Radically different
Background: Remote measurement technology (RMT) is increasingly used in health research to collect real-world data relevant to clinical states (eg, sleep, activity, and stress). Concerns exist about the impact of remote tracking via personal devices and wearables on individuals with or at risk of eating disorders (EDs) by promoting a focus on exercise, diet, and appearance. There is a lack of research applying RMT to EDs. Objective: This study aimed to explore how smartphone- and wearable-based RMTs influence eating-, exercise-, and weight-related experiences among individuals with a history of or at risk of EDs and to identify perceived benefits, harms, and recommendations for their use in this population. Methods: In total, 14 semistructured interviews were conducted with former participants of Remote Assessment of Disease and Relapse: Major Depressive Disorder, a 2-year digital health study tracking depression outcomes via RMTs. Participants were included in this follow-up if they had disclosed a history of a comorbid ED or were within the at-risk age range (18-30 years) for EDs during Remote Assessment of Disease and Relapse: Major Depressive Disorder and displayed subclinical ED symptoms (Eating Disorder Diagnostic Scale). Interviews explored the impact of app engagement and wearables (Fitbits) on food, activity, and weight-related behaviors and attitudes. Template analysis was adopted to capture themes guided by the focus on ED-relevant domains. Results: In total, 6 themes captured participants’ experiences with RMTs across clinical status and presentation. Participants broadly appreciated the convenience and reflective potential, while some described emotional strain linked to constant self-tracking. Health data impacted participants’ eating and exercise habits through a dynamic process from awareness to cognition to action, fostering healthy routines or obsessive patterns, depending on emotional state, ED presentation, and recovery stage. Self-tracking appeared to mirror illness stage, supporting ED recovery among those with greater distance from illness, but risking reinforcement of compulsive patterns among those with residual or emerging symptoms. Participants’ recommendations for future studies in EDs stressed balancing autonomy with safeguards for vulnerable individuals. Conclusions: These exploratory findings, drawn from individuals with lived ED experience and young people at subclinical risk, suggest that RMT use was shaped by recovery stage and contextual factors, rather than being inherently beneficial or harmful. While findings should not be interpreted as evidence of RMT safety or acceptability in ED cohorts broadly, they raise important questions about ethical RMT design, including the selection of wearables, access to data, and researcher communication with participants.
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Background: Acute kidney injury (AKI) is a frequent and serious complication among hospitalized patients, particularly in critical care settings, where its incidence can exceed 50%. AKI is associated with increased mortality, prolonged hospitalization, dialysis dependence, and higher health care costs. Although the KDIGO (Kidney Disease: Improving Global Outcomes) guidelines emphasize supportive care, hemodynamic optimization, and avoidance of nephrotoxins, their implementation remains inconsistent, partly due to the lack of timely risk stratification. Recent advances in artificial intelligence have enhanced early prediction and detection of AKI, offering new opportunities to improve patient outcomes and intensive care unit (ICU) efficiency. The U-Care Renal Platform (UCRP; U-Care Medical S.r.l), a Conformité Européenne (CE)–marked artificial intelligence–powered medical device, integrates directly with the ICU electronic health record to continuously analyze patient data and predict the risk of moderate or severe AKI within 24 hours, providing actionable, guideline-based recommendations. While the predictive performance of UCRP has been validated previously, its real-world impact on clinical and operational outcomes in the ICU remains underexplored. Objective: This single-center uncontrolled before-and-after implementation study aims to evaluate the association between UCRP implementation and selected ICU clinical and operational outcomes in routine practice at SCIAS Hospital, Barcelona. Methods: This study was conducted as a retrospective service evaluation of a workflow-embedded clinical decision support system between March 2023 and March 2025. It included 202 postsurgical adult ICU patients. Outcomes of interest were assessed by comparing preimplementation and postimplementation periods. Months during which the UCRP was inactive were excluded from the analysis (total excluded duration: 10 months; 5 in the preimplementation period and 5 in the postimplementation period). The outcomes included the incidence of moderate-to-severe AKI (KDIGO stages 2 and 3), the use of nephrotoxic medications, the frequency of hypotensive episodes among patients with AKI, and the ICU length of stay. Results: During the postimplementation period, lower rates of moderate-to-severe AKI (9/99, 9.1% vs 12/103, 11.7%), nephrotoxic drug administration, and hypotensive episodes among patients with AKI were observed compared with the preimplementation period. Conclusions: Integration of the UCRP into ICU workflows was associated with differences in selected AKI-related process and intermediate clinical outcomes in this single-center uncontrolled before-and-after implementation study. However, given the study design, causal relationships cannot be established, and the findings should be interpreted as preliminary signals requiring confirmation in larger, controlled, and multicenter studies, including patient-centered outcomes.
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Background: Digital remote monitoring technologies, including smartphones and wearables, offer promising avenues for early detection of psychosis relapse. However, selecting devices that are acceptable to participants and produce high-quality data remains challenging. Objective: The aim of this nested pilot study was to assess the acceptability and data quality of 3 commercially available wearable devices in people with psychosis recruited to the CONNECT cohort study. Methods: Participants recruited to the CONNECT study before July 31, 2024, were included in the pilot study and selected 1 of 3 wearable devices: a Fitbit Charge 5, Samsung Galaxy Watch 5, or Apple Watch SE. Baseline demographics were compared between device groups. Acceptability of devices to participants was assessed through a Wearable Device Satisfaction Questionnaire after 3 months of use, with the proportion of positive responses to each question calculated and compared. Data completeness was also assessed by calculating the number (and percentage) of valid days of step count, heart rate, and sleep data, and comparing between groups. Data quality was assessed through summarizing the amount of troubleshooting required, additional metrics available from the wearables, and continuity of data completeness by calculating the proportion of participants with at least 3 days of heart rate data per week for the first 20 weeks of follow-up. Predefined criteria were used to determine the next steps for the wider CONNECT study: if one device was superior, this would be selected; if none were found to be superior and the Fitbit was found to be noninferior, then Fitbit would be retained. Results: Of the first 107 participants recruited to CONNECT, 105 were included in the pilot study evaluation. The Samsung Galaxy Watch was selected most frequently by participants (46/105, 43.8%), followed by the Apple Watch (27/105, 25.7%), and Fitbit Charge (23/105, 21.9%). Differences in participant demographics were observed across device groups. Self-reported acceptability after use did not differ substantially between devices. However, in terms of data completeness, the median proportion of valid heart rate data days was significantly lower for Samsung Galaxy (median 31.2%, IQR 8.5%-46.0%) compared to Fitbit (median 80.1%, IQR 26.7%-95.0%; =.003) and Apple Watch (median 49.3%, IQR 21.5%-86.0%; =.02). There was no significant difference between Fitbit and Apple Watch. Similar patterns were observed for step count and sleep data. The Samsung Galaxy Watch required more frequent troubleshooting for data flow issues and lacked additional physiological metrics, available from the other devices. Conclusions: Due to comparatively lower data quality and technical performance, the Samsung Galaxy Watch was discontinued for use in the subsequent phase of the CONNECT study. The study highlights the importance of incorporating nested evaluations of devices in long-term research.
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