scholarly journals Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers

10.2196/22532 ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. e22532
Author(s):  
Pia S de Boer ◽  
Alexander J A M van Deursen ◽  
Thomas J L van Rompay
Keyword(s):  
General Population ◽  
Internet Of Things ◽  
Performance Test ◽  
Action Plan ◽  
Health Condition ◽  
Plan Execution ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Internet Skills ◽  
Skill Component

Background The health internet-of-things (IoT) can potentially provide insights into the present health condition, potential pitfalls, and support of a healthier lifestyle. However, to enjoy these benefits, people need skills to use the IoT. These IoT skills are expected to differ across the general population, thereby causing a new digital divide. Objective This study aims to assess whether a sample of the general Dutch population can use health IoT by focusing on data and strategic IoT skills. Furthermore, we determine the role of gender, age, and education, and traditional internet skills. Methods From April 1, 2019, to December 12, 2019, 100 individuals participated in this study. Participants were recruited via digital flyers and door-to-door canvassing. A selective quota sample was divided into equal subsamples of gender, age, and education. Additional inclusion criteria were smartphone possession and no previous experience of using activity trackers. This study was conducted in 3 waves over a period of 2 weeks. In wave 1, a questionnaire was administered to measure the operational, mobile, and information internet skills of the participants, and the participants were introduced to the activity tracker. After 1 week of getting acquainted with the activity tracker, a task-based performance test was conducted in wave 2 to measure the levels of data IoT skills and the strategic IoT skill component—action plan construction. A week after the participants were asked to use the activity tracker more deliberately, a performance test was then conducted in wave 3 to measure the level of the strategic IoT skill component—action plan execution. Results The participants successfully completed 54% (13.5/25) of the data IoT skill tasks. Regarding strategic IoT tasks, the completion rates were 56% (10.1/18) for action plan construction and 43% (3.9/9) for action plan execution. None of the participants were able to complete all the data IoT skill tasks, and none of the participants were able to complete all the strategic IoT skill tasks regarding action plan construction or its execution. Age and education were important determinants of the IoT skill levels of the participants, except for the ability to execute an action plan strategically. Furthermore, the level of information internet skills of the participants contributed to their level of data IoT skills. Conclusions This study found that data and strategic IoT skills of Dutch citizens are underdeveloped with regard to health purposes. In particular, those who could benefit the most from health IoT were those who had the most trouble using it, that is, the older and lower-educated individuals.

scholarly journals Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers (Preprint)

2020 ◽  
Author(s):  
Pia S de Boer ◽  
Alexander J A M van Deursen ◽  
Thomas J L van Rompay
Keyword(s):  
General Population ◽  
Internet Of Things ◽  
Performance Test ◽  
Action Plan ◽  
Health Condition ◽  
Plan Execution ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Internet Skills ◽  
Skill Component

BACKGROUND The health internet-of-things (IoT) can potentially provide insights into the present health condition, potential pitfalls, and support of a healthier lifestyle. However, to enjoy these benefits, people need skills to use the IoT. These <i>IoT skills</i> are expected to differ across the general population, thereby causing a new digital divide. OBJECTIVE This study aims to assess whether a sample of the general Dutch population can use health IoT by focusing on data and strategic IoT skills. Furthermore, we determine the role of gender, age, and education, and <i>traditional</i> internet skills. METHODS From April 1, 2019, to December 12, 2019, 100 individuals participated in this study. Participants were recruited via digital flyers and door-to-door canvassing. A selective quota sample was divided into equal subsamples of gender, age, and education. Additional inclusion criteria were smartphone possession and no previous experience of using activity trackers. This study was conducted in 3 waves over a period of 2 weeks. In wave 1, a questionnaire was administered to measure the operational, mobile, and information internet skills of the participants, and the participants were introduced to the activity tracker. After 1 week of getting acquainted with the activity tracker, a task-based performance test was conducted in wave 2 to measure the levels of data IoT skills and the strategic IoT skill component—<i>action plan construction</i>. A week after the participants were asked to use the activity tracker more deliberately, a performance test was then conducted in wave 3 to measure the level of the strategic IoT skill component—<i>action plan execution</i>. RESULTS The participants successfully completed 54% (13.5/25) of the data IoT skill tasks. Regarding strategic IoT tasks, the completion rates were 56% (10.1/18) for action plan construction and 43% (3.9/9) for action plan execution. None of the participants were able to complete all the data IoT skill tasks, and none of the participants were able to complete all the strategic IoT skill tasks regarding action plan construction or its execution. Age and education were important determinants of the IoT skill levels of the participants, except for the ability to execute an action plan strategically. Furthermore, the level of information internet skills of the participants contributed to their level of data IoT skills. CONCLUSIONS This study found that data and strategic IoT skills of Dutch citizens are underdeveloped with regard to health purposes. In particular, those who could benefit the most from health IoT were those who had the most trouble using it, that is, the older and lower-educated individuals.

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

Internet of things-based cloud computing platform for analyzing the physical health condition

2021 ◽  
pp. 1-15
Author(s):  
Mengyao Cui ◽  
Seung-Soo Baek ◽  
Rubén González Crespo ◽  
R. Premalatha
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Health Monitoring ◽  
Tracking System ◽  
Disease Diagnosis ◽  
Health Condition ◽  
Computing Platform ◽  
Concept Model ◽  
Patient Health ◽  
Cloud Servers

BACKGROUND: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient’s healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health. OBJECTIVE: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients. METHOD: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient’s eye movement. The collected data are used in the cloud database to evaluate the patient’s health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers. RESULTS: The experimental results show that patient health monitoring is a reliable way to improve health effectively.

Impact of activity trackers on secondary prevention in patients with coronary artery disease: a systematic review and meta-analysis (Preprint)

2021 ◽  
Author(s):  
Toshiki Kaihara ◽  
Valent Intan-Goey ◽  
Martijn Scherrenberg ◽  
Maarten Falter ◽  
Ines Frederix ◽  
...  
Keyword(s):  
Secondary Prevention ◽  
Positive Impact ◽  
Meta Analysis ◽  
Peak Oxygen Consumption ◽  
Cochrane Library ◽  
Major Adverse Cardiovascular Events ◽  
Cvd Risk ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Peak Vo2

BACKGROUND Ischemic heart disease (IHD) is related to high rates of morbidity and mortality among cardiovascular diseases (CVD). Activity trackers have been used in cardiac rehabilitation (CR) in the last years. However, their effectiveness to influence outcomes after IHD is debated. OBJECTIVE This review summarizes the latest data of impact of activity trackers on CVD risk and outcomes. METHODS Articles from 1986 to 2020 in English were searched by electronic databases (PubMed, Cochrane Library, Embase). Inclusion criteria were: randomized controlled trials of IHD secondary prevention using an activity tracker which include at least peak oxygen consumption (VO2), major adverse cardiovascular events (MACE), quality of life (QoL), LDL-cholesterol (LDL-C) as outcomes. Meta-analysis and qualitative analysis were performed. RESULTS After removing duplicates, 604 articles were included and the screening identified a total of 11 articles. Compared to control groups, intervention groups with activity trackers significantly increased peak VO2 (mean difference 1.54; 95% CI [0.50–2.57]; P=.004) and decreased MACE (risk ratio 0.51; 95% CI [0.31–0.86]; P=.01). Heterogeneity was low (I2=0%) for MACE and high (I2=51%) for peak VO2. Intervention with an activity tracker also has positive impact on QoL in qualitative analyses. There was no between-group difference in LDL-C. CONCLUSIONS CR using activity trackers has a positive and multi-faceted effect on peak VO2, MACE, and QoL in patients with IHD.

scholarly journals TTEthernet Transmission in Software-Defined Distributed Robot Intelligent Control System

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Caibing Liu ◽  
Fang Li ◽  
Guohao Chen ◽  
Xin Huang
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Performance Test ◽  
New Technologies ◽  
Simulation Software ◽  
Utilization Rate ◽  
Intelligent Control System ◽  
Time Performance ◽  
Industrial Iot ◽  
Smart Technologies

With the integration of new technologies such as smart technologies and cloud computing in the industrial Internet of Things, the complexity of industrial IoT applications is increasing. Real-time performance and determinism are becoming serious challenges for system implementation in these Internet of Things systems, especially in critical security areas. This paper provides a framework for a software-defined bus-based intelligent robot system and designs scheduling algorithms to make TTEthernet play the role of scheduling in the framework. Through the framework, the non-real-time and uncertainties problem of distributed robotic systems can be solved. Moreover, a fragment strategy was proposed to solve the problem of large delay caused by Rate-Constrained traffic. Experimental results indicate that the improved scheme based on fragmentation strategy proposed in this paper can improve the real-time performance of RC traffic to a certain extent. Besides, this paper made a performance test and comparison experiments of the improved scheme in the simulation software to verify the feasibility of the improved scheme. The result showed that the delay of Rate-Constrained traffic was reduced and the utilization rate of network was improved.

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 Design and Analysis of Trash Monitoring System Prototype Based On Internet of Things (IoT) Using MQTT Protocol

2018 ◽  
Vol 10 (3) ◽  
pp. 113
Author(s):  
Achmad Auliyaa Zulfikri ◽  
Doan Perdana ◽  
Gustommy Bisono
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
System Performance ◽  
Performance Test ◽  
Advanced Technology ◽  
Android Application ◽  
Packet Delivery ◽  
End To End Delay ◽  
System Prototype ◽  
Maximum Limit

On this research,Internet of Things (IoT) as an advanced technology is used to monitor the height of trash from a trash can in order to give notification whether the height of trash is already reach the maximum limit or not yet.To support those needs,we used NodeMCU as microcontroller,ultrasonic sensor,MQTT as IoT protocol,and also Android application to show the data.After we did the system performance test,we got the biggest result of end-to-end delay which is 2.06875 seconds when the packet delivery is set to 1000 ms with 3 active nodes and the smallest result which is 0.26055 seconds when the packet delivery is set to 100 ms with 1 active mode.The biggest result of throughput is 597.17 Bytes/s when the packet delivery is set to 100 ms with 1 active mode and the smallest result is 75.86 Bytes/s when the packet delivery is set to 1000 ms with 3 active nodes.The biggest result of availability and reliability is 99.905% when the packet delivery is set to 1000 ms and the smallest result is 99.833% when the packet delivery is set to 100 ms.

Internet of Things Interpreting a Myth

2019 ◽  
pp. 47-81 ◽  
Author(s):  
Hisham Abusaada ◽  
Abeer Elshater
Keyword(s):  
Content Analysis ◽  
Internet Of Things ◽  
Action Plan ◽  
Internal Displacement ◽  
The Internet ◽  
Internally Displaced Persons ◽  
Displaced Persons ◽  
Internally Displaced ◽  
Digital Methods ◽  
The Internet Of Things

The livability standard still has not considered the chaos city that may stem from or lead to cities of hardship. This chapter rectifies this by making the phenomena of chaos and hardship the centerpiece of the analysis. It depends on the internally displaced persons (IDPs) to display the characteristics of liability and the hardship of living and be the indicators of chaos city. This chapter addresses the non-perceptible processes of the IDPs from outside and inside Cairo in Egypt. This internal displacement supposes the lead-in to chaotic changes in the lifestyles of the cities; it can even be said that they become cities of hardship. The theoretical reading depends on conventional and digital methods (content analysis and the internet of things) to follow these changes, which occur not only due to migrations but also due to ignoring decentralization. The outcomes provide an action plan to create cities free from hardship, displacement, and chaos.

scholarly journals Inequalities in the social use of the Internet of things: A capital and skills perspective

2019 ◽  
Vol 21 (6) ◽  
pp. 1344-1361 ◽  
Author(s):  
Alex van der Zeeuw ◽  
Alexander JAM van Deursen ◽  
Giedo Jansen
Keyword(s):  
Internet Of Things ◽  
Social Relations ◽  
The Internet ◽  
Internet Skills ◽  
The Social ◽  
Use Of The Internet ◽  
Social Use ◽  
Smart Things ◽  
Social Uses ◽  
The Internet Of Things

In this article, we set out to explain different types of social uses of the Internet of Things (IoT) using forms of capital and Internet skills. We argue that the IoT platform entices different manners of social communication that are easily overlooked when focusing on the novelty of smart “things.” How people use the IoT socially is crucial in trying to understand how people create, maintain, or absolve social relations in a networked society. We find inversed effects for social capital, income and education on private use, and on sharing IoT data with a partner. Sharing with acquaintances and strangers is predicted by cultural activities. Sharing IoT data with acquaintances can especially be attributed to social relations that escape the immediate household. We conclude that varying figurations of capital and Internet skills predict how the IoT is used socially.

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