Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

Health behavior models in the age of mobile interventions: are our theories up to the task?

The Oxford handbook of economic geography.

The release of smart phones equipped with a rich set of sensors has enabled human activity recognition on mobile platforms. Monitoring the daily activities and their levels helps in recognizing the health and wellness of the users as a practical application. Mobile phone’s ubiquity, unobtrusiveness, ease of use, communication channels, and playfulness make mobile phones a suitable platform also for inducing behavior change for a healthier and more active lifestyle. In this paper, we provide a review on the activity recognition systems that use integrated sensors in the mobile phone with a special focus on the systems that target personal health and well-being applications. Initially, we provide background information about the activity recognition process, such as the sensors used, activities targeted, and the steps of activity recognition using machine learning algorithms, before listing the challenges of activity recognition on mobile phones. Next, we focus on the classification of existing work on the topic together with a detailed taxonomy. Finally, we investigate the directions for future research.

A Review and Taxonomy of Activity Recognition on Mobile Phones

Over the past century, in most countries, there has been a continual increase in life expectancy primarily due to improvements in public health, nutrition, personal hygiene and medicine. However, these improvements are now coupled with aging population demographics and falling birth rates, which, when combined, are expected to significantly burden the socioeconomic well-being of many of these countries. In fact, never before in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions for the well-being, healthcare and social needs of the elderly. One efficient and cost-effective solution to the problem of elderly/patient care is remote healthcare monitoring so they can continue to live at home rather than in nursing homes or hospitals that are very expensive and with limited spaces. These remote monitoring systems will allow medical personnel to keep track of important physiological signs with reduced human resources, at less cost and in real time. This paper introduces several low-cost, noninvasive, user-friendly sensing and actuating systems using information and communication technologies. Such systems can be used to create engineering solutions to some of the pressing healthcare problems in our society, especially as it pertains to the elderly. One example is the integration of sensors, wireless communications, low-power electronics and intelligent computing to determine health-related information using signals from walking patterns. Such a sensing system will be suitable for prolonged use in a home environment. It will be wearable, noninvasive and non-intrusive, similar to smart socks, smart wrist-bands or smart belts. Other examples such as a smart joint monitor and a smart sleeping environment will be discussed, and future perspectives and research challenges in smart home technologies will be described.

Information and communications technologies for elderly ubiquitous healthcare in a smart home

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

https://www.mdpi.com/1424-8220/19/14/3213/pdf

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

Over the last decade, there has been substantial research interest in the application of accelerometry data for many forms of automated gait and activity analysis algorithms. This paper introduces a summary of new “of-the-shelf” mobile phone handset platforms containing embedded accelerometers which support the development of custom software to implement real time analysis of the accelerometer data. An overview of the main software programming environments which support the development of such software, including Java ME based JSR 256 API, C++ based Motion Sensor API and the Python based “aXYZ” module, is provided. Finally, a sample application is introduced and its performance evaluated in order to illustrate how a standard mobile phone can be used to detect gait activity using such a non-intrusive and easily accepted sensing platform.

Off-the-shelf mobile handset environments for deploying accelerometer based gait and activity analysis algorithms

Numerous laboratory-based studies have been reported on the use of accelerometry for gait and activity analysis particularly among cohorts of elderly subjects. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilizing accelerometers embedded in off-the-shelf cellular handsets that allow medical professionals and caregivers to remotely monitor the activity characteristics of elderly patients in the home or in the community. The use of ubiquitous cellular handsets makes the system far more acceptable to patients and enables the use of the system to be extended beyond healthcare facilities into the home environment. Mobile handset power consumption issues and other relevant handset and mobile handset application characteristics have been investigated in the context of the deployment of the proposed system.

Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets

The analysis of Call Detail Record (CDR) data sets generated by mobile telephony networks has generated much interest in recent years, particularly as an easily accessed source of large volumes of data capable of reflecting the dynamic behavior of humans across a range of temporal and spatial scales. This paper presents a study focused on examining human social and economic behavioral patterns in Uganda through the analysis of a CDR data set generated in a Ugandan mobile telephone network in 2010. By examining the response of subscribers to a service incentivizing higher mobile phone call rates through the offering of discounts, economically motivated differences in subscriber behavior in poorer versus wealthier regions of the country are identified. The paper also presents an analysis which suggests a high degree of social insularity within the regions of Uganda which is most likely related to regionally economic development levels in addition to the high levels of ethnic homogeneity within those...

Comparing Rural and Urban Social and Economic Behavior in Uganda: Insights from Mobile Voice Service Usage

Numerous laboratory based studies have been reported on the use of accelerometry for gait and activity analysis. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilising accelerometers embedded in off-the-shelf cellular handsets which allow medical professionals and caregivers to remotely monitor the activity characteristics of elderly patients in the home or in the community. The use of ubiquitous cellular handsets makes the system far more acceptable to patients and enables use of the system to be extended beyond primary health-care facilities into the home environment. Mobile handset power consumption issues and other relevant handset characteristics for a variety of different COTS handsets are investigated in the context of deploying this system.

Monitoring of activity levels of the elderly in home and community environments using off the shelf cellular handsets

Online Social Networks OSNs provide a venue for virtual interactions and relationships between individuals. In some communities, OSNs also facilitate arranging offline meetings and relationships. FetLife, the world's largest anonymous social network for the BDSM, fetish and kink communities, provides a unique example of an OSN that serves as an interaction space, community organizing tool, and sexual market. In this paper, we present a first look at the characteristics of European members of Fetlife, comprising 504,416 individual nodes with 1,912,196 connections. We looked at user characteristics in terms of gender, sexual orientation, and preferred role. We further examined the homophilic communities and find that women in particular are far more platonically involved on the site than straight males. Our results suggest there are important differences between the FetLife community and conventional OSNs.

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Han Wang

Papers

Off-the-shelf mobile handset environments for deploying accelerometer based gait and activity analysis algorithms

Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets

Comparing Rural and Urban Social and Economic Behavior in Uganda: Insights from Mobile Voice Service Usage

Monitoring of activity levels of the elderly in home and community environments using off the shelf cellular handsets

An Exploration of Fetish Social Networks and Communities