ReviewNPJ digital medicine2019
Best practices for analyzing large-scale health data from wearables and smartphone apps.
Review in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
90 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Objectively measuring the association between the built environment and physical activity: a systematic review and reporting framework.The international journal of behavioral nutrition and physical activity · 2022Pooled it
- Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review.JMIR mHealth and uHealth · 2021Pooled it
- Digital manikins to self-report pain on a smartphone: A systematic review of mobile apps.European journal of pain (London, England) · 2021Pooled it
- Use of wearable biometric monitoring devices to measure outcomes in randomized clinical trials: a methodological systematic review.BMC medicine · 2020Pooled it
- Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Weight Loss Outcomes Among MyFitnessPal Users: Behavioral and Dietary Predictors of Success.Nutrients · 2026Article
- National implementation of a digital Parkinson's disease screening programme in Thailand: reach, adoption, and real-world performance of the CheckPD app.BMC public health · 2026Article
- Determining a Likely Mechanism of Missingness in Repeated Measures Sleep Data From Wearable Fitness Trackers: Longitudinal Analysis.JMIR mHealth and uHealth · 2026Article
- A hand biomechanics dataset of kinematics, kinetics, electromyography, and imaging in healthy adults.Scientific data · 2026Article
- The undue burdens of clinical trial participation: implications for equity, diversity, and inclusion.Trials · 2026Article
- Global physical activity resilience during the COVID-19 pandemic: an observational study of 5.17 million exercise app users across 65 countries.BMJ public health · 2026Article
- Article
- Advances in biomonitoring technologies for women's health.Nature communications · 2025Review
- Article
- A hand biomechanics dataset of kinematics, kinetics, electromyography, and imaging in healthy adults.bioRxiv : the preprint server for biology · 2025Article
- Consumer Wearable Usage to Collect Health Data Among Adults Living in Germany: Nationwide Observational Survey Study.JMIR mHealth and uHealth · 2025Observational
- A Co-Segmentation Algorithm to Predict Emotional Stress From Passively Sensed mHealth Data.Statistics in medicine · 2025Article
- Investigating the Magnitude and Persistence of COVID-19-Related Impacts on Affect and GPS-Derived Daily Mobility Patterns in Adolescence and Emerging Adulthood: Insights From a Smartphone-Based Intensive Longitudinal Study of Colorado-Based Youths From June 2016 to April 2022.Journal of medical Internet research · 2025Article
- Refining Established Practices for Research Question Definition to Foster Interdisciplinary Research Skills in a Digital Age: Consensus Study With Nominal Group Technique.JMIR medical education · 2025Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the "wild", and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.