The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial

The effect of multiple nursing interventions on physical activity and health promotion in the elderly: A randomized controlled trial

Health promotion and physical activity practices for the elderly are important but still not sufficient. This study aimed to determine if the effects of education through home visit, mobile application (Google Fit), group walking, and phone reminder interventions on health promotion and physical activity in the elderly. The study has a randomized controlled design with pretest-posttest interventions and a control group. The study sample consisted of 66 elderly individuals, 33 of whom were assigned to the intervention group and 33 to the control group by randomization. Multiple nursing interventions, including education through home visit, mobile application (Google Fit) use, group walking, and phone reminder messages, were implemented by the researcher in line with Pender's Health Promotion Model. The first month score of the intervention group participants on the healthy lifestyle habits subscale was higher than that of the control group participants (all p-values < 0.05). The effect size was 0.577 (high effect size) and the confidence interval was 0.085-1.07. It can be recommended that interventions with reminders and repetitions regarding healthy lifestyle behaviors to health promotion in the elderly be planned.

Older Veteran Experiences of using Technology during a Multicomponent Telerehabilitation Program: A Convergent Mixed Methods Study

Less than half of U.S. veterans meet physical activity guidelines. Even though changing physical activity can be challenging, prior studies have demonstrated that it is possible. Older adults are using technology to aid in such behavior change. However, research that explores the mechanisms of how technology can aid in behavior change is lacking, especially among older veterans. Thus, the purpose of this secondary, convergent mixed methods study was to explore how older veterans engaged with technologies that were used during a multicomponent telerehabilitation program. The study included veterans aged ≥60 years with ≥3 chronic medical conditions and physical function limitation. Quantitative data were collected during the primary randomized controlled trial, and qualitative data were collected via individual interviews following completion of the telerehabilitation program. Data were merged and then analyzed by high vs. low technology engagement groups. Key similarities and differences between groups were identified in five domains: satisfaction with the virtual environment, coping self-efficacy, perceptions of Annie (automated text messaging platform), experiences using the activity monitor, and self-management skills. Findings can help inform the successful integration of similar technologies into physical rehabilitation programs. Further study is warranted to understand additional factors and mechanisms that influence technology engagement in telerehabilitation.

The moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults

This study aimed to assess the moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults. In the Active for Life trial, 243 inactive adults aged 65+ years were randomised into: (1) tailoring + Fitbit (n = 78), (2) tailoring-only (n = 96) or (3) control (n = 69). For the current study, participants were categorised as having higher (n = 146) or lower (n = 97) social support based on the Duke Social Support Index (DSSI_10). Moderate-to-vigorous physical activity (MVPA) was measured through accelerometers at baseline and post-intervention. A linear mixed model analysis demonstrated that among participants with lower social support, the tailoring + Fitbit participants, but not the tailoring only participants increased their MVPA more than the control. Among participants with higher social support, no differences in MVPA changes were observed between groups. Web-based computer-tailored interventions with Fitbit integration may be more effective in older adults with lower levels of social support.

The Effectiveness of Strategies to Improve User Engagement With Digital Health Interventions Targeting Nutrition, Physical Activity, and Overweight and Obesity: Systematic Review and Meta-Analysis

BACKGROUND

Digital health interventions (DHIs) are effective in improving poor nutrition, physical inactivity, overweight and obesity. There is evidence suggesting that the impact of DHIs may be enhanced by improving user engagement. However, little is known about the overall effectiveness of strategies on engagement with DHIs.

OBJECTIVE

This study aims to assess the overall effectiveness of strategies to improve engagement with DHIs targeting nutrition, physical activity, and overweight or obesity and explore associations between strategies and engagement outcomes. The secondary aim was to explore the impact of these strategies on health risk outcomes.

METHODS

The MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, Scopus, and Academic Source Complete databases were searched up to July 24, 2023. Eligible studies were randomized controlled trials that evaluated strategies to improve engagement with DHIs and reported on outcomes related to DHI engagement (use or user experience). Strategies were classified according to behavior change techniques (BCTs) and design features (eg, supplementary emails). Multiple-variable meta-analyses of the primary outcomes (usage and user experience) were undertaken to assess the overall effectiveness of strategies. Meta-regressions were conducted to assess associations between strategies and use and user experience outcomes. Synthesis of secondary outcomes followed the "Synthesis Without Meta-Analysis" guidelines. The methodological quality and evidence was assessed using the Cochrane risk-of-bias tool, and the Grading of Recommendations Assessment, Development, and Evaluation tool respectively.

RESULTS

Overall, 54 studies (across 62 publications) were included. Pooled analysis found very low-certainty evidence of a small-to-moderate positive effect of the use of strategies to improve DHI use (standardized mean difference=0.33, 95% CI 0.20-0.46; P<.001) and very low-certainty evidence of a small-to-moderate positive effect on user experience (standardized mean difference=0.29, 95% CI 0.07-0.52; P=.01). A significant positive association was found between the BCTs social support (effect size [ES]=0.40, 95% CI 0.14-0.66; P<.001) and shaping knowledge (ES=0.39, 95% CI 0.03-0.74; P=.03) and DHI use. A significant positive association was found among the BCTs social support (ES=0.70, 95% CI 0.18-1.22; P=.01), repetition and substitution (ES=0.29, 95% CI 0.05-0.53; P=.03), and natural consequences (ES=0.29, 95% CI 0.05-0.53; P=.02); the design features email (ES=0.29, 95% CI 0.05-0.53; P=.02) and SMS text messages (ES=0.34, 95% CI 0.11-0.57; P=.01); and DHI user experience. For secondary outcomes, 47% (7/15) of nutrition-related, 73% (24/33) of physical activity-related, and 41% (14/34) of overweight- and obesity-related outcomes reported an improvement in health outcomes.

CONCLUSIONS

Although findings suggest that the use of strategies may improve engagement with DHIs targeting such health outcomes, the true effect is unknown because of the low quality of evidence. Future research exploring whether specific forms of social support, repetition and substitution, natural consequences, emails, and SMS text messages have a greater impact on DHI engagement is warranted.

TRIAL REGISTRATION

PROSPERO CRD42018077333; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=77333.

The Effects of ICT-Based Interventions on Physical Mobility of Older Adults: A Systematic Literature Review and Meta-Analysis

Systematic literature review and meta-analysis were conducted to integrate and analyze intervention studies dealing with the effects of information and communications technology- (ICT-) based interventions on the physical mobility of older adults in the community. The PubMed/MEDLINE, Embase, CINAHL, and Cochrane CENTRAL databases were searched for studies published from January 2000 to December 2022. We used the Risk of Bias 2 (RoB 2) tool to evaluate the quality of the randomized controlled studies in the systematic review. The meta-analysis was performed using a random-effects model. The model was used to calculate the standardized mean difference (SMD) and 95% confidence interval (CI) for both effect measures. I2 tests were used to measure the presence of heterogeneity. Thirty-seven randomized controlled trials were included (2,419 intervention participants), of which 23 were included in the meta-analysis. ICT interventions significantly improved Timed Up and Go (TUG) as a marker of physical mobility variable in older adults (SMD = -0.33, 95% CI: -0.57 to -0.10, p=0.005, I2 = 74.7%). A sensitivity analysis was performed on subgroups, and interventions were found to be effective in improving TUG in the exergame group (SMD = -0.40, 95% CI: -0.72 to -0.08, p < 0.001, I2 = 75.0%) and in the exergame with virtual reality (VR) group (SMD = -0.33, 95% CI: -1.01 to 0.35, p < 0.001, I2 = 91.0%) but both groups showed high heterogeneity. A meta-analysis was also performed on Short Physical Performance Battery (SPPB) but statistically significant results were not found (SMD = -0.19, 95% CI: -0.61 to 0.23, p=0.375, I2 = 87.7%). For the Berg Balance Scale (BBS), the post-intervention scores were significantly better than baseline (SMD = 1.52, 95% CI: 0.48 to 2.57, p=0.004, I2 = 93.5%). However, the number of studies included in the meta-analysis was small and heterogeneity was high, so follow-up studies are needed. This study confirmed that exergames, telecommunication, e-health, information applications, and robots were used as effective ICT-based interventions for improving the physical mobility of older adults. It is necessary to develop and apply more diverse ICT-based interventions that will prevent impairments of mobility and encourage older adults to live more independently, with a higher quality of life, based on extensive research on ICT-based interventions.

Novel 3-arm wait-list controlled trial designs together with mixed-effects analysis improve precision of treatment effect estimators

Clinical trialists have long been searching for approaches to increase statistical power without increasing sample size. Conventional wait-list controlled (WLC) trials are limited to two trial arms and two or three repeated measurements per person. These features limit statistical power. Furthermore, their analysis is usually based on analysis of covariance or mixed effects modelling, with a focus on estimating treatment effect at one time-period after initiation of therapy. We propose two 3-arm WLC trial designs together with a mixed-effects analysis framework. The designs require three or four repeated measurements per person. The analytic framework defines up to three treatment effect estimands, representing the effects at one to three time-periods after initiation of therapy. The precision (inverse of variance) of the treatment effect estimators in the new and conventional trial designs are analytically derived and evaluated in simulations. The results are interpreted in the context of a cognitive training trial in older people. The proposed designs and analysis methods increase the precision level of treatment effect estimators as compared to conventional designs and analyses. Given a target level of statistical power, the proposed methods require a smaller number of participants per trial than the conventional methods, without necessarily increasing the number of measurements per trial. Furthermore, the proposed analytic framework sheds light on the treatment effects at different times after initiation of therapy, which is not usually considered in conventional WLC trial analysis. In situations that a WLC trial is appropriate, the 3-arm designs are useful alternatives to existing 2-arm designs.

Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions

OBJECTIVE

Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time.

METHODS

Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation.

RESULTS

The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms.

CONCLUSION

The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances.

Identifying opportunity, capability and motivation of Sri Lankan 5th grade schoolteachers to implement in-classroom physical activity breaks: A qualitative study

BACKGROUND

Classroom-based physical activity interventions have demonstrated positive effects in reducing sedentary behaviour among school children. However, this is an understudied area, especially in low- and middle-income countries such as Sri Lanka. This study aims to explore teachers' opportunity, capability and motivation relating to the implementation of an in-classroom physical activity breaks programme.

METHODS

Twenty-seven teachers were recruited through snowball sampling and participated in semi-structured telephone interviews from early-January to the mid-June 2022. The Capability, Opportunity, and Motivation Behaviour (COM-B) model was used to guide and deductively thematic analyse the interviews.

RESULTS

21 out of the recruited teachers responded to the full study. The mean age of respondents was 39.24 years old ranging from 27 years to 53 years. Teaching experience of the respondents ranged from three to 37 years, and 57% were female. Three teachers had a degree with a teacher training diploma, while others were having General Certificate of Education in Advanced Level with a teacher training diploma as the highest education qualification. Capability factors such as age, dress code, mask wearing, knowledge, skills and workload of the teachers were identified as important factors in implementing a physical activity breaks intervention in a Sri Lankan classroom setting. Classroom space, facilities, student backgrounds and safety were identified as opportunity factors. Obtaining policy level decisions to implement the activity breaks and managing the time of the activities to reduce time lost in education time were identified as motivational factors.

CONCLUSION

During the intervention development phase, implementation facilitators and barriers must be considered carefully. Behaviour change techniques can be utilised to address the identified COM-B factors to ensure a good implementation of the intervention.

Engagement, acceptability, usability and satisfaction with Active for Life, a computer-tailored web-based physical activity intervention using Fitbits in older adults

BACKGROUND

Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with 'Active for Life,' a web-based physical activity intervention providing computer-tailored physical activity advice to older adults.

METHODS

Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12 weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction.

RESULTS

At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p = .14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37 min in total, p = .56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p = .41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p = .003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options.

CONCLUSIONS

This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901.

The Effectiveness of Wearable Devices in Non-Communicable Diseases to Manage Physical Activity and Nutrition: Where We Are?

Wearable devices are increasingly popular in clinical and non-clinical populations as a tool for exercise prescription, monitoring of daily physical activity and nutrition, and health-related parameters management. In this regard, smart devices not only assist people in pursuing a healthier lifestyle, but also provide a constant stream of physiological and metabolic data for management of non-communicable diseases (NCDs). Although the benefits of lifestyle-based interventions (exercise and nutrition) for NCDs are well known, the potential of wearable devices to promote healthy behaviors in clinical populations is still controversial. In this narrative review, we aimed to discuss the current application of wearable devices in NCDs, highlighting their role in prescribing and monitoring daily physical activity and dietary habits in the population living with chronic diseases. None of the studies considered specifically addressed the efficacy of the use of wearable devices, and limited are those that incorporate monitoring of both physical activity and nutrition for NCDs. However, there is evidence that such devices have helped improve physical activity levels, physical fitness, body composition, and metabolic and psychological parameters. Therefore, the authors believe that the benefits obtained from the use of wearable devices are likely to translate to public health and represent one of the important tools for the development of prevention plans in everyday life and clinical practice for optimal patient management.