The mini review by Manfredini and colleagues including 15 studies of in-hospital falls (2009–2011) in various settings showed that only half of these provided information about time of falling. However, the time pattern of falls may be related to risk factors, as argued by the authors. Knowledge of the time of falling may be useful for preventive measures.1 Therefore, we would like to contribute data available from the Longitudinal Urban Cohort Ageing Study (LUCAS) in-hospital fall database. The LUCAS research consortium is investigating various domains of functional competence in older persons in different settings to improve preventive and healthcare measures.2 One LUCAS subproject's aim is to improve in-hospital fall prevention, as described previously.3-5 Complete standardized reports including the time of falling comprise the data of 3,401 fall events recorded. The distribution of time of falling as depicted at 3-hour intervals is shown in Figure 1. The majority of falls (73–75%), occurred at daytime (6:00 a.m. to midnight), and about a quarter during nights (midnight to 6:00 a.m.). Half of the falls occurred between 9:00 a.m. and 9:00 p.m., but no striking peak frequencies are evident. Comparing the falls' time pattern over an 11-year period, from January 2000 to February 2011, a tendency appears in the direction of slightly reduced falls during daytime, with quite stable fall occurrence during nights. It may be speculated whether this is because of systematic fall prevention started December 1, 2004.3 There are particular time patterns associated with leading diagnostic categories. For example, stroke patients and those with Parkinson's disease had time peaks between 9:00 a.m. and noon; patients with any fractures and fall-related injuries had time peaks between midnight and 3:00 a.m.; whereas patients with cardiovascular diagnoses, congestive heart failure, in particular, fell most frequently between 3:00 a.m. and 6:00 a.m. These time patterns were related to the places where falls occurred.2 Falls in confused patients occurred significantly earlier during hospital stay than in any other patients without confusion, and changes in mobility status are closely related to fall frequency.2, 4 Analyses considering weekdays and weekends did not detect particular days with peak fall frequency. Cognitive impairment is associated with high fall risk. There is a special care unit in our clinic directed to the particular risks and needs of these geriatric patients.6 A preliminary analysis (2010/2011) showed that these patients' fall rate is nearly four times that in patients on the other wards (28.5 vs 7.9/1,000 hospital days). However, no strikingly different time patterns of falling were evident so far. As also shown in other studies, the majority of older patients' in-hospital falls occur at times of usual activities during the day.1, 7 Compared with hospital patients, the time patterns of injurious falls in community-dwelling persons and in nursing home residents aged ≥ 70 were also quite similar, with most falls occurring at daytime.8 Shifts to nighttime have been reported predominantly from psychiatric, gerontopsychiatric departments and hospices, probably reflecting a higher number of delirious patients and those with disturbed day–night rhythm.1 The biphasic circadian time peaks of falls in older hospitalized patients may, in part, be also influenced by case-mix factors.9 The time patterns of in-hospital falls in older patients are related to disease characteristics, the patients' levels of functional competence and actual fall-related activity, environmental and local factors, and factors of patient care as well. In terms of fall preventive measures, these contributing factors should be considered. So far, the time of falls is closely related to patterns of patients' activities, some of which are reasonably explained by functional impairment and disease factors such as nocturia and disorientation. Improvement in fall prevention in hospitals may gain from more-detailed knowledge of fall-related factors.10 One focus of interest will be to better understand the mechanisms of falls. In fact, insights from large enough databases may facilitate proactive fall-prevention efforts in hospitals.10 Conflict of Interest: Wolfgang von Renteln-Kruse, Lilli Neumann, Tom Krause, Stefan Golgert, and Birgit Frilling report no conflict of interest or any financial support received. This work was supported by the German Federal Ministry for Education and Research (BMBF), Berlin (BMBF grant numbers: 01ET0708, 01ET1001A). Author Contributions: Wolfgang von Renteln-Kruse: Study concept and design. Lilli Neumann, Tom Krause, Stefan Golgert: Data management and statistical analyses. Wolfgang von Renteln-Kruse, Lilli Neumann, Birgit Frilling: Data interpretation and preparation of the manuscript. Sponsor's Role: The funding source had no role in the design and conduct of the study; in the collection, analyses, and interpretation of the data; or in the preparation, review, or approval of the manuscript.