Effectiveness of the mHealth intervention 'MyDayPlan' to increase physical activity: an aggregated single case approach

Feasibility Test of Personalized (N-of-1) Trials for Increasing Middle-Aged and Older Adults' Physical Activity

PURPOSE

To test the effectiveness and feasibility of a remotely delivered intervention to increase physical activity (walking) in middle-aged and older adults.

DESIGN

This study used a personalized (N-of-1) trial design.

SETTING

This study took place at a major healthcare system from November 2021 to February 2022.

SUBJECTS

Sixty adults (45-75 years, 92% female, 80% white) were recruited.

INTERVENTION

A 10-week study comprising a 2-week baseline, followed by four 2-week periods where four behavior change techniques (BCTs) - self-monitoring, goal setting, action planning, and feedback - were delivered one at a time in random order.

MEASURES

Activity was measured by a Fitbit, and intervention components delivered by email/text. Average daily steps were compared between baseline and intervention. Participants completed satisfaction items derived from the System Usability Scale and reported attitudes and opinions about personalized trials.

RESULTS

Participants rated personalized trial components as feasible and acceptable. Changes in steps between baseline and intervention were not significant, but a large heterogeneity of treatment effects existed, suggesting some participants significantly increased walking while others significantly decreased.

CONCLUSIONS

Our intervention was well-accepted but use of BCTs delivered individually did not result in a significant increase in steps. Feasibility and heterogeneity of treatment effects support adopting a personalized trial approach to optimize intervention results.

A randomized controlled mHealth trial that evaluates social comparison-oriented gamification to improve physical activity, sleep quantity, and quality of life in young adults

INTRODUCTION

The integration of gamification in mHealth interventions presents a novel approach to enhance user engagement and health outcomes. This study aims to evaluate whether comparison-oriented gamification can effectively improve various aspects of health and well-being, including physical activity, sedentary behavior, sleep, and overall quality of life among young adults.

METHODS

Potential 107 young adults (from 19 to 28 years old) participated in an 8-week trial. Participants were assigned to either a gamified mHealth intervention (LevantApp) with daily leaderboards and progress bars (n = 53, 26 % dropped-out), or a control condition without gamification (n = 52, 29 % dropped-out). Physical activity (number of steps, moderate and moderate-to-vigorous physical activity -MVPA-) and sleep quantity were measured objectively via accelerometry and subjectively using the International Physical Activity Questionnaire(IPAQ), Pittsburgh Sleep Quality Index(PSQI), Sedentary Behavior Questionnaire(SBQ), and Short Form Health Survey(SF-36).

RESULTS

This mHealth intervention with social comparison-oriented gamification significantly improved moderate physical activity to a greater extent than the control group. Additionally, the intervention group showed improvements in the number of steps, moderate physical activity, sedentary time, emotional wellbeing, and social functioning. However, no significant group by time interaction was observed. No significant differences were observed in sleep quality or quantity.

CONCLUSION

s: The LevantApp gamified mHealth intervention was effective in improving moderate physical activity, physical functioning, and role-emotional in young adults. No significant effects were found on step counts, MVPA or sleep, suggesting that while gamification can enhance specific aspects of physical activity and quality of life, its impact may vary across different outcomes.

The Synergistic Effect of Nurse Proactive Phone Calls With an mHealth App Program on Sustaining App Usage: 3-Arm Randomized Controlled Trial

BACKGROUND

Although mobile health application (mHealth app) programs have effectively promoted disease self-management behaviors in the last decade, usage rates have tended to fall over time.

OBJECTIVE

We used a case management approach led by a nurse and supported by a health-social partnership team with the aim of sustaining app usage among community-dwelling older adults and evaluated the outcome differences (i.e, self-efficacy, levels of depression, and total health service usages) between those who continued to use the app.

METHODS

This was a 3-arm randomized controlled trial. A total of 221 older adults with hypertension, diabetes, or chronic pain were randomized into 3 groups: mHealth (n=71), mHealth with interactivity (mHealth+I; n=74), and the control (n=76). The mHealth application was given to the mHealth and mHealth+I groups. The mHealth+I group also received 8 proactive calls in 3 months from a nurse to encourage use of the app. The control group received no interventions. Data were collected at preintervention (T1), postintervention (T2), and at 3 months' postintervention (T3) to ascertain the sustained effect.

RESULTS

A total of 37.8% of mHealth+I and 18.3% of mHealth group participants continued using the mHealth app at least twice per week until the end of the sixth month. The difference in app usage across the 2 groups between T2 and T3 was significant (χ²₁=6.81, P=.009). Improvements in self-efficacy (β=4.30, 95% CI 0.25-8.35, P=.04) and depression levels (β=-1.98, 95% CI -3.78 to -0.19, P=.03) from T1 to T3 were observed in the mHealth group participants who continued using the app. Although self-efficacy and depression scores improved from T1 to T2 in the mHealth+I group, the mean values decreased at T3. Health service usage decreased for all groups from T1 to T2 (β=-1.38, 95% CI -1.98 to -0.78, P<.001), with a marginal increase at T3.

CONCLUSIONS

The relatively low rates of mHealth app usage at follow-up are comparable to those reported in the literature. More work is needed to merge the technology-driven and in-person aspects of mHealth.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03878212; https://clinicaltrials.gov/ct2/show/NCT03878212.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1159/000509129.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial

BACKGROUND

The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking.

METHODS

We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months.

DISCUSSION

The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05351359, 28/04/2022).

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.

Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach

The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1-H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1-H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R 2) was found within the range of 0.224-0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.

A Theory-Informed, Personalized mHealth Intervention for Adolescents (Mobile App for Physical Activity): Development and Pilot Study

Background

Evidence suggests that physical activity (PA) during childhood and adolescence is crucial as it usually results in adequate PA levels in adulthood. Given the ubiquitous use of smartphones by adolescents, these devices may offer feasible means to reach young populations and deliver interventions aiming to increase PA participation and decrease sedentary time. To date, very few studies have reported smartphone-based interventions promoting PA for adolescents. In addition, most available fitness apps do not include the latest evidence-based content.

Objective

This paper described the systematic development of a behavior change, theory-informed Mobile App for Physical Activity intervention with personalized prompts for adolescents aged 16 to 18 years. The within-subject trial results provided the first evidence of the general effectiveness of the intervention based on the outcomes step count, sedentary time, and moderate to vigorous PA (MVPA) minutes. The effectiveness of the intervention component personalized PA prompt was also assessed.

Methods

A 4-week within-subject trial with 18 healthy adolescents aged 16 to 18 years was conducted (mean age 16.33, SD 0.57 years). After the baseline week, the participants used the Mobile App for Physical Activity intervention (Fitbit fitness tracker+app), which included a daily personalized PA prompt delivered via a pop-up notification. A paired 1-tailed t test was performed to assess the effectiveness of the intervention. Change-point analysis was performed to assess the effectiveness of a personalized PA prompt 30 and 60 minutes after prompt delivery.

Results

The results showed that the intervention significantly reduced sedentary time in adolescents during the first week of the trial (t₁₇=−1.79; P=.04; bootstrapped P=.02). This trend, although remaining positive, diminished over time. Our findings indicate that the intervention had no effect on metabolic equivalent of task–based MVPA minutes, although the descriptive increase may give reason for further investigation. Although the results suggested no overall change in heart rate–based MVPA minutes, the results from the change-point analyses suggest that the personalized PA prompts significantly increased heart rate per minute during the second week of the study (t₁₆=1.84; P=.04; bootstrapped P=.04). There were no significant increases in participants’ overall step count; however, the personalized PA prompts resulted in a marginally significant increase in step counts per minute in the second week of the study (t₁₇=1.35; P=.09; bootstrapped P=.05).

Conclusions

The results of the trial provide preliminary evidence of the benefit of the Mobile App for Physical Activity intervention for modest yet significant reductions in participants’ sedentary time and the beneficial role of personalized PA prompts. These results also provide further evidence of the benefits and relative efficacy of personalized activity suggestions for inclusion in smartphone-based PA interventions. This study provides an example of how to guide the development of smartphone-based mobile health PA interventions for adolescents.