Unhealthy lifestyles, environment, well-being and health capability in rural neighbourhoods: a community-based cross-sectional study
Authors: Azul Anabela Marisa , Almendra Ricardo , Quatorze Marta , Loureiro Adriana , Reis Flávio , Tavares Rui , Mota-Pinto Anabela , Cunha António , Rama Luís , Malva João Oliveira , Santana Paula , Ramalho-Santos João ,   HeaLIQs4Cities consortium, Cunha António , Pardal André , Peixoto Eugénia , Guardado Diana , Zwaving Marieke , De La Blanca Eduardo Briones Pérez , van der Heijden Roel A. , Van’t Jagt Ruth Koops , Bultje Daan , Malva João , Reis Flávio , Rama Luís , Veríssimo Manuel , Teixeira Ana , Lima Margarida , Santos Lèlita , Palavra Filipe , Ferreira Pedro , Pinto Anabela Mota , Santana Paula , Almendra Ricardo , Loureiro Adriana , Viana Inês , Quatorze Marta , Azul Anabela Marisa , Ramalho-Santos João , Sandholdt Catharina Thiel , Kristiansen Maria 
Published: September 2021

Background

Non-communicable diseases are a leading cause of health loss worldwide, in part due to unhealthy lifestyles. Metabolic-based diseases are rising with an unhealthy body-mass index (BMI) in rural areas as the main risk factor in adults, which may be amplified by wider determinants of health. Changes in rural environments reflect the need of better understanding the factors affecting the self-ability for making balanced decisions. We assessed whether unhealthy lifestyles and environment in rural neighbourhoods are reflected into metabolic risks and health capability.

Methods

We conducted a community-based cross-sectional study in 15 Portuguese rural neighbourhoods to describe individuals’ health functioning condition and to characterize the community environment. We followed a qualitatively driven mixed-method design to gather information about evidence-based data, lifestyles and neighbourhood satisfaction (incorporated in eVida technology), within a random sample of 270 individuals, and in-depth interviews to 107 individuals, to uncover whether environment influence the ability for improving or pursuing heath and well-being.

Results

Men showed to have a 75% higher probability of being overweight than women (p-value = 0.0954); and the reporting of health loss risks was higher in women (RR: 1.48; p-value = 0.122), individuals with larger waist circumference (RR: 2.21; IC: 1.19; 4.27), overweight and obesity (RR: 1.38; p-value = 0.293) and aged over 75 years (RR: 1.78; p-value = 0.235; when compared with participants under 40 years old). Metabolic risks were more associated to BMI and physical activity than diet (or sleeping habits). Overall, metabolic risk linked to BMI was higher in small villages than in municipalities. Seven dimensions, economic development, built (and natural) environment, social network, health care, demography, active lifestyles, and mobility, reflected the self-perceptions in place affecting the individual ability to make healthy choices. Qualitative data exposed asymmetries in surrounding environments among neighbourhoods and uncovered the natural environment and natural resources specifies as the main value of rural well-being.

Conclusions

Metabolic risk factors reflect unhealthy lifestyles and can be associated with environment contextual-dependent circumstances. People-centred approaches highlight wider socioeconomic and (natural) environmental determinants reflecting health needs, health expectations and health capability. Our community-based program and cross-disciplinary research provides insights that may improve health-promoting changes in rural neighbourhoods.