Open source smartphone app and tools for measuring, quantifying, and visualizing technology use

Geyer, Kris; Ellis, David; Shaw, Heather; Davidson, Brittany I.

doi:10.31234/osf.io/eqhfa

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…a more nuanced approach to understand both the costs and benefits of specific smartphone applications that can be monitored remotely (Geyer et al, 2020). Recent work has shown that although total time spent using smartphones had r = .16 effect sizes with anxiety and depression (matching our work), certain categories of applications have beneficial relationships (e.g., time spent reading books) (David et al, 2018).…”

Section: Choice Of Measurement Impacts Relationshipssupporting

confidence: 70%

Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health.

Shaw¹,

Ellis²,

Geyer³

et al. 2020

Technology, Mind, and Behavior

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Section: Choice Of Measurement Impacts Relationshipssupporting

confidence: 70%

Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health.

Shaw¹,

Ellis²,

Geyer³

et al. 2020

Technology, Mind, and Behavior

Self Cite

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“…Our findings, however, indicate only a modest association between self-reports and usage logs, leading us to conclude that self-report measures of media use may not be a valid stand-in for more objective measures. Notwithstanding the potential biases affecting log-data 5,35,39,40 , if these measures are taken to be a valid reflection of actual usage 5,21,32,41,85 , our findings raise important concerns about the validity of findings and conclusions across many areas of the social sciences in which self-reported media use is a central outcome or explanatory variable.…”

Section: Discussionmentioning

confidence: 86%

A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use

et al. 2021

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There is widespread public and academic interest in understanding the uses and effects of digital media. Scholars primarily use self-report measures of the quantity or duration of media use as proxies for more objective measures, but the validity of these self-reports remains unclear. Advancements in data collection techniques have produced a collection of studies indexing both self-reported and log-based measures. To assess the alignment between these measures, we conducted a meta-analysis of this research. Based on 106 effect sizes, we found that self-reported media use only moderately correlates with logged measurements, that self-reports were rarely an accurate reflection of logged media use, and that measures of problematic media use show an even smaller association with usage logs. These findings raise concerns about the validity of findings relying solely on self-reported measures of media use. The materials needed to reproduce the analysis and an article preprint are available at: https://osf.io/dhx48/.

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“…23 . Data from smartphones 24,25 , online forums 26,27 , fitness trackers 28,29 or pedometers 30,31 , sociometric badges 32,33 , devices to check air quality (e.g., particulate matter) 28 Digital footprint 'The digital traces each one of us leaves behind as we conduct our lives,' 20…”

Section: Digital What?mentioning

confidence: 99%

The crossroads of digital phenotyping

Davidson

2022

General Hospital Psychiatry

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The term 'Digital Phenotyping' has started to appear with increasing regularity in medical research, especially within psychiatry. This aims to bring together digital traces (e.g., from smartphones), medical data (e.g., electronic health records), and lived experiences (e.g., daily activity, location, social contact), to better monitor, intervene, and diagnose various psychiatric conditions. However, is this notion any different from digital traces or the quantified self? While digital phenotyping has the potential to transform and revolutionize medicine as we know it; there are a number of challenges that must be addressed if research is to blossom. At present, these issues include; (1) methodological issues, for example, the lack of clear theoretical links between digital markers (e.g., battery life, interactions with smartphones) and condition relapses, (2) the current tools being employed, where they typically have a number of security or privacy issues, and are invasive by nature, (3) analytical methods and approaches, where I question whether research should start in larger-scale epidemiological scale or in smaller (and potentially highly vulnerable) patient populations as is the current norm, (4) the current lack of security and privacy regulation adherence of apps used, and finally, (5) how do such technologies become integrated into various healthcare systems? This aims to provide deep insight into how the Digital Phenotyping could provide huge promise if we critically reflect now and gather clinical insights with a number of other disciplines such as epidemiology, computer-and the social sciences to move forward.

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Open source smartphone app and tools for measuring, quantifying, and visualizing technology use

Cited by 10 publications

References 18 publications

Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health.

Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health.

A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use

The crossroads of digital phenotyping

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