Changing Behavioral Lifestyle Risk Factors Related to Cognitive Decline in Later Life Using a Self-Motivated eHealth Intervention in Dutch Adults
Changing behavioral lifestyle risk factors related to cognitive decline in later life using a self-motivated eHealth intervention in Dutch adults
Number of pages
SourceJournal of Medical Internet Research, 18, 6, (2016), pp. e171, article e171
Article / Letter to editor
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SW OZ DCC NRP
Journal of Medical Internet Research
SubjectRadboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 1: Alzheimer`s disease DCMN: Donders Center for Medical Neuroscience; DI-BCB_DCC_Theme 3: Plasticity and Memory; Neuropsychology and rehabilitation psychology; Neuro- en revalidatiepsychologie
BACKGROUND: Our labor force is aging, but aged workers are not yet coached on how to stay cognitively fit for the job. OBJECTIVE: In this study, we tested whether a self-motivated, complex eHealth intervention could improve multiple health-related behaviors that are associated with cognitive aging among working Dutch adults. METHODS: This quasi-experimental prospective study with a pre-post design was conducted with employees of Dutch medium to large companies. All employees with Internet access, a good understanding of the Dutch language, and who provided digital informed consent were eligible to participate. In total, 2972 participants (2110/2972, 71.11% females) with a mean (standard deviation, SD) age of 51.8 (SD 12.9) years were recruited; 2305 became active users of the intervention, and 173 completed the 1-year follow-up. This self-motivated eHealth lifestyle intervention stimulates participants to set personally relevant, monthly health behavior change goals using Goal Attainment Scaling and to realize these goals by implementing behavior change techniques grounded in behavior change theory. The primary outcomes were the goal-setting success rate and the change in overall lifestyle score from baseline to the 1-year follow-up; the score was based on physical activity, diet, smoking, alcohol, sleep, and stress scores. The secondary outcomes were the changes in body weight, body mass index, specific lifestyle characteristics, and website usage. RESULTS: A total of 1212 participants set 2620 behavior change goals; 392 participants assessed 1089 (1089/2288, 47.59%) goals and successfully achieved 422 (422/1089, 38.75%) of these goals. Among the goal-setting participants in follow-up, this led to a +0.81-point improvement (95% CI 0.49-1.13, P<.001) in overall lifestyle (d=0.32) and weight loss of 0.62 kg (95% CI -1.16 to -0.07, P=.03). These participants also showed significant improvement in 8 out of 11 specific lifestyle components. CONCLUSIONS: Among an adult Dutch population, this eHealth intervention resulted in lifestyle changes in behavioral risk factors associated with cognitive decline, and these improvements lasted over the period of 1 year. Given the general aging of our workforce, this eHealth intervention opens new avenues for the widespread use of cost-effective self-motivated prevention programs aimed at prevention of early-stage cognitive decline and more self-management of their risk factors. TRIAL REGISTRATION: Nederlands Trial Register: NTR4144; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4144 (Archived by WebCite at http://www.webcitation.org/6cZzwZSg3).
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