Physical function and capacity tests are widely used for assessing health across various clinical conditions. However, traditional assessments may not accurately capture real-world health conditions reliably and frequently. Sensors, smartphones and wearable devices offer the potential to bridge this gap by collecting data in everyday life that may better reflect participants’ physical capabilities, and could be used to predict clinical outcomes and the performance of physical tests. However, there is a lack of comprehensive reviews and consensus in the field. This work reviews the literature on passively collected data from digital health technology in relation to physical function and capacity tests and informs future investigations in this domain. A systematic literature search was conducted following the PRISMA guidelines on 3 databases. Our analysis identifies cardiovascular and neurodegenerative diseases as the most frequently studied conditions, and wearables embedding inertial sensors as the most common device type. Most studies rely on one week-long data collection. Associations between physical test outcomes and metrics such as step count and activity intensity show correlations as high as 0.89 when machine learning is introduced. This review provides a comprehensive summary of current research on the use of digital health technology in free-living conditions and the clinical significance of data when associated with physical tests.

INTRODUCTION: Parkinson's disease (PD) is typically assessed during short clinical visits using rating scales such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). These assessments provide only a snapshot of symptom severity and may not capture fluctuations in daily life. In this study, we examined whether wrist-worn actigraphy can be used to estimate MDS-UPDRS scores in people with Parkinson's disease (PwP).

METHODS: Continuous accelerometer recordings at 25 Hz were collected over up to 28 days using GeneActiv devices. From these recordings, three feature representations were derived: non-embedding actigraphy features, self-supervised accelerometer embeddings, and a combined feature set. A small set of regression models was evaluated using strict leave-one-participant-out cross-validation (LOPO-CV).

RESULTS: Estimation performance varied across targets and feature sets. The strongest result was observed for MDS-UPDRS Part IV, where non-embedding features with Elastic Net achieved a mean absolute error (MAE) of 1.6 and a correlation of 0.83 between estimated and actual values. The combined feature set performed best for Part I (MAE = 3.0, r = 0.60), Part III (MAE = 8.2, r = 0.47), and the total MDS-UPDRS score (MAE = 13.3, r = 0.49), whereas non-embedding features performed best for Part II (MAE = 2.7, r = 0.61). Embedding-only models were competitive for some outcomes, but were not the best overall.

DISCUSSION: Overall, the results show that month-long wrist-worn actigraphy contains information related to PD severity in daily life, although estimation accuracy remains limited and depends on the MDS-UPDRS target. Wearable-derived measures may therefore provide complementary information to clinical assessments, particularly for motor complications.

People with Parkinson’s disease (PwP) engage in diverse self-care practices, including tracking medication and symptoms. While consumer-oriented health technologies have supported such practices, specialized technologies for Parkinson’s disease (PD) are increasingly being integrated into clinical care. This paper reports on a design inquiry including PwP and healthcare professionals aimed at eliciting needs for meaningful representations of PD symptom-tracking data. We conducted a toolkit-based data elicitation workshop with 8 PwP experienced with sensor-based active tests to assess motor symptoms, alongside 15 interviews with clinicians, including neurologists, nurses, and physiotherapists. Interviews combined dashboard prototypes centered on simulated active test data with collaborative prompt-based prototyping. Our findings show that PwP and clinicians rely on one another to interpret symptom-tracking data, and that the resulting data representations may influence the focus of clinical encounters. Active test data is found particularly valuable for nurses and physiotherapists, while highlighting tensions in using such data to support neurologists’ medication-related decision-making without increasing testing burdens for PwP.

Family caregivers play a critical role in chronic conditions care, yet digital support for their well-being remains limited. While prior HCI work has focused on task coordination, value elicitation, and information management, this project supports family caregivers in identifying and making sense of their own needs. Building on a clinical screening instrument in colorectal cancer care, we explore how such a tool can be adapted through an iterative, HCI-led process to provide value in everyday life. Through informed internal design sessions with Care Science, Computer Science, and HCI researchers, and a workshop and prototype testing with family caregivers, we integrate the screening instrument into a family caregiver-oriented self-care platform. This process addresses framing, data representation, and interaction flow, acknowledging family caregivers' demands and needs. As work in progress, we indicate how formative HCI evaluation can be combined with health-oriented outcome assessment to adapt clinical instruments into caregiver-centered digital health interventions.

Introduction: This study investigates the potential of digital health technologies (DHTs), such as wearable devices and smartphones, to complement traditional submaximal functional capacity tests, such as the 6 min walk test (6MWT) and the timed up and go test (TUG). While these traditional tests are widely used due to their simplicity and relevance to daily living activities, they have limitations, including infrequent administration and the need for clinical observation. DHT offers continuous, real-world monitoring, which may accurately reflect patients' health status and effectively inform clinical decisions. However, there is a need to establish the validity of the data and metrics computed through DHT and understand patient perspectives on using such technology.

Methods and analysis: This is an observational pilot study (Synergy Digital Health study) that aims at linking wearable data with traditional test outcomes and assessing participants' acceptance and usage of such DHT. A cohort of 30 cardiovascular patients from Oxford University Hospitals, UK, and 30 community-dwelling elderly people from social centres in Helsingborg, Sweden, will use wearable devices for 2 months in free-living conditions, they will fill out technology acceptance questionnaires (AQs), have baseline assessments and perform physical tests such as the 6MWT and TUG using the Mobistudy smartphone app. Subgroups will participate in codesign workshops to identify experience-based design recommendations for the technology. Quantitative and qualitative methods will be adopted to analyse the collected data.

Ethics and dissemination: The study protocol received ethical approval in Sweden from the Etikprövningsmyndigheten (2024-04886-01) and in the UK from the National Health Service (NHS) Research Ethics Committees (Iras project ID: 340870), in accordance with local regulations. All participants are asked for written informed consent. The results of the study will be shared via scientific journals and conferences.

BACKGROUND: Timely, individually tailored support for family caregivers of cancer patients is stressed, reinforcing the importance of implementing screening tools in clinical practice.

AIM: This study aims to evaluate the validity and reliability of the Swedish CancerSupportSource-Caregiver among 145 Swedish family caregivers of persons diagnosed with cancer.

METHODS: We evaluated the validity and reliability of the Swedish CancerSupportSource-Caregiver among 145 Swedish family caregivers of persons diagnosed with cancer who responded to the Swedish CancerSupportSource-Caregiver, sociodemographic questions, and the Hospital Anxiety and Depression Scale. Psychometric analyses were performed using descriptive statistics and classical test theory to evaluate data quality, targeting, scaling assumptions, and internal validity. Construct validity was assessed through confirmatory factor analysis; criterion validity through concurrent validity; and reliability through internal consistency.

RESULT: Overall, in the sample, evaluations demonstrated generally satisfactory psychometric properties with respect to data quality, targeting, and scaling assumptions. The hypothesized five-domain model showed an acceptable fit to the data, although there were indices that it could be improved. Item loadings were generally high, supporting the proposed construct structure. Further, assessments of the criterion validity were satisfactory. However, the evaluations of internal validity and internal consistency indicated redundancy, mainly within the emotional well-being domain.

CONCLUSION: The Swedish CancerSupportSource-Caregiver demonstrated preliminary satisfactory abilities to screen for support needs and psychological distress among Swedish family caregivers of persons diagnosed with cancer. Further evaluations in larger samples, using Rasch measurement theory, could provide a deeper understanding of the functioning of items and response options.

Smartwatches and fitness trackers are widely used to monitor physiology and physical activity, for example, by counting the number of steps over time. Step count has been associated with health outcomes and it is therefore important that its measurement is reliable and that the algorithms used to derive it from inertial sensors are transparent.We reproduced 7 open-source algorithms for step counting for wrist-worn devices: 3 peak detectors (Bangle simple, Espruino, Oxford) 3 periodicity detectors (Block-autocorrelation, Windowed-autocorrelation, Windowed-FFT) and one Dummy algorithm that assumes a constant step rate when movement is detected. The algorithms were benchmarked against a dataset collected from 20 healthy participants who wore the open source Bangle.js smartwatch on the wrist, and a custom inertial sensor unit on the right foot as reference while performing 4 activities: resting, low intensity activity with no walking, indoor walking on a treadmill with 3 speeds and outdoor walking over a fixed path with stops.We compared the step count computed from the accelerometry collected through the smartwatch with the one collected on the foot on 30 s segments. Results show that the most accurate algorithm is the one based on windowed autocorrelation with a 22±30% mean absolute percentage error over segments where walking is present. Interestingly, the Dummy algorithm had higher accuracy than the peak detectors, which highlights the importance of motion detection strategies in these algorithms.

BACKGROUND: The 6-minute walk test (6MWT) measures exercise capacity in cardiorespiratory, neurological, and musculoskeletal conditions. It consists of observing how far a patient can walk in 6 minutes and is usually performed in a corridor in a clinic. During the COVID-19 pandemic, as health care systems cancelled nonurgent outpatient appointments, many tests were conducted online. At Oxford University Hospitals National Health Service Foundation Trust, patients followed up on by cardiovascular outpatient clinics were asked to use the open-source Timed Walk app to perform the 6MWT in their community as a substitute for the regular tests in the clinic.

OBJECTIVE: This study aimed to assess the clinical usefulness of the app within the context of the pandemic.

METHODS: Consented patients were invited to perform a 6MWT outdoors using the app at least once a month and report the results through periodic telephone calls and visits. Clinical decisions made for the same cohort were registered, with a focus on the effect of the app in supporting decision-making. Data collected through the app during the study period were compared with 6MWTs performed in the prepandemic period.

RESULTS: This study was conducted between October 2021 and December 2022. A total of 55 participants consented (n=25, 45% female; mean age 44.80, SD 17.49 y). In total, 741 events were logged. A total of 51 medical decisions were made for 25 patients; in 41% (21/51) of the decisions, the app played a role, affecting 44% (11/25) of the patients. Between 2018 and 2022, a cohort of 49 patients for whom data were available performed 63 6MWTs in the clinic (18 in 2021), whereas the same patients performed 605 tests using the app in 2022 (ie, October 2021 to December 2022).

CONCLUSIONS: The use of the Timed Walk app for remote 6MWTs allowed clinicians to obtain frequent and objective indications of the status of the patients during the pandemic, compensating for the absence of regular clinic appointments and providing 33 times more tests than in the prepandemic period. These tests supported approximately half of the clinical decisions made regarding the consented patients, showing that the app is useful in clinical practice.

The use of fitness trackers, smartwatches, and wrist-worn devices has been increasing globally. These devices can measure physical activity, sleep, and health-related measurements like heart rate and heart rate variability using Photoplethysmography (PPG). Research indicates that PPG measurements can be less accurate on darker skin compared to lighter skin due to the higher presence of melanin, a light-absorbing substance in dark skin. This paper addresses the impact of melanin on the accuracy of heart rate measurements on different skin colors using four commercial smartwatches of different brands: two inexpensive devices (<100€) and two higher end devices (>200). We analyze the accuracy of these smartwatches on 12 individuals with 6 different skin types categorized using the Fitzpatrick scale while controlling for external factors. The collected data from the smartwatches are compared to a reference sensor that uses electrocardiography (ECG) measurements with electrodes placed around the chest. Three different tests are conducted wearing the devices, with no movement, with circular hand motions and while walking. With the presented results, it was concluded that the 4 smart-watches measurement accuracy is not dependent on specific skin types. Accuracy varies across devices with the more expensive ones not always being the most precise.

Functional capacity testing is essential for assessing mobility changes, which can impact independence across various populations and health conditions. This study aims to implement instrumented function tests using a combination of affordable sensors, including sensorized mats, sensorized shoes, smartphones, and smartwatches. The goal is to provide objective, reliable, and detailed data on test outcomes, such as gait analysis. We have created a dataset from 6 participants of varying ages, each performing 5 standardized functional tests: Timed Up and Go, 30-Second Chair Rise, Locomo challenge, 10-meter walk, and 40-meter walk. Alongside the dataset, we have developed a tool for visualizing the sensor signals and marking key events to facilitate data analysis. This dataset is intended to support researchers in developing algorithms for extracting test-specific parameters, and for comparing sensors in terms of quality of the signals and ease of setup.