Accuracy of Monocular Two-Dimensional Pose Estimation Compared With a Reference Standard for Kinematic Multiview Analysis: Validation Study

Background: Expensive optoelectronic systems, considered the gold standard, require a laboratory environment and the attachment of markers, and they are therefore rarely used in everyday clinical practice. Two-dimensional (2D) human pose estimations for clinical purposes allow kinematic analyses to be carried out via a camera-based smartphone app. Since clinical specialists highly depend on the validity of information, there is a need to evaluate the accuracy of 2D pose estimation apps. Objective: The aim of the study was to investigate the accuracy of the 2D pose estimation of a mobility analysis app (Lindera-v2), using the PanopticStudio Toolbox data set as a reference standard. The study aimed to assess the differences in joint angles obtained by 2D video information generated with the Lindera-v2 algorithm and the reference standard. The results can provide an important assessment of the adequacy of the app for clinical use. Methods: To evaluate the accuracy of the Lindera-v2 algorithm, 10 video sequences were analyzed. Accuracy was evaluated by assessing a total of 30,000 data pairs for each joint (10 joints in total), comparing the angle data obtained from the Lindera-v2 algorithm with those of the reference standard. The mean differences of the angles were calculated for each joint, and a comparison was made between the estimated values and the reference standard values. Furthermore, the mean absolute error (MAE), root mean square error, and symmetric mean absolute percentage error of the 2D angles were calculated. Agreement between the 2 measurement methods was calculated using the intraclass correlation coefficient (ICC[A,2]). A cross-correlation was calculated for the time series to verify whether there was a temporal shift in the data. Results: The mean difference of the Lindera-v2 data in the right hip was the closest to the reference standard, with a mean value difference of –0.05° (SD 6.06°). The greatest difference in comparison with the baseline was found in the neck, with a measurement of –3.07° (SD 6.43°). The MAE of the angle measurement closest to the baseline was observed in the pelvis (1.40°, SD 1.48°). In contrast, the largest MAE was observed in the right shoulder (6.48°, SD 8.43°). The medians of all acquired joints ranged in difference from 0.19° to 3.17° compared with the reference standard. The ICC values ranged from 0.951 (95% CI 0.914-0.969) in the neck to 0.997 (95% CI 0.997-0.997) in the left elbow joint. The cross-correlation showed that the Lindera-v2 algorithm had no temporal lag. Conclusions: The results of the study indicate that a 2D pose estimation by means of a smartphone app can have excellent agreement compared with a validated reference standard. An assessment of kinematic variables can be performed with the analyzed algorithm, showing only minimal deviations compared with data from a massive multiview system

Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System

Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis

Digital pen technology for conducting cognitive assessments: a cross-over study with older adults

Many digitalized cognitive assessments exist to increase reliability, standardization, and objectivity. Particularly in older adults, the performance of digitized cognitive assessments can lead to poorer test results if they are unfamiliar with the computer, mouse, keyboard, or touch screen. In a cross-over design study, 40 older adults (age M = 74.4 ± 4.1 years) conducted the Trail Making Test A and B with a digital pen (digital pen tests, DPT) and a regular pencil (pencil tests, PT) to identify differences in performance. Furthermore, the tests conducted with a digital pen were analyzed manually (manual results, MR) and electronically (electronic results, ER) by an automized system algorithm to determine the possibilities of digital pen evaluation. ICC(2,k) showed a good level of agreement for TMT A (ICC(2,k) = 0.668) and TMT B (ICC(2,k) = 0.734) between PT and DPT. When comparing MR and ER, ICC(2,k) showed an excellent level of agreement in TMT A (ICC(2,k) = 0.999) and TMT B (ICC(2,k) = 0.994). The frequency of pen lifting correlates significantly with the execution time in TMT A (r = 0.372, p = 0.030) and TMT B (r = 0.567, p < 0.001). A digital pen can be used to perform the Trail Making Test, as it has been shown that there is no difference in the results due to the type of pen used. With a digital pen, the advantages of digitized testing can be used without having to accept the disadvantages

App-Based Evaluation of Older People’s Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study

Background: Falls and the risk of falling in older people pose a high risk for losing independence. As the risk of falling progresses over time, it is often not adequately diagnosed due to the long intervals between contacts with health care professionals. This leads to the risk of falling being not properly detected until the first fall. App-based software able to screen fall risks of older adults and to monitor the progress and presence of fall risk factors could detect a developing fall risk at an early stage prior to the first fall. As smartphones become more common in the elderly population, this approach is easily available and feasible. Objective: The aim of the study is to evaluate the app Lindera Mobility Analysis (LIN). The reference standards determined the risk of falling and validated functional assessments of mobility. Methods: The LIN app was utilized in home- and community-dwelling older adults aged 65 years or more. The Berg Balance Scale (BBS), the Tinetti Test (TIN), and the Timed Up & Go Test (TUG) were used as reference standards. In addition to descriptive statistics, data correlation and the comparison of the mean difference of analog measures (reference standards) and digital measures were tested. Spearman rank correlation analysis was performed and Bland-Altman (B-A) plots drawn. Results: Data of 42 participants could be obtained (n=25, 59.5%, women). There was a significant correlation between the LIN app and the BBS (r=-0.587, P<.001), TUG (r=0.474, P=.002), and TIN (r=-0.464, P=.002). B-A plots showed only few data points outside the predefined limits of agreement (LOA) when combining functional tests and results of LIN. Conclusions: The digital app LIN has the potential to detect the risk of falling in older people. Further steps in establishing the validity of the LIN app should include its clinical applicability

Self-monitoring as an instrument to improve older adults’ health behavior

Einleitung: Bei zahlreichen Erkrankungen ist eine aktive Einbindung von Patienten von großer Bedeutung. Mit Hilfe der psychologischen Methode des Selbstmonitorings können eigene Verhaltensweisen dargestellt und analysiert und somit Verhaltensänderungen erzielt werden. Technische Systeme wie Gesundheits-Apps ermöglichen neue Wege der „Selbstvermessung“. Methodik: Zur Analyse des Nutzungsverhaltens von Senioren sowie des individuellen Nutzens bei der Verwendung einer Gesundheits-App wurde eine Studie mit 30 Senioren durchgeführt (Akzeptanzstudie). Die über 60-jährigen Probanden ohne chronische Erkrankungen nutzten eine Gesundheits-App zum Selbstmonitoring gesundheitsbezogener Verhaltensweisen für vier Wochen. Dabei wurde das Nutzungsverhalten anhand subjektiver Angaben der Probanden und mit Hilfe automatisch generierter Loggingdaten analysiert. Weiterhin wurde der Einfluss der Nutzung auf gesundheitsbezogene Parameter wie die Medikamentenadhärenz und die Lebensqualität untersucht. Anschließend wurde eine weitere Studie mit älteren Diabetikern (n=36; Alter > 60 Jahre), welche die Gesundheits-App für 12 Wochen nutzten, durchgeführt, um die Verwendung über einen längeren Zeitraum und die Effektivität im Diabetesmanagement zu ermitteln (Diabetesstudie). Ergebnisse: Sowohl ältere Probanden ohne chronische Erkrankungen als auch ältere Diabetiker nutzten die Gesundheits-App in großem Umfang. Es konnte in beiden Studien eine Verbesserung hinsichtlich der Medikamentenadhärenz und des Gesundheitsverhaltens gezeigt werden. Auf die körperliche Aktivität hatte die Nutzung der App in beiden Studien keine Auswirkungen. Die Gebrauchstauglichkeit (Usability) der Gesundheits-App wurde in beiden Studien positiv bewertet. Jedoch ergeben sich für Patienten mit einer chronischen Erkrankung besondere Anforderungen. Schlussfolgerung: Die Nutzung einer Gesundheits-App kann sowohl für Probanden mit Diabetes Typ II als auch für Probanden ohne chronische Erkrankungen zahlreiche Vorteile hinsichtlich Gesundheitsverhalten und Compliance mit sich bringen. Dazu müssen jedoch bei der Entwicklung die besonderen Bedürfnisse von Senioren berücksichtigt werden. Umfangreiche Schulungs- und Supportkonzepte sind ebenfalls notwendig, damit Senioren eine Gesundheits-App langfristig nutzen.Background: The active involvement of patients in their healthcare is crucial in a number of diseases. Applying the psychological method of self-monitoring can help to reveal and analyze individual behavior, and as a result achieve change in behavior. Technological systems such as health apps allow new ways of “self-measurement”. Methods: A study with 30 adults aged 60 and older was conducted in order to analyze both the usage behavior of seniors and the individual benefits obtained from health apps (acceptance study). People with chronic diseases were excluded from this study. The participants used the app over a period of 4 weeks with the aim to self-monitor health-related behavior. Usage behavior was analyzed with automatically generated logging data and additional subjective reporting of the participants. Furthermore, the effects of use on health-related parameters such as medication compliance and quality of life were examined. This was followed by another study with older diabetics (age > 60, n=36). They used the app over a period of 12 weeks, in order to evaluate usage behavior over an extended period and to analyze the efficacy within diabetes management (diabetes study). Results: Both older participants without chronic diseases and older diabetics used the health app to a great extent. Medication compliance and health-related behavior was improved in both studies. Conversely, there was no positive impact on physical activity. The usability of the investigated health app was rated positive in both studies. Particular demands are required for patients with chronic diseases. Conclusion: The use of a health app can have various advantages for patients with diabetes type II and for people without chronic diseases regarding their health behavior and compliance. In order to achieve those, specific requirements for seniors have to be taken into account at the development stage. Comprehensive training and support concepts are necessary in order to motivate older adults to use the app in the long run

Digitale Lösungen für das Gesundheits- und Krankheitsmanagement älterer Menschen – Effektivität, Usability und Akzeptanz

Die Landschaft im Gesundheitswesen ist derzeit von zwei großen Entwicklungssträngen geprägt. Zum einen erfolgt ein gesellschaftlicher Wandel durch die Demografische Entwicklung der Bevölkerung und dem damit zusammenhängenden Fachkräftemangel im Gesundheitswesen sowie durch die immer älter werdende Bevölkerung. Der zweite große Entwicklungsstrang – die Digitalisierung im Gesundheitswesen – kann dabei unterstützen den Herausforderungen, die mit dem Demografischen Wandel einhergehen zu begegnen. Sowohl in der Wirtschaft – mit der Zunahme des Angebots an digitalen Lösungen – als auch in der Politik mit der Verabschiedung des Digitalen Versorgungsgesetzes, der Einführung der elektronischen Patientenakte oder der Zulassung Digitaler Gesundheitsanwendungen (DiGA) und Digitaler Pflegeanwendungen (DiPA) ist das Thema aktuell und voraussichtlich in Zukunft präsent. Trotz der Vielzahl an digitalen Lösungen findet in der Praxis erst ein sehr geringer Anteil dieser Lösungen Anwendung. Als Ursache werden verschiedene Möglichkeiten in der Literatur genannt: (1) die Fokussierung von Entwickler*innen digitaler Lösungen auf den zweiten Gesundheitsmarkt und damit außerhalb der regulären Gesundheitsversorgung, (2) fehlende Evidenz zur Effektivität und Gebrauchstauglichkeit der digitalen Lösungen, (3) fehlende Konzepte zur Schulung von Kompetenzen der Anwender*innen im Umgang mit digitalen Lösungen. Die vorliegende Arbeit befasst sich mit digitalen Lösungen für Patient*innen und dabei insbesondere für ältere Menschen. Dazu werden digitale Lösungen für die Bereiche Prävention, Diagnostik und Therapie mit den dazugehörigen Chancen und Barrieren dargestellt und der aktuelle Stand der Forschung mit besonderem Blick auf Gebrauchstauglichkeit, Effektivität und Akzeptanz für die drei genannten Versorgungsbereiche aufgezeigt. In sechs eigenen Studien erfolgte die Auseinandersetzung mit Wearables und Apps zu Steigerung der körperlichen Aktivität und Verringerung der Sturzgefahr; im Bereich der Prävention, mit digitalen Unterstützungsmöglichkeiten in der Diagnostik und die Abgrenzung zu papierbasierten Verfahren; und im Bereich der Therapie mit der Möglichkeit zu Steigerung der Medikamentenadhärenz und Therapietreue mit Hilfe einer Smartphone App. In diesen Studien konnte ein Verständnis zur Nutzung digitaler Lösungen durch ältere Menschen aufgebaut, Handlungsempfehlungen zur zielgruppengerechten Entwicklung und Gestaltung digitaler Lösungen abgeleitet und ein weiterer Schritt zur Erhöhung der Evidenz hinsichtlich Effektivität in der Versorgung, Gebrauchstauglichkeit und Akzeptanz geleistet werden. Nachdem zunächst ältere Menschen im Fokus standen, erfolgte in der abschließenden Diskussion die gesamtgesellschaftliche Einordnung der Studienergebnisse in den aktuellen Stand der Forschung. Abschließend wird im Ausblick ein Überblick zu aktuellen Entwicklungssträngen im Gesundheitswesen vor dem Hintergrund der Digitalisierung und der Corona-Pandemie gegeben

Chancen &amp; Barrieren in der Mobilen Rehabilitation – eine qualitative Erhebung mit medizinischem Personal, Koordinatoren, Patienten &amp; Angehörigen

Ziel: Um das Konzept der mobilen Rehabilitation in Deutschland (MoRe) zu verbessern und zu erweitern, war das Ziel der vorliegenden Studie, bestehende Problem- und Handlungsfelder zu identifizieren und daraus Lösungsansätze zu entwickeln. Zur Erhebung von Problemen und Barrieren in der MoRe wurden leitfadengestützte Interviews mit den an der Versorgung beteiligten Akteuren (Medizinisches Personal, Koordinatoren, Patienten und Angehörige) durchgeführt. Es ließen sich fünf Problemfelder in der MoRe identifizieren: Terminkoordination, Kommunikation und Informationsweitergabe, Zusammenarbeit mit Externen, Dokumentation und Rahmenbedingungen. Die berichteten Probleme des medizinischen Personals und der Koordinatoren stimmten dabei weitestgehend überein. Die in der MoRe auftretenden Barrieren decken sich zu großen Teilen mit den generell in der Pflegepraxis diskutierten Handlungsfeldern. Für einzelne Barrieren können technische Entwicklungen einen geeigneten Lösungsansatz darstellen.   Chances and Barriers in Mobile Rehabilitation – a Qualitative Study with Health Professionals, Coordinators, Patients and Family Members In order to further develop and broaden the concept of mobile rehabilitation in Germany, the aim of this study was to identify existing problems and fields of actions and to derive solutions for them. To identify problems and barriers in mobile rehabilitation, guided interviews were performed with all health professionals and coordinators involved in mobile rehabilitation as well as with patients and family members. Five areas associated with specific problems could be identified: Coordination of appointments, communication and information flow, collaboration with external organizations, documentation and regulatory conditions. The presented fields of problems were identical between health professionals and coordinators to a great extent. The detected problems corresponded to those problems articulated in other fields of care such as ambulatory nursing. Solutions for these problems could include technology-based developments. JEL–Klassifizierung: I1

Digitization of neuropsychological diagnostics: a pilot study to compare three paper-based and digitized cognitive assessments

Background and objective: The number of people suffering from dementia is increasing worldwide and so is the need for reliable and economical diagnostic instruments. Therefore, the aim of this study was to compare the processing times of the neuropsychological tests Trail Making Tests A and B (TMT-A/B) and Color-Word Interference Test (CWIT), which were performed in both digital and paper versions. Methods: The pilot study was conducted among 50 healthy participants (age 65–83 years) using a randomized crossover design. The correlations and differences in the individual processing times of the two test versions were statistically analyzed. Further research questions concerned the influence of the individual usage of technology and the technology commitment of participants as well as the influence of the assessed usability on participants’ performance. Results: Between the two versions (paper-based vs. digital) statistically significant correlations were found in all tests, e.g., TMT-A r(48) = 0.63, p < 0.01; TMT-B rs(48) = 0.77, p < 0.001). The mean value comparison showed statistically significant differences, e.g., interference table (CWIT) t(49) = 11.24, p < 0.01). Correlations with medium effect were found between the differences in processing times and the individual usage of computer (e.g., rs(48) = − 0.31) and smartphone (rs(48) =  − 0.29) and between the processing times of the TMT-B and the usability (rs(48) = 0.29). Conclusions: The high correlations between the test procedures appear promising. However, the differences found in the processing times of the two test versions require validation and standardization of digitized test procedures before they can be used in practice