Smartwatch-Based Interventions for People With Dementia: User-Centered Design Approach.

BACKGROUND: Assistive technologies can help people living with dementia maintain their everyday activities. Nevertheless, there is a gap between the potential and use of these materials. Involving future users may help close this gap, but the impact on people with dementia is unclear. OBJECTIVE: We aimed to determine if user-centered development of smartwatch-based interventions together with people with dementia is feasible. In addition, we evaluated the extent to which user feedback is plausible and therefore helpful for technological improvements. METHODS: We examined the interactions between smartwatches and people with dementia or people with mild cognitive impairment. All participants were prompted to complete 2 tasks (drinking water and a specific cognitive task). Prompts were triggered using a smartphone as a remote control and were repeated up to 3 times if participants failed to complete a task. Overall, 50% (20/40) of the participants received regular prompts, and 50% (20/40) received intensive audiovisual prompts to perform everyday tasks. Participants' reactions were observed remotely via cameras. User feedback was captured via questionnaires, which included topics like usability, design, usefulness, and concerns. The internal consistency of the subscales was calculated. Plausibility was also checked using qualitative approaches. RESULTS: Participants noted their preferences for particular functions and improvements. Patients struggled with rating using the Likert scale; therefore, we assisted them with completing the questionnaire. Usability (mean 78 out of 100, SD 15.22) and usefulness (mean 9 out of 12) were rated high. The smartwatch design was appealing to most participants (31/40, 76%). Only a few participants (6/40, 15%) were concerned about using the watch. Better usability was associated with better cognition. The observed success and self-rated task comprehension were in agreement for most participants (32/40, 80%). In different qualitative analyses, participants' responses were, in most cases, plausible. Only 8% (3/40) of the participants were completely unaware of their irregular task performance. CONCLUSIONS: People with dementia can have positive experiences with smartwatches. Most people with dementia provided valuable information. Developing assistive technologies together with people with dementia can help to prioritize the future development of functional and nonfunctional features.

End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks.

In Holter monitoring, the precise detection of standard heartbeats and ventricular premature contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture with the aim of enhancing PVC detection in Holter recordings. Training data comprised the Icentia 11k and INCART DB datasets, as well as our custom dataset. The model's efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, and NST, as well as another custom dataset that was specifically compiled by the authors encompassing challenging real-world examples. The results underscore the 1D U-Net model's prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust, balanced accuracy accentuates the model's equitable performance in discerning both false positives and false negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there's a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

Automated fall risk assessment of elderly using wearable devices.

INTRODUCTION: Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. METHODS: In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automatedalgorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists. RESULTS: There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests. CONCLUSIONS: The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.

Electronic health technology for the assessment of physical activity and eating habits in children and adolescents with overweight and obesity IDA.

OBJECTIVE: It was the goal of the trial to study the impact of electronic healthcare technology into treatment. METHODS: One hundred and twenty-four children/adolescents (females 56%, age 13.5±2.8 years, height 1.64±0.13 m, weight 85.4±23.0 kg, body-mass index (BMI) 31.3±5.2 kg/m(2), BMI-standard deviation score (SDS) 2.50±0.5) were included. To assess physical activity and eating habits, a mobile motion sensor integrated into a mobile phone with digital camera was used. RESULTS: The children/adolescents had a significant weight reduction of 7.1±3.0 kg. BMI/BMI-SDS decreased (p<0.01). Intensity (14.1±6.4 activity units) and duration of physical activity (290.4±92.6 min/day) were assessed with sensors. Time walking: median 45.5 (range, 2.5-206.5), running 8.0 (range, 0-39.5), cycling 27.7 (range, 0-72.5), car driving 23.7 (range, 0-83.0) min/day. Comparing self-reported physical activity (walking 292.9 (range, 9.6-496.1), running 84.8 (range, 8.4-130.2) min/day) with assessment with sensors there were significant differences (p<0.01). Duration of physical activity documented by children/adolescents was higher than the assessment with motion sensors (walking 292.9 vs 45.5 min, p<0.01, running 84.8 vs 8.0 min, p<0.01). Sensor derived energy intake was higher than recommended (469.14±88.75 kcal vs 489.03±108.25 kcal, p=0.09). Performing multivariate analysis the following parameters showed associations with weight reduction (R-square=0.75): body weight (ß=-0.95, p<0.01), C-reactive protein (CRP, ß=0.15, p=0.07), physical activity, time spent in activities measured with sensors (ß=-0.18, p=0.04), stress management (ß=0.16, p=0.06), body fat mass at onset of the trial (ß=0.45, p<0.01) and body shape (ß=-0.25, p=0.01). CONCLUSION: The innovative mobile movement detection system is highly accepted by children and adolescents. The system is able to augment existing weight reduction and stabilization strategies.

Identification of determinants for weight reduction in overweight and obese children and adolescents.

We conducted a pilot trial of electronic technology integrated into the treatment of children and adolescents who are overweight or obese. A total of 30 patients (mean age 14 years, mean BMI 32.7 kg/m(2)) were admitted to our hospital to participate in a structured treatment and teaching programme (STTP). To assess physical activity and eating habits, a mobile motion sensor board (MoSeBo) or a sensor for physical activity, integrated into a mobile phone with digital camera (DiaTrace) was used. Over an average period of four days of monitoring, the mean intensity (15.4 activity units) and duration of physical activity (267 min/d) were recorded with the mobile sensors. The mean time spent walking was 64 min/d, running 11 min/d, cycling 24 min/d and car driving 21 min/d. There were significant differences (P < 0.001) between self-reported physical activity and objective assessment: in general the duration of physical activity documented by children and adolescents was much higher than the objective assessment. Similarly, the real caloric intake was higher than the self-estimates (P = 0.085). A multivariate analysis showed that the following variables were significantly associated with weight reduction in the hospital STTP (R-squared = 0.59): high motivation, intrafamilial conflicts, duration of physical activity assessed with the MoSeBo/DiaTrace system, and the body fat mass at onset of therapy. All children and adolescents included in the trial completed it. Although the MoSeBo/DiaTrace system was used for a relatively short period in each patient, the high acceptance demonstrated that it could be integrated into therapy easily.