Wearable Technology and Mobile Applications for...

Mobile Information Systems

Wearable Technology and Mobile Applications for Healthcare

Lead Guest Editor: Iván García-MagariñoGuest Editors: Dilip Sarkar, Zahia Guessoum, and Raquel Lacuesta

Wearable Technology and Mobile Applicationsfor Healthcare

Mobile Information Systems

Wearable Technology and Mobile Applicationsfor Healthcare

Lead Guest Editor: Iván García-MagariñoGuest Editors: Dilip Sarkar, Zahia Guessoum, and Raquel Lacuesta

Copyright © 2019 Hindawi. All rights reserved.

This is a special issue published in “Mobile Information Systems.” All articles are open access articles distributed under the Creative Com-mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Editorial Board

Mari C. Aguayo Torres, SpainRamon Aguero, SpainMarkos Anastassopoulos, UKMarco Anisetti, ItalyClaudio Agostino Ardagna, ItalyJose M. Barcelo-Ordinas, SpainAlessandro Bazzi, ItalyLuca Bedogni, ItalyPaolo Bellavista, ItalyNicola Bicocchi, ItalyPeter Brida, SlovakiaCarlos T. Calafate, SpainMaría Calderon, SpainJuan C. Cano, SpainSalvatore Carta, ItalyYuh-Shyan Chen, TaiwanWenchi Cheng, ChinaMassimo Condoluci, SwedenAntonio de la Oliva, SpainAlmudena Díaz Zayas, Spain

Filippo Gandino, ItalyJorge Garcia Duque, SpainL. J. García Villalba, SpainMichele Garetto, ItalyRomeo Giuliano, ItalyProsanta Gope, UKJavier Gozalvez, SpainFrancesco Gringoli, ItalyCarlos A. Gutierrez, MexicoRavi Jhawar, LuxembourgPeter Jung, GermanyAdrian Kliks, PolandDik Lun Lee, Hong KongDing Li, USAJuraj Machaj, SlovakiaSergio Mascetti, ItalyElio Masciari, ItalyMaristella Matera, ItalyFranco Mazzenga, ItalyEduardo Mena, Spain

Massimo Merro, ItalyAniello Minutolo, ItalyJose F. Monserrat, SpainRaul Montoliu, SpainMario Muñoz-Organero, SpainFrancesco Palmieri, ItalyJosé J. Pazos-Arias, SpainMarco Picone, ItalyVicent Pla, SpainAmon Rapp, ItalyDaniele Riboni, ItalyPedro M. Ruiz, SpainMichele Ruta, ItalyStefania Sardellitti, ItalyFilippo Sciarrone, ItalyFloriano Scioscia, ItalyMichael Vassilakopoulos, GreeceLaurence T. Yang, CanadaJinglan Zhang, Australia

Contents

Wearable Technology andMobile Applications for HealthcareIván García-Magariño , Dilip Sarkar , and Raquel LacuestaEditorial (2 pages), Article ID 6247094, Volume 2019 (2019)

Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with FeatureExtraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal as InformationSourceCarlos E. Galván-Tejada , Laura A. Zanella-Calzada , Hamurabi Gamboa-Rosales ,Jorge I. Galván-Tejada, Nubia M. Chávez-Lamas, Ma. del Carmen Gracia-Cortés,Rafael Magallanes-Quintanar, and José M. Celaya-PadillaResearch Article (12 pages), Article ID 8269695, Volume 2019 (2019)

Empirical Study Based on the Perceptions of Patients and Relatives about the Acceptance of WearableDevices to ImproveTheir Health and Prevent Possible DiseasesFrancisco D. Guillén-Gámez and María J. Mayorga-FernándezResearch Article (12 pages), Article ID 4731048, Volume 2019 (2019)

An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of WearableHealthcare DevicesShahla Asadi , Rusli Abdullah, Mahmood Safaei, and Shah NazirResearch Article (9 pages), Article ID 8026042, Volume 2019 (2019)

Gait Assessment of Younger and Older Adults with Portable Motion-Sensing Methods: A User StudyRunting Zhong, Pei-Luen Patrick Rau , and Xinghui YanResearch Article (13 pages), Article ID 1093514, Volume 2019 (2019)

AHybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine LearningAlgorithmsAmin Ul Haq , Jian Ping Li , Muhammad Hammad Memon , Shah Nazir , and Ruinan SunResearch Article (21 pages), Article ID 3860146, Volume 2018 (2019)

An Empirical Evaluation on Vibrotactile Feedback forWristband SystemFeng Wang , Wanna Zhang, and Wei LuoResearch Article (8 pages), Article ID 4878014, Volume 2018 (2019)

http://orcid.org/0000-0002-2726-6760http://orcid.org/0000-0001-9102-4291http://orcid.org/0000-0002-4773-4904http://orcid.org/0000-0002-7635-4687http://orcid.org/0000-0002-8049-8077http://orcid.org/0000-0002-9498-6602http://orcid.org/0000-0001-6470-526Xhttp://orcid.org/0000-0003-3749-1264http://orcid.org/0000-0002-8199-2122http://orcid.org/0000-0003-0126-9944http://orcid.org/0000-0002-5713-8612http://orcid.org/0000-0002-7774-5604http://orcid.org/0000-0003-2192-1450http://orcid.org/0000-0002-8680-1831http://orcid.org/0000-0003-0126-9944http://orcid.org/0000-0002-6181-9816http://orcid.org/0000-0002-9847-7805

EditorialWearable Technology and Mobile Applications for Healthcare

Iván Garcı́a-Magariño ,1 Dilip Sarkar ,2 and Raquel Lacuesta 3,4

1Department of Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University of Madrid,Madrid, Spain2Department of Computer Science, Department of Electrical and Computer Engineering, University of Miami, Miami, FL, USA3Department of Computer Science and Engineering of Systems, Polytechnic University School of Teruel, University of Zaragoza,Teruel, Spain4Aragón Health Research Institute, IIS Aragón, University of Zaragoza, Zaragoza, Spain

Correspondence should be addressed to Iván Garcı́a-Magariño; [email protected]

Received 2 May 2019; Accepted 2 May 2019; Published 21 May 2019

Copyright © 2019 Iván Garcı́a-Magariño et al. (is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Wearable technology (WT) and mobile applications (apps)are providing support for continuous health monitoring ofpeople in a wide range of diseases, including psychological andphysical. WT and apps can be especially useful in the agingpopulation [1] for tracking the evolution of certain symptoms,providing motivational engagement and supporting tele-medicine with remote monitoring. WT and apps are some ofthe pillars in the mHealth research [2]. Among most com-mons applications, apps can (a) geolocate lost people withneurodegenerative impairment, (b) collate patient-reportedoutcome measures and patient-reported experience measures(PROMs and PREMs), (c) automatically evaluate earlysymptoms of some neurodegenerative diseases by assessingfeatures such as memory, and (d) keep track of emotionalevolution like in the EmoPaint app. WT can (1) monitor thephysical activity during the day by counting steps, which isuseful in some rehabilitation or degenerative diseases, (2)track the heart’s activity to ensure that the wearer’s activitykeeps the heart rate in a healthy and nonrisky range, and (3)check seeping patterns for ensuring the proper rest.

(is special issue includes six works about the latestadvances in WT and apps, including (a) hardware in-novation about multisemantic vibrotactile for feedback, (b)intelligent analyses of sensor readings for predictions, (c)gait assessment, and (d) analysis of survey data about users’perceptions.

WT and apps have boosted a number of solutions withthe aim of improving health and quality of people’s life. In

this special issue, F. D. Guillén-Gámez and M. J. Mayorga-Fernández analyzed the perceptions of patients and familymembers regarding the use of these new technologies. (eyalso analyzed these perceptions in relation to the age andgender of participants. (eir study showed an increase intechnology interest in young women, the influence of age onthe use of wearable devices, and the importance of privacyand confidence in the use of these technologies.

(ese technologies also can change health monitoringand open door for new monitoring systems for betterhealthcare delivery opportunities. Another factor to payattention in this field is the necessity for predicting andprioritizing individuals’ influential factors which impact onthe process for adoption of wearable healthcare devices byconsumers. (e work of S. Asadi et al. of this special issueindicates that the perceived usefulness is one of the pre-dictors that are more significant in the adoption of thesedevices. Other factors are health interest, perceived ease ofuse, initial trust, and consumers’ innovativeness.

In the context of emotions, there is a large variety of appsthat can track emotions, and generally with self-reportedmethods. Some apps rely on simply asking users to choosetheir emotions from questionnaires with scales such asPANAS (Positive and Negative Affect Schedule). Other appsare based on the estimation of emotions based on theanalysis of other self-reported information. For instance, theEmoPaint app estimated emotions based on the analysis ofself-reported bodily sensations relying on the initial

HindawiMobile Information SystemsVolume 2019, Article ID 6247094, 2 pageshttps://doi.org/10.1155/2019/6247094

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prototypes proposed by L. Nummenmaa et al. [3]. (is appalso allowed the user to provide feedback about theiremotions to keep an emotion diary with more accurateinformation and to let the app automatically learn from thefeedback to customize the predictions for each user. Inaddition, the EmoPose app allowed users to report 3Demotional poses and detect emotions from these. (e un-derlying method is based on the initial emotional body posespresented by K. Schindler et al. [4]. However, WT can au-tomatically detect emotions from sensor data without theneed for self-reporting information by users. C. E. Galván-Tejada et al. reported that sensor data are useful for detectingdepression episodes in unipolar and bipolar patients. (eyused genetic algorithms for analyzing the activity signal froma smartband.

In some WT and apps, artificial intelligence (AI) plays akey role in predicting user emotions and diseases from theavailable data. In the case of emotions, EmoPaint usedcomputer vision techniques (i.e., analysis of color histo-grams in different body areas), EmoPose applied case-basedreasoning, and the work reported in this special issue by C. E.Galván-Tejada et al. is based on genetic algorithms. A. U.Haq et al. presented a hybrid intelligent system for classi-fying people with heart disease and healthy people. (eirexperiments with the Cleveland heart disease datasetcompared the performance of seven well-known classifiersand three feature selection algorithms for this purpose.

WT is still limited in the feedback of eye-free scenarios.In this special issue, F. Wang et al. proposed a newmechanism for providing multisemantic vibrotactile feed-back in wristbands. In particular, they evaluated a wristbandsystem with five vibration patterns, using different vibrationmotors located in different places and orientations of thewristband. (eir experimentation showed that participantswere able to distinguish different vibration patterns with90% accuracy.

Furthermore, WTcan be used for gait assessment. In thisspecial issue, R. Zhong et al. presented an approach forevaluating gait with four smart bracelets in wrists and anklesconnected to an Android app in a smartphone and a websitebased on Microsoft Azure. (eir user study revealed theutility of their approach for assessing gait in Chinese adultsand provided feedback about which aspects could be im-proved from user experience viewpoint like the resultvisualization.

In the expansion of WT and apps for healthcare, someframeworks focus on the agile software development of theseapps, like FAMAP (a framework for developing mHealthapps) [5]. (is framework empowers the development ofdifferent apps andWTs for healthcare incorporating big dataanalytics and AI (e.g., including support for advanced agent-based simulators for predicting the repercussion of certaintreatments or patient situations). In particular, EmoPaintand EmoPose apps were developed with FAMAP.

(is special issue has presented the advances on mon-itoring users with WT and apps for healthcare, consideringusers’ perceptions, emotion tracking, AI, vibrotactile feed-back, and gait assessment. However, it is worth highlightingthat, besides WT and apps, the interest of Internet of things

(IoT) is continuously increasing in the context of healthcare[6], since IoT can automatically collate with big data fromusers on their home environment. Due to the huge amountof generated information from IoT, commonly IoT use fogcomputing for efficiently handling all these data. Similartechniques could be applied in WT and apps. In order toproperly improve this expanding field, WTand apps need tosupport green computing and ensure the appropriate levelsof security and privacy. We hope that this special issueencourages works towards future healthcare systems thatintegrateWT, apps, and IoT tomonitor patients for applyingAI techniques and big data analytics for accurately detectingpatterns in patients and predicting possible relevant medicalconditions. We also hope that researchers will be motivatedby this special issue to continue the research inWTand appsfor healthcare considering the aforementioned challenges.

Conflicts of Interest

(e guest editors do not have any conflicts of interestconcerning this special issue.

Acknowledgments

(e editors acknowledge the support of Zahia Guessoumfrom the University of Reims Champagne-Ardenne, Reims,France.

Iván Garćıa-MagariñoDilip Sarkar

Raquel Lacuesta

References

[1] S. Malwade, S. S. Abdul, M. Uddin et al., “Mobile and wearabletechnologies in healthcare for the ageing population,” Com-puter Methods and Programs in Biomedicine, vol. 161,pp. 233–237, 2018.

[2] J. D. Cameron, A. Ramaprasad, and T. Syn, “An ontology ofand roadmap for mHealth research,” International Journal ofMedical Informatics, vol. 100, pp. 16–25, 2017.

[3] L. Nummenmaa, E. Glerean, R. Hari, and J. K. Hietanen,“Bodily maps of emotions,” Proceedings of the NationalAcademy of Sciences, vol. 111, no. 2, pp. 646–651, 2014.

[4] K. Schindler, L. Van Gool, and B. de Gelder, “Recognizingemotions expressed by body pose: a biologically inspired neuralmodel,” Neural Networks, vol. 21, no. 9, pp. 1238–1246, 2008.

[5] I. Garćıa-Magariño, M. Gonzalez Bedia, and G. Palacios-Navarro, “FAMAP: a framework for developing m-healthapps,” in Trends and Advances in Information Systems andTechnologies. WorldCIST’18 2018, Advances in IntelligentSystems and Computing, pp. 850–859, Springer, Cham,Switzerland, 2018.

[6] A.A.Mutlag,M.K.AbdGhani,N.Arunkumar,M.A.Mohammed,and O. Mohd, “Enabling technologies for fog computing inhealthcare IoT systems,” Future Generation Computer Systems,vol. 90, pp. 62–78, 2019.

2 Mobile Information Systems

Research ArticleDepressionEpisodesDetection inUnipolarandBipolarPatients:AMethodology with Feature Extraction and Feature Selection withGenetic Algorithms Using Activity Motion Signal asInformation Source

Carlos E. Galván-Tejada ,1 Laura A. Zanella-Calzada ,1 Hamurabi Gamboa-Rosales ,1

Jorge I. Galván-Tejada,1 Nubia M. Chávez-Lamas,2 Ma. del Carmen Gracia-Cortés,2

Rafael Magallanes-Quintanar,1 and José M. Celaya-Padilla1

1Unidad Académica de Ingenieŕıa Eléctrica, Universidad Autónoma de Zacatecas, Jardı́n Juarez 147, Centro,98000 Zacatecas, Zac, Mexico2Cĺınica Comunitaria de Tacoaleche, Unidad Académica de Odontoloǵıa, Universidad Autónoma de Zacatecas,Jardı́n Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico

Correspondence should be addressed to Hamurabi Gamboa-Rosales; [email protected]

Received 18 November 2018; Revised 15 February 2019; Accepted 11 March 2019; Published 23 April 2019

Guest Editor: Dilip Sarkar

Copyright © 2019 Carlos E. Galván-Tejada et al. 1is is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in anymedium, provided the original work isproperly cited.

Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positiveaspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life.Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias iscommonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, EcologicalMomentary Assessment (EMA) reports have been proposed and widely used. 1ese reports includes data of the behaviour,feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays includesmartphones and other wearables such as smartwatches. In this study is proposed amethodology to detect depressive patients withthe motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using thissignal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, isdone. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of informationrequired to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 ofarea under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motionsignal can achieve a 0.647 AUC.1ese results allow us to conclude that using the activity signal from a smartband, it is possible todistinguish between depressive states, providing a preliminary and automated tool to specialists for the diagnosis of depressionalmost in real time.

1. Introduction

1e definition of health issued by the World Health Or-ganization (WHO) says that “health is a state of completephysical, mental and social well-being and not only ofdisease or infirmity.” More than 350 million people in theworld suffer from depression, and this can become a serious

health problem, especially when it is of long duration andmoderate to severe intensity, and can cause great sufferingand disrupt work, school, family, economic, and emotionalactivities, among others. In the worst case, it can lead tosuicide, which is the cause of approximately 1 million deathsannually [1]. In Latin America, there is a high rate of mentalhealth problems in the infant and youth population; about


mailto:[email protected]://orcid.org/0000-0002-7635-4687http://orcid.org/0000-0002-8049-8077http://orcid.org/0000-0002-9498-6602https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2019/8269695

20% of this population has disorders that require in-terventions of health services, but this number is under-estimated due to the tendency of adolescents to hide anddisguise their own problems to adults and their lack ofconfidence to access therapeutic structures [2]. Depression isa mental disorder characterized fundamentally by depressivemood, loss of interest, and enjoyment of the positive aspectsof life and fatigue, which impoverish the quality of life andgenerate difficulties in the family, work, and social envi-ronment of those who suffer it.

Depression can manifest itself regardless of age, gender,socioeconomic status, and academic program and canpresent with primary symptoms that do not encompassmood changes and even change cognitive function, so it isnot difficult for any individual to become depressed [3].1ere is no doubt that studying the sociodemographicfactors of age, gender, socioeconomic stratum, and family inadolescent students is relevant, due to the relationship thatmay exist between these and themanifestation of depression.1is is why, in the global context, we find a series of studiesthat report high rates of depression in this population [4].

When referring to depression, we include those mooddisorders with depressive symptoms, which include unipolarmajor depressive disorder, dysthymia, and mood disordersdue to medical illness with depressive symptoms, amongothers. Despite the variety of alterations, by default, whenspeaking of depression, reference is made to the majorunipolar depressive disorder. 1is disorder is considered themain cause of years of life lost due to disability (AVPD)according to the Global Burden of Disease Study (GBDS),conducted by the WHO [1].

One in four people suffer from one or more mood orbehavioural disorders throughout their lives, and between50% and 70% of those with a major or minor depressiveepisode have a predisposition to develop a new one in thenext 5 years, which generates great impact on the globaleconomy by the cost of psychotherapeutic and pharmaco-logical management and by the avoidant personality dis-order (AVPD), which for 2010 was 2.5 trillion dollarsassociated with major depressive disorder, with a projectedincrease to 6 trillion by 2030. 1e prognosis would improvewith timely and adequate psychological, social, and phar-macological management.

1e efficacy of the current treatment of depression needsto be increased since its prevalence is very high worldwideand only half of patients experience complete remission withfirst-line treatments (pharmacotherapy and psychotherapy)within two years [1, 5].

Within the framework of preventive actions or psy-chological rehabilitation, the instrumentation to obtain astandardized measure of depression is to act from a scientificframework. 1erefore, it is necessary to have evaluationinstruments that demonstrate the best evidence of validityand reliability to support inferences about the early detectionof some symptoms of depression. One of these measures isthe State-Rask Depression Inventory [6], which has ad-vantages over some known instruments, such as the Self-Administered Depression Scale [7] and the Beck-II De-pression Inventory [8]. Another of the most representative

characteristics is that it allows us to differentiate between thepersons’ current experience (state) and their habitual way ofbehaving (trait) with regard to the affective component ofdepression [6].1is is of great clinical value in differentiatingexperience in two time frames and specifically targeting oneof the constituent areas of depression: affective disorders [6].Classical methods to achieve correct monitoring of de-pression states (unipolar and bipolar) in patients are done byreports from patients’ recall. Nevertheless, this type ofmonitoring is prone to bias commonly, in addition tochanges in the behaviour and understanding the real worldas is reported by Shiffman et al. [9]. Another type of methodto overcome these problems is Ecological Momentary As-sessment (EMA). 1is type of report includes behaviour,feelings, and other types of activities as close as possible tothe moment of the experience in real-life situations [9]. Oneimprovement to these types of reports is done by the increaseof wearable devices (for instance, smartwatches andsmartglasses) and smartphones, which includes differenttypes of sensors (motion sensors, gyroscopes, and acceler-ometers), allowing EMAmeasurements to be done almost inreal time, helping to monitor mental illness and give a closeview to provide treatments, interventions, and increase thecoverage of mental health services in the population withoutthe need of new specific proposal devices or modifyingadding sensors to the environment where the patients areliving.

One device currently used to achieve mental healthillness supervision is the smartphones and similar ones likesmartwatches. One proposal is presented by Gravenhorstet al. [10]; they discussed how mobile phones can increasethe effectiveness of mental disorders treatment by two mainapproaches: in one hand, the implementation of human-computer interfaces for therapy and secondly, collection ofimportant data from patients’ daily lives to be recorded bythe current state and the development of their mentalproblems; they also discussed the advantages and drawbacksof the most promising technologies for detecting disorderslike depression or bipolar disorder.

Other interesting approach is given by Firth et al.’s [11]study; they demonstrate that psychological interventionswith a smartphone as a clinical tool can reduce anxiety inschizophrenia patients. Torous et al. [12], in their study,provide data on psychiatric patients and the relationshipwith the use and interest of utilizing mobile applications tomonitor their mental health conditions. In this study, resultspresented show that 50% of patients from all age groups areinterested and will use mobile applications to monitor theirmental health condition to control their illness. Bayindiret al. [13] present a systematic review of different works thatfocus in the use of mobile phone sensors to detect humanbehavior characteristics, describing activity detection atdifferent abstraction levels of activity and characterizinghealth-related activities, like physical exercise and sleeping.

Additionally to the use of applications, these devicesinclude several embedded sensors that have been used toacquire contextual information and several niches [14, 15],including activity recognition [16] and particularly activitythat helps to find mental disorders [17]. For instance,


Gruenerbl et al. [18] demonstrate that inertial sensors andGPS traces can be used as a measurement device in psy-chiatric diagnosis, through amethodology based on a featureextraction of physical motion levels and travel patterns, anda classification analysis using a naı̈ve Bayes technique. Reeceet al. [19] identify depressed subjects using uploaded photosof Instagram based on a random forest technique. Grünerblet al. [20] propose the classification of depressed and maniacstates in bipolar patients based on smartphone data. Max-huni et al. [21] classify bipolar patients through audio, motoractivity, and questionnaires. Berle et al. [22] propose anapproach using motor activity information to revealschizophrenia and depression patterns. Koo et al. [23]present a research of the utility of the combination ofbiomarkers related to different approaches, such as motoractivity based on actigraphymeasurements, showing that thediscrimination of patients based on these biomarkers im-proves in the identification of depressed subjects. Averillet al. [24] examine the psychomotor change in depressiveepisodes based on the activity levels measured by actigraphyin order to know the response of the depression treatment,concluding that the early change in simple activity andpsychomotor speed allows one to measure the treatmentresponse in depressed patients. Garcia-Ceja et al. [15]performed an analysis of data collected through actigraphyapplying machine learning to classify depressed patients,finding that the data contain information that allows de-termination of the depression status of a subject. Huguetet al. [25] present a review to identify self-help apps that areavailable for depressed people. 1e apps that offer cognitivebehavioural therapy (CBT) or behavioural activation (BA)are evaluated since the low level of adherence to the coreingredients of the CBTand BA models causes that the utilityof these apps is questionable. It was possible to conclude thatthe application of superior scientific, technological, and legalknowledge is required to improve the credibility of the appsfor people with depression.

On the other hand, Mohr et al. [26] provide a review ofsensing research related to mental health, where a layeredand hierarchical model is provided for the translation of rawsensor data into markers of behaviors and states related tomental health. Finally, in the work of Guntuku et al. [27] isreviewed the study of predicting mental illness using socialmedia, including screening surveys, public sharing onTwitter, and themembership in an online forum, concludingthat automated detection methods are useful to identifydepressed or individuals at risk through the monitoring ofpassive activity in social media.

1e aim of this work is to study the signal generated by asmartbands accelerometer to detect depressive statesthrough the activity of patients and to propose a featureextraction (using the temporal and spectral evolution of thesignal), as well as a clever feature selection based on a geneticalgorithm approach to minimize the data required toidentify these depressive states allowing an almost real-timenoninvasive diagnosis. In this type of disease, early symp-tomatic detection can significantly increase the developmentof an effective treatment and contribute to the prevention ofthis type of psychopathology.

One of the main advantages proposed in this work is thesimplicity in the data acquisition since the device used isnoninvasive, has a small size, and does not hinder dailyactivities, which is a benefit compared to other devices thatcan be interposed in day-to-day tasks, in addition to usingmultiple sensors for the acquisition of different types of data,which is not necessary in this approach because the samepurpose is achieved with a single source of acquisition,obtaining the information required for the extraction offeatures that allowed the classification of depressive patients.

1is paper is organized as follows: in Section 2 is detaileddescribed the materials used for the development of thisresearch, as well as the set of the stages of the methodologyproposed. 1en, Section 3 presents the results obtained.Section 4 is referred to the discussion developed based on theresults previously, and finally, Section 5 shows the con-clusions of this work.

2. Materials and Methods

1e methodology proposed in this research consists in fivemain stages, shown in Figure 1. 1e data used for the de-velopment of this work is acquired from the “Depresjon”dataset (A). 1ese data are initially subjected to a datapreprocessing (B) step, in order to select the samples andsubjects for further analysis, to normalize the data, and toeliminate missing values. 1en, the feature extraction (C) isperformed, obtaining temporal and frequency statisticalfeatures, which are submitted to a feature selection (D) step,using the genetic algorithm (GA) “Galgo.” Finally, the set ofselected features is evaluated, measuring its fitness in theclassification of controls and cases, based on a random forest(RF) technique and a statistical analysis (E).

2.1. Data Description. 1e Depresjon dataset is a collectionof data that contains the motor activity of patients moni-tored with an actigraph watch held on the right wrist. 1eactigraph watch is called “Actiwatch” (model AW4), de-veloped by Cambridge Neurotechnology Ltd, England. 1eActiwatch measures activity levels, and the sampling fre-quency is 32Hz, recording movements over 0.05 g. Move-ments equal a corresponding voltage, which is stored as anactivity count in the memory of the Actiwatch, and thenumber of counts is proportional to the intensity of themovement. 1e activity counts were recorded in intervals ofone minute.

1e database contains the data for the controls (absenceof depression, 32 subjects) and for the cases (presence ofdepression, 23 subjects). 1e features collected for eachsubject were divided in two categories, actigraph datarecorded over time and Montgomery Åsberg DepressionRating Scale (MADRS) scores. 1e data collected over timeinclude the features “timestamp” (one minute intervals),“date” (date of measurement), and “activity” (activitymeasurement from the actigraph watch). In addition,MADRS scores include the features “number” (patientidentifier), “day” (number of days of measurements),“gender” (1: female/2: male), “age” (age in age groups),

Mobile Information Systems 3

“afftype” (1: bipolar II, 2: unipolar depressive, and 3: bipolarI), “melanch” (1: melancholia; 2: no melancholia), “in-patient” (1: inpatient; 2: outpatient), “edu” (educationgrouped in years), “marriage” (1: married or cohabiting; 2:single), “work” (1: working or studying; 2: unemployed/sickleave/pension), “madrs1” (MADRS score when measure-ment started), and “madrs2” (MADRS when measurementstopped) [28].

For this work only the features over time were used.

2.2. Data Preprocessing. 1e data preprocessing consists inthree main steps, the selection of samples and subjects, thenormalization of the data, and the elimination of incompletecases presented as NA (not available).

1e number of samples collected is not consistent,differing in the number of minutes recorded for each subject,so a selection of subjects and samples was made in order topresent a balanced amount of data referring to controls andcases.1e selection of the samples is carried out keeping onlythe first value of the 60 acquired data in the minutesequivalent to one hour, counting now the activity in intervalsof one hour, whereas the selection of subjects depended onthe amount of data resulting from the selection of samples,selecting the first four controls present in the dataset and thefirst five cases. 1is number of subjects allows balance of thenumber of samples for cases and controls.

1en, in the normalization, the data are adjusted in orderto obtain a normal distribution, presenting a mean� 1 and astandard deviation� 0, and it is calculated with Equation (1),where zi represents the normalized value, xi represents thesample, µ is the mean of the total data, and σ is the standarddeviation of the total data:

zi �xi − μσ

. (1)

Finally, the missing data are eliminated, removing all therows with presence of NAs, in order to avoid problems in thesubsequent analysis.

2.3. Feature Extraction. 1e feature extraction is performedusing two types of data, temporal and frequency data. 1etemporal data are directly used from the time-dependent

Depresjon data, which were collected from the activity of thesubjects through the actigraph watch.

On the other hand, the frequency data are obtainedthrough the calculation of the Fourier transform of the time-dependent Depresjon data.

1en, for each type of data, 14 statistical parameters areextracted, presented in Table 1, obtaining a total of 38features.

1e names of the features correspondent to the temporaldata are “tKurtosis,” “tSesgo,” “tQ01,” “tQ05,” “tQ25,”“tQ75,” “tQ95,” “tQ99,” “tMedia,” “tSD,” “tVarianza,”“tTrimMedia,” “tCV,” and “tICV,” while the names of thefeatures correspondent to the frequency data are “fKurtosis,”“fSesgo,” “fQ01,” “fQ05,” “fQ25,” “fQ75,” “fQ95,” “fQ99,”“fMedia,” “fSD,” “fVarianza,” “fTrimMedia,” “fCV,” and“fICV.”

2.4. Feature Selection. In this stage, the 38 features extractedare subjected to a feature selection based on a GA approach.GAs are a stochastic strategy that has been widely used in theanalysis of data and they consist in a sequence of stages thatstarts with a random set of models and develops good localsolutions reproducing the natural selection process usingmeasures such as (1) higher rate of replication of the moreaccurate feature subsets, (2) mutation to generate differentchromosomes, and (3) crossover to improve the combina-tions of the chromosomes.

A validation measure is calculated in combinationduring the selection process, testing the sets of chromo-somes, ensuring that the multivariate feature selection issuitable. 1e aim of the GA is to minimize the score cal-culated by the fitness function, converging then into a so-lution, being therefore possible to select the most significantpredictive subset of n features [29].

For this work, the genetic algorithm “Galgo” is used.Galgo is a package implemented under the R language,which is oriented to select models with high fitness and toanalyze them, as well as for the reconstruction and char-acterization of representative summary models.

1e procedure of Galgo begins with a random pop-ulation of feature or gene subsets or chromosomes of adefined size (n), which are assessed through a fitnessfunction for their ability to predict or classify the desirable

Depresjon database

Dataacquisition

Datapreprocessing

Selection of samplesand subjectsNormalizationElimination of NAs

Temporal statisticalfeaturesFrequency statistical features

Featureextraction

Featureselection

Genetic algorithm

Random forestStatistical analysis

Validation

(a)

(i)(i)

(i)

(i)

(i)

(ii)

(ii)(ii)

(iii)

(b) (c) (d) (e)

Figure 1: Flowchart of the methodology followed.


outcome or the dependent variable, obtaining a certain valueof accuracy. 1e classification methods that can be used inthe internal procedure of Galgo are k-nearest-neighbors,discriminant functions, nearest centroid, support vectormachine, neural networks, and random forest.

1e main idea of the process is to replace the firstpopulation with a new one, including variants of chromo-somes that achieved a higher classification accuracy, and torepeat this procedure until a desired accuracy is reached.1eprogressive changes of the chromosomes are performedthrough a series of operators that simulate the process ofnatural selection, selection, mutation, and crossover.

1e proportion of the solution space increases with theevolution of independent chromosome populations inpartially isolated environments, known as niches, andchromosomes can migrate from one niche to another, inorder to ensure the recombination of good solutions. A set ofniches is called world [30].

1is process is carried out in four main steps:

(i) First, the analysis is configured, specifying the inputand the outcome features, as well as a series ofparameters that will guide the behaviour of theprocess, such as the classification model, the desiredaccuracy, and the error estimation scheme, amongothers.1e classificationmodel can be selected fromthe implemented or can be defined by a function ofthe user; while the error estimation can be defined intwo levels, with a training/test validation strategyusing variant random splits, and in the internaltraining process using a k-fold cross-validation,random splits, or re-substitution error.

(ii) 1en, the search of relevant multivariate modelsbegins with a random population of chromosomesin each cycle of the procedure. 1e number ofchromosomes developed needs to be large enoughto ensure that the greatest amount of solutions wasfound and to achieve this, two approaches aredesigned to provide information of the chromo-some composition, the level of convergence of the

solutions, and the evolution of the fitness values,diagnosing the stability of the populations.

(iii) A refinement and analysis of the population of theselected chromosomes is carried out, since not allthe genes included in the best chromosome may becontributing in a significant way to the fitness value.1erefore, a backward selection strategy is imple-mented to obtain a model contained by genes thatsignificantly contribute to the accuracy of the result.

(iv) Finally, the development of a significant statisticalmodel is obtained from the population of the se-lected chromosomes. For this step, a forward se-lection strategy is included, and its operation isbased on a stepwise inclusion adding the mostfrequent genes of the chromosome population.

1e configuration of the analysis for this study iscomposed by 200 generations, five genes per chromosome, adesired accuracy of 0.99, and “nearest centroid” as classi-fication model, and an error estimation scheme was used across-validation approach.

2.5. Classification Analysis. 1e classification analysis wascarried out through a RF method, looking for the classifi-cation of subjects in two different states, depressed (labeledas “1”) and not depressed (labeled as “0”).

RF is a machine learning technique that presents twomain approaches, classification and regression, and itsperformance is based on decision trees. In the classificationoption, RF provides estimators of a Bayes classifier,f : R↦y, minimizing the error classification P(Y≠f(X)).

Roughly, an ensemble of trees grows, constructed withrandom vectors that generate each of the trees, deciding theclass to which the data correspond by voting, where themajority of the class votes determine the RF prediction. 1isprocess causes that the generalization error merges to alimiting value, thus improving the classification accuracy ofthe system [31].

Specifically, the trees are created using a subset ofbootstrap samples with replacement, L1, . . . , Lntree (of atraining set L), known as a bagging approach, which meansthat one same sample can be selected several times for theclassification analysis while the others samples may not beselected.

Every decision tree is independently constructed withoutany pruning, and each node is divided through a splittingrule using a specific number of features, mtry, randomlyselected.

1e splitting rule is added to the estimators calculatedfrom the trees, represented as f1, . . . , fntree. A response valueis subsequently obtained from the new point, which consistsin the construction of the following equation:

f(x) � argmax 1≤ c≤C ntree

k�11

fk(x)�c. (2)

1e forest is growing up to a defined number of trees, ntree,and by this step, the algorithm creates tree that present two

Table 1: Statistical features extracted from the temporal and fre-quency data.

Feature DescriptionMean μ � 1/nni�1xiStandard deviation sd �

��

Ni�1(xi − μ)

2/(N− 1)

Variance sd2 � 1/nni�1(xi − μ)2

Trimmed mean Mean with outliers trimmedCoefficient of variation CV � sd/μInverse coefficient of variation ICV � μ/sdKurtosis K � μ/σSkewness∗ S � (μ− υ)/σQuantiles∗: 1, 5, 25, 75, 95,99% Q[i](p) � (1− cx[j] + cx[j + 1])

∗] represents the median value; 1 ≤ i ≤ 9; (j−m)/n ≤ p < (j−m+ 1)/n;x[j] represents the jth order statistic; n represents the sample size; c is infunction of j and g, where j � floor(np + m) and g � np + m− j; and mrepresents a constant determined by the sample quantile type.


main characteristics, high variance and low bias. 1e finalclassification decision is calculated through the arithmeticmean of the class assignment probabilities of the total numberof trees.1en, an evaluation step is performed using a new setof unlabelled data input with the decision trees developed inthe ensemble, giving each tree a vote for a class. 1e class thatcollects the greatest number of votes is the one selected.

Around two thirds of the total samples are usually usedfor the training of the trees, and they are referred as in− bagsamples; then, with the remaining one third samples, re-ferred as out− of − the− bag samples, an internal cross-validation is realized for the estimation of the model per-formance [32].

1e estimation of this error is known as out-of-bag(OOB) error. 1is value measures the misclassificationrate for the classification of the OOB samples. 1is meansthat a feature, Xj, is important if when breaking the re-lationship between Xj and Y, the error of the predictionincreases, and the error of the prediction in each tree, f, isevaluated with the OOB sample using

R(f, L) �1

|L|

i: Xi,Yi( )εL

1f Xi( ≠Yi

.(3)

It is important to note that according to literature, theclassification accuracy is less sensitive to ntree than to mtry;therefore, since RF is a computationally efficient classifierthat does not present problems of overfitting, ntree can be anumber as large as possible. On the other hand, the mtryparameter is usually defined by the square root of the totalnumber of input features [33].

For the development of this study, the number of treesselected is ntree � 2000, and the number of features at eachsplit, mtry, is calculated according to the number of featuresas ��p√ , with p being the number of features.

2.6. Validation. 1e validation stage is based on three pa-rameters, the AUC as a single value quantity of the ROCcurve, specificity, and sensitivity.

1e ROC curve has been a widely used tool for theevaluation of binary classification models since it presents aseries of characteristics that allow the correct interpretationof the results, such as the intuitive visual interpretation of thecurve, easy comparison among multiple models, and theAUC value [34].

1e calculation of the classifier’s performance throughthe ROC curve provides a suitable operating point, called asdecision threshold, for the parameterization of the classi-fication model.

A classification problem presents two possible outputs,“correct” and “incorrect,” for each class of the model. Anorderly way to present this information is through a con-fusion matrix, a table that shows the differences between thereal and the predicted classes. 1e values contained in aconfusion matrix are the true positives (TP), true negatives(TN), false positives (FP), and false negatives (FN); besides,the value of the row totals with the truly negatives (CN) andtruly positives (CP) examples, and the value of the column

totals with the predicted negative (RN) and the predictedpositive (RP) examples [35].

1e sensitivity is a parameter referred to as the ability tocorrectly identify those data with a condition, and it iscalculated with the following equation:

sensitivity(1− β) �TpCp

. (4)

On the other hand, the specificity is a parameter referredto as the ability to correctly identify those data without acondition, and it is calculated with the following equation:

specificity(1− α) �TnCn

. (5)

Finally, the plotted values of the sensitivity and thespecificity in conjunction represent the decision threshold ofthe ROC curve.1e AUC value of the curve can be calculatedthrough trapezoidal integration, as shown in the followingequation:

AUC � i

1− βi · Δα( +12

[Δ(1− β) · Δα], (6)

where Δ(1− β) � (1− βi)− (1 + βi−1) and Δα � αi + αi−1[35].

All the analysis is carried out in “R” (version 3.4.4), a freesoftware environment designed for statistical computingand graphics [36]. 1e libraries required for this analysis are“Galgo” (version 1.2-01) [37], “pROC” (version 1.11.0) [38],“e1071” (version 1.7-0) [39], “randomForest” (version 4.6-14) [40], “caret” (version 6.0-79) [41], and “rminer” (version1.4.2) [42].

3. Results

1e results of this research are presented in this section.1rough the first step of this methodology, which was thedata acquisition, the number of subjects selected for thesubsequent analysis was five for cases and four for controls,in order to balance the number of samples in both datasets.

1en, the feature extraction allowed collection of a seriesof 38 statistical features, which of the total, 14 belong to thetime data and the remaining to the frequency data. It isimportant to remind that the frequency data were calculatedthrough the Fourier transform of the time data.

For the third stage, a feature selection based on the GA,Galgo, is carried out, obtaining a series of graphs that allowobservation of the performance of the data through thedevelopment of the different models created in the evolutionof the algorithm. Figure 2 presents a graph of the frequencypercentage with which each feature appeared within thedifferent models developed, positioning each featureaccording to its order of appearance, from highest to lowest,where those features in black present the highest frequencyand those features in gray present the lowest. According tothis graph, the most significant features, according to theirappearance frequency, are “tCV,” “tQ99,” “fCV,” and“tVarianza.” (Tables 2 and 3).


1en, in Figure 3 is shown a graph of the fitness per-formance throughout the evolution of the 200 generations ofthe GA, where it is possible to observe that the average fitnessreaches a stable behaviour, with a value of around 0.63.

Figure 4 presents a heat map of the best chromosomepresented by the GA, contained by a model of five chro-mosomes, “tCV,” “tQ25,” “tQ99,” “tICV,” and “tCV.”

1en, the best chromosome is subjected to a forwardselection step, where for each feature added to the model, itsaverage fitness was calculated, as shown in Figure 5.According to this graph, the model reaches its best averagefitness, as well as stability, with three features, “tCV,” “tQ99,”and “fCV.”

Finally, in Figure 6 is present a heat map of the finalmodel obtained through backward elimination step, con-tained by two time features, “tCV” and “tQ99.”

In the classification analysis, a RF approach is used,measuring the OOB error in order to know the accuracyclassification reached through the model selected in the

previous step. In Table 4 is present the confusion matrixobtained through the classification of subjects using the totalset of features and the respective error values for each of theclasses. 1e OOB error obtained was of 26.95%. In Table 4 ispresent the confusion matrix obtained through the classi-fication of subjects using the best chromosome and therespective error values for each of the classes.1e OOB errorobtained was of 30.52%. Finally, In Table 4 is present theconfusion matrix obtained through the classification ofsubjects using the final model and the respective error valuesfor each of the classes. 1e OOB error obtained was of35.97%.

For the last stage of this work, a validation step wasperformed, calculating the ROC curves of the models, shownin Figure 7, where in Figure 7(a) is present the ROC curve ofthe model contained by the total features and its respectiveAUC value, which obtained a sensitivity of 0.751 and aspecificity of 0.717. 1en, in Figure 7(b) is present the ROCcurve of the model contained by the best chromosome andits respective AUC value, which obtained a sensitivity of0.699 and a specificity of 0.694. Finally, in Figure 7(c) ispresent the ROC curve of the model contained by the finalmodel and its respective AUC value, which obtained asensitivity of 0.684 and a specificity of 0.611.

4. Discussion

In this section, the results obtained are discussed. From thetotal 38 statistical features extracted, a feature selection isperformed based in Galgo. Initially, Galgo developed thegraph shown in Figure 2, which provides the information ofthe frequency with which the features are part of the dif-ferent chromosomes developed, ordered by rank fromhighest to lowest.

According to Figure 2, the four most significant featuresor the features that presented the highest frequency were

250

200

150

100

50

0

–50

–100

Rank

+ fr

eque

ncy

0 5 10 15 20 25Genes

0%

50%

100%

150%

200%

250%

Expected random frequency = 19 (18.5)

tCVtQ9

9

fCV

fSesgotVa

rianza

tlCV tQ25

tTrimM

edia

tKurtos

istSes

gotQ7

5tQ9

5tMe

diatSDfTri

mMediatQ0

1tQ0

5fQ0

1fQ0

5fQ2

5fQ7

5fQ9

5fQ9

9fMe

diafSD fVa

rianza

flCV

Figure 2: Graph of the frequency of appearance of the features within the models developed in the genetic algorithm, positioned fromhigher to lower rank.

Table 2: Confusion matrix of the subjects classification based onthe RF approach using the complete set of features.

TruePredicted

Control Case ErrorControl 819 326 0.284Case 331 962 0.255

Table 3: Confusion matrix of the subjects classification based onthe RF approach using the best chromosome of the GA.

TruePredicted



those presented in black, of which three correspond to thetemporal features and one to the frequency features, whichmeans that temporal data are presenting more significantinformation than frequency data for the classification ofsubjects.

1en, in Figure 3 is shown a graph of the average fitnessbehaviour through the different generations of the GA, beingpossible to observe that the greatest change occurs at thebeginning of the graph, within the first 50 generations, wherethe GA is in the process of finding the best combination ofgenes to obtain a chromosome suitable for classification.Subsequently, a relatively stable value is reached aroundgeneration 80, obtaining an average fitness value of around0.63 in the last generation.

At the end of the 200 generations, the best chromosomeobtained is presented in Figure 4, contained by the fivefeatures presented in the heat map, of which four correspondto the time features and the remaining corresponds to thefrequency features. 1e first feature of the best chromosomecorresponds to the frequency feature, “fCV”, referred to thecoefficient of variation (CV), which is related to the standarddeviation and the mean value, where the higher the value ofthe standard deviation compared to themean, the higher willbe the CV and vice versa. 1is feature may imply that thefrequency data could be presenting significant variationsamong its values between cases and controls, being able todistinguish between both classes.

1e second feature is “tICV”, referred to the inversecoefficient of variation (ICV), which may imply a similarmeaning than the feature “fCV”, where the time data can bepresenting significant information in the distribution of thedata that allows one to distinguish between depressed andnondepressed subjects.

1en, there are present the features “tQ25” and “tQ99,”which represent the 25 and the 99 quantiles, respectively.Quantiles are points of regular intervals of the distributionfunction of a random variable. 1erefore, these two featuresmay imply that, in these data intervals, the most significantinformation or the greatest differences between both classesare presented because taking into account that data on theamount of activity carried out as a function of time are beinganalyzed and that quantile data are arranged in ascendingorder, it is possible that by comparing the variations in theamount of activity correspondingly, that is, the greater ac-tivity of depressed patients against the higher activity ofnondepressed patients, a difference is presented meaningful.

1e fifth feature is “tCV,” which represents the same as“fCV” but with time data. 1is feature may imply that theinformation of the physical activity of patients is presentingdifferences in the standard deviation and the mean between

0 50 100 150 200Generation

0.63

0.62

0.61

0.60

0.59

Fitn

ess

Mean (all)Mean (unfinish)

Figure 3: Graph of fitness achieved in each generation of the genetic algorithm.

5 : 26 : tCV

3 : 18 : tQ25

4 : 21 : tQ99

2 : 27 : tlCV

1 : 12 : fCV

– +Value

Figure 4: Heat map of the best chromosome calculated by thegenetic algorithm.


the two possible classes that provide support for the correctclassification.

On the other hand, in Figure 5 is presented a graph of thebehaviour of the average fitness when the features aresubjected to a forward selection step. For each feature that isadded to the model, the average fitness, the fitness of eachclass, and the total fitness are measured, in order to know thebehaviour that the model has when including the in-formation of the features and in this way to select the

adequate number of features, avoiding having nonsignificantinformation. According to the graph, the model achievesstability from the third feature, reaching an average fitness of0.636.

1e last step of the feature selection consisted of a robustgene back elimination (RGBE) step, in order to removeredundant information, obtaining a final model containedby two features, presented in the heat map of Figure 6. 1ismodel is dependent on the mean, standard deviation, andthe 99 quantile values, which according to the previous steps,these measures provide data that allow the classification ofthe classes.

1en, a RF approach is used for the classification analysis,comparing the OOB error obtained through three differentmodels: one model is contained by the total set of initialfeatures, and it obtained an OOB error of 26.95%; the secondmodel is contained by the features of the best chromosomeobtained with GA, and it obtained an OOB error of 30.52%;and the third model is contained by the final model obtainedthrough the RGBE step, obtaining an OOB error of 35.97%.

1e OOB error values allow one to know the percentageof data that was misclassified during the construction of thedecision trees that form the random forest, and as is possibleto observe, this value increases when the model contains lessfeatures, presenting the lowest OOB error in the modelcontained by the total set of features; nevertheless, evenwhen the final model presents an error 10% higher than thefirst model, the percentage that is correctly classified remainsstatistically significant. Besides, it is important to remarkthat the number of features contained in the final model issignificantly smaller than that contained in the first model;therefore, the information required for the classification ismuch smaller, thus reducing the computational cost for theanalysis of the data.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

0.70

0.65

0.60

0.55

0.50

Aver

age f

itnes

s

Overall (3657)0 (1708)1 (1949)Average (2)

tCV

: 26

tQ99

: 21

fCV

: 12

tVar

ianz

a : 2

4

fSes

go :

1

tlCV

: 27

flCV

: 13

tQ25

: 18

tQ01

: 16

tQ05

: 17

tTrim

Med

ia :

25

tKur

tosis

: 14

tSes

go :

15

tQ75

: 19

tQ95

: 20

tMed

ia :

22

tSD

: 23

fTrim

Med

ia :

11

fQ01

: 2

fQ05

: 3

fQ25

: 4

fQ75

: 5

fQ95

: 6

fQ99

: 7

fMed

ia :

8

fSD

: 9

fVar

ianz

a : 1

0

(1)

(2)

(3)

(4)

(5)

0.6362

Figure 5: Graph of average fitness obtained through a forward selection.

2 : 26 : tCV

1 : 21 : tQ99

–Value+

Figure 6: Heat map of the final chromosome obtained through arobust gene back elimination.

Table 4: Confusion matrix of the subjects classification based onthe RF approach using the final model selected.

TruePredicted



Also, from the validation that RF performs internally, aseries of confusion matrices were obtained in order to ex-plain the OOB errors obtained. In Table 4 is present the truepositives (� 962) and true negatives (� 819), as well as theerror value for controls (� 0.284) and for cases (� 0.255), forthe model contained by the total set of features, where it isshown that even when the error is higher for controls, bothclasses present similar values in the classification error.1en, Table 4 shows the true positives (� 920), true negatives(� 774), error value for controls (� 0.324), and error value forcases (� 0.288), obtained through the classification using thebest chromosome, where it is evident that the error valuesincrease for both classes; nevertheless, the classificationcontinues presenting a statistically significant aptitude eventhough the quantity of features contained in the model wasreduced by around 86.84%. In Table 4 is present the truepositives (� 690) and true negatives (� 871), and the errorvalue for controls (� 0.397) and for cases (� 0.326), showingthat, evidently, by reducing the number of features of thebest chromosome, the error is increased again, especially forcontrols, which may represent that the activity registered bycontrols could be confused with the activity of cases inspecific moments of time, for example, in the hours of sleep.In addition, could also be confused the time of greatestactivity for both classes when in the case of controls, physicalactivity is not very energetic. However, this problem ofconfusion in the classification can be solved by increasingthe number of samples in both classes, taking into accountthat it is important that the training of the algorithms have abalanced amount of data.

In the validation stage, the specificity and sensitivityallowed support of the previous results, obtaining highervalues in the evaluation of sensitivity than in specificity,although it is important to note that the results of thevalidation presented significant values for the three modelsevaluated.

1en, the ROC curve is calculated for each of the models,as shown in Figure 7, where Figure 7(a) represents the curveobtained using the total set of features, Figure 7(b) repre-sents the curve obtained using the best chromosome, andFigure 7(c) represents the curve obtained using the finalmodel, obtaining AUC values of 0.734 > 0.697 > 0.647,respectively. 1e AUC value is reduced by decreasing thenumber of features of the models; however, the difference

between the AUC of the model that contains 100% of thefeatures and the final model, which only contains 5.26% ofthe features, is not representative taking into account thatthe AUC remains statistically significant in the final model.1erefore, the ability of the final model to classify cases fromcontrols remains significant despite the limited amount ofinformation used, thus benefiting the computational costnecessary to carry out the classification.

Finally, in Table 5, a comparison between differenttechniques based on the same approach is shown, collectingdata through actigraphy in order to identify depressed pa-tients, where according to the results is possible to concludethat all works present statistical significant results; however,the complexity of the methodologies and the quantity ofcharacteristics related to different information used on eachresearch, as well as the information sources, are greater thanthose proposed in this work, since it was only necessary theextraction of a reduced set of statistical features from adatabase collected by a single sensor from a small set ofpatients, presenting as one of the main contributions thesimplicity of the experimentation made for the classificationof subjects with presence of depression obtaining statisticallysignificant results, in addition to presenting a lower com-putational cost than that presented in the mentioned worksdue to the small amount of data.

5. Conclusions

In this research is proposed a methodology composed by aseries of steps which mainly includes a feature selection, aclassification analysis, and a validation, in order to find therelationship between a series of statistical features, based ontime and frequency continuous values acquired in a specifictime and the possible condition of depression.

It is important to remark that the number of subjectsallows one to obtain significant results; nevertheless, thisnumber of samples can be increased in order to mainlyimprove the result of the true negatives, which presentsgreater error than true positives. On the other hand, theextracted statistical features show that the information theycontain provides a description of the main characteristics ofa patient’s full-day activity that allows differentiation be-tween depressed and nondepressed subjects.

1.0 0.8 0.6 0.4 0.2 0.0

1.0

0.8

0.6

0.4

0.2

0.0 AUC = 0.734

Specificity

Sens

itivi

ty

(a)

1.0 0.8 0.6 0.4 0.2 0.0

1.0

0.8

0.6

0.4

0.2

0.0 AUC = 0.697

Specificity

Sens

itivi

ty

(b)

1.0 0.8 0.6 0.4 0.2 0.0

1.0

0.8

0.6

0.4

0.2

0.0 AUC = 0.647

Specificity

Sens

itivi

ty

(c)

Figure 7: ROC curves obtained using the (a) complete features, (b) best chromosome, and (c) final chromosome.


1e feature selection through the GA provides a bestchromosome which is subsequently reduced to a modelcontained by two features. 1ese two features are statis-tical descriptors of temporary data that according to thevalidation step, despite presenting a greater error in thedifferentiation of cases and controls than if the whole setof features is used, the results remain statistically sig-nificant, thus allowing having a contained model with areduced amount of features that automatically classifiesdepressed subjects of nondepressed subjects with signif-icant fitness.

In addition, it is worth noting that one of the greatestadvantages of the model being significantly reduced is that itis also reduced in its computational cost, making it easier toaccess it, since it does not require specialized software orhardware for its implementation.

Besides, one of the main benefits demonstrated in thiswork is the values with high precision obtained through asimple methodology using a single source of data, which incomparison with other works, where it is necessary to usemore than one source for the data acquisition and a series ofdifferent techniques for the classification analysis, this ap-proach provides simplicity and statistically significant resultsfor less processing steps and computational cost.

1en, it is possible to conclude that the methodologyimplemented in this study allows one to know that evidently,there is an association between the recorded daily activity ofa patient and the condition of his depressive state. Besides,the results obtained are sustained according to what is re-ported in the literature, where among the symptoms pre-sented by patients with depression is the slowness ofmovement, poor body gesticulation, and the feeling of fa-tigue, tending to show lower levels of activity than subjectswho do not have this condition.

1erefore, through this work is obtained a preliminarytool for the possible support in the diagnosis of the spe-cialists to know the state of health of a patient according tohis state of presence or absence of depression, based on thelevel of activity he has in a full day.

Data Availability

1eDepresjon data used to support the findings of this studyhave been deposited in the “control” and “condition” re-positories. 1is dataset can be accesed in http://datasets.simula.no/depresjon/ and/or can be directly downloadedfrom http://doi.org/10.5281/zenodo.1219550.

Conflicts of Interest

1e authors declare that there are no conflicts of interestregarding the publication of this paper.

Authors’ Contributions

Carlos E. Galván-Tejada and Laura A. Zanella-Calzadacontributed equally to this work.

References

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[2] R. Leyva-Jiménez, A. M. Hernández-Juárez, G. Nava-Jiménez,and V. López-Gaona, “Depresión en adolescentes y funcio-namiento familiar,” Revista Médica del Instituto Mexicano delSeguro Social, vol. 45, 2007.

[3] S. Goldman, “Developmental epidemiology of depressivedisorders,” Child and Adolescent Psychiatric Clinics of NorthAmerica, vol. 21, no. 2, pp. 217–235, 2012.

[4] L. Mendoza, L. d. P. S. Peinado, L. A. D. Mart́ınez, andA. Campo-Arias, “Prevalencia de sintomatologı́a depresiva enniños y niñas escolares de bucaramanga, Colombia,” RevistaColombiana de Psiquiatr, vol. 33, pp. 163–171, 2004.

[5] M. E. Buelna Serrano, L. Gutiérrez Herrera, and S. ÁvilaSaldoval, “El desarrollo de la economı́a de consumo en elcontexto del mundo bipolar de mediados del siglo xx. unavisión retrospectiva,” Análisis Económico, vol. 30, 2015.

[6] D. Agudelo, G. Buela-Casal, and C. D. Spielberger, “Ansiedady depresión: el problema de la diferenciación a través de losśıntomas,” Salud Mental, vol. 30, pp. 33–41, 2007.

[7] W. W. K. Zung, “A self-rating depression scale,” Archives ofgeneral psychiatry, vol. 12, no. 1, pp. 63–70, 1965.

[8] A. T. Beck, R. A. Steer, and G. K. Brown, Beck DepressionInventory-II, vol. 78pp. 490–498, San Antonio, TX, USA, 1996.

[9] S. Shiffman, A. A. Stone, and M. R. Hufford, “Ecologicalmomentary assessment,” Annual Review of Clinical Psychol-ogy, vol. 4, no. 1, pp. 1–32, 2008.

[10] F. Gravenhorst, A. Muaremi, J. Bardram et al., “Mobilephones as medical devices in mental disorder treatment: anoverview,” Personal and Ubiquitous Computing, vol. 19, no. 2,pp. 335–353, 2015.

[11] J. Firth, J. Cotter, R. Elliott, P. French, and A. R. Yung, “Asystematic review and meta-analysis of exercise interventionsin schizophrenia patients,” Psychological Medicine, vol. 45,no. 7, pp. 1343–1361, 2015.

Table 5: Comparison with related works on identification of depression.

Work Description

Averill et al. [24] Examining the response of the depression treatment based on the simple activity and psychomotorspeed measured through actigraphy identifying depressed episodes.

Garcia-Ceja et al. [15] Analyzing data collected through actigraphy comparing different machine learning techniques toclassify depressed subjects.

Gershon et al. [43] Identifying activity patterns from locomotor activity collected by actigraphy extracting a series ofprincipal components, discriminating depressive days from other states.

Koo et al. [23] Identification of depressed patients through the combination of biomarkers related to executivedysfunctions, motor activity, and neurophysiological patterns activity, among others.


http://datasets.simula.no/depresjon/http://datasets.simula.no/depresjon/http://doi.org/10.5281/zenodo.1219550

[12] J. Torous, R. Friedman, and M. Keshavan, “Smartphoneownership and interest in mobile applications to monitorsymptoms of mental health conditions,” JMIR mHealth anduHealth, vol. 2, no. 3, p. e34, 2014.

[13] L. Bayındır, “A survey of people-centric sensing studies uti-lizing mobile phone sensors,” Journal of Ambient Intelligenceand Smart Environments, vol. 9, no. 4, pp. 421–448, 2017.

[14] J. P. Garćıa-Vázquez, M. D. Rodŕıguez, Á. G. Andrade, andJ. Bravo, “Supporting the strategies to improve elders’ med-ication compliance by providing ambient aids,” Personal andUbiquitous Computing, vol. 15, no. 4, pp. 389–397, 2011.

[15] E. Garcia-Ceja, C. E. Galván-Tejada, and R. Brena, “Multi-view stacking for activity recognition with sound and ac-celerometer data,” Information Fusion, vol. 40, pp. 45–56,2018.

[16] E. Garcia-Ceja and R. Brena, “Activity recognition usingcommunity data to complement small amounts of labeledinstances,” Sensors, vol. 16, no. 6, p. 877, 2016.

[17] E. Garcia-Ceja, V. Osmani, and O. Mayora, “Automatic stressdetection in working environments from smartphones’ ac-celerometer data: a first step,” IEEE Journal of Biomedical andHealth Informatics, vol. 20, no. 4, pp. 1053–1060, 2016.

[18] A. Gruenerbl, “Using smart phone mobility traces for thediagnosis of depressive and manic episodes in bipolar pa-tients,” in Proceedings of the 5th Augmented Human In-ternational Conference, Kobe, Japan, March 2014.

[19] A. G. Reece and C. M. Danforth, “Instagram photos revealpredictive markers of depression,” EPJ Data Science, vol. 6,p. 15, 2017.

[20] A. Grunerbl, A. Muaremi, V. Osmani et al., “Smartphone-based recognition of states and state changes in bipolardisorder patients,” IEEE Journal of Biomedical and HealthInformatics, vol. 19, no. 1, pp. 140–148, 2015.

[21] A. Maxhuni, A. Muñoz-Meléndez, V. Osmani, H. Perez,O. Mayora, and E. F. Morales, “Classification of bipolardisorder episodes based on analysis of voice and motor ac-tivity of patients,” Pervasive and Mobile Computing, vol. 31,pp. 50–66, 2016.

[22] J. O. Berle, E. R. Hauge, K. J. Oedegaard, F. Holsten, andO. B. Fasmer, “Actigraphic registration of motor activityreveals a more structured behavioural pattern in schizo-phrenia than in major depression,” BMC Research Notes,vol. 3, no. 1, p. 149, 2010.

[23] P. C. Koo, C. Berger, G. Kronenberg et al., “Combinedcognitive, psychomotor and electrophysiological biomarkersin major depressive disorder,” European Archives of Psychi-atry and Clinical Neuroscience, pp. 1–10, 2018, In press.

[24] I. R. Averill, M. Crowe, C. M. Frampton et al., “Clinical re-sponse to treatment in inpatients with depression correlateswith changes in activity levels and psychomotor speed,”Australian &New Zealand Journal of Psychiatry, vol. 52, no. 7,pp. 652–659, 2018.

[25] A. Huguet, S. Rao, P. J. McGrath et al., “A systematic review ofcognitive behavioral therapy and behavioral activation appsfor depression,” PLoS One, vol. 11, no. 5, Article ID e0154248,2016.

[26] D. C. Mohr, M. Zhang, and S. M. Schueller, “Personal sensing:understanding mental health using ubiquitous sensors andmachine learning,” Annual review of clinical psychology,vol. 13, no. 1, pp. 23–47, 2017.

[27] S. C. Guntuku, D. B. Yaden, M. L. Kern, L. H. Ungar, andJ. C. Eichstaedt, “Detecting depression and mental illness onsocial media: an integrative review,” Current Opinion inBehavioral Sciences, vol. 18, pp. 43–49, 2017.

[28] E. Garcia-Ceja, “Depresjon: a motor activity database ofdepression episodes in unipolar and bipolar patients,” inProceedings of the 9th ACM on Multimedia Systems Confer-ence, MMSys’18, ACM, New York, NY, USA, 2018.

[29] D. Paul, R. Su, M. Romain, V. Sébastien, V. Pierre, andG. Isabelle, “Feature selection for outcome prediction inoesophageal cancer using genetic algorithm and randomforest classifier,” Computerized Medical Imaging andGraphics, vol. 60, pp. 42–49, 2017.

[30] V. Trevino and F. Falciani, “Galgo: an r package for multi-variate variable selection using genetic algorithms,” Bio-informatics, vol. 22, no. 9, pp. 1154–1156, 2006.

[31] N. Dogru and A. Subasi, “Traffic accident detection usingrandom forest classifier,” in Proceedings of the Learning andTechnology Conference, Jinan, China, May 2018.

[32] A. Liaw andM.Wiener, Breiman and Cutler’s Random Forestsfor Classification And Regression, R package Version 4.6-12, RFoundation for Statistical Computing, Vienna, Austria, 2015.

[33] M. Belgiu and L. Drăguţ, “Random forest in remote sensing: areview of applications and future directions,” ISPRS Journal ofPhotogrammetry and Remote Sensing, vol. 114, pp. 24–31,2016.

[34] T. Saito and M. Rehmsmeier, “Precrec: fast and accurateprecision-recall and ROC curve calculations in R,” Bio-informatics, vol. 33, no. 1, pp. 145–147, 2017.

[35] A. P. Bradley, “1e use of the area under the roc curve in theevaluation of machine learning algorithms,” Pattern recog-nition, vol. 30, no. 7, pp. 1145–1159, 1997.

[36] R Core Team, A Language and Environment for StatisticalComputing, R Foundation for Statistical Computing, Vienna,Austria, 2018.

[37] V. Trevino and F. Falciani: “Galgo: genetic algorithms formultivariate statistical models from large-scale functionalgenomics data, R package version 1.4”, 2018.

[38] X. Robin, N. Turck, A. Hainard et al., “Proc: an open-sourcepackage for r and s+ to analyze and compare roc curves,” BMCBioinformatics, vol. 12, p. 77, 2011.

[39] D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, andF. Leisch, E1071: Misc Functions of the Department of Sta-tistics, Probability Leory Group (Formerly: E1071), TU Wien,R package version 1.7-0, 2018.

[40] A. Liaw and M. Wiener, “Classification and regression byrandomforest,” R News, vol. 2, pp. 18–22, 2002.

[41] M. K. C. Jed Wing, Caret: Classification and RegressionTraining, 2018, R package version 6.0-79.

[42] P. Cortez, Rminer: Data Mining Classification and RegressionMethods, 2016, R package version 1.4.2.

[43] A. Gershon, N. Ram, S. L. Johnson, A. G. Harvey, andJ. M. Zeitzer, “Daily actigraphy profiles distinguish depressiveand interepisode states in bipolar disorder,” Clinical Psy-chological Science, vol. 4, no. 4, pp. 641–650, 2016.


Research ArticleEmpirical Study Based on the Perceptions of Patients andRelatives about the Acceptance of Wearable Devices to ImproveTheir Health and Prevent Possible Diseases

Francisco D. Guillén-Gámez 1 and Marı́a J. Mayorga-Fernández 2

1Department of Research and Diagnostic Methods, Faculty of Education, Pontifical University of Salamanca, C/ Henry Collet,52-70, 37007 Salamanca, Spain2Department of Didactics and School Organization, Faculty of Education Sciences of Málaga, University of Málaga,Avda. Cervantes, 2, 29071 Málaga, Spain

Correspondence should be addressed to Francisco D. Guillén-Gámez; [email protected]

Received 7 November 2018; Accepted 21 February 2019; Published 1 April 2019

Guest Editor: Dilip Sarkar

Copyright © 2019 Francisco D. Guillén-Gámez and Maŕıa J. Mayorga-Fernández.0is is an open access article distributed underthe Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.

In recent years, there has been a technological revolution affecting all areas of life, including health. Following this trend, a series ofelectronic devices called wearable technology have emerged with the aim of improving the quality of people’s life. 0ese devicesare made up of tools that help to prevent the development of chronic diseases and fight against the aging of society by monitoringthe vital signs of users. Despite the advantages of using these devices from medical and health points of view, it is necessary toknow the opinion of users regarding their use. For this reason, this study has been proposed.0e purpose of this study is to analysethe perceptions of the subjects involved in the research regarding the acceptance of wearable devices, taking into account variables,such as age and gender. To this end, a nonexperimental, ex post facto design has been composed that combines descriptive andinferential techniques, with a sample of 606 patients and relatives belonging to a public health centre in the community of Madrid.

1. Introduction

Current households increasingly rely on consumer elec-tronic devices which are intended both for leisure and forimproving daily housework. One type of technological de-vice that is presently having a large impact is the wearabledevice (wearable technology). According to Vijayalakshmiet al. [1], in 2018, approximately 210 million units ofwearable devices were produced, amounting revenues ofmore than USD 30 billion.

In recent years, the internet has passed through severalphases: the first focused on information, while the secondfocused on people. However, different researchers predictthat a new phase will occur, the next decade being the In-ternet of 0ings (IoT), in which there will be millions ofwearable devices (including medical devices) that use bigdata and will be connected to the network in order to ex-change data with one other [2, 3].

Raskovic et al. [4] and Yang et al. [5] define wearabletechnology as a set of electronic devices (for example, smartwatches, sports shoes with built-in GPS, and wristbandscontrolling the state of health) that are incorporated in someparts of the body (for example, footwear, clothes, and smartglasses) and interact continuously with the user in order toperform a specific function. 0e launch of Google Glassesand Apple’s Apple Watch has marked a turning point in theincorporation of these technologies in the daily routines ofpeople. 0ey have become particularly useful in the fields ofmedicine and health [6–8].

Health systems fight against the aging of the populationand the development of chronic diseases, which can beprevented, such as hypertension, diabetes, and cardiovas-cular diseases [9, 10]. In response to these challenges, amultitude of researchers have found wearable technologyand health-related apps to be a possible solution to improvethe quality of people’s life [11–13]. In this way, Canhoto and


mailto:[email protected]://orcid.org/0000-0001-6470-526Xhttp://orcid.org/0000-0003-3749-1264https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2019/4731048

Arp [14] affirm that wearable technology products canprovide highly accurate metrics regarding the vital condi-tions of people. Consequently, users will be able to monitorand access information concerning their health more easilyin order for physicians to both prevent diseases and helppeople, collecting evidence and objective data for thetreatment of different types of diseases [15].

Due to the aging trend of the population of developedcountries, these wearable technologies will allow differentlife situations of patients or elderly people to be monitored,specifically in terms of the vital signs that guarantee theirhealth and well-being [16, 17]. For example, these devicescan measure a user’s heart rate, blood pressure, respiratoryfunction, and the level of calories burned during exercise andsteps walked, among other aspects [18, 19]. As Yetisen et al.[13] state, medical data can be sent to a healthcare providerto receive therapeutic feedback or can act automatically byother devices in the network.

Despite the advantages of using wearable devices frommedical and health points of view, it is necessary to know theopinion of users regarding the acceptance of these devicesaccording to medical use and prevention of future diseasessince there is currently little research that focuses on thisstudy problem. In order to broaden this issue, the presentstudy has been raised, objective of which is to analyse theperceptions of the subjects involved in the research re-garding the acceptance of wearable devices as health tools inrelation to their age and gender.

2. Theoretical Framework

2.1. Development of Instruments toMeasure the Acceptance ofWearable Devices. Although the market for smart watchesand other wearable devices is rapidly increasing, estimatesshow that current sales are still relatively low [20]. A lot ofresearch has focused on the acceptance of consumers of newtechnologies and intelligent devices [21–23]. However, fewstudies have been carried out so far to measure the level ofacceptance in the field of wearable health technologies.

For example, Chuah et al. [24] created a theoreticalinstrument based on the technological acceptance of smartwatches. 0e instrument was completed by 226 studentsfrom the University of Malaysia, where 77.9% were femaleand the average age of the sample was 21.4 years. 0e in-strument they created was composed of nine dimensions,among which the following four components stand out:perceived utility, ease of use, attitudes of use, and purchaseintention. Among the findings, the authors found that theease of use of smart watches is indirectly related to theattitude towards the use of these wearable devices. 0e sameresults regarding wearable technologies were found by Chae[25] and Basoglu et al. [26].

Along the same lines, Dehghani et al. [27] developed aninstrument on the intentions of using wearable devices. Todo this, the authors used a sample of 385 users, who an-swered a survey divided into six subdimensions, highlightingthe attractive aesthetic scale, operational comfort, use ofmotivation, healthology, and intended use. 0e resultsshowed that the aesthetic appeal was positively related to the

intention of use, although the healthology dimension had nosignificant effect on the intended use, which was in contrastto the results obtained by Dehghani [28]. In addition, theintended and continued use of these devices was positivelyrelated to the actual use. Some of these findings are cor-roborated by the studies of Rauschnabel and Ro [29] andChuah et al. [24], which determine that users see wearabledevices as fashion accessories and that this significantlyaffects their intention to use them.

On the other hand, in recent years, new and increasinglysophisticated online attacks have appeared in order to hackthe vulnerability of these technological devices and theconfidentiality of information in databases [30–32], anddifferent authors have investigated about it [33, 34]. Forinstance, Das et al. [35] discovered privacy leaks in Bluetoothactivation between the exercise tracker and the smartphone,including user tracking, user activity detection, and iden-tification of people. In the same context, Langone et al. [36]analysed the link between different wearable devices andcommunication via Bluetooth, and they identified severalsecurity problems in a set of commercial portable devices.

For this reason, any company or medical institution thatuses wearable devices to monitor and save medical pa-rameters of its patients should seek solutions, together withtheir creators, in order not to lose information and confi-dentiality of use [37–39]. Different investigations haveanalysed users’ perception about the possible loss of privacyof their medical data when using wearable devices. Forexample, Spagnolli et al. [40] and Wen et al. [41] claim thatprivacy loss in sharing this data or the fact that someone canaccess it significantly threatens the acceptance of their use.

On the other hand, it is necessary to consider the theoryof Ziefle and Wilkowska [21], who affirm that demographicvariables, such as age, can significantly bias the acceptanceand use made by patients of technological devices related tohealth and medical care. Some scholars have already in-vestigated this topic [42, 43].

For example, Arning and Ziefle [44] evaluated thegeneral attitude and confidence in the use of e-Healthtechnologies, using a sample of 52 university students and 52adults. 0ey found that the adult group reported a signifi-cantly more positive attitude towards e-Health technologiesthan the younger group. However, contradictory findingswere found in the research of Röcker et al. [45], who de-termined that older adults may be more resistant or un-decided in adopting novel technological devices than youngadults due to different cultural, educational, and culturalfactors and previous experiences.

On the other hand, Rupp et al. [46] analysed, with asample of 103 participants in an age range of 18 to 83 years,the motivation and confidence to use wearable technologies.0us, they used different subdimensions, highlighting thereliability and privacy of these types of software.0e authorsdetermined that age influenced usability. 0ey concludedthat older adults often had more difficulty than youngeradults learning the use of new technologies, which couldeven lead to anxiety. Similar results have been found withrespect to mobile health services, with differences in the ageof the user groups anal

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