How do different delivery schedules of tailored web-based physical activity advice for breast cancer survivors influence intervention use and efficacy?

Short, Camille E.; Rebar, Amanda L.; James, Erica L.; Duncan, Mitch J.; Courneya, KS; Plotnikoff, Ronald C.; Crutzen, Rik; Vandelanotte, Corneel

doi:10.1007/s11764-016-0565-0

Cited by 55 publications

(96 citation statements)

References 44 publications

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“…Our feasibility data were favorable, but attrition rates were high. The study's attrition rate was comparable to previous web-based intervention studies [48][49][50], but higher than others [24,26,[29][30][31]34]. We believe that functionality challenges contributed to high attrition rates.…”

Section: Discussionsupporting

confidence: 55%

“…In particular, some participants were meeting guidelines for physical activity or dietary intake before joining the study, which may have lowered our estimated effect size between study conditions. Prior studies have observed stronger effects among survivors not meeting guidelines to lifestyle behaviors at the baseline assessment [49]. Furthermore, our attrition rate was high, and many survivors did not complete the baseline assessment, and therefore not randomized.…”

Section: Limitationsmentioning

confidence: 90%

See 1 more Smart Citation

A Lifestyle Intervention via Email in Minority Breast Cancer Survivors: Randomized Parallel-Group Feasibility Study (Preprint)

Paxton¹,

Hajek²,

Newcomb³

et al. 2017

Preprint

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Section: Discussionsupporting

confidence: 55%

Section: Limitationsmentioning

confidence: 90%

A Lifestyle Intervention via Email in Minority Breast Cancer Survivors: Randomized Parallel-Group Feasibility Study (Preprint)

Paxton¹,

Hajek²,

Newcomb³

et al. 2017

Preprint

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show abstract

“…However, it is it not known whether this affects the attrition rates of the eHealth program. Furthermore, self-regulation techniques (eg, action planning, coping planning, and monitoring) play an important role in many behavior change theories [14-16] and are therefore often implemented in eHealth interventions (eg, see [17-19]). These techniques are theory-based and elicit behavior change [20].…”

Section: Introductionmentioning

confidence: 99%

A Self-Regulation-Based eHealth Intervention to Promote a Healthy Lifestyle: Investigating User and Website Characteristics Related to Attrition

Mispel¹,

Poppe²,

Crombez³

et al. 2017

J Med Internet Res

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BackgroundeHealth interventions can reach large populations and are effective in increasing physical activity (PA) and fruit and vegetable intake. Nevertheless, the effects of eHealth interventions are overshadowed by high attrition rates. Examining more closely when users decide to leave the intervention can help eHealth developers to make informed decisions about which intervention components should be reshaped or simply removed. Investigating which users are more likely to quit an intervention can inform developers about whether and how their intervention should be adapted to specific subgroups of users.ObjectiveThis study investigated the pattern of attrition in a Web-based intervention to increase PA, fruit, and vegetable intake. The first aim was to describe attrition rates according to different self-regulation components. A second aim was to investigate whether certain user characteristics are predictors for start session completion, returning to a follow-up session and intervention completion.MethodsThe sample consisted of 549 adults who participated in an online intervention, based on self-regulation theory, to promote PA and fruit and vegetable intake, called “MyPlan 1.0.” Using descriptive analysis, attrition was explored per self-regulation component (eg, action planning and coping planning). To identify which user characteristics predict completion, logistic regression analyses were conducted.ResultsAt the end of the intervention program, there was an attrition rate of 78.2% (330/422). Attrition rates were very similar for the different self-regulation components. However, attrition levels were higher for the fulfillment of questionnaires (eg, to generate tailored feedback) than for the more interactive components. The highest amount of attrition could be observed when people were asked to make their own action plan. There were no significant predictors for first session completion. Yet, two subgroups had a lower chance to complete the intervention, namely male users (OR: 2.24, 95% CI=1.23-4.08) and younger adults (OR: 1.02, 95% CI=1.00-1.04). Furthermore, younger adults were less likely to return to the website for the first follow-up after one week (OR: 1.03, 95% CI=1.01-1.04).ConclusionsThis study informs us that eHealth interventions should avoid the use of extensive questionnaires and that users should be provided with a rationale for several components (eg, making an action plan and completing questions). Furthermore, future interventions should focus first on motivating users for the behavior change before guiding them through action planning. Though, this study provides no evidence for removal of one of the self-regulation techniques based on attrition rates. Finally, strong efforts are needed to motivate male users and younger adults to complete eHealth interventions.

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“…The sample also included a predominantly white, high SES population, possibly due to the requirement of an iPad for participation in this study, thus limiting the generalizability of the results. Despite their high SES, the participants reported similar fatigue, depression, and satisfaction with life scores when compared with other studies . Future studies may consider targeting diverse populations as women of diverse racial/ethnic backgrounds report poorer QoL in comparison with their non‐Latino white counterparts .…”

Section: Study Limitationsmentioning

confidence: 61%

Effects of physical activity on psychological well‐being outcomes in breast cancer survivors from prediagnosis to posttreatment survivorship

et al. 2018

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How do different delivery schedules of tailored web-based physical activity advice for breast cancer survivors influence intervention use and efficacy?

Abstract: This study will be useful for informing the design of future web-based interventions targeting breast cancer survivors.

Cited by 55 publications

References 44 publications

A Lifestyle Intervention via Email in Minority Breast Cancer Survivors: Randomized Parallel-Group Feasibility Study (Preprint)

A Lifestyle Intervention via Email in Minority Breast Cancer Survivors: Randomized Parallel-Group Feasibility Study (Preprint)

A Self-Regulation-Based eHealth Intervention to Promote a Healthy Lifestyle: Investigating User and Website Characteristics Related to Attrition

Effects of physical activity on psychological well‐being outcomes in breast cancer survivors from prediagnosis to posttreatment survivorship

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