Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies

Objectives: Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing. Design: Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time). Data sources: Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970–July 2021). Eligibility criteria for selecting studies: Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals. Results: Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as ‘low risk’ or ‘some concerns’. Summary: Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed

A Model for Using Physiological Conditions for Proactive Tourist Recommendations

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended

Remote biometrical monitoring system via IoT

Os sistemas de Internet of Things (IoT) estão a experienciar um rápido crescimento devido à sua aplicabilidade em vários domínios, desde cidades inteligentes até aos cuidados de saúde. Nestes sistemas, os dispositivos comunicam entre si, ou com a infraestrutura, recorrendo a comunicações machine-to-machine (M2M). Uma vez que muitos destes dispositivos são simples, com escassa capacidade de processamento, foram desenvolvidos protocolos M2M como o Constrained Application Protocol (CoAP) e o Messaging Queue Telemetry Transport (MQTT), bem como frameworks de suporte de comunicações M2M. Apesar dos desenvolvimentos nesta tecnologia, ainda são encontrados desafios no desenvolvimento de aplicações M2M e IoT a nível da interoperabilidade, escalabilidade e padronização, por exemplo. Consequentemente, vários standards M2M foram desenvolvidos para superar estes desafios, sendo o oneM2M um deles. Atualmente, existem vários dispositivos disponíveis com uma interface WiFi embebida, o que significa que quando inseridos num sistema IoT, não necessitam de uma gateway (GW) para o acesso à Internet, uma vez que o WiFi é uma tecnologia omnipresente na sociedade atual. Esta é uma característica fundamental visto que diminui o custo global do sistema. Além disso, estes dipositivos, como o módulo ESP32, oferecem modos de poupança de energia que permitem explorar recursos de gestão de energia definidos pelo standard IEEE 802.11. As instituições de cuidados de saúde procuram oferecer os melhores serviços em termos de confiabilidade, segurança e conforto aos seus pacientes. Recentemente, tecnologias IoT foram abordadas, desenvolvidas e utilizadas para melhorar o serviço aos pacientes. O trabalho proposto nesta dissertação é um sistema de monitorização contínua via IoT capaz de monitorizar os sinais vitais de um paciente e apresentá-los aos profissionais de saúde. Para além disso, o sistema pode ser utilizado em diversos cenários desde salas de emergência, uso doméstico até à competição desportiva. O sistema possui dois componentes principais: um dispositivo wearable com uma antena WiFi e um sistema de monitorização orientado ao profissional de saúde. O wearable é composto por um sensor fotopletismográfico (PPG) MAX30100/MAX30102 para medir o ritmo cardíaco e os níveis de saturação de oxigénio no sangue, um ESP32 com uma antena WiFi incorporada para processar e enviar os dados do sensor para o sistema de monitorização e, finalmente, uma bateria de Lítio Polímero (LiPo) para fornecer energia aos dois componentes mencionados. No que refere ao sistema de monitorização, este é composto por uma base de dados orientada a eventos temporais para armazenar todos os dados necessários, um software de visualização gráfica para a visualização dos sinais vitais do paciente e, por fim, uma Interface Gráfica com o objetivo de ser um painel de controlo para todo o sistema. Para além disso, o sistema segue a norma oneM2M devido a questões de interoperabilidade relativas à arquitetura, e implementa o modelo de comunicação publisher-subscriber pois este é eficiente em termos de sensorização e monitorização remota. Por último, o objetivo desta dissertação é desenvolver um sistema de monitorização de baixo custo focado na gestão energética e que ao mesmo tempo não comprometa a sua confiabilidade e robustez.Internet of Things (IoT) systems are experiencing rapid growth due to their applicability in several domains, from smart cities to healthcare among many. In these systems, devices communicate with each other, or with infrastructure, resorting to machine-to-machine (M2M) communications. Since many of these devices are simple systems, with weak processing capacity, lightweight M2M protocols were developed such as Constrained Application Protocol (CoAP) and Messaging Queue Telemetry Transport (MQTT) as well as frameworks to support M2M communications. As expected, there are challenges when developing M2M and IoT applications: interoperability, scalability, standardisation, among others. Therefore, several M2M standards were created to overcome these issues, with oneM2M being one of them. Nowadays, there are multiple devices available that have an embedded WiFi interface, thus, when inserted in an IoT system, these devices do not need a gateway (GW) to access the Internet since WiFi is one of the most common technologies at Internet boundary. This is a key feature because it increases the system's pervasiveness as well as the overall cost of the system. Additionally, these devices, such as the ESP32 module, offer sleep modes that allow exploiting the power management features by the IEEE 802.11 standard. Healthcare institutions always strive to provide the best services concerning the reliability, safety and comfort of the patients. To do so, IoT technologies have been embraced and developed in recent years to improve these services. The work proposed in this dissertation is an end-to-end continuous monitoring system via IoT capable of monitoring a patient's vital signs and displaying them to the medical personnel. Moreover, the system can be applied to a wide range of application scenarios from emergency wards and home environment to sports training and competition. The system has two major components, a low-cost and low-power WiFi-enabled wearable device for the user and, at the upper end, a monitoring interface for the medical personnel. The wearable is composed by a MAX30100/MAX30102 PhotoPletysmoGraphy (PPG) sensor to measure the heart rate and oxygen saturation levels, an ESP32 with a built-in WiFi antenna to process and send the sensor data to the monitoring system and, finally, a Lithium Polymer (LiPo) battery to power-up the previous two components. At the upper end, the monitoring interface is composed of a time-series database to store all the data, a graphics visualisation software of patient's vital signs and a Graphic User Interface (GUI) serving as a control panel. Additionally, the system relies on the oneM2M standard for the interoperability concerning the architecture and follows a publish-subscribe communication model due to its efficiency in sensing and remote monitoring. Furthermore, the goal of this dissertation is to develop a low-cost and energy-efficient monitoring system while not compromising the reliability and robustness of traditional machines and systems

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies.

Objectives Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing. Design Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time). Data sources Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970-July 2021). Eligibility criteria for selecting studies Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals. Results Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as 'low risk' or 'some concerns'. Summary Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed. PROSPERO registration number CRD42021230894

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT