Andrea Schaller

Zusammenfassung: Digitale bewegungsbezogene Versorgungsleistungen (dbVL) gewinnen zunehmend in der deutschen Gesundheitsversorgung an Bedeutung, während aktuell Qualitätsanforderungen und Wirksamkeitskriterien noch einem dynamischen Entwicklungsprozess unterliegen. Ziel des Beitrags ist eine begriffliche Einordnung von dbVL sowie die systematische Darstellung des Status quo der Einsatzmöglichkeiten und der Qualitätsanforderungen in der Versorgungspraxis. Für die begriffliche Einordnung wurden bestehende Perspektiven der Bewegungsversorgung sowie internationale Klassifikationssysteme digitaler Gesundheitsinterventionen berücksichtigt. Gesetzliche Rahmenbedingungen sowie spezifische Qualitäts- und Wirksamkeitsanforderungen für die Versorgung mit dbVL wurden aus verfügbaren, versorgungsrelevanten Dokumenten systematisch synthetisiert. In allen Versorgungskontexten ermöglichen die gesetzlichen Rahmenbedingungen grundsätzlich den Einsatz von dbVL. Die Analyse zeigt jedoch eine erhebliche, teils sektoral bedingte Heterogenität und Fragmentierung hinsichtlich der Qualitäts- und Wirksamkeitsanforderungen. Die Analyse verdeutlicht den Bedarf einheitlicher und kontextübergreifender Systematisierungen, Qualitätsanforderungen und Evaluationskriterien für dbVL. Zentrale abgeleitete Positionen sind die Einbindung bewegungsbezogener Expertise, nutzerinnen- und nutzerzentrierte Entwicklung und technologieadäquate Evaluationsansätze. Zugleich wird sichtbar, dass die derzeitige sektorale Fragmentierung konsensfähige, kontextübergreifende Evidenz- und Nutzenkonzepte sowie perspektivisch eine kohärentere regulatorische Neu-/Umordnung erfordert.

Purpose: Total hip arthroplasty (THA) and total knee arthroplasty (TKA) as treatments for osteoarthritis increasingly affect working-age adults. However, little is known about the development of work ability after THA and TKA. This study investigated different trajectory classes of work ability after THA and TKA. Methods: This prospective study included 146 working patients (THA n = 51, TKA n = 95). They received THA/ TKA treatment as usual, including surgery, rehabilitation, and optional post-rehabilitation programmes. The primary outcome work ability was assessed five times (before surgery, end of rehabilitation, 3-month, 6-month, and 12-month follow-up) with the Work Ability Score. Additionally, the questionnaires included sociodemographic, health-, rehabilitation-, and work-related data. We applied a latent class growth analysis to identify distinct trajectory classes of work ability over 12 months, and examined associated factors using univariable multinominal logistic regression analyses in MPlus. Results: We found three distinct trajectory classes of work ability within one year after THA/ TKA: no improvement (24.5%), delayed improvement (36.6%), and early improvement in work ability (38.9%). Preoperative predictors for work ability trajectory classes were sex, pain intensity, functional ability in everyday life, and risk of work-related disabilities. Furthermore, work ability trajectory classes were associated with participation in the T-RENA post-rehabilitation programme, and with pain intensity one year after having started rehabilitation. Conclusion: Despite the fact that most patients showed improvements in work ability after THA and TKA, there is a noteworthy proportion who did not. Further studies are needed to enhance identification of these individuals, and to offer targeted interventions. Trial registration: The study is registered in the German Clinical Trials Register (DRKS00029630).

Zusammenfassung: Der Übergang in das Studium ist eine kritische Lebensphase, die durch die Ablösung vom Elternhaus, die Gestaltung des beruflichen Werdegangs und den Aufbau eines unabhängigen Lebens geprägt ist. Globale Krisen (Klima, Pandemie, Kriege) sind weitere potenzielle Stressfaktoren, die sich negativ auf die Gesundheit (GZ) auswirken können. Das Gesundheitsverhalten (GV) und die Gesundheitskompetenz (GK) sind wichtige Ressourcen zur Erhaltung der Gesundheit und zur Bewältigung des Studiums. Ziel der Studie ist es, 1.) den aktuellen GZ, das GV und die GK von Studierenden zu erfassen sowie 2.) Zusammenhänge von Geschlecht, studienbezogenen Faktoren, GV und GZ bzw. GK zu identifizieren.An einer deutschen Hochschule (FH) wurde eine Querschnittsstudie, orientiert an der STROBE-Checkliste, durchgeführt. Die Daten wurden vom Mai bis November 2022 mittels eines Online-Fragebogens erhoben. Es wurden Daten zum GZ (bestehend aus Lebenszufriedenheit, Schlafqualität und wahrgenommener Stress), zum GV (bestehend körperlicher Aktivität, Ernährungs- und Rauchverhalten, Alkohol- und Suchtmittelkonsum) und der GK erfasst. Die Auswertung inkludierte deskriptive Statistiken sowie multiple lineare Regressionsanalysen zur Bewertung der Zusammenhänge.Es wurden N=550 Teilnehmer (♀: 74%) einbezogen. Der GZ wurde als gut bis sehr gut empfunden (81,3%), die Lebenszufriedenheit von 13% als hoch eingestuft, der Stress als hoch (62,6%) und die Schlafqualität (58,1%) als gut wahrgenommen. Das GV offenbarte ein ausreichend körperliches Aktivitätsniveau (57,4%), ein gutes Ernährungs- (58,6%) und Alkoholkonsumverhalten (70,4%). Die GK zeigte sich bei 42,7% als ausreichend. Ein hoher GZ (p<0,004, korr. R²: ,254; f²: 0,34) war mit männlichem Geschlecht (β=0,124, p=0,031), höherer Lebenszufriedenheit (β=0,050, p<0,001), besserer Schlafqualität (β=–0,044, p<0,001) und gesunder Ernährung (β=0,016, p=0,005) assoziiert. Die GK (p=0,022, korr. R²: 0,076; f²: 0,08) war mit dem Masterabschluss (β=0,505, p=0,049), besserer Schlafqualität (β=–0,158, p=0,004), gesunder Ernährung (β=0,059, p=0,031), geringerem Alkoholkonsum (β=–0,209, p=0,017) und höherer Lebenszufriedenheit (β=0,067, p=0,022) assoziiert.Die Ergebnisse belegen einen guten Gesundheitszustand, offenbaren zugleich gezielten Handlungsbedarf beim Gesundheitsverhalten und der Gesundheitskompetenz. Hochschulen sollten daher bei der Interventionsplanung den Fokus nicht ausschließlich auf den Gesundheitszustand legen.

Background: Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people’s health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated. Objective: The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes? Methods: A systematic scoping literature review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized. Results: A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework. Conclusions: Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines. Trial Registration: OSF Registries osf.io/bfswp; https://osf.io/bfswp

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people’s health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated. Objective: The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes? Methods: A systematic scoping literature review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized. Results: A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework. Conclusions: Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines. Trial Registration: OSF Registries osf.io/hsu2w; https://osf.io/hsu2w/

Background and Objective: University students (US) face challenging times characterized by home detachment, identity exploration, academic demands, and additional stressors due to the current global situation that can negatively impact health. Health behaviors (HB) and health literacy (HL) are...

Zusammenfassung: Hintergrund: Arbeitslosigkeit hat vielfältige negative Auswirkungen auf die Gesundheit, weswegen besonders langzeitarbeitslose Menschen als gesundheitlich vulnerable und sozial benachteiligte Gruppe gelten. Allgemein wird als ein zentrales Ziel der Prävention und Gesundheitsförderung inzwischen die Förderung der Gesundheitskompetenz benannt. Neben quantitativen Daten sind mit Blick vulnerable, benachteiligte Gruppen und z. B. für die Entwicklung zielgruppenspezifischer Maßnahmen weitergehende qualitative Untersuchungen von Interesse. Die vorliegende qualitative Arbeit erhebt dahingehend Beschreibungen der subjektiven Gesundheitskompetenz von Langzeitarbeitslosen. Methodik: Angelehnt an das integrierte Modell der Gesundheitskompetenz wurden 9 halbstrukturierte, leitfadengestützte Interviews in einer Einrichtung zur Gesundheits- und Beschäftigungsförderung durchgeführt (4 Frauen, 5 Männer; Alter: 55 ± 9 Jahre). Die Auswertung erfolgte nach der Methode der inhaltlich strukturierten Inhaltsanalyse. Ergebnisse: Während die Interviewten von vielfältigen Quellen zum „Finden von Gesundheitsinformationen“ berichten (medizinisch-gesundheitsbezogener Bereich, soziales Umfeld, Medien), deuten sich in den Stufen „Verstehen“ und „Beurteilen“ Schwierigkeiten an. Mit Blick auf das „Anwenden von Gesundheitsinformationen“ wurden Umsetzungsprobleme bzw. fehlende Umsetzungsstrategien benannt. Schlussfolgerungen: Während von der Zielgruppe vielfältige gesundheitsbezogene Quellen genutzt, sollte Schwerpunkte in Maßnahmen zur Gesundheitskompetenzförderung weitergehend bei den Themen Verstehen und Beurteilen von Gesundheitsinformationen liegen. Besonders relevant ist die Vermittlung von Strategien zur Umsetzung gesundheitsförderlicher Verhaltensweisen. Eine Stärkung sozialer Netzwerke sollte zudem zur Verknüpfung von Beschäftigungs- und Gesundheitsförderung fokussiert werden.

Es besteht kein Zweifel daran, dass Bewegung ein wirksames Medikament ist. Das von der Weltgesundheitsorganisation (WHO) empfohlene minimale Aktivitätsniveau erreichen aber längst nicht alle. Um langfristig Menschen zu Bewegung zu motivieren, benötigt es eine Verhaltensänderung. Das Modell der Bewegungsbezogenen Gesundheitskompetenz bildet die Grundlage, um diese Verhaltensänderung in Gang zu bringen.

Background: The German health care system is faced with a serious shortage of nurses. This is associated, amongst other things, with difficult working conditions and work-related health burdens. Workplace health promotion (WHP) is considered a promising approach to promote the health of nurses. The present review aims to give an overview on existing interventions in different nursing settings (acute care hospitals, long-term care (LTC) facilities and home-based long-term care) in Germany. Methods: A systematic literature search was conducted in PubMed and PubPsych. Studies were included if published after 2010 and provided data of intervention studies on workplace health promotion among nurses in Germany (RCTs, non-RCTs, non-controlled intervention studies and pilot studies). The setting in which the study was conducted (acute medical care hospital, inpatient LTC facilities, home-based LTC, cross-setting) as well as the health issue assessed (physical health, mental health and/or violence experience) were extracted. The intervention was reported against the background of the quality criteria for prevention measures of the statutory health insurers in Germany. The results of the studies were presented according to the RE-AIM framework. Results: Eleven studies on WHP for nurses were included, whereof seven studies were conducted in acute medical care hospitals and four in LTC facilities. No study reported results on WHP for nurses working in the setting of home-based LTC. Most studies aimed at improving mental health. The intervention contents and forms of implementation were heterogeneous. According to the RE-AIM criteria, the reporting of most studies showed several limitations, especially a lack of reporting on Implementation and Adoption. Most studies showed no statistically significant effect on the respective outcomes (Effectiveness). Four studies reported results on Maintenance indicating a sustained effectiveness. Conclusion: Despite the high relevance for health promotion for nurses, our review showed a striking lack of intervention studies in this field. From this we derive a high need of tailored interventions, taking into account the setting-specific development, implementation of WHP interventions for nurses. With regard to the evaluation, the RE-AIM criteria should be taken more into account in order to meet the requirements of evaluating complex interventions and thus contribute to evidence development of WHP in nursing. In terms of content, the topic of violence prevention and dealing with experiences of violence should also be taken into account. Regarding the settings, the working conditions and health burdens in LTC facilities, home-based LTC and acute medical hospitals must be considered. Trial registration: PROSPERO registration number: CRD42021231891