A factorial randomised controlled trial to identify efficacious self-regulation techniques in an e- and m-health intervention to target an active lifestyle: study protocol

Within-Person Associations of Accelerometer-Assessed Physical Activity With Time-Varying Determinants in Older Adults: Time-Based Ecological Momentary Assessment Study

BACKGROUND

Despite the availability of physical activity (PA) interventions, many older adults are still not active enough. This might be partially explained by the often-limited effects of PA interventions. In general, health behavior change interventions often do not focus on contextual and time-varying determinants, which may limit their effectiveness. However, before the dynamic tailoring of interventions can be developed, one should know which time-dependent determinants are associated with PA and how strong these associations are.

OBJECTIVE

The aim of this study was to examine within-person associations between multiple determinants of the capability, opportunity, motivation, and behavior framework assessed using Ecological Momentary Assessment (EMA) and accelerometer-assessed light PA, moderate to vigorous PA, and total PA performed at 15, 30, 60, and 120 minutes after the EMA trigger.

METHODS

Observational data were collected from 64 healthy older adults (36/64, 56% men; mean age 72.1, SD 5.6 y). Participants were asked to answer a time-based EMA questionnaire 6 times per day that assessed emotions (ie, relaxation, satisfaction, irritation, and feeling down), the physical complaint fatigue, intention, intention, and self-efficacy. An Axivity AX3 was wrist worn to capture the participants' PA. Multilevel regression analyses in R were performed to examine these within-person associations.

RESULTS

Irritation, feeling down, intention, and self-efficacy were positively associated with subsequent light PA or moderate to vigorous PA at 15, 30, 60, or 120 minutes after the trigger, whereas relaxation, satisfaction, and fatigue were negatively associated.

CONCLUSIONS

Multiple associations were observed in this study. This knowledge in combination with the time dependency of the determinants is valuable information for future interventions so that suggestions to be active can be provided when the older adult is most receptive.

The Effects of Providing a Connected Scale in an App-Based Digital Health Program: Cross-sectional Examination

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Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity

BACKGROUND

Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase.

METHODS

To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi²-tests.

RESULTS

First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X² = 16.92, P < .001; X² = 20.34, P < .001; X² = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans.

CONCLUSIONS

The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level.

TRIAL REGISTRATION

The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach–Based Psychological Determinants

Background

Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored.

Objective

The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition.

Methods

The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used.

Results

The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%).

Conclusions

This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance.

Trial Registration

ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271

International Registered Report Identifier (IRRID)

RR2-10.1186/s13063-019-3456-7

Investigating the Influence of Personalised Gamification on Mobile Survey User Experience

Gamification in healthcare has shown to increase user motivation and treatment adherence. Personalisation strategies have the potential to create sustainable health change and user motivation. In striving for personalisation, surveys are often used as an easy tool to collect information about the user. But lengthy surveys are often tedious and demotivating, provoking bad respondent behaviour which results in a loss of data quality. Gamified surveys can enhance respondent behaviour but are labour-intensive to develop. This study explores the effect of a re-usable mobile survey application, using personalised gamification, on user experience. In an A/B study setting with 28 participants, The Hexad Player Type Framework is used to determine the player type of the user and personalise the survey accordingly. Results have shown that the overall user experience of the gamified application is higher than the traditional survey and 37.5% of gamified users perceived the duration of the survey as shorter than the actual time, compared to 20% of the respondents of the traditional survey. No significant difference in data quality has been detected, as loss in data quality remained limited in both versions. Future work should explore the influence of more elaborate game elements in a larger population.

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

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.

Interventions outside the workplace for reducing sedentary behaviour in adults under 60 years of age

BACKGROUND

Adults spend a majority of their time outside the workplace being sedentary. Large amounts of sedentary behaviour increase the risk of type 2 diabetes, cardiovascular disease, and both all-cause and cardiovascular disease mortality.

OBJECTIVES

Primary • To assess effects on sedentary time of non-occupational interventions for reducing sedentary behaviour in adults under 60 years of age Secondary • To describe other health effects and adverse events or unintended consequences of these interventions • To determine whether specific components of interventions are associated with changes in sedentary behaviour • To identify if there are any differential effects of interventions based on health inequalities (e.g. age, sex, income, employment) SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, Cochrane Database of Systematic Reviews, CINAHL, PsycINFO, SportDiscus, and ClinicalTrials.gov on 14 April 2020. We checked references of included studies, conducted forward citation searching, and contacted authors in the field to identify additional studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and cluster RCTs of interventions outside the workplace for community-dwelling adults aged 18 to 59 years. We included studies only when the intervention had a specific aim or component to change sedentary behaviour.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened titles/abstracts and full-text articles for study eligibility. Two review authors independently extracted data and assessed risk of bias. We contacted trial authors for additional information or data when required. We examined the following primary outcomes: device-measured sedentary time, self-report sitting time, self-report TV viewing time, and breaks in sedentary time.

MAIN RESULTS

We included 13 trials involving 1770 participants, all undertaken in high-income countries. Ten were RCTs and three were cluster RCTs. The mean age of study participants ranged from 20 to 41 years. A majority of participants were female. All interventions were delivered at the individual level. Intervention components included personal monitoring devices, information or education, counselling, and prompts to reduce sedentary behaviour. We judged no study to be at low risk of bias across all domains. Seven studies were at high risk of bias for blinding of outcome assessment due to use of self-report outcomes measures. Primary outcomes Interventions outside the workplace probably show little or no difference in device-measured sedentary time in the short term (mean difference (MD) -8.36 min/d, 95% confidence interval (CI) -27.12 to 10.40; 4 studies; I² = 0%; moderate-certainty evidence). We are uncertain whether interventions reduce device-measured sedentary time in the medium term (MD -51.37 min/d, 95% CI -126.34 to 23.59; 3 studies; I² = 84%; very low-certainty evidence) We are uncertain whether interventions outside the workplace reduce self-report sitting time in the short term (MD -64.12 min/d, 95% CI -260.91 to 132.67; I² = 86%; very low-certainty evidence). Interventions outside the workplace may show little or no difference in self-report TV viewing time in the medium term (MD -12.45 min/d, 95% CI -50.40 to 25.49; 2 studies; I² = 86%; low-certainty evidence) or in the long term (MD 0.30 min/d, 95% CI -0.63 to 1.23; 2 studies; I² = 0%; low-certainty evidence). It was not possible to pool the five studies that reported breaks in sedentary time given the variation in definitions used. Secondary outcomes Interventions outside the workplace probably have little or no difference on body mass index in the medium term (MD -0.25 kg/m², 95% CI -0.48 to -0.01; 3 studies; I² = 0%; moderate-certainty evidence). Interventions may have little or no difference in waist circumference in the medium term (MD -2.04 cm, 95% CI -9.06 to 4.98; 2 studies; I² = 65%; low-certainty evidence). Interventions probably have little or no difference on glucose in the short term (MD -0.18 mmol/L, 95% CI -0.30 to -0.06; 2 studies; I² = 0%; moderate-certainty evidence) and medium term (MD -0.08 mmol/L, 95% CI -0.21 to 0.05; 2 studies, I² = 0%; moderate-certainty evidence) Interventions outside the workplace may have little or no difference in device-measured MVPA in the short term (MD 1.99 min/d, 95% CI -4.27 to 8.25; 4 studies; I² = 23%; low-certainty evidence). We are uncertain whether interventions improve device-measured MVPA in the medium term (MD 6.59 min/d, 95% CI -7.35 to 20.53; 3 studies; I² = 70%; very low-certainty evidence). We are uncertain whether interventions outside the workplace improve self-reported light-intensity PA in the short-term (MD 156.32 min/d, 95% CI 34.34 to 278.31; 2 studies; I² = 79%; very low-certainty evidence). Interventions may have little or no difference on step count in the short-term (MD 226.90 steps/day, 95% CI -519.78 to 973.59; 3 studies; I² = 0%; low-certainty evidence) No data on adverse events or symptoms were reported in the included studies.

AUTHORS' CONCLUSIONS

Interventions outside the workplace to reduce sedentary behaviour probably lead to little or no difference in device-measured sedentary time in the short term, and we are uncertain if they reduce device-measured sedentary time in the medium term. We are uncertain whether interventions outside the workplace reduce self-reported sitting time in the short term. Interventions outside the workplace may result in little or no difference in self-report TV viewing time in the medium or long term. The certainty of evidence is moderate to very low, mainly due to concerns about risk of bias, inconsistent findings, and imprecise results. Future studies should be of longer duration; should recruit participants from varying age, socioeconomic, or ethnic groups; and should gather quality of life, cost-effectiveness, and adverse event data. We strongly recommend that standard methods of data preparation and analysis are adopted to allow comparison of the effects of interventions to reduce sedentary behaviour.

Acceptability and feasibility of the mHealth intervention 'MyDayPlan' to increase physical activity in a general adult population

Background

Electronic health (eHealth) and mobile health (mHealth) interventions have the potential to tackle the worldwide problem of physical inactivity. However, they often suffer from large attrition rates. Consequently, feasibility and acceptability of interventions have become important matters in the creation of e- and mHealth interventions. The aim of this study was to evaluate participants’ opinions regarding acceptability and feasibility of a self-regulation, app-based intervention called ‘MyDayPlan’. ‘MyDayPlan’ provides an innovative daily cycle providing several self-regulation techniques throughout the day that guide users towards an active lifestyle via various self-regulation techniques.

Methods

Semi-structured interviews were conducted with 20 adults after using the app for 2 weeks. A directed content analysis was performed using NVivo Software.

Results

‘MyDayPlan’ was well-received and seems to be feasible and acceptable with inactive adults. The straightforward lay out and ease of use of the app were appreciated. Furthermore, the incorporation of the techniques ‘action planning’, and ‘prompting review of behavioral goals’ was positively evaluated. However, the users gave some recommendations: implementation of activity trackers to self-monitor physical activity could be of added value. Furthermore, increasing intuitiveness by minimizing text input and providing more preprogrammed options could further increase the ease of use. Finally, users indicated that they would benefit from more guidance during the “coping planning” component (barrier identification/problem solving), for example by receiving more tailored examples.

Conclusions

Based on these findings, adaptations will be made to the ‘MyDayPlan’ app before evaluating its effectiveness. Furthermore, involving potential end users and evaluating acceptability and feasibility during the development of an e- and mHealth intervention is key. Also, creating interventions with a large ease of use and straightforward layout that provides tailored support during action and coping planning is key.