A Population-Based Survey

Background: Chronic conditions are an increasing challenge for individuals and the health care system. Smartphones and health apps are potentially promising tools to change health-related behaviors and manage chronic conditions. Objective: The aim of this study was to explore (1) the extent of smartphone and health app use, (2) sociodemographic, medical, and behavioral correlates of smartphone and health app use, and (3) associations of the use of apps and app characteristics with actual health behaviors. Methods: A population-based survey (N=4144) among Germans, aged 35 years and older, was conducted. Sociodemographics, presence of chronic conditions, health behaviors, quality of life, and health literacy, as well as the use of the Internet, smartphone, and health apps were assessed by questionnaire at home visit. Binary logistic regression models were applied. Results: It was found that 61.25% (2538/4144) of participants used a smartphone. Compared with nonusers, smartphone users were younger, did more research on the Internet, were more likely to work full-time and more likely to have a university degree, engaged more in physical activity, and less in low fat diet, and had a higher health-related quality of life and health literacy. Among smartphone users, 20.53% (521/2538) used health apps. App users were younger, less likely to be native German speakers, did more research on the Internet, were more likely to report chronic conditions, engaged more in physical activity, and low fat diet, and were more health literate compared with nonusers who had a smartphone. Health apps focused on smoking cessation (232/521, 44.5%), healthy diet (201/521, 38.6%), and weight loss (121/521, 23.2%). The most common app characteristics were planning (264/521, 50.7%), reminding (188/521, 36.1%), prompting motivation (179/521 34.4%), and the provision of information (175/521, 33.6%). Significant associations were found between planning and the health behavior physical activity, between feedback or monitoring and physical activity, and between feedback or monitoring and adherence to doctor’s advice. Conclusions: Although there were many smartphone and health app users, a substantial proportion of the population was not engaged. Findings suggest age-related, socioeconomic-related, literacy-related, and health-related disparities in the use of mobile technologies. Health app use may reflect a user’s motivation to change or maintain health behaviors. App developers and researchers should take account of the needs of older people, people with low health literacy, and chronic conditions

Using Smartphones and Health Apps to Change and Manage Health Behaviors: A Population-Based Survey

Background: Chronic conditions are an increasing challenge for individuals and the health care system. Smartphones and health apps are potentially promising tools to change health-related behaviors and manage chronic conditions.  Objective: The aim of this study was to explore (1) the extent of smartphone and health app use, (2) sociodemographic, medical, and behavioral correlates of smartphone and health app use, and (3) associations of the use of apps and app characteristics with actual health behaviors.  Methods: A population-based survey (N=4144) among Germans, aged 35 years and older, was conducted. Sociodemographics, presence of chronic conditions, health behaviors, quality of life, and health literacy, as well as the use of the Internet, smartphone, and health apps were assessed by questionnaire at home visit. Binary logistic regression models were applied.  Results: It was found that 61.25% (2538/4144) of participants used a smartphone. Compared with nonusers, smartphone users were younger, did more research on the Internet, were more likely to work full-time and more likely to have a university degree, engaged more in physical activity, and less in low fat diet, and had a higher health-related quality of life and health literacy. Among smartphone users, 20.53% (521/2538) used health apps. App users were younger, less likely to be native German speakers, did more research on the Internet, were more likely to report chronic conditions, engaged more in physical activity, and low fat diet, and were more health literate compared with nonusers who had a smartphone. Health apps focused on smoking cessation (232/521, 44.5%), healthy diet (201/521, 38.6%), and weight loss (121/521, 23.2%). The most common app characteristics were planning (264/521, 50.7%), reminding (188/521, 36.1%), prompting motivation (179/521 34.4%), and the provision of information (175/521, 33.6%). Significant associations were found between planning and the health behavior physical activity, between feedback or monitoring and physical activity, and between feedback or monitoring and adherence to doctor’s advice.  Conclusions: Although there were many smartphone and health app users, a substantial proportion of the population was not engaged. Findings suggest age-related, socioeconomic-related, literacy-related, and health-related disparities in the use of mobile technologies. Health app use may reflect a user’s motivation to change or maintain health behaviors. App developers and researchers should take account of the needs of older people, people with low health literacy, and chronic conditions

Perceived need for treatment and non-utilization of outpatient psychotherapy in old age: two cohorts of a nationwide survey

Beackground: Older adults with mental health problems may benefit from psychotherapy; however, their perceived need for treatment in relation to rates of non-utilization of outpatient psychotherapy as well as the predisposing, enabling, and need factors proposed by Andersen's Model of Health Care Utilization that account for these differences warrant further investigation. Methods: We used two separate cohorts (2014 and 2019) of a weighted nationwide telephone survey in Germany of German-speaking adults with N=12,197 participants. Across the two cohorts, 12.9% (weighted) reported a perceived need for treatment for mental health problems and were selected for further analyses. Logistic Generalized Estimation Equations (GEE) was applied to model the associations between disposing (age, gender, single habiting, rural residency, general health status), enabling (education, general practitioner visit) non-utilization of psychotherapy (outcome) across cohorts in those with a need for treatment (need factor). Results: In 2014, 11.8% of 6087 participants reported a perceived need for treatment due to mental health problems. In 2016, the prevalence increased significantly to 14.0% of 6110 participants. Of those who reported a perceived need for treatment, 36.4% in 2014 and 36.9%in 2019 did not see a psychotherapist - where rates of non-utilization of psychotherapy were vastly higher in the oldest age category (59.3/52.5%; 75+) than in the youngest (29.1/10.7%; aged 18-25). Concerning factors associated with non-utilization, multivariate findings indicated participation in the cohort of 2014 (OR 0.94), older age (55-64 OR 1.02, 65-74 OR 1.47, 75+ OR 4.76), male gender (OR 0.83), lower educational status (OR 0.84), rural residency (OR 1.38), single habiting (OR 1.37), and seeing a GP (OR 1.39) to be related with non-utilization of psychotherapy; general health status was not significantly associated with non-utilization when GP contact was included in the model. Conclusion: There is a strong age effect in terms of non-utilization of outpatient psychotherapy. Individual characteristics of both healthcare professionals and patients and structural barriers may add to this picture. Effective strategies to increase psychotherapy rates in those older adults with unmet treatment needs are required

Health literacy as health knowledge, choice of health-related information sources and health app usage: results from a population-based survey

Hintergrund: Chronische Erkrankungen stellen weltweit die häufigsten Todesursachen dar, denen durch Gesundheitsverhalten wie körperliche Aktivität oder Inanspruchnahme von Vorsorgeuntersuchungen entgegengewirkt werden kann. Die Gesundheitskompetenz mündiger Patienten stellt damit eine Schlüsselkompetenz für die Bereiche Gesundheitsförderung, Prävention und Krankheitsbewältigung dar, deren Facetten jedoch ungenügend beleuchtet sind. Ziel der vorliegenden Arbeit war es, patientenbezogene Faktoren nach dem konzeptuellen Modell der Gesundheitskompetenz (adaptiert, Paasche-Orlow und Wolf, 2007: (a) Gesundheitswissen, (b) Quellen von Gesundheitsinformationen, (c) Nutzung von Gesundheits-Apps) zu identifizieren und Gesundheitsindikatoren in Beziehung zu setzen. Methoden: Die Untersuchung wurde auf Grundlage einer populationsbasierten Querschnittserhebung der deutschen Bevölkerung (N=4144; stratifiziert nach Alter, Geschlecht, Bildung und Bundesland) ab 35 Jahren durchgeführt (Rücklaufquote 55%). Die Befragung erfolgte als Hausbesuch in Form von computergestützte persönliche Interviews (CAPI). Anhand standardisierter Fragebögen wurden Daten zur Soziodemografie, mobiler Technologienutzung, gesundheitsbezogene Indikatoren, Gesundheitskompetenz (HLS-16), Gesundheitswissen sowie Nutzung gesundheitsbezogener Informationsquellen erfasst und mithilfe adjustierter logistischer und linearer Regressionsmodelle ausgewertet. Ergebnisse: (a) Für das Gesundheitswissen von Personen mit und ohne chronische Erkrankungen ergab sich, dass Betroffene mit Erkrankungen der Atemwege und des Bewegungsapparates einen besseren Wissensstand hatten, als nichterkrankte Vergleichspersonen. Niedrigere Wissenswerte fanden sich bei Personen mit chronischen Schmerzen sowie keine Wissensdifferenzen bei Personen, die an Herz-Kreislauf-Erkrankungen und Depressionen litten, verglichen mit Personen ohne diese Erkrankungen. (b) Als relevante Quellen von Gesundheitsinformationen wurden Allgemeinmediziner (72.1%), Fachärzte (39.5%), Apotheker (31.6%) und Internet (31.5%) von den Befragten genannt. Faktoren wie Alter, Anzahl der Krankheiten sowie Handlungsplanung Selbstwirksamkeit für den Erwerb von Gesundheitswissen stehen dabei im Zusammenhang mit der gewählten Informationsquelle zu gesundheitsrelevanten Themen. (c) Zur Nutzung von Gesundheits-Apps wurden 61.3% der Befragten als Smartphone-Nutzer klassifiziert – davon nutzten 20.5% Gesundheits-Apps (Rauchentwöhnung (44.5%), ausgewogene Ernährung (38.6%), Gewichtsreduktion (23.2%) mit den App-Eigenschaften Planung (50.7%), Erinnerung (36.1%), Motivation (34.4%) und Bereitstellung von Informationen (33.6%)). Die Analysen ergaben multivariate Zusammenhänge zwischen der Smartphone-Nutzung bzw. Gesundheits-Apps und Alter, Berufstätigkeit, Bildung, Migrationshintergrund, chronischen Erkrankungen, körperliche Aktivität, fettarme Ernährung, gesundheitsbezogene Lebensqualität, Gesundheitskompetenz und Internetrecherche. Weiter wurden Zusammenhänge zwischen Planung und körperlicher Aktivität, Feedback oder Überwachung/Monitoring und körperliche Aktivität sowie zwischen Feedback oder Überwachung und Einhaltung der ärztlichen Empfehlung festgestellt. Diskussion: Die vorgestellte Untersuchung liefert populationsbasierte Befunde zu Gesundheitskompetenz und Gesundheitswissen, der Wahl von Gesundheitsinformationsquellen und der Nutzung mobiler Gesundheitstechnologien. Sie leistet einen empirischen Beitrag zum besseren Verständnis des mündigen, gesundheitskompetenten Bürgers als Schlüsselfigur in unserm Gesundheitssystem. Die hier gefundenen altersassoziierten, sozioökonomischen und gesundheitsrelevanten Unterschiede gilt es bezüglich der Stärkung von Gesundheitskompetenz sowie weiterer Erarbeitung von Interventionen zu berücksichtigen.Background: Chronical diseases are the most common cause of death globally, although they can be prevented through health-relevant behaviours. Health literacy (HL) of responsible patients therefore constitutes a key competency to promote health via prevention and curing diseases. However, our understanding of HL is still limited. This work aims to identify patient-specific factors according to the conceptual model of HL (adapted, Paasche-Orlow and Wolf, 2007: (a) health-related knowledge, (b) sources of health-related information, (c) usage of health-related apps) that can improve an individual’s HL and associated health outcomes. Methods: A total of 4144 individuals from Germany constitute the stratified and population-based sample (35+ years of age). Standardized questionnaires collected data on socio-demographics, mobile technology usage, health indicators, HL (HLS-16), health-related knowledge and health-related sources of information via computer-assisted personal interviews (CAPI); the data analysis was conducted via adjusted logistic and linear regression models. Results: (a) Health-related knowledge is higher for individuals with health conditions of their respiratory or musculoskeletal system, and lower for chronic pain patients, as compared to individuals without such conditions. There were no differences in knowledge among people with cardiovascular diseases and depression, as compared to individuals without such diseases. (b) Sources for health-related information were general practitioners (72.1%), medical specialists (39.5%), pharmacists (31.6%) and web-based search (31.5%). The use of sources varied by age, number of health conditions, action planning and self-efficacy with which individuals were able to acquire information. (c) Regarding the mobile technology usage, 61.3% were classified as smartphone-users – 20.5% of them used health-apps. The apps related to smoking cessation (44.5%), healthy diet (38.6%), weight loss (23.2%) with app-characteristics like planning (50.7%), reminding (36.1%), promoting motivation (34.4%) and provision of information (33.6%). The analyses find multivariate relationships between the smartphone usage or health-related apps and the user’s age, profession, education, migration background, chronical diseases, physical activity, low-fat diet, health-related quality of life, health-related competence and web-based search. Additionally, it finds relationships between planning and physical activity, monitoring and physical activity, plus between monitoring and adherence to doctoral recommendations. Discussion: The analysis provides representative evidence about HL and knowledge, the choice of information source and the use of mHealth-technologies. It thus makes an empirical contribution to improve our understanding of the key role of health-literated individuals for our health- system. The related differences by age or socio-economic backgrounds constitute relevant information for policy makers and developers to improve HL and conduct future interventions

Patients’ health literacy in relation to the preference for a general practitioner as the source of health information

Background: For many patients, the general practitioner (GP) is the most important point of contact for obtaining information about a wide range of health topics. However, patients with different characteristics may seek health information from different sources, such as friends or the internet. The relationship between patient characteristics and preferences for information sources is understudied. We investigate which information sources are used by patients for health-related questions and how this relates to patients’ sociodemographics, health, and health literacy. Methods: A stratified and population-based survey was conducted to investigate health information sources within the German population over 35 years (n = 4144). Sociodemographics, use of technology, health-related indicators, and health literacy (including self-efficacy and action planning), as well as questions regarding the ratings of multiple health-related information sources, were investigated in personal interviews and analyzed using logistic regression. Results: In our study, GPs were the most important source of information for the patients, followed by medical specialists, pharmacists and the internet. Patient age and number of illnesses were associated with the choice of information source. Furthermore, action planning and self-efficacy for acquiring health knowledge were associated with the selected source of information. Conclusions: Information provider appears to be an important role for GPs, particularly among old and chronically ill patients. GPs should have the specific capabilities to fill this role and should be trained and referred to accordingly. Selfefficacy and action planning for acquiring health knowledge are important patient factors doctors can use for brief inventions during consultations