The Comparative Study of the Relationship Between Smartphone Choice and Socio-Economic Indicators

Jamalova, Maral; Milán, Constantinovits

doi:10.5539/ijms.v11n3p11

Cited by 17 publications

(34 citation statements)

References 24 publications

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“…Purchase of smartphone is a complex decision which is influenced by technical (Bringula et al, 2018), social (Hooi Ting et al, 2011) and economic factors (for example price, monthly income (Bringula et al, 2018)). The relationship between socio-economic indicators and smartphone vendors' market share have been already proved in developed and developing countries (Jamalova & Constantinovits, 2019). Comprehensive researches regarding the effect of mobile phones/smartphones on economic growth are from 2008 (World Bank, 2008), 2011 (Abdel-Wahab & El-Masry, 2011), and 2016 (James, 2016).…”

Section: Consumer Behavior In the Smartphone Marketmentioning

confidence: 99%

Smart for development: Income level as the element of smartphone diffusion

Jamalova¹,

Constantinovits²

2020

10.5267/j.msl

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For understanding the place of income in smartphone diffusion, this paper aims to find out the dependence of smartphone market indicators on income level worldwide and in the developing economies. The strength of the relationship between above-mentioned variables identifies the speed of smartphone diffusion influenced by income. For current purposes, secondary data from 189 countries were analyzed by using Hierarchical Cluster Analysis and Chi-square analysis. The results confirmed that income level influences smartphone purchase and diffusion. However, the strength of the relationship between income and smartphone market indicators is diverse globally and in developing countries. In developing countries, the strength of the relationship is smaller in comparison with the overall results. It means that income level has weak moderating effect in smartphone diffusion, and it cannot be considered as a key indicator of smartphone diffusion in developing countries according to results of the analysis in 2016 and the arithmetic mean of 2013-2017 years.

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Section: Consumer Behavior In the Smartphone Marketmentioning

confidence: 99%

Smart for development: Income level as the element of smartphone diffusion

Jamalova¹,

Constantinovits²

2020

10.5267/j.msl

Self Cite

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“…Apps that are only available through Google Play were not included. Previous research indicates that users of Androids usually fall within a lower socioeconomic barrier than iPhone users, and it may be of interest to characterize any difference in symptom tracking functionality and behavior among the two user groups [ 25 ]. Furthermore, the apps were piloted through a single simulated cycle, which limits our ability to analyze the symptom trend reporting feature some apps presented.…”

Section: Discussionmentioning

confidence: 99%

The real-world applications of the symptom tracking functionality available to menstrual health tracking apps

Adnan¹,

Coull

Jukic

et al. 2021

Current Opinion in Endocrinology, Diabetes &Amp; Obesity

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Purpose of review The goal of this review was to evaluate whether the fields available in iOS mobile phone apps for recording menstrual cycle symptoms are able to be harmonized across platforms for potential use in research, such as aggregated data analysis. Recent findings Symptom tracking capabilities are a common feature among menstrual health apps but have been the subject of limited investigations. Apps differ with respect to which symptoms are included and the rationale for these differences is unclear. Epidemiologic studies are poised to incorporate these data; however, a thorough exploration of symptom tracking functionality across apps is required. Summary Our review finds that the language used to describe symptoms and the specificity with which symptoms are collected varies greatly across the most used iOS tracking apps. Although some apps allow numerical and qualitative description of symptoms, such as sleep and mood, most simply record the presence or absence of a symptom. Collaborative efforts between clinicians and researchers to guide what and how data is collected may allow tracking apps to realize their potential diagnostic applicability. Regardless, with the increasing use of menstrual health tracking apps, it is imperative that data retrieved from such apps can realize its potential in the research and clinical ecosystems.

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“…There exists a potential selection bias due to the accessibility and affordability of Android phones as opposed to iOS-based phones. Research shows that Android phones are generally more affordable and financially available to those with a lower socioeconomic status (SES) [ 20 ] and that there are higher rates of anxiety and depression present among lower SES populations, possibly due to inferior or limited medical treatment [ 21 ]. Thus, this technological criterion may create a biased sample based on SES and the subsequent mental health disorders precipitated by the living conditions of those within this SES.…”

Section: Discussionmentioning

confidence: 99%

Remote Monitoring Telemedicine (REMOTE) Platform for Patients With Anxiety Symptoms and Alcohol Use Disorder: Protocol for a Case-Control Study

Hernández¹,

Khalilian²,

Caballería³

et al. 2020

JMIR Res Protoc

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Background Monitoring mental health outcomes has traditionally been based on heuristic decisions, often based on scarce, subjective evidence, making the clinical decisions made by professionals, as well as the monitoring of these diseases, subject to flaws. However, the digital phenotype, which refers to the analysis of data collected by measuring human behavior with mobile sensors and smart bracelets, is a promising tool for filling this gap in current clinical practice. Objective The objectives of this study are to develop the digital phenotyping of patients with alcohol use disorder and anxiety symptoms using data collected from a mobile device (ie, smartphone) and a wearable sensor (ie, Fitbit) and to analyze usability and patient satisfaction with the data collection service provided by the app. Methods We propose to conduct a study among a group of 60 participants split into two subgroups—experimental and control—of 30 participants each. The experimental group will be recruited by physicians from the Hospital Clínic de Barcelona, and the control group will be recruited on a volunteer basis through fliers and social media. All participants will go through pretraining to ensure technological capability and understanding of tasks, then each participant will download the HumanITcare app and will be given a wearable sensor (ie, Fitbit). Throughout the 4-month period, participants will be monitored on a range of factors, including sleep cycle, heart rate, movement patterns, and sociability. All data from the wearable sensors and the mobile devices will be saved and sent to the HumanITcare server. Participants will be asked to complete weekly questionnaires about anxiety, depression, and alcohol use disorder symptoms. Research assistants will ensure timely responses. The data from both sensors will then be compared to the questionnaire responses to determine how accurately the devices can predict the same symptoms. Results The recruitment phase was completed in November 2019 and all the data were collected by the end of December 2019. Data are being processed; this process is expected to be completed by October 2020. Conclusions This study was created and conducted as a pilot study with the Hospital Clínic de Barcelona, with the purpose of exploring the feasibility of our approach. The study is focused on patients diagnosed with anxiety and alcohol use disorder, but participants were also monitored for depressive symptoms throughout the trial, although these were not part of the initial inclusion criteria. A limitation to our study was the exclusive use of Android smartphones over iOS devices; this could result in a potential selection bias, due to the accessibility and affordability of Android phones as opposed to iOS-based phones. Another limitation might be that reviews of usability and satisfaction could be confounded by factors such as age and familiarity. An additional function that we might add in future studies is the ability for patients to manage their own data. International Registered Report Identifier (IRRID) DERR1-10.2196/16964

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The Comparative Study of the Relationship Between Smartphone Choice and Socio-Economic Indicators

Cited by 17 publications

References 24 publications

Smart for development: Income level as the element of smartphone diffusion

Smart for development: Income level as the element of smartphone diffusion

The real-world applications of the symptom tracking functionality available to menstrual health tracking apps

Remote Monitoring Telemedicine (REMOTE) Platform for Patients With Anxiety Symptoms and Alcohol Use Disorder: Protocol for a Case-Control Study

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