Direito Artur, Dale Leila Pfaeffli, Shields Emma, Dobson Rosie, Whittaker Robyn, Maddison Ralph
National Institute for Health Innovation, University of Auckland, 261 Morrin Rd, Auckland 1072, New Zealand.
BMC Public Health. 2014 Jun 25;14:646. doi: 10.1186/1471-2458-14-646.
There has been a recent proliferation in the development of smartphone applications (apps) aimed at modifying various health behaviours. While interventions that incorporate behaviour change techniques (BCTs) have been associated with greater effectiveness, it is not clear to what extent smartphone apps incorporate such techniques. The purpose of this study was to investigate the presence of BCTs in physical activity and dietary apps and determine how reliably the taxonomy checklist can be used to identify BCTs in smartphone apps.
The top-20 paid and top-20 free physical activity and/or dietary behaviour apps from the New Zealand Apple App Store Health & Fitness category were downloaded to an iPhone. Four independent raters user-tested and coded each app for the presence/absence of BCTs using the taxonomy of behaviour change techniques (26 BCTs in total). The number of BCTs included in the 40 apps was calculated. Krippendorff's alpha was used to evaluate interrater reliability for each of the 26 BCTs.
Apps included an average of 8.1 (range 2-18) techniques, the number being slightly higher for paid (M = 9.7, range 2-18) than free apps (M = 6.6, range 3-14). The most frequently included BCTs were "provide instruction" (83% of the apps), "set graded tasks" (70%), and "prompt self-monitoring" (60%). Techniques such as "teach to use prompts/cues", "agree on behavioural contract", "relapse prevention" and "time management" were not present in the apps reviewed. Interrater reliability coefficients ranged from 0.1 to 0.9 (Mean 0.6, SD = 0.2).
Presence of BCTs varied by app type and price; however, BCTs associated with increased intervention effectiveness were in general more common in paid apps. The taxonomy checklist can be used by independent raters to reliably identify BCTs in physical activity and dietary behaviour smartphone apps.
最近,旨在改变各种健康行为的智能手机应用程序(应用)大量涌现。虽然纳入行为改变技术(BCTs)的干预措施已被证明更有效,但尚不清楚智能手机应用在多大程度上采用了这些技术。本研究旨在调查体育活动和饮食类应用中行为改变技术的存在情况,并确定分类检查表用于识别智能手机应用中行为改变技术的可靠性。
从新西兰苹果应用商店健康与健身类别中下载排名前20的付费和前20的免费体育活动和/或饮食行为应用到一部iPhone手机上。四名独立评估者使用行为改变技术分类法(共26种行为改变技术)对每个应用进行用户测试,并对是否存在行为改变技术进行编码。计算这40个应用中包含的行为改变技术数量。使用克里彭多夫阿尔法系数评估26种行为改变技术中每种技术的评分者间信度。
应用平均包含8.1种(范围为2 - 18种)技术,付费应用(M = 9.7,范围为2 - 18种)的技术数量略高于免费应用(M = 6.6,范围为3 - 14种)。最常包含的行为改变技术是“提供指导”(占应用的83%)、“设定分级任务”(70%)和“促使自我监测”(60%)。诸如“教导使用提示/线索”、“就行为契约达成一致”、“预防复发”和“时间管理”等技术在所审查的应用中未出现。评分者间信度系数范围为0.1至0.9(平均值0.6,标准差 = 0.2)。
行为改变技术的存在因应用类型和价格而异;然而,与干预效果增强相关的行为改变技术在付费应用中通常更为常见。独立评估者可以使用分类检查表可靠地识别体育活动和饮食行为智能手机应用中的行为改变技术。