Can reminders improve adherence to regular physical activity and exercise recommendations in people over 60 years old?

Wolner-Strohmeyer, Gudrun; Kéilani, Mohammad; Mähr, Bruno; Morawetz, Eva; Zdravkovic, Andrej; Wagner, Barbara; Palma, Stefano; Mickel, M; Jordakieva, Galateja; Crevenna, Richard

doi:10.1007/s00508-020-01699-6

Cited by 2 publications

(1 citation statement)

References 17 publications

(23 reference statements)

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“…For example, the position of the second opinion in duplicate practices, the limited selection for the clinical characteristic in the generic drug rate, and the clinical need or severity in the access frequency were mentioned. In this study, the concept of adherence was expanded and applied in view of trends in the awareness and understanding of moral hazards and concordance in clinical practice; therefore, the definition of active involvement of patients may be controversial in the future [60][61][62][63].…”

Section: Limitationsmentioning

confidence: 99%

Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

Takura

Goto

Honda³

2021

BMC Med

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Background Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. Methods This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. Results Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). Conclusions ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.

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Section: Limitationsmentioning

confidence: 99%

Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

Takura

Goto

Honda³

2021

BMC Med

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Protocol for the Move Often eVery Day (MOV’D) Pilot: A Multi-Component, Fully-Remote, Peer-Supported Exercise Snack Behavior Change Intervention (Preprint)

Moore,

Monteiro,

Aikens

et al. 2024

Preprint

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BACKGROUND Prolonged sitting and lack of moderate to vigorous physical activity represent two independent risk factors for myriad poor health outcomes. The negative effects of prolonged sitting can be ameliorated with as little as 2 minutes of large muscle movement. Further, cardiovascular benefits from moderate to vigorous activity can be accumulated throughout the day in short bouts rather than require continuous long bouts. Taken together, “exercise snacks” provide a way to both interrupt prolonged sitting and accumulate moderate to vigorous physical activity during a sedentary work day. OBJECTIVE The current protocol describes the feasibility and acceptability pilot of Move Often eVery Day (MOV'D), a fully-remote, peer-supported behavioral intervention to interrupt prolonged bouts of sitting at work with exercise snack breaks. METHODS The MOV’D pilot study aimed to recruit approximately 60-80 participants who work full-time in a sedentary occupation. Using a randomized 2-group design, participants were randomly assigned either to the “Fitbit Control'' or the “MOV’D” experimental group. The pilot study had a 4-week active intervention and a 4-week follow-up with assessments at baseline, 4-weeks, and 8-weeks. The Fitbit control group received a Fitbit to self-monitor their physical activity prior to getting intervention material at the end of the study. In addition to the Fitbit, the MOV’D intervention features included: a private social support group chat seeded with daily experimenter prompts; weekly, 5-minute behavior change technique videos; daily self-monitoring, and daily exercise snack suggestion videos. RESULTS Study enrollment began in March 2022 and concluded in June 2022. Data collection concluded in October 2022. We enrolled 70 participants and 68 participants completed all study assessments. CONCLUSIONS This protocol integrates findings from education, behavioral sciences, sedentary behavior, and exercise physiology to promote building an exercise snack habit at work: taking short, intense exercise breaks to break up prolonged sitting. Results from this pilot study will assess the feasibility and acceptability of the MOV’D intervention. CLINICALTRIAL NCT05360485

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Can reminders improve adherence to regular physical activity and exercise recommendations in people over 60 years old?

Cited by 2 publications

References 17 publications

Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

Protocol for the Move Often eVery Day (MOV’D) Pilot: A Multi-Component, Fully-Remote, Peer-Supported Exercise Snack Behavior Change Intervention (Preprint)

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