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Antenatal depression impacts 7-20% of women globally, and can have serious consequences for both the mother and the infant. Preventative interventions are effective, but are cost-efficient only among those at high risk. As such, being able to predict and identify those at risk is invaluable for reducing the burden of care and adverse consequences, as well as improving treatment outcomes. While several approaches have been proposed in the literature for the automatic prediction of depressive states, there is a scarcity of research on automatic prediction of perinatal depression. Moreover, while there exist some works on the automatic prediction of postpartum depression using data collected in clinical settings and applied the model to a smartphone application, to the best of our knowledge, no previous work has investigated the automatic prediction of late antenatal depression using data collected via a smartphone app in the first and second trimesters of pregnancy. This study utilizes data measuring various aspects of self-reported psychological, physiological and behavioral information, collected from 915 women in the first and second trimesters of pregnancy using a smartphone app designed for perinatal depression. By applying machine learning algorithms on these data, this paper explores the possibility of automatic early detection of antenatal depression (i.e., during week 36 to week 42 of pregnancy) in everyday life without the administration of healthcare professionals. We compare uni-modal and multi-modal models and identify predictive markers related to antenatal depression. With multi-modal approach the model reaches a BAC of 0.75, and an AUC of 0.82.

This study explores some features of slider bars in the context of a multi-device web survey. Using data collected among the students of the University of Trento in 2015 and 2016 by means of two web surveys ( N = 6,343 and 4,124) including two experiments, we investigated the effect of the initial position of the handle and the presence of numeric labels on answers provided using slider bars. It emerged that the initial position of the handle affected answers and that the number of rounded scores increased with numeric feedback. Smartphone respondents appeared more sensitive to the initial position of the handle but also less affected by the presence of numeric labels resulting in a lower tendency to rounding. Yet, outcomes on anchoring were inconclusive. Overall, no relevant differences have been detected between tablet and PC respondents. Understanding to what extent interactive and engaging tools such as slider bars can be successfully employed in multi-device surveys without affecting data quality is a key challenge for those who want to exploit the potential of web-based and multi-device data collection without undermining the quality of measurement.

With the rise of mobile surveys comes the need for shorter questionnaires. We investigate the modularization of an existing questionnaire in the Longitudinal Internet Study for the Social Sciences (LISS) Panel in the Netherlands. We randomly divided respondents into a normal length survey condition, a condition where the same survey was split into 3 parts, and a condition where the survey was split into 10 parts. Respondents received the parts consecutively at regular intervals over a 1-month period. We discuss response rates, data quality measures, and respondents’ evaluation of the questionnaire. Our results indicate higher start rates when the survey is cut into smaller parts but also higher dropout rates. However, the fraction of missing information is lower in the 3- and 10-part conditions. More respondents use their mobile phone for survey completion when the survey is shorter. We find fewer item missings and satisficing in shorter surveys. We find no effect on neutral and extreme responding nor on estimates of the validity of answers. People with low and high education and young and old evaluate shorter surveys better than the normal length survey.

This paper presents the findings derived from a mobile friendly web survey, during which all invitations and reminders were sent as text messages via short message service (SMS) to the mobile telephones of the target group. The web survey under study was conducted for the National Theatre of Northern Greece (NTNG), the largest theatrical organization of Greece. The list of mobile phone numbers was provided by the organization and the aim of the survey was to collect data from people who have attended events organized by NTNG. The paper examines the impact of various design study features such as pre-notifications, time and day of SMS delivery, lag between invitations and reminders on survey response. It is demonstrated that SMS pre-notifications can significantly improve response rates. The paper suggests that when the mobile phone numbers of the target group are available, it is feasible to conduct a large-scale web survey using SMS as the only contact mode.

In this paper we discuss the implications of using mobile devices for online survey completion. With more and more people accessing online surveys on mobile devices, online surveys need to be redesigned in order to be able to meet the characteristics of mobile device usage, such as small screens and short messaging. We discuss mobile friendly design by focussing on survey layout, the length of the survey, special features, and the decision of making the survey app or browser based. Further, we discuss the different sensors that can be used to augment or replace survey questions, and respondents’ willingness to share sensor data. We end with three examples of surveys conducted by Statistics Netherlands, where sensors are used for active and passive measurement in mobile surveys.

As survey costs increase and response rates decrease, researchers are looking for alternative methods to collect data from study subjects. Passively collected data may offer a way to reduce the burden on research subjects while also collecting high-quality data needed for social science research. Examples of passive data collection tools are applications installed on mobile devices and sensors in subjects’ homes or worn on the body. In this study, we focus on always-on location data collected from subjects’ iPhones. To explore the promise of passively collected data to augment and improve survey data, we conducted a 2-week pilot study with 24 subjects. We discuss the utility of always-on location data and the challenges researchers may encounter when they incorporate location data in their analyses.

In this paper we discuss the implications of using mobile devices for online survey completion. With more and more people accessing online surveys on mobile devices, online surveys need to be redesigned in order to be able to meet the characteristics of mobile device usage, such as small screens and short messaging. We discuss mobile friendly design by focussing on survey layout, the length of the survey, special features, and the decision of making the survey app or browser based. Further, we discuss the different sensors that can be used to augment or replace survey questions, and respondents’ willingness to share sensor data. We end with three examples of surveys conducted by Statistics Netherlands, where sensors are used for active and passive measurement in mobile surveys.

A sizable minority of all web surveys are nowadays completed on smartphones. People who choose a smartphone for Internet-related tasks are different from people who mainly use a PC or tablet. Smartphone use is particularly high among the young and urban. We have to make web surveys attractive for smartphone completion in order not to lose these groups of smartphone users. In this paper we study how to encourage people to complete surveys on smartphones in order to attract hard-to-reach subgroups of the population. We experimentally test new features of a survey-friendly design: we test two versions of an invitation letter to a survey, a new questionnaire lay-out, and autoforwarding. The goal of the experiment is to evaluate whether the new survey design attracts more smartphone users, leads to a better survey experience on smartphones and results in more respondents signing up to become a member of a probability-based online panel. Our results show that the invitation letter that emphasizes the possibility for smartphone completion does not yield a higher response rate than the control condition, nor do we find differences in the socio-demographic background of respondents. We do find that slightly more respondents choose a smartphone for survey completion. The changes in the layout of the questionnaire do lead to a change in survey experience on the smartphone. Smartphone respondents need 20% less time to complete the survey when the questionnaire includes autoforwarding. However, we do not find that respondents evaluate the survey better, nor are they more likely to become a member of the panel when asked at the end of the survey. We conclude with a discussion of autoforwarding in web surveys and methods to attract smartphone users to web surveys., methods, data, analyses, Vol 13, No 2 (2019)

Mixed-mode surveys have been around since the late 1980s. In the past thirty years, major changes in technology and society influenced and changed data collection and survey methodology. However, in those years, mixed-mode strategies remained part of the daily survey practice, although the type of mix implemented followed the changes in technology and data collection methods. In this paper, I summarize the state of the art in traditional mixed-mode surveys and discuss implications for mixed device surveys., Survey Research Methods, Vol 12, No 2 (2018)

With the rise of mobile surveys comes the need for shorter questionnaires. We investigate the modularization of an existing questionnaire in the Longitudinal Internet Study for the Social Sciences (LISS) Panel in the Netherlands. We randomly divided respondents into a normal length survey condition, a condition where the same survey was split into 3 parts, and a condition where the survey was split into 10 parts. Respondents received the parts consecutively at regular intervals over a 1-month period. We discuss response rates, data quality measures, and respondents’ evaluation of the questionnaire. Our results indicate higher start rates when the survey is cut into smaller parts but also higher dropout rates. However, the fraction of missing information is lower in the 3- and 10- part conditions. More respondents use their mobile phone for survey completion when the survey is shorter. We find fewer item missings and satisficing in shorter surveys. We find no effect on neutral and extreme responding nor on estimates of the validity of answers. People with low and high education and young and old evaluate shorter surveys better than the normal length survey.

Background: Personal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky. Objective: We explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky. Methods: A total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio. Results: OpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher’s perspective, and hindering practicality in the field. Conclusions: OpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations. [JMIR Res Protoc 2017;6(6):e124]

Background: We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment. Objective: Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps. Methods: Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information. Results: The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects. Conclusions: The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes. [JMIR Mhealth Uhealth 2016;4(2):e46]

En el presente trabajo se efectúa una revisión de los diferentes estudios sobre el fenómeno de la isla de calor urbana (ICU) realizados en ciudades mediterráneas. De este modo se realizará una reseña de cada estudio, presentando los métodos utilizados y las principales conclusiones. Destaca cómo en Grecia y España es donde aparecen un mayor número de trabajos realizados y de ciudades estudiadas. En especial, la ciudad de Atenas es la que aglutina una mayor numero de estudios y de más diversas temáticas. Respecto a los tamaños de las ciudades, se puede observar como existe una diversidad de rangos, estando representadas las grandes, las medianas y las pequeñas., The research carries out a review of different studies about urban heat island (ICU) in mediterranean cities. In this way, it shows a brief description about the methods used by the researchers and the main results for each study. In Greece and Spain is where there are a larger number of studies and the cities analysed. Especially, Athens is the city that has a greater number of researches and more different topics. In relation to the size of cities, it can notice that exits diversity of ranges, being represented the big, medium and small ones.

опыт эмпирического исследования с использованием анкетирования на мобильных телефонах показал ограничения репрезентативности данного метода в сравнении с традиционными телефонным интервью в условиях единого методического инструментария и способа формирования выборки., Research conducted using mobile questionnaire has shown that mobile surveys exhibit external validity issues when compared to traditional telephone interviews in case of identical survey design and sampling technique.

This paper shows that computer users and smartphone users taking part in a web survey optimized for smartphones give responses of almost the same quality. Combining a design of one question in each page and innovative page navigation methods, we can get high quality data by both computer and smartphone users. The two groups of users are also compared with regard to their precisely measured item response times. The analysis shows that using a smartphone instead of a computer increases about 20% the geometric mean of item response times. The data analyzed in this paper were collected by a smartphone-friendly web survey. All question texts are short and the response buttons are large and easy to use. As a result, there are no significant interactions between smartphone use and either the length of the question or the age of the respondent. Thus, the longer response times among smartphone users should be explored in other causes, such as the likelihood of smartphone users being distracted by their environment., methods, data, analyses, Vol 9, No 2 (2015)