Filtering by
- Language: English
![137500-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/137500-Thumbnail%20Image.png?versionId=UVmZe.ACHD4qY4sHdMywKQJpIX2BKZin&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T025745Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f543e586929a5090f054c090eafd0bdf4e147635b852c9e1b97e53e054e59481&itok=ZKy5gvsG)
etworking and a tool for stress coping methods.
With this blog, it is my objective to aid my peers who might need help recognizing and coping with stress by the following methods:
a) Actualize the burden of Stress—Chronic stress is a burden and can be overwhelming if not managed. By disclosing my own stressors, it is my hope that peers will identify with me, so that I can then change the way they view and handle the stress.
b) Discuss the psychological and physical effects of stress on the body—It is my intent to clarify how unmanaged chronic stress can affect the physical and mental health and how acute stress is normal and healthy.
c) Share my coping methods that I have found effective in five minute or less videos with blurbs about how and why they are effective. I believe showing them to you in these mostly raw and unedited videos help maintain the current theme I am going for—keep things as real and raw as possible. Hopefully, these raw videos will help peers visualize working coping methods!
![137394-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/137394-Thumbnail%20Image.png?versionId=90z3_fUiWZHdpGnTMsYuYYzRqF6dU4Yr&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T025745Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=3ba3a048641b97dd06c7aca1f6347ccf4014546a19439336be8f4c3e79a3ce7e&itok=QupqmRkw)
![136672-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/136672-Thumbnail%20Image.png?versionId=bY.O.XpxW72_JMH5bvPmXurkzbxsUwWg&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T025745Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=68e32374c6e1d4e30b37804c37b4a25b5cd3f4da29084c43e6cf3d1ac2cd3844&itok=VUkT0CiZ)
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.
![136248-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/136248-Thumbnail%20Image.png?versionId=KCDSU8w7qO5AQsJpFwdIuKFrpHdRyljA&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240612/us-west-2/s3/aws4_request&X-Amz-Date=20240612T072220Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=114840c475ce00dbc9e979d0fca71b6677c6b30ac826842a560e5e579aeb66ed&itok=BSAn35ij)
![131059-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/131059-Thumbnail%20Image.png?versionId=GZhoZNqM8Mma6lddPZm85.LMSnjV0YyK&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T010630Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=e81461fbbcce9a3f4ce099b6465363c168937c1055ec318c115303438c8dca3b&itok=WoQJqC_4)
![130366-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130366-Thumbnail%20Image.png?versionId=Y3UFsAqEPFLGR1T0ZQLJRZ.sD6BFOtMt&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240605/us-west-2/s3/aws4_request&X-Amz-Date=20240605T194838Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=718c194d50060ecbf7fd1c1a1829bc11b4c67ad11f1dacf98718f9a82e48c8c2&itok=6MENkW_C)
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.
![130368-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130368-Thumbnail%20Image.png?versionId=LALL_5UB0cy0KcxV0OCrvVK2OSmAdyPk&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240530/us-west-2/s3/aws4_request&X-Amz-Date=20240530T153906Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=2e332ad25dbb9b6e85a43640f71d0de9ca437144727e2b55bb063d73fe3e58e5&itok=SyYZawqT)
Weight gain during the childbearing years and failure to lose pregnancy weight after birth contribute to the development of obesity in postpartum Latinas.
Methods
Madres para la Salud [Mothers for Health] was a 12-month, randomized controlled trial exploring a social support intervention with moderate-intensity physical activity (PA) seeking to effect changes in body fat, fat tissue inflammation, and depression symptoms in sedentary postpartum Latinas. This report describes the efficacy of the Madres intervention.
Results
The results show that while social support increased during the active intervention delivery, it declined to pre-intervention levels by the end of the intervention. There were significant achievements in aerobic and total steps across the 12 months of the intervention, and declines in body adiposity assessed with bioelectric impedance.
Conclusions
Social support from family and friends mediated increases in aerobic PA resulting in decrease in percent body fat.
![130328-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130328-Thumbnail%20Image.png?versionId=ov2WBtsDyvmqP.kLalI_7ZL8cbmflzR7&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240605/us-west-2/s3/aws4_request&X-Amz-Date=20240605T083850Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=c2c8ef379831fc9e756608725eb28706b07d8152ab193cfec8becd3292be170d&itok=1WHTF29u)
Obese Latino adolescents are disproportionately impacted by insulin resistance and type 2 diabetes. Prediabetes is an intermediate stage in the pathogenesis of type 2 diabetes and represents a critical opportunity for intervention. However, to date, no diabetes prevention studies have been conducted in obese Latino youth with prediabetes, a highly vulnerable and underserved group. Therefore, we propose a randomized-controlled trial to test the short-term (6-month) and long-term (12-month) efficacy of a culturally-grounded, lifestyle intervention, as compared to usual care, for improving glucose tolerance and reducing diabetes risk in 120 obese Latino adolescents with prediabetes.
Methods
Participants will be randomized to a lifestyle intervention or usual care group. Participants in the intervention group will attend weekly nutrition and wellness sessions and physical activity sessions twice a week for six months, followed by three months of booster sessions. The overall approach of the intervention is framed within a multilevel Ecodevelopmental model that leverages community, family, peer, and individual factors during the critical transition period of adolescence. The intervention is also guided by Social Cognitive Theory and employs key behavioral modification strategies to enhance self-efficacy and foster social support for making and sustaining healthy behavior changes. We will test intervention effects on quality of life, explore the potential mediating effects of changes in body composition, total, regional, and organ fat on improving glucose tolerance and increasing insulin sensitivity, and estimate the initial incremental cost effectiveness of the intervention as compared with usual care for improving glucose tolerance.
Discussion
The proposed trial builds upon extant collaborations of a transdisciplinary team of investigators working in concert with local community agencies to address critical gaps in how diabetes prevention interventions for obese Latino youth are developed, implemented and evaluated. This innovative approach is an essential step in the development of scalable, cost-effective, solution oriented programs to prevent type 2 diabetes in this and other populations of high-risk youth.