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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.

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.

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.

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)

In this paper I review three key technology-related trends: 1) big data, 2) non-probability samples, and 3) mobile data collection. I focus on the implications of these trends for survey research and the research profession. With regard to big data, I review a number of concerns that need to be addressed, and argue for a balanced and careful evaluation of the role that big data can play in the future. I argue that these developments are unlikely to replace transitional survey data collection, but will supplement surveys and expand the range of research methods. I also argue for the need for the survey research profession to adapt to changing circumstances., Survey Research Methods, Vol 7, No 3 (2013)