Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data.

BACKGROUND: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in-depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time-consuming and costly but also require systematic and cost-effective approaches. OBJECTIVE: The main objective of this study was to investigate the feasibility of training an end-to-end deep learning (DL) model by directly inputting time-series activity tracking and sleep data obtained from consumer-grade wrist-worn activity trackers to identify comorbid depression and anxiety. METHODS: To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non-time-series data. The basic structure of the DL model was designed to process mixed-input data and perform multi-label-based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short-term memory (LSTM), were applied to process the time-series data, and model selection was conducted by comparing the performances of the hyperparameters. RESULTS: This study achieved significant results in the multi-label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed-input DL model based on activity tracking data. The comparison of hyper-parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results. CONCLUSIONS: This study can be considered as the first to develop a mixed-input DL model based on activity tracking data for the multi-label identification of late-life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer-grade wrist-worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi-label classification framework for identifying the complex symptoms of depression and anxiety.

Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review.

Given the importance of young children's postures and movements to health and development, robust objective measures are required to provide high-quality evidence. This study aimed to systematically review the available evidence for objective measurement of young (0-5 years) children's posture and movement using machine learning and other algorithm methods on accelerometer data. From 1663 papers, a total of 20 papers reporting on 18 studies met the inclusion criteria. Papers were quality-assessed and data extracted and synthesised on sample, postures and movements identified, sensors used, model development, and accuracy. A common limitation of studies was a poor description of their sample data, yet over half scored adequate/good on their overall study design quality assessment. There was great diversity in all aspects examined, with evidence of increasing sophistication in approaches used over time. Model accuracy varied greatly, but for a range of postures and movements, models developed on a reasonable-sized (n > 25) sample were able to achieve an accuracy of >80%. Issues related to model development are discussed and implications for future research outlined. The current evidence suggests the rapidly developing field of machine learning has clear potential to enable the collection of high-quality evidence on the postures and movements of young children.

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation.

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation.

Pain self-management plus activity tracking and nurse-led support in adults with chronic low back pain: feasibility and acceptability of the problem-solving pain to enhance living well (PROPEL) intervention.

BACKGROUND: Chronic low back pain can lead to individual suffering, high medical expenditures, and impaired social well-being. Although the role of physical activity in pain management is well established, the underlying mechanisms of biological and clinical outcomes are unknown. This study aimed to assess the feasibility and acceptability of a pain self-management intervention, Problem-Solving Pain to Enhance Living Well, which employs wearable activity tracking technology and nurse consultations for people with chronic low back pain. METHODS: This one-arm longitudinal study recruited 40 adults aged 18-60 years with chronic low back pain. Over 12 weeks, participants watched 10 short video modules, wore activity trackers, and participated in nurse consultations every 2 weeks. At baseline and the 12-week follow-up, they completed study questionnaires, quantitative sensory testing, and blood sample collection. RESULTS: Forty participants were recruited, and their mean age was 29.8. Thirty-two participants completed the survey questionnaire, quantitative sensory testing, Fitbit activity tracker, and bi-weekly nurse consultation, and 25 completed the evaluation of biological markers. The overall satisfaction with the Problem-Solving Pain to Enhance Living Well video modules, nurse consultations, and Fitbit in pain management was rated as excellent. No adverse events were reported. Between the baseline and 12-week follow-up, there was a significant decrease in pain intensity and interference and an increase in the warm detection threshold at the pain site. CONCLUSIONS: Despite concerns about the participant burden due to multidimensional assessment and intensive education, the feasibility of the Problem-Solving Pain to Enhance Living Well intervention was favorable. Technology-based self-management interventions can offer personalized strategies by integrating pain phenotypes, genetic markers, and physical activity types affecting pain conditions. TRIAL REGISTRATION: This pilot study was registered with ClinicalTrials.gov [NCT03637998, August 20, 2018]. The first participant was enrolled on September 21, 2018.

A Case Study of Upper Limb Robotic-Assisted Therapy Using the Track-Hold Device.

The Track-Hold System (THS) project, developed in a healthcare facility and therefore in a controlled and protected healthcare environment, contributes to the more general and broad context of Robotic-Assisted Therapy (RAT). RAT represents an advanced and innovative rehabilitation method, both motor and cognitive, and uses active, passive, and facilitating robotic devices. RAT devices can be equipped with sensors to detect and track voluntary and involuntary movements. They can work in synergy with multimedia protocols developed ad hoc to achieve the highest possible level of functional re-education. The THS is based on a passive robotic arm capable of recording and facilitating the movements of the upper limbs. An operational interface completes the device for its use in the clinical setting. In the form of a case study, the researchers conducted the experimentation in the former Tabarracci hospital (Viareggio, Italy). The case study develops a motor and cognitive rehabilitation protocol. The chosen subjects suffered from post-stroke outcomes affecting the right upper limb, including strength deficits, tremors, incoordination, and motor apraxia. During the first stage of the enrolment, the researchers worked with seven patients. The researchers completed the pilot with four patients because three of them got a stroke recurrence. The collaboration with four patients permitted the generation of an enlarged case report to collect preliminary data. The preliminary clinical results of the Track-Hold System Project demonstrated good compliance by patients with robotic-assisted rehabilitation; in particular, patients underwent a gradual path of functional recovery of the upper limb using the implemented interface.

Moving minds: Mental health and wellbeing benefits of a 50-day workplace physical activity program.

The aim of this study was to assess changes in mental health and wellbeing measures across a 50-day physical activity workplace program. The secondary aims assessed the relationship between demographic and pre-program physical activity self-reported variables, mental health, wellbeing and program engagement measures. The study utilized a naturalistic longitudinal design with a study population of 2903 people. Participants were engaged in the 10,000 step daily physical activity program for 50-days and measures of engagement were tracked. 1320 participants provided full pre/post-program data across a range of standardized mental health and wellbeing measures alongside demographic and program engagement measures. For individuals providing pre and post program data there was a significant reduction in anxiety (18.2%, p = .008), stress (13.0%, p = .014) and sleep related impairment (6.9%, p < .001) alongside a significant improvement in overall wellbeing (6.7%, p = .001). The data further showed no significant mental health differences were identified between individuals who recorded below versus equal to or above 10,000 steps. Regression analyses indicated numerous group and personal variables impacted mental health, wellbeing and program engagement. The study highlights improvements in a range of mental health and wellbeing scores occurred over the 50-day activity program for people who complete the program. Finally, the study identified a range of protective and risk factors for mental health benefits of these programs and level of engagement. Whilst there were similarities in the pre-program mental health and wellbeing scores of those who completed and those lost to follow-up, further research is required to better characterize and understand this group.

Children's outdoor active mobility behaviour and neighbourhood safety: a systematic review in measurement methods and future research directions.

BACKGROUND: Numerous studies have examined the association between safety and primary school-aged children's forms of active mobility. However, variations in studies' measurement methods and the elements addressed have contributed to inconsistencies in research outcomes, which may be forming a barrier to advancing researchers' knowledge about this field. To assess where current research stands, we have synthesised the methodological measures in studies that examined the effects of neighbourhood safety exposure (perceived and measured) on children's outdoor active mobility behaviour and used this analysis to propose future research directions. METHOD: A systematic search of the literature in six electronic databases was conducted using pre-defined eligibility criteria and was concluded in July 2020. Two reviewers screened the literature abstracts to determine the studies' inclusion, and two reviewers independently conducted a methodological quality assessment to rate the included studies. RESULTS: Twenty-five peer-reviewed studies met the inclusion criteria. Active mobility behaviour and health characteristics were measured objectively in 12 out of the 25 studies and were reported in another 13 studies. Twenty-one studies overlooked spatiotemporal dimensions in their analyses and outputs. Delineations of children's neighbourhoods varied within 10 studies' objective measures, and the 15 studies that opted for subjective measures. Safety perceptions obtained in 22 studies were mostly static and primarily collected via parents, and dissimilarities in actual safety measurement methods were present in 6 studies. The identified schematic constraints in studies' measurement methods assisted in outlining a three-dimensional relationship between 'what' (determinants), 'where' (spatial) and 'when' (time) within a methodological conceptual framework. CONCLUSIONS: The absence of standardised measurement methods among relevant studies may have led to the current diversity in findings regarding active mobility, spatial (locality) and temporal (time) characteristics, the neighbourhood, and the representation of safety. Ignorance of the existing gaps and heterogeneity in measures may impact the reliability of evidence and poses a limitation when synthesising findings, which could result in serious biases for policymakers. Given the increasing interest in children's health studies, we suggested alternatives in the design and method of measures that may guide future evidence-based research for policymakers who aim to improve children's active mobility and safety.

"Someone's Got My Back": Older People's Experience of the Coaching for Healthy Ageing Program for Promoting Physical Activity and Preventing Falls.

The Coaching for Healthy Ageing trial evaluated the impact on physical activity (PA) and falls based on a year-long intervention in which participants aged 60+ receive a home visit, regular health coaching by physiotherapists, and a free activity monitor. This interview study describes the participants' experiences of the intervention and ideas for improvement. The authors sampled purposively for maximum variation in experiences. The data were analyzed thematically by two researchers. Most of the 32 participants reported that the intervention increased PA levels, embedded activities, and generated positivity about PA. They were motivated by quantified PA feedback, self-directed goals, and person-centered coaching. Social connectivity motivated some, but the intervention did not support this well. The intervention structure allowed participants to trial and embed activities. Autonomy and relatedness were emphasized and should be included in future program theory. The authors identified synergistic effects, likely "essential ingredients," and potential areas for improving this and similar interventions.

Knowledge Discovery on IoT-Enabled mHealth Applications.

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification.

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments.

Real-Time Data Collection to Examine Relations Between Physical Activity and Affect in Adults With Mental Illness.

Adults with serious mental illness engage in limited physical activity, which contributes to significant health disparities. This study explored the use of both ecological momentary assessments (EMAs) and activity trackers in adults with serious mental illness to examine the bidirectional relationship between activity and affect with multilevel modeling. Affective states were assessed up to seven times per day using EMA across 4 days. The participants (n = 20) were equipped with a waist-worn accelerometer to measure moderate to vigorous physical activity. The participants had a mean EMA compliance rate of 88.3%, and over 90% of completed EMAs were matched with 30-min windows of accelerometer wear. The participants who reported more positive affect than others had a higher probability of engaging in moderate to vigorous physical activity. Engaging in more moderate to vigorous physical activity than one's usual was associated with more negative affect. This study begins to address the effect of momentary mood on physical activity in a population of adults that is typically difficult to reach.

Quantifying multi-dimensional attributes of human activities at various geographic scales based on smartphone tracking.

BACKGROUND: Advancement in location-aware technologies, and information and communication technology in the past decades has furthered our knowledge of the interaction between human activities and the built environment. An increasing number of studies have collected data regarding individual activities to better understand how the environment shapes human behavior. Despite this growing interest, some challenges exist in collecting and processing individual's activity data, e.g., capturing people's precise environmental contexts and analyzing data at multiple spatial scales. METHODS: In this study, we propose and implement an innovative system that integrates smartphone-based step tracking with an app and the sequential tile scan techniques to collect and process activity data. We apply the OpenStreetMap tile system to aggregate positioning points at various scales. We also propose duration, step and probability surfaces to quantify the multi-dimensional attributes of activities. RESULTS: Results show that, by running the app in the background, smartphones can measure multi-dimensional attributes of human activities, including space, duration, step, and location uncertainty at various spatial scales. By coordinating Global Positioning System (GPS) sensor with accelerometer sensor, this app can save battery which otherwise would be drained by GPS sensor quickly. Based on a test dataset, we were able to detect the recreational center and sports center as the space where the user was most active, among other places visited. CONCLUSION: The methods provide techniques to address key issues in analyzing human activity data. The system can support future studies on behavioral and health consequences related to individual's environmental exposure.

Statistical Platform for Individualized Behavioral Analyses Using Biophysical Micro-Movement Spikes.

Wearable biosensors, such as those embedded in smart phones, can provide data to assess neuro-motor control in mobile settings, at homes, schools, workplaces and clinics. However, because most machine learning algorithms currently used to analyze such data require several steps that depend on human heuristics, the analyses become computationally expensive and rather subjective. Further, there is no standardized scale or set of tasks amenable to take advantage of such technology in ways that permit broad dissemination and reproducibility of results. Indeed, there is a critical need for fully objective automated analytical methods that easily handle the deluge of data these sensors output, while providing standardized scales amenable to apply across large sections of the population, to help promote personalized-mobile medicine. Here we use an open-access data set from Kaggle.com to illustrate the use of a new statistical platform and standardized data types applied to smart phone accelerometer and gyroscope data from 30 participants, performing six different activities. We report full distinction without confusion of the activities from the Kaggle set using a single parameter (linear acceleration or angular speed). We further extend the use of our platform to characterize data from commercially available smart shoes, using gait patterns within a set of experiments that probe nervous systems functioning and levels of motor control.

Occupational physical activity in brewery and office workers.

Active lifestyles are beneficial to health and well-being but our workplaces may not be inherently supportive of physical activity at work. With the increasing use of technology in the workplace, many jobs are becoming more sedentary. The purpose of this study was to characterize levels of occupational physical activity (OPA) among active and sedentary workers. Two types of activity trackers (Fitbit Charge HR and Hexoskin) were used to assess activity measures (steps, heart rate, and energy expenditure) among workers during one full work shift. The first objective of the study was to assess the agreement between two types of accelerometer-based activity trackers as measures of occupational physical activity. The second objective of this study was to assess differences in measures of OPA among workers in generally physically active (brewery) and sedentary (office) work environments. Occupational physical activity data were collected from 50 workers in beer-brewing tasks and 51 workers in office work tasks. The 101 subjects were from the brewing service sector, a call center, and an engineering office within a manufacturing facility. A two-factor repeated measures analysis of variance (ANOVA) was used to assess the two activity tracking devices while two-sample t-tests were used to compare the two worker groups. There were statistically significant differences in total steps and mean heart rate between the two devices. When comparing the two groups of workers there were statistically significant differences in measures of steps, mean heart rate, and energy expenditure. The results of the present study provide quantitative evidence that levels of OPA should be identified for different work groups.

In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injury.

BACKGROUND: Although commercially available activity trackers can aid in tracking therapy and recovery of patients, most devices perform poorly for patients with irregular movement patterns. Standard machine learning techniques can be applied on recorded accelerometer signals in order to classify the activities of ambulatory subjects with incomplete spinal cord injury in a way that is specific to this population and the location of the recording-at home or in the clinic. METHODS: Subjects were instructed to perform a standardized set of movements while wearing a waist-worn accelerometer in the clinic and at-home. Activities included lying, sitting, standing, walking, wheeling, and stair climbing. Multiple classifiers and validation methods were used to quantify the ability of the machine learning techniques to distinguish the activities recorded in-lab or at-home. RESULTS: In the lab, classifiers trained and tested using within-subject cross-validation provided an accuracy of 91.6%. When the classifier was trained on data collected in the lab but tested on at home data, the accuracy fell to 54.6% indicating distinct movement patterns between locations. However, the accuracy of the at-home classifications, when training the classifier with at-home data, improved to 85.9%. CONCLUSION: Individuals with unique movement patterns can benefit from using tailored activity recognition algorithms easily implemented using modern machine learning methods on collected movement data.

A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications.

The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6%, and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products.

Mobility abilities mediate the association of a more active lifestyle with mobility disability in older adults.

BACKGROUND: Few studies have analyzed sensor-derived metrics of mobility abilities and total daily physical activity (TDPA). We tested whether sensor-derived mobility metrics and TDPA indices are independently associated with mobility disabilities. METHODS: This cohort study derived mobility abilities from a belt-worn sensor that recorded annual supervised gait testing. TDPA indices were obtained from a wrist-worn activity monitor. Mobility disability was determined by self-report and inability to perform an 8-feet walk task. Baseline associations of mobility metrics and TDPA (separately and together) were examined with logistic regressions and incident associations (average 7 years follow-up) with Cox models. Mediation analysis quantified the extent mobility metrics mediate the association of TDPA with mobility disability. RESULTS: 724 ambulatory older adults (mean age 82 years, 77.4% female) were studied. In separate models, mobility abilities (e.g. step time variability, turning angular velocity) and TDPA were related to mobility disabilities. Examined together in a single model, mobility abilities remained associated with mobility disabilities, while TDPA was attenuated. This attenuation of TDPA could be explained by mediation analysis that showed about 50% of TDPA associations with mobility disabilities is mediated via mobility abilities (prevalent mobility disability 54%, incident mobility disability 40%, incident loss of ambulation 50%; all p's<0.001). CONCLUSIONS: Sensor-derived mobility metrics assess more diverse facets of mobility. These metrics mediate approximately half of the association of higher levels of daily physical activity with reduced mobility disability in older adults. Findings may inform the design of targeted interventions to reduce mobility disability in late life.

CFTrack: Advanced Diagnostic, Monitoring, and Tracking Device for Cystic Fibrosis Care.

Cystic fibrosis (CF) is a genetic disorder that primarily affects the respiratory, digestive, and reproductive systems. In the United States, approximately 32,000 individuals, spanning both children and adults, suffer from CF, and roughly 1,000 new cases are diagnosed annually. The current gold standard for CF diagnosis is the sweat test, yet this method is plagued by issues such as being time-consuming, expensive, challenging to replicate, and lacking treatment monitoring capabilities. In contrast, the emerging field of wearable sweat biosensors has gained significant attention due to their potential for noninvasive health monitoring. Despite this, there remains a conspicuous absence of a wearable sweat biosensor tailored specifically for CF diagnosis and monitoring. Here, this study introduces a flexible wearable sweat biosensor, named CFTrack, designed to address the unique challenges associated with CF. This proposed CFTrack biosensor not only facilitates CF diagnosis but also enables the monitoring of medication treatment effectiveness and tracks therapy activities. In addition, it operates in a self-powered and customized manner, ensuring seamless integration into the daily lives of individuals with CF. Given that sweat tests and fitness routines are the predominant methods for diagnosing and treating cystic fibrosis patients, respectively, the proposed CFTrack biosensor leverages ion concentration in sweat for diagnostic purposes. Additionally, it incorporates a motion-tracking function to monitor physical activity, providing a comprehensive approach to CF management. To evaluate the feasibility of the proposed CFTrack biosensor, a comprehensive evaluation has been performed including numerical simulations, theoretical analyses, and experimental tests. The results demonstrate the efficacy of the proposed CFTrack biosensor in diagnosing and monitoring CF conditions while also showcasing its ability to effectively track the progress of patients undergoing physical therapy. The proposed CFTrack biosensor resolves key issues associated with existing sweat sensors including high energy consumption, intricate fabrication procedures, and the absence of continuous monitoring capabilities. By addressing these challenges, the proposed sweat biosensor aims to revolutionize CF diagnosis and monitoring, offering a more efficient and user-friendly alternative to current methods.

Smartphone-based activity tracking for spine patients: Current technology and future opportunities.

Activity trackers and wearables allow accurate determination of physical activity, basic vital parameters, and tracking of complex medical conditions. This review attempts to provide a roadmap for the development of these applications, outlining the basic tools available, how they can be combined, and what currently exists in the marketplace for spine patients. Various types of sensors currently exist to measure distinct aspects of user movement. These include the accelerometer, gyroscope, magnetometer, barometer, global positioning system (GPS), Bluetooth and Wi-Fi, and microphone. Integration of data from these sensors allows detailed tracking of location and vectors of motion, resulting in accurate mobility assessments. These assessments can have great value for a variety of healthcare specialties, but perhaps none more so than spine surgery. Patient-reported outcomes (PROMs) are subject to bias and are difficult to track frequently - a problem that is ripe for disruption with the continued development of mobility technology. Currently, multiple mobile applications exist as an extension of clinical care. These include Manage My Surgery (MMS), SOVINITY-e-Healthcare Services, eHealth System, Beiwe Smartphone Application, QS Access, 6WT, and the TUG app. These applications utilize sensor data to assess patient activity at baseline and postoperatively. The results are evaluated in conjunction with PROMs. However, these applications have not yet exploited the full potential of available sensors. There is a need to develop smartphone applications that can accurately track the functional status and activity of spine patients, allowing a more quantitative assessment of outcomes, in contrast to legacy PROMs.