Innovative applications of new technologies for longitudinal measurement
All network activities in this third thematic area will focus on new technologies for data collection and the receptiveness of aging populations to the use of these technologies. Within the scope of methodological innovation in this third area are wearable devices for the collection of health outcomes (e.g., fitbits); passive data collection via smartphone sensors (e.g., steps walked per day, travel patterns); the use of focus groups and participatory design workshops to better understand the receptiveness of aging populations to the use of these devices for measurement; and expanding measurement capabilities (e.g., event-triggered data collection, health trackers (apps), ecological momentary assessment, biomeasures, cognition, etc.).
Annette Jäckle, Alexander Wenz, Jonathan Burton, Mick P Couper.
Increasing Participation in a Mobile App Study: The Effects of a Sequential Mixed-Mode Design and In-Interview Invitation.
Journal of Survey Statistics and Methodology, Volume 10, Issue 4, September 2022, Pages 898–922, https://doi.org/10.1093/jssam/smac006.
Mobile apps are an attractive and versatile method of collecting data in the social and behavioral sciences. In samples of the general population, however, participation in app-based data collection is still rather low. In this article, we examine two potential ways of increasing participation and potentially reducing participation bias in app-based data collection: (1) inviting sample members to a mobile app study within an interview rather than by post and (2) offering a browser-based follow-up to the mobile app. We use experimental data from Spending Study 2, collected on the Understanding Society Innovation Panel and on the Lightspeed UK online access panel. Sample members were invited to download a spending diary app on their smartphone or use a browser-based online diary to report all their purchases for one month. The results suggest that inviting sample members to an app study within a face-to-face interview increases participation rates but does not bring in different types of participants. In contrast, the browser-based alternative can both increase participation rates and reduce biases in who participates if offered immediately once the app had been declined. We find that the success of using mobile apps for data collection hinges on the protocols used to implement the app.
Jäckle, A., Burton, J., Couper, M. P., & Lessof, C. (2019).
Participation in a mobile app survey to collect expenditure data as part of a large-scale probability household panel: coverage and participation rates and biases.
Survey Research Methods, 13(1), 23–44. https://doi.org/10.18148/srm/2019.v1i1.7297.
This paper examines non-response in a mobile app study designed to collect expenditure data. We invited 2,383 members of the nationally representative Understanding Society Innovation Panel in Great Britain to download an app to record their spending on goods and services: participants were asked to scan receipts or report spending directly in the app every day for a month. We examine participation at different stages of the process. We further use data from the prior wave of the panel to examine the prevalence of potential barriers to participation, including access, ability and willingness to use different mobile technologies, and biases in the types of people who participate, considering socio-demographic characteristics, financial position and financial behaviours. While the participation rate was low, drop out was also low: over 80% of participants remained in the study for the full month. The main barriers to participation were access to, and frequency of use of mobile devices, willingness to download an app for a survey, and general cooperativeness with the survey. While there were strong biases in who participated in terms of socio-demographic characteristics (with women, younger, and more educated sample members being more likely to participate), and in terms of financial behaviours (with respondents who already use mobile devices to monitor their finances being more likely to participate), we found no biases in correlates of spending.
Wenz, A., Jäckle, A., & Couper, M. P. (2019).
Willingness to use mobile technologies for data collection in a probability household panel.
Survey Research Methods, 13(1), 1–22. https://doi.org/10.18148/srm/2019.v1i1.7298.
We asked members of the Understanding Society Innovation Panel about their willingness to participate in various data collection tasks on their mobile devices. We find that stated willingness varies considerably depending on the type of activity involved: respondents are less willing to participate in tasks that involve downloading and installing an app, or where data are collected passively. Stated willingness also varies between smartphones and tablets, and between types of respondents: respondents who report higher concerns about the security of data collected with mobile technologies and those who use their devices less intensively are less willing to participate in mobile data collection tasks.
Antoun, Christopher, Jonathan Katz, Josef Argueta, and Lin Wang. 2018.
Design heuristics for effective smartphone questionnaires.
Social Science Computer Review 36:557-574.
Smartphones an be very useful in survey research, but it is important the surveys are properly optimized to be completed on a smartphone. This can include selecting questions that are easier to complete on a smartphone, ensure that questions are fit to the width of smartphone screen to prevent horizontal scrolling, and choose simpler question types over more complex ones.
Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F., & Trappmann, M. (2022).
Missing data and other measurement quality issues in mobile geolocation sensor data.
Social Science Computer Review, 40, 212-235. 10.1177/0894439320944118.
Smartphones can provide valuable insight into human behavior patterns that cannot be measured through surveys. Although this data can be very useful and convenient to collect, there is some question around the quality of smartphone data, as errors can arise through geolocation measurement, or different individuals sharing a smartphone.
Bai, Y., Tompkins, C., Gell, N., Dione, D., Zhang, T., & Byun, W. (2021).
Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting.
PloS one, 16(5), e0251975. https://doi.org/10.1371/journal.pone.0251975.
The results of the study showed that the average step counts recorded by different devices were fairly close, ranging from 11,734 to 11,922 steps. The consumer monitors tested in the study generally provided accurate estimates of step counts, and the Charge2 and Apple2 devices performed reasonably well in heart rate estimation. However, all devices underestimated MVPA in real-life situations, which suggests that caution should be exercised when using these devices to track higher-intensity physical activities.
Blackwood, J., Suzuki, R., Webster, N., Karczewski, H., Ziccardi, T., & Shah, S. (2022).
Use of activPAL to measure physical activity in community dwelling older adults, a systematic review.
Archives of Rehabilitation Research and Clinical Translation, 100190.
The activPAL device is typically used to collect data on step count and walking, sit-to-stand transitions, and sedentary time in older adults. Older adults tend to be more sedentary than other age groups, making data collected from the activPAL device especially useful for current and future research on physical activity in older adults.
Borger, Jay N., Reto Huber and Arko Ghosh. 2019.
Capturing sleep-wake cycles by using day-to-day smartphone touchscreen interactions.
npj Digital Medicine 2:73.
Smartphone technology can be utilized to measure a variety of daily activities, including the sleep-wake cycle. The data collected by smartphones can be further enhanced and verified by participant sleep diaries and actigraphy (a small device that is worn like a watch and monitors sleep activity).
Clarke-Deelder, E., Rokicki, S., McGovern, M. E., Birabwa, C., Cohen, J. L., Waiswa, P., & Abbo, C. (2022).
Levels of depression, anxiety, and psychological distress among Ugandan adults during the first wave of the COVID-19 pandemic: cross-sectional evidence from a mobile phone-based population survey.
Global mental health (Cambridge, England), 9, 274–284. https://doi.org/10.1017/gmh.2022.28.
The study found that 29.2% of the participants experienced moderate psychological distress, with 12.1% reporting severe distress. Factors such as difficulty accessing medical care, worries about COVID-19, concerns about interactions with police during lockdown measures, and spending more time at home were associated with higher levels of distress. The findings highlight the urgent need to address the mental health consequences of the pandemic and consider mental health in policy responses, especially in low- and middle-income countries (LMICs).
Couper, M., & Jäckle, A. (2023).
Participation and Selection Bias in the SHARE Accelerometer Study (SAS).
Paper presented at the Mobile Apps and Sensors in Surveys Workshop, Manchester, UK, June, https://massworkshop.org/2022/04/13/materials_2023/couper-jackle-mass-2023-06-22/.
This study involved a subsample of respondents from 10 countries wearing an accelerometer on their upper thigh for 8 days. While some evidence supported the “healthy volunteer” hypothesis, indicating overrepresentation of healthier individuals in the fully-adherent group, biases and patterns were not necessarily consistent. Addressing the challenge of obtaining consent and minimizing delays between consent and task onset were identified as important factors in improving participation rates.
Cornesse, C, Krieger, U, Sohnius, M-L, et al.
From German Internet Panel to Mannheim Corona Study: Adaptable probability-based online panel infrastructures during the pandemic.
J R Stat Soc Series A. (2022); 185, 773– 797. https://doi.org/10.1111/rssa.12749.
COVID-19 has created an immediate need for frequently updated information surrounding the societal impacts of the pandemic. The Mannheim Corona Study (MCS) was designed to collect this data, based on the existing probability-based online panel infrastructure of the German Internet Panel (GIP).
Edwardson, C. L., Winkler, E. A., Bodicoat, D. H., Yates, T., Davies, M. J., Dunstan, D. W., & Healy, G. N. (2017).
Considerations when using the activPAL monitor in field-based research with adult populations.
Journal of sport and health science, 6(2), 162-178.
Lack of physical activity is related to reduced health outcomes, therefore making it extremely important to understand the mechanisms by which health outcomes are influenced by physical activity. Wearable devices such as the activPAL can be very useful in collecting data in field-based research settings on physical activity.
Fingerman, Karen L., Meng Huo, Susan T. Charles, and Debra J. Umberson. 2020.
Variety is the spice of late life: Social integration and daily activity.
The Journals of Gerontology: Series B 75:377-88.
Android cellphone devices can be used to track daily activities, as well as record sound that is detected near the device. Through this mechanism, in a sample of largely Black and Hispanic persons, it was found that participants were more likely to watch television while alone, and also reported increased feelings of loneliness while watching television.
Gatz, M., Schneider, S., Meijer, E., Darling, J. E., Orriens, B., Liu, Y., & Kapteyn, A. (2022).
Identifying cognitive impairment among older participants in a Nationally Representative internet panel.
The Journals of Gerontology: Series B, 78(2), 201-209. doi:10.1093/geronb/gbac172.
Cognitive impairment is a major health issue impacting many older adults, making it imperative to have indicators of cognitive functioning. Web and phone surveys have shown to be useful in measuring cognitive functioning, allowing for the development of a cognitive impairment score.
Hu, M., Freedman, V. A., Ehrlich, J. R., Reed, N. S., Billington, C., & Kasper, J. D. (2021).
Collecting Objective Measures of Visual and Auditory Function in a National in-Home Survey of Older Adults.
The Journals of survey statistics and methodology, 9(2), 309–334. https://doi.org/10.1093/jssam/smaa044.
The National Health and Aging Trends Study (NHATS) has developed a protocol to objectively measure visual and auditory function in Medicare beneficiaries aged 65 and older. The protocol includes vision tests for distance and near acuity, contrast sensitivity, and a hearing test using a tablet platform. The study found that objective measures were more effective in identifying visual and auditory impairments and had stronger associations with demographic factors. Overall, the study demonstrated that objective visual and auditory functioning can be successfully measured through an interviewer-administered home-based protocol.
Keusch, F., Bähr, S., Haas, G., Kreuter, F., Trappmann, M., & Eckman, S. (2022).
Non‐participation in smartphone data collection using research apps.
The Journals of the Royal Statistical Society: Series A (Statistics in Society), 185(S2). doi:10.1111/rssa.12827.
Utilizing smartphone applications can be an extremely useful tool for administering surveys and collecting behavioral data. The likelihood of using a smartphone application was found to be lower for women and older adults, as well as for those with higher levels of educational attainment.
Keusch, F. & Conrad F. G. (2022).
Using smartphones to capture and combine self-reports and passively measured behavior in social research. Journal of Survey Statistics and Methodology, 10, 863-885.
Smartphones make it possible to collect both self-reported data as well as passively measured behavior data simultaneously. Utilizing these data sources in conjunction with one another can be especially useful in measuring and contextualizing data, but can be difficult to implement due to concerns around privacy and individual’s willingness to share their smartphone data.
Keusch, F., Wenz, A., & Conrad, F. (2022).
Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors.
Computers in Human Behavior, 127, 107054. 10.1016/j.chb.2021.107054.
Smartphones can be extremely useful in collecting data about daily activities (sleep, physical activity, etc.), as they are typically in close proximity to their users. The feasibility of using smartphones to collect this sort of information depends largely on the ability to translate raw smartphone sensor data to activity outcomes, which can be influenced by both sociodemographic and smartphone-related characteristics.
Leister, K. R., Garay, J., & Barreira, T. V. (2022).
Validity of a Novel Algorithm to Detect Bedtime, Wake Time, and Sleep Time in Adults.
Journal for the Measurement of Physical Behaviour, 5(2), 76-84.
The activPAL Technologies’ CREA algorithm can be used to track bedtime, wake time, and sleep time, but there are marked differences between the measured sleep and wake times using the activPAL and ActiGraph devices. The activPAL algorithm appeared to overestimated sleep time by measuring earlier bedtimes and later wake times.
McCarthy, Hannah, Henry W. W. Potts, and Abigail Fisher. 2021.
Physical Activity Behavior Before, During, and After COVID-19 Restrictions: Longitudinal Smartphone-Tracking Study of Adults in the United Kingdom.
Journal of Medical Internet Research 23(2):e23701.
The COVID-19 pandemic had a significant impact on physical activity. Using the BetterPoints smartphone application, physical activity was measured throughout the COVID-19 lockdown, and found that participants who were most active prior to the pandemic demonstrated the most significant decreases in physical activity at the onset of the pandemic. Additionally, older adult participants were found to have a less significant decrease in physical activity at the beginning of the pandemic, but became more sedentary as the pandemic progressed.
Olmsted-Hawala, Erica, Elizabeth Nichols, Brian Falcone, Ivonne J. Figueroa, Christopher Antoun, and Lin Wang. 2018.
Optimal data entry designs in mobile web surveys for older adults.
In Human Aspects of IT for the Aged Population. Acceptance, Communication and Participation. ITAP 2018. Lecture Notes in Computer Science, edited by Jia Zhou and Gavriel Salvendy. 335-54. Cham: Springer.
Smartphones can be very useful in administering web surveys, but special survey design considerations must be taken into account when creating web surveys for the older adult population. It is important to clearly label the survey and its navigation tools to ensure the highest level of usability within this population.
Rivas, Alda, Christopher Antoun, Shelley Feuer, Thomas Mathew, Elizabeth Nichols, Erica Olmsted-Hawala, and Lin Wang. 2022.
Comparison of Three Navigation Button Designs in Mobile Survey for Older Adults.
Survey Practice, August. https://doi.org/10.29115/SP-2022-0005.
Given the increase in utilization of smartphones to conduct web surveys, it has become increasingly more important to consider the challenges that older adults may face when taking mobile surveys. Using a within-subjects experimental design (the same participants tested each survey design), this study determined that a below-content design promotes higher data quality and participant satisfaction, and is therefore recommended for use in mobile surveys for older adults.
Stopczynski, Arkadiusz, Vedran Sekara, Piotr Sapiezynski, Andrea Cuttone, Mette My Madsen, Jakob Eg Larsen, and Sune
Measuring large scale social networks with high resolution.
PLOS One 9(4):e95978.
Bluetooth and Wi-Fi networks can be very useful in collecting information about social networks within a specific location, and can be utilized to make connections within aging populations residing within assisted living facilities. This social network data can also be connected to relevant health data.
Trappmann, M., Bähr, S., Malich, S., Keusch, F., Schwarz, S., Haas, G.-C., & Kreuter, F. (2022).
Augmenting survey data with other data types: Is there a threat to panel retention?
Journal of Survey Statistics and Methodology. Published online before print June 28, 2022. 10.1093/jssam/smac023.
Linking trace data to existing panel survey data can improve the analysis potential of the data, but can be sometimes difficult to implement due to requiring additional consent and completion of tasks from survey participants. In order to address these concerns, a group of participants were invited to install a smartphone app to share additional data, but this request was found to decrease panel retention in the following wave.
York Cornwell, Erin, and Kathleen A. Cagney. 2020.
Neighborhood disorder and distress in real time: Evidence from a
smartphone-based study of older adults.
Journal of Health and Social Behavior 61:521-43.
GPS technology can be used to track activity and location, and determine activity patterns between different populations. Those with lower educational attainment levels and socieoeconomic statuses were found to spend more time outside of their residential areas, likely due to lesser resources. Additionally, poverty rates in nonresidential areas tended to be lower.