Increase in Daily Steps During the Early Phase of a Physical Activity Intervention for Type 2 Diabetes as a Predictor of Intervention Outcome

Predictors associated with an increase in daily steps among people with prediabetes or type 2 diabetes participating in a two-year pedometer intervention

BACKGROUND

This study aimed to explore predictors associated with intermediate (six months) and post-intervention (24 months) increases in daily steps among people with prediabetes or type 2 diabetes participating in a two-year pedometer intervention.

METHODS

A secondary analysis was conducted based on data from people with prediabetes or type 2 diabetes from two intervention arms of the randomised controlled trial Sophia Step Study. Daily steps were measured with an ActiGraph GT1M accelerometer. Participants were divided into two groups based on their response to the intervention: Group 1) ≥ 500 increase in daily steps or Group 2) a decrease or < 500 increase in daily steps. Data from baseline and from six- and 24-month follow-ups were used for analysis. The response groups were used as outcomes in a multiple logistic regression together with baseline predictors including self-efficacy, social support, health-related variables, intervention group, demographics and steps at baseline. Predictors were included in the regression if they had a p-value < 0.2 from bivariate analyses.

RESULTS

In total, 83 participants were included. The mean ± SD age was 65.2 ± 6.8 years and 33% were female. At six months, a lower number of steps at baseline was a significant predictor for increasing ≥ 500 steps per day (OR = 0.82, 95% CI 0.69-0.98). At 24 months, women had 79% lower odds of increasing ≥ 500 steps per day (OR = 0.21, 95% CI 0.05-0.88), compared to men. For every year of increase in age, the odds of increasing ≥ 500 steps per day decreased by 13% (OR = 0.87, 95% CI 0.78-0.97). Also, for every step increase in baseline self-efficacy, measured with the Self-Efficacy for Exercise Scale, the odds of increasing ≥ 500 steps per day increased by 14% (OR = 1.14, 95% CI 1.02-1.27).

CONCLUSIONS

In the Sophia Step Study pedometer intervention, participants with a lower number of steps at baseline, male gender, lower age or higher baseline self-efficacy were more likely to respond to the intervention with a step increase above 500 steps per day. More knowledge is needed about factors that influence response to pedometer interventions.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02374788.

Feasibility, Usability, and Effectiveness of a Machine Learning-Based Physical Activity Chatbot: Quasi-Experimental Study

BACKGROUND

Behavioral eHealth and mobile health interventions have been moderately successful in increasing physical activity, although opportunities for further improvement remain to be discussed. Chatbots equipped with natural language processing can interact and engage with users and help continuously monitor physical activity by using data from wearable sensors and smartphones. However, a limited number of studies have evaluated the effectiveness of chatbot interventions on physical activity.

OBJECTIVE

This study aims to investigate the feasibility, usability, and effectiveness of a machine learning-based physical activity chatbot.

METHODS

A quasi-experimental design without a control group was conducted with outcomes evaluated at baseline and 6 weeks. Participants wore a Fitbit Flex 1 (Fitbit LLC) and connected to the chatbot via the Messenger app. The chatbot provided daily updates on the physical activity level for self-monitoring, sent out daily motivational messages in relation to goal achievement, and automatically adjusted the daily goals based on physical activity levels in the last 7 days. When requested by the participants, the chatbot also provided sources of information on the benefits of physical activity, sent general motivational messages, and checked participants' activity history (ie, the step counts/min that were achieved on any day). Information about usability and acceptability was self-reported. The main outcomes were daily step counts recorded by the Fitbit and self-reported physical activity.

RESULTS

Among 116 participants, 95 (81.9%) were female, 85 (73.3%) were in a relationship, 101 (87.1%) were White, and 82 (70.7%) were full-time workers. Their average age was 49.1 (SD 9.3) years with an average BMI of 32.5 (SD 8.0) kg/m2. Most experienced technical issues were due to an unexpected change in Facebook policy (93/113, 82.3%). Most of the participants scored the usability of the chatbot (101/113, 89.4%) and the Fitbit (99/113, 87.6%) as at least "OK." About one-third (40/113, 35.4%) would continue to use the chatbot in the future, and 53.1% (60/113) agreed that the chatbot helped them become more active. On average, 6.7 (SD 7.0) messages/week were sent to the chatbot and 5.1 (SD 7.4) min/day were spent using the chatbot. At follow-up, participants recorded more steps (increase of 627, 95% CI 219-1035 steps/day) and total physical activity (increase of 154.2 min/week; 3.58 times higher at follow-up; 95% CI 2.28-5.63). Participants were also more likely to meet the physical activity guidelines (odds ratio 6.37, 95% CI 3.31-12.27) at follow-up.

CONCLUSIONS

The machine learning-based physical activity chatbot was able to significantly increase participants' physical activity and was moderately accepted by the participants. However, the Facebook policy change undermined the chatbot functionality and indicated the need to use independent platforms for chatbot deployment to ensure successful delivery of this type of intervention.

The importance of physical activity in management of type 2 diabetes and COVID-19

Over time, various guidelines have emphasised the importance of physical activity and exercise training in the management of type 2 diabetes, chronic diseases, including cardiovascular disease and musculoskeletal disorders. The aim of this review is to evaluate the effectiveness of physical activity in people with type 2 diabetes and COVID-19. Most research to date indicates that people with type 2 diabetes who engage in both aerobic and resistance exercise see the greatest improvements in insulin sensitivity. Physical activity is now also known to be effective at reducing hospitalisation rates of respiratory viral diseases, such as COVID-19, due to the beneficial impacts of exercise on the immune system. Preliminary result indicates that home-based exercise may be an essential component in future physical activity recommendations given the current COVID-19 pandemic and the need for social distancing. This home-based physical exercise can be easily regulated and monitored using step counters and activity trackers, enabling individuals to manage health issues that benefit from physical exercise.

A qualitative investigation of activity measurement and change following a mind-body activity program for chronic pain

Increasing activity is important yet challenging among people with chronic pain. Objective assessment of activity using accelerometers (i.e., ActiGraph) has gained popularity, but reactivity to assessment and non-adherence to wearing the ActiGraph may bias data. We explored: 1) experiences of wearing the ActiGraph to identify ways to increase adherence and facilitate accurate assessment of activity; and 2) barriers and facilitators to increasing activity following two mind-body activity programs to optimize future interventions. We conducted semi-structured exit interviews with 13 participants with heterogenous chronic pain who completed a randomized controlled trial of two mind-body activity programs. We analyzed transcripts using a framework approach to generate themes. Participants indicated that receiving reminders increased ActiGraph wear adherence and reported that program skills such as gratitude, pacing, mindfulness, and deep breathing were facilitators to increasing activity, while medical problems were barriers. Interventions aiming to increase activity may benefit from emphasizing mind-body skills and sending ActiGraph wear reminders.