A Deep Learning-Based Platform for Workers' Stress Detection Using Minimally Intrusive Multisensory Devices.

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously.

Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach.

Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation.

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.

Multi-Sensor Platform for Predictive Air Quality Monitoring.

Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people's health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden rise in CO2 levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people's comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware-software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO2 trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants' physical activity, temperature, humidity and CO2 in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days.

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy.

Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.

Benchmarking of Contactless Heart Rate Measurement Systems in ARM-Based Embedded Platforms.

Heart rate monitoring is especially important for aging individuals because it is associated with longevity and cardiovascular risk. Typically, this vital parameter can be measured using wearable sensors, which are widely available commercially. However, wearable sensors have some disadvantages in terms of acceptability, especially when used by elderly people. Thus, contactless solutions have increasingly attracted the scientific community in recent years. Camera-based photoplethysmography (also known as remote photoplethysmography) is an emerging method of contactless heart rate monitoring that uses a camera and a processing unit on the hardware side, and appropriate image processing methodologies on the software side. This paper describes the design and implementation of a novel pipeline for heart rate estimation using a commercial and low-cost camera as the input device. The pipeline's performance was tested and compared on a desktop PC, a laptop, and three different ARM-based embedded platforms (Raspberry Pi 4, Odroid N2+, and Jetson Nano). The results showed that the designed and implemented pipeline achieved an average accuracy of about 96.7% for heart rate estimation, with very low variance (between 1.5% and 2.5%) across processing platforms, user distances from the camera, and frame resolutions. Furthermore, benchmark analysis showed that the Odroid N2+ platform was the most convenient in terms of CPU load, RAM usage, and average execution time of the algorithmic pipeline.

Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform.

Sarcopenia is a geriatric condition characterized by a loss of strength and muscle mass, with a high impact on health status, functional independence and quality of life in older adults. [d=TT, ]To reduce the effects of the disease, just the diagnostic is not enough, it is necessary more than recognition.To reduce the effects of the disease, it is important to recognize the level and progression of sarcopenia early. Surface electromyography is becoming increasingly relevant for the prevention and diagnosis of sarcopenia, also due to a wide diffusion of smart and minimally invasive wearable devices suitable for electromyographic monitoring. The purpose of this work is manifold. The first aim is the design and implementation of a hardware/software platform. It is based on the elaboration of surface electromyographic signals extracted from the Gastrocnemius Lateralis and Tibialis Anterior muscles, useful to analyze the strength of the muscles with the purpose of distinguishing three different "confidence" levels of sarcopenia. The second aim is to compare the efficiency of state of the art supervised classifiers in the evaluation of sarcopenia. The experimentation stage was performed on an "augmented" dataset starting from data acquired from 32 patients. The latter were distributed in an unbalanced manner on 3 "confidence" levels of sarcopenia. The obtained results in terms of classification accuracy demonstrated the ability of the proposed platform to distinguish different sarcopenia "confidence" levels, with highest accuracy value given by Support Vector Machine classifier, outperforming the other classifiers by an average of 7.7%.

Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults.

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.

Vision-Based Road Rage Detection Framework in Automotive Safety Applications.

Drivers' road rage is among the main causes of road accidents. Each year, it contributes to more deaths and injuries globally. In this context, it is important to implement systems that can supervise drivers by monitoring their level of concentration during the entire driving process. In this paper, a module for Advanced Driver Assistance System is used to minimise the accidents caused by road rage, alerting the driver when a predetermined level of rage is reached, thus increasing the transportation safety. To create a system that is independent of both the orientation of the driver's face and the lighting conditions of the cabin, the proposed algorithmic pipeline integrates face detection and facial expression classification algorithms capable of handling such non-ideal situations. Moreover, road rage of the driver is estimated through a decision-making strategy based on the temporal consistency of facial expressions classified as "anger" and "disgust". Several experiments were executed to assess the performance on both a real context and three standard benchmark datasets, two of which containing non-frontal-view facial expression and one which includes facial expression recorded from participants during driving. Results obtained show that the proposed module is competent for road rage estimation through facial expression recognition on the condition of multi-pose and changing in lighting conditions, with the recognition rates that achieve state-of-art results on the selected datasets.

Customized Prevention Trials Could Resolve the Controversy of the Effects of Omega-3 Fatty Acids on Cancer.

Literature data revealed that the benefits of consuming omega-3 fatty acid (n-3FA) supplements such as fish oil with the goal of reducing the incidence of chronic diseases such as cardiovascular disease and cancer remain controversial. The purpose of this commentary is to discuss factors that may account for the inconsistency of results across different studies. Critical review of the published data, including our own preclinical and clinical studies, strongly suggests that customized clinical prevention trials are needed to resolve the above-mentioned controversy. Specifically, in order to develop a personalized cancer prevention strategy, more attention should be given to multiple factors including the dose of the n-3FA, the specific placebo used as a comparator, duration of administration, type of intervention (primary vs secondary prevention trial), specific compound (DHA vs EPA vs their metabolites), the ratio of n-3FA:n-6FA and the target population tested (high vs average risk).

Omega-3 Fatty Acids Responsive Proteins and Reduction in Breast Density in Obese Postmenopausal Women.

We reported that breast density (BD) was inversely correlated with the plasma level of DHA in postmenopausal obese, but not in nonobese, women given Lovaza (n-3FA). To identify protein biomarkers for the possible differential effect of n-3FA on BD between obese and nonobese women, an iTRAQ method was performed to analyze plasma from obese and lean women at each time point (baseline, 12 and 24-months, n = 10 per group); 173 proteins with >95% confidence (Unuses Score >1.3 and local false discovery rate estimation <5%) were identified. Comparative analysis between various groups identified several differentially expressed proteins (hemopexin precursor, vitamin D binding protein isoform 1 precursor [VDBP], fibronectin isoform 10 precursor [FN], and α-2 macroglobulin precursor [A2M]). Western blot analysis was performed to verify the differential expression of proteins in the iTRAQ study, and those found to be altered in a tumor protective fashion by an n-3FA rich diet in our previous preclinical study; gelsolin, VDBP, and FN were altered by n-3FA in a manner consistent with reduction in inflammation in obese women. To test the impact of our findings on breast cancer risk reduction by n-3FA, a posthoc analysis revealed that n-3FA administration reduced BD selectively in obese postmenopausal women.

Docosahexaenoic Acid in Combination with Dietary Energy Restriction for Reducing the Risk of Obesity Related Breast Cancer.

There is strong evidence that obesity poses a significant risk factor for postmenopausal breast cancer. There are multiple mechanisms by which obesity can predispose to breast cancer, prominent among which is the creation of a pro-inflammatory milieu systemically in the visceral and subcutaneous tissue, as well as locally in the breast. Although dietary intervention studies have shown in general a favorable effect on biomarkers of breast cancer risk, it is still unclear whether losing excess weight will lower the risk. In this manuscript, we will review the evidence that omega-3 fatty acids, and among them docosahexaenoic acid (DHA) in particular, may reduce the risk of obesity related breast cancer primarily because of their pleotropic effects which target many of the systemic and local oncogenic pathways activated by excess weight. We will also review the evidence indicating that intentional weight loss (IWL) induced by dietary energy restriction (DER) will augment the tumor protective effect of DHA because of its complementary mechanisms of action and its ability to reverse the obesity-induced alterations in fatty acid metabolism predisposing to carcinogenesis. We believe that the combination of DER and DHA is a promising safe and effective intervention for reducing obesity-related breast cancer risk which needs to be validated in appropriately designed prospective, randomized clinical trials.

Lysosomal degradation of CD44 mediates ceramide nanoliposome-induced anoikis and diminished extravasation in metastatic carcinoma cells.

The ceramide nanoliposome (CNL) has shown promise in being able to treat a variety of primary tumors. However, its potential for treating metastatic cancer remains unknown. In this study, we demonstrate that CNL increases anoikis while preventing cancer cell extravasation under both static and physiological fluid flow conditions. Mechanistically, CNL limits metastases by decreasing CD44 protein levels in human breast and pancreatic cancer cells via lysosomal degradation of CD44, independent of palmitoylation or proteasome targeting. siRNA down-regulation of CD44 mimics CNL-induced anoikis and diminished extravasation of cancer cells. Taken together, our data indicate that ceramide limits CD44-dependent cancer cell migration, suggesting that CNL could be used to prevent and treat solid tumor metastasis.

Fish Oil Enhances T Cell Function and Tumor Infiltration and Is Correlated With a Cancer Prevention Effect in HER-2/neu But Not PyMT Transgenic Mice.

Few studies have explored the effects of omega-3 polyunsaturated fatty acid (n-3 PUFA) supplementation on immune modulation in murine models of mammary carcinogenesis. HER-2/neu and PyMT mice were randomized to 2 dietary interventions: AIN-93G-based diet with 1) 11% of diet (per gram weight) as corn oil (CO) or 2) 10% of diet as menhaden fish oil plus 1% of diet as corn oil (FO). FO significantly reduced the incidence and multiplicity of tumors (P < 0.001) in HER-2/neu, but not PyMT mice. FO-fed mice had significantly larger splenocyte counts than CO-fed mice in both the HER-2/neu and PyMT models; and in both models this was comprised of an increase in most cell types, including Gr-1(+)/CD11b(+) cells. T cells from FO-fed HER-2/neu mice produced significantly more interleukin-2 (P = 0.004) and interferon-γ (P = 0.012) in response to in vitro stimulation with anti-CD3 (0.5 µg/ml). Lastly, FO-fed HER-2/neu mice had significantly more tumor immune infiltrates than CO-fed mice, including NK1.1(+), F4/80(+), and Gr-1(+)/CD11b(+) cells (P ≤ 0.05). Greater Th1 cytokine production and significantly more tumor immune infiltrates in FO-fed Her2/neu mice may account for the cancer prevention effect of fish oil in this model.

Influence of omega-3 fatty acids on Tamoxifen-induced suppression of rat mammary carcinogenesis.

We report here a detailed time course study of the individual and combined chemopreventive effects of Tamoxifen (Tam) and a high fish oil (FO) diet on multiple histologic parameters of mammary carcinogenesis. Groups of female Sprague-Dawley rats were injected ip with 1-methyl-1-nitrosourea at 50 days of age and assigned to either a control diet (20% corn oil [CO]) or a FO-rich diet (10% FO + 10% CO) in the presence and absence of Tam in the diet (0.6 ppm). Rats were sacrificed at weeks 4 (before palpable tumors), 8 and 12 (when ∼90% of control rats had palpable tumors). Our results demonstrate a major effect of Tam in inhibiting the development of early preneoplastic lesions. FO, while having a marginal protective effect of it own, enhanced the antitumor action of Tam on all histologic parameters of carcinogenesis, although the effects of the combination were not statistically different from those of Tam alone. The combination of FO and Tam was the only intervention that induced regression of established preneoplastic lesions. We also found that in contrast to plasma, only target tissue n-3 fatty acids (FAs) levels correlated with select tissue biomarkers of carcinogenesis whose expression was altered in a manner predictive of a protective effect. Our results demonstrating the potentially superior chemopreventive efficacy of Tam and n-3FA have important translational implications. Our data also emphasize the importance of local factors in affecting target tissue levels and biologic effects of n-3FA.

Differential impact of body mass index on absolute and percent breast density: implications regarding their use as breast cancer risk biomarkers.

Percent breast density (PBD), a commonly used biomarker of breast cancer risk (BCR), is confounded by the influence of non-dense breast tissue on its measurement and factors, such as BMI, which have an impact on non-dense tissue. Consequently, BMI, a potent BCR factor, is, paradoxically, negatively correlated with PBD. We propose that absolute breast density (ABD) is a more accurate biomarker of BCR. We used a volumetric method to compare the correlation between PBD and ABD with baseline demographics and dietary and physical activity variables in a group of 169 postmenopausal women enrolled in a clinical trial prior to any intervention. As expected, a strong negative correlation between PBD and BMI was observed (Rho = -0.5, p < 5e(-12)). In contrast, we observed a strong, previously not well established, positive correlation of BMI with ABD (Rho = 0.41, p < 2.5e(-8)), which supports the use of ABD as a more accurate indicator of BCR. Correction of PBD by BMI did not frequently provide the same information as ABD. In addition, because of the strong influence of BMI on ABD, many correlations between dietary variables and ABD did not emerge, until adjustment was made for BMI. ABD corrected by BMI should be the gold standard BD measurement. These findings identify the optimal measurement of BD when testing the influence of an intervention on BD as a biomarker of BCR.

Lifestyle Modifications and Breast Cancer Risk.

Lifestyle modifications have been shown to be effective in reducing breast cancer [...].

Complementarity between Microbiome and Immunity May Account for the Potentiating Effect of Quercetin on the Antitumor Action of Cyclophosphamide in a Triple-Negative Breast Cancer Model.

Immunotherapy targeting program cell death protein 1 (PD-1) in addition to chemotherapy has improved the survival of triple-negative breast cancer (TNBC) patients. However, the development of resistance and toxicity remain significant problems. Using the translationally relevant 4T1 mouse model of TNBC, we report here that dietary administration of the phytochemical quercetin enhanced the antitumor action of Cyclophosphamide, a cytotoxic drug with significant immunogenic effects that is part of the combination chemotherapy used in TNBC. We observed that quercetin favorably modified the host fecal microbiome by enriching species such as Akkermansia muciniphilia, which has been shown to improve response to anti-PD-1 therapy. We also show that quercetin and, to a greater extent, Cyclophosphamide increased the systemic frequency of T cells and NK cells. In addition, Cyclophosphamide alone and in combination with quercetin reduced the frequency of Treg, which is consistent with an antitumor immune response. On the other hand, Cyclophosphamide did not significantly alter the host microbiome, suggesting complementarity between microbiome- and immune-mediated mechanisms in potentiating the antitumor action of Cyclophosphamide by quercetin. Overall, these results support the potential for microbiota-centered dietary intervention to overcome resistance to chemoimmunotherapy in TNBC.

Fish oil alters tamoxifen-modulated expression of mRNAs that encode genes related to differentiation, proliferation, metastasis, and immune response in rat mammary tumors.

We have previously shown that a fish oil (FO)-rich diet increased the chemopreventive efficacy of tamoxifen (Tam) against N-methyl-N-nitrosourea (MNU)-induced rat mammary carcinogenesis. Herein, we provide evidence that Tam treatment modifies gene expression of mammary tumors depending upon the type of dietary fat fed to the animals. Rats initiated with MNU and treated with Tam were fed a diet rich in corn oil or FO. After 8 wk, cribriform tumors were collected and gene expression analysis was performed. Increased RNA expression of genes such as SerpinB10, Wisp2, and Apod in tumors from FO-treated rats is indicative of highly differentiated tumors. Decreased expression of H19 and Igf2 mRNA in Tam-treated groups, and Gamma Synuclein mRNA in the FO + Tam group may be related to tumor growth impairment and lower metastatic capacity. Change in the expression of genes associated with immunity in animals in the FO + Tam group may suggest a shift in the immune response. These data show that, although Tam modulates the expression of genes leading to tumor growth impairment, further modulations of genes are influenced by FO. FO modulation of Tam changes in gene expression accounts for its enhancing chemopreventive effect against MNU-induced mammary carcinogenesis. Supplemental materials are available for this article. Go to the publisher's online edition of Nutrition and Cancer to view the supplemental file.

Analysis of Time to Diagnosis and Outcomes Among Adults With Primary Hyperparathyroidism.

Importance: Despite access to routine laboratory evaluation, primary hyperparathyroidism (PHP) remains underdiagnosed and undertreated. Objective: To determine the consequences associated with missed diagnoses and prolonged time to diagnosis and treatment of PHP. Design, Setting, and Participants: This is a retrospective cohort study of patients older than 40 years with 2 instances of hypercalcemia during 2010 to 2020 and 3 years of follow-up. Patients were recruited from 63 health care organizations in the TriNetX Research Network. Data analysis was performed from January 2010 to September 2020. Exposures: Elevated serum calcium. Main Outcomes and Measures: Existing symptoms and diagnoses associated with PHP (osteoporosis, fractures, urolithiasis, major depressive disorder, anxiety, hypertension, gastroesophageal reflux disease, malaise or fatigue, joint pain or myalgias, constipation, insomnia, polyuria, weakness, abdominal pain, headache, nausea, amnesia, and gallstones) compared in patients deemed high-risk and without a diagnosis and matched controls, and those who experienced times from documented hypercalcemia to diagnosis and diagnosis to treatment within or beyond 1 year. Results: There were 135 034 patients analyzed (96 554 women [72%]; 28 892 Black patients [21%] and 88 010 White patients [65%]; 3608 Hispanic patients [3%] and 98 279 non-Hispanic patients [73%]; mean [SD] age, 63 [10] years). Two groups without a documented diagnosis of PHP were identified as high risk: 20 176 patients (14.9%) with parathyroid hormone greater than or equal to 50 pg/mL and 24 905 patients (18.4%) with no parathyroid hormone level obtained or recorded explanation for hypercalcemia. High-risk patients experienced significantly increased rates of all associated symptoms and diagnoses compared with matched controls. Just 9.7% of those with hypercalcemia (13 136 patients) had a diagnosis of PHP. Compared with individuals who received a diagnosis within 1 year of hypercalcemia, those whose workup exceeded 1 year had significantly increased rates of major depressive disorder, anxiety, hypertension, gastroesophageal reflux disease, malaise or fatigue, joint pain or myalgias, polyuria, weakness, abdominal pain, and headache at 3 years. The rate of osteoporosis increased from 17.1% (628 patients) to 25.4% (935 patients) over the study period in the group with delayed diagnosis. Among those with a diagnosis, 5280 patients (40.2%) underwent parathyroidectomy. Surgery beyond 1 year of diagnosis was associated with significantly increased rates of osteoporosis and hypertension at 3 years after diagnosis compared with those treated within 1 year. Conclusions and Relevance: Many patients were at high risk for PHP without a documented diagnosis. Complications in these patients, as well as those who received a diagnosis after prolonged workup or time to treatment, resulted in patient harm. System-level interventions are necessary to ensure proper diagnosis and prompt treatment of PHP.