背景:为了改善从意图到改变健康行为的过渡,行动计划是一种经常使用的行为变化方法。就工具性和特殊性而言,行动计划的质量对于支持健康行为的成功改变至关重要。直到现在,关于行动计划生成的预测因素和高质量行动计划的预测因素知之甚少,因此,本研究调查了这些预测因素。
方法:使用基于网络的计算机定制干预,进行了一项随机对照试验,以改善体力活动(PA)和水果和蔬菜(FV)的消耗。在为期8周的干预期间,干预组(n=346)的参与者被指导(逐步)制定自己的行动计划,以改善他们的健康行为.人口特征,社会认知,和健康行为在基线时通过自我报告进行评估.通过干预的两个模块中的服务器注册来跟踪参与者是否生成了行动计划。
结果:40.9%和20.7%的参与者使用了有关身体活动和水果和蔬菜消费的干预措施的行动计划部分,分别。我们发现,在基线时进行体育锻炼的参与者不太可能制定有关体育锻炼的行动计划。关于制定水果和蔬菜行动计划,具有高风险感知和每天吃水果和蔬菜的强烈意愿的参与者更多地使用了这种行为的行动计划部分。最后,体育活动(96.6%)和水果和蔬菜消费(100%)的大部分行动计划都是有帮助的,大约一半的行动计划具有高度特异性(PA=69.6%/FV=59.7%).行动计划的特异性与具有关系和低水平的负面结果预期相关。
结论:风险感知和意图是使用行动计划应用的预测因素。在要求参与者制定行动计划之前,应在有关健康行为变化的干预措施中优先考虑增加改变行为的动机。这也将使干预适合无动机的人。对于那些在干预之前已经表现出期望的健康行为的参与者,行动计划可能不太相关。然而,使用指导的逐步方法来制定行动计划,从而产生了非常有用和具体的行动计划,并可能被纳入有关健康行为变化的其他干预措施。
背景:荷兰试验注册:NTR3706,ClinicalTrials.gov:NCT01909349。
BACKGROUND: In order to improve the transition from an intention to a change in health behaviour, action planning is a frequently used behavioural change method. The quality of action plans in terms of instrumentality and specificity is important in terms of supporting a successful change in health behaviour. Until now, little has been known about the predictors of action plan generation and the predictors of high quality action plans and, therefore, the current
study investigates these predictors.
METHODS: A randomised controlled
trial was conducted to improve physical activity (PA) and fruit and vegetable (FV) consumption using a web-based computer tailored intervention. During the 8-week intervention period, participants in the intervention arm (n = 346) were guided (step-by-step) to generate their own action plans to improve their health behaviours. Demographic characteristics, social cognitions, and health behaviour were assessed at baseline by means of self-reporting. Whether participants generated action plans was tracked by means of server registrations within two modules of the intervention.
RESULTS: The action planning component of the intervention regarding physical activity and fruit and vegetable consumption was used by 40.9 and 20.7 % of the participants, respectively. We found that participants who were physically active at baseline were less likely to generate action plans concerning physical activity. With regards to generating fruit and vegetable action plans, participants with a high risk perception and a strong intention to eat fruit and vegetables on a daily basis made more use of the action planning component for this behaviour. Finally, the large majority of the action plans for physical activity (96.6 %) and fruit and vegetable consumption (100 %) were instrumental and about half of the action plans were found to be highly specific (PA = 69.6 %/FV = 59.7 %). The specificity of the action plans is associated with having a relationship and low levels of negative outcome expectancies.
CONCLUSIONS: Risk perception and intention are predictors of using the application of action planning. Increasing the motivation to change behaviour should be prioritised in interventions concerning changes in health behaviour before participants are asked to generate action plans. This would also make the intervention suitable for unmotivated people. For those participants who already perform the desired health behaviour prior to the intervention, action plans might be less relevant. Nevertheless, using a guided step-by-step approach to generate action plans resulted in highly instrumental and specific action plans and might be integrated into other interventions concerning changes in health behaviour.
BACKGROUND: Netherlands
Trial Register: NTR 3706, ClinicalTrials.gov: NCT01909349 .