scholarly journals The development of ‘Make One Small Change’: an e-health intervention for the workplace developed using the Person-Based Approach

2019 ◽  
Vol 5 ◽  
pp. 205520761985285
Author(s):  
Ana Howarth ◽  
Jose Quesada ◽  
Todd Donnelly ◽  
Peter R Mills
Keyword(s):  
Digital Health ◽  
Intervention Development ◽  
Health Intervention ◽  
Health Interventions ◽  
Self Monitoring ◽  
User Input ◽  
Online Behaviour ◽  
Guiding Principles ◽  
Small Change ◽  
Evidenced Based

Background The application of digital health interventions is widespread and many employers are implementing employee e-health programs. Intended to enhance productivity by increasing wellbeing, workplace interventions often lack evidence of effectiveness and have low rates of retention. Use of the person-based approach (PBA) is one solution, which offers a systematic framework for developing effective digital health interventions. This paper describes the application of the PBA to the development of ‘Make one small change’ (Cigna MSC™), an online behaviour change system for lifestyle habits focused on resilience, movement, eating and sleep. Method and results The development of Cigna MSC™ took place over four stages with colleagues ( n = 79) across Cigna globally. Application of the PBA entailed using high amounts of qualitative data to inform development and a cyclical process of ‘listening, applying and delivering’ was adhered to throughout. Early stages involved review of current literature and the collection of feedback in relation to existing interventions. Combined, results revealed key intervention development issues that were then used to form guiding principles. Guiding principles ensured intervention objectives translated into relevant design features. The final stages of evaluation included testing images, text and content approaches. Feedback dictated that the intervention should be fun, easy to use and include milestones for self-monitoring. The resulting version was finalised and made ready to pilot so future analysis can be made in relation to real-world engagement and the embedded evaluative content can be used to provide evidence of intervention effectiveness. Conclusions Using the PBA, which was evolved specifically to improve development of digital interventions, resulted in a workplace intervention embedded with in-depth user input combined with evidenced-based theory. This paper illustrates how using a rigorous methodology can drive the creation of an effective digital health intervention that uniquely allows for refinement at each stage.

scholarly journals Social Cognitive Theories and Electronic Health Design: Scoping Review (Preprint)

2018 ◽  
Author(s):  
Patricia Grace-Farfaglia
Keyword(s):  
Scoping Review ◽  
Social Cognitive ◽  
Digital Health ◽  
Health Intervention ◽  
Health Interventions ◽  
Design Features ◽  
Social Design ◽  
Web Based ◽  
Cognitive Theories ◽  
Social Cognitive Theories

BACKGROUND There are several social cognitive theories (SCTs) and models that support platform design in electronic health (eHealth) promotion trials. The rationale for this scoping review was to determine how social design features (informational aid, expressive support, gaming, and tailored content) are used to promote self-efficacy, engagement, knowledge, and behavior change. OBJECTIVE This study aimed to review a broad spectrum of digital health interventions in the literature seeking trials that use SCTs for the design of eHealth applications. METHODS The author conducted a systematic scoping review of 161 Web-based health interventions from published randomized clinical trials using 1 or more tools to address the social cognitive determinants in their website design from January 2006 to April 2016. An iterative approach was used in the selection of studies and data extraction. The studies were analyzed for quality and coded for type of social design features employed. RESULTS Expressive interaction tools were found in 48.6% (54/111) of studies categorized as a strong recommendation by the Joanna Briggs Institute criteria. Overall, less than half of the studies addressed participant social support and motivational needs (43.8%). The vast majority of studies (100%) relied on the use of the Web for delivery of informational aid and tailored content for the individual participant (75.9%). CONCLUSIONS This review fills a research gap by linking social theory to Web strategy to improve the impact and sustainability of eHealth interventions. A Digital Health Intervention Model was developed to provide a framework to enhance future Web-based health intervention design and execution.

scholarly journals A Next Generation Assets-Based Public Health Intervention Development Model: The Public as Innovators (Preprint)

2018 ◽  
Author(s):  
Christiana von Hippel
Keyword(s):  
Public Health ◽  
Public Health Intervention ◽  
Intervention Development ◽  
Health Intervention ◽  
Health Interventions ◽  
Development Model ◽  
User Innovation ◽  
The Public ◽  
Wide Range ◽  
Health Experts

UNSTRUCTURED In the public health field, the design of interventions has long been considered to be the province of public health experts. In this paper, I explore an important complement to the traditional model: the design, prototyping, and implementation of innovative public health interventions by the public (users) themselves. These user interventions can then be incorporated by public health experts, who in turn design, support, and implement improvements and diffusion strategies as appropriate for the broader community. The context and support for this proposed new public health intervention development model builds upon user innovation theory, which has only recently begun to be applied to research and practice in medicine and provides a completely novel approach in the field of public health. User innovation is an assets-based model in which end users of a product, process, or service are the locus of innovation and often more likely than producers to develop the first prototypes of new approaches to problems facing them. This occurs because users often possess essential context-specific information about their needs paired with the motivation that comes from directly benefiting from any solutions they create. Product producers in a wide range of fields have, in turn, learned to profit from the strengths of these user innovators by supporting their grass-roots, leading-edge designs and field experiments in various ways. I explore the promise of integrating user-designed and prototyped health interventions into a new assets-based public health intervention development model. In this exploration, a wide range of lead user methods and positive deviance studies provide examples for identification of user innovation in populations, community platforms, and healthcare programs. I also propose action-oriented and assets-based next steps for user-centered public health research and practice to implement this new model. This approach will enable us to call upon the strengths of the communities we serve as we develop new methods and approaches to more efficiently and effectively intervene on the varied complex health problems they face.

scholarly journals Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

2020 ◽  
Author(s):  
Helene Schroé ◽  
Delfien Van Dyck ◽  
Annick De Paepe ◽  
Louise Poppe ◽  
Wen Wei Loh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Behaviour Change ◽  
Action Planning ◽  
Health Intervention ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Self Monitoring ◽  
Coping Planning ◽  
The Impact

Abstract BackgroundE- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB).MethodsIn a 2(action planning: present vs absent) x2(coping planning: present vs absent) x2(self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335,age = 35.8,28.1% men) or SB (n = 138,age = 37.8,37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB.ResultsFirst, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735,p = 0.007) and reduced SB (t=-2.573,p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302,p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8,849,p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918,p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590,p = 0.014;x2 = 17.722,p < 0.001;x2 = 4.552,p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389,p = 0.031) and self-monitoring alone (x2 = 8.858,p = 003), respectively.ConclusionsThis study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registrationThis study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017, http://clinicaltrials.gov/ct2/show/NCT03274271

scholarly journals Black American and Latinx Parent/Caregiver Participation in Digital Health Obesity Interventions for Children: A Systematic Review

2021 ◽  
Vol 3 ◽  
Author(s):  
Jennifer Sanchez-Flack ◽  
Joanna Buscemi ◽  
Alexander O'Donnell ◽  
Margaret H. Clark Withington ◽  
Marian Fitzgibbon
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Obesity Prevention ◽  
Weight Status ◽  
Digital Health ◽  
Health Intervention ◽  
Health Interventions ◽  
Black American ◽  
Eligibility Criteria ◽  
Prevention Interventions

Parents/caregivers are consistently described as integral targets given their influential role in supporting and managing behaviors such as diet and physical activity. Identifying effective obesity prevention interventions to enhance and sustain parent participation is needed. Digital obesity prevention interventions are a promising strategy to improve parent/caregiver participation. Digital health interventions demonstrate acceptable participation and retention among parents/caregivers. However, our understanding of digital obesity prevention interventions targeting Black American and Latinx parents/caregivers is limited. This systematic review aims to identify Black American and Latinx parents'/caregivers' level of participation in digital obesity prevention and treatment interventions and determine the relationship between parent/caregiver participation and behavioral and weight status outcomes. This review adheres to PRISMA guidelines and is registered in PROSPERO. Eligibility criteria include: intervention delivered by digital technology, targeted Black American and Latinx parents/caregivers of young children (2–12 years), reported parent/caregiver participation outcomes, targeted diet or physical activity behaviors, and randomized controlled trial study design. Searches were conducted in September 2020 in ERIC, PsychInfo, PubMed, and Web of Science. Initial searches returned 499 results. Four reviewers screened records against eligibility criteria and 12 studies met inclusion criteria. Across all studies, parent/caregiver participation ranged from low to high. Only half of the included studies reported significant improvements in behavioral or weight status outcomes for parents/caregivers and/or children. Of these studies, three reported high parental/caregiver participation rates, and three reported high satisfaction rates. These findings suggest that participation and satisfaction may impact behavior change and weight status. The small number of studies indicates that additional research is needed to determine whether engagement or other factors predict responsiveness to the digital health intervention. Our results lay the groundwork for developing and testing future digital health interventions with the explicit goal of parental/caregiver participation and considers the need to expand our digital health intervention research methodologies to address obesity inequities among diverse families better.

scholarly journals Associations Between Digital Health Intervention Engagement, Physical Activity, and Sedentary Behavior: Systematic Review and Meta-analysis

10.2196/23180 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23180
Author(s):  
Matthew Mclaughlin ◽  
Tessa Delaney ◽  
Alix Hall ◽  
Judith Byaruhanga ◽  
Paul Mackie ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Subjective Experience ◽  
Digital Health ◽  
Meta Analysis ◽  
Grey Literature ◽  
Positive Association ◽  
Health Intervention ◽  
Health Interventions ◽  
Vote Counting

Background The effectiveness of digital health interventions is commonly assumed to be related to the level of user engagement with the digital health intervention, including measures of both digital health intervention use and users’ subjective experience. However, little is known about the relationships between the measures of digital health intervention engagement and physical activity or sedentary behavior. Objective This study aims to describe the direction and strength of the association between engagement with digital health interventions and physical activity or sedentary behavior in adults and explore whether the direction of association of digital health intervention engagement with physical activity or sedentary behavior varies with the type of engagement with the digital health intervention (ie, subjective experience, activities completed, time, and logins). Methods Four databases were searched from inception to December 2019. Grey literature and reference lists of key systematic reviews and journals were also searched. Studies were eligible for inclusion if they examined a quantitative association between a measure of engagement with a digital health intervention targeting physical activity and a measure of physical activity or sedentary behavior in adults (aged ≥18 years). Studies that purposely sampled or recruited individuals on the basis of pre-existing health-related conditions were excluded. In addition, studies were excluded if the individual engaging with the digital health intervention was not the target of the physical activity intervention, the study had a non–digital health intervention component, or the digital health interventions targeted multiple health behaviors. A random effects meta-analysis and direction of association vote counting (for studies not included in meta-analysis) were used to address objective 1. Objective 2 used vote counting on the direction of the association. Results Overall, 10,653 unique citations were identified and 375 full texts were reviewed. Of these, 19 studies (26 associations) were included in the review, with no studies reporting a measure of sedentary behavior. A meta-analysis of 11 studies indicated a small statistically significant positive association between digital health engagement (based on all usage measures) and physical activity (0.08, 95% CI 0.01-0.14, SD 0.11). Heterogeneity was high, with 77% of the variation in the point estimates explained by the between-study heterogeneity. Vote counting indicated that the relationship between physical activity and digital health intervention engagement was consistently positive for three measures: subjective experience measures (2 of 3 associations), activities completed (5 of 8 associations), and logins (6 of 10 associations). However, the direction of associations between physical activity and time-based measures of usage (time spent using the intervention) were mixed (2 of 5 associations supported the hypothesis, 2 were inconclusive, and 1 rejected the hypothesis). Conclusions The findings indicate a weak but consistent positive association between engagement with a physical activity digital health intervention and physical activity outcomes. No studies have targeted sedentary behavior outcomes. The findings were consistent across most constructs of engagement; however, the associations were weak.

scholarly journals A Digital Health Intervention for Weight Management for Latino Families Living in Rural Communities: Perspectives and Lessons Learned During Development

10.2196/20679 ◽  
2020 ◽  
Vol 4 (8) ◽  
pp. e20679
Author(s):  
Zenong Yin ◽  
Vanessa L Errisuriz ◽  
Martin Evans ◽  
Devasena Inupakutika ◽  
Sahak Kaghyan ◽  
...  
Keyword(s):  
Health Care ◽  
Weight Management ◽  
Access To Health Care ◽  
Underserved Populations ◽  
Digital Health ◽  
Intervention Strategy ◽  
Health Intervention ◽  
Lessons Learned ◽  
Health Interventions ◽  
Rural Residents

Rural residents face numerous challenges in accessing quality health care for management of chronic diseases (eg, obesity, diabetes), including scarcity of health care services and insufficient public transport. Digital health interventions, which include modalities such as internet, smartphones, and monitoring sensors, may help increase rural residents’ access to health care. While digital health interventions have become an increasingly popular intervention strategy to address obesity, research examining the use of technological tools for obesity management among rural Latino populations is limited. In this paper, we share our experience developing a culturally tailored, interactive health intervention using digital technologies for a family-oriented, weight management program in a rural, primarily Latino community. We describe the formative research that guided the development of the intervention, discuss the process of developing the intervention technologies including issues of privacy and data security, examine the results of a pilot study, and share lessons learned. Our experience can help others design user-centered digital health interventions to engage underserved populations in the uptake of healthy lifestyle and disease management skills.

scholarly journals Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

2020 ◽  
Author(s):  
Helene Schroé ◽  
Delfien Van Dyck ◽  
Annick De Paepe ◽  
Louise Poppe ◽  
Wen Wei Loh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Behaviour Change ◽  
Action Planning ◽  
Health Intervention ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Self Monitoring ◽  
Coping Planning ◽  
The Impact

Abstract Background E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs.Methods In a 2 (action planning: present vs absent) x2 (coping planning: present vs absent) x2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n=335,age=35.8,28.1% men) or SB (n=138,age=37.8,37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. Results First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t=2.735,p=0.007) and reduced SB (t=-2.573,p=0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t=2.302,p=0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2=8,849,p=0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2=3.918,p=0.048). To increase PA, action planning was always more effective in combination with coping planning (x2=5.590,p=0.014;x2=17.722,p<0.001;x2=4.552,p=0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2=4.389,p=0.031) and self-monitoring alone (x2=8.858,p=003), respectively.Conclusions This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registration This study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017, http://clinicaltrials.gov/ct2/show/NCT03274271

scholarly journals Assessing Therapeutic Alliance in the Context of mHealth Interventions for Mental Health Problems: Development of the Mobile Agnew Relationship Measure (mARM) Questionnaire (Preprint)

2017 ◽  
Author(s):  
Katherine Berry ◽  
Amy Salter ◽  
Rohan Morris ◽  
Susannah James ◽  
Sandra Bucci
Keyword(s):  
Mental Health ◽  
Therapeutic Alliance ◽  
Digital Health ◽  
Health Intervention ◽  
Health Interventions ◽  
Health Staff ◽  
Service Users ◽  
Face To Face ◽  
Mental Health Interventions ◽  
Stage 1

BACKGROUND Digital health interventions in the form of smartphone apps aim to improve mental health and enable people access to support as and when needed without having to face the stigma they may experience in accessing services. If we are to evaluate mobile health (mHealth) apps and advance scientific understanding, we also need tools to help us understand in what ways mHealth interventions are effective or not. The concept of therapeutic alliance, a measure of the quality of the relationship between a health care provider and a service user, is a key factor in explaining the effects of mental health interventions. The Agnew Relationship Measure (ARM) is a well-validated measure of therapeutic alliance in face-to-face therapy. OBJECTIVE This study presented the first attempt to (1) explore service users’ views of the concept of relationship within mHealth mental health interventions and (2) adapt a well-validated face-to-face measure of therapeutic alliance, the Agnew Relationship Measure (ARM), for use with mHealth interventions. METHODS In stage 1, we interviewed 9 mental health service users about the concept of therapeutic alliance in the context of a digital health intervention and derived key themes from interview transcripts using thematic analysis. In stage 2, we used rating scales and open-ended questions to elicit views from 14 service users and 10 mental health staff about the content and face validity of the scale, which replaced the word “therapist” with the word “app.” In stage 3, we used the findings from stages 1 and 2 to adapt the measure with the support of a decision-making algorithm about which items to drop, retain, or adapt. RESULTS Findings suggested that service users do identify relationship concepts when thinking about mHealth interventions, including forming a bond with an app and the ability to be open with an app. However, there were key differences between relationships with health professionals and relationships with apps. For example, apps were not as tailored and responsive to each person’s unique needs. Furthermore, apps were not capable of portraying uniquely human-like qualities such as friendliness, collaboration, and agreement. We made a number of changes to the ARM that included revising 16 items; removing 4 items due to lack of suitable alternatives; and adding 1 item to capture a key theme derived from stage 1 of the study (“The app is like having a member of my care team in my pocket”). CONCLUSIONS This study introduces the mHealth version of the ARM, the mARM, that has good face and content validity. We encourage researchers to include this easy-to-use tool in digital health intervention studies to gather further data about its psychometric properties and advance our understanding of how therapeutic alliance influences the efficacy of mHealth interventions. CLINICALTRIAL International Standard Randomized Controlled Trial Number (ISRCTN) 34966555; http://www.isrctn.com/ISRCTN34966555 (Archived by WebCite at http://www.webcitation.org/6ymBVwKif)

scholarly journals Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Helene Schroé ◽  
Delfien Van Dyck ◽  
Annick De Paepe ◽  
Louise Poppe ◽  
Wen Wei Loh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Behaviour Change ◽  
Action Planning ◽  
Health Intervention ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Self Monitoring ◽  
Coping Planning ◽  
The Impact

Abstract Background E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs. Methods In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. Results First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = − 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively. Conclusions This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future. Trial registration This study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017.

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