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      Using an eHealth Intervention to Stimulate Health Behavior for the Prevention of Cognitive Decline in Dutch Adults: A Study Protocol for the Brain Aging Monitor

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          Abstract

          Background

          Internet-delivered intervention programs are an effective way of changing health behavior in an aging population. The same population has an increasing number of people with cognitive decline or cognitive impairments. Modifiable lifestyle risk factors such as physical activity, nutrition, smoking, alcohol consumption, sleep, and stress all influence the probability of developing neurodegenerative diseases such as Alzheimer’s disease.

          Objective

          This study aims to answer two questions: (1) Is the use of a self-motivated, complex eHealth intervention effective in changing multiple health behaviors related to cognitive aging in Dutch adults in the work force, especially those aged 40 and over? and (2) Does this health behavior change result in healthier cognitive aging patterns and contribute to preventing or delaying future onset of neurodegenerative syndromes?

          Methods

          The Brain Aging Monitor study uses a quasi-experimental 2-year pre-posttest design. The Brain Aging Monitor is an online, self-motivated lifestyle intervention program. Recruitment is done both in medium to large organizations and in the Dutch general population over the age of 40. The main outcome measure is the relationship between lifestyle change and cognitive aging. The program uses different strategies and modalities such as Web content, email, online newsletters, and online games to aid its users in behavior change. To build self-regulatory skills, the Brain Aging Monitor offers its users goal-setting activities, skill-building activities, and self-monitoring.

          Results

          Study results are expected to be published in early 2016.

          Conclusions

          This study will add to the body of evidence on the effectiveness of eHealth intervention programs with the combined use of state-of-the-art applied games and established behavior change techniques. This will lead to new insights on how to use behavior change techniques and theory in multidimensional lifestyle eHealth research, and how these techniques and theories apply when they are used in a setting where no professional back-end is available.

          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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          Most cited references37

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          High self-control predicts good adjustment, less pathology, better grades, and interpersonal success.

          What good is self-control? We incorporated a new measure of individual differences in self-control into two large investigations of a broad spectrum of behaviors. The new scale showed good internal consistency and retest reliability. Higher scores on self-control correlated with a higher grade point average, better adjustment (fewer reports of psychopathology, higher self-esteem), less binge eating and alcohol abuse, better relationships and interpersonal skills, secure attachment, and more optimal emotional responses. Tests for curvilinearity failed to indicate any drawbacks of so-called overcontrol, and the positive effects remained after controlling for social desirability. Low self-control is thus a significant risk factor for a broad range of personal and interpersonal problems.
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            Meta-analysis of internet-delivered interventions to increase physical activity levels

            Many internet-delivered physical activity behaviour change programs have been developed and evaluated. However, further evidence is required to ascertain the overall effectiveness of such interventions. The objective of the present review was to evaluate the effectiveness of internet-delivered interventions to increase physical activity, whilst also examining the effect of intervention moderators. A systematic search strategy identified relevant studies published in the English-language from Pubmed, Proquest, Scopus, PsychINFO, CINHAL, and Sport Discuss (January 1990 – June 2011). Eligible studies were required to include an internet-delivered intervention, target an adult population, measure and target physical activity as an outcome variable, and include a comparison group that did not receive internet-delivered materials. Studies were coded independently by two investigators. Overall effect sizes were combined based on the fixed effect model. Homogeneity and subsequent exploratory moderator analysis was undertaken. A total of 34 articles were identified for inclusion. The overall mean effect of internet-delivered interventions on physical activity was d = 0.14 (p = 0.00). Fixed-effect analysis revealed significant heterogeneity across studies (Q = 73.75; p = 0.00). Moderating variables such as larger sample size, screening for baseline physical activity levels and the inclusion of educational components significantly increased intervention effectiveness. Results of the meta-analysis support the delivery of internet-delivered interventions in producing positive changes in physical activity, however effect sizes were small. The ability of internet-delivered interventions to produce meaningful change in long-term physical activity remains unclear.
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              A behavior change model for internet interventions.

              The Internet has become a major component to health care and has important implications for the future of the health care system. One of the most notable aspects of the Web is its ability to provide efficient, interactive, and tailored content to the user. Given the wide reach and extensive capabilities of the Internet, researchers in behavioral medicine have been using it to develop and deliver interactive and comprehensive treatment programs with the ultimate goal of impacting patient behavior and reducing unwanted symptoms. To date, however, many of these interventions have not been grounded in theory or developed from behavior change models, and no overarching model to explain behavior change in Internet interventions has yet been published. The purpose of this article is to propose a model to help guide future Internet intervention development and predict and explain behavior changes and symptom improvement produced by Internet interventions. The model purports that effective Internet interventions produce (and maintain) behavior change and symptom improvement via nine nonlinear steps: the user, influenced by environmental factors, affects website use and adherence, which is influenced by support and website characteristics. Website use leads to behavior change and symptom improvement through various mechanisms of change. The improvements are sustained via treatment maintenance. By grounding Internet intervention research within a scientific framework, developers can plan feasible, informed, and testable Internet interventions, and this form of treatment will become more firmly established.
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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications Inc. (Toronto, Canada )
                1929-0748
                Oct-Dec 2015
                10 November 2015
                : 4
                : 4
                : e130
                Affiliations
                [1] 1Department of Geriatric Medicine Radboud University Medical Center NijmegenNetherlands
                [2] 2Radboud Alzheimer Center Radboud University Medical Center NijmegenNetherlands
                [3] 3Institute of Social Sciences HAN University of Applied Sciences NijmegenNetherlands
                [4] 4Department of Human Resource Management Faculty of Economics and Management HAN University of Applied Sciences NijmegenNetherlands
                [5] 5Behavioral Science Institute Faculty of Psychology Radboud University Nijmegen NijmegenNetherlands
                [6] 6Norwegian School of Hotel Mangement University of Stavanger StavangerNorway
                [7] 7Donders Insitute for Brain, Cognition and Behaviour Radboud University Nijmegen NijmegenNetherlands
                [8] 8Department of Medical Psychology Radboud University Medical Center NijmegenNetherlands
                Author notes
                Corresponding Author: Teun Aalbers teun.aalbers@ 123456radboudumc.nl
                Author information
                http://orcid.org/0000-0002-1002-5485
                http://orcid.org/0000-0002-1582-9300
                http://orcid.org/0000-0001-5002-3372
                http://orcid.org/0000-0002-9122-7387
                http://orcid.org/0000-0002-7107-8900
                http://orcid.org/0000-0003-1397-1677
                Article
                v4i4e130
                10.2196/resprot.4468
                4704922
                26554496
                92c04b52-1094-40ed-ad41-acbf8d834af8
                ©Teun Aalbers, Maria AE Baars, Li Qin, Annet de Lange, Roy PC Kessels, Marcel GM Olde Rikkert. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 10.11.2015.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 26 March 2015
                : 22 July 2015
                : 4 August 2015
                : 5 August 2015
                Categories
                Original Paper
                Original Paper

                cognition,healthy lifestyle,ehealth,internet,prevention,applied games,protocol

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