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This analysis examines the geography of subjective wellbeing within a single country via a novel dataset consisting of more than 26,000 respondents embedded in 3100 postal code areas in Finland. We include a detailed indicator on the level of urbanity of the respondent’s location derived from a 250 × 250 m GIS grid, contextual measures of the postal code area´s socioeconomic status as well as proximity to the nearest urban locality and capital city. This analytical framework model makes it possible to examine both individual and contextual determinants for perceived quality of life (QoL). In addition, we include individual-level measures on mental health (Mental Health Inventory MHI-5) and satisfaction with housing and neighbourhood characteristics. The results show that when controlling for socioeconomic factors living in an inner urban area or a neighbourhood (postal code area) with a high unemployment rate are associated with lower QoL and. Also, the share of population with a tertiary education in a postal code area has a positive effect for individual QoL. However, the effects of inner urban living and unemployment rate become insignificant when including mental health indicators and perceived loneliness. In sum, the results confirm and add more detail to earlier findings on lower QoL in urban context and connect living in inner urban area to mental health indicators. As such, the analysis provides further evidence for the positive QoL effects of more rural living while having an access to health and other services.

Background This methodological paper describes the integration of the European Health Interview Survey wave 3 (EHIS 3) into the National FinSote Survey (FinSote2019/EHIS). Finnish Institute for Health and Welfare (THL) conducted the survey in Finland. Methods FinSote 2019/EHIS was conducted as a cross-sectional health, welfare and services survey. A simple random sampling was used to recruit 15 000 individuals aged 15 years and older. Data collection was carried out by self-administered questionnaire that was available in web and paper form. To minimize the respondent burden socio-demographic background variables were obtained from administrative registers. In Finland, EHIS wave 3 was conducted as a part of the National FinSote survey. The questionnaire included both EHIS 3 modules on health status, health care, health determinants (excluding derogation variables), social background variables and additional national questions. In data processing, two datasets were formed: national dataset for sustaining time series as well as monitoring nationally relevant topics and EHIS dataset including the quality and validation rules specified by Eurostat. Results In total, 6,251 questionnaires were completed. The response rate was 44 % in EHIS data (aged 15+) and 45 % in national FinSote data (aged 20+). The representativeness of the results was increased by applying weighting procedures. The sample size was just large enough to achieve the precision requirements determined by Eurostat. Access to micro data of the EHIS 3 will be provided by Eurostat and to the FinSote 2019/EHIS by THL. Conclusions Integrating EHIS wave 3 into an existing national monitoring system was challenging in Finland. The harmonization might lead to interruptions of existing national time trends. Inclusion of some national well-being and social care variables might be irrelevant to respondent aged 15-19 years. Key messages Conducting EHIS as a part of National FinSote survey in Finland was cost-effective but challenging. Cultural differences in regards to living conditions in younger respondents and health care system made it challenging to integrate EHIS into national survey.

The use of wearable technology, which is often acquired to support well-being and a healthy lifestyle, has become popular in Western countries. At the same time, healthcare is gradually taking the first steps to introduce wearable technology into patient care, even though on a large scale the evidence of its’ effectiveness is still lacking. The objective of this study was to identify the factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the Finnish adult population (20–99) and among older adults (65–99). The study utilized a cross-sectional population survey of Finnish adults aged 20 and older (n = 6,034) to analyse non-causal relationships between wearable technology use and the users’ characteristics. Logistic regression models of wearable technology use were constructed using statistically significant sociodemographic, well-being, health, benefit, and lifestyle variables. Both in the general adult population and among older adults, wearable technology use was associated with getting aerobic physical activity weekly according to national guidelines and with marital status. In the general adult population, wearable technology use was also associated with not sleeping enough and agreeing with the statement that social welfare and healthcare e-services help in taking an active role in looking after one’s own health and well-being. Younger age was associated with wearable technology use in the general adult population but for older adults age was not a statistically significant factor. Among older adults, non-use of wearable technology went hand in hand with needing guidance in e-service use, using a proxy, or not using e-services at all. The results support exploration of the effects of wearable technology use on maintaining an active lifestyle among adults of all ages.

Issue In Finland, municipalities are responsible for promoting public health on local level. However, there hasn’t been comparable nationwide information on health promotion processes and resources in different municipalities available. In order to enhance evidence-based management, a nationwide online database and user interface called TEAviisari (http://teaviisari.fi/en/) was released in 2010. Description TEAviisari is based on a generic health promotion capacity-building framework consisting of seven dimensions: commitment, management, monitoring and needs assessment, resources, common practices, participation, and other core functions. Each dimension consists of several indicators. TEAviisari aims to make measures taken by local authorities visible and to provide comparable and objective indicators for the management, planning, and evaluation of health promotion activities in different sectors of administration in all municipalities. Results Most of the data are collected biennially with an electronic form by municipal informants, and complemented with register data. Sectors covered are primary health care; comprehensive, upper secondary and vocational education; sport and physical activity; culture; and municipal management. Exceptionally high coverage (76%-97%) supports the quality of the follow-up data. In order to simplify the interpretation, all data are displayed as summary scores ranging from zero to 100, where 100 stands for a desirable quality. It is possible to drill down into more detailed information, all the way down to single indicators. Lessons Our work shows that it is possible to collect comparable data on health promotion practices and resources in municipalities. TEAviisari offers access to relevant, interpreted information for decision-makers on all levels, serving as an assessment and planning tool for the local government, making their actions transparent to the residents, and providing information for national policy-making.