Leah L Murphy, Ben J Dascombe, Beatrice Murawski, Anna T Rayward, Wendy J Brown, Ronald C Plotnikoff, Corneel Vandelanotte, Elizabeth G Holliday, Mitch J Duncan
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The PAS groups received a pedometer and accessed a smartphone/tablet \"app\" with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0-30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0-10.0], Mid [10.1-20.0], High [20.1-30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome.</p><p><strong>Trial registration: </strong>ACTRN12617000376347; ACTRN12617000680369.</p><p><strong>Results: </strong>During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months.</p><p><strong>Conclusion: </strong>These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions.</p><p><strong>Trial registration number: </strong> ACTRN12617000376347; ACTRN12617000680369.</p>","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":" ","pages":"4"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11960321/pdf/","citationCount":"0","resultStr":"{\"title\":\"Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: secondary analyses from two randomised controlled trials.\",\"authors\":\"Leah L Murphy, Ben J Dascombe, Beatrice Murawski, Anna T Rayward, Wendy J Brown, Ronald C Plotnikoff, Corneel Vandelanotte, Elizabeth G Holliday, Mitch J Duncan\",\"doi\":\"10.1186/s44167-022-00013-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep.</p><p><strong>Methods: </strong>This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet \\\"app\\\" with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0-30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. 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引用次数: 0
摘要
背景：在为期12周的以身体活动和睡眠为目标的移动健康行为干预中，研究用户参与度与活动-睡眠模式之间的关系。方法：该二次分析使用了来自两项随机对照试验（RCT, [Synergy and Refresh]）的数据，旨在改善缺乏运动且睡眠不足的成年人的身体活动和睡眠（PAS）。两项随机对照试验均包括PAS干预组（n = 190） [Synergy n = 80；Control (CON n = 135 [Synergy n = 80；刷新n = 55])。PAS小组收到了一个计步器，并访问了一个带有行为改变策略的智能手机/平板电脑“应用程序”，以及电子邮件/短信支持。使用基于自我报告测量的活动-睡眠行为指数（ASI）对活动-睡眠模式进行量化。干预使用情况量化为干预期间应用程序使用频率、强度和持续时间的综合评分（范围：0-30）。在基线、3个月和6个月时进行评估。使用广义线性模型检查使用和ASI之间的关系。对照组和干预使用组（低[0-10.0]，中[10.1-20.0]，高[20.1-30.0]）之间的ASI差异采用调整基线值的广义线性混合模型进行检查。试验注册：ACTRN12617000376347；ACTRN12617000680369。结果：在3个月的干预期间，平均（±sd）使用评分为18.9±9.5。在3个月（回归系数[95%CI]: 0.45[0.22, 0.68]）和6个月（0.48[0.22,0.74]）时，干预组使用评分与ASI之间存在弱相关性。3个月时，中用药组（Mean [95%CI] = 57.51[53.99, 61.04]）和高用药组（60.09[57.52,62.67]）的ASI评分显著高于（优于）对照组（51.91[49.58,54.24]），低用药组（47.49[41.87,53.12]）差异不显著。只有高剂量组和对照组之间的差异在6个月时仍然存在。结论：这些发现表明，虽然较高的干预使用率与行为改善有关，但这种关联的微弱程度表明，其他因素也可能影响移动医疗干预中的行为改变。试验注册号：ACTRN12617000376347；ACTRN12617000680369。
Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: secondary analyses from two randomised controlled trials.
Background: To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep.
Methods: This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet "app" with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0-30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0-10.0], Mid [10.1-20.0], High [20.1-30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome.
Results: During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months.
Conclusion: These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions.
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