DetectStress: A Novel Stress Detection System Based on Smartphone and Wireless Physical Activity Tracker

2018 ◽  
pp. 67-80
Author(s):  
B. Padmaja ◽  
V. V. Rama Prasad ◽  
K. V. N. Sunitha ◽  
N. Chandra Sekhar Reddy ◽  
C. H. Anil
Keyword(s):  
Physical Activity ◽  
Detection System ◽  
Stress Detection ◽  
Activity Tracker

Efficacy of an activity tracker-and app intervention to increase physical activity in the entire family: Pilot single-arm intervention study (Preprint)

2019 ◽  
Author(s):  
Stephanie Schoeppe ◽  
Jo Salmon ◽  
Susan L. Williams ◽  
Deborah Power ◽  
Stephanie Alley ◽  
...  
Keyword(s):  
Physical Activity ◽  
Intervention Study ◽  
Post Intervention ◽  
Entire Family ◽  
Physical Activity Guidelines ◽  
Mothers And Fathers ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Family Program ◽  
Intervention Efficacy

BACKGROUND Interventions using activity trackers and smartphone apps have demonstrated their ability to increase physical activity in children and adults. However, they have not been tested in entire families. Further, few family-centred interventions have actively involved both parents, and assessed intervention efficacy separately for children, mothers and fathers. OBJECTIVE This study aimed to examine the short-term efficacy of an activity tracker and app intervention to increase physical activity in the entire family (children, mothers and fathers). METHODS This was a pilot single-arm intervention study with pre-post measures. Between 2017-2018, 40 families (58 children aged 6-10 years, 39 mothers, 33 fathers) participated in the 6-week Step it Up Family program in Queensland, Australia. Using commercial activity trackers combined with apps (Garmin Vivofit Jr for children, Vivofit 3 for adults), the intervention included individual and family-level goal-setting, self-monitoring, performance feedback, family step challenges, family social support and modelling, weekly motivational text messages, and an introductory session delivered face-to-face or via telephone. Parent surveys were used to assess intervention efficacy measured as pre-post intervention changes in moderate-to-vigorous physical activity (MVPA) in children, mothers and fathers. RESULTS Thirty-eight families completed the post intervention survey (95% retention). At post intervention, MVPA had increased in children by 58 min/day (boys: 54 min/day, girls: 62 min/day; all P < .001). In mothers, MVPA increased by 27 min/day (P < .001), and in fathers, it increased by 31 min/day (P < .001). Furthermore, the percentage of children meeting Australia’s physical activity guidelines for children (≥60 MVPA min/day) increased from 34% to 89% (P < .001). The percentage of mothers and fathers meeting Australia’s physical activity guidelines for adults (≥150 MVPA min/week) increased from 8% to 57% (P < .001) in mothers, and from 21% to 68% (P < .001) in fathers. CONCLUSIONS Findings suggest that an activity tracker and app intervention is an efficacious approach to increasing physical activity in entire families to meet national physical activity guidelines. The Step it Up Family program warrants further testing in a larger, randomised controlled trial to determine its long-term impact. CLINICALTRIAL No trial registration as this is not an RCT. It is a pilot single-arm intervention study

scholarly journals Design of weak magnetic signal detection system for residual stress detection

2021 ◽  
Vol 632 ◽  
pp. 042064
Author(s):  
Yanyu Wei ◽  
Dixiang Chen ◽  
Weihong Zhou ◽  
Lihui Liu
Keyword(s):  
Residual Stress ◽  
Signal Detection ◽  
Detection System ◽  
Stress Detection ◽  
Magnetic Signal

scholarly journals The EnergyKids Pilot Study: Comparing Energy Balance of Primary School Children during School and Summer Camp

Nutrients ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 92
Author(s):  
Cinzia Franchini ◽  
Alice Rosi ◽  
Cristian Ricci ◽  
Francesca Scazzina
Keyword(s):  
Physical Activity ◽  
Energy Balance ◽  
Weight Status ◽  
Summer Camp ◽  
Negative Energy ◽  
Primary School Children ◽  
Food Diary ◽  
Activity Tracker ◽  
School Time ◽  
Study Participants

Children’s energy requirements may vary during school and summer camp days. To evaluate energy balance during these two periods, seventy-eight children (45% females, 8–10 years) living in Parma, Italy, were enrolled in this observational study. Participants completed a 3-day food diary and wore an activity tracker for three consecutive days during a school- and a summer camp-week to estimate energy intake (EI) and energy expenditure (TEE). Height and body weight were measured at the beginning of each period to define children’s weight status. BMI and EI (school: 1692 ± 265 kcal/day; summer camp: 1738 ± 262 kcal/day) were similar during both periods. Both physical activity and TEE (summer camp: 1948 ± 312; school: 1704 ± 263 kcal/day) were higher during summer camp compared to school time. Therefore, energy balance was more negative during summer camp (−209 ± 366 kcal/day) compared to school time (−12 ± 331 kcal/day). Similar results were observed when males and females were analyzed separately but, comparing the sexes, males had a higher TEE and a more negative energy balance than females, during both periods. The results strongly suggest that an accurate evaluation of children’s energy balance, that considers both diet and physical activity, is needed when planning adequate diets for different situations.

scholarly journals Effects of Activity Tracker-Based Counselling and Live-Web Exercise on Breast Cancer Survivors during Italy COVID-19 Lockdown

2021 ◽  
Vol 6 (2) ◽  
pp. 50
Author(s):  
Andrea Di Blasio ◽  
Teresa Morano ◽  
Federica Lancia ◽  
Gianluca Viscioni ◽  
Angelo Di Iorio ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Light Intensity ◽  
Physical Exercise ◽  
Cancer Survivors ◽  
Breast Cancer Survivors ◽  
Sedentary Time ◽  
Physical Activities ◽  
Activity Tracker ◽  
Personal Feedback

Background: To prevent and fight the increase of daily sedentary time and to promote and stimulate the positive effects of physical activity and exercise on health, both traditional interventions and new strategies are important for breast cancer survivors (BCS). The research goal was to compare the effects of weekly personal feedback, based on objectively measured physical activity, on the trends of both daily sedentary time and on the physical activity of BCS (E− group) with those of an intervention also including online supervised physical exercise sessions (E+ group), during the Italy COVID-19 lockdown. Methods: The Italian COVID-19 emergency allowed the possibility to also observe the effects of social and personal limitations. A total of 51 BCS were studied over an 18-week period and had an objective registration of day-to-day sedentary time, physical activity, and sleep. Both subsamples received weekly or fortnight personal feedback. Data were analysed considering four key periods, according to the COVID-19 emergency steps. Results: Statistical analysis showed an additive effect for sedentary time and a multiplicative effect both for light-to vigorous and light-intensity physical activities. The E− group had a high overall sedentary time and a different trend of light-to vigorous and light-intensity physical activities, with a reduction from the 1st to the 2nd periods (national and personal restrictions), showing a significant rise just at the end of the national restrictions. Conclusions: The use of an activity tracker and its accompanying app, with the reception of weekly tailored advice and supervised online physical exercise sessions, can elicit proper physical activity recomposition in BCS in the COVID-19 era.

scholarly journals Support Vector Machine based Stress Detection System to manage COVID-19 pandemic related stress from ECG signal

2021 ◽  
Vol 20 (1) ◽  
pp. 8-16
Author(s):  
Md Fahim Rizwan ◽  
Rayed Farhad ◽  
Md. Hasan Imam
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Health Condition ◽  
Cardiac Patients ◽  
Gaussian Kernel ◽  
Training Dataset ◽  
Support Vector ◽  
Portable Devices ◽  
Stress Detection ◽  
Specialist Doctor

This study represents a detailed investigation of induced stress detection in humans using Support Vector Machine algorithms. Proper detection of stress can prevent many psychological and physiological problems like the occurrence of major depression disorder (MDD), stress-induced cardiac rhythm abnormalities, or arrhythmia. Stress induced due to COVID -19 pandemic can make the situation worse for the cardiac patients and cause different abnormalities in the normal people due to lockdown condition. Therefore, an ECG based technique is proposed in this paper where the ECG can be recorded for the available handheld/portable devices which are now common to many countries where people can take ECG by their own in their houses and get preliminary information about their cardiac health. From ECG, we can derive RR interval, QT interval, and EDR (ECG derived Respiration) for developing the model for stress detection also. To validate the proposed model, an open-access database named "drivedb” available at Physionet (physionet.org) was used as the training dataset. After verifying several SVM models by changing the ECG length, features, and SVM Kernel type, the results showed an acceptable level of accuracy for Fine Gaussian SVM (i.e. 98.3% for 1 min ECG and 93.6 % for 5 min long ECG) with Gaussian Kernel while using all available features (RR, QT, and EDR). This finding emphasizes the importance of including ventricular polarization and respiratory information in stress detection and the possibility of stress detection from short length data(i.e. form 1 min ECG data), which will be very useful to detect stress through portable ECG devices in locked down condition to analyze mental health condition without visiting the specialist doctor at hospital. This technique also alarms the cardiac patients form being stressed too  much which might cause severe arrhythmogenesis.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

scholarly journals An Activity Tracker–Guided Physical Activity Program for Patients Undergoing Radiotherapy: Protocol for a Prospective Phase III Trial (OnkoFit I and II Trials) (Preprint)

2021 ◽  
Author(s):  
Franziska Hauth ◽  
Barbara Gehler ◽  
Andreas Michael Nieß ◽  
Katharina Fischer ◽  
Andreas Toepell ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Functional Assessment ◽  
Exercise Program ◽  
Daily Step ◽  
Patients With Cancer ◽  
Activity Tracker ◽  
Cancer Related Fatigue ◽  
Daily Step Count

BACKGROUND The positive impact that physical activity has on patients with cancer has been shown in several studies over recent years. However, supervised physical activity programs have several limitations, including costs and availability. Therefore, our study proposes a novel approach for the implementation of a patient-executed, activity tracker–guided exercise program to bridge this gap. OBJECTIVE Our trial aims to investigate the impact that an activity tracker–guided, patient-executed exercise program for patients undergoing radiotherapy has on cancer-related fatigue, health-related quality of life, and preoperative health status. METHODS Patients receiving postoperative radiotherapy for breast cancer (OnkoFit I trial) or neoadjuvant, definitive, or postoperative treatment for other types of solid tumors (OnkoFit II trial) will be randomized (1:1:1) into 3-arm studies. Target accrual is 201 patients in each trial (50 patients per year). After providing informed consent, patients will be randomized into a standard care arm (arm A) or 1 of 2 interventional arms (arms B and C). Patients in arms B and C will wear an activity tracker and record their daily step count in a diary. Patients in arm C will receive personalized weekly targets for their physical activity. No further instructions will be given to patients in arm B. The target daily step goals for patients in arm C will be adjusted weekly and will be increased by 10% of the average daily step count of the past week until they reach a maximum of 6000 steps per day. Patients in arm A will not be provided with an activity tracker. The primary end point of the OnkoFit I trial is cancer-related fatigue at 3 months after the completion of radiotherapy. This will be measured by the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire. For the OnkoFit II trial, the primary end point is the overall quality of life, which will be assessed with the Functional Assessment of Cancer Therapy-General sum score at 6 months after treatment to allow for recovery after possible surgery. In parallel, blood samples from before, during, and after treatment will be collected in order to assess inflammatory markers. RESULTS Recruitment for both trials started on August 1, 2020, and to date, 49 and 12 patients have been included in the OnkoFit I and OnkoFit II trials, respectively. Both trials were approved by the institutional review board prior to their initiation. CONCLUSIONS The OnkoFit trials test an innovative, personalized approach for the implementation of an activity tracker–guided training program for patients with cancer during radiotherapy. The program requires only a limited amount of resources. CLINICALTRIAL ClinicalTrials.gov NCT04506476; https://clinicaltrials.gov/ct2/show/NCT04506476. ClinicalTrials.gov NCT04517019; https://clinicaltrials.gov/ct2/show/NCT04517019. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/28524

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