The relationship of rate and pause features to the communicative participation of people living with ALS.

INTRODUCTION/AIMS: Many people living with amyotrophic lateral sclerosis (PALS) report restrictions in their day-to-day communication (communicative participation). However, little is known about which speech features contribute to these restrictions. This study evaluated the effects of common speech symptoms in PALS (reduced overall speaking rate, slowed articulation rate, and increased pausing) on communicative participation restrictions. METHODS: Participants completed surveys (the Communicative Participation Item Bank-short form; the self-entry version of the ALS Functional Rating Scale-Revised) and recorded themselves reading the Bamboo Passage aloud using a smartphone app. Rate and pause measures were extracted from the recordings. The association of various demographic, clinical, self-reported, and acoustic speech features with communicative participation was evaluated with bivariate correlations. The contribution of salient rate and pause measures to communicative participation was assessed using multiple linear regression. RESULTS: Fifty seven people living with ALS participated in the study (mean age = 61.1 years). Acoustic and self-report measures of speech and bulbar function were moderately to highly associated with communicative participation (Spearman rho coefficients ranged from rs = 0.48 to rs = 0.77). A regression model including participant age, sex, articulation rate, and percent pause time accounted for 57% of the variance of communicative participation ratings. DISCUSSION: Even though PALS with slowed articulation rate and increased pausing may convey their message clearly, these speech features predict communicative participation restrictions. The identification of quantitative speech features, such as articulation rate and percent pause time, is critical to facilitating early and targeted intervention and for monitoring bulbar decline in ALS.

Returning Individual Research Results from Digital Phenotyping in Psychiatry.

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.

Perceptions of Mobile Health Technology in Elective Surgery: A Qualitative Study of North American Surgeons.

OBJECTIVES: To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth. BACKGROUND: Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited. METHODS: This was an exploratory qualitative study following the Consolidated Criteria for Reporting Qualitative Research. Purposive sampling was used to identify surgeons across the United States and Canada. The Consolidated Framework for Implementation Research informed development of a semistructured interview guide. Video-based interviews were conducted (September-November 2020) and interview transcripts were thematically analyzed. RESULTS: Thirty surgeons from 8 specialties and 6 North American regions were interviewed. Surgeons identified opportunities to integrate mHealth data pre- operatively (eg, expectation-setting, decision-making) and during recovery (eg, remote monitoring, earlier detection of adverse events) among higher risk patients. Perceived advantages of mHealth data compared with surgical and patient-reported outcomes included easier data collection, higher interpretability and objectivity of mHealth data, and the potential to develop more patientcentered and functional measures of health. Surgeons identified a variety of implementation facilitators and barriers around surgeon- and patient buy-in, integration with electronic medical records, regulatory/reimbursement concerns, and personnel responsible for mHealth data. Surgeons described similar considerations regarding perceptions of mHealth among patients, including the potential to address or worsen existing disparities in surgical care. CONCLUSIONS: These findings have the potential to inform the effective and equitable implementation of mHealth for the purposes of supporting patients and surgical care teams throughout the delivery of surgical care.

Randomized trial of inosine for urate elevation in amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Higher urate levels are associated with improved ALS survival in retrospective studies, however whether raising urate levels confers a survival advantage is unknown. In the Safety of Urate Elevation in Amyotrophic Lateral Sclerosis (SURE-ALS) trial, inosine raised serum urate and was safe and well-tolerated. The SURE-ALS2 trial was designed to assess longer term safety. Functional outcomes and a smartphone application were also explored. METHODS: Participants were randomized 2:1 to inosine (n = 14) or placebo (n = 9) for 20 weeks, titrated to serum urate of 7-8 mg/dL. Primary outcomes were safety and tolerability. Functional outcomes were measured with the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R). Mobility and ALSFRS-R were also assessed by a smartphone application. RESULTS: During inosine treatment, mean urate ranged 5.68-6.82 mg/dL. Treatment-emergent adverse event (TEAE) incidence was similar between groups (p > .10). Renal TEAEs occurred in three (21%) and hypertension in one (7%) of participants randomized to inosine. Inosine was tolerated in 71% of participants versus placebo 67%. Two participants (14%) in the inosine group experienced TEAEs deemed related to treatment (nephrolithiasis); one was a severe adverse event. Mean ALSFRS-R decline did not differ between groups (p = .69). Change in measured home time was similar between groups. Digital and in-clinic ALSFRS-R correlated well. DISCUSSION: Inosine met pre-specified criteria for safety and tolerability. A functional benefit was not demonstrated in this trial designed for safety and tolerability. Findings suggested potential utility for a smartphone application in ALS clinical and research settings.

Abnormal uterine bleeding patterns determined through menstrual tracking among participants in the Apple Women's Health Study.

BACKGROUND: Use of menstrual tracking data to understand abnormal bleeding patterns has been limited because of lack of incorporation of key demographic and health characteristics and confirmation of menstrual tracking accuracy. OBJECTIVE: This study aimed to identify abnormal uterine bleeding patterns and their prevalence and confirm existing and expected associations between abnormal uterine bleeding patterns, demographics, and medical conditions. STUDY DESIGN: Apple Women's Health Study participants from November 2019 through July 2021 who contributed menstrual tracking data and did not report pregnancy, lactation, use of hormones, or menopause were included in the analysis. Four abnormal uterine bleeding patterns were evaluated: irregular menses, infrequent menses, prolonged menses, and irregular intermenstrual bleeding (spotting). Monthly tracking confirmation using survey responses was used to exclude inaccurate or incomplete digital records. We investigated the prevalence of abnormal uterine bleeding stratified by demographic characteristics and used logistic regression to evaluate the relationship of abnormal uterine bleeding to a number of self-reported medical conditions. RESULTS: There were 18,875 participants who met inclusion criteria, with a mean age of 33 (standard deviation, 8.2) years, mean body mass index of 29.3 (standard deviation, 8.0), and with 68.9% (95% confidence interval, 68.2-69.5) identifying as White, non-Hispanic. Abnormal uterine bleeding was found in 16.4% of participants (n=3103; 95% confidence interval, 15.9-17.0) after accurate tracking was confirmed; 2.9% had irregular menses (95% confidence interval, 2.7-3.1), 8.4% had infrequent menses (95% confidence interval, 8.0-8.8), 2.3% had prolonged menses (95% confidence interval, 2.1-2.5), and 6.1% had spotting (95% confidence interval, 5.7-6.4). Black participants had 33% higher prevalence (prevalence ratio, 1.33; 95% confidence interval, 1.09-1.61) of infrequent menses compared with White, non-Hispanic participants after controlling for age and body mass index. The prevalence of infrequent menses was increased in class 1, 2, and 3 obesity (class 1: body mass index, 30-34.9; prevalence ratio, 1.31; 95% confidence interval, 1.13-1.52; class 2: body mass index, 35-39.9; prevalence ratio, 1.25; 95% confidence interval, 1.05-1.49; class 3: body mass index, >40; prevalence ratio, 1.51; 95% confidence interval, 1.21-1.88) after controlling for age and race/ethnicity. Those with class 3 obesity had 18% higher prevalence of abnormal uterine bleeding compared with healthy-weight participants (prevalence ratio, 1.18; 95% confidence interval, 1.02-1.38). Participants with polycystic ovary syndrome had 19% higher prevalence of abnormal uterine bleeding compared with participants without this condition (prevalence ratio, 1.19; 95% confidence interval, 1.08-1.31). Participants with hyperthyroidism (prevalence ratio, 1.34; 95% confidence interval, 1.13-1.59) and hypothyroidism (prevalence ratio, 1.17; 95% confidence interval, 1.05-1.31) had a higher prevalence of abnormal uterine bleeding, as did those reporting endometriosis (prevalence ratio, 1.28; 95% confidence interval, 1.12-1.45), cervical dysplasia (prevalence ratio, 1.20; 95% confidence interval, 1.03-1.39), and fibroids (prevalence ratio, 1.14; 95% confidence interval, 1.00-1.30). CONCLUSION: In this cohort, abnormal uterine bleeding was present in 16.4% of those with confirmed menstrual tracking. Black or obese participants had increased prevalence of abnormal uterine bleeding. Participants reporting conditions such as polycystic ovary syndrome, thyroid disease, endometriosis, and cervical dysplasia had a higher prevalence of abnormal uterine bleeding.

Machine Learning and the Digital Measurement of Psychological Health.

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

Smartphone-based Assessment of Preoperative Decision Conflict and Postoperative Physical Activity Among Patients Undergoing Cancer Surgery: A Prospective Cohort Study.

OBJECTIVE: To determine the prevalence of clinically significant decision conflict (CSDC) among patients undergoing cancer surgery and associations with postoperative physical activity, as measured through smartphone accelerometer data. BACKGROUND: Patients with cancer face challenging treatment decisions, which may lead to CSDC. CSDC negatively affects patient-provider relationships, psychosocial functioning, and health-related quality of life; however, physical manifestations of CSDC remain poorly characterized. METHODS: Adult smartphone-owners undergoing surgery for breast, skin-soft-tissue, head-and-neck, or abdominal cancer (July 2017-2019) were approached. Patients downloaded the Beiwe application that delivered the Decision Conflict Scale (DCS) preoperatively and collected smartphone accelerometer data continuously from enrollment through 6 months postop-eratively. Restricted-cubic-spline regression, adjusting for a priori potential confounders (age, type of surgery, support status, and postoperative complications) was used to determine trends in postoperative daily physical activity among patients with and without CSDC (DCS score >25/100). RESULTS: Among 99 patients who downloaded the application, 85 completed the DCS (86% participation rate). Twenty-three (27%) reported CSDC. These patients were younger (mean age 48.3 years [standard deviation 14.2]-vs-55.0 [13.3], P = 0.047) and more frequently lived alone (22%-vs-6%, P = 0.042). There were no differences in preoperative physical activity (115.4 minutes [95%CI 90.9, 139.9]-vs-110.8 [95%CI 95.7, 126.0], P = 0.753). Adjusted postoperative physical activity was lower among patients reporting CSDC at 30 days (difference 33.1 minutes [95%CI 5.93,60.2], P = 0.017), 60 days 35.5 [95%CI 8.50, 62.5], P = 0.010 and 90 days 31.8 [95%CI 5.44, 58.1], P = 0.018 postoperatively. CONCLUSIONS: CSDC was prevalent among patients who underwent cancer surgery and associated with lower postoperatively daily physical activity. These data highlight the importance of addressing modifiable decisional needs of patients through enhanced shared decision-making.

Attempts to conceive and the COVID-19 pandemic: data from the Apple Women's Health Study.

BACKGROUND: Previous studies have suggested that emergent events may affect pregnancy planning decisions. However, few have investigated the effect of factors related to the COVID-19 pandemic on pregnancy planning, measured by attempting conception, and how attempting conception status may differ by individual-level factors, such as social status or educational level. OBJECTIVE: This study aimed to examine the effects of factors related to the COVID-19 pandemic, until March 2021, on attempting conception status and to assess the effect measure modification by educational level and subjective social status. STUDY DESIGN: We conducted a longitudinal analysis within a subgroup of 21,616 participants in the Apple Women's Health Study who enrolled from November 2019 to March 2021, who met the inclusion criteria, and who responded to the monthly status menstrual update question on attempting conception status (yes or no). Participants reporting hysterectomy, pregnancy, lactation, or menopause were excluded. We used generalized estimating equation methodology to fit logistic regression models that estimate odds ratios and 95% confidence intervals for the association between the proportion of participants attempting conception and the month of response (compared with a prepandemic reference month of February 2020) while accounting for longitudinal correlation and adjusting for age, race and ethnicity, and marital status. We stratified the analysis by social status and educational level. RESULTS: We observed a trend of reduced odds of attempting conception, with an 18% reduction in the odds of attempting conception in August 2020 and October 2020 compared with the prepandemic month of February 2020 (August odds ratio: 0.82 [95% confidence interval, 0.70-0.97]; October odds ratio: 0.82 [95% confidence interval, 0.69-0.97). The participants with lower educational level (no college education) experienced a sustained reduction in the odds of attempting to conceive from June 2020 to March 2021 compared with February 2020, with up to a 24% reduction in the odds of attempting to conceive in October 2020 (odds ratio, 0.76; 95% confidence interval, 0.59-0.96). Among participants that were college educated, we observed an initial reduction in the odds of attempting to conceive starting in July 2020 (odds ratio 0.73; 95% confidence interval, 0.54-0.99) that returned near prepandemic odds. Moreover, we observed a reduction in the odds of attempting to conceive among those with low subjective social status, with a decline in the odds of attempting to conceive beginning in July 2020 (odds ratio, 0.83; 95% confidence interval, 0.63-1.10) and continuing until March 2021 (odds ratio, 0.79; 95% confidence interval, 0.59-1.06), with the greatest reduction in odds in October 2020 (odds ratio, 0.67; 95% confidence interval, 0.50-0.91). CONCLUSION: Among women in the Apple Women's Health Study cohort, our findings suggested a reduction in the odds of attempting to conceive during the COVID-19 pandemic, until March 2021, particularly among women of lower educational level and lower perceived social status.

Design and methods of the Apple Women's Health Study: a digital longitudinal cohort study.

BACKGROUND: Prospective longitudinal cohorts assessing women's health and gynecologic conditions have historically been limited. OBJECTIVE: The Apple Women's Health Study was designed to gain a deeper understanding of the relationship among menstrual cycles, health, and behavior. This paper describes the design and methods of the ongoing Apple Women's Health Study and provides the demographic characteristics of the first 10,000 participants. STUDY DESIGN: This was a mobile-application-based longitudinal cohort study involving survey and sensor-based data. We collected the data from 10,000 participants who responded to the demographics survey on enrollment between November 14, 2019 and May 20, 2020. The participants were asked to complete a monthly follow-up through November 2020. The eligibility included installed Apple Research app on their iPhone with iOS version 13.2 or later, were living in the United States, being of age greater than 18 years (19 in Alabama and Nebraska, 21 years old in Puerto Rico), were comfortable in communicating in written and spoken English, were the sole user of an iCloud account or iPhone, and were willing to provide consent to participate in the study. RESULTS: The mean age at enrollment was 33.6 years old (±standard deviation, 10.3). The race and ethnicity was representative of the US population (69% White and Non-Hispanic [6910/10,000]), whereas 51% (5089/10,000) had a college education or above. The participant geographic distribution included all the US states and Puerto Rico. Seventy-two percent (7223/10,000) reported the use of an Apple Watch, and 24.4% (2438/10,000) consented to sensor-based data collection. For this cohort, 38% (3490/9238) did not respond to the Monthly Survey: Menstrual Update after enrollment. At the 6-month follow-up, there was a 35% (3099/8972) response rate to the Monthly Survey: Menstrual Update. 82.7% (8266/10,000) of the initial cohort and 95.1% (2948/3099) of the participants who responded to month 6 of the Monthly Survey: Menstrual Update tracked at least 1 menstrual cycle via HealthKit. The participants tracked their menstrual bleeding days for an average of 4.44 (25%-75%; range, 3-6) calendar months during the study period. Non-White participants were slightly more likely to drop out than White participants; those remaining at 6 months were otherwise similar in demographic characteristics to the original enrollment group. CONCLUSION: The first 10,000 participants of the Apple Women's Health Study were recruited via the Research app and were diverse in race and ethnicity, educational attainment, and economic status, despite all using an Apple iPhone. Future studies within this cohort incorporating this high-dimensional data may facilitate discovery in women's health in exposure outcome relationships and population-level trends among iPhone users. Retention efforts centered around education, communication, and engagement will be utilized to improve the survey response rates, such as the study update feature.

Feasibility and performance of smartphone-based daily micro-surveys among patients recovering from cancer surgery.

AIMS: Daily micro-surveys, or the high-frequency administration of patient-reported outcome measures (PROMs), may provide real-time, unbiased assessments of health-related quality of life (HRQoL). We evaluated the feasibility and accuracy of daily micro-surveys using a smartphone platform among patients recovering from cancer surgery. METHODS: In a prospective study (2017-2019), patients undergoing cancer surgery downloaded a smartphone application that administered daily micro-surveys comprising five randomly selected items from the Short Form-36 (SF-36). Micro-surveys were administered without replacement until the entire SF-36 was administered weekly. The full-length SF-36 was also administered preoperatively and 4, 12, and 24 weeks postoperatively. We assessed response and completion rates between the micro-surveys and full-length SF-36, as well as agreement of responses using Bland-Altman (B&A) analyses. RESULTS: Ninety-five patients downloaded the application and were followed for a mean of 131 days [SD ± 85]. Response rates for the full-length SF-36 and micro-surveys was 76% [95%CI 69, 83], and 34% [95%CI 26, 39]. Despite lower response rates, more SF-36 surveys were collected using the daily micro-surveys compared to the intermittent full-length SF-36 (9.9 [95%CI 8.4, 12.6] vs. 3.0 [95%CI 2.8, 3.3], respectively). B&A analyses demonstrated lack of agreement between micro-surveys and SF-36. However, agreement improved with higher micro-survey completion rate. Eighty-five percent of participants reported that daily micro-surveys were not burdensome. CONCLUSION: This study suggests that collection of daily micro-surveys among patients recovering from cancer surgery is feasible using smartphones in the early postoperative period. Future implementation of daily micro-surveys may more granularly describe momentary HRQoL changes through a greater volume of self-reported survey data.

Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data.

Smartphones can be used to collect granular behavioral data unobtrusively, over long time periods, in real-world settings. To detect aberrant behaviors in large volumes of passively collected smartphone data, we propose an online anomaly detection method using Hotelling's T-squared test. The test statistic in our method was a weighted average, with more weight on the between-individual component when the amount of data available for the individual was limited and more weight on the within-individual component when the data were adequate. The algorithm took only an O(1) runtime in each update, and the required memory usage was fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity, and specificity, was consistently better than or equal to the offline method that it was built upon, depending on the sample size of the individual data. Future applications of our method include early detection of surgical complications during recovery and the possible prevention of the relapse of patients with serious mental illness.

Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data.

Physical activity patterns can reveal information about one's health status. Built-in sensors in a smartphone, in comparison to a patient's self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone's acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.

Inferring mobility measures from GPS traces with missing data.

With increasing availability of smartphones with Global Positioning System (GPS) capabilities, large-scale studies relating individual-level mobility patterns to a wide variety of patient-centered outcomes, from mood disorders to surgical recovery, are becoming a reality. Similar past studies have been small in scale and have provided wearable GPS devices to subjects. These devices typically collect mobility traces continuously without significant gaps in the data, and consequently the problem of data missingness has been safely ignored. Leveraging subjects' own smartphones makes it possible to scale up and extend the duration of these types of studies, but at the same time introduces a substantial challenge: to preserve a smartphone's battery, GPS can be active only for a small portion of the time, frequently less than $10\%$, leading to a tremendous missing data problem. We introduce a principled statistical approach, based on weighted resampling of the observed data, to impute the missing mobility traces, which we then summarize using different mobility measures. We compare the strengths of our approach to linear interpolation (LI), a popular approach for dealing with missing data, both analytically and through simulation of missingness for empirical data. We conclude that our imputation approach better mirrors human mobility both theoretically and over a sample of GPS mobility traces from 182 individuals in the Geolife data set, where, relative to LI, imputation resulted in a 10-fold reduction in the error averaged across all mobility features.

Smartphone Global Positioning System (GPS) Data Enhances Recovery Assessment After Breast Cancer Surgery.

PURPOSE: We sought to determine whether smartphone GPS data uncovered differences in recovery after breast-conserving surgery (BCS) and mastectomy, and how these data aligned with self-reported quality of life (QoL). METHODS: In a prospective pilot study, adult smartphone-owners undergoing breast surgery downloaded an application that continuously collected smartphone GPS data for 1 week preoperatively and 6 months postoperatively. QoL was assessed with the Short-Form-36 (SF36) via smartphone delivery preoperatively and 4 and 12 weeks postoperatively. Endpoints were trends in daily GPS-derived distance traveled and home time, as well as SF36 Physical (PCS) and Mental Component Scores (MCS) comparing BCS and mastectomy patients. RESULTS: Thirty-one patients were included. Sixteen BCS and fifteen mastectomy patients were followed for a mean of 201 (SD 161) and 174 (107) days, respectively. There were no baseline differences in demographics, PCS/MCS, home time, or distance traveled. Through 12 weeks postoperatively, mastectomy patients spent more time at home [e.g., week 4: 16.7 h 95% CI (14.3, 19.6) vs. 11.0 h (9.4, 12.9), p < 0.001] and traveled shorter distances [e.g., week 4: 52.5 km 95% CI (36.1, 76.0) vs. 107.7 km (75.8-152.9), p = 0.009] compared with BCS patients. There were no significant QoL differences throughout the study as measured by the MCS [e.g., week 4 difference: 7.83 95% CI (- 9.02, 24.7), p = 0.362] or PCS [e.g., week 4 difference: 8.14 (- 6.67, 22.9), p = 0.281]. GPS and QoL trends were uncorrelated (ρ < ± 0.26, p > 0.05). CONCLUSIONS: Differences in BCS and mastectomy recovery were successfully captured using smartphone GPS data. These data may describe currently unmeasured aspects of physical and mental recovery, which could supplement traditional and QoL outcomes to inform shared decision-making.

The AURORA Study: a longitudinal, multimodal library of brain biology and function after traumatic stress exposure.

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions.

Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS.

INTRODUCTION: Passive data from smartphone sensors may be useful for health-care research. Our aim was to use the coronavirus disease-2019 (COVID-19) pandemic as a positive control to assess the ability to quantify behavioral changes in people with amyotrophic lateral sclerosis (ALS) from smartphone data. METHODS: Eight participants used the Beiwe smartphone application, which passively measured their location during the COVID-19 outbreak. We used an interrupted time series to quantify the effect of the US state of emergency declaration on daily home time and daily distance traveled. RESULTS: After the state of emergency declaration, median daily home time increased from 19.4 (interquartile range [IQR], 15.4-22.0) hours to 23.7 (IQR, 22.2-24.0) hours and median distance traveled decreased from 42 (IQR, 13-83) km to 3.7 (IQR, 1.5-10.3) km. The participant with the lowest functional ability changed behavior earlier. This participant stayed at home more and traveled less than the participant with highest functional ability, both before and after the state of emergency. DISCUSSION: We provide evidence that smartphone-based digital phenotyping can quantify the behavior of people with ALS. Although participants spent large amounts of time at home at baseline, the COVID-19 state of emergency declaration reduced their mobility further. Given participants' high level of daily home time, it is possible that their exposure to COVID-19 could be less than that of the general population.

Correction: Huang, E.J., and Onnela, J.P. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data. Sensors 2020, 20(13), 3706.

The authors wish to make the following corrections to this paper [...].

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data.

Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.

Influence of Peer Physicians on Intensity of End-of-Life Care for Cancer Decedents.

BACKGROUND: The intensity of end-of-life care varies substantially both within and between areas. Differing practice patterns of individual physicians are likely influenced by their peers. OBJECTIVE: To assess whether intensity of end-of-life care previously provided by a physician's peers influences patterns of care at the end-of-life for that physician's patients. RESEARCH DESIGN: Observational study. SUBJECTS: A total of 185,947 fee-for-service Medicare enrollees with cancer who died during 2006-2010 who were treated by 26,383 physicians. MEASURES: Spending in the last month of life, >1 emergency room visit, >1 hospitalization, intensive care unit admission in the last month of life, chemotherapy within 2 weeks of death, no/late hospice, terminal hospitalization. RESULTS: Mean (SD) spending in the last month of life was $16,237 ($17,124). For each additional $1000 of spending for a peer physician's patients in the prior year, spending for the ego physician's patients was $83 higher (P<0.001). Among physicians with peers both in and out of their practice, more of the peer effect was explained by physicians outside of the practice ($72 increase for each $1000 increase by peer physicians' patients, P<0.001) than peer physicians in the practice ($27 for each $1000 increase by within-practice peer physicians' patients, P=0.01). Results were similar across the other measures of end-of-life care intensity. CONCLUSIONS: Physician's peers exert influence on the intensity of care delivered to that physician's patients at the end-of-life. Physician education efforts led by influential providers and provider organizations may have potential to improve the delivery of high-value end-of-life care.

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders.

PURPOSE OF REVIEW: Sleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions. RECENT FINDINGS: To date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes. Success of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.