Socio-economic factors, mood, primary care utilization, and quality of life as predictors of intervention cessation and chronic stress in a type 2 diabetes prevention intervention (PREVIEW Study).

Teodora Handjieva-Darlenska, Wolfgang Schlicht, Roslyn Muirhead, Jennie Brand-Miller, Elli Jalo, Mikael Fogelholm, Marta P Silvestre, Sally D Poppitt, Georgi Assenov Bogdanov, Maija Huttunen-Lenz, J Alfredo Martinez, Kelly Mackintosh, Gareth Stratton, Moira A Taylor, Ian Macdonald, Tanja Adam, Anne Raben

Journal: BMC public health 2023;23(1):1666

PMID: 37649005

Free Full Text on BioMed Central View on PubMed

Plain Language Summary

Type 2 Diabetes (T2D) poses a significant global health burden, with sedentary lifestyles, unhealthy diets, and overweight as key risk factors. Consequences of T2D can be serious, encompassing physical and psychological aspects of health, thus adversely impacting on individuals’ Quality of Life (QoL). The aim of this study was to examine baseline QoL, social support, primary care utilisation, and mood as predictors of intervention cessation and chronic stress among participants with prediabetes. This study was part of the PREVIEW study, which is a 3-year randomised controlled trial comprising a 2-month weight-loss phase for all participants and a 34-month weight maintenance phase for those who lost≥8% of their baseline body mass during the initial weight loss phase. For the weight maintenance phase, participants were randomised to four different intervention arms. Results showed that participants with children, women, and those with higher socio-economic status (SES) were more likely to quit the intervention early. Factors such as lower QoL, lack of family support, and primary care utilisation were associated with cessation. Additionally, lower QoL and higher mood disturbances were linked to chronic stress. Authors concluded that public health strategies should consider individual states and life situations to improve the completion rates of preventive interventions. The design of such interventions should incorporate flexibility to cater to individual needs.

Abstract

BACKGROUND

Sedentary lifestyle and unhealthy diet combined with overweight are risk factors for type 2 diabetes (T2D). Lifestyle interventions with weight-loss are effective in T2D-prevention, but unsuccessful completion and chronic stress may hinder efficacy. Determinants of chronic stress and premature cessation at the start of the 3-year PREVIEW study were examined.

METHODS

Baseline Quality of Life (QoL), social support, primary care utilization, and mood were examined as predictors of intervention cessation and chronic stress for participants aged 25 to 70 with prediabetes (n = 2,220). Moderating effects of sex and socio-economic status (SES) and independence of predictor variables of BMI were tested.

RESULTS

Participants with children, women, and higher SES quitted intervention earlier than those without children, lower SES, and men. Lower QoL, lack of family support, and primary care utilization were associated with cessation. Lower QoL and higher mood disturbances were associated with chronic stress. Predictor variables were independent (p ≤ .001) from BMI, but moderated by sex and SES.

CONCLUSIONS

Policy-based strategy in public health should consider how preventive interventions may better accommodate different individual states and life situations, which could influence intervention completion. Intervention designs should enable in-built flexibility in delivery enabling response to individual needs.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01777893.

Address: Institute of Nursing Science, University of Education Schwäbisch Gmünd, Oberbettringerstraße 200, 73525, Schwäbisch Gmünd, Germany. [email protected].; Department of Nutrition, Exercise and Sports, University of Copenhagen, 1958, Frederiksberg, Denmark.; Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Denmark.; Department of Nutrition and Movement Sciences, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands.; MRC/ARUK Centre for Musculoskeletal Ageing Research, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, University of Nottingham, School of Life Sciences, Nottingham, NG7 2UH, UK.; Nestle Institute of Health Sciences, Nestle Research, Route du Jorat 57, 1000, Lausanne 26, CH, Switzerland.; University of Nottingham, School of Life Sciences, Nottingham, NG7 2UH, UK.; Sport and Exercise Sciences, Swansea University, Swansea, West Glamorgan, UK.; Applied Sports, Technology, Exercise and Medicine Research Centre, Swansea University, Swansea, West Glamorgan, UK.; Department of Medicine and Endocrinology, University of Valladolid, Valladolid, Spain.; CIBER Fisiopatología Obesidad Y Nutrición (CIBERobn), Instituto de Salud Carlos III, IMDEAfood Madrid, 28029, Madrid, Spain.; Department of Pharmacology and Toxicology, Medical University of Sofia, Sofia, 1000, Bulgaria.; Department of Medicine, University of Auckland, Human Nutrition Unit, School of Biological Sciences, Auckland, 1024, New Zealand.; Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland, 1024, New Zealand.; Nutrition & Metabolism, CINTESIS, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.; Department of Food and Nutrition, University of Helsinki, 00014, Helsinki, Finland.; School of Life and Environmental Sciences and Charles Perkins Centre, University of Sydney, Camperdown, NSW, 2006, Australia.; Department of Exercise and Health Sciences, University of Stuttgart, 70569, Stuttgart, Germany.

DOI: 10.1186/s12889-023-16569-9