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Measuring Positive Mental Health

   

Added on  2020-04-13

12 Pages1757 Words293 Views
Measuring Positive Mental Health 1Mental Health – Measuring Positive Mental HealthNameCourse NumberDateFaculty Name

Measuring Positive Mental Health 2Mental Health – Measuring Positive Mental HealthIntroduction Mental health status can be measured based on predetermined scale(s), which focus onthe state of human mind during a specified period. Several factors determine the mental state ofan individual amongst which include physical exercises and age among other possibleconfounders such as gender and ethnicity (NHS Health Scotland, 2017). Observations and testsare required to determine the mental state of a person. It is believed that people who engage inmore physical activities are more likely to have better mental health compared to their fellows(Pratt, 2013). In this study, two groups of women will be compared on basis of their mentalstatus. The groups include those studied at baseline and were not involved in any form ofphysical activities and the others received free sessions, hence the intervention group.Information recorded for purposes of research analysis based on the three periods for the twogroups (Elder, Evans and Nizette, 2009). The aim of the analysis is to see whether there is acorrelation between the amount of activity and their Mental Health wellbeing score. Their BMIis monitored for change to see whether the activity has an effect on it. Participants are monitoredfrom pre-contemplation to maintenance level using a pedometer. Physical activity stage ofchange (PASOC) is checked at 3, 12 and 24 months.Research Questions1.Does Offering exercise sessions to the physically restricted help encourage and maintain a healthy mental wellbeing state?The wellbeing of a person is affected by the physical state of the body. Therefore, increasingthe rate physical activities, the chance of mental state improvements is elevated (Choi, 2014). 2.Is there a significant association between physical activity stage of change at each timepoint 1, 2 & 3 and offering exercise sessions? Due to the expected changes in physical activity stage of change between the groups, wepresume that there will be a significant association between the participant's groups and thephysical activity stage (Choi, 2014). 3.Is age a confounder for the prediction of a participant’s group using the measurements ofthe third recording as the predictors?

Measuring Positive Mental Health 3The rate of physical exercises gradually reduces as women get to older ages. Therefore, ageis a possible confounder in the prediction of whether a participant in the control or interventiongroup (Sedgwick, 2013). Specific Hypothesis1.Offering exercise sessions to the physically restricted help encourage and maintain a healthy mental wellbeing stateNull hypothesis: Offering exercise sessions has no effect on encouraging or maintaining a healthy mental Wellbeing state2.The physical state of change at each time is proportionally related to offering exercise sessionsNull hypothesis: There is no association between offering exercise and the physical state of change. 3.Age is a potential confounder in the modelling of individuals benefiting from exercise sessionsNull hypothesis: Age is not a confounder in the modelling of individuals benefiting from exercise sessionsDescriptive StatisticsThere were 89 (32.5%) participants in the intervention group and 185 (67.5%) in the controlgroup. Table 1: Disability, its effects in activities and promotion of parents/guardiansYesNoN/ADisability? 37 (13.5%)237 (86.5%)Disability Limit Activity? 28 (10.2%)10 (3.6%)236 (86.1%)Parent/Guardian?181 (66.1%)93 (33.9%)Table 2: Physical activity stage summaryPhysical activity stageStage 1Stage 2Stage 3Pre-contemplation12 (4.4%)10 (3.6%) 7 (2.6%)Contemplation 58 (21.2%)50 (18.2%) 22 (8%) Preparation 47 (17.2%)45 16.4%) 77 (28.1%)Decision/Action19 (6.9%)28 (10.2%) 37 (13.5%)Maintenance 138 (50.4%)141 (51.5%)131 (47.8%)

Measuring Positive Mental Health 4Table 3: Employment status summaryFrequencyEmployed173 (63.1%)Unemployed28 (10.2%)Self-employed7 (2.6%)Student19 (6.6%)Carer5 (1.8%)Retired42 (15.3%)63.1% of the women who participated in the study were employed, 10.2% unemployed, 2.6%self-employed, 6.6% were students, 1.8% carers and 15.3% were retired. Table 4: Age distribution of the participantsMeanStandard DeviationMinimumMaximum Age43.3915.271689Figure 1: Distribution of age in baseline and session groupsThe average is a woman who participated in the study was 43.39years with a standard deviationof 15.27years. The distribution of age between the two groups is approximately normal.

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