detrimental trends consistent with previous studies between sedentary behavior and cardiometabolic outcomes including HDL, LDL, and total cholesterol. MVPA demonstrated beneficial associations with lipoproteins including HDL, LDL, total cholesterol, and triglycerides. We observed that high levels of MVPA may be able to partially attenuate the negative effects of highly sedentary behavior on fasting glucose, total cholesterol, and LDL levels. Overall, sedentary behavior indicated deleterious associations with cardiometabolic outcomes. Future directions for this study could examine a more at-risk population or a highly active population for further assessment of CMR biomarkers and the effects of behavior.
In this study, we evaluated the association between sedentary time, light-intensity PA (LPA), and moderate-vigorous PA (MVPA) and CMR biomarkers (high density lipoprotein level, low density lipoprotein level, triglycerides, fasting glucose, total cholesterol, blood pressure, and body mass index). Additionally, we examined if the detrimental association between sedentary time and CMR biomarkers is partially or fully attenuated by MVPA. Baseline objective physical activity and cardiometabolic risk data from a two-arm-cluster randomized trial (Stand&Move@work) were used in this study. Multilevel models clustered by worksite evaluated the fixed effects and interaction between MVPA and sedentary time on CMR. Data from 630 sedentary working adults (from 24 worksites) were included in the analysis. The sample was mainly middle aged (44.6±11.2) females (74%) with race distributions as follows; 70.5% white, 13.8% hispanic, 4.1% black, 5.1% asian, and 2.1% other. Our study showed detrimental trends consistent with previous studies between sedentary behavior and cardiometabolic outcomes including HDL, LDL, and total cholesterol. MVPA demonstrated beneficial associations with lipoproteins including HDL, LDL, total cholesterol, and triglycerides. We observed that high levels of MVPA may be able to partially attenuate the negative effects of highly sedentary behavior on fasting glucose, total cholesterol, and LDL levels. Overall, sedentary behavior indicated deleterious associations with cardiometabolic outcomes. Future directions for this study could examine a more at-risk population or a highly active population for further assessment of CMR biomarkers and the effects of behavior.
ContributorsMeyer, Emily Camille (Author) / Buman, Matthew (Thesis director) / Toledo, Meynard (Committee member) / Pereira, Mark (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
While type 2 diabetes (T2D) rates have soared, the number of Americans classified as ‘prediabetic’ has also increased. Despite this, current preventative approaches are costly and often not without undue side-effects. Instead, behavioral lifestyle approaches hold promise in reducing conversion rates of T2D as the latest treatment option that could mitigate and transform disease management. However, present interventions do not possess the scope necessary for implementation in a realistic, scalable way that can target the large at-risk population.
The application (app) “BeWell24” mitigates this diabetes risk through targeting sleep, physical activity, sedentary behavior, and diet, and is being delivered through mHealth technology to attenuate the higher-risk of the prediabetic Veteran population. In order for full scale dissemination, this thesis examines a provider perspective of the ‘Post-intervention interview guide’, performed with a Phoenix Veterans Affairs Health Care System (PVAHCS) provider. It then suggests revisions to the interview guide based on the provider’s interview and existing literature. This thesis also emphasizes the rationale behind these proposed changes to be organized in line with the iPARIHS framework (integrated Promoting Action on Research Implementation in Health Services).
Overall, the provider responded positively to BeWell24 and the ‘Post-intervention interview guide’, with constructive suggestions for each question in the interview guide. The main theme of the provider’s answers and comments were to prioritize efficiency and preserve standard clinical flow. A revised interview guide is provided, which prospectively presents as a more brief and focused interview organized by the iPARIHS framework. This revised interview guide could aid in the clarity of provider responses, specifically for the prospective interviews of the ongoing larger BeWell24 study and future studies.
The application (app) “BeWell24” mitigates this diabetes risk through targeting sleep, physical activity, sedentary behavior, and diet, and is being delivered through mHealth technology to attenuate the higher-risk of the prediabetic Veteran population. In order for full scale dissemination, this thesis examines a provider perspective of the ‘Post-intervention interview guide’, performed with a Phoenix Veterans Affairs Health Care System (PVAHCS) provider. It then suggests revisions to the interview guide based on the provider’s interview and existing literature. This thesis also emphasizes the rationale behind these proposed changes to be organized in line with the iPARIHS framework (integrated Promoting Action on Research Implementation in Health Services).
Overall, the provider responded positively to BeWell24 and the ‘Post-intervention interview guide’, with constructive suggestions for each question in the interview guide. The main theme of the provider’s answers and comments were to prioritize efficiency and preserve standard clinical flow. A revised interview guide is provided, which prospectively presents as a more brief and focused interview organized by the iPARIHS framework. This revised interview guide could aid in the clarity of provider responses, specifically for the prospective interviews of the ongoing larger BeWell24 study and future studies.
ContributorsWojtas, Abby Ann (Author) / Buman, Matthew (Thesis director) / Larouche, Miranda (Committee member) / Epstein, Dana (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Mobile health or "mHealth" defines a broad spectrum of medical or public health practice supported by mobile devices. The patient's perception of mobile health applications is the key point in confronting whether or not patients will utilize the tools at their disposal As such, the primary aim of this study was to examine participant feedback through quantitative and qualitative measures using the Therapy Evaluation Questionnaire and a patient interview, respectively, to further understand the patient rated acceptability of using BeWell24 and SleepWell24 for improving health outcomes. For BeWell24, it was hypothesized that patients who received the Multicomponent version would report higher acceptability scores than those randomized to the Health Education version. Furthermore, in regard to SleepWell24, it was hypothesized that the SleepWell24 patient would provide positive feedback and suggestions regarding their own experience with the SleepWell24 app. Data from this thesis was pulled from two ongoing randomized controlled trials currently being conducted at the Phoenix Veteran Affairs Health Care Service (PVACHS) and Mayo Clinic hospitals. Means, standard deviations, frequencies, and percentages were commuted to summarize demographics and TEQ scores. In addition, key concepts from a qualitative interview with a SleepWell24 participant were derived. The results showed a greater acceptability of the multicomponent versions of BeWell24 and SleepWell24 but a lower TEQ score of perceived usability. mHealth implementations pose a potential to become an important part of the health sector for establishing innovative approaches to delivering care, and while benefits have been highly praised, it is clear that the perceptions of mHealth must be positive if the technology is to transcend into a practical clinical setting.
ContributorsJimenez, Asael (Author) / Buman, Matthew (Thesis director) / Epstein, Dana (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Sleep diaries and actigraphy are two common methods used to assess sleep subjectively and objectively, respectively. Compared to the gold standard of sleep assessment, polysomnography, sleep diaries and actigraphic methods are more cost-effective and simpler to use. This study aimed to compare the sleep parameters derived from actigraphy and sleep diaries (total sleep time, sleep onset latency, number of awakenings, wake after sleep onset, percentage of time awake, and sleep efficiency). Based on results from previous similar studies, it was hypothesized that the sleep diaries would overestimate the total sleep time parameter and underestimate wake parameters. Twenty healthy young adults without sleep problems volunteered to participate. The participants wore an Actiwatch 2 on their wrist and filled out a sleep diary every morning for the duration of six days. A high intraclass correlation coefficient value between subjective and objective sleep was found for the parameter total sleep time, even though total sleep time was found to be slightly overestimated by the sleep diaries. Sleep onset latency, wake after sleep onset, number of awakenings, percentage of time awake, and sleep efficiency were underestimated by the sleep diaries and did not have high correlation values. Based off of the ICC results, there does not seem to be a strong correlation between the Actiwatch 2 and the sleep diaries, but looking at the Bland Altman plots, there seems to be agreement between the methods.
ContributorsRameshkumar, Aarthi (Author) / Buman, Matthew (Thesis director) / Petrov, Megan (Committee member) / Diaz-Piedra, Carolina (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2016-12