Reactivity to UV Radiation Exposure Monitoring Using Personal Exposure Devices for Skin Cancer Prevention: Longitudinal Observational Study

Parsons, Bridget G.; Nagelhout, Elizabeth; Wankier, Ali P.; Hu, Nan; Lensink, Riley; Zhu, Angela; Nottingham, Katy; Grossman, Douglas; Jensen, Jakob D.; Wu, Yelena P.

doi:10.2196/29694

Cited by 5 publications

(11 citation statements)

References 48 publications

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“…The most dominant study design was observational (n=28, 60.9%) [ 17 , 20 , 21 , 23 - 25 , 27 , 28 , 31 , 32 , 34 , 36 - 40 , 42 - 47 , 49 - 51 , 57 , 58 , 60 ], followed by randomized trials (n=10, 21.7%) [ 19 , 22 , 26 , 30 , 35 , 41 , 52 - 55 ], post hoc analyses of RCTs (n=5, 10.9%) [ 18 , 29 , 56 , 59 , 61 ], and post hoc analyses of observational studies (n=3, 6.5%) [ 16 , 33 , 48 ].…”

Section: Resultsmentioning

confidence: 99%

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Dlima¹,

Shevade²,

Menezes³

et al. 2022

JMIR Bioinform Biotech

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Background Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. Objective The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. Methods We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. Results A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. Conclusions Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build “digital phenotypes” to personalize digital health interventions and treatment plans.

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Section: Resultsmentioning

confidence: 99%

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Dlima¹,

Shevade²,

Menezes³

et al. 2022

JMIR Bioinform Biotech

View full text Add to dashboard Cite

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“…We chose an attention-control group over no intervention so that we could objectively measure UVR exposure in both groups and increase engagement in the control group over the 12-week intervention. A 2021 study that used the Shade device and control mobile application found that unintentional tanning and time spent outside decreased among children (aged 8-17) who wore the device for a two-week period; however, no changes in UVR exposure or sunburn were observed [29]. Notably, the study also observed that sunscreen use decreased over the study period, with the authors concluding this change could dampen the detection of intervention effects.…”

Section: Plos Onementioning

confidence: 96%

A UVR-sensor wearable device intervention to reduce sun exposure in melanoma survivors: Results from a randomized controlled trial

et al. 2023

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Background Melanoma survivors are at increased risk of developing a second primary melanoma; however, some report sub-optimal sun behaviors and sunburns. We tested the effectiveness of a wearable device with ultraviolet radiation (UVR)-sensing technology to improve sun behaviors and reduce sunburns in cutaneous melanoma survivors. Materials and methods We conducted a randomized controlled trial using Shade 2, a commercially available wrist device that measures UVR. The intervention group received the device and mobile application notifications about their exposure and prompts to use sunscreen. The control group received the device and a separate research mobile application without information about their exposure or notifications. Participants wore the device for 12 weeks and self-reported sun behaviors before, during, and after the intervention. The primary outcome was a composite score of sun protection behaviors at week 12. Results 386 participants were randomized (186 control, 182 intervention). Most were female and 5+ years past their first melanoma diagnosis. The average age was 56 years. Most (93%) completed the study, though 40% experienced device issues. No meaningful differences were observed in self-reported sun protection behaviors at week 12 (controls 3.0±0.5 vs. intervention 2.9±0.5, p = 0.06), any sunburn during the intervention period (controls 14.4% vs. intervention 12.7%, p = 0.75), or average daily objective UVR exposure (controls median 87 vs. intervention 83 J/m2, p = 0.43). Conclusion Wearing a device that measured and alerted melanoma survivors to UVR exposure did not result in different sun behaviors, exposure, or sunburns relative to controls. The technology needs refinement before further attempts to assess the effectiveness of self-monitoring UVR exposure. Clinical trials registration NCT03927742.

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“…Another typical application is sun protection. A study on the correlation between wearing an ultraviolet (UV) monitoring device and UV exposure showed that outdoor activity time and sunscreen use time among parents and children changed significantly after wearing the monitoring device for 2 weeks [ 25 ], highlighting the potential effect of preventing skin cancer using wearable UV monitoring devices.…”

Section: Current Applications Of Digital Medicine In Oncologymentioning

confidence: 99%

Applications of digital Medicine in oncology: Prospects and challenges

Zhang

et al. 2022

Cancer Innovation

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The current state of oncology medical services is not encouraging and is unable to fully meet the needs of patients with cancer. In recent years, rapidly developing artificial intelligence technology and gradual advancements in mobile phones, sensors, and wearable devices, which have made these more compact, affordable, and popular, have greatly expanded the development of digital medicine. Digital medicine refers to clinical evidence‐based technology and products with a direct impact on disease management and research. Integrating digital medicine into clinical practice has the advantages of broader applicability, greater cost‐effectiveness, better accessibility, and improved diagnostic and therapeutic performance. Digital medicine has emerged in different clinical application scenarios, including cancer prevention, screening, diagnosis, and treatment, as well as clinical trials. Additionally, big data generated from digital medicine can be used to improve levels of clinical diagnosis and treatment. However, digital medicine also faces many challenges, including security regulation and privacy protection, product usability, data management, and optimization of algorithms. In summary, the application and development of digital medicine in the field of cancer face numerous opportunities and challenges.

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Reactivity to UV Radiation Exposure Monitoring Using Personal Exposure Devices for Skin Cancer Prevention: Longitudinal Observational Study

Cited by 5 publications

References 48 publications

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

A UVR-sensor wearable device intervention to reduce sun exposure in melanoma survivors: Results from a randomized controlled trial

Applications of digital Medicine in oncology: Prospects and challenges

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