Background/Aims: Hemodialysis (HD) patients are less active than their healthy counterparts and frequently experience poor sleep. Our aims were to objectively quantify activity and sleep quality in HD patients of an urban population and to determine the effect of providing feedback on activity. Methods: Activity parameters and sleep parameters were collected by a commercially available activity tracker in 29 chronic HD patients. Patients in the feedback group were provided with their activity and sleep data during each HD treatment. Questionnaires were administered at the beginning and at the end of the study. Results: On average, patients walked 8,454 steps/day and slept 349 min/night. Only 28% of the patients were sedentary, defined as walking <5,000 steps/day. Providing feedback did not increase the activity in this urban population. Patients walked significantly less on Sundays compared to other days of the week: 7,024 steps on Sundays vs. 8,633 steps on HD days and 8,732 on non-HD days. It was also found that patients experienced poor sleep quality. HD treatments during shift 1 (6 a.m. to 10 a.m.) interfered with sleep patterns. Most patients reported that physical activity became more important to them after the 5-week period. The tracking device was very well accepted. Conclusion: Interventions to increase physical activity on Sundays could improve physical activity levels overall. Prospective studies are necessary to further explore the use of tracking devices to identify patients at risk and to implement targeted interventions.

Background/Aims: Hemodialysis (HD) patients are less active than their healthy counterparts. They are often plagued with sleep disorders that affect the quality of their sleep. Our aim was to objectively quantify activity and sleep quality among HD patients in a suburban HD population. Methods: Activity and sleep parameters were measured using a commercially available activity tracker in 29 HD patients from Baton Rouge, LA, USA. Patients in the feedback group received their activity and sleep data at each dialysis treatment. In addition, questionnaires were administered at the beginning and end of the study period. Patients were stratified based on activity levels and sleep quality. Results: Patients walked an average of 5,281 steps/day and slept 370.5 min/night. Informing patients about their daily number of steps taken, did not increase activity. Only 3% of the population followed were active, defined as walking more than 10,000 steps per day. Patients walked significantly less on dialysis days compared to the other days of the week. Many of the patients experienced poor sleep quality, with patients in the first shift experiencing the greatest disturbance to their sleep/wake cycle. Conclusion: Patients in a suburban environment walked much less than those in a previously studied urban population. They rarely met the recommended goal of 10,000 steps/day, even on non-dialysis days. Interventions to increase physical activity may target any day of the week, particularly HD days. Prospective, long-term studies are needed to evaluate the use of activity trackers in dialysis patients and their impact on physical activity.

Background/Aims: Neighborhood walkability is associated with indicators of health in the general population. We explored the association between neighborhood walkability and daily steps in hemodialysis (HD) patients. Methods: We measured daily steps over 5 weeks using Fitbit Flex (Fitbit, San Francisco, CA, USA) and retrieved Walk Score® (WS) data by patient’s home ZIP code (www.walkscore.com; 0 = poorest walkability; 100 = greatest walkability). Results: HD patients took a mean of 6,393 ± 3,550 steps/day (n = 46). Median WS of the neighborhood where they resided was 28. Patients in an above-median WS (n = 27) neighborhood took significantly more daily steps compared to those (n = 19) in a below-median WS neighborhood (7,514 ± 3,900 vs. 4,800 ± 2,228 steps/day; p < 0.001, t test). Daily steps and WS were directly correlated (R = 0.425; p = 0.0032, parametric test; R = 0.359, p = 0.0143, non-parametric test). Conclusion: This is the first study conducted among HD patients to indicate a direct relationship between neighborhood walkability and the actual steps taken. These results should be considered when designing initiatives to increase and improvise exercise routines in HD populations.

Background Dialysis patients are typically inactive and their physical activity (PA) decreases over time. Uremic toxicity has been suggested as a potential causal factor of low PA in dialysis patients. Post-dilution high-volume online hemodiafiltration (HDF) provides greater higher molecular weight removal and studies suggest better clinical/patient-reported outcomes compared with hemodialysis (HD). Methods HDFIT was a randomized controlled trial at 13 clinics in Brazil that aimed to investigate the effects of HDF on measured PA (step counts) as a primary outcome. Stable HD patients (vintage 3–24 months) were randomized to receive HDF or high-flux HD. Treatment effect of HDF on the primary outcome from baseline to 3 and 6 months was estimated using a linear mixed-effects model. Results We randomized 195 patients (HDF 97; HD 98) between August 2016 and October 2017. Despite the achievement of a high convective volume in the majority of sessions and a positive impact on solute removal, the treatment effect HDF on the primary outcome was +538 [95% confidence interval (CI) −330 to 1407] steps/24 h after dialysis compared with HD, and was not statistically significant. Despite a lack of statistical significance, the observed size of the treatment effect was modest and driven by steps taken between 1.5 and 24.0 h after dialysis, in particular between 20 and 24 h (+197 steps; 95% CI −95 to 488). Conclusions HDF did not have a statistically significant treatment effect on PA 24 h following dialysis, albeit effect sizes may be clinically meaningful and deserve further investigation.

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

Introduction: Dialysis patients suffer from poor sleep duration and quality. We examined the self-reported sleep duration in patients randomized to either high-volume hemodiafiltration (HDF) or high flux hemodialysis (HD). Methods: Patients from 13 Brazilian dialysis clinics were enrolled in the HDFIT randomized controlled trial (RCT) investigating the impact of HDF on physical activity and self-reported outcomes. Self-reported sleep duration was taken from patient diaries recording sleep start and end time over a week during baseline, months 3 and 6, respectively. Sleep duration was analyzed by shift and nights relative to dialysis. Results: The HDFIT study enrolled 197 patients; sleep data were available in 173 patients (87 HD; 86 HDF). Patients’ age was 53 ± 15 years, 57% were white, 72% were male, 34% had diabetes, Kt/V was 1.54 ± 0.40, and albumin 3.97 ± 0.36 g/dL. Most patients reported sleeping 510–530 min/night. At 3 months, HDF patients slept 513 ± 71 min/night, HD patients 518 ± 76 min/night. At 6 months, HDF patients slept 532 ± 74 min/night, HD patients 519 ± 80 min/night. At baseline, 1st shift patients slept 406 ± 86 min the night before HD, 534 ± 64 min the night after HD, and 496 ± 99 min the night between 2 non-HD days. Compared to patients in the 2nd and 3rd shifts, patients dialyzed in the 1st shift slept less in the night before dialysis. Similar patterns were seen after 3 and 6 months. Conclusion: In our RCT, the dialysis modality (HDF vs. HD) had no effect on self-reported sleep duration. In both groups, dialysis in the 1st shift adversely affected self reported sleep duration.

Hemodialysis (HD) patients are less active than their healthy counterparts; this is associated with higher mortality. Healthcare workers observe their patients only during HD, which accounts for about 7% of the week. Knowing more about what occurs in between sessions, particularly with respect to physical activity, may improve patient care and prognosis. Yet without a standard method to measure interdialytic activity, it is difficult to compare the effect of interventions. However, it is unclear how interdialytic activity can be accurately measured. Since activity associated with quality of life is multi-dimensional, objective and subjective tools should be used in conjunction. While commercially available tracking devices can be seamlessly incorporated into everyday life and can increase awareness of user's activity, their validation is needed in the HD population. Fertile topics for research should include the relationship between objective and subjective measures in HD patients, and the investigation of physical activity in non-ambulatory HD patients.

BackgroundIn hemodialysis patients, a third vaccination is frequently administered to augment protection against coronavirus disease 2019 (COVID-19). However, the newly emerged B.1.1.159 (Omicron) variant may evade vaccinal protection more easily than previous strains. It is of clinical interest to better understand the neutralizing activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants after booster vaccine or COVID-19 infection in these mostly immunocompromised patients.MethodsHemodialysis patients from four dialysis centers were recruited between June 2021 and February 2022. Each patient provided a median of six serum samples. SARS-CoV-2 neutralizing antibodies (nAbs) against wild type (WT) or Omicron were measured using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit.ResultsForty-two patients had three doses of mRNA1273. Compared to levels prior to the third dose, nAb-WT increased 18-fold (peak at day 23) and nAb-Omicron increased 23-fold (peak at day 24) after the third dose. Peak nAb-WT exceeded peak nAb-Omicron 27-fold. Twenty-one patients had COVID-19 between December 24, 2021, and February 2, 2022. Following COVID-19, nAb-WT and nAb-Omicron increased 12- and 40-fold, respectively. While levels of vaccinal and post-COVID nAb-WT were comparable, post-COVID nAb-Omicron levels were 3.2 higher than the respective peak vaccinal nAb-Omicron. Four immunocompromised patients having reasons other than end-stage kidney disease have very low to no nAb after the third dose or COVID-19.ConclusionsOur results suggest that most hemodialysis patients have a strong humoral response to the third dose of vaccination and an even stronger post-COVID-19 humoral response. Nevertheless, nAb levels clearly decay over time. These findings may inform ongoing discussions regarding a fourth vaccination in hemodialysis patients.