PDF(Pubmed)
Abstract：
UNASSIGNED: Strategies to increase physical activity (PA) and improve nutrition would contribute to substantial health benefits in the population, including reducing the risk of several types of cancers. The increasing accessibility of digital technologies mean that these tools could potentially facilitate the improvement of health behaviours among young people.
UNASSIGNED: We conducted a review of systematic reviews to assess the available evidence on digital interventions aimed at increasing physical activity and good nutrition in sub-populations of young people (school-aged children, college/university students, young adults only (over 18 years) and both adolescent and young adults (<25 years)).
UNASSIGNED: Searches for systematic reviews were conducted across relevant databases including KSR Evidence (www.ksrevidence.com), Cochrane Database of Systematic Reviews (CDSR) and Database of Abstracts of Reviews of Effects (DARE; CRD). Records were independently screened by title and abstract by two reviewers and those deemed eligible were obtained for full text screening. Risk of bias (RoB) was assessed with the Risk of Bias Assessment Tool for Systematic Reviews (ROBIS) tool. We employed a narrative analysis and developed evidence gap maps.
UNASSIGNED: Twenty-four reviews were included with at least one for each sub-population and employing a range of digital interventions. The quality of evidence was limited with only one of the 24 of reviews overall judged as low RoB. Definitions of \"digital intervention\" greatly varied across systematic reviews with some reported interventions fitting into more than one category (i.e., an internet intervention could also be a mobile phone or computer intervention), however definitions as reported in the relevant reviews were used. No reviews reported cancer incidence or related outcomes. Available evidence was limited both by sub-population and type of intervention, but evidence was most pronounced in school-aged children. In school-aged children eHealth interventions, defined as school-based programmes delivered by the internet, computers, tablets, mobile technology, or tele-health methods, improved outcomes. Accelerometer-measured (Standardised Mean Difference [SMD] 0.33, 95% Confidence Interval [CI]: 0.05 to 0.61) and self-reported (SMD: 0.14, 95% CI: 0.05 to 0.23) PA increased, as did fruit and vegetable intake (SMD: 0.11, 95% CI: 0.03 to 0.19) (review rated as low RoB, minimal to considerable heterogeneity across results). No difference was reported for consumption of fat post-intervention (SMD: -0.06, 95% CI: -0.15 to 0.03) or sugar sweetened beverages(SSB) and snack consumption combined post-intervention (SMD: -0.02, 95% CI:-0.10 to 0.06),or at the follow up (studies reported 2 weeks to 36 months follow-up) after the intervention (SMD:-0.06, 95% CI: -0.15 to 0.03) (review rated low ROB, minimal to substantial heterogeneity across results). Smartphone based interventions utilising Short Messaging Service (SMS), app or combined approaches also improved PA measured using objective and subjective methods (SMD: 0.44, 95% CI: 0.11 to 0.77) when compared to controls, with increases in total PA [weighted mean difference (WMD) 32.35 min per day, 95% CI: 10.36 to 54.33] and in daily steps (WMD: 1,185, 95% CI: 303 to 2,068) (review rated as high RoB, moderate to substantial heterogeneity across results). For all results, interpretation has limitations in terms of RoB and presence of unexplained heterogeneity.
UNASSIGNED: This review of reviews has identified limited evidence that suggests some potential for digital interventions to increase PA and, to lesser extent, improve nutrition in school-aged children. However, effects can be small and based on less robust evidence. The body of evidence is characterised by a considerable level of heterogeneity, unclear/overlapping populations and intervention definitions, and a low methodological quality of systematic reviews. The heterogeneity across studies is further complicated when the age (older vs. more recent), interactivity (feedback/survey vs. no/less feedback/surveys), and accessibility (type of device) of the digital intervention is considered. This underscores the difficulty in synthesising evidence in a field with rapidly evolving technology and the resulting challenges in recommending the use of digital technology in public health. There is an urgent need for further research using contemporary technology and appropriate methods.
UNASSIGNED: We conducted a review of systematic reviews to assess the available evidence on digital interventions aimed at increasing physical activity and good nutrition in sub-populations of young people (school-aged children, college/university students, young adults only (over 18 years) and both adolescent and young adults (<25 years)).
UNASSIGNED: Searches for systematic reviews were conducted across relevant databases including KSR Evidence (www.ksrevidence.com), Cochrane Database of Systematic Reviews (CDSR) and Database of Abstracts of Reviews of Effects (DARE; CRD). Records were independently screened by title and abstract by two reviewers and those deemed eligible were obtained for full text screening. Risk of bias (RoB) was assessed with the Risk of Bias Assessment Tool for Systematic Reviews (ROBIS) tool. We employed a narrative analysis and developed evidence gap maps.
UNASSIGNED: Twenty-four reviews were included with at least one for each sub-population and employing a range of digital interventions. The quality of evidence was limited with only one of the 24 of reviews overall judged as low RoB. Definitions of \"digital intervention\" greatly varied across systematic reviews with some reported interventions fitting into more than one category (i.e., an internet intervention could also be a mobile phone or computer intervention), however definitions as reported in the relevant reviews were used. No reviews reported cancer incidence or related outcomes. Available evidence was limited both by sub-population and type of intervention, but evidence was most pronounced in school-aged children. In school-aged children eHealth interventions, defined as school-based programmes delivered by the internet, computers, tablets, mobile technology, or tele-health methods, improved outcomes. Accelerometer-measured (Standardised Mean Difference [SMD] 0.33, 95% Confidence Interval [CI]: 0.05 to 0.61) and self-reported (SMD: 0.14, 95% CI: 0.05 to 0.23) PA increased, as did fruit and vegetable intake (SMD: 0.11, 95% CI: 0.03 to 0.19) (review rated as low RoB, minimal to considerable heterogeneity across results). No difference was reported for consumption of fat post-intervention (SMD: -0.06, 95% CI: -0.15 to 0.03) or sugar sweetened beverages(SSB) and snack consumption combined post-intervention (SMD: -0.02, 95% CI:-0.10 to 0.06),or at the follow up (studies reported 2 weeks to 36 months follow-up) after the intervention (SMD:-0.06, 95% CI: -0.15 to 0.03) (review rated low ROB, minimal to substantial heterogeneity across results). Smartphone based interventions utilising Short Messaging Service (SMS), app or combined approaches also improved PA measured using objective and subjective methods (SMD: 0.44, 95% CI: 0.11 to 0.77) when compared to controls, with increases in total PA [weighted mean difference (WMD) 32.35 min per day, 95% CI: 10.36 to 54.33] and in daily steps (WMD: 1,185, 95% CI: 303 to 2,068) (review rated as high RoB, moderate to substantial heterogeneity across results). For all results, interpretation has limitations in terms of RoB and presence of unexplained heterogeneity.
UNASSIGNED: This review of reviews has identified limited evidence that suggests some potential for digital interventions to increase PA and, to lesser extent, improve nutrition in school-aged children. However, effects can be small and based on less robust evidence. The body of evidence is characterised by a considerable level of heterogeneity, unclear/overlapping populations and intervention definitions, and a low methodological quality of systematic reviews. The heterogeneity across studies is further complicated when the age (older vs. more recent), interactivity (feedback/survey vs. no/less feedback/surveys), and accessibility (type of device) of the digital intervention is considered. This underscores the difficulty in synthesising evidence in a field with rapidly evolving technology and the resulting challenges in recommending the use of digital technology in public health. There is an urgent need for further research using contemporary technology and appropriate methods.
摘要：
■增加体力活动（PA）和改善营养的策略将为人口带来巨大的健康益处，包括降低几种癌症的风险。数字技术的日益普及意味着这些工具可能有助于改善年轻人的健康行为。
■我们进行了系统评价，以评估有关旨在增加年轻人亚人群（学龄儿童，学院/大学生，仅限年轻人（18岁以上）以及青少年和年轻人（<25岁））。
■在包括KSREvidence（www。ksrevidence.com)，Cochrane系统评论数据库（CDSR）和效果评论摘要数据库（DARE；CRD）。记录由两名审阅者通过标题和摘要独立筛选，并获得被认为合格的记录进行全文筛选。使用系统评价偏差风险评估工具(ROBIS)工具评估偏差风险(RoB)。我们采用了叙事分析并开发了证据差距图。
■纳入了24条评价，每个亚人群至少有一条评价，并采用了一系列数字干预措施。证据的质量有限，在24篇综述中，只有一篇被认为RoB较低。“数字干预”的定义在不同的系统评价中差异很大，一些报告的干预措施属于一个以上的类别(即，互联网干预也可以是移动电话或计算机干预)，然而,使用了相关综述中报告的定义.没有评论报告癌症发病率或相关结果。现有证据受到亚人群和干预类型的限制，但是证据在学龄儿童中最为明显。在学龄儿童电子健康干预中，定义为互联网提供的校本课程，电脑,片剂,移动技术，或者远程保健方法，改善结果。加速度计测量（标准化平均差[SMD]0.33，95％置信区间[CI]：0.05至0.61）和自我报告（SMD：0.14，95％CI：0.05至0.23）PA增加，水果和蔬菜摄入量也是如此(SMD：0.11，95%CI：0.03至0.19)(评价为低RoB，结果之间的异质性最小到相当大)。干预后的脂肪消费量（SMD：-0.06，95％CI：-0.15至0.03）或含糖饮料（SSB）和零食消费量联合干预后（SMD：-0.02，95％CI：-0.10至0.06）没有差异，或在干预后的随访（研究报告2周至36个月的随访）（SMD：-0.06，95％CI：-0.15至0.03）（评价为低ROB，结果之间的最小到实质性异质性)。基于智能手机的干预利用短消息服务(SMS)，与对照组相比，应用或组合方法也改善了使用客观和主观方法测量的PA(SMD：0.44，95%CI：0.11至0.77)，随着总PA[加权平均差(WMD)每天32.35分钟的增加，95％CI：10.36至54.33]和每日步数（大规模杀伤性武器：1,185，95％CI：303至2,068）（评估为高RoB，结果之间的中度到实质性异质性)。对于所有结果，解释在RoB和存在无法解释的异质性方面存在局限性。
■这次审查发现了有限的证据，表明数字干预措施有可能增加PA和，在较小程度上,改善学龄儿童的营养。然而,影响可能很小，而且基于不太可靠的证据。证据的特点是相当大的异质性，不清楚/重叠的人群和干预定义，系统评价的方法论质量较低。当年龄(年龄较大vs.最近)，交互性(反馈/调查与没有/更少的反馈/调查)，并考虑了数字干预的可访问性(设备类型)。这突显了在技术快速发展的领域中综合证据的困难，以及在推荐在公共卫生中使用数字技术方面所带来的挑战。迫切需要使用当代技术和适当的方法进行进一步的研究。
■我们进行了系统评价，以评估有关旨在增加年轻人亚人群（学龄儿童，学院/大学生，仅限年轻人（18岁以上）以及青少年和年轻人（<25岁））。
■在包括KSREvidence（www。ksrevidence.com)，Cochrane系统评论数据库（CDSR）和效果评论摘要数据库（DARE；CRD）。记录由两名审阅者通过标题和摘要独立筛选，并获得被认为合格的记录进行全文筛选。使用系统评价偏差风险评估工具(ROBIS)工具评估偏差风险(RoB)。我们采用了叙事分析并开发了证据差距图。
■纳入了24条评价，每个亚人群至少有一条评价，并采用了一系列数字干预措施。证据的质量有限，在24篇综述中，只有一篇被认为RoB较低。“数字干预”的定义在不同的系统评价中差异很大，一些报告的干预措施属于一个以上的类别(即，互联网干预也可以是移动电话或计算机干预)，然而,使用了相关综述中报告的定义.没有评论报告癌症发病率或相关结果。现有证据受到亚人群和干预类型的限制，但是证据在学龄儿童中最为明显。在学龄儿童电子健康干预中，定义为互联网提供的校本课程，电脑,片剂,移动技术，或者远程保健方法，改善结果。加速度计测量（标准化平均差[SMD]0.33，95％置信区间[CI]：0.05至0.61）和自我报告（SMD：0.14，95％CI：0.05至0.23）PA增加，水果和蔬菜摄入量也是如此(SMD：0.11，95%CI：0.03至0.19)(评价为低RoB，结果之间的异质性最小到相当大)。干预后的脂肪消费量（SMD：-0.06，95％CI：-0.15至0.03）或含糖饮料（SSB）和零食消费量联合干预后（SMD：-0.02，95％CI：-0.10至0.06）没有差异，或在干预后的随访（研究报告2周至36个月的随访）（SMD：-0.06，95％CI：-0.15至0.03）（评价为低ROB，结果之间的最小到实质性异质性)。基于智能手机的干预利用短消息服务(SMS)，与对照组相比，应用或组合方法也改善了使用客观和主观方法测量的PA(SMD：0.44，95%CI：0.11至0.77)，随着总PA[加权平均差(WMD)每天32.35分钟的增加，95％CI：10.36至54.33]和每日步数（大规模杀伤性武器：1,185，95％CI：303至2,068）（评估为高RoB，结果之间的中度到实质性异质性)。对于所有结果，解释在RoB和存在无法解释的异质性方面存在局限性。
■这次审查发现了有限的证据，表明数字干预措施有可能增加PA和，在较小程度上,改善学龄儿童的营养。然而,影响可能很小，而且基于不太可靠的证据。证据的特点是相当大的异质性，不清楚/重叠的人群和干预定义，系统评价的方法论质量较低。当年龄(年龄较大vs.最近)，交互性(反馈/调查与没有/更少的反馈/调查)，并考虑了数字干预的可访问性(设备类型)。这突显了在技术快速发展的领域中综合证据的困难，以及在推荐在公共卫生中使用数字技术方面所带来的挑战。迫切需要使用当代技术和适当的方法进行进一步的研究。
