Smartwatch Application Research Articles

A smartwatch-based CPR feedback device improves chest compression quality among health care professionals and lay rescuers

Cardiopulmonary resuscitation (CPR) quality significantly impacts patient outcomes during cardiac arrests. With advancements in health care technology, smartwatch-based CPR feedback devices have emerged as potential tools to enhance CPR delivery. This study evaluated a novel smartwatch-based CPR feedback device in enhancing chest compression quality among health care professionals and lay rescuers. A single-center, open-label, randomized crossover study was conducted with 30 subjects categorized into 3 groups based on rescuer category. The Relay Response BLS smartwatch application was compared to a defibrillator-based feedback device (Zoll OneStep CPR Pads). Following an introduction to the technology, subjects performed chest compressions in 3 modules: baseline unaided, aided by the smartwatch-based feedback device, and aided by the defibrillator-based feedback device. Outcome measures included effectiveness, learnability, and usability. Across all groups, the smartwatch-based device significantly improved mean compression depth effectiveness (68.4% vs 29.7%; P < .05) and mean rate effectiveness (87.5% vs 30.1%; P< .05), compared to unaided compressions. Compression variability was significantly reduced with the smartwatch-based device (coefficient of variation: 14.9% vs 26.6%), indicating more consistent performance. Fifteen of 20 professional rescuers reached effective compressions using the smartwatch-based device in an average 2.6 seconds. A usability questionnaire revealed strong preference for the smartwatch-based device over the defibrillator-based device. The smartwatch-based device enhances the quality of CPR delivery by keeping compressions within recommended ranges and reducing performance variability. Its user-friendliness and rapid learnability suggest potential for widespread adoption in both professional and lay rescuer scenarios, contributing positively to CPR training and real-life emergency responses.

A compact dual-band frequency reconfigurable antenna with pin diode integration for smartwatch application

ABSTRACT In this article, a novel frequency reconfigurable antenna for Wi-Fi and WiMAX applications is proposed. The antenna uses PIN diodes for frequency reconfigurability, allowing it to seamlessly switch between two distinct frequency bands centred around the resonant frequencies of 4.1 GHz in the ON state and 5.2 GHz in the OFF state. The input reflection coefficient obtained from the designed antenna is below −10 dB. The measured and simulated gain of the antenna is 4 dBi and 2.65 dBi at resonating frequencies 4.1 GHz and 5.2 GHz the measured and simulated gain is 3.2 dBi and 2.93 dBi, respectively. The radiation pattern exhibits the same for both states. Optimization of the antenna’s dimensional parameters is done with the help of CST simulation software and the electrical parameter such as input reflection coefficient is measured using a Vector Network Analyzer to validate the prototype. The proposed antenna is fabricated on FR-4 substrate material with a thickness of 1.6 mm and a relative permittivity of 4.4. The proposed antenna’s size is 30 × 30 × 1.6 mm3 which is compact and suitable for smartwatch applications. The proposed antenna is compact and remains a good candidate for integration with the smartwatch.

Feasibility of a wearable self-management application for patients with COPD at home: a pilot study

BackgroundAmong people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the effects on their self-management.MethodsWe developed a COPD self-management application for smartphones and smartwatches. Participants were provided with the app on a smartphone and a smartwatch, as well as a cellular data plan and followed for 6 months. We measured usage of the different smartphone app functions. For the primary outcome, we examined the change in self-management from baseline to the end of follow up. Secondary outcomes include changes in self-efficacy, quality of life, and COPD disease control.ResultsThirty-four patients were enrolled and followed. Mean age was 69.8 years, and half of the participants were women. The most used functions were recording steps through the smartwatch, entering a daily symptom questionnaire, checking oxygen saturation, and performing breathing exercises. There was no significant difference in the primary outcome of change in self-management after use of the app or in overall total scores of health-related quality of life, disease control or self-efficacy.ConclusionWe found older patients with COPD would engage with a COPD smartphone and smartwatch application, but this did not result in improved self-management. More research is needed to determine if a smartphone and smartwatch application can improve self-management in people with COPD.Trial registrationClinicalTrials.Gov NCT03857061, First Posted February 27, 2019.

Patient Acceptance of Self-Monitoring on a Smartwatch in a Routine Digital Therapy: A Mixed-Methods Study

Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have the potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK’s National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with a smartwatch with those from an additional 34 patients without a smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on a smartwatch and reflect on the conduct of human-computer interaction research evaluating user acceptance of mental health technologies.

Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper, showing the variability of noise-related distractions, thermal comfort, and associated context. The results show that participants experienced at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and the collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants’ needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.

Enhancing quality of life: Human-centered design of mobile and smartwatch applications for assisted ambient living

&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; Assisted ambient living interfaces are technologies designed to improve the quality of life for people who require assistance with daily activities. They are crucial for individuals to maintain their independence for as long as possible. To this end, these interfaces have to be user-friendly, intuitive, and accessible, even for those who are not tech-savvy. Research in recent years indicates that people find it uncomfortable to wear invasive or large intrusive devices to monitor health status, and poor user interface design implies a lack of user engagement. &lt;strong&gt;Methods:&lt;/strong&gt; This paper presents the design and implementation of non-intrusive mobile and smartwatch applications for detecting older adults when executing their routines. The solution uses an intuitive mobile application to set up beacons and incorporates biometric data acquired from the smartwatch to measure bio-signals correlated to the user’s location. User testing and interface evaluation are carried out using the User Experience Questionnaire (UEQ). &lt;strong&gt;Results:&lt;/strong&gt; Six older adults participated in the evaluation of the interfaces. Results show that users found the interaction to be excellent in all the parameters of the UEQ in the evaluation of the mobile interface. For the smartwatch application, results vary from above average to excellent. &lt;strong&gt;Conclusions:&lt;/strong&gt; The applications are intuitive and easy to use, and data obtained from integrating systems is essential to link information and provide feedback to the user.&lt;/p&gt;

Developing a Smartwatch-Based Healthcare Application: Notes to Consider.

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.

In-the-Wild Affect Analysis of Children with ASD Using Heart Rate.

Recognizing the affective state of children with autism spectrum disorder (ASD) in real-world settings poses challenges due to the varying head poses, illumination levels, occlusion and a lack of datasets annotated with emotions in in-the-wild scenarios. Understanding the emotional state of children with ASD is crucial for providing personalized interventions and support. Existing methods often rely on controlled lab environments, limiting their applicability to real-world scenarios. Hence, a framework that enables the recognition of affective states in children with ASD in uncontrolled settings is needed. This paper presents a framework for recognizing the affective state of children with ASD in an in-the-wild setting using heart rate (HR) information. More specifically, an algorithm is developed that can classify a participant's emotion as positive, negative, or neutral by analyzing the heart rate signal acquired from a smartwatch. The heart rate data are obtained in real time using a smartwatch application while the child learns to code a robot and interacts with an avatar. The avatar assists the child in developing communication skills and programming the robot. In this paper, we also present a semi-automated annotation technique based on facial expression recognition for the heart rate data. The HR signal is analyzed to extract features that capture the emotional state of the child. Additionally, in this paper, the performance of a raw HR-signal-based emotion classification algorithm is compared with a classification approach based on features extracted from HR signals using discrete wavelet transform (DWT). The experimental results demonstrate that the proposed method achieves comparable performance to state-of-the-art HR-based emotion recognition techniques, despite being conducted in an uncontrolled setting rather than a controlled lab environment. The framework presented in this paper contributes to the real-world affect analysis of children with ASD using HR information. By enabling emotion recognition in uncontrolled settings, this approach has the potential to improve the monitoring and understanding of the emotional well-being of children with ASD in their daily lives.

Feasibility, credence, and usefulness of out-of-office cuffless blood pressure monitoring using smartwatch: a population survey

BackgroundCuffless blood pressure (BP) measurement, enabled by recent advances in wearable devices, allows for BP monitoring in daily life. This study aims to evaluate the feasibility, cresdence, and usefulness of cuffless BP monitoring through a population survey.MethodsDuring the "Daily BP Measurement with Your Galaxy Watch" campaign held by the Korean Society of Hypertension, participants were asked to share their experiences with cuffless BP measurement using a smartwatch application through an online survey. The questionnaire included questions about age, underlying medical conditions, smartwatch utilization, experience with BP calibration, the reliability of BP values measured by a smartwatch, and willingness to use the BP monitoring function in the future.ResultsA total of 1071 participants responded to the survey. The largest age group (decile) was 50-59 years old (33.3%), followed by 40-49 years old (29.9%). Although nearly half of the participants (47.5%) had no chronic diseases, 40.1% reported having hypertension. BP monitoring was the most frequently utilized smartwatch function (95.8%), followed by heart rate measurement (87.1%). 31.8% of participants reported that BP values measured by the smartphone application were "very accurate and helpful," while 63.5% rated them as "slightly lower (44.4%)" or "higher (19.1%)" compared to the standard home BP monitoring device. 93% of the participants reported utilizing the BP monitoring function at least once a week. Regarding the BP calibration process, most participants (93.9%) calibrated the BP measurement application themselves, and 50.8% rated the difficulty level as "very easy."ConclusionCuffless BP measurement using a smartwatch application was feasible in the general population, including the self-calibration process. However, the satisfaction level in terms of accuracy is still modest, indicating a need for further development.

Reducing scratching behavior in atopic dermatitis patients using the EMDR treatment protocol for urge: A pilot study.

Itch, and thereby the scratching behavior, is a common complaint in atopic dermatitis. Scratching damages the skin, which in turn worsens the itch. This itch-scratch cycle perpetuates the skin condition and has a major impact on the patient's quality of life. In addition to pharmacological treatment, psychological interventions show promising results in reducing scratching behavior. To investigate the effect of treatment according the EMDR treatment protocol for urge on scratching behavior of atopic dermatitis patients in a controlled study. This study applies a multiple baseline across subjects design. Six patients were randomly allocated to different baseline lengths and all of them started registration of scratching behavior at the same day, using a mobile phone application. Nocturnal scratching was registered by a smart watch application. The total study duration was 46 days and was equal for all patients. Treatment consisted of two sessions using the EMDR treatment protocol for urge. Furthermore, standardized measures were used to assess disease activity, quality of life, and self-control. The nonoverlap of all pairs effect size was calculated for the daily measure data. One patient dropped out. Visual inspection suggests that the scratching behavior decreased over time in all patients. Furthermore, a moderate effect size of the treatment is found. During the baseline phase, scratching behavior fluctuated considerably and showed a slight negative trend. Outcomes of disease activity decreased over time and patients' self-control and quality of life improved after treatment. Nocturnal scratching behavior did not change after the intervention. The results of the visual analysis of day time scratching behavior, disease activity, quality of life, and self-control seem promising. These findings pave the way for future research into the effect of the new intervention on other skin conditions suffering from scratching behavior, such as prurigo nodularis.

Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and MobilityMonitor.

Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real time in ecological settings. Data come from a sample of 31 participants (Mage=74.7, 51.6% female) who used ROAMM for approximately 2 weeks. We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures. Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance=70.7%) and the majority reported being "likely" to enroll in a 2-year study (77.4%). Some smartwatch features were correlated with their respective traditional measurements (eg, certain GPS-derived life-space mobility features (r=0.50-0.51, p < .05) and ecologically measured pain (r=0.72, p=.01), but others were not (eg, ecologically measured fatigue). ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss.

Implementation of a Mobile Technology-Supported Diaphragmatic Breathing Intervention in Military mTBI With PTSD.

Diaphragmatic breathing is an evidence-based intervention for managing stress and anxiety; however, some military veterans with mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) report challenges to learning and practicing the technique. BreatheWell Wear assists performance of breathing exercises through reminders, biofeedback, and visual, tactile, and auditory guidance. To evaluate feasibility of implementing BreatheWell Wear, a mobile smartwatch application with companion smartphone app, as an intervention for stress management in military veterans with mTBI and PTSD. Thirty veterans with chronic symptoms of mTBI and PTSD recruited from an interdisciplinary, intensive outpatient program participated in this pilot pragmatic clinical trial. Participants were randomly assigned to the experimental (BreatheWell Wear and conventional care) and control (conventional care) groups for 4 weeks. Conventional care included instruction on relaxation breathing and participation in behavioral health therapy. Effects on goal attainment, treatment adherence, diaphragmatic breathing technique knowledge, and stress were measured through surveys and diaries. Changes in symptoms, mood, and well-being were measured pre/postintervention via the Posttraumatic Checklist for DSM-5, Beck Anxiety Inventory, Beck Depression Inventory, and Flourishing Scale. Person-centered goal attainment (t = 4.009, P < .001), treatment adherence (t = 2.742, P = .001), diaphragmatic breathing technique knowledge (t = 1.637, P < .001), and reported ease of remembering to practice (t = -3.075, P = .005) were significantly greater in the experimental group. As expected, measures of PTSD, anxiety, depression, and psychological well-being showed clinically meaningful change in both groups, and both groups demonstrated reduced stress following diaphragmatic breathing. These preliminary findings indicate that BreatheWell Wear may be a clinically feasible tool for supporting diaphragmatic breathing as an intervention in veterans with mTBI and PTSD, and a future effectiveness trial is warranted.

Improved Patient Monitoring with a Novel Multisensory Smartwatch Application.

The design of medical alarms has been heavily criticized in the past decade. Auditory medical alarms have poor learnability, discernibility, and relevance, leading to poor patient outcomes, and alarm fatigue, and overall poor informatic system design. We developed a novel trimodal patient monitoring smartwatch application for patient monitoring. Participants completed two phases: (1) control and (2) our novel trimodal system while identifying alarms (heart rate, oxygenation, and blood pressure) and completing a cognitively demanding task. Alarms were auditory icons presented as either solo or co-alarms. Participant performance was assessed by accuracy and response time (RT) of alarm identification. Using the novel system, accuracy was significantly improved overall (p < 0.01) and in co-alarm situations (p < 0.01), but not for solo alarms (p = 0.484). RT was also significantly faster (p < 0.01) while using the novel system for all alarm types. Participants reported decreased mental workload using the novel system. This feasibility study shows that our novel alarm system performs better than current standards. Improvements in accuracy, RT and perceived mental workload indicate the potential of this system to have a positive impact on medical informatic systems and clinical monitoring, for both the patient and the clinician.

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters – naming chest compression frequency and depth – as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model’s precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of pm 2.22 compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training.

Using Smartwatches to Detect Face Touching.

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20–83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.

GaitWear: a smartwatch application for in-the-wild gait normalisation based on a virtual field study assessing the effects of visual and haptic cueing

ABSTRACT We explore the use of Virtual Reality as a way to simulate field studies via what is known as Virtual Field Studies. This is particularly relevant when inviting participants to the lab is not feasible, or in the case of our work to simulate locomotion in crowded streets from the safety of the lab. We rely on this to assess the effects of four different cues in normalising gait performance in a simulated environment: two baselines from literature (visual and haptic) that have been traditionally explored in the context of a controlled lab environment, and two novel haptic cues that combine temporal and spatial feedback. We compare these in a holistic manner for the first time, capturing not only gait and gaze performance, but usability, perceived workload, and participant preference. Our haptic baseline performed according to the results described in the literature, and together with participants' gaze behaviour and sense of embodiment we start to validate Virtual Field Studies in this domain. We further report that the haptic baseline was the preferred cue by participants, and led to an overall better performance. We conclude with our implementation of GaitWear, a smart watch application that produces this haptic baseline on the fly.

Wearable and telemedicine innovations for Olympic events and elite sport.

Rapid advances in wearable technologies and real-time monitoring have resulted in major inroads in the world of recreational and elite sport. One such innovation is the application of real-time monitoring, which comprises a smartwatch application and ecosystem, designed to collect, process and transmit a wide range of physiological, biomechanical, bioenergetic and environmental data using cloud-based services. We plan to assess the impact of this wireless technology during Tokyo 2020, where this technology could help characterize the physiological and thermal strain experienced by an athlete, as well as determine future management of athletes during a medical emergency as a result of a more timely and accurate diagnosis. Here we describe some of the innovative technologies developed for numerous sports at Tokyo 2020 ranging from race walking (20 km and 50 km events), marathon, triathlon, road cycling (including the time trial event), mountain biking, to potentially team sports played outdoors. A more symbiotic relationship between sport, health and technology needs to be encouraged that harnesses the unique demands of elite sport (e.g., the need for unobtrusive devices that provide real-time feedback) and serves as medical and preventive support for the athlete's care. The implementation of such applications would be particularly welcome in the field of medicine (i.e., telemedicine applications) and the workplace (with particular relevance to emergency services, the military and generally workers under extreme environmental conditions). Laboratory and field-based studies are required in simulated scenarios to validate such emerging technologies, with the field of sport serving as an excellent model to understand and impact disease.

Design of an Android Wear Smartwatch Application as a Wearable Interface to the Diabetes Diary Application

Design of an Android Wear Smartwatch Application as a Wearable Interface to the Diabetes Diary Application

Effects of vibration-guided cardiopulmonary resuscitation with a smartwatch versus metronome guidance cardiopulmonary resuscitation during adult cardiac arrest: a randomized controlled simulation study

BackgroundSmartwatches could be used as a cardiopulmonary resuscitation (CPR) guidance system through its vibration function. This study was conducted to determine whether vibration guidance by a smartwatch application influences CPR performance compared to metronome guided CPR in a simulated noisy setting. MethodsThis study was randomised controlled trial. A total of 130 university students were enrolled. The experiment was conducted using a cardiac arrest model with hands-only CPR. Participants were randomly divided into two groups 1:1 ratio and performed 2-min metronome guidance or vibration guidance compression at the rate of 110/min. Basic life support quality data were compared in simulated noisy environments. ResultsThere were significant differences between the audio and vibration guidance groups in the mean compression rate (MCR). However, there were no significant differences in correct or mean compression depth, correct hand position, and correctly released compression. The vibration guidance group resulted in 109 MCR (Interquartile range [IQR] 108–110), whereas the metronome guidance group resulted in 115 MCR (IQR 112–117) (p < 0.001). ConclusionIn a simulated noisy environment, vibration guided CPR showed to be particularly advantageous in maintaining a desired MCR during hands-only CPR compared to metronome guided CPR.

Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study.

IntroductionPeople living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices.AimThe aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M.Methods‘Watch Your Steps’ was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day.Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type.ResultsFifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background (n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23–67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders.ConclusionIt was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.