Felix Wichum

BACKGROUND Fatigue is burdensome in advanced cancer. Wearable-informed mobile health (mHealth) apps may support monitoring and self-management, but evidence on usability and engagement in this population is limited. OBJECTIVE To evaluate the feasibility, usability, engagement, and patient experience of a wearable-informed mHealth app for fatigue self-management, and the reception of its exploratory digital fatigue score. METHODS In this 4-week, single-arm pilot study, adults with stage III-IV cancer according to the Union for International Cancer Control used the app and an activity tracker. We assessed enrollment, retention, and dropout reasons for feasibility, week-4 usability with the System Usability Scale (SUS) and the German version of the mHealth App Usability Questionnaire (GER-MAUQ), engagement through weekly self-reported use and satisfaction ratings and app usage logs, patient experience through optional weekly free-text responses, and reception of the fatigue score through study-specific ratings. Fatigue, health-related quality of life, and distress were exploratory outcomes assessed at baseline and weekly. Analyses included descriptive statistics, linear mixed models, and correlations. RESULTS Of 38 enrolled participants, 29 completed baseline and 23 completed week 4 (postbaseline retention 79.3%). Dropout respondents (n=5) mainly cited disease burden, and weekly free-text responses mainly described illness-related barriers. Mean SUS was 68.9 (SD 17.8), in the upper marginal range, and GER-MAUQ usefulness (median 4.29) was rated lower than ease of use (median 5.80, P<.001). Weekly active users decreased from 23 in week 1 to 19 in week 4. The fatigue score was used by 20 of 23 participants (87.0%), but its helpfulness was rated low (median 2.0 on a 1-5 scale). CONCLUSIONS The app appeared feasible over 4 weeks in selected patients with advanced cancer, and dropout responses mainly cited illness burden rather than technical or privacy-related barriers. Usability was in the marginal to acceptable range, while usefulness was rated lower than ease of use. Engagement differed mainly in reach across features, and illness-related barriers and rigid daily planning limited use. The fatigue score attracted interest but needs clearer explanations, more actionable recommendations, and independent validation. Clinical outcomes were exploratory, and efficacy requires controlled evaluation.

BACKGROUND Smartwatches are increasingly used for decentralized data collection in clinical research, but the everyday-life settings that make these data attractive also introduce variability. Before a smartwatch-derived feature can support clinical monitoring, its reproducibility must be established: features with low test-retest reliability weaken associations with clinical outcomes and potentially generate non-actionable signals. Reliability is expected to vary by feature type, aggregation window, and data availability, but has not been systematically screened in a clinical cohort. OBJECTIVE This study aimed to evaluate the test-retest reliability of candidate smartwatch-derived features for longitudinal monitoring in adults with advanced cancer and in healthy controls, and to determine how reliability depends on the temporal aggregation window. METHODS In a prospective single-centre observational cohort study, we analysed 8 weeks of Garmin Vivosmart 5 sensor data from 60 adults with advanced cancer, and 20 healthy controls. Out of 80 participants, 77 contributed usable smartwatch data. We examined 35 daily features across seven domains: heart rate variability, heart rate, respiration, oxygen saturation, sleep, activity, and smartwatch-derived stress. Test-retest reliability was quantified as the intraclass correlation coefficient (ICC(2,1)) across adjacent non-overlapping 1-, 3-, and 7-day windows, with 95% CIs from subject-level bootstrap resampling (10,000 resamples). Between-group and therapy-centred contrasts used permutation testing with Benjamini-Hochberg false discovery rate correction. RESULTS Reliability improved with longer aggregation windows in both cohorts. Between 1-day and 7-day windows, median ICC(2,1) increased from 0.53 to 0.73 in controls and from 0.66 to 0.79 in patients. The number of features reaching good-to-excellent reliability, defined as ICC(2,1)≥0.75, increased from 5 of 35 to 15 of 34 in controls and from 12 of 35 to 26 of 35 in patients. Heart rate and heart rate variability features were the most reliable, with 4 of 11 reaching weekly ICC(2,1)≥0.90 in both cohorts. Activity, respiration, sleep and oxygen-saturation features were more sensitive to aggregation window and data availability, showing larger gains from daily to weekly aggregation (e.g., step count ICC increased from 0.30 to 0.73 in controls). Weekly reliability did not differ significantly between patients and controls (median ΔICC=0.036, no feature survived FDR correction, all q>0.05). No feature showed a significant change in reliability around therapy (median ΔICC=0.016, all q>0.05). CONCLUSIONS Weekly aggregation improved the reliability of many smartwatch-derived features, but reliability remained feature specific. Heart rate and heart rate-variability features were consistently reliable, whereas selected sleep and oxygen-saturation features displayed only moderate reliability across all aggregation windows. Reliability was comparable across cohorts and stable around therapy, indicating that feature-wise estimates are transferable across these clinical contexts. Feature-level reliability screening is a prerequisite before smartwatch-derived measures are used in clinical monitoring.

Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.

Exercise therapy is able to reduce symptom burden in advanced cancer patients (ACP). However, ACP daily form differs between days e.g. tumor and therapy induced. We included five ACP to classify individual exercise capacity due to cardiovascular parameters. Features are extracted from the electrocardiogram and then processed with a neural network after feature selection. Results indicate a high classification quality with an F1 score up to 0.95 ± 0.05. Including neuronal networks for training control can potentially help to manage exercise intensity ideal.