Dedicated Smartphone Application Research Articles

Why do sustainable shared mobility practices not proliferate more widely? Insights from digital mobility diaries

Sharing instead of owning consumer goods should significantly reduce their production and the associated environmental damages. However, this ecological promise has yet to be fulfilled, even in mobility, where sharing practices have become mainstream. This is because the most widespread mobility-sharing practice (free-floating carsharing) has the lowest ecological benefit. This study asks why the adoption of more ecologically beneficial shared mobility practices remains lower, addressing this question through digital ethnography. Using digital mobility diaries for three weeks, 21 respondents documented their mobility practices (including various shared mobility practices and private car travel) by uploading pictures, videos and text notes on a dedicated smartphone application. The data show that free-floating carsharing has proliferated because it can leverage the ‘system of automobility’ as its users are relatively monomodal. Other shared mobility services function well; however, we found that they have not proliferated as widely because the overall multimodal mobility mix (e.g. walking, cycling, public transport, on-demand sharing) associated with this form of shared mobility is highly demanding. Better conditions for multimodal mobility, created through appropriate infrastructure, are crucial for sustainable shared mobility practices to spread. However, space limitations mean this will likely come at the expense of car-centred infrastructure and necessitate its exnovation.

Smartphone use and personality: Their effects on sleep quality across groups using mediation analysis.

The rapid rise in smartphone use has led to declining sleep quality. Excessive internet use has been linked to negative impacts on physical and mental health, and individual personality traits (PT) may contribute to internet addiction and mitigate its harmful effects. This study aims to: (1) examine whether PT mediate the relationship between smartphone use and sleep quality, and (2) investigate whether the relationship between smartphone use and sleep quality varies across different gender and age groups. There were 269 participants in the dataset. The daily averages for sleep duration, sleep distraction, and smartphone use were extracted from the usage data acquired through a dedicated smartphone application. Structural equation modeling was employed to investigate the mediating role of personality in the relationship of smartphone use and sleep quality. Results indicated that PT partially mediated the relationship, with a significant negative indirect impact of smartphone use on sleep quality. Age differences were observed, showing distinct patterns between younger and older participants, while no significant gender differences emerged. This study found that excessive smartphone use, coupled with low personality indicators, leads to poor sleep quality. Positive personality traits improve sleep outcomes, with age influencing the impact of smartphone use on sleep. Our findings support and contribute to existing concerns about technology overuse and highlight the need for targeted interventions to promote more beneficial technology design and usage patterns.

Ecological Momentary Assessment of Awake Bruxism Frequency in Patients with Different Temporomandibular Disorders.

Self-reported awake bruxism (AB) has been associated with temporomandibular disorders (TMD). However, the daily amount of AB behavior has not been quantified in pain patients. Therefore, this study aimed to assess AB frequency in patients with myofascial pain and temporomandibular joint (TMJ) pain and compare it to a group of pain-free individuals. Eighty-four individuals belonging to either a TMD group (n = 54) or a healthy control group (n = 30) were selected. AB frequency was obtained by ecological momentary assessment with a dedicated smartphone application that sent sound alerts at random intervals during the day for one week. Upon receiving the alert, the volunteer reported the current muscular condition and/or the teeth position, i.e., relaxed jaw muscle, jaw bracing, teeth contact, teeth clenching, or teeth grinding. Data were evaluated by independent t-test (α = 0.05). During the seven days, AB frequency was 62.1% ± 26.8% for TMD patients and 36.2% ± 27.3% for pain-free subjects (p < 0.001). Mandible bracing was most common in the TMD group (p < 0.001), while teeth contact, clenching, and grinding did not differ between groups. Moreover, no differences were found in AB frequency between myofascial pain and TMJ pain patients. Therefore, TMD patients have higher AB frequency characterized by jaw bracing, irrespective of pain location.

Who uses mHealth apps? Identifying user archetypes of mHealth apps.

This study aims to explore the user archetypes of health apps based on average usage and psychometrics. The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. Two clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional, happy neurotic occasional, and unhappy neurotic frequent users. The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations' needs may help to better invest in effective health technology.

Approximation of Pavement Key Performance Indicators from Crowdsourced Data

The feasibility of Pavement KPI approximation from crowdsourced data is examined. A dedicated smartphone application for the collection of positioning and accelerometer data as well as regular manual driver inputs is used. Smartphone application data is correlated with KPIs determined using pavement defect and International Roughness Index data collected with a Laser Crack Measuring System. Statistical and machine learning models are developed for the approximation of KPIs from smartphone data, and then the resulting models are used to predict pavement KPIs beyond the training dataset. The performance of alternative models is examined and the cost savings of such an approach in the deployment and running of a Pavement Asset Management System.

Quantitative Determination of Ethylene Using a Smartphone-Based Optical Fiber Sensor (SOFS) Coupled with Pyrene-Tagged Grubbs Catalyst.

For rapid and portable detection of ethylene in commercial fruit ripening storage rooms, we designed a smartphone-based optical fiber sensor (SOFS), which is composed of a 15 mW 365 nm laser for fluorescence signal excitation and a bifurcated fiber system for signal flow direction from probe to smartphone. Paired with a pyrene-tagged Grubbs catalyst (PYG) probe, our SOFS showed a wide linearity range up to 350 ppm with a detection limit of 0.6 ppm. The common gases in the warehouse had no significant interference with the results. The device is portable (18 cm × 8 cm × 6 cm) with an inbuilt power supply and replaceable optical fiber sensor tip. The images are processed with a dedicated smartphone application for RGB analysis and ethylene concentration. The device was applied in detection of ethylene generated from apples, avocados, and bananas. The linear correlation data showed agreement with data generated from a fluorometer. The SOFS provides a rapid, compact, cost-effective solution for determination of the fruit ethylene concentration dynamic during ripening for better fruit harvest timing and postharvest management to minimize wastage.

Developing a smart multifunctional outdoor jacket with wearable sensing technology for user health and safety

Over the decades, there has been a sustained effort to use fashion as a medium for delivering digital functionality. The goal is to integrate information technology (IT) into clothing to provide users with functions to assist them in their tasks. Regarding the direction of previous efforts, this study developed a multifunctional smart outdoor jacket prototype that senses, recognizes, responds, and manages various safety risks and potentially hazardous situations and identifies environmental factors that are difficult to predict. The prototype’s research and development (R & D) was carried out through the following steps. First, to determine functions that can practically assist users in outdoor environments and help ensure their health and safety, a user requirement survey subject to expert evaluation was conducted. Six functions were selected: (1) Bluetooth hands-free calling and audio streaming, (2) heart rate monitoring for self-health care, (3) emergency calls to request assistance, (4) temperature-reactive heating to retain body heat for survival, (5) fall detection and automatic emergency calls, and (6) ultraviolet monitoring for self-health care. Next, a wearable system and its garment platform were developed, containing detachable device modules for washability and ease of maintenance. Lastly, a dedicated smartphone application was developed for extended functionality. By exploring the use of clothing in diversifying wearable health care and HAR systems, the study could be used to diversify wearable healthcare and safety platforms.

Smartphone-based evaluation of awake bruxism behaviours in a sample of healthy young adults: findings from two University centres.

A smartphone‐based ecological momentary assessment (EMA) strategy was used to assess the frequency of awake bruxism behaviours, based on the report of five oral conditions (ie relaxed jaw muscles, teeth contact, mandible bracing, teeth clenching and teeth grinding). One hundred and fifty‐three (N = 153) healthy young adults (mean ± SD age = 22.9 ± 3.2 years), recruited in two different Italian Universities, used a dedicated smartphone application that sent 20 alerts/day at random times for seven days. Upon alert receipt, the subjects had to report in real‐time one of the above five possible oral conditions. Individual data were used to calculate an average frequency of the study population for each day. For each condition, a coefficient of variation (CV) of frequency data was calculated as the ratio between SD and mean values over the seven recording days. Average frequency of the different behaviours over the seven days was as follows: relaxed jaw muscle, 76.4%; teeth contact, 13.6%; mandible bracing, 7.0%; teeth clenching, 2.5%; and teeth grinding, 0.5%. No significant differences were found in frequency data between the two University samples. The relaxed jaw muscles condition was more frequent in males (80.7 ± 17.7) than in females (73.4 ± 22.2). The frequency of relaxed jaw muscles condition over the period of observation had a very low coefficient of variation (0.27), while for the different awake bruxism behaviours, CV was in a range between 1.5 (teeth contact) and 4.3 (teeth grinding). Teeth contact was the most prevalent behaviour (57.5–69.7). Findings from this investigation suggest that the average frequency of AB behaviours over one week, investigated using EMA‐approach, is around 23.6%.

A portable, low-cost, smartphone assisted methodology for on-site measurement of NO2 levels in ambient air by selective chemical reactivity and digital image analysis

In this work, we propose a simple, sensitive, selective and inexpensive colourimetric method based on compact and portable instrumentation to monitor nitrogen dioxide (NO2) levels in ambient air, applicable to on-site measurements when needed. The NO2 concentration was determined by monitoring the Griess-Saltzman colourimetric reaction on filter paper pads exposed to ambient air, where NO2 was collected by active sampling (portable pump) on pads coated with triethanolamine (TEA). Digital images of the pads were captured by a smartphone camera inside a box under controlled lighting conditions. The images were analysed without any extraction procedures, using a dedicated smartphone application developed by our research group. The linear working range of the proposed method was from 19.1–191.0 μg m−3 with a correlation coefficient of 0.996. A limit detection of 4.85 μg m−3 was achieved using a sampling time of 60 min and a sampling flow rate of 400 mL min-1. The reproducibility of the proposed methodology was also studied, achieving a relative standard deviation value of 9 % (n = 5). The colourimetric method was tested in ambient air for the on-site determination of hourly NO2 concentrations in an urban area of Badajoz (Spain). The field measurements were validated by orthogonal regression against the reference method, with significant correlation at the 95 % confidence level, and a slope and intercept statistically equal from expected values. An expanded relative uncertainty of 15 % was estimated according to European guidelines for the demonstration of equivalence of ambient air monitoring methods.

Extending Legacy Industrial Machines by a Low-Cost Easy-to-Use IoT Module for Data Acquisition

Industry 4.0 is a paradigm that enhances industrial automation systems with the recent advances in the domain of the Internet of Things (IoT), gaining new possibilities and providing new services. Traditional industrial machines do not have IoT capabilities, and in order to integrate such a machine into Industry 4.0, there is a need for an intermediary device or system that communicates with the machine through its supported communication interfaces and protocols and forwards the communication to the global network. This paper presents the development and experimental validation of a low-cost hardware module that can easily integrate the machine’s existing control unit into the IoT and enable synchronization of the measurements and states of the variables of the machine and its environment with a cloud server. The developed module is universal, can connect to any control unit that is able to communicate through basic RS232 serial communication, and does not require the control unit to have any higher level communication protocol implemented. On the other end, the presented solution uses a dedicated smartphone application to provide remote monitoring and control of the machine through the cloud by using the synchronized variable states, as well as further possibilities for storing, processing, and analyzing the historical data from the system. The developed solution was experimentally validated on an experimental setup consisting of a conveyor belt driven by a three-phase asynchronous electromotor controlled by a programmable logic controller through a variable-frequency drive.

Epidemiological characteristics and therapeutic management of patients with chronic heart failure who use smartphones: Potential impact of a dedicated smartphone application (report from the OFICSel study)

The effectiveness of transitional care services for patients discharged from hospital after acute heart failure is challenging, especially in terms of reducing subsequent heart failure hospitalizations. The increased adoption of smartphone applications in society offers a new opportunity to interact with patients to avoid rehospitalization. Thus, electronic health (e-health) can enhance the impact of existing therapeutic education programmes. To determine the prevalence of smartphone use among patients with chronic heart failure, and to assess the epidemiological characteristics and therapeutic management of these patients, with a broader aim of developing smartphone-based therapeutic education programmes for patients. The French Observatoire français de l'insuffisance cardiaque et du sel (OFICSel) registry was conducted in 2017 by 300 cardiologists, and included both inpatients and outpatients who had been hospitalized for heart failure at least once in the previous 5 years. Data collection included demographic and heart failure-related variables, which were provided by the cardiologist and by the patient via a questionnaire. Among the 2822 patients included, 2517 completed the questionnaire. Of this total, 907 patients (36%) were smartphone users. Compared with non-users, smartphone users were younger, were more frequently men, more frequently lived in cities, had a higher educational level and were more frequently professionally active. Smartphone users less frequently had diabetes, hypertension, atrial fibrillation or ischaemic cardiopathy. Only 22% of patients were actively participating in a therapeutic education programme. Smartphones were used by more than one-third of patients with heart failure in France in 2017, underscoring the feasibility of developing a smartphone application to deliver therapeutic education to the population with chronic heart failure.

Mobile Application for Communication Increases the Efficiency of Organ Procurement and Transplantation.

Background:Donor organ recovery is a complex process involving organ procurement organizations and multiple surgical teams from various transplant centers. Nearly 30% of discarded organs are wasted due to reasons related to improper coordination and communication.Problem Statement:Lack of real-time communication results in many hours of preventable delay between procurement and transplant teams resulting in the high volume of organ waste, clinical frustration, and critical delays.Methods:A Plan-Do-Study-Act performance improvement methodology was utilized to design and implement a dedicated mobile communication application (app). Critical time points in the organ offer, procurement, and transplant processes were analyzed from the Report of Organ Offers, and relation coordination metrics were measured.Processes Addressed:Members of procurement and transplant teams in Iowa were interviewed and a dedicated smartphone application was implemented to replace phone calls, e-mails, faxes, and text messages during upcoming kidney offers from July 31, 2017 to July 31, 2018.Outcomes:Teams reported a substantial increase in clinical productivity and case progress awareness, including a noteworthy reduction in phone calls. The relational coordination data indicated substantially higher relationship and communication quality with the app. The Report of Organ Offer data revealed a 35% increase in organs transplanted and a 50% reduction in time from initial organ offer to transplant with the use of the mobile application.Implications for Practice:The use of a dedicated communication application reduces clinical frustration and delays during the coordination of organ offer, procurement, and transplant. Technologies that improve communication have the potential to improve organ utilization.

Portable low-cost open-source wireless spectrophotometer for fast and reliable measurements.

We demonstrate a low-cost standalone portable spectrophotometer for fast and reliable measurement execution. The data acquired can be both displayed via a dedicated smartphone application or a computer interface, allowing users either to gather and view data on the move or set up a continuous experiment. All design and software files are open-source and are intended for the device to be easily replicable and further customizable to suit specific applications. The assembled device can measure absorption in the wavelength range from 450nm to 750nm with a resolution of 15nm and is housed in a 90×85×58mm casing. Validation of the device was carried out by assessing wavelength accuracy, dynamic range and the signal-to-noise ratio of the system, followed by testing in three different applications where limit of quantification, limit of detection and relative standard deviations were determined. The results indicated better performance than low-cost spectrophotometers, on average being comparable to moderate to high-cost spectrophotometers.

Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

Waiting times are important indicators of the degree of travel time optimisation and other behavioural traits among public transport (PT) passengers. As previous studies have shown, the level and usage of pre-trip information regarding schedule or real-time departures are important factors that influence the potential to realise travel time savings by enabling PT passengers to optimise waiting times. Most empirical evidence regarding the revealed PT travel behaviour concerning information levels is based on manual interviews or traditional travel surveys, in which there is a risk that the actual context of where and when the choice of departure time was made is not taken into account. This paper reports the results of a travel survey based on a dedicated smartphone application applied in a field study in a Swedish mid-size urban and regional context. Context-aware notification prompting was used to allow respondents to state their use of pre-trip information as well as whether they had pre-planned their trip and how contingent planning aids were used for time optimisation. The implications on passenger waiting times of the use of information regarding departure times by passengers were emphasised during analyses of the resulting data, along with personal characteristics, in which auxiliary sources such as timetable data and Automatic Vehicle Location were utilised to determine ground truth trip trajectories and trip-contextual factors. The results indicate the significance of having access to pre-trip information, especially for long trips above one hour’s duration, in order to pre-plan and thereby optimise waiting times. In addition, the use and source of pre-trip information differ among age and gender groups. Trip purpose and time of day to some extent determine waiting times and choice of trip optimisation strategy (arrival or departure time).

Smartphone-based application for EMA assessment of awake bruxism: compliance evaluation in a sample of healthy young adults.

A smartphone-based ecological momentary assessment (EMA) strategy to collect real time data on awake bruxism (AB) has been recently introduced. The aim of this study was to assess the compliance with its use over 1 week in a sample of healthy young adults. Sixty (N = 60) healthy young adults (mean age 24.2 ± 4.1 years) used a dedicated smartphone application that sent 20 alerts at random times throughout the day. Upon alert receipt, the subjects had to report in real time their condition among five possible options: relaxed jaw muscles, teeth contact, teeth clenching, teeth grinding, and mandible bracing. Compliance rate with the app was assessed at the individual and group level in terms of percentage of answered alerts as well as number of days that were needed to reach the targeted observation period of 7 days with a compliance of at least 60%. The mean compliance recorded with the smartphone application was 67.8% of the total alerts. On average, 9.8 ± 3.2 days (range 7-19) have been necessary to achieve the targeted goal of 7 days with a minimum of 60% alerts/day. No gender differences were detected in any compliance data. Response rate was not different during weekdays or weekends. This investigation is the first attempt to assess individual compliance with EMA for reporting awake bruxism. Results suggest that a smartphone-based strategy can have interesting potential. The compliance rate reported in this study will serve as a comparison standpoint for future investigations. Based on the recent multidisciplinary focus on the study of awake bruxism, EMA has emerged as a potential approach for use in the clinical and research settings. This investigation suggests that compliance with such strategy is good, thus making it worthy of adoption for the assessment of AB and its clinical implications.

Ecological Momentary Assessment and Intervention Principles for the Study of Awake Bruxism Behaviors, Part 1: General Principles and Preliminary Data on Healthy Young Italian Adults.

Background: Awake bruxism (AB) is an oral condition that has some uncertainties concerning the epidemiology, also due to the different diagnostic strategies that have been adopted to address it in the research setting. The recent new definition of AB suggests that an ecological momentary assessment (EMA), which enables real-time reporting of the condition under study, can implement knowledge on the topic.Objectives: This article will discuss the general principles of EMA and EMI (Ecological Momentary Intervention) and comment on a preliminary dataset gathered with a smartphone application in a population of Italian young adults.Materials and Methods: A dedicated smartphone application has been used (BruxApp®) on a sample of 30 University students (mean age 24 ± 3.5 years) to record real time report on five specific oral conditions (relaxed jaw muscles, tooth contact, teeth clenching, teeth grinding, mandible bracing) that are related with the spectrum of AB activities. Data were recorded over a 7-day period for two times, with a 1-month interval between the two observation periods. The purpose of collecting data over a second week, 1-month later, was to monitor AB behaviors over time, and test for potential “EMI” effects.Results: Over the first 7 days (T1), the average frequency of relaxed jaw muscles reports at the population level was 62%. Teeth contact (20%) and mandible bracing (14%) were the most frequent AB behaviors. No significant gender differences were detected. One month later, during the second week of data collection (T2), the frequency of the conditions was as follows: relaxed jaw muscles 74%, teeth contact 11% and mandible bracing 13%.Conclusions: These data recorded do not allow any generalization due the unrepresentativeness of the study population. On the other hand, they can be used as templates for future comparisons to get deeper into the study of natural fluctuations of AB behaviors as well as into the potential biofeedback effect of an ecological momentary assessment/intervention. It is important to recognize that the use of smartphone technology may help to set range of values for AB frequency in otherwise healthy individuals, in order to stand as comparisons for selected populations with risk or associated factors.

Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild

Parkinson’s Disease (PD) is a neurodegenerative disorder with early non-motor/motor symptoms that may evade clinical detection for years after the disease onset due to their mildness and slow progression. Digital health tools that process densely sampled data streams from the daily human-mobile interaction can objectify the monitoring of behavioral patterns that change due to the appearance of early PD-related signs. In this context, touchscreens can capture micro-movements of fingers during natural typing; an unsupervised activity of high frequency that can reveal insights for users’ fine-motor handling and identify motor impairment. Subjects’ typing dynamics related to their fine-motor skills decline, unobtrusively captured from a mobile touchscreen, were recently explored in-the-clinic assessment to classify early PD patients and healthy controls. In this study, estimation of individual fine motor impairment severity scores is employed to interpret the footprint of specific underlying symptoms (such as brady-/hypokinesia (B/H-K) and rigidity (R)) to keystroke dynamics that cause group-wise variations. Regression models are employed for each fine-motor symptom, by exploiting features from keystroke dynamics sequences from in-the-clinic data captured from 18 early PD patients and 15 controls. Results show that R and B/H-K UPDRS Part III single items scores can be predicted with an accuracy of 78% and 70% respectively. The generalization power of these trained regressors derived from in-the-clinic data was further tested in a PD screening problem using data harvested in-the-wild for a longitudinal period of time (mean±std : 7±14 weeks) via a dedicated smartphone application for unobtrusive sensing of their routine smartphone typing. From a pool of 210 active users, data from 13 self-reported PD patients and 35 controls were selected based on demographics matching with the ones in-the-clinic setting. The results have shown that the estimated index achieve {0.84 (R),0.80 (B/H −K)} ROC AUC, respectively, with {sensitivity/speci ficity : 0.77/0.8 (R),0.92/0.63 (B/H −K)}, on classifying PD and controls in-the-wild setting. Apparently, the proposed approach constitutes a step forward to unobtrusive remote screening and detection of specific early PD signs from mobile-based human-computer interaction, introduces an interpretable methodology for the medical community and contributes to the continuous improvement of deployed tools and technologies in-the-wild.

Complementing or Substituting? News in an Era of Multiple Platforms and Second Screens

ABSTRACTThe article analyzes contemporary patterns of news watching, concentrating on the experience of watching news through multiple platforms and second screens (i.e., not only via TV sets but across additional new media channels), and while performing additional communication activities simultaneously (i.e., media multitasking). The data is based on the documentation of viewings through a dedicated smartphone application by 110 subjects for 4 days. Only 10 of them can be labeled “traditional” viewers who did not report even once on viewing news video content not on television or performing other media activities parallel to viewing. The findings demonstrate that contemporary news consumers are not loyal to television as a medium of exposure to news video content, and have diverse repertoires of news viewing, where new media platforms play a significant role.

Physical activity is increased by a 12-week semiautomated telecoaching programme in patients with COPD: a multicentre randomised controlled trial.

RationaleReduced physical activity (PA) in patients with COPD is associated with a poor prognosis. Increasing PA is a key therapeutic target, but thus far few strategies have been found effective in this patient group.ObjectivesTo investigate the effectiveness of a 12-week semiautomated telecoaching intervention on PA in patients with COPD in a multicentre European randomised controlled trial.Methods343 patients from six centres, encompassing a wide spectrum of disease severity, were randomly allocated to either a usual care group (UCG) or a telecoaching intervention group (IG) between June and December 2014. This 12-week intervention included an exercise booklet and a step counter providing feedback both directly and via a dedicated smartphone application. The latter provided an individualised daily activity goal (steps) revised weekly and text messages as well as allowing occasional telephone contacts with investigators. PA was measured using accelerometry during 1 week preceding randomisation and during week 12. Secondary outcomes included exercise capacity and health status. Analyses were based on modified intention to treat.Main resultsBoth groups were comparable at baseline in terms of factors influencing PA. At 12 weeks, the intervention yielded a between-group difference of mean, 95% CI (lower limit – upper limit; ll-ul) +1469, 95% CI (971 to 1965) steps/day and +10.4, 95% CI (6.1 to 14.7) min/day moderate PA; favouring the IG (all p≤0.001). The change in 6-min walk distance was significantly different (13.4, 95% CI (3.40 to 23.5) m, p<0.01), favouring the IG. In IG patients, an improvement could be observed in the functional state domain of the clinical COPD questionnaire (p=0.03) compared with UCG. Other health status outcomes did not differ.ConclusionsThe amount and intensity of PA can be significantly increased in patients with COPD using a 12-week semiautomated telecoaching intervention including a step counter and an application installed on a smartphone.Trial registration number:NCT02158065.

Smart City Mobility Application--Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data.

Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process).