Comparing national device-based physical activity surveillance systems: a systematic review.

BACKGROUND: Physical activity surveillance systems are important for public health monitoring but rely mostly on self-report measurement of physical activity. Integration of device-based measurements in such systems can improve population estimates, however this is still relatively uncommon in existing surveillance systems. This systematic review aims to create an overview of the methodology used in existing device-based national PA surveillance systems. METHODS: Four literature databases (PubMed, Embase.com, SPORTDiscus and Web of Science) were searched, supplemented with backward tracking. Articles were included if they reported on population-based (inter)national surveillance systems measuring PA, sedentary time and/or adherence to PA guidelines. When available and in English, the methodological reports of the identified surveillance studies were also included for data extraction. RESULTS: This systematic literature search followed the PRISMA guidelines and yielded 34 articles and an additional 18 methodological reports, reporting on 28 studies, which in turn reported on one or multiple waves of 15 different national and 1 international surveillance system. The included studies showed substantial variation between (waves of) systems in number of participants, response rates, population representativeness and recruitment. In contrast, the methods were similar on data reduction definitions (e.g. minimal number of valid days, non-wear time and necessary wear time for a valid day). CONCLUSIONS: The results of this review indicate that few countries use device-based PA measurement in their surveillance system. The employed methodology is diverse, which hampers comparability between countries and calls for more standardized methods as well as standardized reporting on these methods. The results from this review can help inform the integration of device-based PA measurement in (inter)national surveillance systems.

Sharing Data Collected with Smartphone Sensors: Willingness, Participation, and Nonparticipation Bias.

Smartphone sensors allow measurement of phenomena that are difficult or impossible to capture via self-report (e.g., geographical movement, physical activity). Sensors can reduce respondent burden by eliminating survey questions and improve measurement accuracy by replacing/augmenting self-reports. However, if respondents who are not willing to collect sensor data differ on critical attributes from those who are, the results can be biased. Research on the mechanisms of willingness to collect sensor data mostly comes from (nonprobability) online panels and is hypothetical (i.e., asks participants about the likelihood of participation in a sensor-based study). In a cross-sectional general population randomized experiment, we investigate how features of the request and respondent characteristics influence willingness to share (WTS) and actually sharing smartphone-sensor data. We manipulate the request to either mention or not mention (1) how participation will benefit the participant, (2) participants' autonomy over data collection, and (3) that data will be kept confidential. We assess nonparticipation bias using the administrative records. WTS and actually sharing varies by sensor task, participants' autonomy over data sharing, their smartphone skills, level of privacy concerns, and attitudes toward surveys. Fewer people agree to share photos and a video than geolocation, but all who agreed to share photos or a video actually did. Some nonresponse and nonparticipation biases are substantial and make each other worse, but others jointly reduce the overall bias. Our findings suggest that sensor-data-sharing decisions depend on sample members' situation when asked to share and the nature of the sensor task rather than the sensor type.

Understanding Willingness to Share Smartphone-Sensor Data.

The growing smartphone penetration and the integration of smartphones into people's everyday practices offer researchers opportunities to augment survey measurement with smartphone-sensor measurement or to replace self-reports. Potential benefits include lower measurement error, a widening of research questions, collection of in situ data, and a lowered respondent burden. However, privacy considerations and other concerns may lead to nonparticipation. To date, little is known about the mechanisms of willingness to share sensor data by the general population, and no evidence is available concerning the stability of willingness. The present study focuses on survey respondents' willingness to share data collected using smartphone sensors (GPS, camera, and wearables) in a probability-based online panel of the general population of the Netherlands. A randomized experiment varied study sponsor, framing of the request, the emphasis on control over the data collection process, and assurance of privacy and confidentiality. Respondents were asked repeatedly about their willingness to share the data collected using smartphone sensors, with varying periods before the second request. Willingness to participate in sensor-based data collection varied by the type of sensor, study sponsor, order of the request, respondent's familiarity with the device, previous experience with participating in research involving smartphone sensors, and privacy concerns. Willingness increased when respondents were asked repeatedly and varied by sensor and task. The timing of the repeated request, one month or six months after the initial request, did not have a significant effect on willingness.

Disentangling mode-specific selection and measurement bias in social surveys.

A large-scale mixed-mode experiment linked to the Dutch Crime Victimization Survey was conducted in 2011. The experiment consisted of two waves; one wave with random assignment to one of the modes web, paper, telephone and face-to-face, and one follow-up wave to the full sample with interviewer modes only. The objective of the experiment is to estimate total mode effects and more specifically the corresponding mode effect components arising from undercoverage, nonresponse and measurement. In this paper, mode-specific selection and measurement bias are defined, and estimators for the bias terms based on the experimental design are introduced and discussed. The proposed estimators are applied to a number of key survey variables from the Labour Force Survey and the Crime Victimization Survey.