Will Ke Wang

Background: Oxygen saturation is a crucial metric used for monitoring patients with lung disease or respiratory illness who are at risk of hypoxemia (low blood oxygen saturation). Early and accurate identification of abnormal oxygen saturation is important for these patients, who may develop significant desaturation and hypoxemia symptoms during their daily activities. Objective: This study aimed to evaluate the accuracy of Apple Watch Series 7 and a clinical-grade pulse oximeter, Masimo MightySat Rx, under hypoxemia and to assess whether measurement error is influenced by the oxygen desaturation rate (ODR). Methods: We calculated the ODR of each measurement and conducted a comparative analysis of the displayed oxygen saturation readings from both the Masimo MightySat Rx finger pulse oximeter and Apple Watch Series 7 with arterial blood oxygen saturation (SaO ₂ ) readings obtained from a blood gas analyzer. Results: Both the Masimo MightySat Rx pulse oximeter and the Apple Watch Series 7 tended to overestimate oxygen saturation. The pulse oximeter readings were more likely to fall within 2% of the acceptable (as specified by Masimo) peripheral oxygen saturation (SpO _₂ ) error range than the Apple Watch (49.03% vs 32.14%). Notably, both devices had limitations under low oxygen saturation levels (rms ) of 3.52% (95% CI 3.18%‐3.86%) and 5.82% (95% CI 5.32%‐6.31%) for the Masimo MightySat Rx and Apple Watch Series 7, respectively. Among the blood oxygen measurements taken during a high ODR (ie, ≥2% SpO ₂ per minute), which is a rate clinically correlated with sleep apnea, the A _rms increased slightly by 0.75% for the Masimo MightySat Rx and decreased by 0.28% for the Apple Watch Series 7. Conclusions: Both devices consistently overestimated SpO ₂ , with accuracy declining notably during hypoxemia. The Apple Watch Series 7 mean bias suggests a likelihood of missing instances of hypoxemia, particularly at arterial oxygen saturation values below but close to 88%. Both the Apple Watch Series 7 and Masimo MightySat Rx exhibited A _rms values exceeding the US Food and Drug Administration threshold under conditions of hypoxemia. While past studies have implicated high ODRs in increasing measurement error, we found no statistically significant relationship between ODR and measurement error for either device. Overall, our findings of SpO ₂ overestimation and high A _rms values underscore the need for caution when interpreting oxygen saturation values from these devices. The small sample size and limited diversity in skin tone and age restrict the generalizability of our findings. Future studies should include larger and more diverse populations to evaluate the performance of wearable-based pulse oximetry.

Daylight Saving Time (DST) remains contentious: some policymakers highlight behavioral benefits, while others emphasize health risks. Existing evidence relies largely on aggregated data and strict modeling assumptions, obscuring individual-level patterns and yielding associational rather than causal insights. We address these limitations using objective, longitudinal Fitbit measures from the NIH All of Us Research Program. Avoiding strict modeling assumptions, we employed a natural difference-in-differences design using Arizona (no DST) as a control against neighboring Mountain Time states (observing DST). Contrary to the common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 while reducing evening steps by 180; spring showed the opposite. Importantly, these treatment effects varied by demographics: older adults (65+) and lower-income individuals showed greater morning reallocation during fall DST, while younger and higher-income adults showed limited adaptation. We also demonstrate that activity reallocation patterns differed across data-driven activity phenotypes (“Morning-,” “Neutral-,” and “Evening Walker”). These disparities suggest that structural factors (e.g., rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioral changes, though differences were clinically insignificant. Our study provides the first large-scale causal analysis of DST’s influence using continuous wearables data, illustrating how observational data can generate real-world evidence to inform health-relevant policies.

Prediabetes (PD), which includes impaired glucose tolerance (IGT) and impaired fasting glucose (IFG), is a dysfunctional metabolic state that often progresses to type 2 diabetes (T2D). Standard screening tools such as random glucose and hemoglobin A1c frequently miss early or intermittent dysglycemia and cannot distinguish underlying physiological differences relevant for targeted intervention. Although the oral glucose tolerance test (OGTT) detects more PD and T2D and identifies high-risk individuals earlier, its clinical use is limited by the need for repeated venous sampling and in-clinic administration. We introduce the mobile OGTT (mOGTT), which leverages continuous glucose monitoring (CGM) to capture high-resolution glycemic responses to a standardized glucose challenge outside clinical settings. In a population spanning a broad range of glycemic health, we establish preliminary normative mOGTT values, characterize their relationship to clinical OGTT thresholds, and assess concordance with A1c. We show that CGM-derived analogs of OGTT metrics improve detection and phenotyping of dysglycemia and propose the Glucose Challenge Response Index (GCRI), a composite measure of glycemic health. Finally, we demonstrate the generalizability of GCRI-based subphenotypes in an out-of-sample cohort. These results help facilitate an efficient and scalable approach for conveniently detecting and quantifying early-stage glucose dysregulation.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Background: Digital sensing solutions represent a convenient, objective, relatively inexpensive method that could be leveraged for assessing symptoms of various health conditions. Recent progress in the capabilities of digital sensing products has targeted the measurement of scratching during sleep, traditionally referred to as nocturnal scratching, in patients with atopic dermatitis or other skin conditions. Many solutions measuring nocturnal scratch have been developed; however, a lack of efforts toward standardization of the measure’s definition and contextualization of scratching during sleep hampers the ability to compare different technologies for this purpose. Objective: We aimed to address this gap and bring forth unified measurement definitions for nocturnal scratch. Methods: We performed a narrative literature review of definitions of scratching in patients with skin inflammation and a targeted literature review of sleep in the context of the period during which such scratching occurred. Both searches were limited to English language studies in humans. The extracted data were synthesized into themes based on study characteristics: scratch as a behavior, other characterization of the scratching movement, and measurement parameters for both scratch and sleep. We then developed ontologies for the digital measurement of sleep scratching. Results: In all, 29 studies defined inflammation-related scratching between 1996 and 2021. When cross-referenced with the results of search terms describing the sleep period, only 2 of these scratch-related papers also described sleep-related variables. From these search results, we developed an evidence-based and patient-centric definition of nocturnal scratch: an action of rhythmic and repetitive skin contact movement performed during a delimited time period of intended and actual sleep that is not restricted to any specific time of the day or night. Based on the measurement properties identified in the searches, we developed ontologies of relevant concepts that can be used as a starting point to develop standardized outcome measures of scratching during sleep in patients with inflammatory skin conditions. Conclusions: This work is intended to serve as a foundation for the future development of unified and well-described digital health technologies measuring nocturnal scratching and should enable better communication and sharing of results between various stakeholders taking part in research in atopic dermatitis and other inflammatory skin conditions.

Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.

BACKGROUND Digital sensing solutions represent a convenient, objective, relatively inexpensive method that could be leveraged for assessing symptoms of various health conditions. Recent progress in the capabilities of digital sensing products has targeted the measurement of scratching during sleep, traditionally referred to as nocturnal scratching, in patients with atopic dermatitis or other skin conditions. Many solutions measuring nocturnal scratch have been developed; however, a lack of efforts toward standardization of the measure’s definition and contextualization of scratching during sleep hampers the ability to compare different technologies for this purpose. OBJECTIVE We aimed to address this gap and bring forth unified measurement definitions for nocturnal scratch. METHODS We performed a narrative literature review of definitions of scratching in patients with skin inflammation and a targeted literature review of sleep in the context of the period during which such scratching occurred. Both searches were limited to English language studies in humans. The extracted data were synthesized into themes based on study characteristics: scratch as a behavior, other characterization of the scratching movement, and measurement parameters for both scratch and sleep. We then developed ontologies for the digital measurement of sleep scratching. RESULTS In all, 29 studies defined inflammation-related scratching between 1996 and 2021. When cross-referenced with the results of search terms describing the sleep period, only 2 of these scratch-related papers also described sleep-related variables. From these search results, we developed an evidence-based and patient-centric definition of nocturnal scratch: an action of rhythmic and repetitive skin contact movement performed during a delimited time period of intended and actual sleep that is not restricted to any specific time of the day or night. Based on the measurement properties identified in the searches, we developed ontologies of relevant concepts that can be used as a starting point to develop standardized outcome measures of scratching during sleep in patients with inflammatory skin conditions. CONCLUSIONS This work is intended to serve as a foundation for the future development of unified and well-described digital health technologies measuring nocturnal scratching and should enable better communication and sharing of results between various stakeholders taking part in research in atopic dermatitis and other inflammatory skin conditions.

Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestible, and implantable are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used in modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents non-deep learning models commonly used for time series classification in biomedical applications that achieve high performance. Objective: We performed a systematic review to characterize the techniques used in time series classification of digital clinical measures throughout all stages of data processing and model building. Methods: We conducted a literature search on PubMed, and the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles reporting academic research on digital clinical measures in the five year period between June 2016 and June 2021. We identified and categorized research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely-used machine learning classifiers was the most commonly used technique and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also second best classification technique. Wavelet-based classification models (8 out of 135 articles) were also common. Electroencephalogram (29 out of 135 articles) was the most common data type used as an input. Accuracy was the most commonly reported performance metric, with 67.65% of articles reporting on accuracy. In this review paper, we provide summaries of signal pre-processing methods, feature engineering and selection methods, time series models, as well as model interpretations. Importantly, we found that about 50% of the papers only report one performance metric, which may result in a skewed view of overall performance. Conclusion: While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation– many different methods have proven successful.

Objective: Sleep is vital to supporting adolescent behavioral health and functioning; however, sleep disturbances remain under-recognized and undertreated in many health care settings. One barrier is the complexity of sleep, which makes it difficult for providers to determine which aspects—beyond sleep duration—may be most important to assess and treat to support adolescent health. This study examined associations between 2 sleep indices (regularity and timing) and adolescent behavioral health and functioning over and above the impact of shortened/fragmented sleep. Method: Eighty-nine adolescents recruited from the community (mean age = 14.04, 45% female participants) completed 7 days/nights of actigraphy and, along with a parent/guardian, reported on behavioral health (internalizing and externalizing symptoms) and psychosocial functioning. Stepwise linear regressions examined associations between sleep timing and regularity and behavioral/functional outcomes after accounting for shortened/fragmented sleep. Results: Delayed sleep timing was associated with greater self-reported internalizing (F[6,82] = 11.57, p = 0.001) and externalizing (F[6,82] = 11.12, p = 0.001) symptoms after accounting for shortened/fragmented sleep. Irregular sleep was associated with greater self-reported and parent-reported externalizing symptoms (self: F[7,81] = 6.55, p = 0.01; parent: F[7,80] = 6.20, p = 0.01) and lower psychosocial functioning (self: F[7,81] = 6.03, p = 0.02; parent: F[7,78] = 3.99, p < 0.05) after accounting for both shortened/fragmented sleep and delayed sleep timing. Conclusion: Sleep regularity and timing may be critical for understanding the risk of poor behavioral health and functional deficits among adolescents and as prevention and intervention targets. Future work should focus on developing and evaluating convenient, low-cost, and effective methods for addressing delayed and/or irregular adolescent sleep patterns in real-world health care settings.