Background: Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. Objective: This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. Methods: We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results: In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. Conclusions: The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences.

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.

Background: Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. Objective: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

Medication adjustments in Parkinson’s disease (PD) are driven by patient subjective report and clinicians’ rating of motor feature severity (such as bradykinesia and tremor). As patients may be seen by different clinicians at different visits, this study aims to determine the inter-rater reliability of upper limb motor function assessment among clinicians treating people with PD (PwPD). PwPD performed six standardised hand movements from the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), while two cameras simultaneously recorded. Eight clinicians independently rated tremor and bradykinesia severity using a visual analogue scale. We compared intraclass correlation coefficient (ICC) before and after a training/calibration session where high-variance participant videos were reviewed and MDS-UPDRS instructions discussed. In the first round, poor agreement was observed for most hand movements, with best agreement for resting tremor (ICC 0.66 bilaterally; right hand 95 % CI 0.50–0.82; left hand: 0.50–0.81). Postural tremor (left hand) had poor agreement (ICC 0.14; 95 % CI 0.04–0.33), as did wrist pronation-supination (right hand ICC 0.34; 95 % CI 0.19–0.56). In post-training rating exercises, agreements improved, especially for the right hand. Best agreement was observed for hand open-close ratings in the left hand (ICC 0.82, 95 % CI 0.64–0.94) and resting tremor in the right hand (ICC 0.92, 95 % CI 0.83–0.98). Discrimination between right and left hand features by raters also improved, except in resting tremor (disimprovement) and wrist pronation-supination (no change). Clinicians vary in rating video-recorded PD upper limb motor features, especially bradykinesia, but this can be improved somewhat with training.

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here.

Background: Wearable devices have the potential to provide reliable and objective assessment and monitoring of Parkinson disease (PD). During design, there is often a focus on technical performance, accuracy, and reliability, with less emphasis on the user experience. Objective: This study explored the user experience of a novel prototype wearable device (wrist- and ankle-worn) to record limb movements (accelerations and angular velocities), and physiological data (eg, photoplethysmography and heart rate information for estimation of blood pressure). Methods: This qualitative study used internet-based semistructured interviews with people with PD, following wearing prototype devices for 24 hours at home. Interviews were audio-recorded and transcripts analyzed using a hybrid deductive and inductive approach. Results: Six people with PD, 3 male and 3 female, aged 52-83 years, with mild-to-moderate PD (Hoehn and Yahr scale score ≤3; median MDS-UPDRS [Movement Disorder Society - Unified Parkinson’s Disease Rating Scale] score of 72/199) and cognition within normal limits (6-item Cognitive Impairment Test median score 0), with an average disease duration of 8 years took part in the study. Participants were overall positive toward the device, finding it generally comfortable, light in weight and noninvasive. Five out of 6 participants reported minor problems related to strap adjustability and challenges specific to PD. The prototype was comfortable, but this was a lesser priority than robustness, the device not hindering their usual clothing choices (device size or outward projection, or both), and adjustability for fit, including the need to switch to an elasticated strap. In particular, a discreet design was important, as some individuals may feel self-conscious about wearing visible condition-specific products, due to stigma. The ankle-worn device was perceived as unfamiliar and nondiscreet, with some participants likening it to a prisoner tracking system and dressing specifically to conceal it. The wrist-worn device was considered more user-friendly, especially if designed discreetly and resembling more familiar devices. Five of the 6 participants believed their health care teams should have access to the data, particularly relating to their symptoms, fluctuations, and medication. Three also wished to access these data for self-management. One participant was hesitant regarding the potential benefits of technology to support PD management, preferring to use feedback data personally and relying on their health care team’s usual assessment to guide decisions. Across the group, desirable device technical features included symptom prediction, reminder prompts, and support for medication management. Despite concerns about stigma, most participants were willing to wear PD-specific devices, believing that they could aid better symptom management. Conclusions: In summary, wearable devices must be discreet, robust, comfortable, and easily applied to promote adherence regardless of technical specifications.