The Value of Removing Daily Obstacles via Everyday Problem-Solving Theory: Developing an Applied Novel Procedure to Increase Self-Efficacy for Exercise

Personalized mHealth Intervention (StepAdd) for Increasing Physical Activity in Japanese Patients With Type 2 Diabetes: Secondary Analysis of Social Cognitive Theory Measurements of a Single-Arm Pilot Study

Background

A 12-week pilot of the StepAdd mobile health (mHealth) behavior change intervention based on social cognitive theory (SCT) saw an 86.7% increase in mean daily step counts among patients with type 2 diabetes. Due to the lack of exploration of theoretical implications in mHealth intervention studies, there is a need to understand the mechanism underlying the behavioral change to inform the future design of digital therapeutics.

Objective

This study aimed to examine the SCT drivers underlying the mean increase in exercise among Japanese patients with type 2 diabetes who participated in the StepAdd intervention.

Methods

This is a post hoc analysis of data collected in the single-arm pilot study of the 32 patients who completed the StepAdd intervention. The StepAdd app uses self-mastery and coping strategies to increase self-efficacy and thus increase walking. Self-mastery was measured by the goal completion (GC) rate, which is the percentage of days in which patients met these adapting goals. The use of coping strategies was measured by the strategy implementation (SI) rate, which is the percentage of days in which patients applied their selected coping strategies. We assessed correlations between GC, SI, and self-efficacy to increase walking via linear regression and analyzed relationships via structural equation modeling.

Results

We found statistically significant support for the SCT approach, including a correlation coefficient (ρ) of 0.649 between step increase and GC rate (P<.001); a ρ of 0.497 between the coping SI rate and self-efficacy increase (P=.004); a ρ of 0.446 between GC rate and self-mastery increase (P=.01); and a ρ of 0.355 between self-regulation increase and step increase (P=.046), giving us insight into why the behavior intervention succeeded. We also found significant correlations between self-efficacy for barriers and self-efficacy for task-specific behavior (ρ=0.358; P=.04), as well as self-regulation and self-efficacy for task-specific behavior (ρ=0.583; P<.001). However, a cross-lagged panel modeling analysis found no significant evidence that changes in self-efficacy preceded behavior changes in line with SCT.

Conclusions

Self-mastery and coping strategies contributed to the walking behavior change in StepAdd, supporting the SCT model of behavior change. Future research is needed to better understand the causal pathways proposed by SCT.

An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial

Background

The increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs.

Objective

The primary aim was to investigate the impact of the Samsung-developed i80 BPM app, implementing ML for exercise prescription, on user satisfaction and exercise intensity among the general population. The secondary objective was to assess the effectiveness of ML-generated exercise programs for remote prescription of exercise to members of the public.

Methods

Participants were randomized to complete 3 exercise sessions per week for 12 weeks using the i80 BPM mobile app, crossing over weekly between intervention and control conditions. The intervention condition involved individualizing exercise sessions using RL, based on user preferences such as exercise difficulty, selection, and intensity, whereas under the control condition, exercise sessions were not individualized. Exercise intensity (measured by the 10-item Borg scale) and user satisfaction (measured by the 8-item version of the Physical Activity Enjoyment Scale) were recorded after the session.

Results

In total, 62 participants (27 male and 42 female participants; mean age 43, SD 13 years) completed 559 exercise sessions over 12 weeks (9 sessions per participant). Generalized estimating equations showed that participants were more likely to exercise at a higher intensity (intervention: mean intensity 5.82, 95% CI 5.59-6.05 and control: mean intensity 5.19, 95% CI 4.97-5.41) and report higher satisfaction (RL: mean satisfaction 4, 95% CI 3.9-4.1 and baseline: mean satisfaction 3.73, 95% CI 3.6-3.8) in the RL model condition.

Conclusions

The findings suggest that RL can effectively increase both the intensity with which people exercise and their enjoyment of the sessions, highlighting the potential of ML to enhance remote exercise interventions. This study underscores the benefits of personalized exercise prescriptions in increasing adherence and satisfaction, which are crucial for the long-term effectiveness of fitness programs. Further research is warranted to explore the long-term impacts and potential scalability of RL-enhanced exercise apps in diverse populations. This study contributes to the understanding of digital health interventions in exercise science, suggesting that personalized, app-based exercise prescriptions may be more effective than traditional, nonpersonalized methods. The integration of RL into exercise apps could significantly impact public health, particularly in enhancing engagement and reducing the global burden of physical inactivity.

Physical activity and perceived barriers in individuals with moderate-to-severe traumatic brain injury

INTRODUCTION

Regular physical activity (PA), especially aerobic exercise, may benefit cognitive function in middle-aged and older adults, but promoting regular PA in individuals with traumatic brain injury (TBI) remains a challenge.

OBJECTIVE

To characterize PA and perceived barriers to PA in younger (<45 years) and middle age and older (≥45 years) individuals ≥1 year after moderate-to-severe TBI.

DESIGN

Multicenter survey study.

SETTING

Community.

PARTICIPANTS

Persons who met the following criteria were included in the study: (1) 18 years and older; (2) English speaking; (3) History of moderate-to-severe TBI; (4) Followed in a TBI Model Systems Center for at least 1 year; and (5) Able to complete the survey independently.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURE(S)

PA level measured by Rapid Assessment of Physical Activity questionnaire (RAPA) and self-reported barriers to PA.

RESULTS

A total of 472 participants completed the survey (response rate of 21%). More individuals in the younger group (<45 years old) met Centers for Disease Control and Prevention (CDC) recommended aerobic PA guidelines compared to the middle-aged and older group (≥ 45 years old) (62% vs 36%, p < .001). Lack of motivation, lack of time, and fatigue were the most reported barriers. Perceived barriers to PA varied by age and PA level: the middle-aged and older individuals (≥ 45 years old) were more likely to report no barriers and inactive individuals (RAPA ≤5) more likely to report lack of motivation and money, pain, and lack of resources.

CONCLUSION

Participants ≥45 years of age were less likely to meet the CDC PA guidelines than younger individuals after moderate-to-severe TBI. Because perceived barriers to PA varied between age groups and PA levels, individualized approaches may be needed to promote PA in this population.

Construction and validation of the life roles self-efficacy scale for young adults in school-to-work transition

Self-efficacy is widely regarded as a key factor in shaping one’s own career. To date, self-efficacy has usually been measured on a one-dimensional basis without taking into account the interaction between the various social roles involved in career development. The social roles were described by Donald E. Super in his career development theory. Adopting this framework, we provide a questionnaire to simultaneously measure self-efficacy in Super’s five described social roles. This work presents the development and validation of a new questionnaire entitled the Life Roles Self-Efficacy Scale (LRSES). The questionnaire has been developed based on a series of surveys: the first survey (N = 347) aimed to establish the exploratory factor analysis (EFA) and basic psychometric properties of the tool; the second survey (N = 494) aimed to verify the confirmatory factor analysis of the method (CFA) and reliability parameters with regard to a new sample; and the third survey (N = 109) explored the consistency of results over time. Statistical analysis confirmed this tool to be accurate for assessing one’s self-efficacy in school-to-work transitions.

My Problems Are Solvable: Idiographic Methods Offset Age Differences in Interpersonal Problem Solving Among Young, Middle-Aged, and Older Adults

This study tested the hypothesis that older adults retain high levels of everyday problem solving performance when confronting problems of maximal ecological relevance, identified through idiographic methods. Younger, middle-aged, and older adults completed a daily challenge questionnaire (DCQ) in which they reported problems of maximal personal relevance or idiographic problems. The large majority of the problems reported were interpersonal. We then assessed performance on an everyday problem-solving task in which participants generated solutions for idiographic problems as well as problems generated by group matched research participants representing each of two other age groups (e.g., older adults received their own problems plus problems generated by matched younger and middle-aged adults). Performance was measured by computing the total number of safe and effective solutions provided. Results fully supported our hypothesis; adults of all ages showed higher performance when solving their idiographic problems.

Preventive strategies to reduce depressive symptoms in overweight and obese young adults

This study examined the relationships among problem-solving, physical activity self-efficacy, leisure-time physical activity, and depressive symptoms in overweight/obese young adults vulnerable to many health risks. Data from 96 young adults were used. The mean age and body mass index were 24.0±3.3 years old, and 36.9±7.9, respectively. There was a positive association between physical activity self-efficacy and leisure-time physical activity in African Americans, but not in non-African Americans. Better problem solving was associated with fewer depressive symptoms regardless of gender and race.