Kathrin Wunsch, Janis Fiedler, Sebastian Hubenschmid, Harald Reiterer, Britta Renner, Alexander Woll
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Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps. <strong>Objective:</strong> The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family–based setting. <strong>Methods:</strong> A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (<i>t</i><sub>0</sub>) and postintervention (<i>t</i><sub>1</sub>) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. Between <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub>, families of the intervention group (IG) used the SF app individually and collaboratively for 3 consecutive weeks, whereas families in the control group (CG) received no treatment. Four weeks following <i>t</i><sub>1</sub>, a follow-up assessment (<i>t</i><sub>2</sub>) was completed by participants, consisting of all questionnaire items to assess the stability of the intervention effects. Multilevel analyses were implemented in R (R Foundation for Statistical Computing) to acknowledge the hierarchical structure of persons (level 1) clustered in families (level 2). <strong>Results:</strong> Overall, 48 families (CG: n=22, 46%, with 68 participants and IG: n=26, 54%, with 88 participants) were recruited for the study. Two families (CG: n=1, 2%, with 4 participants and IG: n=1, 2%, with 4 participants) chose to drop out of the study owing to personal reasons before <i>t</i><sub>0</sub>. Overall, no evidence for meaningful and statistically significant increases in PA and HE levels of the intervention were observed in our physically active study participants (all <i>P</i>>.30). <strong>Conclusions:</strong> Despite incorporating behavior change techniques rooted in family life and psychological theories, the SF intervention did not yield significant increases in PA and HE levels among the participants. The results of the study were mainly limited by the physically active participants and the large age range of children and adolescents. Enhancing intervention effectiveness may involve incorporating health literacy, just-in-time adaptive interventions, and more advanced features in future app development. 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Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps. <strong>Objective:</strong> The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family–based setting. <strong>Methods:</strong> A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (<i>t</i><sub>0</sub>) and postintervention (<i>t</i><sub>1</sub>) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. 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引用次数: 0
摘要
背景:许多智能手机应用程序都以身体活动(PA)和健康饮食(HE)为目标,但有关这些应用程序对初始化和维持行为改变(尤其是儿童和青少年)的有效性的经验证据仍然有限。社会环境会影响个人行为;因此,在设计移动医疗(mHealth)应用程序时,需要考虑家庭等核心环境。研究目的本研究旨在评估以理论和证据为基础的移动医疗干预措施(称为 SMARTFAMILY [SF])在家庭集体环境中针对 PA 和 HE 的有效性。研究方法根据行为改变理论和技术开发、实施了一款智能手机应用程序,并在集体家庭环境中进行了分组随机对照试验评估。基线(t0)和干预后(t1)的测量包括作为主要结果的运动量(自我报告和加速度测量)和高血压测量(自我报告的水果和蔬菜摄入量)。次要结果(自我报告)包括内在动力、特定行为自我效能和家庭健康氛围。在 t0 至 t1 期间,干预组(IG)的家庭连续 3 周单独或合作使用 SF 应用程序,而对照组(CG)的家庭则没有接受任何治疗。t1 之后的四周,参与者完成了后续评估(t2),包括所有问卷项目,以评估干预效果的稳定性。使用 R(R 统计计算基础)进行多层次分析,以确认以家庭(第二层)为单位的个人(第一层)聚类的层次结构。结果研究共招募了 48 个家庭(CG:n=22,46%,68 人参与;IG:n=26,54%,88 人参与)。有两个家庭(CG:n=1,2%,4 人参与;IG:n=1,2%,4 人参与)由于个人原因在 t0 前选择退出研究。总体而言,在我们的体力活动研究参与者中,没有观察到干预措施对 PA 和 HE 水平有有意义和统计学意义的提高(所有 P>.30)。结论:尽管采用了植根于家庭生活和心理学理论的行为改变技术,但自立干预并未显著提高参与者的运动量和运动负荷水平。研究结果主要受限于参加体育锻炼的参与者以及儿童和青少年的年龄跨度较大。要提高干预效果,可能需要在未来的应用程序开发中纳入健康知识、及时适应性干预和更先进的功能。需要进一步开展研究,以便更好地了解干预参与情况,并根据个人情况制定移动医疗干预措施,从而提高初级预防工作的有效性。试验注册:德国临床试验注册 DRKS00010415; https://drks.de/search/en/trial/DRKS00010415
An mHealth Intervention Promoting Physical Activity and Healthy Eating in a Family Setting (SMARTFAMILY): Randomized Controlled Trial
Background: Numerous smartphone apps are targeting physical activity (PA) and healthy eating (HE), but empirical evidence on their effectiveness for the initialization and maintenance of behavior change, especially in children and adolescents, is still limited. Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps. Objective: The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family–based setting. Methods: A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (t0) and postintervention (t1) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. Between t0 and t1, families of the intervention group (IG) used the SF app individually and collaboratively for 3 consecutive weeks, whereas families in the control group (CG) received no treatment. Four weeks following t1, a follow-up assessment (t2) was completed by participants, consisting of all questionnaire items to assess the stability of the intervention effects. Multilevel analyses were implemented in R (R Foundation for Statistical Computing) to acknowledge the hierarchical structure of persons (level 1) clustered in families (level 2). Results: Overall, 48 families (CG: n=22, 46%, with 68 participants and IG: n=26, 54%, with 88 participants) were recruited for the study. Two families (CG: n=1, 2%, with 4 participants and IG: n=1, 2%, with 4 participants) chose to drop out of the study owing to personal reasons before t0. Overall, no evidence for meaningful and statistically significant increases in PA and HE levels of the intervention were observed in our physically active study participants (all P>.30). Conclusions: Despite incorporating behavior change techniques rooted in family life and psychological theories, the SF intervention did not yield significant increases in PA and HE levels among the participants. The results of the study were mainly limited by the physically active participants and the large age range of children and adolescents. Enhancing intervention effectiveness may involve incorporating health literacy, just-in-time adaptive interventions, and more advanced features in future app development. Further research is needed to better understand intervention engagement and tailor mHealth interventions to individuals for enhanced effectiveness in primary prevention efforts. Trial Registration: German Clinical Trials Register DRKS00010415; https://drks.de/search/en/trial/DRKS00010415
期刊介绍:
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636.
The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.
JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.