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1.
Sensors (Basel) ; 22(1)2021 Dec 25.
Article in English | MEDLINE | ID: mdl-35009667

ABSTRACT

Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20-69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants' postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson's correlations (rp) and limits of the agreement (LoA%). The within-session test-retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ rp ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed.


Subject(s)
Mobile Applications , Gait , Humans , Postural Balance , Reproducibility of Results , Smartphone , Walking , Walking Speed
2.
Sensors (Basel) ; 22(1)2021 Dec 28.
Article in English | MEDLINE | ID: mdl-35009713

ABSTRACT

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.


Subject(s)
Telemedicine , Tinnitus , Delivery of Health Care , Humans , Smartphone , Surveys and Questionnaires
3.
Sensors (Basel) ; 22(1)2022 Jan 05.
Article in English | MEDLINE | ID: mdl-35009950

ABSTRACT

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects' real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub's technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.


Subject(s)
Wearable Electronic Devices , Electroencephalography , Humans , Smartphone , Surveys and Questionnaires
4.
PLoS One ; 17(1): e0262105, 2022.
Article in English | MEDLINE | ID: mdl-34986171

ABSTRACT

OBJECTIVE: To evaluate the use of a COVID-19 app containing relevant information for healthcare workers (HCWs) in hospitals and to determine user experience. METHODS: A smartphone app (Firstline) was adapted to exclusively contain local COVID-19 policy documents and treatment protocols. This COVID-19 app was offered to all HCWs of a 900-bed tertiary care hospital. App use was evaluated with user analytics and user experience in an online questionnaire. RESULTS: A total number of 1168 HCWs subscribed to the COVID-19 app which was used 3903 times with an average of 1 minute and 20 seconds per session during a three-month period. The number of active users peaked in April 2020 with 1017 users. Users included medical specialists (22.3%), residents (16.5%), nurses (22.2%), management (6.2%) and other (26.5%). Information for HCWs such as when to test for SARS-CoV-2 (1214), latest updates (1181), the COVID-19 telephone list (418) and the SARS-CoV-2 / COVID-19 guideline (280) were the most frequently accessed advice. Seventy-one users with a mean age of 46.1 years from 19 different departments completed the questionnaire. Respondents considered the COVID-19 app clear (54/59; 92%), easy-to-use (46/55; 84%), fast (46/52; 88%), useful (52/56; 93%), and had faith in the information (58/70; 83%). The COVID-19 app was used to quickly look up something (43/68; 63%), when no computer was available (15/68; 22%), look up / dial COVID-related phone numbers (15/68; 22%) or when walking from A to B (11/68; 16%). Few respondents felt app use cost time (5/68; 7%). CONCLUSIONS: Our COVID-19 app proved to be a relatively simple yet innovative tool that was used by HCWs from all disciplines involved in taking care of COVID-19 patients. The up-to-date app was used for different topics and had high user satisfaction amongst questionnaire respondents. An app with local hospital policy could be an invaluable tool during a pandemic.


Subject(s)
COVID-19 , Health Personnel , Hospitals , Mobile Applications , Health Policy , Humans , Information Dissemination , SARS-CoV-2 , Smartphone
5.
Indian J Public Health ; 65(4): 340-344, 2021.
Article in English | MEDLINE | ID: mdl-34975075

ABSTRACT

Background: While a smartphone can be a hugely productive tool, excessive use of this device can interfere with work, education, our physical and mental health, and productivity. Nowadays, we do not just use our smartphones, but we rely on them. Objectives: The present study aims to develop and validate an instrument measuring the problematic use of smartphones among adults in a rural area of West Bengal, India. Methods: The questionnaire on problematic use of smartphone is a self-designed tool. The items were selected by literature review. The psychometric properties of the questionnaire were assessed by content validity, construct validity, and reliability. Exploratory factor analysis was performed to identify the factors. Results: Forty-two items were generated by literature review. After final analysis, the main questionnaire contained 28 items with 5 domains, namely "impulsive use of phone," "dependence," "impaired control," "denial," "decreased productivity," and "emotional attachment." The Cronbach's alpha value for three domains was found to be >0.7 and >0.8 for the other three domains. Conclusion: Excessive mobile phone use is associated with various adverse consequences which is emerging as a public health problem in a large number of population in India. Problematic use of smartphone questionnaire is a valid and reliable tool to assess the pattern of mobile use among Indian population.


Subject(s)
Rural Population , Smartphone , Adult , Humans , India , Reproducibility of Results , Surveys and Questionnaires
7.
Nutrients ; 13(12)2021 Nov 24.
Article in English | MEDLINE | ID: mdl-34959771

ABSTRACT

As physical inactivity is one of the four leading risk factors for mortality, it should be intensively treated. Therefore, this one-year follow-up study aimed to evaluate the long-term effects of a preventive app to increase physical activity in German adults under real-life circumstances. Data collection took place from July 2019 to July 2021 and included six online questionnaires. Physical activity was studied as the primary outcome based on MET-minutes per week (metabolic equivalent). Secondary outcomes included health-related quality of life based on a mental (MCS) and physical health component summary score (PCS). At the time of publication, 46/65 participants completed the study (median 52 years, 81.5% women). A significant increase of physical activity was observed in people with a low/moderate baseline activity during the first four months of follow-up (median increase by 490 MET-minutes per week, p < 0.001, r = 0.649). Both MCS (median increase by 2.8, p = 0.006, r = 0.344) and PCS (median increase by 2.6, p < 0.001, r = 0.521) significantly increased during the first two months and the BMI significantly decreased during the first six months after the intervention (median decrease by 0.96 kg/m2, p < 0.001, r = 0.465). Thus, this study provides evidence for the medium-term impact of the app, since the effects decreased over time. However, due to the chosen study design and a sizeable loss to follow-up, the validity of these findings is limited.


Subject(s)
Exercise/statistics & numerical data , Health Promotion/methods , Mobile Applications , Body Mass Index , Female , Follow-Up Studies , Germany , Humans , Male , Middle Aged , Outcome Assessment, Health Care , Quality of Life , Smartphone , Time Factors
8.
Sensors (Basel) ; 21(24)2021 Dec 08.
Article in English | MEDLINE | ID: mdl-34960273

ABSTRACT

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04-1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17-0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.


Subject(s)
Micro-Electrical-Mechanical Systems , Pedestrians , Algorithms , Gravitation , Humans , Smartphone
9.
Sensors (Basel) ; 21(24)2021 Dec 09.
Article in English | MEDLINE | ID: mdl-34960339

ABSTRACT

Chaos theory and its extension into cryptography has generated significant applications in industrial mixing, pulse width modulation and in electric compaction. Likewise, it has merited applications in authentication mechanisms for wireless power transfer systems. Wireless power transfer (WPT) via resonant inductive coupling mechanism enables the charging of electronic devices devoid of cords and wires. In practice, the key to certified charging requires the use of an authentication protocol between a transmitter (charger) and receiver (smartphone/some device). Via the protocol, a safe level and appropriate charging power can be harvested from a charger. Devoid of an efficient authentication protocol, a malicious charger may fry the circuit board of a receiver or cause a permanent damage to the device. In this regard, we first propose a chaos-based key exchange authentication protocol and analyze its robustness in terms of security and computational performance. Secondly, we theoretically demonstrate how the protocol can be applied to WPT systems for the purposes of charger to receiver authentication. Finally, we present insightful research problems that are relevant for future research in this paradigm.


Subject(s)
Electric Power Supplies , Wireless Technology , Electricity , Electronics , Smartphone
10.
Sensors (Basel) ; 21(24)2021 Dec 10.
Article in English | MEDLINE | ID: mdl-34960368

ABSTRACT

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.


Subject(s)
Deep Learning , Smartphone , Human Activities , Humans , Motion , Neural Networks, Computer
11.
Sensors (Basel) ; 21(24)2021 Dec 10.
Article in English | MEDLINE | ID: mdl-34960371

ABSTRACT

This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user's mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices.


Subject(s)
Disabled Persons , Wheelchairs , Algorithms , Quality of Life , Smartphone
12.
Sensors (Basel) ; 21(24)2021 Dec 13.
Article in English | MEDLINE | ID: mdl-34960412

ABSTRACT

The recent development of the smartphone Global Navigation Satellite System (GNSS) chipsets, such as Broadcom BCM47755 and Qualcomm Snapdragon 855 embedded, makes instantaneous and cm level real-time kinematic (RTK) positioning possible with Android-based smartphones. In this contribution we investigate the instantaneous single-baseline RTK performance of Samsung Galaxy S20 and Google Pixel 4 (GP4) smartphones with such chipsets, while making use of dual-frequency L1 + L5 Global Positioning System (GPS), E1 + E5a Galileo, L1 + L5 Quasi-Zenith Satellite System (QZSS) and B1 BeiDou Navigation Satellite System (BDS) code and phase observations in Dunedin, New Zealand. The effects of locating the smartphones in an upright and lying down position were evaluated, and we show that the choice of smartphone configuration can affect the positioning performance even in a zero-baseline setup. In particular, we found non-zero mean and linear trends in the double-differenced carrier-phase residuals for one of the smartphone models when lying down, which become absent when in an upright position. This implies that the two assessed smartphones have different antenna gain pattern and antenna sensitivity to interferences. Finally, we demonstrate, for the first time, a near hundred percent (98.7% to 99.9%) instantaneous RTK integer least-squares success rate for one of the smartphone models and cm level positioning precision while using short-baseline experiments with internal and external antennas, respectively.


Subject(s)
Search Engine , Smartphone , Biomechanical Phenomena , Geographic Information Systems , Language
13.
Sensors (Basel) ; 21(24)2021 Dec 14.
Article in English | MEDLINE | ID: mdl-34960443

ABSTRACT

This paper proposes a high-order MIMO antenna operating at 3.5 GHz for a 5G new radio. Using an eighth-mode substrate integrated waveguide (EMSIW) cavity and considering a typical smartphone scenario, a two-element MIMO antenna is developed and extended to a twelve-element MIMO. These MIMO elements are closely spaced, and by employing multiple diversity techniques, high isolation is achieved without using a decoupling network. The asymmetric EMSIW structures resulted in radiation pattern diversity, and their orthogonal placement provides polarization diversity. The radiation characteristics and diversity performance are parametrically optimized for a two-element MIMO antenna. The experimental results exhibited 6.0 dB and 10.0 dB bandwidths of 250 and 100 MHz, respectively. The measured and simulated radiation patterns are closely matched with a peak gain of 3.4 dBi and isolation ≥36 dB. Encouraged with these results, higher-order MIMO, namely, four- and twelve-element MIMO are investigated, and isolation ≥35 and ≥22 dB are achieved, respectively. The channel capacity is found equal to 56.37 bps/Hz for twelve-element MIMO, which is nearly 6.25 times higher than the two-element counterpart. The hand and head proximity analysis reveal that the proposed antenna performances are within the acceptable limit. A detailed comparison with the previous works demonstrates that the proposed antenna offers a simple, low-cost, and compact MIMO antenna design solution with a high diversity performance.


Subject(s)
Smartphone , Wireless Technology , Equipment Design , Records
14.
Sensors (Basel) ; 21(24)2021 Dec 14.
Article in English | MEDLINE | ID: mdl-34960448

ABSTRACT

The use of GPS positioning and navigation capabilities in mobile phones is present in our daily lives for more than a decade, but never with the centimeter level of precision that can actually be reached with several of the most recent smartphones. The introduction of the new GNSS systems (Global Navigation Satellite Systems), the European system Galileo, is opening new horizons in a wide range of areas that rely on precise georeferencing, namely the mass market smartphones apps. The constant growth of this market has brought new devices with innovative capabilities in hardware and software. The introduction of the Android 7 by Google, allowing access to the GNSS raw code and phase measurements, and the arrival of the new chip from Broadcom BCM47755 providing dual frequency in some smartphones came to revolutionize the positioning performance of these devices as never seen before. The Xiaomi Mi8 was the first smartphone to combine those features, and it is the device used in this work. It is well known that it is possible to obtain centimeter accuracy with this kind of device in relative static positioning mode with distances to a reference station up to a few tens of kilometers, which we also confirm in this paper. However, the main purpose of this work is to show that we can also get good positioning accuracy using long baselines. We used the ability of the Xiaomi Mi8 to get dual frequency code and phase raw measurements from the Galileo and GPS systems, to do relative static positioning in post-processing mode using wide baselines, of more than 100 km, to perform precise surveys. The results obtained were quite interesting with RMSE below 30 cm, showing that this type of smartphone can be easily used as a low-cost device, for georeferencing and mapping applications. This can be quite useful in remote areas where the CORS networks are not dense or even not available.


Subject(s)
Cell Phone , Smartphone , Data Collection , Geographic Mapping , Software
15.
Sensors (Basel) ; 21(24)2021 Dec 16.
Article in English | MEDLINE | ID: mdl-34960503

ABSTRACT

The fit of a lower limb prosthetic socket is critical for user comfort and the quality of life of lower limb amputees. Sockets are conventionally produced using hand-crafted patient-based casting techniques. Modern digital techniques offer a host of advantages to the process and ultimately lead to improving the lives of amputees. However, commercially available scanning equipment required is often expensive and proprietary. Smartphone photogrammetry could offer a low cost alternative, but there is no widely accepted imaging technique for prosthetic socket digitisation. Therefore, this paper aims to determine an optimal imaging technique for whole socket photogrammetry and evaluate the resultant scan measurement accuracy. A 3D printed transtibial socket was produced to create digital and physical twins, as reference models. The printed socket was photographed from 360 positions and simplified genetic algorithms were used to design a series of experiments, whereby a collection of photos were processed using Autodesk ReCap. The most fit technique was used to assess accuracy. The accuracy of the socket wall volume, surface area and height were 61.63%, 99.61% and 99.90%, respectively, when compared to the digital reference model. The scanned model had a wall thickness ranging from 2.075 mm at the top to 7.758 mm towards the base of the socket, compared to a consistent thickness of 2.025 mm in the control model. The technique selected did not show sufficient accuracy for clinical application due to the degradation of accuracy nearer to the base of the socket interior. However, using an internal wall thickness estimation, scans may be of sufficient accuracy for clinical use; assuming a uniform wall thickness.


Subject(s)
Artificial Limbs , Smartphone , Humans , Photogrammetry , Prosthesis Design , Quality of Life
16.
Sensors (Basel) ; 21(24)2021 Dec 17.
Article in English | MEDLINE | ID: mdl-34960517

ABSTRACT

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users' daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either "potentially COVID-19 infected" or "no evident signs of infection". We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).


Subject(s)
COVID-19 , Wearable Electronic Devices , Artificial Intelligence , Humans , SARS-CoV-2 , Smartphone
17.
Int J Environ Res Public Health ; 18(24)2021 12 10.
Article in English | MEDLINE | ID: mdl-34948631

ABSTRACT

Despite the potential risks of excessive smartphone use for maladaptive outcomes, the link between smartphone use and aggression remains less understood. Furthermore, prior findings are inconclusive due to a narrow focus on limited aspects of smartphone use (e.g., screen time) and reliance on self-reported assessments of smartphone use. Therefore, using objective measures of smartphone use, we sought to examine the associations between several key indices of smartphone use-screen time, checking behaviors, and addictive tendency-and multifaceted aggression (i.e., confrontation, anger, and hostility). In a cross-sectional study, we administered a series of questionnaires assessing aggressive tendencies (i.e., The Aggression Questionnaire) and various aspects of smartphone use (N = 253, Mage = 21.8 years, female = 73.2%). Using structural equation modeling, we found that smartphone checking and addictive smartphone use predicted only hostility. In contrast, both objective and subjective measures of screen time did not predict any facets of aggression. These results highlight differing impacts of various indices of smartphone use on aggression and imply that excessive checking and addictive smartphone use are problematic smartphone-use behaviors that require more targeted interventions with respect to hostility.


Subject(s)
Behavior, Addictive , Internet Addiction Disorder , Adult , Aggression , Cross-Sectional Studies , Female , Humans , Latent Class Analysis , Smartphone , Surveys and Questionnaires , Young Adult
18.
Int J Environ Res Public Health ; 18(24)2021 12 14.
Article in English | MEDLINE | ID: mdl-34948774

ABSTRACT

Smartphones have become the primary devices for accessing the online world. The potential for smartphone use to become problematic has come into increasing focus. Students and young adults have been shown to use their smartphones at high rates and may be at risk for problematic use. There is limited research evaluating interventions for problematic smartphone use. The present research aimed to develop and evaluate a digital intervention for problematic smartphone use in a student population. A mixed-method case series design was used. The participants were 10 students with mild-moderate dependency on the online world (measured via a self-report questionnaire). An intervention comprising goal setting, personalised feedback, mindfulness, and behavioural suggestions was delivered via a smartphone application. Time spent on smartphones was measured objectively through the same application. Changes in problematic technology use, wellbeing, mindfulness, and sleep were also evaluated. The findings indicate that the intervention resulted in a reduction in self-reported problematic smartphone use, but not screen time. The findings also indicate that over the course of participation, there was a positive influence on wellbeing, online dependency, mindfulness, and sleep. However, the mechanisms of change could not be determined. The study provides preliminary evidence that a light-touch, smartphone-delivered package is an acceptable and effective intervention for students wishing to better manage their problematic smartphone use.


Subject(s)
Mindfulness , Mobile Applications , Humans , Screen Time , Smartphone , Students , Young Adult
19.
Int J Environ Res Public Health ; 18(24)2021 12 10.
Article in English | MEDLINE | ID: mdl-34948664

ABSTRACT

In the last decades, the use of Information and Communication Technologies (ICTs) has progressively spread to society and public administration. Health is one of the areas in which the use of ICTs has more intensively developed through what is now known as eHealth. That area has recently included mHealth. Spanish health system has stood out as one of the benchmarks of this technological revolution. The development of ICTs applied to health, especially since the outbreak of the pandemic caused by SARS Cov-2, has increased the range of health services delivered through smartphones and the development of subsequent specialized apps. Based on the data of a Survey on Use and Attitudes regarding eHealth in Spain, the aim of this research was to conduct a comparative analysis of the different eHealth and mHealth user profiles. The results show that the user profile of eHealth an mHealth services in Spain is not in a majority. Weaknesses are detected both in the knowledge and use of eHealth services among the general population and in the usability or development of their mobile version. Smartphones can be a democratizing vector, as for now, access to eHealth services is only available to wealthy people, widening inequality.


Subject(s)
COVID-19 , Telemedicine , Humans , SARS-CoV-2 , Smartphone , Spain
20.
PLoS One ; 16(12): e0261023, 2021.
Article in English | MEDLINE | ID: mdl-34936651

ABSTRACT

Since the outbreak of Covid-19, the use of digital devices, especially smartphones, remarkably increased. Smartphone use belongs to one's daily routine, but can negatively impact physical and mental health, performance, and relationships if used excessively. The present study aimed to investigate potential correlates of problematic smartphone use (PSU) severity and the mechanisms underlying its development. Data of 516 smartphone users from Germany (Mage = 31.91, SDage = 12.96) were assessed via online surveys in April and May 2021. PSU severity was significantly negatively associated with sense of control. In contrast, it was significantly positively linked to fear of missing out (FoMO), repetitive negative thinking (RNT), and daily time spent on smartphone use. In a moderated mediation analysis, the negative relationship between sense of control and PSU severity was significantly mediated by FoMO. RNT significantly moderated the positive association between FoMO and PSU severity. Specifically, the higher the RNT, the stronger the relationship between FoMO and PSU. The present findings disclose potential mechanisms that could contribute to PSU. Potential ways of how to reduce PSU severity are discussed.


Subject(s)
COVID-19/psychology , Internal-External Control , Smartphone/statistics & numerical data , Adolescent , Adult , Aged , Behavior, Addictive/psychology , COVID-19/epidemiology , Fear/psychology , Germany/epidemiology , Humans , Middle Aged , Thinking , Young Adult
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