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Record W2965689557 · doi:10.14740/jocmr3894

Adherence to Medication, Physical Activity and Diet in Older Adults With Diabetes: Its Association With Cognition, Anxiety and Depression

2019· article· en· W2965689557 on OpenAlexvenueno aff
Rosa Mendes, Sónia Martins, Lia Fernandes

Bibliographic record

VenueJournal of Clinical Medicine Research · 2019
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnxietyDepression (economics)Context (archaeology)Hospital Anxiety and Depression ScaleDiabetes mellitusOutpatient clinicCognitionOdds ratioPsychiatryInternal medicineClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Adherence to medication, physical activity (PA) and diet in diabetes mellitus (DM) patients is crucial for its good management, avoiding acute and chronic complications. There are several risk factors associated with non-adherence, including cognitive impairment, depression and anxiety. Nevertheless, studies on therapeutic adherence in older patients with DM are scarce. In this context, the present study aimed to analyze whether adherence to medication, PA and diet are associated with cognitive impairment, anxiety and depression. It also aimed to identity predictors of medication non-adherence. METHODS: A cross-sectional study of older patients (≥ 65 years old) with DM was carried out in the Outpatient Department of Internal Medicine Service of CHUSJ-Porto, Portugal. Those unable to communicate were excluded. Cognition (mini-mental state examination), anxiety and depression (hospital anxiety and depression scale) were assessed. Adherence to medication, PA and diet was measured, based on self-reporting patient/family, questionnaires, physician clinical opinion, hemoglobin test and pharmacy records. Patient groups were compared, using the Mann-Whitney or the Kruskal-Wallis test for continuous variables and the Chi-square test for paired categorical variables (significance level of 0.05). The odds ratio (OR) was calculated to identify independent predictors of non-adherence to medication. RESULTS: The final sample (n = 94) had a mean age of 75.2 years (standard deviation: 6.7) and mostly were female (53.2%), married (63.8%) and with a low education level (61.7%). Also, 22.3% with cognitive impairment, 16% with depression and 23.4% with anxiety were found. Patients non-adherent to medication had higher depression (P = 0.048) and anxiety (P = 0.010), compared to adherents/partial adherents. Patients non-adherent to PA showed higher anxiety (P = 0.035) and depression (P = 0.004), compared to adherents. Non-adherents to PA had more cognitive impairment than adherents (26.3% vs. 0%; P = 0.034). Patients who had insulin prescribed presented a higher risk of non-adherence to medication (OR: 4.041, 95% confidence interval (CI): 1.404 - 11.628; P = 0.010). Also, the risk of non-adherence to medication is higher by an increase of one unit in anxiety (OR: 1.252, 95% CI: 1.046 - 1.499; P = 0.014). CONCLUSIONS: Higher anxiety and depression were associated with non-adherence to medication and to PA. Insulin prescribed and high anxiety scores were predictors of medication non-adherence. This study appears to contribute to the knowledge about the influence of cognitive and psychological factors in therapeutic adherence in these older diabetic patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.479
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations88
Published2019
Admission routes1
Has abstractyes

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