Irregular Cycles, Ovulatory Disorders, and Cardiometabolic Conditions in a US-Based Digital Cohort.

Importance: Polycystic ovary syndrome (PCOS), characterized by irregular menstrual cycles and hyperandrogenism, is a common ovulatory disorder. Having an irregular cycle is a potential marker for cardiometabolic conditions, but data are limited on whether the associations differ by PCOS status or potential interventions. Objective: To evaluate the association of PCOS, time to regularity since menarche (adolescence), and irregular cycles (adulthood) with cardiometabolic conditions. Design, Setting, and Participants: This cross-sectional study used a large, US-based digital cohort of users of the Apple Research application on their iPhone. Eligibility criteria were having ever menstruated, living in the US, being at age of consent of at least 18 years (or 19 years in Alabama and Nebraska or 21 years in Puerto Rico), and being able to communicate in English. Participants were enrolled between November 14, 2019, and December 13, 2022, and completed relevant surveys. Exposures: Self-reported PCOS diagnosis, prolonged time to regularity (not spontaneously establishing regularity within 5 years of menarche), and irregular cycles. Main Outcomes and Measures: The primary outcome was self-reported cardiometabolic conditions, including obesity, prediabetes, type 1 and 2 diabetes, high cholesterol, hypertension, metabolic syndrome, arrhythmia, congestive heart failure, coronary artery disease, heart attack, heart valve disease, stroke, transient ischemic attack (TIA), deep vein thrombosis, and pulmonary embolism measured using descriptive statistics and logistic regression to estimate prevalence odds ratios (PORs) and 95% CIs. Effect modification by lifestyle factors was also estimated. Results: The study sample (N = 60 789) had a mean (SD) age of 34.5 (11.1) years, with 12.3% having PCOS and 26.3% having prolonged time to regularity. Among a subset of 25 399 participants who completed the hormonal symptoms survey, 25.6% reported irregular cycles. In covariate-adjusted logistic regression models, PCOS was associated with a higher prevalence of all metabolic and several cardiovascular conditions, eg, arrhythmia (POR, 1.37; 95% CI, 1.20-1.55), coronary artery disease (POR, 2.92; 95% CI, 1.95-4.29), heart attack (POR, 1.79; 95% CI, 1.23-2.54), and stroke (POR, 1.66; 95% CI, 1.21-2.24). Among participants without PCOS, prolonged time to regularity was associated with type 2 diabetes (POR, 1.24; 95% CI, 1.05-1.46), hypertension (POR, 1.09; 95% CI, 1.01-1.19), arrhythmia (POR, 1.20; 95% CI, 1.06-1.35), and TIA (POR, 1.33; 95% CI, 1.01-1.73), and having irregular cycles was associated with type 2 diabetes (POR, 1.36; 95% CI, 1.08-1.69), high cholesterol (POR, 1.17; 95% CI, 1.05-1.30), arrhythmia (POR, 1.21; 95% CI, 1.02-1.43), and TIA (POR, 1.56; 95% CI, 1.06-2.26). Some of these associations were modified by high vs low body mass index or low vs high physical activity. Conclusions and Relevance: These findings suggest that PCOS and irregular cycles may be independent markers for cardiometabolic conditions. Early screening and intervention among individuals with irregular menstrual cycles may be beneficial.

Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study.

BACKGROUND: Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. OBJECTIVE: Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("cross-body" validation), manually ascertained ground truth ("visually assessed" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("commercial wearable" validation). METHODS: We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. RESULTS: In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%. CONCLUSIONS: This study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings.

Background: Step counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. Objective: Our goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("internal" validation), manually ascertained ground truth ("manual" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("wearable" validation). Methods: We used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. Results: In the internal validation datasets, participants performed 751.7±581.2 (mean±SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4±359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2±2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %. Conclusions: This study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

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.

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.

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.

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.

Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures.

OBJECTIVE: Patient-reported outcome measures (PROMs) are currently the gold standard to evaluate patient physical performance and ability to recover after spine surgery. However, PROMs have significant limitations due to the qualitative and subjective nature of the information reported as well as the impossibility of using this method in a continuous manner. The smartphone global positioning system (GPS) can be used to provide continuous, quantitative, and objective information on patient mobility. The aim of this study was to use daily mobility features derived from the smartphone GPS to characterize the perioperative period of patients undergoing spine surgery and to compare these objective measurements to PROMs, the current gold standard. METHODS: Eight daily mobility features were derived from smartphone GPS data in a population of 39 patients undergoing spine surgery for a period of 2 months starting 3weeks before surgery. In parallel, three different PROMs for pain (visual analog scale [VAS]), disability (Oswestry Disability Index [ODI]) and functional status (Patient-Reported Outcomes Measurement Information System [PROMIS]) were serially measured. Segmented linear regression analysis was used to assess trends before and after surgery. The Student paired t-test was used to compare pre- and postoperative PROM scores. Pearson's correlation was calculated between the daily average of each GPS-based mobility feature and the daily average of each PROM score during the recovery period. RESULTS: Smartphone GPS features provided data documenting a reduction in mobility during the immediate postoperative period, followed by a progressive and steady increase with a return to baseline mobility values 1 month after surgery. PROMs measuring pain, physical performance, and disability were significantly different 1 month after surgery compared to the 2 immediate preoperative weeks. The GPS-based features presented moderate to strong linear correlation with pain VAS and PROMIS physical score during the recovery period (Pearson r > 0.7), whereas the ODI and PROMIS mental scores presented a weak correlation (Pearson r approximately 0.4). CONCLUSIONS: Smartphone-derived GPS features were shown to accurately characterize perioperative mobility trends in patients undergoing surgery for spine-related diseases. Features related to time (rather than distance) were better at describing patient physical and performance status. Smartphone GPS has the potential to be used for the development of accurate, noninvasive and personalized tools for patient mobility monitoring after surgery.

Expected Versus Experienced Health-Related Quality of Life Among Patients Recovering From Cancer Surgery: A Prospective Cohort Study.

Patient expectations of the impact of surgery on postoperative health-related quality of life (HRQL) may reflect the effectiveness of patient-provider communication. We sought to compare expected versus experienced HRQL among patients undergoing cancer surgery. METHODS: Adults undergoing cancer surgery were eligible for inclusion (2017-2019). Preoperatively, patients completed a smartphone-based survey assessing expectations for HRQL 1 week and 1, 3, and 6 months postoperatively based on the 8 short-form 36 (SF36) domains (physical functioning, physical role limitations, pain, general health, vitality, social functioning, emotional role limitations, and mental health). Experienced HRQL was then assessed through smartphone-based SF36 surveys 1, 3, and 6 months postoperatively. Correlations between 1- and 6-month trends in expected versus experienced HRQL were determined. RESULTS: Among 101 consenting patients, 74 completed preoperative expectations and SF36 surveys (73%). The mean age was 54 years (SD 14), 49 (66%) were female, and the most common operations were for breast (34%) and abdominal (31%) tumors. Patients expected HRQL to worsen 1 week after surgery and improve toward minimal disability over 6 months. There was poor correlation (≤±0.4) between 1- and 6-month trends in expected versus experienced HRQL in all SF36 domains except for moderate correlation in physical functioning (0.50, 95% confidence interval [0.22-0.78], P < 0.001) and physical role limitations (0.41, 95% confidence interval [0.05-0.77], P = 0.024). Patients expected better HRQL than they experienced. CONCLUSIONS: Preoperative expectations of postoperative HRQL correlated poorly with lived experiences except in physical health domains. Surgeons should evaluate factors which inform expectations around physical and psychosocial health and use these data to enhance shared decision-making.

Can mHealth interventions improve quality of life of cancer patients? A systematic review and meta-analysis.

mHealth can be used to deliver interventions to optimize Health-related quality of life (HRQoL) of cancer patients. In this systematic-review and meta-analysis, we explored the possible impact of health interventions delivered via mHealth tools on HRQoL of cancer patients. The systematic literature search was performed on July 20, 2019, to identify studies that evaluated the impact of mHealth intervention on HRQoL of cancer patients. We identified 25 studies (17 randomized controlled trials and 8 pre-post design studies; 957 patients) that evaluated mHealth interventions. The most commonly studied mHealth interventions included physical activity/ fitness interventions (9 studies), cognitive behavioral therapy (6 studies), mindfulness/ stress management (3 studies). In the majority of studies, mHealth interventions were associated with an improved HRQoL of cancer patients. The meta-analysis of the identified studies supported the positive effect of mHealth interventions for HRQoL of cancer patients. mHealth interventions are promising for improving HRQoL of cancer patients.

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.

Association of Physician Peer Influence With Subsequent Physician Adoption and Use of Bevacizumab.

Importance: Understanding adoption of new cancer therapies may help identify opportunities to increase use for high-value indications. Objective: To determine whether use of bevacizumab in 2005 to 2006 by oncologists' peers was associated with greater bevacizumab use among oncologists in 2007 to 2010. Design, Setting, and Participants: This cohort study of physicians and their patients took place in 51 randomly selected hospital referral regions in the United States. Participants were 44 012 fee-for-service Medicare beneficiaries aged 65 years or older with cancers of the colorectum, lung, breast, kidney, brain, or ovary treated by 3261 oncologists in 2005 to 2010 and assigned to one of 252 communities. Data were analyzed in 2017 to 2018. Exposures: Among patients treated with chemotherapy during 2007 to 2010 by an oncologist who had not treated patients with bevacizumab in 2005 to 2006, models assessed the association of bevacizumab use with rates of bevacizumab use in their physician's community of connected physicians in 2005 to 2006. Models adjusted for patient and physician characteristics and physician, practice, and community random effects. Main Outcomes and Measures: Receipt of bevacizumab. Results: A total of 34 750 patients (14 126 [40.6%] aged ≥75 years; 21 321 [61.4%] female) with cancers of the colorectum, lung, breast, kidney, brain, and ovary were treated with chemotherapy in 2005 to 2006 in the 51 hospital referral regions. Among 9262 patients treated in 2007 to 2010 by 829 physicians whose patients did not use bevacizumab in 2005 to 2006, 3654 (39.5%) were aged 75 years or older and 6227 (67.2%) were female. The rate of bevacizumab use relative to other chemotherapy in 2007 to 2010 by tertile of use (bevacizumab for <4.4%, 4.4%-6.2%, and >6.2% of all patients receiving chemotherapy) among their physician's peers in 2005 to 2006 was 10.0%, 9.5%, and 13.6%, respectively. After adjustment, use of bevacizumab in 2007 to 2010 was greater among physicians in communities with the highest rates of bevacizumab use in 2005 to 2006 compared with those whose peers were in the lowest tertile of bevacizumab use in 2005 to 2006 (adjusted odds ratio, 1.64; 95% CI, 1.20-2.25). Conclusions and Relevance: This study found that an increase in oncologists' adoption and use of bevacizumab in the years after its approval was associated with their peer physicians being earlier adopters. As organizations seek to provide better care at lower costs, interventions that leverage physician ties may help to promote adoption of high-value use of new cancer treatments and deimplementation of low-value therapies.

Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery.

Importance: Patient-generated health data captured from smartphone sensors have the potential to better quantify the physical outcomes of surgery. The ability of these data to discriminate between postoperative trends in physical activity remains unknown. Objective: To assess whether physical activity captured from smartphone accelerometer data can be used to describe postoperative recovery among patients undergoing cancer operations. Design, Setting, and Participants: This prospective observational cohort study was conducted from July 2017 to April 2019 in a single academic tertiary care hospital in the United States. Preoperatively, adults (age ≥18 years) who spoke English and were undergoing elective operations for skin, soft tissue, head, neck, and abdominal cancers were approached. Patients were excluded if they did not own a smartphone. Exposures: Study participants downloaded an application that collected smartphone accelerometer data continuously for 1 week preoperatively and 6 months postoperatively. Main Outcomes and Measures: The primary end points were trends in daily exertional activity and the ability to achieve at least 60 minutes of daily exertional activity after surgery among patients with vs without a clinically significant postoperative event. Postoperative events were defined as complications, emergency department presentations, readmissions, reoperations, and mortality. Results: A total of 139 individuals were approached. In the 62 enrolled patients, who were followed up for a median (interquartile range [IQR]) of 147 (77-179) days, there were no preprocedural differences between patients with vs without a postoperative event. Seventeen patients (27%) experienced a postoperative event. These patients had longer operations than those without a postoperative event (median [IQR], 225 [152-402] minutes vs 107 [68-174] minutes; P < .001), as well as greater blood loss (median [IQR], 200 [35-515] mL vs 25 [5-100] mL; P = .006) and more follow-up visits (median [IQR], 2 [2-4] visits vs 1 [1-2] visits; P = .002). Compared with mean baseline daily exertional activity, patients with a postoperative event had lower activity at week 1 (difference, -41.6 [95% CI, -75.1 to -8.0] minutes; P = .02), week 3 (difference, -40.0 [95% CI, -72.3 to -3.6] minutes; P = .03), week 5 (difference, -39.6 [95% CI, -69.1 to -10.1] minutes; P = .01), and week 6 (difference, -36.2 [95% CI, -64.5 to -7.8] minutes; P = .01) postoperatively. Fewer of these patients were able to achieve 60 minutes of daily exertional activity in the 6 weeks postoperatively (proportions: week 1, 0.40 [95% CI, 0.31-0.49]; P < .001; week 2, 0.49 [95% CI, 0.40-0.58]; P = .003; week 3, 0.39 [95% CI, 0.30-0.48]; P < .001; week 4, 0.47 [95% CI, 0.38-0.57]; P < .001; week 5, 0.51 [95% CI, 0.42-0.60]; P < .001; week 6, 0.73 [95% CI, 0.68-0.79] vs 0.43 [95% CI, 0.33-0.52]; P < .001). Conclusions and Relevance: Smartphone accelerometer data can describe differences in postoperative physical activity among patients with vs without a postoperative event. These data help objectively quantify patient-centered surgical recovery, which have the potential to improve and promote shared decision-making, recovery monitoring, and patient engagement.

Social network analysis of group position, popularity, and sleep behaviors among U.S. adolescents.

OBJECTIVE: Studies have documented the salience of social networks for a range of health outcomes and behaviors among adolescents, but sleep has received far less attention. We examined whether adolescents' network positions relative to cohesive friendship groups and popularity among peers are associated with their sleep behaviors and whether the associations differ by gender. METHODS: We analyzed friendship data on 2,550 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to identify participants' network group positions (member, isolate, liaison) and popularity (number of friendship nominations received). Respondents provided self-reports of sleep duration, insomnia symptoms, and sleep insufficiency. We evaluated the relationships between respondents' group positions and popularity with their sleep behaviors using linear and Poisson regression, adjusting for socio-demographics, self-rated health, smoking and drinking status, integration in non-peer contexts (school, family, and religion), and friends' sleep. RESULTS: Results from the total sample show that liaisons report increased risk of sleep insufficiency compared to group members. Higher popularity status is associated with shorter sleep duration and greater sleep insufficiency. Stratifying by gender, popular girls report shorter sleep duration (ß = -2.68 min for each additional friendship nomination; 95% CI [-4.75, -.61]) and greater sleep insufficiency (RR = 1.04; 95% CI [1.01, 1.07]) compared to less popular girls. Girls who are liaisons experience more sleep insufficiency (RR = 1.28; 95% CI [1.07, 1.53]) compared to group member girls. In contrast, isolated boys reported more insomnia symptoms (RR = 2.19; 95% CI [1.20, 3.98]) compared to group members. CONCLUSION: Popularity may have hidden costs for girls' sleep, while social isolation seems to be detrimental for boys' sleep. These results suggest that a social network perspective is valuable for studying sleep health and might inform targeted interventions to improve adolescents' sleep outcomes.

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.

The HOPE Pilot Study: Harnessing Patient-Reported Outcomes and Biometric Data to Enhance Cancer Care.

PURPOSE: Integrating patient-reported outcomes (PROs) into clinical practice is an increasingly promising strategy for improving patients' symptoms, communication, and clinical outcomes. The objective of the current study was to assess the feasibility, acceptability, and perceived effectiveness of a mobile health intervention that was designed to collect PROs and activity data as a measure of health status. PATIENTS AND METHODS: This work was a pilot intervention with 10 patients with gynecologic cancers who received palliative chemotherapy. The HOPE (Helping Our Patients Excel) study used wearable accelerometers to assess physical activity and the Beiwe research platform to collect PROs, stratify patient responses by risk, provide tailored symptom management, and notify patients and clinicians of high-risk symptoms. Feasibility and acceptability were assessed through enrollment and adherence rates, and perceived effectiveness was evaluated by patients and oncologists at study completion. RESULTS: The approach-to-consent rate was 100%, and participants were 90% and 70% adherent to the wearable accelerometers and smartphone surveys, respectively. Participants' mean daily step count was 3,973 (standard deviation [SD], 2,305 steps) and increased from week 1 (mean, 3,520 steps; SD, 1,937 steps) to week 3 (mean, 4,136 steps; SD, 1,578 steps). Active monitoring of participants' heart rates, daily steps, and PROs throughout the study identified anomalies in participants' behavior patterns that suggested poor health for two patients (20%). Patients and clinicians indicated that the intervention improved physical activity, communication, and symptom management. CONCLUSION: A mobile health intervention that collects PROs and activity data as a measure of health status is feasible, acceptable, and was perceived to be effective in improving symptom management in patients with advanced gynecologic cancers. A larger, multisite, randomized clinical trial to assess the efficacy of the HOPE intervention on patients' symptoms, health-related quality of life, clinical outcomes, and health care use is warranted.

Biomarker correlation network in colorectal carcinoma by tumor anatomic location.

BACKGROUND: Colorectal carcinoma evolves through a multitude of molecular events including somatic mutations, epigenetic alterations, and aberrant protein expression, influenced by host immune reactions. One way to interrogate the complex carcinogenic process and interactions between aberrant events is to model a biomarker correlation network. Such a network analysis integrates multidimensional tumor biomarker data to identify key molecular events and pathways that are central to an underlying biological process. Due to embryological, physiological, and microbial differences, proximal and distal colorectal cancers have distinct sets of molecular pathological signatures. Given these differences, we hypothesized that a biomarker correlation network might vary by tumor location. RESULTS: We performed network analyses of 54 biomarkers, including major mutational events, microsatellite instability (MSI), epigenetic features, protein expression status, and immune reactions using data from 1380 colorectal cancer cases: 690 cases with proximal colon cancer and 690 cases with distal colorectal cancer matched by age and sex. Edges were defined by statistically significant correlations between biomarkers using Spearman correlation analyses. We found that the proximal colon cancer network formed a denser network (total number of edges, n = 173) than the distal colorectal cancer network (n = 95) (P < 0.0001 in permutation tests). The value of the average clustering coefficient was 0.50 in the proximal colon cancer network and 0.30 in the distal colorectal cancer network, indicating the greater clustering tendency of the proximal colon cancer network. In particular, MSI was a key hub, highly connected with other biomarkers in proximal colon cancer, but not in distal colorectal cancer. Among patients with non-MSI-high cancer, BRAF mutation status emerged as a distinct marker with higher connectivity in the network of proximal colon cancer, but not in distal colorectal cancer. CONCLUSION: In proximal colon cancer, tumor biomarkers tended to be correlated with each other, and MSI and BRAF mutation functioned as key molecular characteristics during the carcinogenesis. Our findings highlight the importance of considering multiple correlated pathways for therapeutic targets especially in proximal colon cancer.

Assessing the impact of colonoscopy complications on use of colonoscopy among primary care physicians and other connected physicians: an observational study of older Americans.

OBJECTIVES: Psychological biases can distort treatment decision-making. The availability heuristic is one such bias, wherein events that are recent, vivid or easily imagined are readily 'available' to memory and are therefore judged more likely to occur than expected based on epidemiological data. We assessed if the occurrence of a serious colonoscopy complication for a primary care physician's patient influenced colonoscopy rates for the physician's other patients. DESIGN: Longitudinal study with time-varying exposure variables. SETTING/PARTICIPANTS: Individuals living in 51 hospital referral regions across the USA identified based on enrolment in fee-for-service Medicare during 2005-2010. We assigned patients to a primary care physician based on office visits during the prior 2 years. EXPOSURES: For each physician in each month, we calculated the proportion of patients assigned to them who had a colonoscopy. We identified two serious complications of which the primary care provider would very likely be aware: gastrointestinal bleed or perforation leading to hospitalisation or death within 14 days of colonoscopy. MAIN OUTCOME MEASURES: We employed Poisson regression models including physician fixed effects to assess the change in number of colonoscopies in the four quarters following an adverse colonoscopy event. RESULTS: We identified 5 360 191 patients assigned to 30 704 physicians. 4864 physicians (16%) had at least one patient with an adverse event. The estimated change in the quarterly number of colonoscopies among physicians' patients was significantly lower in quarter 2 following an adverse colonoscopy event (change=-2.1% (95% CI -3.4 to -0.8%)), before returning to the rate expected in the absence of an adverse event. CONCLUSIONS: Having a patient experience a serious adverse colonoscopy event was associated with a small and temporary decline in colonoscopy rates among a physician's other patients. This finding provides empirical evidence for the influence of notable adverse events on care, possibly due to the availability heuristic.

Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial.

BACKGROUND: Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV- persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat and pre-exposure prophylaxis strategies. METHODS: We simulated 1000 trials, each consisting of 32 villages with 200 individuals per village. At baseline, we randomized the universal test-and-treat strategy. Then, after 3 years of follow-up, we considered four strategies for targeting pre-exposure prophylaxis: (1) all HIV- individuals who self-identify as high risk, (2) all HIV- individuals who are identified by their HIV+ partner (serodiscordant couples), (3) highly connected HIV- individuals, and (4) the HIV- contacts of a newly diagnosed HIV+ individual (a ring-based strategy). We explored two possible trial designs, and all villages were followed for a total of 7 years. For each village in a trial, we used a stochastic block model to generate bipartite (male-female) networks and simulated an agent-based epidemic process on these networks. We estimated the individual and combined intervention effects with a novel targeted maximum likelihood estimator, which used cross-validation to data-adaptively select from a pre-specified library the candidate estimator that maximized the efficiency of the analysis. RESULTS: The universal test-and-treat strategy reduced the 3-year cumulative HIV incidence by 4.0% on average. The impact of each pre-exposure prophylaxis strategy on the 4-year cumulative HIV incidence varied by the coverage of the universal test-and-treat strategy with lower coverage resulting in a larger impact of pre-exposure prophylaxis. Offering pre-exposure prophylaxis to serodiscordant couples resulted in the largest reductions in HIV incidence (2% reduction), and the ring-based strategy had little impact (0% reduction). The joint effect was larger than either individual effect with reductions in the 7-year incidence ranging from 4.5% to 8.8%. Targeted maximum likelihood estimation, data-adaptively adjusting for baseline covariates, substantially improved power over the unadjusted analysis, while maintaining nominal confidence interval coverage. CONCLUSION: Our simulation study suggests that nesting a pre-exposure prophylaxis study within an ongoing trial can lead to combined intervention effects greater than those of universal test-and-treat alone and can provide information about the efficacy of pre-exposure prophylaxis in the presence of high coverage of treatment for HIV+ persons.